content
stringlengths 6
1.03M
| input_ids
sequencelengths 4
535k
| ratio_char_token
float64 0.68
8.61
| token_count
int64 4
535k
|
---|---|---|---|
using BinaryBuilder, Pkg
name = "SPIRV_LLVM_Translator"
repo = "https://github.com/KhronosGroup/SPIRV-LLVM-Translator.git"
# These are the platforms we will build for by default, unless further
# platforms are passed in on the command line
platforms = expand_cxxstring_abis(supported_platforms())
# Bash recipe for building across all platforms
script = raw"""
cd SPIRV-LLVM-Translator
install_license LICENSE.TXT
CMAKE_FLAGS=()
# Release build for best performance
CMAKE_FLAGS+=(-DCMAKE_BUILD_TYPE=Release)
# Install things into $prefix
CMAKE_FLAGS+=(-DCMAKE_INSTALL_PREFIX=${prefix})
# Explicitly use our cmake toolchain file and tell CMake we're cross-compiling
CMAKE_FLAGS+=(-DCMAKE_TOOLCHAIN_FILE=${CMAKE_TARGET_TOOLCHAIN})
CMAKE_FLAGS+=(-DCMAKE_CROSSCOMPILING:BOOL=ON)
# Tell CMake where LLVM is
CMAKE_FLAGS+=(-DLLVM_DIR="${prefix}/lib/cmake/llvm")
# Build the library
CMAKE_FLAGS+=(-DBUILD_SHARED_LIBS=ON)
cmake -B build -S . -GNinja ${CMAKE_FLAGS[@]}
ninja -C build -j ${nproc} llvm-spirv install
install -Dm755 build/tools/llvm-spirv/llvm-spirv${exeext} -t ${bindir}
"""
# The products that we will ensure are always built
products = Product[
LibraryProduct(["libLLVMSPIRVLib", "LLVMSPIRVLib"], :libLLVMSPIRV),
ExecutableProduct("llvm-spirv", :llvm_spirv),
]
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] | 2.589537 | 497 |
<reponame>tmigot/MPCC.jl
using Documenter
using MPCC
makedocs(
sitename = "MPCC.jl",
format = Documenter.HTML(assets = ["assets/style.css"], prettyurls = get(ENV, "CI", nothing) == "true"),
modules = [MPCC],
pages = [
"Home" => "index.md",
"API" => "api.md",
"Examples and tutorials" => "tutorial.md",
]
)
# Documenter can also automatically deploy documentation to gh-pages.
# See "Hosting Documentation" and deploydocs() in the Documenter manual
# for more information.
deploydocs(repo = "github.com/tmigot/MPCC.jl")#
#https://juliadocs.github.io/Documenter.jl/stable/man/hosting/ ?
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] | 2.431227 | 269 |
# MadNLP.jl.
# Created by <NAME> (<EMAIL>)
mutable struct MonolevelPartition
g::Graph
nparts::Int
part::Vector{Int}
end
mutable struct MonolevelStruc
V::Vector{Int}
new_nbr::Vector{Int}
end
mutable struct BilevelPartition
g_lower::Graph
nparts_lower::Int
part_lower::Vector{Int}
V_lower::Vector{Vector{Int}}
g_upper::Graph
nparts_upper::Int
part_upper::Vector{Int}
end
mutable struct BilevelStruc
V_lower::Vector{Int}
V_upper::Vector{Int}
new_nbr_upper::Vector{Int}
end
mutable struct TwoStagePartition
nparts::Int
part::Vector{Int}
end
get_current_V(mls::MonolevelStruc) = mls.V
get_current_size(mls::MonolevelStruc) = length(mls.V)
get_full_size(mlp::MonolevelPartition) = nv(mlp.g)
function MonolevelPartition(csc::SparseMatrixCSC,part,nparts;max_size=0.)
g = Graph(csc)
isempty(part) && (part=partition(g,nparts,alg=:KWAY))
return MonolevelPartition(g,nparts,part)
end
function MonolevelStruc(mlp::MonolevelPartition,k;max_size=0.)
V = findall(mlp.part.==k)
new_nbr = expand!(V,mlp.g,max_size)
return MonolevelStruc(V,new_nbr)
end
function expand!(mls::MonolevelStruc,mlp::MonolevelPartition,max_size)
mls.new_nbr = expand!(mls.V,mlp.g,max_size,new_nbr=mls.new_nbr)
return
end
get_current_V(bls::BilevelStruc) = bls.V_lower
get_current_size(bls::BilevelStruc) = length(bls.V_upper)
get_full_size(blp::BilevelPartition) = blp.nparts_lower
function BilevelPartition(csc,part_lower,nparts_lower,part_upper,nparts_upper;max_size=0.)
g_lower = Graph(csc)
isempty(part_lower) && (part_lower= partition(g_lower,nparts_lower,alg=:KWAY))
V_lower = Vector{Vector{Int}}(undef,nparts_lower)
@blas_safe_threads for k=1:nparts_lower
V_lower[k] = findall(part_lower.==k)
end
g_upper = Graph(nparts_lower)
for e in edges(g_lower)
add_edge!(g_upper,part_lower[src(e)],part_lower[dst(e)])
end
isempty(part_upper) && (part_upper = partition(g_upper,nparts_upper,alg=:KWAY))
return BilevelPartition(g_lower,nparts_lower,part_lower,V_lower,g_upper,nparts_upper,part_upper)
end
function BilevelStruc(blp::BilevelPartition,k;max_size=0.)
V_upper = findall(blp.part_upper.==k)
new_nbr_upper = expand!(V_upper,blp.g_upper,max_size)
V_lower = vcat(blp.V_lower[V_upper]...)
return BilevelStruc(V_lower,V_upper,new_nbr_upper)
end
function TwoStagePartition(csc::SparseMatrixCSC,part,nparts)
if isempty(part) || findfirst(x->x==0.,part) == nothing
g = Graph(csc)
isempty(part) && (part = partition(g,nparts,alg=:KWAY))
mark_boundary!(g,part)
end
return TwoStagePartition(nparts,part)
end
Graph(csc::SparseMatrixCSC) = Graph(getelistcsc(csc.colptr,csc.rowval))
getelistcsc(colptr,rowval) = [Edge(i,Int(j)) for i=1:length(colptr)-1 for j in @view rowval[colptr[i]:colptr[i+1]-1]]
function expand!(bls::BilevelStruc,blp::BilevelPartition,max_size)
orig_size = length(bls.V_upper)
bls.new_nbr_upper = expand!(bls.V_upper,blp.g_upper,max_size,new_nbr=bls.new_nbr_upper)
bls.V_lower = vcat(blp.V_lower[bls.V_upper]...)
return
end
function expand!(V_om,g::Graph,max_size;
new_nbr=[])
if isempty(new_nbr)
new_nbr = Int[]
for v in V_om
append!(new_nbr,neighbors(g,v))
end
unique!(new_nbr)
setdiff!(new_nbr,V_om)
end
old_nbr = V_om
while (length(V_om) + length(new_nbr) < max_size) && length(V_om) < nv(g) && !isempty(new_nbr)
append!(V_om,new_nbr)
old_old_nbr = old_nbr
old_nbr=new_nbr
new_nbr = Int[]
for v in old_nbr
append!(new_nbr,neighbors(g,v))
end
unique!(new_nbr)
setdiff!(new_nbr,old_old_nbr)
setdiff!(new_nbr,old_nbr)
end
return new_nbr
end
function mark_boundary!(g,part)
for e in edges(g)
(part[src(e)]!=part[dst(e)] && part[src(e)]!= 0 && part[dst(e)] != 0) &&
(part[src(e)] = 0; part[dst(e)] = 0)
end
end
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] | 2.019861 | 2,014 |
function spiral!(r)
a = 1
s = Nord
while !ismarker(r)
for i in 1:a
if ismarker(r)
break
end
move!(r, s)
end
s = rotate_right(s)
for i in 1:a
if ismarker(r)
break
end
move!(r, s)
end
if ismarker(r)
break
end
s = rotate_right(s)
a += 1
end
end | [
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] | 1.478405 | 301 |
using Test
using PowerModels
using NetDecOPF
using DualDecomposition
using Ipopt
const DD = DualDecomposition
sub_optimizer = optimizer_with_attributes(Ipopt.Optimizer, "print_level" => 0, "warm_start_init_point" => "yes")
optimizer = optimizer_with_attributes(Ipopt.Optimizer, "print_level" => 0, "warm_start_init_point" => "yes")
file = "../examples/case5.m"
data = parse_file(file)
# Partition network
partitions = metis_cluster(data, 2)
dn_model = decompose(data, partitions, ACRPowerModel, NetDecOPF.build_acopf_with_free_lines)
# dn_model = decompose(data, partitions, W_ACRModel, NetDecOPF.build_acopf_with_free_lines)
algo = init_DD_algo(dn_model)
set_subnet_optimizer!(dn_model, sub_optimizer)
# Change parameters (for example)
params = BM.Parameters()
BM.set_parameter(params, "maxiter", 3000)
BM.set_parameter(params, "ϵ_s", 1.e-5)
# Lagrange master method
LM = DD.BundleMaster(BM.ProximalMethod, optimizer, params)
DD.run!(algo, LM)
@test isapprox(DD.dual_objective_value(algo), 17551, rtol = 0.1)
# @show DD.dual_solution(algo)
| [
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] | 2.567237 | 409 |
using DistributionsAD
using Turing, Random, Test, LinearAlgebra
using Turing: Variational
using Turing.Variational: TruncatedADAGrad, DecayedADAGrad, AdvancedVI
include("../test_utils/AllUtils.jl")
@testset "advi.jl" begin
@turing_testset "advi constructor" begin
Random.seed!(0)
N = 500
s1 = ADVI()
q = vi(gdemo_default, s1)
c1 = rand(q, N)
end
@numerical_testset "advi inference" begin
@testset for opt in [TruncatedADAGrad(), DecayedADAGrad()]
Random.seed!(1)
N = 500
alg = ADVI(10, 5000)
q = vi(gdemo_default, alg; optimizer = opt)
samples = transpose(rand(q, N))
chn = Chains(reshape(samples, size(samples)..., 1), ["s", "m"])
# TODO: uhmm, seems like a large `eps` here...
check_gdemo(chn, atol = 0.5)
end
end
@turing_testset "advi different interfaces" begin
Random.seed!(1234)
target = MvNormal(ones(2))
logπ(z) = logpdf(target, z)
advi = ADVI(10, 1000)
# Using a function z ↦ q(⋅∣z)
getq(θ) = TuringDiagMvNormal(θ[1:2], exp.(θ[3:4]))
q = vi(logπ, advi, getq, randn(4))
xs = rand(target, 10)
@test mean(abs2, logpdf(q, xs) - logpdf(target, xs)) ≤ 0.07
# OR: implement `update` and pass a `Distribution`
function AdvancedVI.update(d::TuringDiagMvNormal, θ::AbstractArray{<:Real})
return TuringDiagMvNormal(θ[1:length(q)], exp.(θ[length(q) + 1:end]))
end
q0 = TuringDiagMvNormal(zeros(2), ones(2))
q = vi(logπ, advi, q0, randn(4))
xs = rand(target, 10)
@test mean(abs2, logpdf(q, xs) - logpdf(target, xs)) ≤ 0.05
end
end
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] | 1.977477 | 888 |
module SubmodularMaximization
#
# Hack in python imports for now
#
using PyCall
const tikzplotlib = PyNULL()
const ag = PyNULL()
function __init__()
try
copy!(tikzplotlib, pyimport("tikzplotlib"))
catch e
println("Could not import tikzplotlib, trying matploblib2tikz instead")
copy!(tikzplotlib, pyimport("matplotlib2tikz"))
end
copy!(ag, pyimport("mpl_toolkits.axes_grid1"))
end
#
# End hack
#
using Base.Iterators
import Base.<
# Abstract interface
export PartitionProblem, PartitionElement, ElementArray, Solution,
get_num_agents, solve_block, objective, evaluate_solution, empty
# Explicit interface
export ExplicitPartitionProblem, get_element, get_agent, get_center
# General methods that probably need updates
export marginal_gain, compute_weight, compute_weight_matrix, mean_weight,
total_weight, extract_triangle, visualize_solution
# Agent specification for coverage problems
export Agent, generate_agents,
generate_colors,
visualize_agents,
get_element_indices,
independent
# Solvers
export solve_optimal, solve_worst, solve_myopic, solve_random, solve_sequential
# Max now uses isless to exclude types that do not have a total order. We would
# like to have something like this defined for solutions but probably should not
# break things. This is a good compromise
partial_max(a, b) = ifelse(b < a, a, b)
partial_min(a, b) = ifelse(b > a, a, b)
# Interface
# get_block(Agent) = <array of objects associated with agents' block of the
# partition matroid>
# get_center(Agent) = <agent center>
# (Note really a general property but currently defined for all agents)
# plot_element(x) = <Nothing>
# make_agent(agent_specification) = < agent >::AgentType
struct Agent{T}
center::Array{Float64, 1}
radius::Float64
sensors::Array{T, 1}
end
get_block(agent::Agent) = agent.sensors
get_center(agent::Agent) = agent.center
function generate_agents(agent_specification, num_agents)
[make_agent(agent_specification) for agent in 1:num_agents]
end
#
#
# Abstract partition matroid problems
#
# This interface is suitable for cases where the matroid is defined implicitly
# and solved suboptimally
#
# Interface
#
abstract type PartitionProblem{PartitionElement} end
# Define dependent types
PartitionElement(::PartitionProblem{T}) where T = T
# Subtypes should redefine this method
PartitionElement(::Type{<:PartitionProblem{T}}) where T = T
# Defines the structure of a solution for the given matroid or its type
ElementArray(::T) where T = ElementArray(T)
ElementArray(::Type{T}) where T = Vector{PartitionElement(T)}
# Solutions consist of their value and a set of solution elements
struct Solution{PartitionElement}
value::Float64
elements::Vector{PartitionElement}
end
objective(p::PartitionProblem, X) = error("Objective not defined for ",
typeof(p))
# Define the solution for individual solution elements
objective(p::PartitionProblem{T}, X::T) where T = objective(p, [X])
evaluate_solution(p::PartitionProblem, X) =
Solution(objective(p, X), X)
# Compare solutions by value
<(a::Solution, b::Solution) = a.value < b.value
# empty array (vector) of solution elements
empty(::T) where T <: PartitionProblem = ElementArray(T)()
empty(::Type{T}) where T <: PartitionProblem = ElementArray(T)()
# By default, store the representation of the matroid in something like a vector
# vector
get_num_agents(p::PartitionProblem) = length(p.partition_matroid)
# This should be an optimal or subotimal solver that outputs the appropriate
# PartitionElement for a block given prior selections
solve_block(p::PartitionProblem, block::Integer, selections::Vector) =
error("Single agent (block) solver not defined for ", typeof(p))
#
# Concrete partition matroid problems
#
# The objective provides f({x} | Y)
# as objective(x, Y)
#
# The inputs are elements of the blocks
#
# The partition matroid is a Vector of the blocks of the partition matroid.
# The blocks contain whatever the elements of the ground set correspond to.
# For example, the blocks may contain specifications of coverage regions.
# Solution elemnts for concrete partition matroids are indices within the array
# of blocks
#
# (agent_index, block_index)
const ExplicitSolutionElement = Tuple{Int64,Int64}
struct ExplicitPartitionProblem <: PartitionProblem{ExplicitSolutionElement}
objective::Function
partition_matroid::Vector
end
get_element(problem::ExplicitPartitionProblem, x) =
get_element(problem.partition_matroid, x)
get_element(partition_matroid, x) =
get_block(partition_matroid[x[1]])[x[2]]
get_agent(problem::ExplicitPartitionProblem, x) =
get_agent(problem.partition_matroid, x)
get_agent(partition_matroid::Vector, x) = partition_matroid[x]
get_center(p::PartitionProblem, x) = get_center(get_agent(p, x))
objective(p::ExplicitPartitionProblem, X::Vector{ExplicitSolutionElement}) =
p.objective(map(x->get_element(p.partition_matroid, x), X))
marginal_gain(f, x, Y) = f(vcat([x], Y)) - f(Y)
compute_weight(f, x, y) = f([x]) - marginal_gain(f, x, [y])
compute_weight(f, X::Array, Y::Array) =
maximum([compute_weight(f, x, y) for x in X, y in Y])
# Solve explicit partition matroids by iteration over blocks given a set of
# prior selections
function solve_block(p::ExplicitPartitionProblem, block_index::Integer,
selections::Vector)
block::ElementArray(p) = get_element_indices(p, block_index)
_, index = findmax(map(x->objective(p, [selections; x]), block))
block[index]
end
function compute_weight_matrix(p::PartitionProblem)
indices = get_element_indices(p.partition_matroid)
n = length(p.partition_matroid)
weights = zeros(n, n)
f(x) = objective(p, x)
for ii in 2:n, jj in 1:ii-1
w = compute_weight(f, indices[ii], indices[jj])
weights[ii, jj] = w
weights[jj, ii] = w
end
weights
end
function mean_weight(W::Array)
n = size(W,1)
twice_edges = n * (n - 1)
sum(W) / twice_edges
end
total_weight(W::Array) = sum(W) / 2
mean_weight(p::PartitionProblem) = mean_weight(compute_weight_matrix(p))
total_weight(p::PartitionProblem) = total_weight(compute_weight_matrix(p))
function extract_triangle(A)
values = Float64[]
for ii in 2:size(A, 1), jj in 1:ii-1
push!(values, A[ii, jj])
end
values
end
# indexing and solver tools
# Construct index set for ease in manipulation of the matroid
#
# The resulting index is an array of arrays whereas each array corresponds
# to indices for an element in a block of the partition matroid
get_element_indices(p::ExplicitPartitionProblem, xs...) =
get_element_indices(p.partition_matroid, xs...)
function get_element_indices(agents::Vector, block::Integer)
map(1:length(get_block(agents[block]))) do block_index
(block, block_index)
end
end
function get_element_indices(agents::Vector)
map(agents, 1:length(agents)) do agent, agent_index
get_element_indices(agents, agent_index)
end
end
# A set is independent if it contains at most one assignment to each agent
# Tuple consist of agent and agent-index
independent(x) = length(x) == length(Set(map(first, x)))
# Returns true if any element of Y can be added to X
can_augment(x, Y::Vector) = any(y->independent(vcat(x, y)), Y)
# Can agent index add solution elements to x in a partition matroid
can_augment(x, index::Int64) = !in(index, map(first, x))
# Can a given solution be added to x
can_augment(x, e::ExplicitSolutionElement) = can_augment(x, first(e))
include("src/utils.jl")
include("src/visualization.jl")
include("src/normal_lookup_table.jl")
include("src/solvers.jl")
# Support for solvers with range-based communication graphs
export make_adjacency_matrix, plot_adjacency, plot_shortest_path,
make_hop_adjacency, neighbors, shortest_path, path_distance, is_connected,
generate_connected_problem, solve_multi_hop
export communication_span, communication_messages, communication_volume
export MultiHopSolver, SequentialCommunicationSolver, AuctionSolver,
LocalAuctionSolver
include("src/communications_graphs_solvers.jl")
include("src/coverage/coverage.jl")
include("src/coverage/probabilistic_coverage.jl")
include("src/target_tracking/target_tracking.jl")
include("src/target_tracking/filtering.jl")
include("src/target_tracking/information.jl")
include("src/target_tracking/target_coverage.jl")
include("src/target_tracking/single_robot_solver.jl")
include("src/target_tracking/multi_robot_solvers.jl")
include("src/target_tracking/multi_robot_target_tracking_problem.jl")
include("src/target_tracking/multi_robot_target_coverage_problem.jl")
include("src/target_tracking/pairwise_weights.jl")
include("src/target_tracking/experiment_tools.jl")
include("src/target_tracking/visualization.jl")
end
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function integral(A::AbstractInterpolation, t::Number)
bw, fw = samples(A)
idx = max(1+bw, min(searchsortedlast(A.t, t), length(A.t) - fw))
_integral(A, idx, t)
end
function integral(A::AbstractInterpolation, t1::Number, t2::Number)
bw, fw = samples(A)
# the index less than or equal to t1
idx1 = max(1+bw, min(searchsortedlast(A.t, t1), length(A.t) - fw))
# the index less than t2
idx2 = max(2+bw, min(searchsortedlast(A.t, t2), length(A.t) - fw))
if A.t[idx2] == t2
idx2-=1
end
total = zero(eltype(A))
for idx in idx1:idx2
lt1 = idx == idx1 ? t1 : A.t[idx]
lt2 = idx == idx2 ? t2 : A.t[idx+1]
total += _integral(A, idx, lt2)-_integral(A, idx, lt1)
end
total
end
samples(A::LinearInterpolation{<:AbstractVector}) = (0, 1)
function _integral(A::LinearInterpolation{<:AbstractVector{<:Number}}, idx::Number, t::Number)
t1 = A.t[idx]
t2 = A.t[idx+1]
u1 = A.u[idx]
u2 = A.u[idx+1]
t^2*(u1 - u2)/(2*t1 - 2*t2) + t*(t1*u2 - t2*u1)/(t1 - t2)
end
samples(A::ConstantInterpolation{<:AbstractVector}) = (0, 1)
function _integral(A::ConstantInterpolation{<:AbstractVector}, idx::Number, t::Number)
if A.dir === :left
# :left means that value to the left is used for interpolation
return A.u[idx]*t
else
# :right means that value to the right is used for interpolation
return A.u[idx+1]*t
end
end
samples(A::QuadraticInterpolation{<:AbstractVector}) = (0, 2)
function _integral(A::QuadraticInterpolation{<:AbstractVector{<:Number}}, idx::Number, t::Number)
t1 = A.t[idx]
t2 = A.t[idx+1]
t3 = A.t[idx+2]
u1 = A.u[idx]
u2 = A.u[idx+1]
u3 = A.u[idx+2]
(t^3*(-t1*u2 + t1*u3 + t2*u1 - t2*u3 - t3*u1 + t3*u2)/
(3*t1^2*t2 - 3*t1^2*t3 - 3*t1*t2^2 + 3*t1*t3^2 + 3*t2^2*t3 - 3*t2*t3^2) +
t^2*(t1^2*u2 - t1^2*u3 - t2^2*u1 + t2^2*u3 + t3^2*u1 - t3^2*u2)/
(2*t1^2*t2 - 2*t1^2*t3 - 2*t1*t2^2 + 2*t1*t3^2 + 2*t2^2*t3 - 2*t2*t3^2) +
t*(t1^2*t2*u3 - t1^2*t3*u2 - t1*t2^2*u3 + t1*t3^2*u2 + t2^2*t3*u1 - t2*t3^2*u1)/
(t1^2*t2 - t1^2*t3 - t1*t2^2 + t1*t3^2 + t2^2*t3 - t2*t3^2))
end
samples(A::QuadraticSpline{<:AbstractVector{<:Number}}) = (0, 1)
function _integral(A::QuadraticSpline{<:AbstractVector{<:Number}}, idx::Number, t::Number)
t1 = A.t[idx]
t2 = A.t[idx+1]
u1 = A.u[idx]
z1 = A.z[idx]
z2 = A.z[idx+1]
t^3*(z1 - z2)/(6*t1 - 6*t2) + t^2*(t1*z2 - t2*z1)/(2*t1 - 2*t2) + t*(-t1^2*z1 - t1^2*z2 + 2*t1*t2*z1 + 2*t1*u1 - 2*t2*u1)/(2*t1 - 2*t2)
end
samples(A::CubicSpline{<:AbstractVector{<:Number}}) = (0, 1)
function _integral(A::CubicSpline{<:AbstractVector{<:Number}}, idx::Number, t::Number)
t1 = A.t[idx]
t2 = A.t[idx+1]
u1 = A.u[idx]
u2 = A.u[idx+1]
z1 = A.z[idx]
z2 = A.z[idx+1]
h2 = A.h[idx+1]
(t^4*(-z1 + z2)/(24*h2) + t^3*(-t1*z2 + t2*z1)/(6*h2) +
t^2*(h2^2*z1 - h2^2*z2 + 3*t1^2*z2 - 3*t2^2*z1 - 6*u1 + 6*u2)/(12*h2) +
t*(h2^2*t1*z2 - h2^2*t2*z1 - t1^3*z2 - 6*t1*u2 + t2^3*z1 + 6*t2*u1)/(6*h2))
end | [
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] | 1.705984 | 1,738 |
<filename>docs/make.jl
using Documenter, Interpolations
makedocs(
sitename="Interpolations.jl",
modules=[Interpolations],
format=Documenter.HTML(prettyurls = get(ENV, "CI", nothing)=="true"),
pages=["Home" => "index.md",
"General usage" => "interpolations.md",
"Interpolation algorithms" => "control.md",
"Extrapolation" => "extrapolation.md",
"Convenience Constructors" => "convenience-construction.md",
"Library" => "api.md"]
)
deploydocs(repo="github.com/JuliaMath/Interpolations.jl")
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] | 2.555556 | 207 |
<reponame>lukketotte/EpdTest.jl
using EpdTest
using Test
@testset "EpdTest.jl" begin
μ,σ,p = 0., 1., 1.
y = [0.2, -1., 2.2, 3.1]
@test EpdTest.epdTest(y, μ, σ, p) === -1.4366494480275978
end
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] | 1.837838 | 111 |
# The branching factors for the various INode levels, as determined by
# _config_for_size(N). A NodeConfig remains valid until the number of dictionary
# entries N reaches next_config_size - changing configurations is handled by
# insert!(dict,h,k,v).
struct NodeConfig
inode_capacity::Vector{Int}
next_config_size::Int
end
function _config_for_size2(N::Int)::NodeConfig
if N < 16
return NodeConfig(Int64[], 16)
elseif N < 65536
# For small dictionaries, use inode_capacity=4, and height increasing with N.
num_bits = ceil(log2(N+1))
leaf_bits = min(num_bits,4)
num_inodes = max(1, convert(Int, ceil((num_bits-leaf_bits)/2)))
inode_capacity = [4 for i in 1:num_inodes]
next_config_size = 2^(4+2*(num_inodes))
else
# For large dictionaries, we use height 7, and inode capacity increasing with
# N. We partition ceil(log2(N)) bits among 7 levels, and to grow capacity as
# N increases we add a single bit to one level.
# We will have 1/7 of the bits at the leaf node, so 6/7 of the bits will be
# partitioned among the inodes. We start by giving all levels the same number
# of bits, then take care of the remainder by putting one extra bit at deeper
# levels.
num_bits = ceil(log2(N+1))
leaf_bits = max(4, convert(Int, floor(num_bits/7)))
inode_bits = num_bits - leaf_bits
m = convert(Int, floor(inode_bits/6))
inode_capacity = [2^m for i in 1:6]
have_bits = m*6
for k=6:-1:1
if have_bits < inode_bits
inode_capacity[k] *= 2
@dassert1 inode_capacity[k] <= 256 # N <= 2^56
have_bits += 1
else
break
end
end
next_config_size = convert(Int, 2^(floor(log2(N))+1))
end
shifts = [0 for i in 1:6]
return NodeConfig(inode_capacity, next_config_size)
end
const config1 = _config_for_size2(1)
const config16 = _config_for_size2(16)
const config64 = _config_for_size2(64)
const config256 = _config_for_size2(256)
function _config_for_size(N::Int)::NodeConfig
# For N < 1024, use one of the pre-allocated instances config1/config16/
# config64/config256, to reduce space use by small dictionaries.
if N < 64
if N < 16
@dassert1 config1.next_config_size == 16
return config1
else
@dassert1 config16.next_config_size == 64
return config16
end
elseif N < 1024
if N < 256
@dassert1 config64.next_config_size == 256
return config64
else
@dassert1 config256.next_config_size == 1024
return config256
end
else
return _config_for_size2(N)
end
end
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] | 2.255387 | 1,253 |
<reponame>simonbyrne/CUDA.jl<filename>deps/discovery.jl
function resolve(path)
if islink(path)
dir = dirname(path)
resolve(joinpath(dir, readlink(path)))
else
path
end
end
# return a list of valid directories, resolving symlinks and pruning duplicates
function valid_dirs(dirs)
map!(resolve, dirs, dirs)
filter(isdir, unique(dirs))
end
## generic discovery routines
function library_versioned_names(name::String, version::Union{Nothing,VersionNumber,String}=nothing)
names = String[]
if Sys.iswindows()
# Windows encodes the version in the filename
if version isa VersionNumber
append!(names, ["$(name)$(Sys.WORD_SIZE)_$(version.major)$(version.minor).$(Libdl.dlext)",
"$(name)$(Sys.WORD_SIZE)_$(version.major).$(Libdl.dlext)"])
elseif version isa String
push!(names, "$(name)$(Sys.WORD_SIZE)_$(version).$(Libdl.dlext)")
elseif version === nothing
push!(names, "$(name)$(Sys.WORD_SIZE).$(Libdl.dlext)")
end
# some libraries (e.g. CUTENSOR) are shipped without the word size-prefix
if version isa VersionNumber
append!(names, ["$(name)_$(version.major)$(version.minor).$(Libdl.dlext)",
"$(name)_$(version.major).$(Libdl.dlext)"])
elseif version isa String
push!(names, "$(name)_$(version).$(Libdl.dlext)")
elseif version === nothing
push!(names, "$(name).$(Libdl.dlext)")
end
elseif Sys.isapple()
# macOS puts the version number before the dylib extension
if version isa VersionNumber
append!(names, ["lib$(name).$(version.major).$(version.minor).$(Libdl.dlext)",
"lib$(name).$(version.major).$(Libdl.dlext)"])
elseif version isa String
push!(names, "lib$(name).$(version).$(Libdl.dlext)")
elseif version === nothing
push!(names, "lib$(name).$(Libdl.dlext)")
end
elseif Sys.isunix()
# most UNIX distributions ship versioned libraries (also see JuliaLang/julia#22828)
if version isa VersionNumber
append!(names, ["lib$(name).$(Libdl.dlext).$(version.major).$(version.minor).$(version.patch)",
"lib$(name).$(Libdl.dlext).$(version.major).$(version.minor)",
"lib$(name).$(Libdl.dlext).$(version.major)"])
elseif version isa String
push!(names, "lib$(name).$(Libdl.dlext).$(version)")
elseif version === nothing
push!(names, "lib$(name).$(Libdl.dlext)")
end
elseif version === nothing
push!(names, "lib$name.$(Libdl.dlext)")
end
return names
end
"""
find_library(name, version; locations=String[])
Wrapper for Libdl.find_library, performing a more exhaustive search:
- variants of the library name (including version numbers, platform-specific tags, etc);
- various subdirectories of the `locations` list, and finally system library directories.
Returns the full path to the library.
"""
function find_library(name::String, version::Union{Nothing,VersionNumber,String}=nothing;
locations::Vector{String}=String[])
@debug "Request to look for library $name $version" locations
# figure out names
all_names = library_versioned_names(name, version)
# figure out locations
all_locations = String[]
for location in locations
push!(all_locations, location)
push!(all_locations, joinpath(location, "lib"))
if Sys.WORD_SIZE == 64
push!(all_locations, joinpath(location, "lib64"))
push!(all_locations, joinpath(location, "libx64"))
end
if Sys.iswindows()
push!(all_locations, joinpath(location, "bin"))
push!(all_locations, joinpath(location, "bin", Sys.WORD_SIZE==64 ? "x64" : "Win32"))
end
end
@debug "Looking for library $(join(all_names, ", "))" locations=all_locations
name_found = Libdl.find_library(all_names, all_locations)
if isempty(name_found)
return nothing
end
# find the full path of the library (which Libdl.find_library doesn't guarantee to return)
path = Libdl.dlpath(name_found)
@debug "Found library $(basename(path)) at $(dirname(path))"
return path
end
"""
find_binary(name; locations=String[])
Similar to `find_library`, performs an exhaustive search for a binary in various
subdirectories of `locations`, and finally PATH by using `Sys.which`.
"""
function find_binary(name::String; locations::Vector{String}=String[])
@debug "Request to look for binary $name" locations
# figure out locations
all_locations = String[]
for location in locations
push!(all_locations, location)
push!(all_locations, joinpath(location, "bin"))
end
# we look in PATH too by using `Sys.which` with unadorned names
@debug "Looking for binary $name" locations=all_locations
all_paths = [name; [joinpath(location, name) for location in all_locations]]
for path in all_paths
try
program_path = Sys.which(path)
if program_path !== nothing
@debug "Found binary $path at $program_path"
return program_path
end
catch
# some system disallow `stat` on certain paths
end
end
return nothing
end
## CUDA-specific discovery routines
const cuda_releases = [v"1.0", v"1.1",
v"2.0", v"2.1", v"2.2",
v"3.0", v"3.1", v"3.2",
v"4.0", v"4.1", v"4.2",
v"5.0", v"5.5",
v"6.0", v"6.5",
v"7.0", v"7.5",
v"8.0",
v"9.0", v"9.1", v"9.2",
v"10.0", v"10.1", v"10.2",
v"11.0", v"11.1", v"11.2", v"11.3", v"11.4"]
const cuda_library_versions = Dict(
v"11.0.1" => Dict(
# NOTE: encountered this version in a Docker container; not sure where it came from.
"cupti" => "2020.1.0", # wtf
"nvtx" => v"11.0.167",
"cublas" => v"11.0.0", #.191
"cufft" => v"10.1.3", #.191
"curand" => v"10.2.0", #.191
"cusolver" => v"10.4.0", #.191
"cusparse" => v"11.0.0", #.191
),
v"11.0.2" => Dict(
"cupti" => "2020.1.0", # wtf
"nvtx" => v"11.0.167",
"cublas" => v"11.0.0", #.191
"cufft" => v"10.1.3", #.191
"curand" => v"10.2.0", #.191
"cusolver" => v"10.4.0", #.191
"cusparse" => v"11.0.0", #.191
),
v"11.0.3" => Dict(
"cupti" => "2020.1.1", # docs mention 11.0.221
"nvtx" => v"11.0.167",
"cublas" => v"11.2.0", #.252
"cufft" => v"10.2.1", #.245
"curand" => v"10.2.1", #.245
"cusolver" => v"10.6.0", #.245
"cusparse" => v"11.1.1", #.245
),
v"11.1.0" => Dict(
"cupti" => "2020.2.0", # docs mention 11.1.69
"nvtx" => v"11.1.74",
"cublas" => v"11.2.1", #.74
"cufft" => v"10.3.0", #.74
"curand" => v"10.2.2", #.74
"cusolver" => v"11.0.0", #.74
"cusparse" => v"11.2.0", #.275
),
v"11.1.1" => Dict(
"cupti" => "2020.2.1", # docs mention 11.1.105
"nvtx" => v"11.1.74",
"cublas" => v"11.3.0", #.106
"cufft" => v"10.3.0", #.105
"curand" => v"10.2.2", #.105
"cusolver" => v"11.0.1", #.105
"cusparse" => v"11.3.0", #.10
),
v"11.2.0" => Dict(
"cupti" => "2020.3.0", # docs mention 11.2.67
"nvtx" => v"11.2.67",
"cublas" => v"11.3.1", #.68
"cufft" => v"10.4.0", #.72
"curand" => v"10.2.3", #.68
"cusolver" => v"11.0.2", #.68
"cusparse" => v"11.3.1", #.68
),
v"11.2.1" => Dict(
"cupti" => "2020.3.1", # docs mention 11.2.135
"nvtx" => v"11.2.67",
"cublas" => v"11.4.1", #.1026
"cufft" => v"10.4.0", #.135
"curand" => v"10.2.3", #.135
"cusolver" => v"11.1.0", #.135
"cusparse" => v"11.4.0", #.135
),
v"11.2.2" => Dict(
"cupti" => "2020.3.1", # docs mention 11.2.152
"nvtx" => v"11.2.152",
"cublas" => v"11.4.1", #.1043
"cufft" => v"10.4.1", #.152
"curand" => v"10.2.3", #.152
"cusolver" => v"11.1.0", #.152
"cusparse" => v"11.4.1", #.1152
),
v"11.3.0" => Dict(
"cupti" => "2021.1.0", # docs mention 11.3.58
"nvtx" => v"11.3.58",
"cublas" => v"11.4.2", #.10064
"cufft" => v"10.4.2", #.58
"curand" => v"10.2.4", #.58
"cusolver" => v"11.1.1", #.58
"cusparse" => v"11.5.0", #.58
),
v"11.3.1" => Dict(
"cupti" => "2021.1.1", # docs mention 11.3.111
"nvtx" => v"11.3.109",
"cublas" => v"11.5.1", #.109
"cufft" => v"10.4.2", #.109
"curand" => v"10.2.4", #.109
"cusolver" => v"11.1.2", #.109
"cusparse" => v"11.6.0", #.109
),
v"11.4.0" => Dict(
"cupti" => "2021.2.0", # docs mention 11.4.65
"nvtx" => v"11.4.43",
"cublas" => v"11.5.2", #.43
"cufft" => v"10.5.0", #.43
"curand" => v"10.2.5", #.43
"cusolver" => v"11.2.0", #.43
"cusparse" => v"11.6.0", #.43
),
)
function cuda_library_version(library, toolkit_version)
if library == "nvtx"
v"1"
elseif toolkit_version >= v"11"
# starting with CUDA 11, libraries are versioned independently
if !haskey(cuda_library_versions, toolkit_version)
error("CUDA.jl does not yet support CUDA $toolkit_version; please file an issue.")
end
# HACK: generalize this?
if library == "cusolverMg"
library = "cusolver"
end
if library == "cublasLt"
library = "cublas"
end
cuda_library_versions[toolkit_version][library]
else
toolkit_version
end
end
const cuda_library_names = Dict(
"nvtx" => "nvToolsExt"
)
# only for nvdisasm, to discover the CUDA toolkit version
const cuda_binary_versions = Dict(
v"11.0.1" => Dict(
# NOTE: encountered this version in a Docker container; not sure where it came from.
"nvdisasm" => v"11.0.167"
),
v"11.0.2" => Dict(
"nvdisasm" => v"11.0.194"
),
v"11.0.3" => Dict(
"nvdisasm" => v"11.0.221"
),
v"11.1.0" => Dict(
"nvdisasm" => v"11.1.74"
),
v"11.1.1" => Dict(
"nvdisasm" => v"11.1.74" # ambiguous!
),
v"11.2.0" => Dict(
"nvdisasm" => v"11.2.67"
),
v"11.2.1" => Dict(
"nvdisasm" => v"11.2.135"
),
v"11.2.2" => Dict(
"nvdisasm" => v"11.2.152"
),
v"11.3.0" => Dict(
"nvdisasm" => v"11.3.58",
"ptxas" => v"11.3.58"
),
v"11.3.1" => Dict(
"nvdisasm" => v"11.3.58", # ambiguous!
"ptxas" => v"11.3.109"
),
v"11.3.1" => Dict(
"nvdisasm" => v"11.4.43"
),
)
# simplified find_library/find_binary entry-points,
# looking up name aliases and known version numbers
# and passing the (optional) toolkit dirs as locations.
function find_cuda_library(library::String, toolkit_dirs::Vector{String},
toolkit_version::VersionNumber)
toolkit_release = VersionNumber(toolkit_version.major, toolkit_version.minor)
# figure out the location
locations = toolkit_dirs
## CUPTI is in the "extras" directory of the toolkit
if library == "cupti"
toolkit_extras_dirs = filter(dir->isdir(joinpath(dir, "extras")), toolkit_dirs)
cupti_dirs = map(dir->joinpath(dir, "extras", "CUPTI"), toolkit_extras_dirs)
append!(locations, cupti_dirs)
end
## NVTX is located in an entirely different location on Windows
if library == "nvtx" && Sys.iswindows()
if haskey(ENV, "NVTOOLSEXT_PATH")
dir = ENV["NVTOOLSEXT_PATH"]
@debug "Looking for NVTX library via environment variable" dir
else
program_files = ENV[Sys.WORD_SIZE == 64 ? "ProgramFiles" : "ProgramFiles(x86)"]
dir = joinpath(program_files, "NVIDIA Corporation", "NvToolsExt")
@debug "Looking for NVTX library in the default directory" dir
end
isdir(dir) && push!(locations, dir)
end
version = cuda_library_version(library, toolkit_version)
name = get(cuda_library_names, library, library)
find_library(name, version; locations=locations)
end
find_cuda_binary(name::String, toolkit_dirs::Vector{String}=String[]) =
find_binary(name; locations=toolkit_dirs)
"""
find_toolkit()::Vector{String}
Look for directories where (parts of) the CUDA toolkit might be installed. This returns a
(possibly empty) list of paths that can be used as an argument to other discovery functions.
The behavior of this function can be overridden by defining the `CUDA_PATH`, `CUDA_HOME` or
`CUDA_ROOT` environment variables, which should point to the root of the CUDA toolkit.
"""
function find_toolkit()
dirs = String[]
# look for environment variables to override discovery
envvars = ["CUDA_PATH", "CUDA_HOME", "CUDA_ROOT"]
filter!(var -> haskey(ENV, var) && ispath(ENV[var]), envvars)
if !isempty(envvars)
paths = unique(map(var->ENV[var], envvars))
if length(paths) > 1
@warn "Multiple CUDA environment variables set to different values: $(join(paths, ", "))"
end
@debug "Looking for CUDA toolkit via environment variables $(join(envvars, ", "))"
append!(dirs, paths)
return dirs
end
# look for the compiler binary (in the case PATH points to the installation)
ptxas_path = find_binary("ptxas")
if ptxas_path !== nothing
ptxas_dir = dirname(ptxas_path)
if occursin(r"^bin(32|64)?$", basename(ptxas_dir))
ptxas_dir = dirname(ptxas_dir)
end
@debug "Looking for CUDA toolkit via ptxas binary" path=ptxas_path dir=ptxas_dir
push!(dirs, ptxas_dir)
end
# look for the runtime library (in the case LD_LIBRARY_PATH points to the installation)
libcudart_path = find_library("cudart")
if libcudart_path !== nothing
libcudart_dir = dirname(libcudart_path)
if occursin(r"^(lib|bin)(32|64)?$", basename(libcudart_dir))
libcudart_dir = dirname(libcudart_dir)
end
@debug "Looking for CUDA toolkit via CUDA runtime library" path=libcudart_path dir=libcudart_dir
push!(dirs, libcudart_dir)
end
# look in default installation directories
default_dirs = String[]
if Sys.iswindows()
# CUDA versions are installed in separate directories under a single base dir
program_files = ENV[Sys.WORD_SIZE == 64 ? "ProgramFiles" : "ProgramFiles(x86)"]
basedir = joinpath(program_files, "NVIDIA GPU Computing Toolkit", "CUDA")
if isdir(basedir)
entries = map(dir -> joinpath(basedir, dir), readdir(basedir))
append!(default_dirs, entries)
end
else
# CUDA versions are installed in unversioned dirs, or suffixed with the version
basedirs = ["/usr/local/cuda", "/opt/cuda"]
for ver in cuda_releases, dir in basedirs
push!(default_dirs, "$dir-$(ver.major).$(ver.minor)")
end
append!(default_dirs, basedirs)
push!(default_dirs, "/usr/lib/nvidia-cuda-toolkit")
push!(default_dirs, "/usr/share/cuda")
end
reverse!(default_dirs) # we want to search starting from the newest CUDA version
default_dirs = valid_dirs(default_dirs)
if !isempty(default_dirs)
@debug "Looking for CUDA toolkit via default installation directories" dirs=default_dirs
append!(dirs, default_dirs)
end
# filter
dirs = valid_dirs(dirs)
@debug "Found CUDA toolkit at $(join(dirs, ", "))"
return dirs
end
# figure out the CUDA toolkit version (by looking at the output of a tool like `nvdisasm`)
function parse_toolkit_version(tool, tool_path::String)
# parse the version string
verstr = withenv("LANG"=>"C") do
read(`$tool_path --version`, String)
end
m = match(r"\bV(?<major>\d+).(?<minor>\d+).(?<patch>\d+)\b", verstr)
if m === nothing
@error "Could not parse CUDA version info (\"$verstr\"); please file an issue."
return nothing
end
version = VersionNumber(parse(Int, m[:major]),
parse(Int, m[:minor]),
parse(Int, m[:patch]))
if version >= v"11"
# starting with CUDA 11, binaries are versioned independently
# NOTE: we can't always tell, e.g. nvdisasm is the same in CUDA 11.1.0 and 11.1.1.
# return the lowest version to ensure compatibility.
for toolkit_version in sort(collect(keys(cuda_binary_versions)))
if haskey(cuda_binary_versions[toolkit_version], tool) &&
cuda_binary_versions[toolkit_version][tool] == version
@debug "CUDA toolkit identified as $toolkit_version (providing $tool $version)"
return toolkit_version
end
end
@error "CUDA.jl does not yet support CUDA with $tool $version; please file an issue."
return nothing
else
@debug "CUDA toolkit identified as $version"
return version
end
end
"""
find_libdevice(toolkit_dirs::Vector{String})
Look for the CUDA device library supporting `targets` in any of the CUDA toolkit directories
`toolkit_dirs`. On CUDA >= 9.0, a single library unified library is discovered and returned
as a string. On older toolkits, individual libraries for each of the targets are returned as
a vector of strings.
"""
function find_libdevice(toolkit_dirs)
@debug "Request to look for libdevice" locations=toolkit_dirs
# figure out locations
dirs = String[]
for toolkit_dir in toolkit_dirs
push!(dirs, toolkit_dir)
push!(dirs, joinpath(toolkit_dir, "libdevice"))
push!(dirs, joinpath(toolkit_dir, "nvvm", "libdevice"))
end
# filter
dirs = valid_dirs(dirs)
@debug "Look for libdevice" locations=dirs
for dir in dirs
path = joinpath(dir, "libdevice.10.bc")
if isfile(path)
@debug "Found unified device library at $path"
return path
end
end
return nothing
end
"""
find_libcudadevrt(toolkit_dirs::Vector{String})
Look for the CUDA device runtime library in any of the CUDA toolkit directories
`toolkit_dirs`.
"""
function find_libcudadevrt(toolkit_dirs)
locations = toolkit_dirs
@debug "Request to look for libcudadevrt " locations
name = nothing
if Sys.isunix()
name = "libcudadevrt.a"
elseif Sys.iswindows()
name = "cudadevrt.lib"
else
error("No support for discovering the CUDA device runtime library on your platform, please file an issue.")
end
# figure out locations
all_locations = String[]
for location in locations
push!(all_locations, location)
if Sys.iswindows()
if Sys.WORD_SIZE == 64
push!(all_locations, joinpath(location, "lib", "x64"))
elseif Sys.WORD_SIZE == 32
push!(all_locations, joinpath(location, "lib", "Win32"))
end
else
push!(all_locations, joinpath(location, "lib"))
if Sys.WORD_SIZE == 64
push!(all_locations, joinpath(location, "lib64"))
end
end
end
@debug "Looking for CUDA device runtime library $name" locations=all_locations
paths = filter(isfile, map(location->joinpath(location, name), all_locations))
if isempty(paths)
return nothing
else
path = first(paths)
@debug "Found CUDA device runtime library $(basename(path)) at $(dirname(path))"
return path
end
end
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] | 2.077262 | 9,772 |
<reponame>JuliaPOMDP/SARSOP.jl<filename>test/cancer.jl<gh_stars>1-10
S = [:healthy, :in_situ, :invasive, :death]
A = [:wait, :test, :treat]
O = [:pos, :neg]
γ = 0.99
s0 = Deterministic(:healthy)
term = Set([:death])
function T(s, a, sp)
if s == :healthy
if sp == :in_situ
return 0.02
elseif sp == s
return 0.98
else
return 0.0
end
elseif s == :in_situ
if a == :treat
if sp == :healthy
return 0.60
elseif sp == s
return 0.40
else
return 0.0
end
else #a == :test || a == :wait
if sp == :invasive
return 0.10
elseif sp == s
return 0.90
else
return 0.0
end
end
elseif s == :invasive
if a == :treat
if sp == :healthy
return 0.20
elseif sp == :death
return 0.20
elseif sp == s
return 0.60
else
return 0.0
end
else
if sp == :death
return 0.60
elseif sp == s
return 0.40
else
return 0.0
end
end
else # s == :death
return 0.25
end
end
function Z(a, sp, o)
if a == :test
if sp == :healthy
if o == :pos
return 0.05
else
return 0.95
end
elseif sp == :in_situ
if o == :pos
return 0.80
else
return 0.20
end
else
if o == :pos
return 1.0
else
return 0.0
end
end
elseif a == :treat
if sp == :in_situ || sp == :invasive
if o == :pos
return 1.0
else
return 0.0
end
else
if o == :pos
return 0.0
else
return 1.0
end
end
else #a == :wait
if o == :pos
return 0.0
else
return 1.0
end
end
end
function R(s, a)
if s == :death
return 0.0
elseif a == :wait
return 1.0
elseif a == :test
return 0.80
else
return 0.10
end
end
c = DiscreteExplicitPOMDP(S, A, O, T, Z, R, γ, s0, terminals=term);
function evalSolver(m)
results = Dict{String, Float64}()
for (key, solver) in ["SARSOP"=>SARSOPSolver()]
policy = solve(solver, m)
N = 10000
rsum = 0.0
for i in 1:N
rsum += simulate(RolloutSimulator(max_steps=500), m, policy)
end
results[key] = rsum/N
end;
return results
end
results_c = evalSolver(c);
@show results_c["SARSOP"]
@test results_c["SARSOP"] >= 62.5
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] | 1.63337 | 1,822 |
<filename>test/adapt.jl
using DimensionalData, Test, Unitful, Adapt
struct CustomArray{T,N} <: AbstractArray{T,N}
arr::Array
end
CustomArray(x::Array{T,N}) where {T,N} = CustomArray{T,N}(x)
Adapt.adapt_storage(::Type{<:CustomArray}, xs::Array) = CustomArray(xs)
Base.size(x::CustomArray, y...) = size(x.arr, y...)
Base.getindex(x::CustomArray, y...) = getindex(x.arr, y...)
Base.count(x::CustomArray) = count(x.arr)
@testset "Metadata" begin
@test adapt(CustomArray, Metadata(:a=>"1", :b=>"2")) == NoMetadata()
end
@testset "Dimension" begin
d = X([1:10...]; metadata=Metadata(:a=>"1", :b=>"2"))
d1 = adapt(CustomArray, d)
@test val(d1) isa CustomArray
@test val(d1).arr == [1:10...]
@test metadata(d1) == NoMetadata()
end
@testset "DimArray" begin
A = rand(4, 5)
da = DimArray(A, (X, Y))
da1 = adapt(CustomArray, da)
@test parent(da1) isa CustomArray
@test parent(da1).arr == A
@test metadata(da1) == NoMetadata()
end
@testset "DimStack" begin
A = rand(4, 5)
B = rand(4, 5)
ds = DimStack((a=A, b=B), (X, Y))
ds1 = adapt(CustomArray, ds)
@test parent(ds1[:a]) isa CustomArray
@test parent(ds1[:a]).arr == A
@test parent(ds1[:b]) isa CustomArray
@test parent(ds1[:b]).arr == B
@test metadata(ds) == NoMetadata()
end
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7,
9310,
8,
6624,
1400,
9171,
14706,
3419,
198,
437,
198
] | 2.28223 | 574 |
<gh_stars>0
using Klara
function plogtarget(x::Vector{Float64})
s2 = exp(x[1])
nx = length(x)-1
-0.5*((x[1]-0.0)^2/9.0+dot(x[2:end], x[2:end])/s2+nx*log(s2))
end
p = BasicContMuvParameter(:p, logtarget=plogtarget)
model = likelihood_model(p, false)
sampler = SliceSampler(1., 5)
mcrange = BasicMCRange(nsteps=100000, burnin=10000)
v0 = Dict(:p=>[10.; zeros(4)])
job = BasicMCJob(model, sampler, mcrange, v0)
run(job)
chain = output(job)
mean(chain)
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] | 2.066667 | 225 |
#Define Simulation state that also contains the control variables using the DEDataVector
#interface from the DifferentialEquations package
import DifferentialEquations: ODEProblem
mutable struct SimState{Float64} <: DEDataVector{Float64}
x::Vector{Float64} #State variables
u::Vector{Float64} #Control Variables
F::Type{<:AbstractFrame} #FrameType
end #SimState
SimState(x::Vector{Float64}, u::Vector{Float64}) = SimState(x, u, ICRF)
# Create New ODE interface that uses x_dot = f(x,u,t), instead of x_dot = f(x,t)
#Also, make sure that integration is done in ICRF reference frame
function ODEProblem(f::Function, x0::Vector{Float64}, u0::Vector{Float64}, t0::Float64, tend::Float64)
y0 = SimState(x0,u0)
return ODEProblem(f,y0,(t0,tend))
end
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] | 2.879699 | 266 |
<filename>src/blocksizes.jl
##############
# BlockSizes #
##############
# Keeps track of the sizes of all the blocks in the `BlockArray`
immutable BlockSizes{N}
sizes::NTuple{N, Vector{Int}}
end
Base.:(==)(a::BlockSizes, b::BlockSizes) = a.sizes == b.sizes
BlockSizes{N}(sizes::Vararg{Vector{Int}, N}) = BlockSizes(sizes)
Base.getindex(block_sizes::BlockSizes, i) = block_sizes.sizes[i]
Base.getindex(block_sizes::BlockSizes, i, j) = block_sizes.sizes[i][j]
function blocksize{N}(block_sizes::BlockSizes{N}, i::NTuple{N, Int})
return ntuple(k->block_sizes[k, i[k]], Val{N})
end
function Base.show{N}(io::IO, block_sizes::BlockSizes{N})
if N == 0
print(io, "[]")
else
print(io, block_sizes.sizes[1])
for i in 2:N
print(io, "×", block_sizes.sizes[i])
end
end
end
nblocks{N}(block_sizes::BlockSizes{N}) = ntuple(i -> length(block_sizes[i]), Val{N})
nblocks(block_sizes::BlockSizes, i::Int) = length(block_sizes[i])
Base.copy{N}(block_sizes::BlockSizes{N}) = BlockSizes(ntuple(i -> copy(block_sizes[i]), Val{N}))
# Computes the global range of an Array that corresponds to a given block_index
@generated function globalrange{N}(block_sizes::BlockSizes, block_index::NTuple{N, Int})
start_indices_ex = Expr(:tuple, [:(1 + _cumsum(block_sizes[$i], block_index[$i]-1)) for i=1:N]...)
indices_ex = Expr(:tuple, [:(start_indices[$i]:start_indices[$i] + block_sizes[$i, block_index[$i]] - 1) for i = 1:N]...)
return quote
@inbounds start_indices = $start_indices_ex
@inbounds indices = $indices_ex
return indices
end
end
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220,
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1441,
36525,
198,
220,
220,
220,
886,
198,
437,
198
] | 2.269553 | 716 |
"""
MultiSampler
A sampler that makes it easy to sample from a vector of independent samplers.
##### Fields
- `samplers::T` : the independent samplers.
"""
struct MultiSampler{T<:AbstractVector{<:Sampleable}} <: Sampleable{Multivariate, Discrete}
samplers::T
end
"""
Base.show(io::IO, multisampler::MultiSampler) -> nothing
Show a `MultiSampler` in a human-friendly manner.
"""
function Base.show(io::IO, multisampler::MultiSampler)
println(io, typeof(multisampler))
println(io, " independent samplers: ")
for sampler in multisampler.samplers
println(io, " ", sampler)
end
return nothing
end
"""
MultiSampler(graph::AbstractGraph, nsamplers::Integer) -> MultiSampler
Construct and return a `MultiSampler` with `nsamplers` `NeighborSampler`s defined w.r.t.
`graph`.
##### Complexity
- Time complexity: `O(nsamplers)`.
"""
function MultiSampler(graph::AbstractGraph, nsamplers::Integer)
return MultiSampler([NeighborSampler(graph, rand(1:nv(graph))) for _ in 1:nsamplers])
end
"""
Base.rand(rng::AbstractRNG,
multisampler::MultiSampler{<:AbstractVector{<:NeighborSampler}}) -> (
Vector{Int})
Draw from each `NeighborSampler` independently and return a vector of the samples.
##### Complexity
- Time complexity: `O(nsamplers)`.
Use this for e.g., simulating multiple-agent random walks on a graph.
"""
function Base.rand(rng::AbstractRNG,
multisampler::MultiSampler{<:AbstractVector{<:NeighborSampler}})
return [rand(sampler) for sampler in multisampler.samplers]
end
"""
Base.rand(rng::AbstractRNG,
multisampler::MultiSampler{<:AbstractVector{<:Sampleable}}) -> Vector{Float64}
Draw from each `sampler` independently and return a vector of the samples.
##### Complexity
- Time complexity: `O(nsamplers(multisampler))`.
Use this for e.g., simulating multiple-agent random walks on an integer lattice.
"""
function Base.rand(rng::AbstractRNG,
multisampler::MultiSampler{<:AbstractVector{<:Sampleable}})
# convert to float for type stability
return [float(rand(sampler)) for sampler in multisampler.samplers]
end
"""
nsamplers(multisampler::MultiSampler)
Return the number of samplers.
##### Complexity
- Time complexity: `O(1)`.
"""
function nsamplers(multisampler::MultiSampler)
return length(multisampler.samplers)
end
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] | 2.693694 | 888 |
<gh_stars>1-10
#=
Find local bindings
Downstreams: completions.jl, goto.jl, datatip.jl, refactor.jl
=#
struct LocalBinding
name::String
verbatim::String
span::UnitRange{Int64}
line::Int
expr::EXPR
end
struct LocalScope
name::String
verbatim::String
span::UnitRange{Int64}
line::Int
children::Vector{Union{LocalBinding,LocalScope}}
expr::EXPR
end
const LocalBS = Union{LocalBinding,LocalScope}
struct ActualLocalBinding
name::String
verbatim::String
root::String
line::Int
locality::Float64
expr::EXPR
end
function ActualLocalBinding(bs::LocalBS, root::String, line::Int, byteoffset::Int)
locality = distance(line, byteoffset, bs.line, bs.span)
return ActualLocalBinding(bs.name, bs.verbatim, root, bs.line, locality, bs.expr)
end
"""
locals(text::String, line::Int, col::Int)::Vector{ActualLocalBinding}
Returns local bindings in `text`, while computing localities based on `line` and `col`.
"""
function locals(text::String, line::Int, col::Int)::Vector{ActualLocalBinding}
expr = CSTParser.parse(text, true)
traverse_expr!(expr)
bindings = localbindings(expr, text)
actual_localbindings(bindings, line, byteoffset(text, line, col))
end
function localbindings(expr, text, bindings = LocalBS[], pos = 1, line = 1)
# binding
bind = bindingof(expr)
hs = hasscope(expr)
if bind !== nothing && !hs
verbatim = str_value_verbatim(bind, text, pos)
range = pos:pos+expr.span
push!(bindings, LocalBinding(bind.name, verbatim, range, line, expr))
end
if hs
typof(expr) === CSTParser.Kw && return bindings
# destructure multiple returns
if ismultiplereturn(expr)
for arg in expr
# don't update `pos` & `line`, i.e.: treat all the multiple returns as same
localbindings(arg, text, bindings, pos, line)
end
# properly detect the parameters of a method with where clause: https://github.com/JunoLab/Juno.jl/issues/404
elseif iswhereclause(expr)
for arg in expr
localbindings(arg, text, bindings, pos, line)
line += countlines(arg, text, pos)
pos += arg.fullspan
end
else
# find local binds in a scope
# calculate fields for `LocalScope` first
verbatim = str_value_verbatim(expr, text, pos)
range = pos:pos+expr.span
name = bind === nothing ? "" : bind.name
children = LocalBS[]
for arg in expr
localbindings(arg, text, children, pos, line)
line += countlines(arg, text, pos)
pos += arg.fullspan
end
push!(bindings, LocalScope(name, verbatim, range, line, children, expr))
end
return bindings
end
# look for more local bindings if exists
for arg in expr
localbindings(arg, text, bindings, pos, line)
line += countlines(arg, text, pos)
pos += arg.fullspan
end
return bindings
end
function byteoffset(text, line, col)
byteoffset = 1
current_line = 1
current_char = 0
for c in text
if line == current_line
current_char += 1
c === '\n' && break
end
current_char == col && break
byteoffset += @static VERSION >= v"1.1" ? ncodeunits(c) : ncodeunits(string(c))
c === '\n' && (current_line += 1)
end
byteoffset
end
function actual_localbindings(bindings, line, byteoffset)
actual_bindings = _actual_localbindings(bindings, line, byteoffset)
filter!(b -> !isempty(b.name), actual_bindings)
sort!(actual_bindings, lt = (b1, b2) -> b1.locality < b2.locality)
return @static VERSION ≥ v"1.1" ? unique!(b->b.name, actual_bindings) : unique(b->b.name, actual_bindings)
end
function _actual_localbindings(bindings, line, byteoffset, root = "", actual_bindings = ActualLocalBinding[])
for bind in bindings
push!(actual_bindings, ActualLocalBinding(bind, root, line, byteoffset))
if bind isa LocalScope && byteoffset in bind.span
_actual_localbindings(bind.children, line, byteoffset, bind.name, actual_bindings)
end
end
return actual_bindings
end
function distance(line, byteoffset, defline, defspan)
abslinediff = abs(line - defline)
absbytediff = abs(byteoffset - defspan[1]) # tiebreaker for bindings on the same line
diff = if byteoffset in defspan
Inf
elseif line < defline
(defline - line)*10 # bindings defined *after* the current line have a lower priority
else
line - defline
end
diff + absbytediff*1e-6
end
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1525,
1513,
733,
9,
16,
68,
12,
21,
198,
437,
198
] | 2.412093 | 1,968 |
export Surgery
module Surgery
using CSV
using ..Empirikos: BinomialSample
const DATA = joinpath(@__DIR__, "surgery.csv")
function load_table()
CSV.File(DATA)
end
function ebayes_samples()
tbl = load_table()
Zs = BinomialSample.(tbl.s, tbl.n)
end
end
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] | 2.481481 | 108 |
<filename>src/wrappers/x86_64-linux-gnu-cxx11.jl
# Autogenerated wrapper script for SCIP_jll for x86_64-linux-gnu-cxx11
export libscip
using bliss_jll
using GMP_jll
using CompilerSupportLibraries_jll
using Ipopt_jll
using Zlib_jll
JLLWrappers.@generate_wrapper_header("SCIP")
JLLWrappers.@declare_library_product(libscip, "libscip.so.7.0")
function __init__()
JLLWrappers.@generate_init_header(bliss_jll, GMP_jll, CompilerSupportLibraries_jll, Ipopt_jll, Zlib_jll)
JLLWrappers.@init_library_product(
libscip,
"lib/libscip.so",
RTLD_LAZY | RTLD_DEEPBIND,
)
JLLWrappers.@generate_init_footer()
end # __init__()
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] | 2.202703 | 296 |
export proxLLR!, normLLR
"""
proxLLR!(x::Vector{T}, λ::Float64=1e-6; kargs...) where T
proximal map for LLR regularization using singular-value-thresholding
# Arguments
* `x::Vector{T}` - Vector to apply proximal map to
* `λ::Float64` - regularization parameter
* `shape::Tuple{Int}=[]` - dimensions of the image
* `blockSize::Tuple{Int}=[2;2]` - size of patches to perform singluar value thresholding on
* `randshift::Bool=true` - randomly shifts the patches to ensure translation invariance
"""
function proxLLR!(x::Vector{T}, λ; shape::NTuple{N,TI}=error(),
blockSize::NTuple{N,TI}=ntuple(_-> 2, N), randshift::Bool=true) where {T, N,TI <: Integer}
x = reshape(x, tuple(shape..., length(x) ÷ prod(shape)))
block_idx = CartesianIndices(blockSize)
K = size(x)[end]
if randshift
# Random.seed!(1234)
shift_idx = (Tuple(rand(block_idx))..., 0)
xs = circshift(x, shift_idx)
else
xs = x
end
ext = mod.(shape,blockSize)
pad = mod.(blockSize .- ext, blockSize)
if any(pad .!= 0)
xp = zeros(T, (shape .+ pad)..., K)
xp[CartesianIndices(x)] .= xs
else
xp = xs
end
xᴸᴸᴿ = Array{T}(undef, prod(blockSize), K)
for i ∈ CartesianIndices(StepRange.(0, blockSize, shape .- 1))
@views xᴸᴸᴿ .= reshape(xp[i .+ block_idx,:], :, K)
# threshold singular values
SVDec = svd!(xᴸᴸᴿ)
proxL1!(SVDec.S,λ)
xp[i .+ block_idx,:] .= reshape(SVDec.U * Diagonal(SVDec.S) * SVDec.Vt, blockSize..., :)
end
if any(pad .!= 0)
xs .= xp[CartesianIndices(xs)]
end
if randshift
x .= circshift(xs, -1 .* shift_idx)
end
x = vec(x)
return x
end
"""
normLLR(x::Vector{T}, λ::Float64; kargs...) where T
returns the value of the LLR-regularization term.
Arguments are the same is in `proxLLR!`
"""
function normLLR(x::Vector{T}, λ::Float64; shape::NTuple{N,TI}, L=1, blockSize::NTuple{N,TI}=ntuple(_-> 2, N), randshift::Bool=true, kargs...) where {N, T, TI <: Integer}
Nvoxel = prod(shape)
K = floor(Int,length(x)/(Nvoxel*L))
normᴸᴸᴿ = 0.
for i = 1:L
normᴸᴸᴿ += blockNuclearNorm(x[(i-1)*Nvoxel*K+1:i*Nvoxel*K], shape; blockSize=blockSize, randshift=randshift, kargs...)
end
return λ*normᴸᴸᴿ
end
function blockNuclearNorm(x::Vector{T}, shape::NTuple{N,TI}; blockSize::NTuple{N,TI}=ntuple(_-> 2, N),
randshift::Bool=true, kargs...) where {N, T, TI <: Integer}
x = reshape( x, tuple( shape...,floor(Int64, length(x)/prod(shape)) ) )
Wy = blockSize[1]
Wz = blockSize[2]
if randshift
srand(1234)
shift_idx = [rand(1:Wy) rand(1:Wz) 0]
x = circshift(x, shift_idx)
end
ny, nz, K = size(x)
# reshape into patches
L = floor(Int,ny*nz/Wy/Wz) # number of patches, assumes that image dimensions are divisble by the blocksizes
xᴸᴸᴿ = zeros(T,Wy*Wz,L,K)
for i=1:K
xᴸᴸᴿ[:,:,i] = im2colDistinct(x[:,:,i], (Wy,Wz))
end
xᴸᴸᴿ = permutedims(xᴸᴸᴿ,[1 3 2])
# L1-norm of singular values
normᴸᴸᴿ = 0.
for i = 1:L
SVDec = svd(xᴸᴸᴿ[:,:,i])
normᴸᴸᴿ += norm(SVDec.S,1)
end
return normᴸᴸᴿ
end
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] | 2.055995 | 1,518 |
function importSR(SIMurl::String,LOCurl::String)
#1.. Import Data
#Load Data with colnames:
#mol_ID|loc_ID|framenumber|pos_x|pos_y
#[Int64,Int64,Int64,Float64,Float64,Float64,Float64,Float64,Float64,Float64]
#DataSim = CSV.read("/Users/Patrick/Documents/GitHub/BayesianEstimation/STORM/Simulationen_01-24-19/sim_Tet.csv",
#DataSim = CSV.read("/Users/Patrick/Documents/GitHub/BayesianEstimation/STORM/ORI_SIM/locs_gamma_conf.csv",
# print("importing DataSim\n")
# DataSim = CSV.read(SIMurl,
# types=[Int64,Int64,Int64,Float64,Float64],
# header=["label_ID","oligomer_ID","framenum","x_nm","y_nm"], datarow=2, DataFrame)
print("importing DataSim\n")
DataSim = CSV.read(SIMurl,
types=[Int64,Int64,Float64,Float64,Int64],
header=["label_ID","framenum","x_nm","y_nm","oligomer_ID"], datarow=2, DataFrame)
#RealLoc = CSV.read("/Users/Patrick/Documents/GitHub/BayesianEstimation/STORM/Simulationen_01-24-19/sim_Tet_locs.csv",
#RealLoc = CSV.read("/Users/Patrick/Documents/GitHub/BayesianEstimation/STORM/ORI_SIM/labels_gamma.csv",
print("importing RealLoc\n")
RealLoc = CSV.read(LOCurl,
types=[Int64,Float64,Float64,Int64],
#header=["label_ID","x_nm","y_nm"], datarow=2, DataFrame)
header=["label_ID","x_nm","y_nm","oligomer_ID"], datarow=2, DataFrame)
return DataSim, RealLoc
end
export importSR
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<gh_stars>0
"""
load(file::String, vari::String; poly = Array{Float64}([]), start_date::Tuple, end_date::Tuple, data_units::String = "")
Returns a ClimGrid type with the data in **file** of variable **vari** inside the polygon **poly**. Metadata is built-in the ClimGrid type, from the netCDF attributes.
Inside the ClimgGrid type, the data is stored into an AxisArray data type, with time, longitude/x and latitude/y dimensions.
The polygon provided should be in the -180, +180 longitude format. If the polygon crosses the International Date Line, the polygon should be splitted in multiple parts (i.e. multi-polygons).
Options for data_units are for precipitation : "mm", which converts the usual "kg m-2 s-1" unit found in netCDF files. For temperature : "Celsius", which converts the usual "Kelvin" unit.
Temporal subsetting can be done by providing start_date and end-date Tuples of length 1 (year), length 3 (year, month, day) or 6 (hour, minute, second).
**Note:** load uses [CF conventions](http://cfconventions.org/). If you are unable to read the netCDF file with load, the user will need to read it with low-level functions available in [NetCDF.jl package](https://github.com/JuliaGeo/NetCDF.jl) or [NCDatasets.jl](https://github.com/Alexander-Barth/NCDatasets.jl) or re-create standartized netCDF files.
"""
function load(file::String, vari::String; poly = ([]), start_date::Tuple=(Inf,), end_date::Tuple=(Inf,), data_units::String = "")
# TODO this file is a complete mess, but it works. Clean it up!
# Get attributes
ds = NCDatasets.Dataset(file)
attribs_dataset = ds.attrib
attribs = Dict(attribs_dataset)
# The following attributes should be set for netCDF files that follows CF conventions.
# project, institute, model, experiment, frequency
project = ClimateTools.project_id(attribs_dataset)
institute = ClimateTools.institute_id(attribs_dataset)
model = ClimateTools.model_id(attribs_dataset)
experiment = ClimateTools.experiment_id(attribs_dataset)
frequency = ClimateTools.frequency_var(attribs_dataset)
runsim = ClimateTools.runsim_id(attribs_dataset)
grid_mapping = ClimateTools.get_mapping(keys(ds))
# Get dimensions names
latname, latstatus = getdim_lat(ds)
lonname, lonstatus = getdim_lon(ds)
dataunits = ds[vari].attrib["units"]
latunits = ds[latname].attrib["units"]
lonunits = ds[lonname].attrib["units"]
caltype = ""
try
caltype = ds["time"].attrib["calendar"]
catch
caltype = "standard"
end
# Create dict with latname and lonname
dimension_dict = Dict(["lon" => lonname, "lat" => latname])
# lat_raw = NetCDF.ncread(file, latname)
lat_raw = nomissing(ds[latname][:], NaN)
# lon_raw = NetCDF.ncread(file, lonname)
lon_raw = nomissing(ds[lonname][:], NaN)
# Get variable attributes
varattrib = Dict(ds[vari].attrib)
if latstatus # means we don't have a "regular" grid
# Get names of grid
latgrid_name = latgridname(ds)
longrid_name = longridname(ds)
# latgrid = NetCDF.ncread(file, latgrid_name)
latgrid = nomissing(ds[latgrid_name][:], NaN)
# longrid = NetCDF.ncread(file, longrid_name)
longrid = nomissing(ds[longrid_name][:], NaN)
# Ensure we have a grid
if ndims(latgrid) == 1 && ndims(longrid) == 1
longrid, latgrid = ndgrid(lon_raw, lat_raw)
map_attrib = Dict(["grid_mapping" => "Regular_longitude_latitude"])
end
map_attrib = build_grid_mapping(ds, grid_mapping)
varattrib["grid_mapping"] = grid_mapping
else # if no grid provided, create one
longrid, latgrid = ndgrid(lon_raw, lat_raw)
map_attrib = Dict(["grid_mapping" => grid_mapping])
varattrib["grid_mapping"] = grid_mapping
end
# =====================
# TIME
# Get time resolution
timeV = ds["time"][:]
if frequency == "N/A" || !ClimateTools.@isdefined frequency
try
try
frequency = string(diff(timeV)[2])
catch
frequency = string(diff(timeV)[1])
end
catch
frequency = "N/A"
end
end
timeattrib = Dict(ds["time"].attrib)
T = typeof(timeV[1])
idxtimebeg, idxtimeend = timeindex(timeV, start_date, end_date, T)
timeV = timeV[idxtimebeg:idxtimeend]
# ==================
# Spatial shift if grid is 0-360.
rotatedgrid = false
if sum(longrid .> 180) >= 1
rotatedgrid = true
# Shift 360 degrees grid to -180, +180 degrees
longrid_flip = ClimateTools.shiftgrid_180_west_east(longrid)
# Shift 360 degrees vector to -180, +180 degrees
lon_raw_flip = ClimateTools.shiftvector_180_west_east(lon_raw)
else
longrid_flip = longrid # grid is already "flipped" by design
end
# ===================
# GET DATA
data_pointer = ds[vari]
if !isempty(poly)
# Test to see if the polygon crosses the meridian
meridian = ClimateTools.meridian_check(poly)
# Build mask based on provided polygon
msk = inpolygrid(longrid_flip, latgrid, poly)
if sum(isnan.(msk)) == length(msk) # no grid point inside polygon
throw(error("No grid points found inside the provided polygon"))
end
if rotatedgrid
# Regrid msk to original grid if the grid have been rotated to get proper index for data extraction
msk = ClimateTools.shiftarray_east_west(msk, longrid_flip)
end
#Extract data based on mask
data_ext = ClimateTools.extractdata(data_pointer, msk, idxtimebeg, idxtimeend)
data_ext = nomissing(data_ext, NaN)
#new mask (e.g. representing the region of the polygon)
begin
I = Base.findall(!isnan, msk)
idlon, idlat = (getindex.(I, 1), getindex.(I, 2))
end
minXgrid = minimum(idlon)
maxXgrid = maximum(idlon)
minYgrid = minimum(idlat)
maxYgrid = maximum(idlat)
msk = msk[minXgrid:maxXgrid, minYgrid:maxYgrid]
data_mask = applymask(data_ext, msk) # needed when polygon is not rectangular
if rotatedgrid
# Regrid msk to shifted grid if the grid have been rotated to get final mask
#shiftarray_west_east(msk, longrid)
end
# Get lon_raw and lat_raw for such region
lon_raw = lon_raw[minXgrid:maxXgrid]
lat_raw = lat_raw[minYgrid:maxYgrid]
if map_attrib["grid_mapping"] == "Regular_longitude_latitude"
lon_raw = ClimateTools.shiftvector_180_west_east(lon_raw)
end
# Idem for longrid and latgrid
if meridian
longrid = ClimateTools.shiftgrid_180_east_west(longrid)#grideast, gridwest)
longrid = longrid[minXgrid:maxXgrid, minYgrid:maxYgrid]
latgrid = latgrid[minXgrid:maxXgrid, minYgrid:maxYgrid]
# Re-translate to -180, 180 if 0, 360
if rotatedgrid
longrid_flip = ClimateTools.shiftgrid_180_west_east(longrid)
data = permute_west_east(data_mask, longrid)#idxwest, idxeast)
msk = ClimateTools.permute_west_east(msk, longrid)
# TODO Try to trim padding when meridian is crossed and model was on a 0-360 coords
# idlon, idlat = findn(.!isnan.(msk))
# minXgrid = minimum(idlon)
# maxXgrid = maximum(idlon)
# minYgrid = minimum(idlat)
# maxYgrid = maximum(idlat)
#
# msk = msk[minXgrid:maxXgrid, minYgrid:maxYgrid]
# data = data[:, minXgrid:maxXgrid, minYgrid:maxYgrid]
# lon_raw_flip = lon_raw[minXgrid:maxXgrid]
#
# longrid_flip = longrid[minXgrid:maxXgrid, minYgrid:maxYgrid]
# longrid_flip = ClimateTools.shiftgrid_180_west_east(longrid_flip)
# latgrid = latgrid[minXgrid:maxXgrid, minYgrid:maxYgrid]
# data = applymask(data, msk)
else
data = data_mask
end
else
if rotatedgrid # flip in original grid
longrid = ClimateTools.shiftgrid_180_east_west(longrid) #grideast, gridwest)
end
longrid = longrid[minXgrid:maxXgrid, minYgrid:maxYgrid]
latgrid = latgrid[minXgrid:maxXgrid, minYgrid:maxYgrid]
if rotatedgrid
longrid_flip = shiftgrid_180_west_east(longrid)
lon_raw_flip = shiftvector_180_east_west(lon_raw)
data = permute_west_east(data_mask, longrid)#idxwest, idxeast)
msk = ClimateTools.permute_west_east(msk, longrid)
else
data = data_mask
end
end
elseif isempty(poly) # no polygon clipping
msk = Array{Float64}(ones((size(data_pointer, 1), size(data_pointer, 2))))
data_ext = ClimateTools.extractdata(data_pointer, msk, idxtimebeg, idxtimeend)
# replace_missing!(data_ext)
data_ext = nomissing(data_ext, NaN)
# data_ext = convert(data_ext, Float32)
if rotatedgrid
# Flip data "west-east"
data = ClimateTools.permute_west_east(data_ext, longrid)
else
data = data_ext
end
end
if rotatedgrid
longrid .= longrid_flip
lon_raw .= lon_raw_flip
end
# Convert units of optional argument data_units is provided
if data_units == "Celsius" && (vari == "tas" || vari == "tasmax" || vari == "tasmin") && dataunits == "K"
data .-= 273.15
dataunits = "°C"
varattrib["units"] = "Celsius"
# @warn "Using Celsius can be problematic for arithmetic operations. Best practice is to keep Kelvin and only convert to Celsius at the end with the overloaded ClimateTools.uconvert function."
end
if data_units == "mm" && vari == "pr" && (dataunits == "kg m-2 s-1" || dataunits == "mm s-1")
factor = timeresolution(ds["time"])
# factor = pr_timefactor(rez)
data .*= factor.value
dataunits = "mm"
varattrib["standard_name"] = "precipitation"
varattrib["units"] = "mm"
end
# Create AxisArray from variable "data"
if ndims(data) == 3
# Convert data to AxisArray
dataOut = AxisArray(data, Axis{Symbol(lonname)}(lon_raw), Axis{Symbol(latname)}(lat_raw), Axis{:time}(timeV))
elseif ndims(data) == 4 # this imply a 3D field (height component)
# Get level vector
plev = ds["plev"][:]
# Convert data to AxisArray
dataOut = AxisArray(data, Axis{Symbol(lonname)}(lon_raw), Axis{Symbol(latname)}(lat_raw), Axis{:plev}(plev), Axis{:time}(timeV))
else
throw(error("load takes only 3D and 4D variables for the moment"))
end
C = ClimGrid(dataOut, longrid=longrid, latgrid=latgrid, msk=msk, grid_mapping=map_attrib, dimension_dict=dimension_dict, timeattrib=timeattrib, model=model, frequency=frequency, experiment=experiment, run=runsim, project=project, institute=institute, filename=file, dataunits=dataunits, latunits=latunits, lonunits=lonunits, variable=vari, typeofvar=vari, typeofcal=caltype, varattribs=varattrib, globalattribs=attribs)
close(ds)
# NetCDF.close(ncfile)
return C
end
"""
load(files::Array{String,1}, vari::String; poly = ([]), start_date::Date = Date(-4000), end_date::Date = Date(-4000), data_units::String = "")
Loads and merge the files contained in the arrar files.
"""
function load(files::Array{String,1}, vari::String; poly=([]), start_date::Tuple=(Inf,), end_date::Tuple=(Inf,), data_units::String="")
nfiles = length(files)
C = Array{ClimGrid}(undef, nfiles) # initialize # TODO better initialization
datesort = Array{Any}(undef, nfiles)
Cout = []
p = Progress(nfiles*2, 3, "Loading files: ")
for ifile = 1:nfiles
C[ifile] = load(files[ifile], vari, poly = poly, start_date=start_date, end_date=end_date, data_units=data_units)
datesort[ifile] = get_timevec(C[ifile])[1]
next!(p)
end
# Sort files based on timevector to ensure that merge results in amonotone increase in time
idx = sortperm(datesort)
C = C[idx]
for imod = 1:nfiles
if imod == 1
Cout = C[imod]
else
Cout = merge(Cout, C[imod])
end
next!(p)
end
return Cout
end
"""
load2D(file::String, vari::String; poly=[], data_units::String="")
Returns a 2D array. Should be used for *fixed* data, such as orography.
"""
function load2D(file::String, vari::String; poly=[], data_units::String="")
# Get attributes
ds = NCDatasets.Dataset(file)
attribs_dataset = ds.attrib
attribs = Dict(attribs_dataset)
project = ClimateTools.project_id(attribs_dataset)
institute = ClimateTools.institute_id(attribs_dataset)
model = ClimateTools.model_id(attribs_dataset)
experiment = ClimateTools.experiment_id(attribs_dataset)
frequency = ClimateTools.frequency_var(attribs_dataset)
runsim = ClimateTools.runsim_id(attribs_dataset)
grid_mapping = ClimateTools.get_mapping(keys(ds))
# Get dimensions names
latname, latstatus = getdim_lat(ds)
lonname, lonstatus = getdim_lon(ds)
dataunits = ds[vari].attrib["units"]
latunits = ds[latname].attrib["units"]
lonunits = ds[lonname].attrib["units"]
# Create dict with latname and lonname
dimension_dict = Dict(["lon" => lonname, "lat" => latname])
# lat_raw = NetCDF.ncread(file, latname)
lat_raw = nomissing(ds[latname][:], NaN)
# lon_raw = NetCDF.ncread(file, lonname)
lon_raw = nomissing(ds[lonname][:], NaN)
# Get variable attributes
varattrib = Dict(ds[vari].attrib)
if latstatus # means we don't have a "regular" grid
# Get names of grid
latgrid_name = latgridname(ds)
longrid_name = longridname(ds)
# latgrid = NetCDF.ncread(file, latgrid_name)
latgrid = nomissing(ds[latgrid_name][:], NaN)
# longrid = NetCDF.ncread(file, longrid_name)
longrid = nomissing(ds[longrid_name][:], NaN)
map_attrib = build_grid_mapping(ds, grid_mapping)
varattrib["grid_mapping"] = grid_mapping
else # if no grid provided, create one
longrid, latgrid = ndgrid(lon_raw, lat_raw)
map_attrib = Dict(["grid_mapping" => grid_mapping])
varattrib["grid_mapping"] = grid_mapping
end
# ==================
# Spatial shift if grid is 0-360.
rotatedgrid = false
if sum(longrid .> 180) >= 1
rotatedgrid = true
# Shift 360 degrees grid to -180, +180 degrees
longrid_flip = ClimateTools.shiftgrid_180_west_east(longrid)
# Shift 360 degrees vector to -180, +180 degrees
lon_raw_flip = ClimateTools.shiftvector_180_west_east(lon_raw)
else
longrid_flip = longrid # grid is already "flipped" by design
end
# ===================
# GET DATA
# data = ds[variable]
data_pointer = ds[vari]
if !isempty(poly)
# Test to see if the polygon crosses the meridian
meridian = ClimateTools.meridian_check(poly)
# Build mask based on provided polygon
msk = inpolygrid(longrid_flip, latgrid, poly)
if sum(isnan.(msk)) == length(msk) # no grid point insode polygon
throw(error("No grid points found inside the provided polygon"))
end
if rotatedgrid
# Regrid msk to original grid if the grid have been rotated to get proper index for data extraction
msk = ClimateTools.shiftarray_east_west(msk, longrid_flip)
end
#Extract data based on mask
data_ext = ClimateTools.extractdata2D(data_pointer, msk)
# replace_missing!(data_ext)
data_ext = nomissing(data_ext, NaN)
# data_ext = convert(data_ext, Float32)
begin
I = Base.findall(!isnan, msk)
idlon, idlat = (getindex.(I, 1), getindex.(I, 2))
end
minXgrid = minimum(idlon)
maxXgrid = maximum(idlon)
minYgrid = minimum(idlat)
maxYgrid = maximum(idlat)
msk = msk[minXgrid:maxXgrid, minYgrid:maxYgrid]
data_mask = applymask(data_ext, msk) # needed when polygon is not rectangular
if rotatedgrid
# Regrid msk to shifted grid if the grid have been rotated to get final mask
#shiftarray_west_east(msk, longrid)
end
# Get lon_raw and lat_raw for such region
lon_raw = lon_raw[minXgrid:maxXgrid]
lat_raw = lat_raw[minYgrid:maxYgrid]
if map_attrib["grid_mapping"] == "Regular_longitude_latitude"
lon_raw = ClimateTools.shiftvector_180_west_east(lon_raw)
end
# Idem for longrid and latgrid
if meridian
longrid = ClimateTools.shiftgrid_180_east_west(longrid)#grideast, gridwest)
longrid = longrid[minXgrid:maxXgrid, minYgrid:maxYgrid]
latgrid = latgrid[minXgrid:maxXgrid, minYgrid:maxYgrid]
# Re-translate to -180, 180 if 0, 360
if rotatedgrid
longrid_flip = ClimateTools.shiftgrid_180_west_east(longrid)
data = permute_west_east(data_mask, longrid)#idxwest, idxeast)
msk = ClimateTools.permute_west_east(msk, longrid)
# TODO Try to trim padding when meridian is crossed and model was on a 0-360 coords
# idlon, idlat = findn(.!isnan.(msk))
# minXgrid = minimum(idlon)
# maxXgrid = maximum(idlon)
# minYgrid = minimum(idlat)
# maxYgrid = maximum(idlat)
#
# msk = msk[minXgrid:maxXgrid, minYgrid:maxYgrid]
# data = data[:, minXgrid:maxXgrid, minYgrid:maxYgrid]
# lon_raw_flip = lon_raw[minXgrid:maxXgrid]
#
# longrid_flip = longrid[minXgrid:maxXgrid, minYgrid:maxYgrid]
# longrid_flip = ClimateTools.shiftgrid_180_west_east(longrid_flip)
# latgrid = latgrid[minXgrid:maxXgrid, minYgrid:maxYgrid]
# data = applymask(data, msk)
else
data = data_mask
end
else
if rotatedgrid # flip in original grid
longrid = shiftgrid_180_east_west(longrid) #grideast, gridwest)
end
longrid = longrid[minXgrid:maxXgrid, minYgrid:maxYgrid]
latgrid = latgrid[minXgrid:maxXgrid, minYgrid:maxYgrid]
if rotatedgrid
longrid_flip = shiftgrid_180_west_east(longrid)
lon_raw_flip = shiftvector_180_east_west(lon_raw)
data = permute_west_east(data_mask, longrid)#idxwest, idxeast)
msk = ClimateTools.permute_west_east(msk, longrid)
else
data = data_mask
end
end
elseif isempty(poly) # no polygon clipping
msk = Array{Float64}(ones((size(data_pointer, 1), size(data_pointer, 2))))
data_ext = extractdata2D(data_pointer, msk)
# replace_missing!(data_ext)
data_ext = nomissing(data_ext, NaN)
# data_ext = convert(data_ext, Float32)
if rotatedgrid
# Flip data "west-east"
data = permute_west_east(data_ext, longrid)
else
data = data_ext
end
end
if rotatedgrid
longrid = longrid_flip
lon_raw = lon_raw_flip
end
# Convert data to AxisArray
dataOut = AxisArray(data, Axis{Symbol(lonname)}(lon_raw), Axis{Symbol(latname)}(lat_raw))
C = ClimGrid(dataOut, longrid=longrid, latgrid=latgrid, msk=msk, grid_mapping=map_attrib, dimension_dict=dimension_dict, model=model, frequency=frequency, experiment=experiment, run=runsim, project=project, institute=institute, filename=file, dataunits=dataunits, latunits=latunits, lonunits=lonunits, variable=vari, typeofvar=vari, typeofcal="fixed", varattribs=varattrib, globalattribs=attribs)
close(ds)
return C
end
model_id(attrib::NCDatasets.Attributes) = get(attrib,"model_id", get(attrib, "parent_source_id", get(attrib,"model","N/A")))
experiment_id(attrib::NCDatasets.Attributes) = get(attrib,"experiment_id",get(attrib,"experiment","N/A"))
project_id(attrib::NCDatasets.Attributes) = get(attrib,"project_id", get(attrib, "mip_era", get(attrib,"project","N/A")))
institute_id(attrib::NCDatasets.Attributes) = get(attrib,"institute_id",get(attrib, "institution_id", get(attrib,"institute","N/A")))
frequency_var(attrib::NCDatasets.Attributes) = get(attrib,"frequency","N/A")
runsim_id(attrib::NCDatasets.Attributes) = get(attrib, "parent_experiment_rip", get(attrib,"driving_model_ensemble_member","N/A"))
"""
getdim_lat(ds::NCDatasets.Dataset)
Returns the name of the "latitude" dimension and the status related to a regular grid. The latitude dimension is usually "latitude", "lat", "y", "yc", "rlat".
"""
function getdim_lat(ds::NCDatasets.Dataset)
if sum(keys(ds.dim) .== "rlat") == 1
return "rlat", true
elseif sum(keys(ds.dim) .== "lat") == 1
return "lat", false
elseif sum(keys(ds.dim) .== "latitude") == 1
return "latitude", false
elseif sum(keys(ds.dim) .== "y") == 1
return "y", true
elseif sum(keys(ds.dim) .== "yc") == 1
return "yc", true
else
error("Manually verify x/lat dimension name")
end
end
"""
getdim_lon(ds::NCDatasets.Dataset)
Returns the name of the "longitude" dimension and the status related to a regular grid. The longitude dimension is usually "longitue", "lon", "x", "xc", "rlon".
"""
function getdim_lon(ds::NCDatasets.Dataset)
if sum(keys(ds.dim) .== "rlon") == 1
return "rlon", true
elseif sum(keys(ds.dim) .== "lon") == 1
return "lon", false
elseif sum(keys(ds.dim) .== "longitude") == 1
return "longitude", false
elseif sum(keys(ds.dim) .== "x") == 1
return "x", false
elseif sum(keys(ds.dim) .== "xc") == 1
return "xc", false
else
error("Manually verify x/lat dimension name")
end
end
"""
latgridname(ds::NCDatasets.Dataset)
Returns the name of the latitude grid when datasets is not on a rectangular grid.
"""
function latgridname(ds::NCDatasets.Dataset)
if in("lat", keys(ds))
return "lat"
elseif in("latitude", keys(ds))
return "latitude"
else
error("Variable name is not supported. File an issue on https://github.com/Balinus/ClimateTools.jl/issues")
end
end
"""
longridname(ds::NCDatasets.Dataset)
Returns the name of the longitude grid when datasets is not on a rectangular grid.
"""
function longridname(ds::NCDatasets.Dataset)
if in("lon", keys(ds))
return "lon"
elseif in("longitude", keys(ds))
return "longitude"
else
error("Variable name is not supported. File an issue on https://github.com/Balinus/ClimateTools.jl/issues")
end
end
"""
extractdata(data, msk, idxtimebeg, idxtimeend)
Returns the data contained in netCDF file, using the appropriate mask and time index. Used internally by `load`.
"""
function extractdata(data, msk, idxtimebeg, idxtimeend)
# idlon, idlat = findn(.!isnan.(msk))
begin
I = Base.findall(!isnan, msk)
idlon, idlat = (getindex.(I, 1), getindex.(I, 2))
end
minXgrid = minimum(idlon)
maxXgrid = maximum(idlon)
minYgrid = minimum(idlat)
maxYgrid = maximum(idlat)
if ndims(data) == 3
dataout = data[minXgrid:maxXgrid, minYgrid:maxYgrid, idxtimebeg:idxtimeend]
# Permute dims
# data = permutedims(data, [3, 1, 2])
elseif ndims(data) == 4
dataout = data[minXgrid:maxXgrid, minYgrid:maxYgrid, :, idxtimebeg:idxtimeend]
# Permute dims
# data = permutedims(data, [4, 1, 2, 3])
end
return dataout
end
function extractdata2D(data, msk)
# idlon, idlat = findn(.!isnan.(msk))
begin
I = Base.findall(!isnan, msk)
idlon, idlat = (getindex.(I, 1), getindex.(I, 2))
end
minXgrid = minimum(idlon)
maxXgrid = maximum(idlon)
minYgrid = minimum(idlat)
maxYgrid = maximum(idlat)
data = data[minXgrid:maxXgrid, minYgrid:maxYgrid]
return data
end
"""
get_mapping(ds::Array{String,1})
Returns the grid_mapping of Dataset *ds*
"""
function get_mapping(K::Array{String,1})
if in("rotated_pole", K)
return "rotated_pole"
elseif in("lambert_conformal_conic", K)
return "lambert_conformal_conic"
elseif in("rotated_latitude_longitude", K)
return "rotated_latitude_longitude"
elseif in("rotated_mercator", K)
return "rotated_mercator"
elseif in("crs", K)
return "crs"
elseif in("polar_stereographic", K)
return "polar_stereographic"
else
return "Regular_longitude_latitude"
end
end
function build_grid_mapping(ds::NCDatasets.Dataset, grid_mapping::String)
if ClimateTools.@isdefined grid_mapping
# map_dim = varattrib[grid_mapping]
map_attrib = Dict(ds[grid_mapping].attrib)
map_attrib["grid_mapping"] = grid_mapping
else
error("File an issue on https://github.com/Balinus/ClimateTools.jl/issues to get the grid supported")
end
return map_attrib
end
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] | 2.379198 | 10,393 |
<reponame>RockwallNest/RubyWithJulia
using CSV
using DataFrames
using StatsPlots
btc_df = CSV.File("./btc_jpy.csv") |> DataFrame
@df btc_df plot(:time, [:best_bid :best_ask :ltp], legend = :bottomright, title="BTC_JPY,Bitflyer.com 12/31/2020")
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] | 2.411765 | 102 |
<reponame>josePereiro/Chemostat_Rath2017.jl
using ProjAssistant
@quickactivate "Chemostat_Rath2017"
@time begin
import DataFrames: DataFrame
import MAT
import CSV
import Chemostat
import Chemostat.MetNets
const Ch = Chemostat
import Chemostat_Rath2017: Human1, RathData
const Rd = RathData
const H1 = Human1
const HG = H1.HumanGEM
end
## ------------------------------------------------------------------
# load model
model = HG.load_humangem_raw_model()
biomass_idx = MetNets.rxnindex(model, HG.HUMAN_BIOMASS_IDER)
## ------------------------------------------------------------------
# Biomass equation
# I will modified the biomass equation (biomass_human) of Human1 model with data
# derived from Niklas (2013): 103–114. https://doi.org/10.1016/j.ymben.2013.01.002. Table1.
# I compute de relation between the total of each group reported in Niklas 2013 with the equivalent
# group found in the model biomass, and then rescaled each group to match the reported total.
# I do not touch the energetic part of the equation, atp + h20 -> adp + h2 + pi
biomass = Dict()
for met_idx in MetNets.rxn_mets(model, biomass_idx)
met = model.mets[met_idx]
biomass[met] = model.S[met_idx, biomass_idx]
end
## ------------------------------------------------------------------
# Carbohydrates
ch_ids = ["m03161c"]
exp_ch_tot = 438.3 * 1e-3 # Niklas (2013): 103–114. https://doi.org/10.1016/j.ymben.2013.01.002. Table1
println("experimental total ch: ", exp_ch_tot)
model_ch_tot = abs.(sum([biomass[met] for met in ch_ids])) # The model did not include directly any carbohydrate
println("model total ch: ", model_ch_tot)
ch_factor = exp_ch_tot/model_ch_tot
println("factor exp/model: ", ch_factor)
## ------------------------------------------------------------------
# RNA
rna_ids = ["m02847c"]
model_rna_tot = abs.(sum([biomass[met] for met in rna_ids]))
println("model total rna: ", model_rna_tot)
exp_rna_tot = 176.9 * 1e-3 # Niklas (2013): 103–114. https://doi.org/10.1016/j.ymben.2013.01.002. Table1
println("experimental total rna: ", exp_rna_tot)
rna_factor = exp_rna_tot/model_rna_tot
println("factor exp/model: ", rna_factor)
## ------------------------------------------------------------------
# DNA
dna_ids = ["m01721n"]
model_dna_tot = abs.(sum([biomass[met] for met in dna_ids]))
println("model total dna: ", model_dna_tot)
exp_dna_tot = 45.3 * 1e-3 # Niklas (2013): 103–114. https://doi.org/10.1016/j.ymben.2013.01.002. Table1
println("experimental total dna: ", exp_dna_tot)
dna_factor = exp_dna_tot/model_dna_tot
println("factor exp/model: ", dna_factor)
## ------------------------------------------------------------------
# Lipids
lip_ids = ["m10014c"]
model_lip_tot = abs.(sum([biomass[met] for met in lip_ids]))
println("model total lipids:", model_lip_tot)
# Niklas (2013): 103–114. https://doi.org/10.1016/j.ymben.2013.01.002. Table1
exp_lip_tot = 202.9 * 1e-3
println("experimental total lipids:", exp_lip_tot)
lip_factor = exp_lip_tot/model_lip_tot
println("factor exp/model:", lip_factor)
# Aminoacids
aa_ids = ["m10013c"]
model_prot_tot = abs.(sum([biomass[met] for met in aa_ids]))
println("model total protein: ", model_prot_tot)
exp_prot_tot = 7462.6 * 1e-3 # Niklas (2013): 103–114. https://doi.org/10.1016/j.ymben.2013.01.002. Table1
println("experimental total protein: ", exp_prot_tot)
prot_factor = exp_prot_tot/model_prot_tot
println("factor exp/model: ", prot_factor)
## ------------------------------------------------------------------
# Rescaling
# Carbohydrates
for ch in ch_ids
biomass[ch] = biomass[ch] * ch_factor
end
# Aminoacids
for aa in aa_ids
biomass[aa] = biomass[aa] * prot_factor
end
# lipids
for lip in lip_ids
biomass[lip] = biomass[lip] * lip_factor
end
# DNA
for dna in dna_ids
biomass[dna] = biomass[dna] * dna_factor
end
# RNA
for rna in rna_ids
biomass[rna] = biomass[rna] * rna_factor
end
## ------------------------------------------------------------------
# save
sdat(HG, biomass,
"niklas_biomass", ".jls";
verbose = true
) | [
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] | 2.780355 | 1,466 |
"""
Axisymmetric Solov'ev equilibra in (R/R₀,Z/R₀,ϕ) coordinates.
Based on Cerfon & Freidberg, Physics of Plasmas 17, 032502, 2010,
and Freidberg, Ideal Magnetohydrodynamics, 2014.
"""
module Solovev
using RecipesBase
using SymEngine: N, symbols, diff, expand, subs
import ..ElectromagneticFields
import ..ElectromagneticFields: code
import ..SolovevAbstract: AbstractSolovevEquilibrium, X, Y, Z, R, r, θ, ϕ, r²
export SolovevEquilibrium, SolovevXpointEquilibrium
function ψ₀(x::AbstractVector{T}, a) where {T <: Number}
x[1]^4 / 8 + a * (x[1]^2 * log(x[1]) / 2 - x[1]^4 / 8 )
end
function ψ₁(x::AbstractVector{T}) where {T <: Number}
one(T)
end
function ψ₂(x::AbstractVector{T}) where {T <: Number}
x[1]^2
end
function ψ₃(x::AbstractVector{T}) where {T <: Number}
x[2]^2 - x[1]^2 * log(x[1])
end
function ψ₄(x::AbstractVector{T}) where {T <: Number}
x[1]^4 - 4 * x[1]^2 * x[2]^2
end
function ψ₅(x::AbstractVector{T}) where {T <: Number}
2 * x[2]^4 - 9 * x[2]^2 * x[1]^2 + 3 * x[1]^4 * log(x[1]) - 12 * x[1]^2 * x[2]^2 * log(x[1])
end
function ψ₆(x::AbstractVector{T}) where {T <: Number}
x[1]^6 - 12 * x[1]^4 * x[2]^2 + 8 * x[1]^2 * x[2]^4
end
function ψ₇(x::AbstractVector{T}) where {T <: Number}
8 * x[2]^6 - 140 * x[2]^4 * x[1]^2 + 75 * x[2]^2 * x[1]^4 - 15 * x[1]^6 * log(x[1]) +
180 * x[1]^4 * x[2]^2 * log(x[1]) - 120 * x[1]^2 * x[2]^4 * log(x[1])
end
function ψ₈(x::AbstractVector{T}) where {T <: Number}
x[2]
end
function ψ₉(x::AbstractVector{T}) where {T <: Number}
x[2] * x[1]^2
end
function ψ₁₀(x::AbstractVector{T}) where {T <: Number}
x[2]^3 - 3 * x[2] * x[1]^2 * log(x[1])
end
function ψ₁₁(x::AbstractVector{T}) where {T <: Number}
3 * x[2] * x[1]^4 - 4 * x[2]^3 * x[1]^2
end
function ψ₁₂(x::AbstractVector{T}) where {T <: Number}
8 * x[2]^5 - 45 * x[2] * x[1]^4 - 80 * x[2]^3 * x[1]^2 * log(x[1]) + 60 * x[2] * x[1]^4 * log(x[1])
end
@doc raw"""
Axisymmetric Solov'ev equilibra in (R/R₀,Z/R₀,ϕ) coordinates.
Based on Cerfon & Freidberg, Physics of Plasmas 17, 032502, 2010,
and Freidberg, Ideal Magnetohydrodynamics, 2014.
The covariant components of the vector potential are given by
```math
A (x, y, \phi) = \left( \frac{B_0 R_0}{2} \, \frac{y}{x} , \, - \frac{B_0 R_0}{2} \, \ln x , \, \psi(x,y) \right)^T ,
```
with $x = R/R_0$ and $y = Z/R_0$.
The normalised poloidal flux $\psi$ is given by
```math
\psi (x,y) = \psi_0 + \sum \limits_{i=1}^{7} c_i \psi_i (x,y) ,
```
with
```math
\begin{aligned}
\psi_{0} &= \frac{x^4}{8} + \alpha \left( \frac{1}{2} x^2 \, \ln x - \frac{x^4}{8} \right) , \\
\psi_{1} &= 1 , \\
\psi_{2} &= x^2 , \\
\psi_{3} &= y^2 - x^2 \, \ln x , \\
\psi_{4} &= x^4 - 4 x^2 y^2 , \\
\psi_{5} &= 2 y^4 9 y^2 x^2 + 3 x^4 \, \ln x - 12 x^2 y^2 \, \ln x , \\
\psi_{6} &= x^6 - 12 x^4 y^2 + 8 x^2 y^4 , \\
\psi_{7} &= 8 y^6 - 140 y^4 x^2 + 75 y^2 x^4 - 15 x^6 \, \ln x + 180 x^4 y^2 \, \ln x - 120 x^2 y^4 \, \ln x .
\end{aligned}
```
This formula describes exact solutions of the Grad-Shafranov equation with up-down symmetry.
The constants $c_i$ are determined from boundary constraints on $\psi$, that are derived from
the following analytic model for a smooth, elongated "D" shaped cross section:
```math
\begin{aligned}
x &= 1 + \epsilon \, \cos (\tau + \delta_0 \, \sin \tau) , \\
y &= \epsilon \kappa \, \sin (\tau) ,
\end{aligned}
```
where $0 \leq \tau < 2 \pi$, $\epsilon = a / R_0$ is the inverse aspect ratio, $\kappa$ the elongation,
and $\sin \delta_0 = \delta$ is the triangularity.
Defining three test points, namely
- the high point $(1 - \delta \epsilon, \kappa \epsilon)$,
- the inner equatorial point $(1 - \epsilon, 0)$,
- and the outer equatorial point $(1 + \epsilon, 0)$,
the following geometric constraints can be posed on the solution:
```math
\begin{aligned}
\psi (1 + \epsilon, 0) &= 0 , \\
\psi (1 - \epsilon, 0) &= 0 , \\
\psi (1 - \delta \epsilon, \kappa \epsilon) &= 0 , \\
\psi_{x} (1 - \delta \epsilon, \kappa \epsilon) &= 0 , \\
\psi_{yy} (1 + \epsilon, 0) &= - N_1 \psi_{x} (1 + \epsilon, 0) , \\
\psi_{yy} (1 - \epsilon, 0) &= - N_2 \psi_{x} (1 - \epsilon, 0) , \\
\psi_{xx} (1 - \delta \epsilon, \kappa \epsilon) &= - N_3 \psi_y (1 - \delta \epsilon, \kappa \epsilon) .
\end{aligned}
```
The first three equations define the three test points, the fourth equations enforces the high
point to be a maximum, and the last three equations define the curvature at the test points.
The coefficients $N_j$ can be found from the analytic model cross section as
```math
\begin{aligned}
N_1 &= \left[ \frac{d^2 x}{dy^2} \right]_{\tau = 0} = - \frac{(1 + \delta_0)^2}{\epsilon \kappa^2} , \\
N_2 &= \left[ \frac{d^2 x}{dy^2} \right]_{\tau = \pi} = \hphantom{-} \frac{(1 - \delta_0)^2}{\epsilon \kappa^2} , \\
N_3 &= \left[ \frac{d^2 x}{dy^2} \right]_{\tau = \pi/2} = - \frac{\kappa}{\epsilon \, \cos^2 \delta_0} .
\end{aligned}
```
For a given value of the constant $a$ above conditions reduce to a set of seven linear
inhomogeneous algebraic equations for the unknown $c_i$, which can easily be solved.
Parameters:
* `R₀`: position of magnetic axis
* `B₀`: B-field at magnetic axis
* `ϵ`: inverse aspect ratio
* `κ`: elongation
* `δ`: triangularity
* `α`: free constant, determined to match a given beta value
"""
struct SolovevEquilibrium{T <: Number} <: AbstractSolovevEquilibrium
name::String
R₀::T
B₀::T
ϵ::T
κ::T
δ::T
α::T
c::Vector{T}
function SolovevEquilibrium{T}(R₀::T, B₀::T, ϵ::T, κ::T, δ::T, α::T, c::Vector{T}) where T <: Number
new("Solovev Equilibrium", R₀, B₀, ϵ, κ, δ, α, c)
end
end
function SolovevEquilibrium(R₀::T, B₀::T, ϵ::T, κ::T, δ::T, α::T) where T <: Number
n = 7
A = zeros(n,n+1)
x₁, x₂, x₃ = symbols("x₁, x₂, x₃")
x = [x₁, x₂, x₃]
ψ = [ψ₁(x), ψ₂(x), ψ₃(x), ψ₄(x), ψ₅(x), ψ₆(x), ψ₇(x), -ψ₀(x,α)]
for i in axes(A,2)
A[1,i] = N(subs(ψ[i], x[1]=>1+ϵ, x[2]=>0))
A[2,i] = N(subs(ψ[i], x[1]=>1-ϵ, x[2]=>0))
A[3,i] = N(subs(ψ[i], x[1]=>1-δ*ϵ, x[2]=>κ*ϵ))
A[4,i] = N(subs(diff(ψ[i], x[1]), x[1]=>1-δ*ϵ, x[2]=>κ*ϵ))
A[5,i] = N(subs(diff(ψ[i], x[2], 2), x[1]=>1+ϵ, x[2]=>0) - (1 + asin(δ))^2 / (ϵ * κ^2) * subs(diff(ψ[i], x[1]), x[1]=>1+ϵ, x[2]=>0))
A[6,i] = N(subs(diff(ψ[i], x[2], 2), x[1]=>1-ϵ, x[2]=>0) + (1 - asin(δ))^2 / (ϵ * κ^2) * subs(diff(ψ[i], x[1]), x[1]=>1-ϵ, x[2]=>0))
A[7,i] = N(subs(diff(ψ[i], x[1], 2), x[1]=>1-δ*ϵ, x[2]=>κ*ϵ) - κ / (ϵ * (1 - δ^2)) * subs(diff(ψ[i], x[2]), x[1]=>1-δ*ϵ, x[2]=>κ*ϵ))
end
c = A[1:n,1:n] \ A[1:n,n+1]
SolovevEquilibrium{T}(R₀, B₀, ϵ, κ, δ, α, c)
end
function init(R₀, B₀, ϵ, κ, δ, α)
SolovevEquilibrium(R₀, B₀, ϵ, κ, δ, α)
end
macro code(R₀, B₀, ϵ, κ, δ, α)
code(init(R₀, B₀, ϵ, κ, δ, α); escape=true)
end
SolovevEquilibriumITER() = SolovevEquilibrium(6.2, 5.3, 0.32, 1.7, 0.33, -0.155)
# SolovevEquilibriumTFTR() = SolovevEquilibrium(2.5, 5.6, 0.345, 1.0, 0.0, )
# SolovevEquilibriumJET() = SolovevEquilibrium(3.0, 3.6, 0.333, 1.7, 0.25, )
SolovevEquilibriumNSTX() = SolovevEquilibrium(0.85, 0.30, 0.78, 2.00, 0.35, 1.0)
# SolovevEquilibriumMAST() = SolovevEquilibrium(0.85, 0.52, 0.77, 2.45, 0.50, )
SolovevEquilibriumFRC() = SolovevEquilibrium(0.0, 0.0, 0.99, 10., 0.7, 0.0)
# SolovevEquilibriumFRC2() = SolovevEquilibrium(0.0, 0.0, 1.00, 10., 1.0, 0.0)
function Base.show(io::IO, equ::SolovevEquilibrium)
print(io, "SolovevEquilibrium Equilibrium with\n")
print(io, " R₀ = ", equ.R₀, "\n")
print(io, " B₀ = ", equ.B₀, "\n")
print(io, " ϵ = ", equ.ϵ, "\n")
print(io, " κ = ", equ.κ, "\n")
print(io, " δ = ", equ.δ, "\n")
print(io, " α = ", equ.α)
end
function ElectromagneticFields.A₃(x::AbstractArray{T,1}, equ::SolovevEquilibrium) where {T <: Number}
( ψ₀(x, equ.α) + equ.c[1] * ψ₁(x)
+ equ.c[2] * ψ₂(x)
+ equ.c[3] * ψ₃(x)
+ equ.c[4] * ψ₄(x)
+ equ.c[5] * ψ₅(x)
+ equ.c[6] * ψ₆(x)
+ equ.c[7] * ψ₇(x) )
end
@doc raw"""
Axisymmetric Solov'ev equilibra with X-point in (R/R₀,Z/R₀,phi) coordinates.
Based on Cerfon & Freidberg, Physics of Plasmas 17, 032502, 2010,
and Freidberg, Ideal Magnetohydrodynamics, 2014.
The covariant components of the vector potential are given by
```math
A (x, y, \phi) = \left( \frac{B_0 R_0}{2} \, \frac{y}{x} , \, - \frac{B_0 R_0}{2} \, \ln x , \, \psi(x,y) \right)^T ,
```
with $x = R/R_0$ and $y = Z/R_0$.
The normalised poloidal flux $\psi$ is given by
```math
\psi (x,y) = \psi_0 + \sum \limits_{i=1}^{12} c_i \psi_i (x,y) ,
```
with
```math
\begin{aligned}
\psi_{0} &= \frac{x^4}{8} + \alpha \left( \frac{1}{2} x^2 \, \ln x - \frac{x^4}{8} \right) , \\
\psi_{1} &= 1 , \\
\psi_{2} &= x^2 , \\
\psi_{3} &= y^2 - x^2 \, \ln x , \\
\psi_{4} &= x^4 - 4 x^2 y^2 , \\
\psi_{5} &= 2 y^4 9 y^2 x^2 + 3 x^4 \, \ln x - 12 x^2 y^2 \, \ln x , \\
\psi_{6} &= x^6 - 12 x^4 y^2 + 8 x^2 y^4 , \\
\psi_{7} &= 8 y^6 - 140 y^4 x^2 + 75 y^2 x^4 - 15 x^6 \, \ln x + 180 x^4 y^2 \, \ln x - 120 x^2 y^4 \, \ln x , \\
\psi_{8} &= y , \\
\psi_{9} &= y x^2 , \\
\psi_{10} &= y^3 - 3 y x^2 \, \ln x , \\
\psi_{11} &= 3 y x^4 - 4 y^3 x^2 , \\
\psi_{12} &= 8 y^5 - 45 y x^4 - 80 y^3 x^2 \, \ln x + 60 y x^4 \, \ln x .
\end{aligned}
```
This formula describes exact solutions of the Grad-Shafranov equation with up-down asymmetry.
The constants $c_i$ are determined from boundary constraints on $\psi$, that are derived from
the following analytic model for a smooth, elongated "D" shaped cross section:
```math
\begin{aligned}
x &= 1 + \epsilon \, \cos (\tau + \arcsin \delta \, \sin \tau) , \\
y &= \epsilon \kappa \, \sin (\tau) ,
\end{aligned}
```
where $0 \leq \tau < 2 \pi$, $\epsilon = a / R_0$ is the inverse aspect ratio, $\kappa$ the elongation,
and $\sin \delta_0 = \delta$ is the triangularity.
Defining four test points, namely
- the high point $(1 - \delta \epsilon, \kappa \epsilon)$,
- the inner equatorial point $(1 - \epsilon, 0)$,
- and the outer equatorial point $(1 + \epsilon, 0)$,
- the position of the X-point $(x_{\mathrm{sep}}, y_{\mathrm{sep}})$,
the following geometric constraints can be posed on the solution:
```math
\begin{aligned}
\psi (1 + \epsilon, 0) &= 0 , \\
\psi (1 - \epsilon, 0) &= 0 , \\
\psi (1 - \delta \epsilon, \kappa \epsilon) &= 0 , \\
\psi (x_{\mathrm{sep}}, y_{\mathrm{sep}}) &= 0 , \\
\psi_{y} (1 + \epsilon, 0) &= 0 , \\
\psi_{y} (1 - \epsilon, 0) &= 0 , \\
\psi_{x} (1 - \delta \epsilon, \kappa \epsilon) &= 0 , \\
\psi_{x} (x_{\mathrm{sep}}, y_{\mathrm{sep}}) &= 0 , \\
\psi_{y} (x_{\mathrm{sep}}, y_{\mathrm{sep}}) &= 0 , \\
\psi_{yy} (1 + \epsilon, 0) &= - N_1 \psi_{x} (1 + \epsilon, 0) , \\
\psi_{yy} (1 - \epsilon, 0) &= - N_2 \psi_{x} (1 - \epsilon, 0) , \\
\psi_{xx} (1 - \delta \epsilon, \kappa \epsilon) &= - N_3 \psi_y (1 - \delta \epsilon, \kappa \epsilon) .
\end{aligned}
```
The first four equations define the four test points, the fifth and sixth equations define the up-down
symmetry, the seventh equations enforces the high point to be a maximum, the eighth and ninth eqaution
set the $x$- and $y$-components of the magnetic field at the X-point to zero, and the last three
equations define the curvature at the first three test points.
The coefficients $N_j$ can be found from the analytic model cross section as
```math
\begin{aligned}
N_1 &= \left[ \frac{d^2 x}{dy^2} \right]_{\tau = 0} = - \frac{(1 + \delta_0)^2}{\epsilon \kappa^2} , \\
N_2 &= \left[ \frac{d^2 x}{dy^2} \right]_{\tau = \pi} = \hphantom{-} \frac{(1 - \delta_0)^2}{\epsilon \kappa^2} , \\
N_3 &= \left[ \frac{d^2 x}{dy^2} \right]_{\tau = \pi/2} = - \frac{\kappa}{\epsilon \, \cos^2 \delta_0} .
\end{aligned}
```
For a given value of the constant $a$ above conditions reduce to a set of seven linear
inhomogeneous algebraic equations for the unknown $c_i$, which can easily be solved.
Parameters:
* `R₀`: position of magnetic axis
* `B₀`: B-field at magnetic axis
* `ϵ`: inverse aspect ratio
* `κ`: elongation
* `δ`: triangularity
* `α`: free constant, determined to match a given beta value
* `xsep`: x position of the X point
* `ysep`: y position of the X point
"""
struct SolovevXpointEquilibrium{T <: Number} <: AbstractSolovevEquilibrium
name::String
R₀::T
B₀::T
ϵ::T
κ::T
δ::T
α::T
xsep::T
ysep::T
c::Vector{T}
function SolovevXpointEquilibrium{T}(R₀::T, B₀::T, ϵ::T, κ::T, δ::T, α::T, xsep::T, ysep::T, c::Vector{T}) where T <: Number
new("Solovev Equilibrium with X-point", R₀, B₀, ϵ, κ, δ, α, xsep, ysep, c)
end
end
function SolovevXpointEquilibrium(R₀::T, B₀::T, ϵ::T, κ::T, δ::T, α::T, xsep::T, ysep::T) where T <: Number
n = 12
A = zeros(n,n+1)
x₁, x₂, x₃ = symbols("x₁, x₂, x₃")
x = [x₁, x₂, x₃]
ψ = [ψ₁(x), ψ₂(x), ψ₃(x), ψ₄(x), ψ₅(x), ψ₆(x), ψ₇(x), ψ₈(x), ψ₉(x), ψ₁₀(x), ψ₁₁(x), ψ₁₂(x), -ψ₀(x,α)]
for i in axes(A,2)
A[ 1,i] = N(subs(ψ[i], x[1]=>1+ϵ, x[2]=>0))
A[ 2,i] = N(subs(ψ[i], x[1]=>1-ϵ, x[2]=>0))
A[ 3,i] = N(subs(ψ[i], x[1]=>1-δ*ϵ, x[2]=>κ*ϵ))
A[ 4,i] = N(subs(ψ[i], x[1]=>xsep, x[2]=>ysep))
A[ 5,i] = N(subs(diff(ψ[i], x[2]), x[1]=>1+ϵ, x[2]=>0))
A[ 6,i] = N(subs(diff(ψ[i], x[2]), x[1]=>1-ϵ, x[2]=>0))
A[ 7,i] = N(subs(diff(ψ[i], x[1]), x[1]=>1-δ*ϵ, x[2]=>κ*ϵ))
A[ 8,i] = N(subs(diff(ψ[i], x[1]), x[1]=>xsep, x[2]=>ysep))
A[ 9,i] = N(subs(diff(ψ[i], x[2]), x[1]=>xsep, x[2]=>ysep))
A[10,i] = N(subs(diff(ψ[i], x[2], 2), x[1]=>1+ϵ, x[2]=>0) - (1 + asin(δ))^2 / (ϵ * κ^2) * subs(diff(ψ[i], x[1]), x[1]=>1+ϵ, x[2]=>0))
A[11,i] = N(subs(diff(ψ[i], x[2], 2), x[1]=>1-ϵ, x[2]=>0) + (1 - asin(δ))^2 / (ϵ * κ^2) * subs(diff(ψ[i], x[1]), x[1]=>1-ϵ, x[2]=>0))
A[12,i] = N(subs(diff(ψ[i], x[1], 2), x[1]=>1-δ*ϵ, x[2]=>κ*ϵ) - κ / (ϵ * (1 - δ^2)) * subs(diff(ψ[i], x[2]), x[1]=>1-δ*ϵ, x[2]=>κ*ϵ))
end
c = A[1:n,1:n] \ A[1:n,n+1]
SolovevXpointEquilibrium{T}(R₀, B₀, ϵ, κ, δ, α, xsep, ysep, c)
end
function SolovevDoubleXpointEquilibrium(R₀::T, B₀::T, ϵ::T, κ::T, δ::T, α::T, xsep::T, ysep::T) where T <: Number
n = 7
A = zeros(n,n+1)
x₁, x₂, x₃ = symbols("x₁, x₂, x₃")
x = [x₁, x₂, x₃]
ψ = [ψ₁(x), ψ₂(x), ψ₃(x), ψ₄(x), ψ₅(x), ψ₆(x), ψ₇(x), -ψ₀(x,α)]
for i in axes(A,2)
A[1,i] = N(subs(ψ[i], x[1]=>1+ϵ, x[2]=>0))
A[2,i] = N(subs(ψ[i], x[1]=>1-ϵ, x[2]=>0))
A[3,i] = N(subs(ψ[i], x[1]=>xsep, x[2]=>ysep))
A[4,i] = N(subs(diff(ψ[i], x[1]), x[1]=>xsep, x[2]=>ysep))
A[5,i] = N(subs(diff(ψ[i], x[2]), x[1]=>xsep, x[2]=>ysep))
A[6,i] = N(subs(diff(ψ[i], x[2], 2), x[1]=>1+ϵ, x[2]=>0) - (1 + asin(δ))^2 / (ϵ * κ^2) * subs(diff(ψ[i], x[1]), x[1]=>1+ϵ, x[2]=>0))
A[7,i] = N(subs(diff(ψ[i], x[2], 2), x[1]=>1-ϵ, x[2]=>0) + (1 - asin(δ))^2 / (ϵ * κ^2) * subs(diff(ψ[i], x[1]), x[1]=>1-ϵ, x[2]=>0))
end
c = vcat(A[1:n,1:n] \ A[1:n,n+1], zeros(5))
SolovevXpointEquilibrium{T}(R₀, B₀, ϵ, κ, δ, α, xsep, ysep, c)
end
function init(R₀, B₀, ϵ, κ, δ, α, xsep, ysep, doublex=false)
if doublex
return SolovevDoubleXpointEquilibrium(R₀, B₀, ϵ, κ, δ, α, xsep, ysep)
else
return SolovevXpointEquilibrium(R₀, B₀, ϵ, κ, δ, α, xsep, ysep)
end
end
macro code_xpoint(R₀, B₀, ϵ, κ, δ, α, xsep, ysep, doublex=false)
code(SolovevXpointEquilibrium(R₀, B₀, ϵ, κ, δ, α, xsep, ysep, doublex); escape=true)
end
SolovevXpointEquilibriumITER() = SolovevXpointEquilibrium(6.2, 5.3, 0.32, 1.7, 0.33, -0.155, 0.88, -0.60)
SolovevXpointEquilibriumNSTX() = SolovevXpointEquilibrium(0.85, 0.3, 0.78, 2.0, 0.35, -0.05, 0.70, -1.71)
SolovevDoubleXpointEquilibriumNSTX() = SolovevDoubleXpointEquilibrium(0.85, 0.3, 0.78, 2.0, 0.35, 0.0, 0.70, -1.71)
function Base.show(io::IO, equ::SolovevXpointEquilibrium)
print(io, "Solovev Xpoint Equilibrium with\n")
print(io, " R₀ = ", equ.R₀, "\n")
print(io, " B₀ = ", equ.B₀, "\n")
print(io, " ϵ = ", equ.ϵ, "\n")
print(io, " κ = ", equ.κ, "\n")
print(io, " δ = ", equ.δ, "\n")
print(io, " α = ", equ.α, "\n")
print(io, " xsep = ", equ.xsep, "\n")
print(io, " ysep = ", equ.ysep)
end
function ElectromagneticFields.A₃(x::AbstractArray{T,1}, equ::SolovevXpointEquilibrium) where {T <: Number}
( ψ₀(x, equ.α) + equ.c[1] * ψ₁(x)
+ equ.c[2] * ψ₂(x)
+ equ.c[3] * ψ₃(x)
+ equ.c[4] * ψ₄(x)
+ equ.c[5] * ψ₅(x)
+ equ.c[6] * ψ₆(x)
+ equ.c[7] * ψ₇(x)
+ equ.c[8] * ψ₈(x)
+ equ.c[9] * ψ₉(x)
+ equ.c[10] * ψ₁₀(x)
+ equ.c[11] * ψ₁₁(x)
+ equ.c[12] * ψ₁₂(x) )
end
function ITER(; xpoint=false)
if xpoint
return SolovevXpointEquilibriumITER()
else
return SolovevEquilibriumITER()
end
end
function NSTX(; xpoint=false)
if xpoint
return SolovevXpointEquilibriumNSTX()
else
return SolovevEquilibriumNSTX()
end
end
function NSTXdoubleX()
SolovevDoubleXpointEquilibriumNSTX()
end
function FRC()
SolovevEquilibriumFRC()
end
macro code_iter(xpoint=false)
code(ITER(xpoint=xpoint); escape=true)
end
macro code_iter_xpoint()
code(ITER(xpoint=true); escape=true)
end
macro code_nstx(xpoint=false)
code(NSTX(xpoint=xpoint); escape=true)
end
macro code_nstx_xpoint(doublex=false)
if doublex
equilibrium = NSTXdoubleX()
else
equilibrium = NSTX(xpoint=true)
end
code(equilibrium; escape=true)
end
macro code_nstx_double_xpoint()
code(NSTXdoubleX(); escape=true)
end
macro code_frc()
code(FRC(); escape=true)
end
@recipe function f(equ::SolovevEquilibrium;
nx = 100, ny = 120, nτ = 200, levels = 50, size = (300,400), aspect_ratio = :equal,
xlims = ( 0.50, 1.50),
ylims = (-0.75, +0.75))
xgrid = LinRange(xlims[1], xlims[2], nx)
zgrid = LinRange(ylims[1], ylims[2], ny)
pot = [ElectromagneticFields.A₃([xgrid[i], zgrid[j], 0.0], equ) / xgrid[i] for i in eachindex(xgrid), j in eachindex(zgrid)]
τ = LinRange(0, 2π, nτ)
boundary_X = 1 .+ equ.ϵ .* cos.(τ .+ asin(equ.δ) .* sin.(τ) )
boundary_Y = equ.ϵ .* equ.κ .* sin.(τ)
aspect_ratio := aspect_ratio
size := size
xlims := xlims
ylims := ylims
levels := levels
legend := :none
@series begin
seriestype := :contour
(xgrid, zgrid, pot')
end
@series begin
seriestype := :path
seriescolor := :red
linewidth := 3
(boundary_X, boundary_Y)
end
end
@recipe function f(equ::SolovevXpointEquilibrium;
nx = 100, ny = 120, levels = 50, size = (300,400),
xlims = ( 0.50, 1.50),
ylims = (-0.75, +0.75))
xgrid = LinRange(xlims[1], xlims[2], nx)
zgrid = LinRange(ylims[1], ylims[2], ny)
pot = [ElectromagneticFields.A₃([xgrid[i], zgrid[j], 0.0], equ) / xgrid[i] for i in eachindex(xgrid), j in eachindex(zgrid)]
seriestype := :contour
aspect_ratio := :equal
size := size
xlims := xlims
ylims := ylims
levels := levels
legend := :none
(xgrid, zgrid, pot')
end
end
module SolovevFRC
import ..Solovev: @code_frc, FRC
export @code, init
var"@code" = var"@code_frc"
init = FRC
end
module SolovevITER
import ..Solovev: @code_iter, ITER
export @code, init
var"@code" = var"@code_iter"
init = ITER
end
module SolovevITERwXpoint
import ..Solovev: @code_iter_xpoint, ITER
export @code, init
var"@code" = var"@code_iter_xpoint"
init() = ITER(xpoint=true)
end
module SolovevNSTX
import ..Solovev: @code_nstx, NSTX
export @code, init
var"@code" = var"@code_nstx"
init = NSTX
end
module SolovevNSTXwXpoint
import ..Solovev: @code_nstx_xpoint, NSTX
export @code, init
var"@code" = var"@code_nstx_xpoint"
init() = NSTX(xpoint=true)
end
module SolovevNSTXwDoubleXpoint
import ..Solovev: @code_nstx_double_xpoint, NSTXdoubleX
export @code, init
var"@code" = var"@code_nstx_double_xpoint"
init = NSTXdoubleX
end
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709,
6,
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1602,
22282,
430,
287,
357,
49,
14,
49,
158,
224,
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] | 1.756838 | 12,576 |
<reponame>emenems/HypoTest<filename>test/runtests.jl
# Run the test from HypoTest folder
using HypoTest
using Test
import Distributions
import Random
# List of test files:
tests = ["momentestim_test.jl",
"histdata_test.jl",
"disttest_test.jl",
"othertest_test.jl"]
# Run all tests in the list
for i in tests
include(i)
end
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] | 2.723577 | 123 |
module NaiveBayes
# package code goes here
include("naive_bayes.jl")
end # module
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] | 2.896552 | 29 |
# Client and server for communicating with the RAFT cluster
using Sockets
const RAFT_API_VERSION = 1
const RAFT_PROTO_VERSION = 1
const RAFT_JULIA_CLIENT = 0x1
const RAFT_PROTO_CLIENT = 0x2
const COMMS_PORT = 2000
function process_preamble(h::UInt64)
size = UInt32(h & 0xffffffff)
version = UInt16((h >> 32) & 0xffff)
flags = UInt16((h >> 48) & 0xffff)
@debug ("size=$size version=$version flags=$flags", " ", h)
(size, version, flags)
end
function check_version(version, flags, lookup_table)
if (flags & RAFT_JULIA_CLIENT) == RAFT_JULIA_CLIENT
(version != RAFT_API_VERSION) && throw(RavanaException("Unsupported API version $(version)"))
elseif (flags & RAFT_PROTO_CLIENT) == RAFT_PROTO_CLIENT
(version != RAFT_PROTO_VERSION) && throw(RavanaException("Unsupported proto version $(version)"))
else
throw(RavanaException("Bad protocol header. Unknown flag $(flags)"))
end
end
# Disassemble protocol header
function get_opt(sock, lookup_table)
(size::UInt32, version::UInt16, flags::UInt16) = process_preamble(read(sock, UInt64))
check_version(version, flags, lookup_table)
(op, argv) = array_to_type(read(sock, size))
!haskey(lookup_table, op) && throw(RavanaException("Invalid op $(op)"))
func = lookup_table[op]
@debug ("op: ", op, " argv: ", argv)
return (op, func, argv)
end
"""
"""
function ravana_server(;address=IPv4(0), port=COMMS_PORT)
@async begin
server = 0 # Init server socket
sockErr = true
@debug ("In ravana_server")
while (sockErr)
try
server = listen(address, port)
sockErr = false
@info ("Starting cluster communication server at $(address):$(port)")
catch e
@info ("Could not listen on port $(port). Trying $(port + 1)")
port += 1 # Try next port
end
end
while true
sock = accept(server)
if isopen(sock) != true
throw(RaftException("Error! Socket not open"))
end
# Disassemble op and arguments
@async begin
try
(op, func, argv) = get_opt(sock, op_table)
# Execute on cluster
ret = raft_cluster_execute(op, func, argv)
# Return result to client
b = byte_array(ret)
write(sock, length(b), b)
catch e
@error ("ravana_server(): Exception! ", e)
b = byte_array(e)
write(sock, length(b), b)
end
close(sock)
end
end
end
end
"""
ravana_client(address, port, op::Int32, argv...)
Low level function that can be called from a Julia prompt/program.
```jldoxctest
julia> Ravana.ravana_client(IPv4(0), 2000, Ravana.OP_GET_NODE_PARAMS)
```
"""
function ravana_client(address, port, op::Int32, argv...)
bytes = byte_array((op, argv))
size = UInt32(length(bytes))
version = UInt16(RAFT_API_VERSION)
flags = UInt16(RAFT_JULIA_CLIENT)
client = connect(address, port)
ret = write(client, size, version, flags, bytes)
ret_size = read(client, Int)
ret = array_to_type(read(client, ret_size))
close(client)
ret
end
# If leader execute op, otherwise redirect to leader
function raft_cluster_execute(op, func, argv)
@debug ("In raft_cluster_execute")
if op == OP_INIT_CLUSTER
ret = raft_execute(op, func, argv) # Bootstrap bypasses append entries
elseif current_state == LEADER
@debug("Leader before executing raft_run_command")
ret = raft_run_command(op, func, argv) # Commits as per RAFT protocol
else
ret = ravana_client(leaderAddress, leaderPort, op, argv) # Redirect to leader
end
ret
end
function init_cluster(;address=IPv4(0), port=2000)
ravana_client(address, port, OP_INIT_CLUSTER, nothing)
end
function get_cluster_config(node::raftNode)
ravana_client(node.name, node.port, OP_GET_CLUSTER_CONF, nothing)
end
get_cluster_config() = get_cluster_config(raftNode(leaderAddress, COMMS_PORT, Int128(0), Int128(0)))
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] | 2.229921 | 1,905 |
function build()
println("Build this thing!")
end
| [
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] | 3.176471 | 17 |
const CerebellarVemusLobuleI = AnatomicalStructure("cerebellar vermus lobule I")
const CerebellarVemusLobuleII = AnatomicalStructure("cerebellar vermus lobule II")
const CerebellarVemusLobuleIII = AnatomicalStructure("cerebellar vermus lobule III")
const CerebellarVemusLobuleIV = AnatomicalStructure("cerebellar vermus lobule IV")
const CerebellarVemusLobuleV = AnatomicalStructure("cerebellar vermus lobule V")
const CerebellarVemusLobuleVI = AnatomicalStructure("cerebellar vermus lobule VI")
const CerebellarVemusLobuleVII = AnatomicalStructure("cerebellar vermus lobule VII")
const CerebellarVemusLobuleVIII = AnatomicalStructure("cerebellar vermus lobule VIII")
const CerebellarVemusLobuleIX = AnatomicalStructure("cerebellar vermus lobule IX")
const CerebellarVemusLobuleX = AnatomicalStructure("cerebellar vermus lobule X")
const FlocculonodularLobe = AnatomicalStructure("flocculonodular lobe")
# TODO sub structures need more about cerebellar lob components
# children = Flocculonodular lobe
const Vestibulocerebellum = BilateralStructure("vestibulocerebellum")
const Archicerebellum = Vestibulocerebellum
# TODO sub structures for Cerebrocerebellum
const Cerebrocerebellum = BilateralStructure("cerebrocerebellum" )
"""
Vermis
* Catani and <NAME>, 2008
* Dell'Acqua et al., 2013
* Meola et al., 2016a; Meola et al., 2016c; Wakana et al., 2004
"""
const Vermis = AnatomicalStructure("vermis")
const CerebellumCortex = BilateralStructure("cerebellum cortex")
#const CerebellumWhiteMatter = AnatomicalStructure("cerebellum white matter")
#= TODO this needs a different separation than white matter vs cortex
const Cerebellum = AnatomicalStructure(
name = "Cerebellum",
children = (
CerebellumCortex,
CerebellumWhiteMatter
)
)
=#
# TODO double check paravermis
const Paravermis = BilateralStructure("paravermis")
# children Vermis and paravermis
const Spinocerebellum = BilateralStructure("spinocerebellum")
const Paleocerebellum = Spinocerebellum
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<filename>src/Reconstruction.jl
export reconstruction
export writePartToImage!, initImage
"""
This is the most high level reconstruction method using the `MDFDatasetStore`
"""
function reconstruction(d::MDFDatasetStore, study::Study, exp::Experiment, recoParams)
!(haskey(recoParams,:SFPath)) && (recoParams[:SFPath] = sfPath( MPIFile( recoParams[:measPath] ) ))
numReco = findReco(d,study,exp,recoParams)
haskey(recoParams,:emptyMeasPath) && recoParams[:emptyMeasPath]!=nothing && (recoParams[:emptyMeas] = MPIFile( recoParams[:emptyMeasPath] ) )
#numReco = findReco(d,study,exp,recoParams)
if numReco > 0
@info "Reconstruction found in MDF dataset store."
reco = getReco(d,study,exp, numReco)
c = loadRecoData(reco.path)
else
c = reconstruction(recoParams)
addReco(d,study,exp, c)
end
return c
end
# The previous function is somewhat redundant in its arguments. In particular
# the measPath and the study/exp are redundant. Should be cleaned up before
# it can be used as a user facing API
#
#function reconstruction(d::MDFDatasetStore, recoParams::Dict)
#
# study = ???
#
# studies = getStudies( activeDatasetStore(m) )
#
# for study in studies
# push!(m.studyStore, (study.date, study.name, study.subject, study.path, true))
# end
#
# m.currentStudy = Study(TreeModel(m.studyStoreSorted)[currentIt,4],
# TreeModel(m.studyStoreSorted)[currentIt,2],
# TreeModel(m.studyStoreSorted)[currentIt,3],
# TreeModel(m.studyStoreSorted)[currentIt,1])
#
# exp = getExperiment(study, recoParams[:measPath])
# return reconstruction(d, study, exp, recoParams)
#end
"""
This is the most high level reconstruction method that performs in-memory reconstruction
"""
function reconstruction(recoParams::Dict)
@info "Performing in-memory reconstruction."
bMeas = MPIFile( recoParams[:measPath] )
!(haskey(recoParams,:SFPath)) && (recoParams[:SFPath] = sfPath( bMeas ))
bSF = MPIFile(recoParams[:SFPath])
c = reconstruction(bSF, bMeas; recoParams...)
# store reco params with image
c.recoParams = recoParams
return c
end
function reconstruction(bMeas::MPIFile; kargs...)
bSF = MPIFile(sfPath(bMeas) )
reconstruction(bSF, bMeas; kargs...)
end
function reconstruction(filenameMeas::AbstractString; kargs...)
bMeas = MPIFile(filenameMeas)
reconstruction(bMeas; kargs...)
end
function reconstruction(filenameSF::AbstractString, filenameMeas::AbstractString; kargs...)
bSF = MPIFile(filenameSF)
bMeas = MPIFile(filenameMeas)
reconstruction(bSF,bMeas; kargs...)
end
function reconstruction(filenameSF::AbstractString, filenameMeas::AbstractString, freq::Array; kargs...)
bSF = MPIFile(filenameSF)
bMeas = MPIFile(filenameMeas)
reconstruction(bSF,bMeas,freq; kargs...)
end
function reconstruction(bSF::Union{T,Vector{T}}, bMeas::MPIFile; kargs...) where {T<:MPIFile}
if haskey(kargs, :periodicMotionCorrection) && kargs[:periodicMotionCorrection]
return reconstructionPeriodicMotion(bSF, bMeas; kargs...)
elseif acqNumPeriodsPerFrame(bMeas) > 1 &&
(acqNumPeriodsPerFrame(bSF) == 1 || typeof(bSF) == MultiMPIFile)
# This branch is only used of the measurements are multi-patch and if the
# system matrix is not fully sampled. This is the case if either we have a
# single system matrix that is reused for-multiple patches, or if we have
# a MultiMPIFile
return reconstructionMultiPatch(bSF, bMeas; kargs...)
else
return reconstructionSinglePatch(bSF, bMeas; kargs...)
end
end
function reconstructionSinglePatch(bSF::Union{T,Vector{T}}, bMeas::MPIFile;
minFreq=0, maxFreq=1.25e6, SNRThresh=-1,maxMixingOrder=-1, numUsedFreqs=-1, sortBySNR=false, recChannels=1:numReceivers(bMeas),
bEmpty = nothing, emptyMeas=bEmpty, bgFrames = 1, fgFrames = 1, varMeanThresh = 0, minAmplification=2,
numPeriodAverages=1, numPeriodGrouping=1, kargs...) where {T<:MPIFile}
freq = filterFrequencies(bSF,minFreq=minFreq, maxFreq=maxFreq,recChannels=recChannels, SNRThresh=SNRThresh,
numUsedFreqs=numUsedFreqs, sortBySNR=sortBySNR, numPeriodAverages=numPeriodAverages,
numPeriodGrouping=numPeriodGrouping)
if varMeanThresh > 0
bEmptyTmp = (emptyMeas == nothing) ? bMeas : emptyMeas
freqVarMean = filterFrequenciesVarMean(bMeas, bEmptyTmp, fgFrames, bgFrames;
thresh=varMeanThresh, minAmplification=minAmplification,
minFreq=minFreq, maxFreq=maxFreq,recChannels=recChannels)
freq = intersect(freq, freqVarMean)
end
# Ensure that no frequencies are used that are not present in the measurement
freq = intersect(freq, filterFrequencies(bMeas, numPeriodAverages=numPeriodAverages,
numPeriodGrouping=numPeriodGrouping))
@debug "selecting $(length(freq)) frequencies"
return reconstruction(bSF, bMeas, freq; emptyMeas=emptyMeas, bgFrames=bgFrames, fgFrames=fgFrames,
numPeriodAverages=numPeriodAverages, numPeriodGrouping=numPeriodGrouping, kargs...)
end
function reconstruction(bSF::Union{T,Vector{T}}, bMeas::MPIFile, freq::Array;
bEmpty = nothing, emptyMeas = bEmpty, bgFrames = 1,
denoiseWeight = 0, redFactor = 0.0, thresh = 0.0,
loadasreal = false, solver = "kaczmarz", sparseTrafo = nothing, saveTrafo=false,
gridsize = gridSizeCommon(bSF), fov=calibFov(bSF), center=[0.0,0.0,0.0], useDFFoV=false,
deadPixels=Int[], bgCorrectionInternal=false, bgDictSize=nothing, bgFramesDict=nothing,
numPeriodAverages=1, numPeriodGrouping=1, kargs...) where {T<:MPIFile}
(typeof(bgFrames) <: AbstractRange && emptyMeas==nothing) && (emptyMeas = bMeas)
bgCorrection = emptyMeas != nothing ? true : bgCorrectionInternal
consistenceCheck(bSF, bMeas)
@debug "Loading System matrix"
S, grid = getSF(bSF, freq, sparseTrafo, solver;
bgCorrection=bgCorrection, loadasreal=loadasreal,
thresh=thresh, redFactor=redFactor, saveTrafo=saveTrafo,
useDFFoV=useDFFoV, gridsize=gridsize, fov=fov, center=center,
deadPixels=deadPixels,numPeriodAverages=numPeriodAverages,
numPeriodGrouping=numPeriodGrouping)
if denoiseWeight > 0 && sparseTrafo == nothing
denoiseSF!(S, shape, weight=denoiseWeight)
end
bgDict = getBackgroundDictionary(bSF, bMeas, freq, bgDictSize, bgFramesDict)
return reconstruction(S, bSF, bMeas, freq, grid, emptyMeas=emptyMeas, bgFrames=bgFrames,
sparseTrafo=sparseTrafo, loadasreal=loadasreal,
bgDict = bgDict,
solver=solver, bgCorrectionInternal=bgCorrectionInternal,
numPeriodAverages=numPeriodAverages, numPeriodGrouping=numPeriodGrouping; kargs...)
end
function reconstruction(S, bSF::Union{T,Vector{T}}, bMeas::MPIFile, freq::Array, grid;
frames = nothing, bEmpty = nothing, emptyMeas= bEmpty, bgFrames = 1, nAverages = 1,
numAverages=nAverages, bgDict = nothing, bgFramesPost = nothing,
sparseTrafo = nothing, loadasreal = false, maxload = 100, maskDFFOV=false,
weightType=WeightingType.None, weightingLimit = 0, solver = "kaczmarz",
spectralCleaning=true, spectralLeakageCorrection=spectralCleaning,
fgFrames=1:10, bgCorrectionInternal=false,
noiseFreqThresh=0.0, channelWeights=ones(3),
numPeriodAverages=1, numPeriodGrouping=1, kargs...) where {T<:MPIFile}
#(typeof(bgFrames) <: AbstractRange && bEmpty==nothing) && (bEmpty = bMeas)
bgCorrection = emptyMeas != nothing ? true : false
@debug "Loading emptymeas ..."
if emptyMeas!=nothing
#if acqNumBGFrames(emptyMeas) > 0
# uEmpty = getMeasurementsFD(emptyMeas, false, frequencies=freq, frames=bgFrames, #frames=measBGFrameIdx(bEmpty),
# numAverages = =length(bgFrames), bgCorrection=bgCorrectionInternal,
# loadasreal=loadasreal, spectralLeakageCorrection=spectralLeakageCorrection)
#else
uEmpty = getMeasurementsFD(emptyMeas, frequencies=freq, frames=bgFrames, numAverages=length(bgFrames),
loadasreal = loadasreal,spectralLeakageCorrection=spectralLeakageCorrection, bgCorrection=bgCorrectionInternal,
numPeriodAverages=numPeriodAverages, numPeriodGrouping=numPeriodGrouping)
if bgFramesPost != nothing
uEmptyPost = getMeasurementsFD(emptyMeas, false, frequencies=freq, frames=bgFramesPost,
numAverages = length(bgFramesPost), bgCorrection=bgCorrectionInternal,
loadasreal = loadasreal, spectralLeakageCorrection=spectralLeakageCorrection,
numPeriodAverages=numPeriodAverages, numPeriodGrouping=numPeriodGrouping)
end
#end
end
frames == nothing && (frames = 1:acqNumFrames(bMeas))
weights = getWeights(weightType, freq, S, weightingLimit=weightingLimit,
emptyMeas = emptyMeas, bMeas = bMeas, bgFrames=bgFrames, bSF=bSF,
channelWeights = channelWeights)
L = -fld(-length(frames),numAverages) # number of tomograms to be reconstructed
p = Progress(L, 1, "Reconstructing data...")
# initialize sparseTrafo
B = linearOperator(sparseTrafo, shape(grid), eltype(S))
@debug "S: $(eltype(S))"
@debug "B: $(eltype(B))"
#initialize output
image = initImage(bSF,bMeas,L,numAverages,grid,false)
currentIndex = 1
iterator = numAverages == 1 ? Iterators.partition(frames,maxload) : Iterators.partition(frames,numAverages*maxload)
for partframes in iterator
@debug "Loading measurements ..."
u = getMeasurementsFD(bMeas, frequencies=freq, frames=partframes, numAverages=numAverages,
loadasreal=loadasreal, spectralLeakageCorrection=spectralLeakageCorrection,
bgCorrection=bgCorrectionInternal, numPeriodAverages=numPeriodAverages, numPeriodGrouping=numPeriodGrouping)
if emptyMeas!=nothing
if bgFramesPost == nothing
u = u .- uEmpty
else
for l=1:length(partframes)
alpha = (partframes[l] - mean(bgFrames)) / (mean(bgFramesPost) - mean(bgFrames))
u[:,:,l] .-= (1-alpha).*uEmpty[:,:,1] .+ alpha.*uEmptyPost[:,:,1]
end
end
end
noiseFreqThresh > 0 && setNoiseFreqToZero(u, freq, noiseFreqThresh, bEmpty = emptyMeas, bMeas = bMeas, bgFrames=bgFrames)
# convert measurement data if neccessary
if eltype(S)!=eltype(u)
@warn "System matrix and measurement have different element data type. Mapping measurment data to system matrix element type."
u = map(eltype(S),u)
end
@debug "Reconstruction ..."
c = reconstruction(S, u, bgDict; sparseTrafo=B, progress=p,
weights=weights, solver=solver, shape=shape(grid), kargs...)
currentIndex = writePartToImage!(image, c, currentIndex, partframes, numAverages)
end
return image
end
function writePartToImage!(image, c, currentIndex::Int, partframes, numAverages)
# permute c's dimensions into image order
colorsize = size(image,1)
spatialsize = size(image,2)*size(image,3)*size(image,4)
inc = -fld(-length(partframes),numAverages)
c = reshape(c,spatialsize,colorsize,inc)
c = permutedims(c,[2,1,3])
# write c to image
image[Axis{:time}(currentIndex:currentIndex+inc-1)] = c[:]
currentIndex += inc
return currentIndex
end
function initImage(bSFs::Union{T,Vector{T}}, bMeas::S, L::Int, numAverages::Int,
grid::RegularGridPositions, loadOnlineParams=false) where {T,S<:MPIFile}
# the number of channels is determined by the number of system matrices
if isa(bSFs,AbstractVector) || isa(bSFs,MultiContrastFile)
numcolors = length(bSFs)
bSF = bSFs[1]
else
numcolors = 1
bSF = bSFs
end
# calculate axis
shp = shape(grid)
pixspacing = (spacing(grid) ./ acqGradient(bMeas)[1] .* acqGradient(bSF)[1])*1000u"mm"
offset = (ffPos(bMeas) .- 0.5 .* calibFov(bSF))*1000u"mm" .+ 0.5 .* pixspacing
dtframes = acqNumAverages(bMeas)*dfCycle(bMeas)*numAverages*1u"s"
# initialize raw array
array = Array{Float32}(undef, numcolors,shp...,L)
# create image
im = makeAxisArray(array, pixspacing, offset, dtframes)
# provide meta data
if loadOnlineParams
imMeta = ImageMeta(im,generateHeaderDictOnline(bSF,bMeas))
else
imMeta = ImageMeta(im,generateHeaderDict(bSF,bMeas))
end
return imMeta
end
"""
Low level reconstruction method
"""
function reconstruction(S, u::Array, bgDict::Nothing=nothing; sparseTrafo = nothing,
lambd=0.0, lambda=lambd, λ=lambda, progress=nothing, solver = "kaczmarz",
weights=nothing, enforceReal=true, enforcePositive=true,
relativeLambda=true, kargs...)
N = size(S,2) #prod(shape)
M = div(length(S), N)
L = size(u)[end]
u = reshape(u, M, L)
c = zeros(N,L)
#c = zeros(real(eltype(u)),N,L) Change by J.Dora
if sum(abs.(λ)) > 0 && solver != "fusedlasso" && relativeLambda
trace = calculateTraceOfNormalMatrix(S,weights)
λ *= trace / N
setlambda(S,λ)
end
solv = createLinearSolver(solver, S; weights=weights, λ=λ,
sparseTrafo=sparseTrafo, enforceReal=enforceReal,
enforcePositive=enforcePositive, kargs...)
progress==nothing ? p = Progress(L, 1, "Reconstructing data...") : p = progress
for l=1:L
d = solve(solv, u[:,l])
if sparseTrafo != nothing
d[:] = sparseTrafo*d #backtrafo from dual space
end
#if typeof(B)==LinearSolver.DSTOperator
# d=onGridReverse(d,shape)
#end
c[:,l] = real( d ) # this one is allocating
next!(p)
sleep(0.001)
end
return c
end
# old code
#if reshapesolution
# c = reshape(c, shape..., L)
#end
#shape(grid)
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] | 2.464887 | 5,582 |
### A Pluto.jl notebook ###
# v0.12.20
using Markdown
using InteractiveUtils
# ╔═╡ 63ba08cc-59a8-11eb-0a0f-27efac60d779
using Pkg, DrWatson
# ╔═╡ 6db218c6-59a8-11eb-2a8b-7107354cf590
begin
#@quickactivate "StatisticalRethinkingStan"
using StanSample, StanOptimize
using StatisticalRethinking
end
# ╔═╡ 51fc19b8-59a8-11eb-2214-15aca59b807b
md" ## Figure 8.2s"
# ╔═╡ 8aaa4bcc-59a8-11eb-2003-f1213b116565
begin
df = CSV.read(sr_datadir("rugged.csv"), DataFrame)
df_africa = df[df.cont_africa .== 1, [:rgdppc_2000, :rugged]]
dropmissing!(df_africa, :rgdppc_2000)
dropmissing!(df_africa, :rugged)
df_africa.log_gdp = log.(df_africa[:, :rgdppc_2000])
scale!(df_africa, [:log_gdp, :rugged])
PRECIS(df_africa)
end
# ╔═╡ 25ee6cd8-6a54-11eb-0a72-3fb09ee63b0e
begin
df_non_africa = df[df.cont_africa .== 0, [:rgdppc_2000, :rugged]]
dropmissing!(df_non_africa, :rgdppc_2000)
dropmissing!(df_non_africa, :rugged)
df_non_africa.log_gdp = log.(df_non_africa[:, :rgdppc_2000])
scale!(df_non_africa, [:log_gdp, :rugged])
end;
# ╔═╡ d7d3e626-6a45-11eb-1820-a3ec98e556b1
stan8_0 = "
data {
int N;
vector[N] G;
vector[N] R;
}
parameters {
real a;
real b;
real<lower=0> sigma;
}
transformed parameters {
vector[N] mu;
mu = a + b * (R - 0.125);
}
model {
a ~ normal(1, 1);
b ~ normal(0, 1);
sigma ~ exponential(1);
G ~ normal(mu, sigma);
}
";
# ╔═╡ a3e7c070-6a46-11eb-072b-3943db854020
begin
data1 = (N = size(df_africa, 1), G = df_africa.log_gdp_s,
R = df_africa.rugged_s)
m8_1s = SampleModel("m8.1s", stan8_0)
rc8_1_1s = stan_sample(m8_1s; data=data1)
if success(rc8_1_1s)
post8_1_1s_df = read_samples(m8_1s, :dataframe)
PRECIS(post8_1_1s_df[:, [:a, :b, :sigma]])
end
end
# ╔═╡ cc11715a-6a54-11eb-1955-d38a021a3bb3
begin
data2 = (N = size(df_non_africa, 1), G = df_non_africa.log_gdp_s,
R = df_non_africa.rugged_s)
rc8_1_2s = stan_sample(m8_1s; data=data2)
if success(rc8_1_2s)
post8_1_2s_df = read_samples(m8_1s, :dataframe)
PRECIS(post8_1_2s_df[:, [:a, :b, :sigma]])
end
end
# ╔═╡ 1968b688-6a49-11eb-2dec-9986830b4a7e
begin
p1 = plotbounds(
df_africa, :rugged, :log_gdp,
post8_1_1s_df, [:a, :b, :sigma];
bounds=[:none, :hpdi],
colors=[:orange, :lightblue],
title="African nations",
xlab="ruggedness",
ylab="log GDP"
)
df_afr = df[df.cont_africa .== 1, [:country, :rgdppc_2000, :rugged]]
df_afr = df_afr[df_afr.rugged .> 4, :]
for (ind, country) in enumerate(df_afr.country)
annotate!([([df_afr.rugged[ind]+0.3], [log(df_afr.rgdppc_2000[ind])+0.15],
Plots.text(df_afr.country[ind], 6, :red, :right))])
end
end
# ╔═╡ 0f36fb46-6a5d-11eb-2d34-91266e770493
begin
p2 = plotbounds(
df_non_africa, :rugged, :log_gdp,
post8_1_2s_df, [:a, :b, :sigma];
bounds=[:none, :hpdi],
colors=[:orange, :lightblue],
title="Non-African nations",
xlab="ruggedness",
ylab="log GDP"
)
df_na = df[:, [:country, :rgdppc_2000, :rugged]]
dropmissing!(df_na, :rgdppc_2000)
dropmissing!(df_na, :rugged)
df_na = df_na[df_na.rugged .> 4, :]
for (ind, country) in enumerate(df_na.country)
println(country)
if !(country in df_afr.country)
annotate!([([df_na.rugged[ind]+0.3],
[log(df_na.rgdppc_2000[ind])+0.15],
Plots.text(df_na.country[ind], 6, :red, :right))])
end
end
plot(p1, p2, layout=(1,2))
end
# ╔═╡ 45767e2e-6a63-11eb-3e12-354a7e32a374
md" ## End of figure 8.2s"
# ╔═╡ Cell order:
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] | 1.807565 | 2,115 |
# This file is a part of Julia. License is MIT: https://julialang.org/license
# OffsetArrays (arrays with indexing that doesn't start at 1)
# This test file is designed to exercise support for generic indexing,
# even though offset arrays aren't implemented in Base.
# OffsetArrays v1.3.0
# No compat patch and docstrings
module OffsetArrays
using Base: tail, @propagate_inbounds
using Base: IdentityUnitRange
export OffsetArray, OffsetMatrix, OffsetVector
struct IdOffsetRange{T<:Integer,I<:AbstractUnitRange{T}} <: AbstractUnitRange{T}
parent::I
offset::T
IdOffsetRange{T,I}(r::I, offset::T) where {T<:Integer,I<:AbstractUnitRange{T}} = new{T,I}(r, offset)
end
# Construction/coercion from arbitrary AbstractUnitRanges
function IdOffsetRange{T,I}(r::AbstractUnitRange, offset::Integer = 0) where {T<:Integer,I<:AbstractUnitRange{T}}
rc, o = offset_coerce(I, r)
return IdOffsetRange{T,I}(rc, convert(T, o+offset))
end
function IdOffsetRange{T}(r::AbstractUnitRange, offset::Integer = 0) where T<:Integer
rc = convert(AbstractUnitRange{T}, r)::AbstractUnitRange{T}
return IdOffsetRange{T,typeof(rc)}(rc, convert(T, offset))
end
IdOffsetRange(r::AbstractUnitRange{T}, offset::Integer = 0) where T<:Integer =
IdOffsetRange{T,typeof(r)}(r, convert(T, offset))
# Coercion from other IdOffsetRanges
IdOffsetRange{T,I}(r::IdOffsetRange{T,I}) where {T<:Integer,I<:AbstractUnitRange{T}} = r
function IdOffsetRange{T,I}(r::IdOffsetRange) where {T<:Integer,I<:AbstractUnitRange{T}}
rc, offset = offset_coerce(I, r.parent)
return IdOffsetRange{T,I}(rc, r.offset+offset)
end
function IdOffsetRange{T}(r::IdOffsetRange) where T<:Integer
return IdOffsetRange(convert(AbstractUnitRange{T}, r.parent), r.offset)
end
IdOffsetRange(r::IdOffsetRange) = r
AbstractUnitRange{T}(r::IdOffsetRange{T}) where {T} = r
AbstractUnitRange{T}(r::IdOffsetRange) where {T} = IdOffsetRange{T}(r)
# TODO: uncomment these when Julia is ready
# # Conversion preserves both the values and the indexes, throwing an InexactError if this
# # is not possible.
# Base.convert(::Type{IdOffsetRange{T,I}}, r::IdOffsetRange{T,I}) where {T<:Integer,I<:AbstractUnitRange{T}} = r
# Base.convert(::Type{IdOffsetRange{T,I}}, r::IdOffsetRange) where {T<:Integer,I<:AbstractUnitRange{T}} =
# IdOffsetRange{T,I}(convert(I, r.parent), r.offset)
# Base.convert(::Type{IdOffsetRange{T,I}}, r::AbstractUnitRange) where {T<:Integer,I<:AbstractUnitRange{T}} =
# IdOffsetRange{T,I}(convert(I, r), 0)
offset_coerce(::Type{Base.OneTo{T}}, r::Base.OneTo) where T<:Integer = convert(Base.OneTo{T}, r), 0
function offset_coerce(::Type{Base.OneTo{T}}, r::AbstractUnitRange) where T<:Integer
o = first(r) - 1
return Base.OneTo{T}(last(r) - o), o
end
# function offset_coerce(::Type{Base.OneTo{T}}, r::IdOffsetRange) where T<:Integer
# rc, o = offset_coerce(Base.OneTo{T}, r.parent)
# Fallback, specialze this method if `convert(I, r)` doesn't do what you need
offset_coerce(::Type{I}, r::AbstractUnitRange) where I<:AbstractUnitRange{T} where T =
convert(I, r), 0
@inline Base.parent(r::IdOffsetRange) = r.parent
@inline Base.axes(r::IdOffsetRange) = (Base.axes1(r),)
@inline Base.axes1(r::IdOffsetRange) = IdOffsetRange(Base.axes1(r.parent), r.offset)
@inline Base.length(r::IdOffsetRange) = length(r.parent)
Base.reduced_index(i::IdOffsetRange) = typeof(i)(first(i):first(i))
# Workaround for #92 on Julia < 1.4
Base.reduced_index(i::IdentityUnitRange{<:IdOffsetRange}) = typeof(i)(first(i):first(i))
for f in [:firstindex, :lastindex]
@eval Base.$f(r::IdOffsetRange) = $f(r.parent) .+ r.offset
end
@inline function Base.iterate(r::IdOffsetRange)
ret = iterate(r.parent)
ret === nothing && return nothing
return (ret[1] + r.offset, ret[2])
end
@inline function Base.iterate(r::IdOffsetRange, i)
ret = iterate(r.parent, i)
ret === nothing && return nothing
return (ret[1] + r.offset, ret[2])
end
@inline Base.first(r::IdOffsetRange) = first(r.parent) + r.offset
@inline Base.last(r::IdOffsetRange) = last(r.parent) + r.offset
@propagate_inbounds Base.getindex(r::IdOffsetRange, i::Integer) = r.parent[i - r.offset] + r.offset
@propagate_inbounds function Base.getindex(r::IdOffsetRange, s::AbstractUnitRange{<:Integer})
return r.parent[s .- r.offset] .+ r.offset
end
@propagate_inbounds function Base.getindex(r::IdOffsetRange, s::IdentityUnitRange)
return IdOffsetRange(r.parent[s .- r.offset], r.offset)
end
@propagate_inbounds function Base.getindex(r::IdOffsetRange, s::IdOffsetRange)
return IdOffsetRange(r.parent[s.parent .+ (s.offset - r.offset)] .+ (r.offset - s.offset), s.offset)
end
# offset-preserve broadcasting
Broadcast.broadcasted(::Base.Broadcast.DefaultArrayStyle{1}, ::typeof(-), r::IdOffsetRange{T}, x::Integer) where T =
IdOffsetRange{T}(r.parent .- x, r.offset)
Broadcast.broadcasted(::Base.Broadcast.DefaultArrayStyle{1}, ::typeof(+), r::IdOffsetRange{T}, x::Integer) where T =
IdOffsetRange{T}(r.parent .+ x, r.offset)
Broadcast.broadcasted(::Base.Broadcast.DefaultArrayStyle{1}, ::typeof(+), x::Integer, r::IdOffsetRange{T}) where T =
IdOffsetRange{T}(x .+ r.parent, r.offset)
Base.show(io::IO, r::IdOffsetRange) = print(io, "OffsetArrays.IdOffsetRange(", first(r), ':', last(r), ")")
# Optimizations
@inline Base.checkindex(::Type{Bool}, inds::IdOffsetRange, i::Real) = Base.checkindex(Bool, inds.parent, i - inds.offset)
struct Origin{T <: Union{Tuple,Int}}
index::T
end
Origin(I::NTuple{N,Int}) where N = Origin{typeof(I)}(I)
Origin(I::CartesianIndex) = Origin(I.I)
Origin(I1::Int, In::Int...) = Origin((I1, In...))
# Origin(0) != Origin((0, )) but they work the same with broadcasting
Origin(n::Int) = Origin{Int}(n)
(o::Origin)(A::AbstractArray) = o.index .- first.(axes(A))
### Low-level utilities ###
_indexoffset(r::AbstractRange) = first(r) - 1
_indexoffset(i::Integer) = 0
_indexoffset(i::Colon) = 0
_indexlength(r::AbstractRange) = length(r)
_indexlength(i::Integer) = i
_indexlength(i::Colon) = Colon()
_offset(axparent::AbstractUnitRange, ax::AbstractUnitRange) = first(ax) - first(axparent)
_offset(axparent::AbstractUnitRange, ax::Integer) = 1 - first(axparent)
abstract type AxisConversionStyle end
struct SingleRange <: AxisConversionStyle end
struct TupleOfRanges <: AxisConversionStyle end
AxisConversionStyle(::Type) = SingleRange()
AxisConversionStyle(::Type{<:CartesianIndices}) = TupleOfRanges()
_convertTupleAbstractUnitRange(x) = _convertTupleAbstractUnitRange(AxisConversionStyle(typeof(x)), x)
_convertTupleAbstractUnitRange(::SingleRange, x) = (convert(AbstractUnitRange{Int}, x),)
_convertTupleAbstractUnitRange(::TupleOfRanges, x) = convert(Tuple{Vararg{AbstractUnitRange{Int}}}, x)
_toAbstractUnitRanges(t::Tuple) = (_convertTupleAbstractUnitRange(first(t))..., _toAbstractUnitRanges(tail(t))...)
_toAbstractUnitRanges(::Tuple{}) = ()
# ensure that the indices are consistent in the constructor
_checkindices(A::AbstractArray, indices, label) = _checkindices(ndims(A), indices, label)
function _checkindices(N::Integer, indices, label)
throw_argumenterror(N, indices, label) = throw(ArgumentError(label * " $indices are not compatible with a $(N)D array"))
N == length(indices) || throw_argumenterror(N, indices, label)
end
# Technically we know the length of CartesianIndices but we need to convert it first, so here we
# don't put it in OffsetAxisKnownLength.
const OffsetAxisKnownLength = Union{Integer,AbstractUnitRange}
const OffsetAxis = Union{OffsetAxisKnownLength,Colon}
const ArrayInitializer = Union{UndefInitializer,Missing,Nothing}
## OffsetArray
struct OffsetArray{T,N,AA<:AbstractArray} <: AbstractArray{T,N}
parent::AA
offsets::NTuple{N,Int}
function OffsetArray{T,N,AA}(parent::AA, offsets::NTuple{N,Int}) where {T,N,AA <: AbstractArray}
@boundscheck overflow_check.(axes(parent), offsets)
new{T,N,AA}(parent, offsets)
end
end
const OffsetVector{T,AA <: AbstractArray} = OffsetArray{T,1,AA}
const OffsetMatrix{T,AA <: AbstractArray} = OffsetArray{T,2,AA}
function overflow_check(r, offset::T) where T
# This gives some performance boost https://github.com/JuliaLang/julia/issues/33273
throw_upper_overflow_error() = throw(ArgumentError("Boundary overflow detected: offset $offset should be equal or less than $(typemax(T) - last(r))"))
throw_lower_overflow_error() = throw(ArgumentError("Boundary overflow detected: offset $offset should be equal or greater than $(typemin(T) - first(r))"))
if offset > 0 && last(r) > typemax(T) - offset
throw_upper_overflow_error()
elseif offset < 0 && first(r) < typemin(T) - offset
throw_lower_overflow_error()
end
end
# Tuples of integers are treated as offsets
# Empty Tuples are handled here
function OffsetArray(A::AbstractArray, offsets::Tuple{Vararg{Integer}})
_checkindices(A, offsets, "offsets")
OffsetArray{eltype(A),ndims(A),typeof(A)}(A, offsets)
end
# These methods are necessary to disallow incompatible dimensions for
# the OffsetVector and the OffsetMatrix constructors
for (FT, ND) in ((:OffsetVector, :1), (:OffsetMatrix, :2))
@eval function $FT(A::AbstractArray{<:Any,$ND}, offsets::Tuple{Vararg{Integer}})
_checkindices(A, offsets, "offsets")
OffsetArray{eltype(A),$ND,typeof(A)}(A, offsets)
end
FTstr = string(FT)
@eval function $FT(A::AbstractArray, offsets::Tuple{Vararg{Integer}})
throw(ArgumentError($FTstr * " requires a " * string($ND) * "D array"))
end
end
## OffsetArray constructors
for FT in (:OffsetArray, :OffsetVector, :OffsetMatrix)
# Nested OffsetArrays may strip off the wrapper and collate the offsets
@eval function $FT(A::OffsetArray, offsets::Tuple{Vararg{Integer}})
_checkindices(A, offsets, "offsets")
$FT(parent(A), map(+, A.offsets, offsets))
end
# In general, indices get converted to AbstractUnitRanges.
# CartesianIndices{N} get converted to N ranges
@eval function $FT(A::AbstractArray, inds::Tuple{Any,Vararg{Any}})
$FT(A, _toAbstractUnitRanges(to_indices(A, axes(A), inds)))
end
# convert ranges to offsets
@eval function $FT(A::AbstractArray, inds::Tuple{AbstractUnitRange,Vararg{AbstractUnitRange}})
_checkindices(A, inds, "indices")
# Performance gain by wrapping the error in a function: see https://github.com/JuliaLang/julia/issues/37558
throw_dimerr(lA, lI) = throw(DimensionMismatch("supplied axes do not agree with the size of the array (got size $lA for the array and $lI for the indices"))
lA = size(A)
lI = map(length, inds)
lA == lI || throw_dimerr(lA, lI)
$FT(A, map(_offset, axes(A), inds))
end
@eval $FT(A::AbstractArray, inds::Vararg) = $FT(A, inds)
@eval $FT(A::AbstractArray, origin::Origin) = $FT(A, origin(A))
end
# array initialization
function OffsetArray{T,N}(init::ArrayInitializer, inds::Tuple{Vararg{OffsetAxisKnownLength}}) where {T,N}
_checkindices(N, inds, "indices")
AA = Array{T,N}(init, map(_indexlength, inds))
OffsetArray{T,N,typeof(AA)}(AA, map(_indexoffset, inds))
end
function OffsetArray{T,N}(init::ArrayInitializer, inds::Tuple) where {T,N}
OffsetArray{T,N}(init, _toAbstractUnitRanges(inds))
end
OffsetArray{T,N}(init::ArrayInitializer, inds::Vararg) where {T,N} = OffsetArray{T,N}(init, inds)
OffsetArray{T}(init::ArrayInitializer, inds::NTuple{N,OffsetAxisKnownLength}) where {T,N} = OffsetArray{T,N}(init, inds)
function OffsetArray{T}(init::ArrayInitializer, inds::Tuple) where {T}
OffsetArray{T}(init, _toAbstractUnitRanges(inds))
end
OffsetArray{T}(init::ArrayInitializer, inds::Vararg) where {T} = OffsetArray{T}(init, inds)
Base.IndexStyle(::Type{OA}) where {OA <: OffsetArray} = IndexStyle(parenttype(OA))
parenttype(::Type{OffsetArray{T,N,AA}}) where {T,N,AA} = AA
parenttype(A::OffsetArray) = parenttype(typeof(A))
Base.parent(A::OffsetArray) = A.parent
Base.eachindex(::IndexCartesian, A::OffsetArray) = CartesianIndices(axes(A))
Base.eachindex(::IndexLinear, A::OffsetVector) = axes(A, 1)
@inline Base.size(A::OffsetArray) = size(parent(A))
@inline Base.size(A::OffsetArray, d) = size(parent(A), d)
@inline Base.axes(A::OffsetArray) = map(IdOffsetRange, axes(parent(A)), A.offsets)
@inline Base.axes(A::OffsetArray, d) = d <= ndims(A) ? IdOffsetRange(axes(parent(A), d), A.offsets[d]) : IdOffsetRange(axes(parent(A), d))
@inline Base.axes1(A::OffsetArray{T,0}) where {T} = IdOffsetRange(axes(parent(A), 1)) # we only need to specialize this one
Base.similar(A::OffsetArray, ::Type{T}, dims::Dims) where T =
similar(parent(A), T, dims)
function Base.similar(A::AbstractArray, ::Type{T}, inds::Tuple{OffsetAxisKnownLength,Vararg{OffsetAxisKnownLength}}) where T
B = similar(A, T, map(_indexlength, inds))
return OffsetArray(B, map(_offset, axes(B), inds))
end
# reshape accepts a single colon
Base.reshape(A::AbstractArray, inds::OffsetAxis...) = reshape(A, inds)
function Base.reshape(A::AbstractArray, inds::Tuple{OffsetAxis,Vararg{OffsetAxis}})
AR = reshape(A, map(_indexlength, inds))
return OffsetArray(AR, map(_offset, axes(AR), inds))
end
# Reshaping OffsetArrays can "pop" the original OffsetArray wrapper and return
# an OffsetArray(reshape(...)) instead of an OffsetArray(reshape(OffsetArray(...)))
Base.reshape(A::OffsetArray, inds::Tuple{OffsetAxis,Vararg{OffsetAxis}}) =
OffsetArray(reshape(parent(A), map(_indexlength, inds)), map(_indexoffset, inds))
# And for non-offset axes, we can just return a reshape of the parent directly
Base.reshape(A::OffsetArray, inds::Tuple{Union{Integer,Base.OneTo},Vararg{Union{Integer,Base.OneTo}}}) = reshape(parent(A), inds)
Base.reshape(A::OffsetArray, inds::Dims) = reshape(parent(A), inds)
Base.reshape(A::OffsetArray, ::Colon) = reshape(parent(A), Colon())
Base.reshape(A::OffsetVector, ::Colon) = A
Base.reshape(A::OffsetVector, ::Tuple{Colon}) = A
Base.reshape(A::OffsetArray, inds::Union{Int,Colon}...) = reshape(parent(A), inds)
Base.reshape(A::OffsetArray, inds::Tuple{Vararg{Union{Int,Colon}}}) = reshape(parent(A), inds)
function Base.similar(::Type{T}, shape::Tuple{OffsetAxis,Vararg{OffsetAxis}}) where {T <: AbstractArray}
P = T(undef, map(_indexlength, shape))
OffsetArray(P, map(_offset, axes(P), shape))
end
Base.fill(v, inds::NTuple{N, Union{Integer, AbstractUnitRange}}) where {N} =
fill!(similar(Array{typeof(v)}, inds), v)
Base.zeros(::Type{T}, inds::NTuple{N, Union{Integer, AbstractUnitRange}}) where {T, N} =
fill!(similar(Array{T}, inds), zero(T))
Base.ones(::Type{T}, inds::NTuple{N, Union{Integer, AbstractUnitRange}}) where {T, N} =
fill!(similar(Array{T}, inds), one(T))
Base.trues(inds::NTuple{N, Union{Integer, AbstractUnitRange}}) where {N} =
fill!(similar(BitArray, inds), true)
Base.falses(inds::NTuple{N, Union{Integer, AbstractUnitRange}}) where {N} =
fill!(similar(BitArray, inds), false)
## Indexing
# Note this gets the index of the parent *array*, not the index of the parent *range*
# Here's how one can think about this:
# Δi = i - first(r)
# i′ = first(r.parent) + Δi
# and one obtains the result below.
parentindex(r::IdOffsetRange, i) = i - r.offset
@inline function Base.getindex(A::OffsetArray{T,N}, I::Vararg{Int,N}) where {T,N}
@boundscheck checkbounds(A, I...)
J = map(parentindex, axes(A), I)
@inbounds parent(A)[J...]
end
@inline function Base.getindex(A::OffsetVector, i::Int)
@boundscheck checkbounds(A, i)
@inbounds parent(A)[parentindex(Base.axes1(A), i)]
end
@propagate_inbounds Base.getindex(A::OffsetArray, i::Int) = parent(A)[i]
@inline function Base.setindex!(A::OffsetArray{T,N}, val, I::Vararg{Int,N}) where {T,N}
@boundscheck checkbounds(A, I...)
J = @inbounds map(parentindex, axes(A), I)
@inbounds parent(A)[J...] = val
A
end
@inline function Base.setindex!(A::OffsetVector, val, i::Int)
@boundscheck checkbounds(A, i)
@inbounds parent(A)[parentindex(Base.axes1(A), i)] = val
A
end
@propagate_inbounds function Base.setindex!(A::OffsetArray, val, i::Int)
parent(A)[i] = val
A
end
# For fast broadcasting: ref https://discourse.julialang.org/t/why-is-there-a-performance-hit-on-broadcasting-with-offsetarrays/32194
Base.dataids(A::OffsetArray) = Base.dataids(parent(A))
Broadcast.broadcast_unalias(dest::OffsetArray, src::OffsetArray) = parent(dest) === parent(src) ? src : Broadcast.unalias(dest, src)
### Special handling for AbstractRange
const OffsetRange{T} = OffsetArray{T,1,<:AbstractRange{T}}
const IIUR = IdentityUnitRange{S} where S<:AbstractUnitRange{T} where T<:Integer
Base.step(a::OffsetRange) = step(parent(a))
@propagate_inbounds Base.getindex(a::OffsetRange, r::OffsetRange) = OffsetArray(a[parent(r)], r.offsets)
@propagate_inbounds function Base.getindex(a::OffsetRange, r::IdOffsetRange)
OffsetArray(a.parent[r.parent .+ (r.offset - a.offsets[1])], r.offset)
end
@propagate_inbounds Base.getindex(r::OffsetRange, s::IIUR) =
OffsetArray(r[s.indices], s)
@propagate_inbounds Base.getindex(a::OffsetRange, r::AbstractRange) = a.parent[r .- a.offsets[1]]
@propagate_inbounds Base.getindex(a::AbstractRange, r::OffsetRange) = OffsetArray(a[parent(r)], r.offsets)
@propagate_inbounds Base.getindex(r::UnitRange, s::IIUR) =
OffsetArray(r[s.indices], s)
@propagate_inbounds Base.getindex(r::StepRange, s::IIUR) =
OffsetArray(r[s.indices], s)
# this method is needed for ambiguity resolution
@propagate_inbounds Base.getindex(r::StepRangeLen{T,<:Base.TwicePrecision,<:Base.TwicePrecision}, s::IIUR) where T =
OffsetArray(r[s.indices], s)
@propagate_inbounds Base.getindex(r::StepRangeLen{T}, s::IIUR) where {T} =
OffsetArray(r[s.indices], s)
@propagate_inbounds Base.getindex(r::LinRange, s::IIUR) =
OffsetArray(r[s.indices], s)
function Base.show(io::IO, r::OffsetRange)
show(io, r.parent)
o = r.offsets[1]
print(io, " with indices ", o+1:o+length(r))
end
Base.show(io::IO, ::MIME"text/plain", r::OffsetRange) = show(io, r)
### Some mutating functions defined only for OffsetVector ###
Base.resize!(A::OffsetVector, nl::Integer) = (resize!(A.parent, nl); A)
Base.push!(A::OffsetVector, x...) = (push!(A.parent, x...); A)
Base.pop!(A::OffsetVector) = pop!(A.parent)
Base.append!(A::OffsetVector, items) = (append!(A.parent, items); A)
Base.empty!(A::OffsetVector) = (empty!(A.parent); A)
# These functions keep the summary compact
function Base.inds2string(inds::Tuple{Vararg{Union{IdOffsetRange,IdentityUnitRange{<:IdOffsetRange}}}})
Base.inds2string(map(UnitRange, inds))
end
Base.showindices(io::IO, ind1::IdOffsetRange, inds::IdOffsetRange...) = Base.showindices(io, map(UnitRange, (ind1, inds...))...)
function Base.showarg(io::IO, a::OffsetArray, toplevel)
print(io, "OffsetArray(")
Base.showarg(io, parent(a), false)
Base.showindices(io, axes(a)...)
print(io, ')')
toplevel && print(io, " with eltype ", eltype(a))
end
function Base.replace_in_print_matrix(A::OffsetArray{<:Any,2}, i::Integer, j::Integer, s::AbstractString)
J = map(parentindex, axes(A), (i,j))
Base.replace_in_print_matrix(parent(A), J..., s)
end
function Base.replace_in_print_matrix(A::OffsetArray{<:Any,1}, i::Integer, j::Integer, s::AbstractString)
ip = parentindex(axes(A,1), i)
Base.replace_in_print_matrix(parent(A), ip, j, s)
end
function no_offset_view(A::AbstractArray)
if Base.has_offset_axes(A)
OffsetArray(A, map(r->1-first(r), axes(A)))
else
A
end
end
no_offset_view(A::OffsetArray) = no_offset_view(parent(A))
end # module
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] | 2.624297 | 7,466 |
"""
set_kit(proj_name, proj_path)
Create a folder named `proj_name` in `proj_path`, as well as a Pluto notebook and an internal file structure inside `proj_path/proj_name`.
File structure and use of the notebook are detailed [here](https://github.com/FellowsFreiesWissen/computational_notebooks).
See also: [`Pluto`](https://juliapackages.com/p/pluto)
# Example
```julia-repl
julia> include("set_kit.jl")
julia> set_kit("example_project", pwd())
```
"""
function set_kit(proj_name::String, proj_path::String)
## Check project path and whether it is empty
println(string("Will create a project at and set your working directory to: ", joinpath(proj_path, proj_name)))
println("Is that correct? Answer yes or no:")
user_ans = readline()
if user_ans == "no"
println("Check help('set_kit') to set up your project's location.")
else
if isdir(joinpath(proj_path, proj_name))
println("There already is a project with this name in this path. Move it or change its name.")
else
## Create project in path
cd(proj_path)
## Create file structure
### first level
mkdir(proj_name)
### main folders of a project
map(mkdir, map(x -> joinpath(proj_name, x),
["results","text", "submission"]))
### main folders of results
map(mkdir, map(x -> joinpath(joinpath(proj_name,"results"), x),
["data", "scripts"]))
map(mkdir, map(x -> joinpath(joinpath(proj_name,"results", "data"), x),
["raw", "process", "metadata"]))
### main folders of text
map(mkdir, map(x -> joinpath(joinpath(proj_name,"text"), x),
["figures","tables", "supplementary", "references"]))
### main folder
touch(joinpath(proj_path, proj_name, "README.txt"))
open(joinpath(proj_path, proj_name, "README.txt"), "w") do io
write(io, "This folder contains the set up for a reproducible workflow as described by https://github.com/FellowsFreiesWissen/computational_notebooks.git\n")
write(io, "\n")
write(io, "The file structure is organized as such:\n")
write(io, "\n")
write(io, "projet_name\n")
write(io, "|-- README.txt\n")
write(io, "|-- main.Rmd\n")
write(io, "|-- results\n")
write(io, "| |-- README.txt\n")
write(io, "| |-- data\n")
write(io, "| | |-- raw\n")
write(io, "| | |-- process\n")
write(io, "| | |-- metadata\n")
write(io, "| |-- scripts\n")
write(io, "|-- text\n")
write(io, "| |-- README.txt\n")
write(io, "| |-- main.doc\n")
write(io, "| |-- figures\n")
write(io, "| |-- tables\n")
write(io, "| |-- supplementary\n")
write(io, "| |-- references\n")
write(io, "|-- submission\n")
write(io, "| |-- README.txt\n")
write(io, "| |-- journal1\n")
write(io, "| |-- first\n")
write(io, "| |-- journal2\n")
write(io, "| |-- first\n")
write(io, "| |-- revisions\n")
end
### results folder
touch(joinpath(proj_path, proj_name, "results/README.txt"))
open(joinpath(proj_path, proj_name, "results/README.txt"), "w") do io
write(io, "This folder contains all files of results or their processing, organized in the following subfolders:")
write(io, "\n")
write(io, "`data/raw`: your raw data files. These should not be protected against any change after the first storage")
write(io, "\n")
write(io, "`data/process`: these are files generated by processing the raw data and these are the ones used in the analysis. It can be a copy of the raw data if that is already ready to use. Any processing of the raw data to generate the files here should be documented in the notebook.")
write(io, "\n")
write(io, "`data/metadata`: files containing information about the data that will be useful for future users, readers, and reviewers of your data (e.g. description of variables names, units, and values)")
write(io, "`scripts`: all code used to process the data, and which, for some reason or another is not included in the notebook because they too cumbersome or not of upmost relevancy for comprehension.")
end
### text folder
touch(joinpath(proj_path, proj_name, "text/README.txt"))
open(joinpath(proj_path, proj_name, "text/README.txt"), "w") do io
write(io, "This folder contains the main text of the manuscript, folders containing the figures and tables (unformatted) to be included in it, as well as a folder with the supplementary material and one with references.")
end
### submission folder
touch(joinpath(proj_path, proj_name, "submission/README.txt"))
open(joinpath(proj_path, proj_name, "submission/README.txt"), "w") do io
write(io, "This folder contains the files specific to journal submissions, e.g. cover letters, submitted versions.")
end
## Create the minimal notebook
touch(joinpath(proj_path, proj_name, string(proj_name, ".jl")))
open(joinpath(proj_path, proj_name, string(proj_name, ".jl")), "w") do io
write(io, "### A Pluto.jl notebook ###")
write(io, "\n\n")
write(io, "using Markdown")
write(io, "\n")
write(io, "using InteractiveUtils")
write(io, "\n\n")
write(io, "# ╔═╡ 307335ba-cf46-11eb-28b0-f199cf048ae6")
write(io, "\n")
write(io, "md\" ## Your title here\"")
write(io, "\n\n")
write(io, "# ╔═╡ dfdfa33f-53c1-45e2-8727-86ac01a09398")
write(io, "\n")
write(io, "md\"\"\"")
write(io, "\n\n")
write(io, "## Brief intro to Pluto notebooks and Markdown syntax")
write(io, "\n\n")
write(io, "Feel free to skip this tutorial if you already know how Pluto noteboos work. Also, make sure to delete it once you submit/share your notebook.")
write(io, "\n\n")
write(io, "This is your notebook. When filling it, you should obey Pluto's own syntax to create cells containing code or text.")
write(io, "\n\n")
write(io, "To write one single line of narrative text, use simple quotes and include \"md\" before the quotes (as done for the title header above). To write several lines of text, use three pairs of quotes, as in this cell.")
write(io, "\n\n")
write(io, "Text written with no special markings will appear without any special formatting when you generate a `.html` or `.pdf` version of this file (click the \"Export\" button above to convert it).")
write(io, "\n\n")
write(io, "It is possible to include code inside Markdown cells (it is not executed, though):")
write(io, "\n\n")
write(io, "- either as `inline code`")
write(io, "\n\n")
write(io, "- or as a block of code:")
write(io, "\n\n")
write(io, "```{julia}")
write(io, "\n")
write(io, "print(\"This is an example of a code block...\")")
write(io, "\n")
write(io, "print(\"for several lines of code\")")
write(io, "\n")
write(io, "```")
write(io, "\n\n")
write(io, "It is also possible to include hyperlinks: for example, this [link to further details on the Markdown syntax used in Pluto notebooks](https://www.juliapackages.com/p/pluto).")
write(io, "\n\n")
write(io, "Cells for code do not need any special marking, unless the cell contains several lines of code. In that case, the code should be included inside a `begin ... end` block.")
write(io, "\n\n")
write(io, "```{julia}")
write(io, "\n")
write(io, "begin")
write(io, "\n")
write(io, " # your lines")
write(io, "\n")
write(io, " # of code")
write(io, "\n")
write(io, "end")
write(io, "\n")
write(io, "```")
write(io, "\n\n")
write(io, "To execute the code in code cell and have its results appear above it, click the \"play\" (\"Run cell\") button below the cells or place your cursor inside the chunk and press `Shift+Enter`.")
write(io, "\n\n")
write(io, "The outputs of a cell are always show, unless the cell is disabled (available in the `...` - \"Actions\" - button on top of the cell). To hide the contents of the cell itself, click the eye icon on top of the cell.")
write(io, "\n")
write(io, "\"\"\"")
write(io, "\n\n")
write(io, "# ╔═╡ 37c7bffc-cf46-11eb-19b8-4175e38e818b")
write(io, "\n")
write(io, "begin")
write(io, "\n")
write(io, "# We suggest having a chunk dedicated to variables containing the paths to the folders related to the project.")
write(io, "\n")
write(io, "# The code in this chunk is not relevant for the reader, and thus is not included in the knitted version (therefore, `include = FALSE`).")
write(io, "\n")
write(io, "# This is also useful if you are not using the folder structure suggested by this kit, and want to preserve your privacy.")
write(io, "\n")
write(io, "# Feel free to edit these paths to adapt them to your needs or not use this suggestion at all.")
write(io, "\n\n")
write(io, "\tdata_dir = joinpath(\"results\", \"data\", \"process\") ## Do NOT play with stuff in data/raw. That is your back-up. Work only on `process`.")
write(io, "\n")
write(io, "\tscripts_dir = joinpath(\"results\", \"scripts\")")
write(io, "\n")
write(io, "\tsuppl_dir = joinpath(\"results\", \"supplementary\")")
write(io, "\n")
write(io, "\tfigs_dir = joinpath(\"text\", \"figures\")")
write(io, "\n")
write(io, "\ttabs_dir = joinpath(\"text\", \"tables\")")
write(io, "\n")
write(io, "end")
write(io, "\n\n")
write(io, "# ╔═╡ f5062ac5-18e0-42fd-8bb7-1602a8298779")
write(io, "\n")
write(io, "md\"## Load data\"")
write(io, "\n\n")
write(io, "# ╔═╡ d4465a06-7d32-11ec-3907-59e1f4d60e2f")
write(io, "\n")
write(io, "# Here, you write the code to read the data from data_dir")
write(io, "\n\n")
write(io, "# ╔═╡ 6d256dbe-edc9-4132-aac7-62a473601034")
write(io, "\n")
write(io, "md\"\"\"")
write(io, "\n")
write(io, "## Data analysis")
write(io, "\n\n")
write(io, "### Figure 1")
write(io, "\n")
write(io, "\"\"\"")
write(io, "\n\n")
write(io, "# ╔═╡ f12401f7-1c08-4800-a64d-88f91f6b59fe")
write(io, "\n")
write(io, "# Here, you write the code to create a graph (Figure 1) included in the main text.")
write(io, "\n")
write(io, "# To avoid the repetition of having the same figure here as in the text, include a line that saves the figure in the figures directory.")
write(io, "\n\n")
write(io, "# ╔═╡ 2af762fe-0173-47eb-98d7-8fa6d3b555d3")
write(io, "\n")
write(io, "md\"### Figure S1\"")
write(io, "\n\n")
write(io, "# ╔═╡ 93c7698d-6e5b-4803-b7c6-90d4da7cd9bf")
write(io, "\n")
write(io, "# your code to plot Figure S1")
write(io, "\n\n")
write(io, "# ╔═╡ Cell order:")
write(io, "\n")
write(io, "# ╠═307335ba-cf46-11eb-28b0-f199cf048ae6")
write(io, "\n")
write(io, "# ╠═dfdfa33f-53c1-45e2-8727-86ac01a09398")
write(io, "\n")
write(io, "# ╠═37c7bffc-cf46-11eb-19b8-4175e38e818b")
write(io, "\n")
write(io, "# ╠═f5062ac5-18e0-42fd-8bb7-1602a8298779")
write(io, "\n")
write(io, "# ╠═d4465a06-7d32-11ec-3907-59e1f4d60e2f")
write(io, "\n")
write(io, "# ╠═6d256dbe-edc9-4132-aac7-62a473601034")
write(io, "\n")
write(io, "# ╠═f12401f7-1c08-4800-a64d-88f91f6b59fe")
write(io, "\n")
write(io, "# ╠═2af762fe-0173-47eb-98d7-8fa6d3b555d3")
write(io, "\n")
write(io, "# ╠═93c7698d-6e5b-4803-b7c6-90d4da7cd9bf")
println("Your project is ready to go")
end
end
end
end
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] | 1.958304 | 7,147 |
<gh_stars>1-10
immutable DisplacementMesh <: ImageTransformation
input_vertices::Matrix{Float64}
output_vertices::Matrix{Float64}
indices::Matrix{Int}
function DisplacementMesh(input_vertices::Matrix{Float64}, output_vertices::Matrix{Float64}, indices::Matrix{Int})
@assert size(input_vertices) == size(output_vertices)
new(input_vertices, output_vertices, indices)
end
end
function DisplacementMesh(field::DisplacementField, img_width::Int, img_height::Int)
input_vertices, output_vertices = _compute_vertices(field, Float64(img_width), Float64(img_height))
indices = _compute_indices(field)
DisplacementMesh(input_vertices, output_vertices, indices)
end
DisplacementMesh(field::DisplacementField, img_size::Tuple{Int,Int}) = DisplacementMesh(field, img_size[1], img_size[2])
DisplacementMesh(field::DisplacementField, img::AbstractImage) = DisplacementMesh(field, size(img, "x"), size(img, "y"))
function Base.show(io::IO, dm::DisplacementMesh)
print(io, "DisplacementMesh (vertices: $(size(dm.input_vertices,1)), triangles: $(size(dm.indices,1)))")
end
@recipe function plot(dm::DisplacementMesh, img::Image)
legend --> false
yflip := true
layout := 2
@series begin
title --> "Input"
seriestype := :image
subplot := 1
img
end
@series begin
title --> "Output"
seriestype := :image
subplot := 2
transform(dm,img)
end
dm
end
@recipe function plot(dm::DisplacementMesh)
fillcolor --> :transparent
legend --> false
yflip := true
layout := 2
n_indices = size(dm.indices,1)
x_in = zeros(n_indices*4)
y_in = zeros(n_indices*4)
x_out = zeros(n_indices*4)
y_out = zeros(n_indices*4)
ti = 1
for i = 1:n_indices
for j = 1:3
x_in[ti] = dm.input_vertices[dm.indices[i, j], 2]
y_in[ti] = dm.input_vertices[dm.indices[i, j], 1]
x_out[ti] = dm.output_vertices[dm.indices[i, j], 2]
y_out[ti] = dm.output_vertices[dm.indices[i, j], 1]
ti += 1
end
x_in[ti] = NaN
y_in[ti] = NaN
x_out[ti] = NaN
y_out[ti] = NaN
ti += 1
end
@series begin
title --> "Input"
seriestype := :shape
subplot := 1
x_in, y_in
end
@series begin
title --> "Output"
seriestype := :shape
subplot := 2
x_out, y_out
end
end
function _transform{T}(dm::DisplacementMesh, img::Image{T})
img_sep = separate(img)
rawdata = convert(Array{Float64, 3}, data(img_sep))
wrp = PiecewiseAffineTransforms.pa_warp(rawdata, dm.input_vertices, dm.output_vertices, dm.indices)
rawdata_new = convert(Array{eltype(T),3}, wrp)
img_sep_new = Image(rawdata_new, properties(img_sep))
if isxfirst(img)
copyproperties(img, permutedims(convert(Image{T}, img_sep_new), [2, 1]))
else
copyproperties(img, convert(Image{T}, img_sep_new))
end
end
function transform{T<:AbstractImage}(dm::DisplacementMesh, img::T)
result = _transform(dm, img)::T
_log_operation!(result, dm)::T
end
function _compute_vertices(field::DisplacementField, img_width::Float64, img_height::Float64)
height, width = size(field.delta_X)
input_vertices = zeros(height*width, 2)
output_vertices = zeros(height*width, 2)
i = 1
for x = 1:width, y = 1:height
input_vertices[i,1] = clamp(clamp(field.y[y], 0., 1.) * img_height, 1., img_height)
input_vertices[i,2] = clamp(clamp(field.x[x], 0., 1.) * img_width, 1., img_width)
output_vertices[i,1] = clamp(input_vertices[i,1] + field.delta_Y[y,x] * img_height, 1., img_height)
output_vertices[i,2] = clamp(input_vertices[i,2] + field.delta_X[y,x] * img_width, 1., img_width)
i = i + 1
end
input_vertices, output_vertices
end
function _compute_indices(field::DisplacementField)
grid_size = size(field.delta_X)
height, width = grid_size
w_half = floor(Int, width/2)
h_half = floor(Int, height/2)
indices = zeros(Int, (height-1)*(width-1)*2, 3)
i = 1
for x = 1:(width-1), y = 1:(height-1)
if (x <= w_half && y <= h_half) || (x > w_half && y > h_half)
# upper left or lower right
# *--*
# |\ |
# | \|
# *--*
indices[i, 1] = sub2ind(grid_size, y, x )
indices[i, 2] = sub2ind(grid_size, y+1, x+1)
indices[i, 3] = sub2ind(grid_size, y+1, x )
i = i + 1
indices[i, 1] = sub2ind(grid_size, y, x )
indices[i, 2] = sub2ind(grid_size, y, x+1)
indices[i, 3] = sub2ind(grid_size, y+1, x+1)
i = i + 1
else
# lower left or upper right
# *--*
# | /|
# |/ |
# *--*
indices[i, 1] = sub2ind(grid_size, y, x )
indices[i, 2] = sub2ind(grid_size, y, x+1)
indices[i, 3] = sub2ind(grid_size, y+1, x )
i = i + 1
indices[i, 1] = sub2ind(grid_size, y+1, x)
indices[i, 2] = sub2ind(grid_size, y, x+1)
indices[i, 3] = sub2ind(grid_size, y+1, x+1)
i = i + 1
end
end
indices
end
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] | 2.033704 | 2,611 |
<filename>docs/make.jl
using Documenter
import Pkg
# Fix for https://github.com/trixi-framework/Trixi.jl/issues/668
if (get(ENV, "CI", nothing) != "true") && (get(ENV, "TRIXI_DOC_DEFAULT_ENVIRONMENT", nothing) != "true")
push!(LOAD_PATH, dirname(@__DIR__))
end
using Trixi
using Trixi2Vtk
# Get Trixi root directory
trixi_root_dir = dirname(@__DIR__)
include(joinpath(trixi_root_dir, "docs", "literate", "make.jl"))
# Copy list of authors to not need to synchronize it manually
authors_text = read(joinpath(trixi_root_dir, "AUTHORS.md"), String)
authors_text = replace(authors_text, "in the [LICENSE.md](LICENSE.md) file" => "under [License](@ref)")
write(joinpath(@__DIR__, "src", "authors.md"), authors_text)
# Define module-wide setups such that the respective modules are available in doctests
DocMeta.setdocmeta!(Trixi, :DocTestSetup, :(using Trixi); recursive=true)
DocMeta.setdocmeta!(Trixi2Vtk, :DocTestSetup, :(using Trixi2Vtk); recursive=true)
# Create tutorials for the following files:
# Normal structure: "title" => "filename.jl"
# If there are several files for one topic and one folder, the structure is:
# "title" => ["subtitle 1" => ("folder 1", "filename 1.jl"),
# "subtitle 2" => ("folder 2", "filename 2.jl")]
files = [
"Adding a new equation" => ["Scalar conservation law" => ("adding_new_equations", "cubic_conservation_law.jl"),
"Nonconservative equation" => ("adding_new_equations", "nonconservative_advection.jl")],
"Differentiable programming" => "differentiable_programming.jl",
"Unstructured meshes with HOHQMesh.jl" => "hohqmesh_tutorial.jl",
]
tutorials = create_tutorials(files)
# Make documentation
makedocs(
# Specify modules for which docstrings should be shown
modules = [Trixi, Trixi2Vtk],
# Set sitename to Trixi
sitename="Trixi.jl",
# Provide additional formatting options
format = Documenter.HTML(
# Disable pretty URLs during manual testing
prettyurls = get(ENV, "CI", nothing) == "true",
# Explicitly add favicon as asset
assets = ["assets/favicon.ico"],
# Set canonical URL to GitHub pages URL
canonical = "https://trixi-framework.github.io/Trixi.jl/stable"
),
# Explicitly specify documentation structure
pages = [
"Home" => "index.md",
"Getting started" => [
"Overview" => "overview.md",
"Visualization" => "visualization.md",
],
"Tutorials" => tutorials,
"Basic building blocks" => [
"Meshes" => [
"Tree mesh" => joinpath("meshes", "tree_mesh.md"),
"Structured mesh" => joinpath("meshes", "structured_mesh.md"),
"Unstructured mesh" => joinpath("meshes", "unstructured_quad_mesh.md"),
"P4est-based mesh" => joinpath("meshes", "p4est_mesh.md"),
"Simplicial mesh" => joinpath("meshes", "mesh_data_meshes.md"),
],
"Time integration" => "time_integration.md",
"Callbacks" => "callbacks.md",
],
"Advanced topics & developers" => [
"Conventions" =>"conventions.md",
"Development" => "development.md",
"GitHub & Git" => "github-git.md",
"Style guide" => "styleguide.md",
"Testing" => "testing.md",
"Performance" => "performance.md",
"Parallelization" => "parallelization.md",
],
"Troubleshooting and FAQ" => "troubleshooting.md",
"Reference" => [
"Trixi.jl" => "reference-trixi.md",
"Trixi2Vtk.jl" => "reference-trixi2vtk.md"
],
"Authors" => "authors.md",
"Contributing" => "contributing.md",
"License" => "license.md"
],
strict = true # to make the GitHub action fail when doctests fail, see https://github.com/neuropsychology/Psycho.jl/issues/34
)
deploydocs(
repo = "github.com/trixi-framework/Trixi.jl",
devbranch = "main",
push_preview = true
)
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] | 2.331432 | 1,753 |
<gh_stars>0
module MNIST
using GZip, Colors
const Gray = Colors.Gray{Colors.N0f8}
const dir = joinpath(@__DIR__, "../../deps/mnist")
function load()
mkpath(dir)
cd(dir) do
for file in ["train-images-idx3-ubyte",
"train-labels-idx1-ubyte",
"t10k-images-idx3-ubyte",
"t10k-labels-idx1-ubyte"]
isfile(file) && continue
info("Downloading MNIST dataset")
download("https://cache.julialang.org/http://yann.lecun.com/exdb/mnist/$file.gz", "$file.gz")
open(file, "w") do io
write(io, GZip.open(read, "$file.gz"))
end
end
end
end
const IMAGEOFFSET = 16
const LABELOFFSET = 8
const NROWS = 28
const NCOLS = 28
const TRAINIMAGES = joinpath(dir, "train-images-idx3-ubyte")
const TRAINLABELS = joinpath(dir, "train-labels-idx1-ubyte")
const TESTIMAGES = joinpath(dir, "t10k-images-idx3-ubyte")
const TESTLABELS = joinpath(dir, "t10k-labels-idx1-ubyte")
function imageheader(io::IO)
magic_number = bswap(read(io, UInt32))
total_items = bswap(read(io, UInt32))
nrows = bswap(read(io, UInt32))
ncols = bswap(read(io, UInt32))
return magic_number, Int(total_items), Int(nrows), Int(ncols)
end
function labelheader(io::IO)
magic_number = bswap(read(io, UInt32))
total_items = bswap(read(io, UInt32))
return magic_number, Int(total_items)
end
function rawimage(io::IO)
img = Array{Gray}(NCOLS, NROWS)
for i in 1:NCOLS, j in 1:NROWS
img[i, j] = reinterpret(Colors.N0f8, read(io, UInt8))
end
return img
end
function rawimage(io::IO, index::Integer)
seek(io, IMAGEOFFSET + NROWS * NCOLS * (index - 1))
return rawimage(io)
end
rawlabel(io::IO) = Int(read(io, UInt8))
function rawlabel(io::IO, index::Integer)
seek(io, LABELOFFSET + (index - 1))
return rawlabel(io)
end
getfeatures(io::IO, index::Integer) = vec(getimage(io, index))
"""
images()
images(:test)
Load the MNIST images.
Each image is a 28×28 array of `Gray` colour values (see Colors.jl).
Returns the 60,000 training images by default; pass `:test` to retreive the
10,000 test images.
"""
function images(set = :train)
load()
io = IOBuffer(read(set == :train ? TRAINIMAGES : TESTIMAGES))
_, N, nrows, ncols = imageheader(io)
[rawimage(io) for _ in 1:N]
end
"""
labels()
labels(:test)
Load the labels corresponding to each of the images returned from `images()`.
Each label is a number from 0-9.
Returns the 60,000 training labels by default; pass `:test` to retreive the
10,000 test labels.
"""
function labels(set = :train)
load()
io = IOBuffer(read(set == :train ? TRAINLABELS : TESTLABELS))
_, N = labelheader(io)
[rawlabel(io) for _ = 1:N]
end
end # module
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] | 2.366432 | 1,138 |
<filename>src/general_variables.jl<gh_stars>0
################################################################################
# BASE EXTENSIONS
################################################################################
# Extend Base.copy for new variable types
Base.copy(v::GeneralVariableRef) = v
Base.copy(v::DispatchVariableRef) = v
# Extend Base.:(==) for GeneralVariableRef
function Base.:(==)(v::T, w::T)::Bool where {T <: GeneralVariableRef}
return v.model === w.model && v.raw_index == w.raw_index &&
v.index_type == w.index_type && v.param_index == w.param_index
end
# Extend Base.:(==) for DispatchVariableRef
function Base.:(==)(v::T, w::U)::Bool where {T <: DispatchVariableRef,
U <: DispatchVariableRef}
return v.model === w.model && v.index == w.index
end
# Extend Base.broadcastable
Base.broadcastable(v::GeneralVariableRef) = Ref(v)
Base.broadcastable(v::DispatchVariableRef) = Ref(v)
# Extend Base.length
Base.length(v::GeneralVariableRef)::Int = 1
Base.length(v::DispatchVariableRef)::Int = 1
# Extend JuMP functions
JuMP.isequal_canonical(v::GeneralVariableRef, w::GeneralVariableRef)::Bool = v == w
JuMP.isequal_canonical(v::DispatchVariableRef, w::DispatchVariableRef)::Bool = v == w
JuMP.variable_type(model::InfiniteModel)::DataType = GeneralVariableRef
# Extract the root name of a variable reference (removes the bracketed container indices)
function _remove_name_index(vref::GeneralVariableRef)::String
name = JuMP.name(vref)
first_bracket = findfirst(isequal('['), name)
if first_bracket === nothing
return name
else
# Hacky fix to handle invalid Unicode
try
return name[1:first_bracket-1]
catch
return name[1:first_bracket-2]
end
end
end
# Define basic attribute getters
_index_type(vref::GeneralVariableRef)::DataType = vref.index_type
_raw_index(vref::GeneralVariableRef)::Int = vref.raw_index
_param_index(vref::GeneralVariableRef)::Int = vref.param_index
################################################################################
# BASIC REFERENCE ACCESSERS
################################################################################
"""
JuMP.index(vref::GeneralVariableRef)::AbstractInfOptIndex
Extend [`JuMP.index`](@ref JuMP.index(::JuMP.VariableRef)) to return the
appropriate index of `vref`.
**Example**
```jldoctest; setup = :(using InfiniteOpt, JuMP; m = InfiniteModel(); @hold_variable(m, vref))
julia> index(vref)
HoldVariableIndex(1)
```
"""
function JuMP.index(vref::GeneralVariableRef)::AbstractInfOptIndex
index_type = _index_type(vref)
if index_type == DependentParameterIndex
return index_type(DependentParametersIndex(_raw_index(vref)),
_param_index(vref))
else
return index_type(_raw_index(vref))
end
end
"""
JuMP.index(vref::DispatchVariableRef)::AbstractInfOptIndex
Extend [`JuMP.index`](@ref JuMP.index(::JuMP.VariableRef)) to return the
appropriate index of `vref`.
"""
function JuMP.index(vref::DispatchVariableRef)::AbstractInfOptIndex
return vref.index
end
"""
JuMP.owner_model(vref::GeneralVariableRef)::InfiniteModel
Extend [`JuMP.owner_model`](@ref JuMP.owner_model(::JuMP.AbstractVariableRef)) to
return the model where `vref` is stored.
**Example**
```jldoctest; setup = :(using InfiniteOpt, JuMP; m = InfiniteModel(); @hold_variable(m, 0 <= vref <= 1))
julia> owner_model(vref)
An InfiniteOpt Model
Feasibility problem with:
Finite Parameters: 0
Infinite Parameters: 0
Variable: 1
Derivatives: 0
Measures: 0
`HoldVariableRef`-in-`MathOptInterface.GreaterThan{Float64}`: 1 constraint
`HoldVariableRef`-in-`MathOptInterface.LessThan{Float64}`: 1 constraint
Names registered in the model: vref
Optimizer model backend information:
Model mode: AUTOMATIC
CachingOptimizer state: NO_OPTIMIZER
Solver name: No optimizer attached.
```
"""
function JuMP.owner_model(vref::GeneralVariableRef)::InfiniteModel
return vref.model
end
"""
JuMP.owner_model(vref::DispatchVariableRef)::InfiniteModel
Extend [`JuMP.owner_model`](@ref JuMP.owner_model(::JuMP.AbstractVariableRef)) to
return the model where `vref` is stored.
"""
function JuMP.owner_model(vref::DispatchVariableRef)::InfiniteModel
return vref.model
end
################################################################################
# DISPATCH VARIABLE MAKERS
################################################################################
"""
dispatch_variable_ref(model::InfiniteModel, index::AbstractInfOptIndex)
Return the variable reference associated the type of `index`. This needs to be
defined for each variable reference type.
"""
function dispatch_variable_ref end
"""
dispatch_variable_ref(vef::GeneralVariableRef)::DispatchVariableRef
Return the concrete [`DispatchVariableRef`](@ref) this associated with `vref`.
This relies on `dispatch_variable_ref` being extended for the index type,
otherwise an `MethodError` is thrown.
"""
function dispatch_variable_ref(vref::GeneralVariableRef)::DispatchVariableRef
model = JuMP.owner_model(vref)
idx = JuMP.index(vref)
return dispatch_variable_ref(model, idx)
end
################################################################################
# CORE DATA METHODS
################################################################################
"""
_add_data_object(model::InfiniteModel, object::AbstractDataObject)::ObjectIndex
Add `object` to the appropriate `CleverDict` in `model` and return the its
index. This needs to be defined for the type of `object`. These definitions
need to use `MOIUC.add_item` to add the object to the `CleverDict`.
"""
function _add_data_object end
"""
_data_dictionary(vref::DispatchVariableRef)::MOIUC.CleverDict
Return the `CleverDict` that stores data objects for the type of `vref`. This
needs to be defined for the type of `vref`.
"""
function _data_dictionary end
"""
_data_dictionary(vref::GeneralVariableRef)::MOIUC.CleverDict
Return the `CleverDict` that stores data objects for the type of `vref`. It
relies on `_data_dictionary` being defined
for the underlying `DispatchVariableRef`, otherwise an `MethodError` is thrown.
"""
function _data_dictionary(vref::GeneralVariableRef)::MOIUC.CleverDict
return _data_dictionary(dispatch_variable_ref(vref))
end
"""
_data_object(vref::DispatchVariableRef)::AbstractDataObject
Return the data object associated with `vref`, in other words the object its
index points to in the `InfiniteModel`. This needs to be defined for the type
of `vref`. This should use `_data_dictionary` to access the `CleverDict` that
the object is stored in.
"""
function _data_object end
"""
_data_object(vref::GeneralVariableRef)::AbstractDataObject
Return the data object associated with `vref`, in other words the object its
index points to in the `InfiniteModel`. It relies on `_data_object` being defined
for the underlying `DispatchVariableRef`, otherwise an `MethodError` is thrown.
"""
function _data_object(vref::GeneralVariableRef)::AbstractDataObject
return _data_object(dispatch_variable_ref(vref))
end
"""
_delete_data_object(vref::DispatchVariableRef)::Nothing
Delete the concrete `AbstractDataObject` associated with `vref`.
"""
function _delete_data_object(vref::DispatchVariableRef)::Nothing
delete!(_data_dictionary(vref), JuMP.index(vref))
return
end
################################################################################
# NAME METHODS
################################################################################
# Dispatch fallback
function JuMP.name(vref::DispatchVariableRef)
throw(ArgumentError("`JuMP.name` not defined for variable reference type " *
"`$(typeof(vref))`."))
end
"""
JuMP.name(vref::GeneralVariableRef)::String
Extend [`JuMP.name`](@ref JuMP.name(::JuMP.VariableRef)) to
return the name of `vref`. It relies on `JuMP.name` being defined
for the underlying `DispatchVariableRef`, otherwise an `ArugmentError` is thrown.
"""
function JuMP.name(vref::GeneralVariableRef)::String
return JuMP.name(dispatch_variable_ref(vref))
end
# Dispatch fallback
function JuMP.set_name(vref::DispatchVariableRef, name::String)
throw(ArgumentError("`JuMP.set_name` not defined for variable reference type " *
"`$(typeof(vref))`."))
end
"""
JuMP.set_name(vref::GeneralVariableRef, name::String)::Nothing
Extend [`JuMP.set_name`](@ref JuMP.name(::JuMP.VariableRef, ::String)) to
set the name of `vref`. It relies on `JuMP.set_name` being defined
for the underlying `DispatchVariableRef`, otherwise an `ArugmentError` is thrown.
"""
function JuMP.set_name(vref::GeneralVariableRef, name::String)::Nothing
return JuMP.set_name(dispatch_variable_ref(vref), name)
end
################################################################################
# VALIDITY METHODS
################################################################################
"""
JuMP.is_valid(model::InfiniteModel, vref::DispatchVariableRef)::Bool
Extend [`JuMP.is_valid`](@ref JuMP.is_valid(::JuMP.Model, ::JuMP.VariableRef)) to
return `Bool` if `vref` is a valid reference.
"""
function JuMP.is_valid(model::InfiniteModel, vref::DispatchVariableRef)::Bool
return model === JuMP.owner_model(vref) &&
haskey(_data_dictionary(vref), JuMP.index(vref))
end
# DependentParameterRef
function JuMP.is_valid(model::InfiniteModel, vref::DependentParameterRef)::Bool
return model === JuMP.owner_model(vref) &&
haskey(_data_dictionary(vref), JuMP.index(vref).object_index)
end
"""
JuMP.is_valid(model::InfiniteModel, vref::GeneralVariableRef)::Bool
Extend [`JuMP.is_valid`](@ref JuMP.is_valid(::JuMP.Model, ::JuMP.VariableRef)) to
return `Bool` if `vref` is a valid reference.
**Example**
```jldoctest; setup = :(using InfiniteOpt, JuMP; model = InfiniteModel(); @hold_variable(model, vref))
julia> is_valid(model, vref)
true
```
"""
function JuMP.is_valid(model::InfiniteModel, vref::GeneralVariableRef)::Bool
return JuMP.is_valid(model, dispatch_variable_ref(vref))
end
################################################################################
# CORE OBJECT METHODS
################################################################################
"""
_core_variable_object(vref::DispatchVariableRef)::Union{InfOptParameter, InfOptVariable, Measure}
Return the core object that `vref` points to. This needs to be extended for type
of `vref`. This should use `_data_object` to access the data object where the
variable object is stored.
"""
function _core_variable_object end
"""
_core_variable_object(vref::GeneralVariableRef)::Union{InfOptParameter, InfOptVariable, Measure}
Return the core object that `vref` points to. This is enabled
with appropriate definitions of `_core_variable_object` for the
underlying `DispatchVariableRef`, otherwise an `MethodError` is thrown.
"""
function _core_variable_object(vref::GeneralVariableRef)
return _core_variable_object(dispatch_variable_ref(vref))
end
"""
_set_core_variable_object(vref::DispatchVariableRef, object)::Nothing
Sets the core object that `vref` points to `object`. This needs to be extended
for types of `vref` and `object`. This should use `_data_object` to access the
data object where the variable object is stored.
"""
function _set_core_variable_object end
################################################################################
# DEPENDENCY METHODS
################################################################################
# Define wrappers for internal usage methods and their templates
for op = (:_infinite_variable_dependencies, :_reduced_variable_dependencies,
:_point_variable_dependencies, :_measure_dependencies,
:_constraint_dependencies, :_derivative_dependencies,
:_derivative_constraint_dependencies, :_parameter_function_dependencies,
:_generative_measures)
@eval begin
# define the api template
func = $op
"""
$func(vref::DispatchVariableRef)::Vector{AbstractInfOptIndex}
Return the indices of these entities that depend on `vref`. This needs to
be extended for type of `vref`. This should use `_data_object` to access the
data object where the name is stored if appropriate.
"""
function $(op) end
# define the dispatch version
"""
$func(vref::GeneralVariableRef)::Vector{AbstractInfOptIndex}
Return the indices of these entities that depend on `vref`. This is enabled
with appropriate definitions of `$func` for the
underlying `DispatchVariableRef`, otherwise an `MethodError` is thrown.
"""
function $op(vref::GeneralVariableRef)
return $op(dispatch_variable_ref(vref))
end
end
end
################################################################################
# USED BY METHODS
################################################################################
# Define the usage method wrappers and their fallbacks
for op = (:used_by_infinite_variable, :used_by_reduced_variable,
:used_by_point_variable, :used_by_measure, :used_by_constraint,
:used_by_objective, :used_by_derivative, :is_used, :has_derivative_constraints,
:used_by_parameter_function)
@eval begin
# define the fallback method
func = $op
function $op(vref::DispatchVariableRef)
str = string("`", func, "` not defined for variable reference type " *
"`$(typeof(vref))`.")
throw(ArgumentError(str))
end
# define the dispatch version
"""
$func(vref::GeneralVariableRef)::Bool
Define `$func` for general variable references. It relies on `$func`
being defined for the underlying `DispatchVariableRef`, otherwise an
`ArugmentError` is thrown. See the underlying docstrings for more
information.
"""
function $op(vref::GeneralVariableRef)::Bool
return $op(dispatch_variable_ref(vref))
end
end
end
################################################################################
# DELETE METHODS
################################################################################
# Dispatch fallback
function JuMP.delete(model::InfiniteModel, vref)
throw(ArgumentError("`JuMP.delete` not defined for variable reference type " *
"`$(typeof(vref))`."))
end
"""
JuMP.delete(model::InfiniteModel, vref::GeneralVariableRef)::Nothing
Extend [`JuMP.delete`](@ref JuMP.delete(::JuMP.Model, ::JuMP.VariableRef)) to
delete `vref` and its dependencies. It relies on `JuMP.delete`
being defined for the underlying `DispatchVariableRef`, otherwise an
`ArugmentError` is thrown.
"""
function JuMP.delete(model::InfiniteModel, vref::GeneralVariableRef)::Nothing
return JuMP.delete(model, dispatch_variable_ref(vref))
end
"""
JuMP.delete(model::InfiniteModel,
prefs::AbstractArray{<:GeneralVariableRef})::Nothing
Extend `JuMP.delete` to delete a group of dependent infinite parameters and
their dependencies. An `ArugmentError` is thrown if `prefs` are not dependent
infinite parameters.
"""
function JuMP.delete(model::InfiniteModel,
prefs::AbstractArray{<:GeneralVariableRef})::Nothing
return JuMP.delete(model, dispatch_variable_ref.(prefs))
end
################################################################################
# PARAMETER METHODS
################################################################################
"""
_parameter_number(pref::DispatchVariableRef)::Int
Return the parameter creation number for `pref` assuming it is an infinite
parameter. This needs to be defined for the type of `pref`. This should use
the `_data_object` to get the number.
"""
function _parameter_number end
"""
_parameter_number(pref::GeneralVariableRef)::Int
Return the parameter creation number for `pref` assuming it is an infinite
parameter. It relies on `_parameter_number` being properly defined for the
underlying `DispatchVariableRef`, otherwise an `MethodError` is thrown.
"""
function _parameter_number(pref::GeneralVariableRef)::Int
return _parameter_number(dispatch_variable_ref(pref))
end
"""
_object_number(pref::DispatchVariableRef)::Int
Return the object number for `pref` assuming it is an infinite
parameter. This needs to be defined for the type of `pref`. This should use
the `_data_object` to get the number.
"""
function _object_number end
"""
_object_number(pref::GeneralVariableRef)::Int
Return the object number for `pref` assuming it is an infinite
parameter. It relies on `_object_number` being properly defined for the
underlying `DispatchVariableRef`, otherwise an `MethodError` is thrown.
"""
function _object_number(pref::GeneralVariableRef)::Int
return _object_number(dispatch_variable_ref(pref))
end
# Define 1 argument user method wrappers and their fallbacks
for op = (:infinite_set, :num_supports, :significant_digits, :has_supports,
:supports, :delete_supports, :fill_in_supports!, :parameter_value,
:derivative_method, :has_generative_supports, :has_internal_supports,
:add_generative_supports, :raw_function, :generative_support_info)
@eval begin
# define the fallback method
func = $op
function $op(pref; kwargs...)
str = string("`", func, "` not defined for variable reference type " *
"`$(typeof(pref))`.")
throw(ArgumentError(str))
end
# define the dispatch version
"""
$func(prefs; [kwargs...])
Define `$func` for general variable references. It relies on `$func`
being defined for the underlying `DispatchVariableRef`, otherwise an
`ArugmentError` is thrown. See the underlying docstrings for more
information. Note that this is a auto generated wrapper and the underlying
method may or may not use `kwargs`.
"""
function $op(prefs::Union{GeneralVariableRef, AbstractArray{<:GeneralVariableRef}}; kwargs...)
return $op(dispatch_variable_ref.(prefs); kwargs...)
end
end
end
# Dispatch fallback
function set_infinite_set(pref, set::AbstractInfiniteSet)
throw(ArgumentError("`set_infinite_set` not defined for variable reference type(s) " *
"`$(typeof(pref))`."))
end
"""
set_infinite_set(pref::GeneralVariableRef, set::InfiniteScalarSet)::Nothing
Specify the scalar infinite set of the infinite parameter `pref` to `set`. Note
this will reset/delete all the supports contained in the
underlying parameter object. Also, errors if `pref` is used
by a measure. An `ArgumentError` is thrown if `pref` is not an infinite parameter.
"""
function set_infinite_set(pref::GeneralVariableRef,
set::InfiniteScalarSet)::Nothing
return set_infinite_set(dispatch_variable_ref(pref), set)
end
"""
set_infinite_set(prefs::AbstractArray{<:GeneralVariableRef},
set::InfiniteArraySet)::Nothing
Specify the multi-dimensional infinite set of the dependent infinite parameters
`prefs` to `set`. Note this will reset/delete all the supports contained in the
underlying [`DependentParameters`](@ref) object. This will error if the not all
of the dependent infinite parameters are included or if any of them are used by
measures. An `ArgumentError` is thrown if `prefs` are not dependent infinite
parameters.
"""
function set_infinite_set(prefs::AbstractArray{<:GeneralVariableRef},
set::InfiniteArraySet)::Nothing
return set_infinite_set(dispatch_variable_ref.(prefs), set)
end
# Better fallbacks for supports
function supports(pref::DispatchVariableRef; kwargs...)
throw(ArgumentError("`supports` not defined for variable reference type(s) " *
"`$(typeof(pref))`."))
end
function supports(prefs::AbstractArray; kwargs...)
throw(ArgumentError("`supports` not defined for variable reference type(s) " *
"`$(typeof(prefs))`."))
end
# Dispatch fallback
function set_supports(pref, supports; kwargs...)
throw(ArgumentError("`set_supports` not defined for variable reference type(s) " *
"`$(typeof(pref))`."))
end
"""
set_supports(pref::GeneralVariableRef, supports::Union{Real, Vector{<:Real}};
[force::Bool = false])::Nothing
Set the support points associated with a single infinite
parameter `pref`. An `ArgumentError` is thrown if `pref` is not an independent
infinite parameter.
"""
function set_supports(pref::GeneralVariableRef,
supports::Union{Real, Vector{<:Real}};
force::Bool = false,
label::Type{<:AbstractSupportLabel} = UserDefined
)::Nothing
return set_supports(dispatch_variable_ref(pref), supports,
force = force, label = label)
end
"""
set_supports(
prefs::Union{Vector{GeneralVariableRef}, AbstractArray{<:GeneralVariableRef}},
supports::Union{Array{<:Real, 2}, AbstractArray{<:Vector{<:Real}}};
[force::Bool = false]
)::Nothing
Set the support points associated with dependent infinite
parameters `prefs`. An `ArgumentError` is thrown if `prefs` is are not
dependent infinite parameters.
"""
function set_supports(prefs::AbstractArray{<:GeneralVariableRef},
supports::Union{Array{<:Real, 2}, AbstractArray{<:Vector{<:Real}}};
label::Type{<:AbstractSupportLabel} = UserDefined,
force::Bool = false
)::Nothing
return set_supports(dispatch_variable_ref.(prefs), supports, label = label,
force = force)
end
# Dispatch fallback
function add_supports(pref, supports; kwargs...)
throw(ArgumentError("`add_supports` not defined for variable reference type(s) " *
"`$(typeof(pref))`."))
end
"""
add_supports(pref::GeneralVariableRef,
supports::Union{Real, Vector{<:Real}})::Nothing
Add the support points `supports` to a single infinite
parameter `pref`. An `ArgumentError` is thrown if `pref` is not an independent
infinite parameter.
"""
function add_supports(pref::GeneralVariableRef,
supports::Union{Real, Vector{<:Real}};
check::Bool = true,
label::Type{<:AbstractSupportLabel} = UserDefined
)::Nothing
return add_supports(dispatch_variable_ref(pref), supports,
check = check, label = label)
end
"""
add_supports(
prefs::Union{Vector{GeneralVariableRef}, AbstractArray{<:GeneralVariableRef}},
supports::Union{Array{<:Real, 2}, AbstractArray{<:Vector{<:Real}}}
)::Nothing
Add the support points `supports` to the dependent infinite
parameters `prefs`. An `ArgumentError` is thrown if `prefs` is are not
dependent infinite parameters.
"""
function add_supports(prefs::AbstractArray{<:GeneralVariableRef},
supports::Union{Array{<:Real, 2}, AbstractArray{<:Vector{<:Real}}};
label::Type{<:AbstractSupportLabel} = UserDefined,
check::Bool = true
)::Nothing
return add_supports(dispatch_variable_ref.(prefs), supports, label = label,
check = check)
end
# Fallback
function JuMP.set_value(vref::DispatchVariableRef, value::Real)
throw(ArgumentError("`JuMP.set_value` not defined for variable reference type " *
"`$(typeof(vref))`."))
end
"""
JuMP.set_value(vref::DispatchVariableRef, value::Real)::Nothing
Extend `JuMP.set_value` to
set the value of `vref`. It relies on `JuMP.set_value`
being defined for the underlying `DispatchVariableRef`, otherwise an
`ArugmentError` is thrown.
"""
function JuMP.set_value(vref::GeneralVariableRef, value::Real)::Nothing
return JuMP.set_value(dispatch_variable_ref(vref), value)
end
# Dispatch fallback
function set_derivative_method(pref::DispatchVariableRef, method)
throw(ArgumentError("`set_derivative_method` not defined for variable reference type(s) " *
"`$(typeof(pref))`."))
end
"""
set_derivative_method(pref::GeneralVariableRef,
method::AbstractDerivativeMethod
)::Nothing
Specify the numerical derivative evaluation technique associated with `pref`.
An `ArgumentError` is thrown if `pref` is not an infinite parameter.
"""
function set_derivative_method(pref::GeneralVariableRef,
method::AbstractDerivativeMethod
)::Nothing
return set_derivative_method(dispatch_variable_ref(pref), method)
end
# Define parameter status setters
for op = (:_set_has_generative_supports, :_set_has_internal_supports,
:_set_has_derivative_constraints)
@eval begin
# define the fallback method
func = $op
function $op(vref::DispatchVariableRef, status)
str = string("`", func, "` not defined for variable reference type " *
"`$(typeof(vref))`.")
throw(ArgumentError(str))
end
# define the dispatch version
function $op(vref::GeneralVariableRef, status::Bool)::Nothing
return $op(dispatch_variable_ref(vref), status)
end
end
end
################################################################################
# VARIABLE METHODS
################################################################################
# Define single argument variable method wrappers and their fallbacks
for op = (:raw_parameter_refs, :parameter_refs, :parameter_list,
:start_value_function, :reset_start_value_function,
:infinite_variable_ref, :eval_supports, :raw_parameter_values,
:parameter_values, :parameter_bounds, :delete_parameter_bounds)
@eval begin
# define the fallback method
func = $op
function $op(vref::DispatchVariableRef)
str = string("`", func, "` not defined for variable reference type " *
"`$(typeof(vref))`.")
throw(ArgumentError(str))
end
# define the dispatch version
"""
$func(vref::GeneralVariableRef)
Define `$func` for general variable references. It relies on `$func`
being defined for the underlying `DispatchVariableRef`, otherwise an
`ArugmentError` is thrown. See the underlying docstrings for more
information.
"""
function $op(vref::GeneralVariableRef)
return $op(dispatch_variable_ref(vref))
end
end
end
# Dispatch fallback
function set_start_value_function(vref::DispatchVariableRef, start)
throw(ArgumentError("`set_start_value_function` not defined for variable reference type " *
"`$(typeof(vref))`."))
end
"""
set_start_value_function(vref::GeneralVariableRef, start::Union{Real, Function})::Nothing
Set the start value function of `vref`. It relies on `set_start_value_function`
being defined for the underlying `DispatchVariableRef`, otherwise an
`ArugmentError` is thrown.
"""
function set_start_value_function(vref::GeneralVariableRef, start)::Nothing
return set_start_value_function(dispatch_variable_ref(vref), start)
end
"""
has_parameter_bounds(vref::GeneralVariableRef)::Bool
Return a `Bool` indicating if `vref` is limited to a sub-domain as defined
by parameter bound.
"""
function has_parameter_bounds(vref::GeneralVariableRef)::Bool
return has_parameter_bounds(dispatch_variable_ref(vref))
end
# Dispatch fallback
function set_parameter_bounds(vref::DispatchVariableRef, bounds; kwargs...)
throw(ArgumentError("`set_parameter_bounds` not defined for variable reference type(s) " *
"`$(typeof(vref))`."))
end
"""
set_parameter_bounds(vref::GeneralVariableRef,
bounds::ParameterBounds{GeneralVariableRef};
[force::Bool = false])::Nothing
Specify a new set of parameter bounds for a hold variable `vref`.
An `ArgumentError` is thrown if `vref` is not a hold variable.
"""
function set_parameter_bounds(vref::GeneralVariableRef,
bounds::ParameterBounds{GeneralVariableRef};
force::Bool = false, _error::Function = error
)::Nothing
return set_parameter_bounds(dispatch_variable_ref(vref), bounds,
force = force, _error = _error)
end
# Dispatch fallback
function add_parameter_bounds(vref::DispatchVariableRef, bounds; kwargs...)
throw(ArgumentError("`add_parameter_bounds` not defined for variable reference type(s) " *
"`$(typeof(vref))`."))
end
"""
add_parameter_bounds(vref::GeneralVariableRef,
bounds::ParameterBounds{GeneralVariableRef}
)::Nothing
Specify more parameter bounds for a hold variable `vref`.
An `ArgumentError` is thrown if `vref` is not a hold variable.
"""
function add_parameter_bounds(vref::GeneralVariableRef,
bounds::ParameterBounds{GeneralVariableRef};
_error::Function = error
)::Nothing
return add_parameter_bounds(dispatch_variable_ref(vref), bounds,
_error = _error)
end
################################################################################
# MEASURE METHODS
################################################################################
# Define measure queries and their fallbacks
for op = (:measure_function, :measure_data, :is_analytic, :expand)
@eval begin
# define the fallback method
func = $op
function $op(mref::DispatchVariableRef)
str = string("`", func, "` not defined for variable reference type " *
"`$(typeof(mref))`.")
throw(ArgumentError(str))
end
# define the dispatch version
"""
$func(mref::GeneralVariableRef)
Define `$func` for general variable references. Errors if `mref` does
not correspond to a `MeasureRef`. See the underlying docstrings for more
information.
"""
function $op(mref::GeneralVariableRef)
return $op(dispatch_variable_ref(mref))
end
end
end
################################################################################
# DERIVATIVE METHODS
################################################################################
# Define measure queries and their fallbacks
for op = (:derivative_argument, :operator_parameter, :evaluate,
:derivative_constraints, :delete_derivative_constraints)
@eval begin
# define the fallback method
func = $op
function $op(dref::DispatchVariableRef)
str = string("`", func, "` not defined for variable reference type " *
"`$(typeof(dref))`.")
throw(ArgumentError(str))
end
# define the dispatch version
"""
$func(dref::GeneralVariableRef)
Define `$func` for general variable references. Errors if `dref` does
not correspond to a `DerivativeRef`. See the underlying docstrings for more
information.
"""
function $op(dref::GeneralVariableRef)
return $op(dispatch_variable_ref(dref))
end
end
end
################################################################################
# VARIABLE INFO METHODS
################################################################################
# Define the 1 argument JuMP variable info methods
for op = (:has_lower_bound, :has_upper_bound, :is_fixed, :is_binary, :is_integer,
:lower_bound, :upper_bound, :fix_value, :start_value,
:set_binary, :set_integer,
:LowerBoundRef, :UpperBoundRef, :FixRef, :BinaryRef, :IntegerRef,
:delete_lower_bound, :delete_upper_bound, :unfix, :unset_binary,
:unset_integer)
@eval begin
# define the fallback method
func = JuMP.$op
function JuMP.$op(vref::DispatchVariableRef)
str = string("`JuMP.", func, "` not defined for variable reference type " *
"`$(typeof(vref))`.")
throw(ArgumentError(str))
end
# define the dispatch version
"""
JuMP.$func(vref::GeneralVariableRef)
Define `JuMP.$func` for general variable references. It relies on `JuMP.$func`
being defined for the underlying `DispatchVariableRef`, otherwise an
`ArugmentError` is thrown. See the underlying docstrings for more
information.
"""
function JuMP.$op(vref::GeneralVariableRef)
return JuMP.$op(dispatch_variable_ref(vref))
end
end
end
# Define the 2 argument setting methods (except for fix)
for op = (:set_lower_bound, :set_upper_bound, :set_start_value)
@eval begin
# define the fallback method
func = JuMP.$op
function JuMP.$op(vref::DispatchVariableRef, value)
str = string("`JuMP.", func, "` not defined for variable reference type " *
"`$(typeof(vref))`.")
throw(ArgumentError(str))
end
# define the dispatch version
"""
JuMP.$func(vref::GeneralVariableRef, value::Real)::Nothing
Define `JuMP.$func` for general variable references. It relies on `JuMP.$func`
being defined for the underlying `DispatchVariableRef`, otherwise an
`ArugmentError` is thrown. See the underlying docstrings for more
information.
"""
function JuMP.$op(vref::GeneralVariableRef, value::Real)::Nothing
return JuMP.$op(dispatch_variable_ref(vref), value)
end
end
end
# Dispatch fallback for JuMP.fix
function JuMP.fix(vref::DispatchVariableRef, value::Real; force::Bool = false)
throw(ArgumentError("`JuMP.fix` not defined for variable reference type " *
"`$(typeof(vref))`."))
end
"""
JuMP.fix(vref::GeneralVariableRef, value::Real; force::Bool = false)::Nothing
Define `JuMP.fix` for general variable references. It relies on `JuMP.fix`
being defined for the underlying `DispatchVariableRef`, otherwise an
`ArugmentError` is thrown. See the underlying docstrings for more
information.
"""
function JuMP.fix(vref::GeneralVariableRef, value::Real;
force::Bool = false)::Nothing
return JuMP.fix(dispatch_variable_ref(vref), value, force = force)
end
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] | 2.70891 | 13,075 |
using PyPlot, Random, TransD_GP, GP, Statistics, HDF5
function geomprogdepth(n, dy, c)
dy*(1.0-c^n)/(1-c)
end
function getn(z, dy, c)
log(1 - z/dy*(1-c))/log(c)
end
function nicenup(g::PyPlot.Figure;fsize=14)
for ax in gcf().axes
ax.tick_params("both",labelsize=fsize)
ax.xaxis.label.set_fontsize(fsize)
ax.yaxis.label.set_fontsize(fsize)
ax.title.set_fontsize(fsize)
if typeof(ax.get_legend_handles_labels()[1]) != Array{Any,1}
ax.legend(loc="best", fontsize=fsize)
end
end
g.tight_layout()
end
n, dz, extendfrac = 18, .5, 1.2
znrange = 1.0:n
zboundaries = geomprogdepth.(znrange, dz, extendfrac)
thickness = [zboundaries[1]; diff(zboundaries)[1:end-1]]
zall = [zboundaries[1]/2; 0.5*(zboundaries[1:end-1] + zboundaries[2:end])]
znall = getn.(zall, dz, extendfrac)
figure()
plot(znall, zall)
xlabel("depth index")
ylabel("depth associated km")
grid()
nicenup(gcf())
f, ax = plt.subplots(1, 2, figsize=(10,5))
ax[1].stem(zboundaries[1:end-1], zboundaries[1:end-1], markerfmt="")
ax[1].stem(zall, zall, "k--", markerfmt=" ")
ax[1].set_xlabel("depth km")
ax[1].set_ylabel("depth km")
ax[2].stem(znrange[1:end-1], znrange[1:end-1], markerfmt="")
ax[2].stem(znall, znall, "k--", markerfmt=" ")
ax[2].set_ylabel("depth index")
ax[2].set_xlabel("depth index")
nicenup(gcf())
f, ax = plt.subplots(1, 2, figsize=(10,5), sharey=true)
ax[1].stem(zall[1:end-1],thickness, "k--", markerfmt=" ")
ax[1].set_xlabel("depth km")
ax[1].set_ylabel("thickness km")
ax[1].yaxis.grid(which="major")
ax[2].stem(znall[1:end-1],thickness, "k--", markerfmt=" ")
ax[2].set_xlabel("depth index")
ax[2].yaxis.grid(which="major")
nicenup(f)
nmin, nmax = 2, 200
λ, δ = [8, 5, 2], 0.1
fbounds = [-2.8 0.25]
demean = true
sdev_prop = 0.1
sdev_pos = [0.5;0.5;0.2]
pnorm = 2.
λx,λy = 100.0, 100.0
dx, dy = 0.05λx, 0.05λy
x = 0:(0.05λx):λx-dx
y = 0:(0.05λy):λy-dy
xall = zeros(3,length(x)*length(y)*length(znall))
for i in 1:size(xall,2)
xid, yid, zid = Tuple(CartesianIndices((length(x),length(y),length(znall)))[i])
xall[:,i] = [x[xid]; y[yid]; znall[zid]]
end
xbounds = zeros(Float64,size(xall,1),2)
for dim in 1:size(xall, 1)
xbounds[dim,:] = [minimum(xall[dim,:]), maximum(xall[dim,:])]
end
## Initialize a model using these options
Random.seed!(2)
opt = TransD_GP.Options(nmin = nmin,
nmax = nmax,
xbounds = xbounds,
fbounds = fbounds,
xall = xall,
λ = λ,
δ = δ,
demean = demean,
sdev_prop = sdev_prop,
sdev_pos = sdev_pos,
pnorm = pnorm,
quasimultid = false
)
@time m = TransD_GP.init(opt)
TransD_GP.birth!(m, opt)
@time for i = 1:48# 17 also works well
TransD_GP.birth!(m, opt)
end
v = reshape(m.fstar,length(x), length(y), length(znall))
meshgrid(xs, ys) = [xs[i] for i in 1:length(xs), j in 1:length(ys)], [ys[j] for i in 1:length(xs), j in 1:length(ys)]
xx,yy = meshgrid(x,y)
f = figure(figsize=(10,10))
l = [7,12, 15, 18]
for i in l
c=(v[:,:,i].-minimum(v))./(maximum(v)-minimum(v))
plot_surface(xx, yy, zall[i]*ones(size(yy)), facecolors=plt.cm.jet(c), shade=false, alpha=0.9)
end
timebirth = true
if timebirth
ts = time(); ndo = 100
for i = 1:ndo
TransD_GP.birth!(m, opt)
TransD_GP.death!(m, opt)
end
dt = time() - ts
@info "avg time per move is $(dt/ndo/2)"
end
yy,zz = meshgrid(y,zall)
l = [1, 18]
for i in l
c=(v[i,:,:].-minimum(v))./(maximum(v)-minimum(v))
plot_surface(x[i]*ones(size(yy)), yy, zz, facecolors=plt.cm.jet(c), shade=false, alpha=0.9)
end
scatter3D(m.xtrain[1,1:m.n], m.xtrain[2,1:m.n], geomprogdepth.(m.xtrain[3,1:m.n], dz, extendfrac), c=m.ftrain[1:m.n], vmin=minimum(v), vmax=maximum(v), s=50, cmap="jet", alpha=0.8)
xlabel("x km")
ylabel("y km")
zlabel("depth km")
cbar = f.colorbar(plt.cm.ScalarMappable(cmap="jet",norm=matplotlib.colors.Normalize(vmin=minimum(v), vmax=maximum(v))),ax=gca())
cbar.set_label(L"\log_{10} \sigma")
gca().zaxis.label.set_fontsize(16); nicenup(gcf())
gca().invert_zaxis()
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] | 1.832526 | 2,478 |
<reponame>ScottJordan/EvaluationOfRLAlgs
function sample_normal!(z, μ, σ, rng)
@. z = μ + σ * randn((rng,), eltype(μ))
end
function logpdf_normal(z, μ, σ)
logp = -log(sqrt(2.0 * π)) - (z - μ)^2 / (2.0 * σ^2) - log(σ)
return logp
end
# something might be wrong with the gradient of the log probability calculation.
mutable struct LinearNormalPolicy{TP, TB, TS} <: AbstractPolicy where {TP<:Real,TB<:AbstractFuncApprox,TS<:Bool}
θ::Array{TP}
ϕ::TB
σ::Array{TP, 1}
μ::Array{TP,1}
action::Array{TP, 1}
feats::Array{TP, 1}
function LinearNormalPolicy(::Type{T}, state_dim::Int, num_actions::Int, sigma, train_sigma::Bool=true) where {T}
θmu = vec(zeros(T,state_dim, num_actions))
σ = ones(T, num_actions) .* sigma
ϕ = IdentityBasis(state_dim)
if train_sigma
θ = cat(θmu, σ, dims=1)
else
θ = θmu
end
a = zeros(tp, num_actions)
μ = zeros(tp, num_actions)
feats = zeros(T, get_num_outputs(ϕ))
new{T,typeof(ϕ),train_sigma}(θ, ϕ, σ, μ, a, feats)
end
function LinearNormalPolicy(ϕ::AbstractFuncApprox, num_actions::Int, sigma, train_sigma::Bool=true)
tp = eltype(get_params(ϕ))
θmu = vec(zeros(tp,get_num_outputs(ϕ), num_actions))
σ = ones(tp, num_actions) .* sigma
if train_sigma
θ = cat(θmu, σ, dims=1)
else
θ = θmu
end
a = zeros(tp, num_actions)
μ = zeros(tp, num_actions)
feats = zeros(tp, get_num_outputs(ϕ))
new{tp,typeof(ϕ),train_sigma}(θ, clone(ϕ), σ, μ, a, feats)
end
end
function get_num_params(π::LinearNormalPolicy)::Int
return length(π.θ)
end
function get_thetamu(π::LinearNormalPolicy{T,TB,false}) where {T,TB}
N = get_num_outputs(π.ϕ)
return reshape(π.θ, N, :)
end
function get_thetamu(π::LinearNormalPolicy{T,TB,true}) where {T,TB}
N = get_num_outputs(π.ϕ)
A = length(π.σ)
return reshape(view(π.θ, 1:N*A), N, A)
end
function call(fun::LinearNormalPolicy{TP,IdentityBasis}, x::Int)::Array{TP,1} where {TP<:Real}
θ = get_thetamu(fun)
return θ[x, :]
end
function call!(out::Array{TP, 1}, fun::LinearNormalPolicy{TP,IdentityBasis}, x::Int) where {TP<:Real}
θ = get_thetamu(fun)
out .= θ[x, :]
end
function call(fun::LinearNormalPolicy{TP,IdentityBasis}, x::Int, y::Int)::TP where{TP<:Real}
θ = get_thetamu(fun)
return θ[x, y]
end
function call(fun::LinearNormalPolicy{TP,IdentityBasis}, x::Array{TP,1})::Array{TP,1} where {TP<:Real}
θ = get_thetamu(fun)
return θ'*x
end
function call!(out::Array{TP, 1}, fun::LinearNormalPolicy{TP,IdentityBasis}, x::Array{TP,1}) where {TP<:Real}
θ = get_thetamu(fun)
out .= θ'*x
end
function call(fun::LinearNormalPolicy{TP}, x::Array{TP,1})::Array{TP,1} where {TP<:Real}
call!(fun.feats, fun.ϕ, x)
θ = get_thetamu(fun)
return θ'*fun.feats
end
function call!(out::Array{TP, 1}, fun::LinearNormalPolicy{TP}, x::Array{TP,1}) where {TP<:Real}
call!(fun.feats, fun.ϕ, x)
θ = get_thetamu(fun)
out .= θ'*fun.feats
end
function call(fun::LinearNormalPolicy{TP,IdentityBasis}, x::Array{TP,1}, y::Int)::TP where {TP<:Real}
θ = get_thetamu(fun)
return dot(θ[:, y],x)
end
function call(fun::LinearNormalPolicy{TP}, x::Array{TP,1}, y::Int)::TP where {TP<:Real}
call!(fun.feats, fun.ϕ, x)
θ = get_thetamu(fun)
return dot(θ[:, y], fun.feats)
end
function get_mean!(π::LinearNormalPolicy, x)
call!(π.μ,π,x)
end
function get_mean(π::LinearNormalPolicy{T}, x)::Array{T,1} where {T<:Real}
get_mean!(π, x)
return π.μ
end
function get_action!(π::LinearNormalPolicy{T}, x, rng::AbstractRNG)::T where {T<:Real}
get_mean!(π, x)
sample_normal!(π.action, π.μ, π.σ, rng)
logp = sum(logpdf_normal.(π.action, π.μ, π.σ))
return logp
end
function grad_mu!(grad, gmu, x)
grad .= x*gmu'
end
function grad_mu!(grad, gmu, x::Int)
grad[x, :] .= gmu
end
function grad_std!(grad, amu, std)
@. grad = (-1 + (amu / std)^2) / std
end
function gradient_logp!(grad::Array{T}, π::LinearNormalPolicy{T, IdentityBasis,true}, x, action)::T where {T<:Real}
fill!(grad, 0.)
θmu = get_thetamu(π)
num_theta = length(θmu)
gtheta = reshape(view(grad, 1:num_theta), size(θmu))
get_mean!(π, x)
std = π.σ
amu = @. (action - π.μ)
gmu = @. amu / std^2
grad_mu!(gtheta, gmu, x)
grad_std!(view(grad, num_theta+1:length(grad)), amu, std)
logp = sum(logpdf_normal.(π.action, π.μ, std)) # TODO make this not redundant computation
return logp
end
function gradient_logp!(grad::Array{T}, π::LinearNormalPolicy{T, IdentityBasis,false}, x, action)::T where {T<:Real}
fill!(grad, 0.)
num_theta = get_num_params(π)
gtheta = reshape(view(grad, 1:num_theta), size(get_thetamu(π)))
get_mean!(π, x)
std = π.σ
amu = @. (action - π.μ)
gmu = @. amu / std^2
grad_mu!(gtheta, gmu, x)
logp = sum(logpdf_normal.(π.action, π.μ, std))
return logp
end
function gradient_logp!(grad::Array{T}, π::LinearNormalPolicy{T,TB,true}, x, action::Int)::T where {T<:Real,TB}
fill!(grad, 0.)
θmu = get_thetamu(π)
num_theta = length(θmu)
gtheta = reshape(view(grad, 1:num_theta), size(θmu))
get_mean!(π, x)
std = π.σ
amu = @. (action - π.μ)
gmu = @. amu / std^2
grad_mu!(gtheta, gmu, π.feats)
grad_std!(view(grad, num_theta+1:length(grad)), amu, std)
logp = sum(logpdf_normal.(π.action, π.μ, std))
return logp
end
function gradient_logp!(grad::Array{T}, π::LinearNormalPolicy{T,TB,false}, x, action::Int)::T where {T<:Real,TB}
fill!(grad, 0.)
num_theta = get_num_params(π)
gtheta = reshape(view(grad, 1:num_theta), size(get_thetamu(π)))
get_mean!(π, x)
std = π.σ
amu = @. (action - π.μ)
gmu = @. amu / std^2
grad_mu!(gtheta, gmu, π.feats)
logp = sum(logpdf_normal.(π.action, π.μ, std))
return logp
end
function get_action_gradient_logp!(grad::Array{T}, π::LinearNormalPolicy{T, IdentityBasis,true}, x, rng::AbstractRNG)::T where {T<:Real}
fill!(grad, 0.)
θmu = get_thetamu(π)
num_theta = length(θmu)
gtheta = reshape(view(grad, 1:num_theta), size(θmu))
get_mean!(π, x)
sample_normal!(π.action, π.μ, π.σ, rng)
logp = sum(logpdf_normal.(π.action, π.μ, std))
std = π.σ
amu = @. (action - π.μ)
gmu = @. amu / std^2
grad_mu!(gtheta, gmu, x)
grad_std!(view(grad, num_theta+1:length(grad)), amu, std)
return logp
end
function get_action_gradient_logp!(grad::Array{T}, π::LinearNormalPolicy{T, IdentityBasis,false}, x, rng::AbstractRNG)::T where {T<:Real}
fill!(grad, 0.)
θmu = get_thetamu(π)
num_theta = length(θmu)
gtheta = reshape(view(grad, 1:num_theta), size(θmu))
get_mean!(π, x)
sample_normal!(π.action, π.μ, π.σ, rng)
logp = sum(logpdf_normal.(π.action, π.μ, std))
std = π.σ
amu = @. (action - π.μ)
gmu = @. amu / std^2
grad_mu!(gtheta, gmu, x)
return logp
end
function get_action_gradient_logp!(grad::Array{T}, π::LinearNormalPolicy{T, TB,true}, x, rng::AbstractRNG)::T where {T<:Real,TB}
fill!(grad, 0.)
θmu = get_thetamu(π)
num_theta = length(θmu)
gtheta = reshape(view(grad, 1:num_theta), size(θmu))
get_mean!(π, x)
sample_normal!(π.action, π.μ, π.σ, rng)
std = π.σ
logp = sum(logpdf_normal.(π.action, π.μ, std))
amu = @. (π.action - π.μ)
gmu = @. amu / std^2
grad_mu!(gtheta, gmu, π.feats)
grad_std!(view(grad, num_theta+1:length(grad)), amu, std)
return logp
end
function get_action_gradient_logp!(grad::Array{T}, π::LinearNormalPolicy{T, TB,false}, x, rng::AbstractRNG)::T where {T<:Real,TB}
fill!(grad, 0.)
θmu = get_thetamu(π)
num_theta = length(θmu)
gtheta = reshape(view(grad, 1:num_theta), size(θmu))
get_mean!(π, x)
sample_normal!(π.action, π.μ, π.σ, rng)
std = π.σ
logp = sum(logpdf_normal.(π.action, π.μ, std))
amu = @. (π.action - π.μ)
gmu = @. amu / std^2
grad_mu!(gtheta, gmu, π.feats)
return logp
end
function set_params!(π::LinearNormalPolicy{T,TB,true}, θ::Array{T}) where {T,TB}
π.θ .= θ
clamp!(view(π.θ, length(π.θ)-length(π.σ)+1:length(π.θ)), 0.001, 100)
π.σ .= π.θ[end-length(π.σ)+1:end]
end
function set_params!(π::LinearNormalPolicy{T,TB,false}, θ::Array{T}) where {T,TB}
π.θ .= θ
end
function get_params(π::LinearNormalPolicy)
π.θ
end
function copy_params!(params::Array{T}, π::LinearNormalPolicy{T}) where {T}
vec(params) .= vec(π.θ)
end
function copy_params(π::LinearSoftmaxPolicy{T})::Array{T} where {T}
return deepcopy(π.θ)
end
function add_to_params!(π::LinearNormalPolicy, Δθ)
@. π.θ += Δθ
end
function add_to_params!(π::LinearNormalPolicy{T,TB,true}, Δθ) where {T,TB}
@. π.θ += Δθ
clamp!(view(π.θ,length(π.θ)-length(π.σ)+1:length(π.θ)), 0.001, 100)
π.σ .= π.θ[end-length(π.σ)+1:end]
end
function clone(π::LinearNormalPolicy{T, TB, TS})::LinearNormalPolicy{T,TB,TS} where {T,TB,TS}
A = length(π.σ)
π₂ = LinearNormalPolicy(π.ϕ, A, π.σ, TS)
π₂.action = deepcopy(π.action)
π₂.μ = deepcopy(π.μ)
return π₂
end
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] | 1.983409 | 4,641 |
struct GUI
id::UUID
widgets::Vector{Widget}
expression::Expr
end
macro gui(expr)
if expr.head != :for
error(
"@gui syntax is @gui for ",
" [<variable>=<domain>,]... <expression> end"
)
end
is_gui = true
quote_widgets, cur_block, cur_bindings, cur_symbols = _manipulate_outer(expr, is_gui)
return quote
cur_widgets = make_gui_list($(quote_widgets...))
cur_listener = $(esc(make_gui_block(cur_block, cur_symbols)))
cur_id = _manipulate_inner(cur_widgets, cur_listener, $(is_gui))
nothing
end
end
macro manipulate(expr)
if expr.head != :for
error(
"@manipulate syntax is @manipulate for ",
" [<variable>=<domain>,]... <expression> end"
)
end
is_gui = false
quote_widgets, cur_block, cur_bindings, cur_symbols = _manipulate_outer(expr, is_gui)
return quote
cur_widgets = make_gui_list($(quote_widgets...))
cur_listener = $(esc(make_gui_block(cur_block, cur_symbols)))
cur_id = _manipulate_inner(cur_widgets, cur_listener, $(is_gui))
nothing
end
end
export @gui, @manipulate
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] | 2.429213 | 445 |
<reponame>JeffBezanson/MemPool.jl<filename>src/lock.jl
# Copied from CUDA.jl/src/pool.jl
"""
NonReentrantLock
Simple non-reentrant lock that errors when trying to reenter on the same task.
"""
struct NonReentrantLock # <: Base.AbstractLock
rl::ReentrantLock
NonReentrantLock() = new(ReentrantLock())
end
function Base.lock(nrl::NonReentrantLock)
@assert !islocked(nrl.rl) || nrl.rl.locked_by !== current_task()
lock(nrl.rl)
end
function Base.trylock(nrl::NonReentrantLock)
@assert !islocked(nrl.rl) || nrl.rl.locked_by !== current_task()
trylock(nrl.rl)
end
Base.unlock(nrl::NonReentrantLock) = unlock(nrl.rl)
# NonReentrantLock may be taken around code that might call the GC, which might
# reenter through finalizers. Avoid that by temporarily disabling finalizers
# running concurrently on this thread.
enable_finalizers(on::Bool) = ccall(:jl_gc_enable_finalizers, Cvoid,
(Ptr{Cvoid}, Int32,), Core.getptls(), on)
macro safe_lock(l, ex)
quote
temp = $(esc(l))
lock(temp)
enable_finalizers(false)
try
$(esc(ex))
finally
unlock(temp)
enable_finalizers(true)
end
end
end
# If we actually want to acquire a lock from a finalizer, we can't cause a task
# switch. As a NonReentrantLock can only be taken by another thread that should
# be running, and not a concurrent task we'd need to switch to, we can safely
# spin.
macro safe_lock_spin(l, ex)
quote
temp = $(esc(l))
while !trylock(temp)
# we can't yield here
end
enable_finalizers(false) # retains compatibility with non-finalizer callers
try
$(esc(ex))
finally
unlock(temp)
enable_finalizers(true)
end
end
end
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] | 2.342675 | 785 |
<reponame>Kitty-Cats/MaxHelpingHand
module MaxHelpingHandMovingFlagTouchSwitch
using ..Ahorn, Maple
@mapdef Entity "MaxHelpingHand/MovingFlagTouchSwitch" MovingFlagTouchSwitch(x::Integer, y::Integer,
flag::String="moving_flag_touch_switch", icon::String="vanilla", persistent::Bool=false,
inactiveColor::String="5FCDE4", movingColor::String="FF8080", activeColor::String="FFFFFF", finishColor::String="F141DF")
const placements = Ahorn.PlacementDict(
"Flag Touch Switch (Moving)\n(max480's Helping Hand + Outback Helper)" => Ahorn.EntityPlacement(
MovingFlagTouchSwitch
)
)
const bundledIcons = String["vanilla", "tall", "triangle", "circle", "diamond", "double", "heart", "square", "wide", "winged"]
Ahorn.editingOptions(entity::MovingFlagTouchSwitch) = Dict{String,Any}(
"icon" => bundledIcons
)
Ahorn.editingOrder(entity::MovingFlagTouchSwitch) = String["x", "y", "width", "height", "inactiveColor", "movingColor", "activeColor", "finishColor"]
function Ahorn.renderSelectedAbs(ctx::Ahorn.Cairo.CairoContext, entity::MovingFlagTouchSwitch)
px, py = Ahorn.position(entity)
sprite = "collectables/outback/movingtouchswitch/container.png"
for node in get(entity.data, "nodes", ())
nx, ny = Int.(node)
theta = atan(py - ny, px - nx)
Ahorn.drawArrow(ctx, px, py, nx + cos(theta) * 8, ny + sin(theta) * 8, Ahorn.colors.selection_selected_fc, headLength=6)
Ahorn.drawSprite(ctx, sprite, nx, ny)
px, py = nx, ny
end
end
function Ahorn.selection(entity::MovingFlagTouchSwitch)
nodes = get(entity.data, "nodes", ())
x, y = Ahorn.position(entity)
sprite = "collectables/outback/movingtouchswitch/container.png"
res = Ahorn.Rectangle[Ahorn.getSpriteRectangle(sprite, x, y)]
for node in nodes
nx, ny = Int.(node)
push!(res, Ahorn.getSpriteRectangle(sprite, nx, ny))
end
return res
end
Ahorn.nodeLimits(entity::MovingFlagTouchSwitch) = 0, -1
function Ahorn.render(ctx::Ahorn.Cairo.CairoContext, entity::MovingFlagTouchSwitch)
icon = get(entity.data, "icon", "vanilla")
iconPath = "objects/touchswitch/icon00.png"
if icon != "vanilla"
iconPath = "objects/MaxHelpingHand/flagTouchSwitch/$(icon)/icon00.png"
end
Ahorn.drawSprite(ctx, "collectables/outback/movingtouchswitch/container.png", 0, 0)
Ahorn.drawSprite(ctx, iconPath, 0, 0)
end
end
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] | 2.587166 | 935 |
<reponame>vtjnash/Coverage.jl
#######################################################################
# Coverage.jl
# Take Julia test coverage results and bundle them up in JSONs
# https://github.com/IainNZ/Coverage.jl
#######################################################################
module Coverage
using Requests
using JSON
# process_cov
# Given a .cov file, return the counts for each line, where the
# lines that can't be counted are denoted with a -1
export process_cov
function process_cov(filename)
fp = open(filename, "r")
lines = readlines(fp)
num_lines = length(lines)
coverage = Array(Union(Nothing,Int), num_lines)
for i = 1:num_lines
cov_segment = lines[i][1:9]
coverage[i] = cov_segment[9] == '-' ? nothing : int(cov_segment)
end
close(fp)
return coverage
end
# process_src_coveralls
# Given a .jl file, return the Coveralls.io dictionary for this
# file by reading in the file and its matching .cov. Don't convert
# to JSON yet, just return dictionary.
# https://coveralls.io/docs/api
# {
# "name" : "$filename"
# "source": "...\n....\n...."
# "coverage": [null, 1, null]
# }
export process_src_coveralls
function process_src_coveralls(filename)
return ["name" => filename,
"source" => readall(filename),
"coverage" => process_cov(filename*".cov")]
end
# create_coveralls_post
# Create the request to submit to Coveralls.io (as a dictionary,
# not a JSON string)
# https://coveralls.io/docs/api
# {
# "service_job_id": "1234567890",
# "service_name": "travis-ci",
# "source_files": [
# {
# "name": "example.rb",
# "source": "def four\n 4\nend",
# "coverage": [null, 1, null]
# },
# {
# "name": "lib/two.rb",
# "source": "def seven\n eight\n nine\nend",
# "coverage": [null, 1, 0, null]
# }
# ]
# }
export create_coveralls_travis_post
function create_coveralls_travis_post(source_files)
return ["service_job_id" => ENV["TRAVIS_JOB_ID"],
"service_name" => "travis-ci",
"source_files" => source_files]
end
# submit_coveralls
# Submit coverage to Coveralls.io
export submit_coveralls
function submit_coveralls(data)
println(JSON.json(data))
post("https://coveralls.io/api/v1/jobs"; data = {"json_file" => JSON.json(data)})
end
end | [
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] | 2.345635 | 1,111 |
# This file is a part of Julia. License is MIT: https://julialang.org/license
"""
Some{T}
A wrapper type used in `Union{Some{T}, Nothing}` to distinguish between the absence
of a value ([`nothing`](@ref)) and the presence of a `nothing` value (i.e. `Some(nothing)`).
Use [`something`](@ref) to access the value wrapped by a `Some` object.
"""
struct Some{T}
value::T
end
promote_rule(::Type{Some{T}}, ::Type{Some{S}}) where {T, S<:T} = Some{T}
promote_rule(::Type{Some{T}}, ::Type{Nothing}) where {T} = Union{Some{T}, Nothing}
convert(::Type{Some{T}}, x::Some) where {T} = Some{T}(convert(T, x.value))
convert(::Type{Union{Some{T}, Nothing}}, x::Some) where {T} = convert(Some{T}, x)
convert(::Type{Union{T, Nothing}}, x::Any) where {T} = convert(T, x)
convert(::Type{Nothing}, x::Any) = throw(MethodError(convert, (Nothing, x)))
convert(::Type{Nothing}, x::Nothing) = nothing
function show(io::IO, x::Some)
if get(io, :typeinfo, Any) == typeof(x)
show(io, x.value)
else
print(io, "Some(")
show(io, x.value)
print(io, ')')
end
end
"""
notnothing(x)
Throw an error if `x === nothing`, and return `x` if not.
"""
notnothing(x::Any) = x
notnothing(::Nothing) = throw(ArgumentError("nothing passed to notnothing"))
"""
something(x, y...)
Return the first value in the arguments which is not equal to [`nothing`](@ref),
if any. Otherwise throw an error.
Arguments of type [`Some`](@ref) are unwrapped.
# Examples
```jldoctest
julia> something(nothing, 1)
1
julia> something(Some(1), nothing)
1
julia> something(missing, nothing)
missing
julia> something(nothing, nothing)
ERROR: ArgumentError: No value arguments present
```
"""
function something end
something() = throw(ArgumentError("No value arguments present"))
something(x::Nothing, y...) = something(y...)
something(x::Some, y...) = x.value
something(x::Any, y...) = x
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] | 2.675599 | 709 |
<filename>src/FEMMDeforLinearModule.jl
"""
FEMMDeforLinearModule
Module for operations on interiors of domains to construct system matrices and
system vectors for linear deformation models.
"""
module FEMMDeforLinearModule
using FinEtools.FTypesModule: FInt, FFlt, FCplxFlt, FFltVec, FIntVec, FFltMat, FIntMat, FMat, FVec, FDataDict
import FinEtools.FENodeSetModule: FENodeSet
import FinEtools.FESetModule: FESet, manifdim
import FinEtools.IntegDataModule: IntegData
import FinEtools.FEMMDeforLinearBaseModule: FEMMDeforLinearAbstract
import FinEtools.DeforModelRedModule: DeforModelRed, DeforModelRed2DAxisymm
import FinEtools.MatDeforModule: MatDefor
import FinEtools.CSysModule: CSys
"""
FEMMDeforLinear{S<:FESet, F<:Function, P<:PropertyDeformationLinear}
Class for linear deformation finite element modeling machine.
"""
mutable struct FEMMDeforLinear{MR<:DeforModelRed, S<:FESet, F<:Function, M<:MatDefor} <: FEMMDeforLinearAbstract
mr::Type{MR}
integdata::IntegData{S, F} # geometry data
mcsys::CSys # updater of the material orientation matrix
material::M # material object
end
function FEMMDeforLinear(mr::Type{MR}, integdata::IntegData{S, F}, material::M) where {MR<:DeforModelRed, S<:FESet, F<:Function, M<:MatDefor}
# @show mr
# @show material.mr
@assert mr === material.mr "Model reduction is mismatched"
@assert (integdata.axisymmetric) || (mr != DeforModelRed2DAxisymm) "Axially symmetric requires axisymmetric to be true"
return FEMMDeforLinear(mr, integdata, CSys(manifdim(integdata.fes)), material)
end
function FEMMDeforLinear(mr::Type{MR}, integdata::IntegData{S, F}, material::M, print) where {MR<:DeforModelRed, S<:FESet, F<:Function, M<:MatDefor}
@show mr
@show material.mr
@assert mr === material.mr "Model reduction is mismatched"
@assert (integdata.axisymmetric) || (mr != DeforModelRed2DAxisymm) "Axially symmetric requires axisymmetric to be true"
return FEMMDeforLinear(mr, integdata, CSys(manifdim(integdata.fes)), material)
end
end
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] | 2.695767 | 756 |
<gh_stars>1-10
# # Single-thread `sum_gather`
include("plots.jl");
# ## Throughput, L1, and last-level (LL) cache misses
#
# (Note: the LL cache miss data may not be available in some machines.)
plt_throughput_cache_miss
# ## Tuned `evals`
plt_evals
| [
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] | 2.628866 | 97 |
module CompatHelper
import Base64
import Dates
import GitHub
import HTTP
import Pkg
import Printf
import TOML
import TimeZones
import UUIDs
include("types.jl")
include("main.jl")
include("assert.jl")
include("ci_service.jl")
include("envdict.jl")
include("get_latest_version_from_registries.jl")
include("get_project_deps.jl")
include("git.jl")
include("new_versions.jl")
include("pull_requests.jl")
include("ssh_keys.jl")
include("stdlib.jl")
include("timestamps.jl")
include("utils.jl")
include("version_numbers.jl")
end # module
| [
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] | 2.918478 | 184 |
<gh_stars>1-10
using LightGraphs
import Base.==
input = joinpath(@__DIR__, "input")
raw = readlines(input)
struct Coord
x::Int
y::Int
end
==(left::Coord, right::Coord) = left.x == right.x && left.y == right.y
function adjacent(coord::Coord)
x, y = coord.x, coord.y
return [
Coord(x, y + 1),
Coord(x, y - 1),
Coord(x + 1, y),
Coord(x - 1, y)
]
end
function map_maze!(cs::Array{String,1}, c2t::Dict{Coord,Char})
for j = 1:length(cs)
line = cs[j]
for i = 1:length(line)
t = line[i]
c2t[Coord(i, j)] = t
end
end
end
function isalpha(char::Union{Char,Nothing})
return char !== nothing && char >= 'A' && char <= 'Z'
end
function portals(c2t::Dict{Coord, Char})
potentials = []
for ct in c2t
c, t = ct
if t >= 'A' && t <= 'Z'
push!(potentials, c)
end
end
portals = Array{Pair{String, Coord}, 1}(undef, (0))
for p in potentials
x, y = p.x, p.y
i = get(c2t, Coord(x - 1, y), nothing)
j = c2t[p]
k = get(c2t, Coord(x + 1, y), nothing)
if i == '.' && isalpha(j)
push!(portals, Pair(string(j, k), Coord(x - 1, y)))
elseif isalpha(i) && k == '.'
push!(portals, Pair(string(i, j), Coord(x + 1, y)))
end
end
for p in potentials
x, y = p.x, p.y
i = get(c2t, Coord(x, y - 1), nothing)
j = c2t[p]
k = get(c2t, Coord(x, y + 1), nothing)
if i == '.' && isalpha(j)
push!(portals, Pair(string(j, k), Coord(x, y - 1)))
elseif isalpha(i) && k == '.'
push!(portals, Pair(string(i, j), Coord(x, y + 1)))
end
end
return portals
end
function warp(coord, portals)
label, _ = first(filter(kv -> kv.second.x == coord.x && kv.second.y == coord.y, portals))
for w in filter(kv -> kv.first == label, portals)
if w.second.x != coord.x && w.second.y != coord.y
return w.second
end
end
end
function seen!(coord::Coord, level::Int, cache::Set{Pair{Coord, Int}})
if Pair(coord, level) in cache
return true
else
push!(cache, Pair(coord, level))
return false
end
end
function explore(start::Coord, finish::Coord, level::Int, c2t::Dict{Coord, Char}, portals::Array{Pair{String, Coord}, 1})
nodes = [(start, 0, level)]
seen = Set{Pair{Coord, Int}}()
warps = [w.second for w in portals]
xs = map(kv -> kv.second.x, portals)
ys = map(kv -> kv.second.y, portals)
minx = minimum(xs)
maxx = maximum(xs)
miny = minimum(ys)
maxy = maximum(ys)
while !isempty(nodes)
node = popfirst!(nodes)
coord, steps, level = node
# skip exploring if we've already started from this node
if seen!(coord, level, seen)
continue
else
## found exit, bail
if coord == finish && level in [0, 1]
return steps
## found exit but not at right level
elseif coord == finish
continue
## not a corridor
elseif c2t[coord] != '.'
continue
elseif coord in warps && coord != start
warp_to = warp(coord, portals)
if (coord.x in [minx, maxx] || coord.y in [miny, maxy]) && level < 0
for neighbor in adjacent(warp_to)
push!(nodes, (neighbor, steps + 2, level + 1))
end
elseif (coord.x in [minx, maxx] || coord.y in [miny, maxy]) && level == 0
continue
elseif level <= 0
for neighbor in adjacent(warp_to)
push!(nodes, (neighbor, steps + 2, level - 1))
end
else
for neighbor in adjacent(warp_to)
push!(nodes, (neighbor, steps + 2, level))
end
end
else
for neighbor in adjacent(coord)
push!(nodes, (neighbor, steps + 1, level))
end
end
end
end
end
c2t = Dict{Coord,Char}()
map_maze!(raw, c2t)
ps = portals(c2t)
_, start = first(filter(n -> n.first == "AA", ps))
_, finish = first(filter(n -> n.first == "ZZ", ps))
p1 = explore(start, finish, 1, c2t, ps)
@assert p1 == 580
p2 = explore(start, finish, 0, c2t, ps)
@assert p2 == 6362
print("-----------------------------------------------------------------------\n")
print("donut maze -- part one\n : $p1\n")
print("donut maze -- part two\n : $p2\n")
print("-----------------------------------------------------------------------\n") | [
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] | 1.976725 | 2,406 |
<reponame>UnofficialJuliaMirrorSnapshots/SimpleANOVA.jl-fff527a3-8410-504e-9ca3-60d5e79bb1e4<gh_stars>0
@testset "Display Tests" begin
@testset "AnovaData" begin
data = [AnovaValue( "Total", 1827.6975, 19),
AnovaResult( "A", 70.3125, 1, 70.3125, 3.0706495, 0.098856175),
AnovaResult( "B", 1386.1125, 1, 1386.1125, 60.533556, 7.9430782e-7),
AnovaResult("A × B", 4.9005, 1, 4.9005, 0.21401199, 0.64987001),
AnovaFactor( "C", 366.372, 16, 22.89825)]
result = AnovaData(data, AnovaFactor[], AnovaFactor[], Float64[], 1)
expectedlines = ["",
"Analysis of Variance Results",
"",
"Effect SS DF MS F p",
"-------------------------------------------------------",
" Total 1827.7 19 ",
" A 70.3125 1 70.3125 3.07065 0.0988562 ",
" B 1386.11 1 1386.11 60.5336 7.94308e-7",
" A × B 4.9005 1 4.9005 0.214012 0.64987 ",
" C 366.372 16 22.8983 ",
""]
@test sprint(show, result) == join(expectedlines, "\n")
end
end
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] | 1.59292 | 904 |
<filename>test/runtest.jl
using Logging
io = open("test_log.txt", "w+")
logger = SimpleLogger(io)
with_logger(logger) do
testdirectory = dirname(@__FILE__)
cd(testdirectory)
directories = readdir()
for directory in directories
if isdir(directory)
include(joinpath(directory, "runtest.jl"))
end
cd(testdirectory)
end
end
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] | 2.35 | 160 |
using Comonicon
using Comonicon.Types
using Comonicon.BuildTools: precompile_script, install
using Test
module Dummy
using Comonicon
using Test
"""
foo foo
# Arguments
- `x`: an argument
# Options
- `--foo <foo>`: foo foo
"""
@cast function foo(x::Int, y; foo::Int = 1, hulu::Float64 = 2.0, flag::Bool = false) where {T}
@test x == 1
@test y == "2.0"
@test foo == 2
@test hulu == 3.0
@test flag == true
end
"""
goo goog gooasd dasdas
goo asdas dasd assadas
# Arguments
- `ala`: ala ahjsd asd wvxzj
- `gaga`: djknawd sddasd kw
# Options
- `-g,--giao <name>`: huhuhuhuhuuhu
# Flags
- `-f,--flag`: dadsa fasf gas
"""
@cast function goo(ala::Int, gaga; giao = "Bob", flag::Bool = false)
@test ala == 1
@test gaga == "2.0"
@test giao == "Sam"
@test flag == true
end
"""
tick tick.
# Arguments
- `xx`: xxxxxxxxxxxx
- `yy`: yyyyyyyyyyyy
"""
@cast function tick(xx::Int, yy::Float64 = 1.0)
@test xx == 1
@test yy in [1.0, 2.0]
end
@main name = "dummy" doc = """
dummy command. dasdas dsadasdnaskdas dsadasdnaskdas
sdasdasdasdasdasd adsdasdas dsadasdas dasdasd dasda
"""
end
@test Dummy.command_main(String["foo", "1.0", "2.0", "--foo", "2", "--hulu=3.0", "--flag"]) == 0
@test Dummy.command_main(String["foo", "1.0", "2.0", "--foo", "2", "--hulu=3.0", "-f"]) == 0
@test Dummy.command_main(String["goo", "1.0", "2.0", "-gSam", "-f"]) == 0
@test_throws ErrorException LeafCommand(
identity;
name = "foo",
options = [Option("huhu"; short = true)],
)
@test Dummy.command_main(String["tick", "1.0", "2.0"]) == 0
@test Dummy.command_main(String["tick", "1.0"]) == 0
@test precompile_script(Dummy) == """
using Main.Dummy;
Main.Dummy.command_main(["-h"]);
Main.Dummy.command_main(["goo", "-h"]);
Main.Dummy.command_main(["tick", "-h"]);
Main.Dummy.command_main(["foo", "-h"]);
"""
empty!(ARGS)
append!(ARGS, ["2", "--opt1", "3"])
"""
ArgParse example implemented in Comonicon.
# Arguments
- `x`: an argument
# Options
- `--opt1 <arg>`: an option
- `-o, --opt2 <arg>`: another option
# Flags
- `-f, --flag`: a flag
"""
@main function main(x; opt1 = 1, opt2::Int = 2, flag = false)
@test flag == false
@test x == "2"
@test opt1 == "3"
@test opt2 == 2
end
Comonicon.install(
Dummy;
bin = Comonicon.PATH.project("test", "bin"),
completion = false,
quiet = false,
)
@test isfile(Comonicon.PATH.project("test", "bin", "dummy"))
@test isfile(Comonicon.PATH.project("test", "bin", "dummy.jl"))
@testset "default_name" begin
@test Comonicon.Parse.default_name("Foo.jl") == "foo"
@test Comonicon.Parse.default_name(sin) == "sin"
end
cmd = @cast(f_issue_47(xs::Int...) = xs)
@testset "issue/#47" begin
@test cmd.args[1].type == Int
@test cmd.args[1].vararg == true
end
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] | 2.239775 | 1,247 |
module DVeTest
using Base.Test
using Quantum
xb = basis(:X,[1:5])
d = DiracVector([1:5], xb)
@test d==d''
@test d[:]==d.coeffs
@test d'*d == 55
@test d*d' == DiracMatrix(kron(d.coeffs, d.coeffs'), d.basis, d.basis')
@test isdual(d,d')
@test filterstates(s->label(s)%2==0, d) == DiracVector([2,4], basis(xb[2], xb[4]))
@test filtercoeffs(c->c%2==0, d) == DiracVector([2,4], basis(xb[2], xb[4]))
@test map(x->x*2, d)==d+d
@test xb[1]'*d == 1
@test d'*xb[1] == 1
@test norm(normalize(d)) < 1+1e-8 && norm(normalize(d)) > 1-1e-8
@test reduce(+,[i*xb[i]' for i=1:length(xb)]) == d'
@test (1/sqrt(2)*xb[1]) + (1/sqrt(2)*xb[2]) == dvec([1/sqrt(2), 1/sqrt(2)], basis(xb[1], xb[2]))
@test ket(:X,1)+ket(:X,1)+ket(:X,1) = dvec([3], basis(xb[1]))
@test ket(:X,1)+(bra(:S,"1")*ket(:X, 1)) * ket(:X,1)==dvec([1+1*bra(:S,"1")*ket(:X,1)], basis(ket(:X,1)))
end | [
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198,
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437
] | 1.843478 | 460 |
using LightGraphs
"""
solve
"""
export solve!
function solve!(::LPModel, ::JuMP.Model, ::AbstractSolveAttribute) <: Bool end
# Will construct the constraint graph and find a feasible solution or deem the problem infeasible
# NB: Assumes the model to have difference constraints only!
function solve!(lpmodel::LPModel, model::JuMP.Model, ::ShortestPath)
A, b = get_constraint_matrices(lpmodel)
edges = fill((0,0), size(A)[1]) # Number of constraints
num_vars = size(A)[2]
distmx = zeros(num_vars, num_vars)
# Get list of edges and distance matrix
index = 1
for row in eachrow(A)
i = findall(x->x==-1, row)[1]
j = findall(x->x==1, row)[1]
edge = (i,j)
edges[index] = edge
distmx[i,j] = b[index]
index += 1
end
# Directed graph
digraph = SimpleDiGraph(Edge.(edges))
# Use all vertices as sources, as we omit the starting source
sources = collect(1:num_vars)
try
bf = bellman_ford_shortest_paths(digraph, sources, distmx)
distances = bf.dists
print("\nFeasible solution found:\n")
show(distances)
print("\n")
# Update to feasible unbounded solution
return true
catch error
if isa(error, LightGraphs.NegativeCycleError)
# Update no feasible solution
print("\nInfeasible solution.\n")
return true
else
throw(error)
return false
end
end
end
"""
function solve!(model::Model, ::ConstantObjective)
# Set optimal objective value (if legal)
MOI.set(model, MOI.ObjectiveValue(), objective_function(model, objective_function_type(model)).constant)
# Check if there is a legal solution for the variables, and set result status accordingly
for (F, S) in list_of_constraint_types(model)
for cref in all_constraints(model, F, S)
setVariable(model, cref, F, S)
end
end
end
function setVariable(model::Model, ::Any, ::VariableRef, ::Union{})
MOI.set(model, MOI.PrimalStatus(), MOI.INFEASIBLE_POINT)
MOI.set(model, MOI.PrimalStatus(), MOI.FEASIBLE_POINT)
end
""" | [
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# %%
# <NAME>
# Code based off Emmanuel's Code
# cd("/Volumes/SSD Hans/Github/MacroFall2020/AdvMacro/Assignment8")
# mkpath("Figures")
# Pkg.add("Plots")
# Pkg.add.(["Optim" ,"Roots","Parameters","Distributions","QuadGK"])
# Pkg.add.(["Latexify","PrettyTables","StatsPlots"])
using Plots
# using LateXStrings # Pkg.add("LaTeXStrings") # https://github.com/stevengj/LaTeXStrings.jl
# Pkg.add("Dierckx")
using Dierckx # Pkg.add("Dierckx") # https://github.com/kbarbary/Dierckx.jl
# Pkg.add("Interpolations")
using Interpolations # Pkg.add("Interpolations") # https://github.com/JuliaMath/Interpolations.jl
# Pkg.add("ForwardDiff")
using ForwardDiff # Pkg.add("ForwardDiff") # https://github.com/JuliaDiff/ForwardDiff.jl
using Optim # Pkg.add("Optim") # https://julianlsolvers.github.io/Optim.jl/stable/
using Optim: converged, maximum, maximizer, minimizer, iterations
using Roots # Pkg.add("Roots") # https://github.com/JuliaMath/Roots.jl
using Parameters # Pkg.add("Parameters") # https://github.com/mauro3/Parameters.jl
using Distributions #Pkg.add("Distributions")
using QuadGK # Pkg.add("QuadGK") # https://juliamath.github.io/QuadGK.jl/latest/
using LinearAlgebra
using Random
using Statistics
using Latexify
# Pkg.add("StatsPlots")
using StatsPlots
# Call Scaled Interpolation Functions
include("../Scaled_Interpolation_Functions.jl")
# Pkg.add("SparseArrays")
using SparseArrays
# mkpath("Figures")
#
# using Random, Distributions
# using Statistics
# using LinearAlgebra
# using Plots
# using Interpolations
# using Dierckx
# using ForwardDiff
# using Optim
# using Optim: converged, maximum, maximizer, minimizer, iterations
# using Roots
# using Parameters
# include("Scaled_Interpolation_Functions.jl")
# # using .VFI_Toolbox
# println(" ")
# println("------------------------")
# println("Hugget (1993) in Julia")
# println("PWD: ",pwd())
# println("Solve Hugget's model with EGM and several implementations of histogram method")
# println("------------------------")
# println(" ")
#-----------------------------------------------------------
#-----------------------------------------------------------
# Paramters and Model Structure
# Generate structure for parameters using Parameters module
# We can set default values for our parameters
@with_kw struct Par
# Model Parameters
α::Float64 = 1/3 ; # Production function
β::Float64 = 0.96 ; # Discount factor
γ::Float64 = 2.0 ; # Relative risk aversion parameter
δ::Float64 = 0.05 ; # Depreciation rate
ρ::Float64 = 0.90 ; # Persistence of labor efficiency process
σ::Float64 = 0.08 ; # Standard devaiation of labor efficiency innovation
z_bar::Float64 = 1; # Reference level for productivity
# VFI Paramters
max_iter::Int64 = 100000; # Maximum number of iterations
dist_tol::Float64 = 1E-6 ; # Tolerance for distance
# Histogram iteration parameters
Hist_max_iter = 10000 ;
Hist_tol = 1E-5 ;
# Histogram iteration parameters
N_eq = 1000 ;
tol_eq = 1E-6 ;
# Minimum consumption for numerical optimization
c_min::Float64 = 1E-16
end
# Allocate paramters to object p for future calling
p = Par()
# Function to distretize AR(1) markov process with Rouwenhorst (1995)
function Rouwenhorst95(N,p::Par)
@unpack ρ,σ=p
# INPUTS:
# ρ: persistence of unerlying AR(1) process where log(z') = ρlog(z)+η
# σ_z: Std dev of inovation η in AR(1) process where η∼N(0,σ^2)
# N: Size of grid for discrete process
# OUTPUT:
# z: All possible values of discretized AR(1) process, equally spaced grid of size N
# Π: Matrix of transition probabilities
# PDF_z: Stationary PDF of z
#---------------------------------------------------------------------------
Π = zeros(N,N)
Π_Nm = zeros(N-1,N-1)
P = (1+ρ)/2
ϕ = σ*(sqrt((N-1)/(1-ρ^2)))
z = range(-ϕ,ϕ;length=N)
if N==2
Π = [P 1-P;1-P P]
else
Π_Nm = Rouwenhorst95(N-1,p)[2]
o = zeros(N-1)
Π = P*[Π_Nm o; o' 0] + (1-P)*[o Π_Nm; 0 o'] + (1-P)*[o' 0; Π_Nm o] + P*[0 o';o Π_Nm]
Π = Π./repeat(sum(Π,dims=2),1,N)
end
PDF_z = pdf.(Binomial(N-1,0.5),(0:N-1))
return (z,Π,P)
end
# Function to get grid on assets
function Make_A_Grid(n_a,θ_a,a_min,a_max,p::Par)
# Get k_grid
if θ_a≠1
a_grid = PolyRange(a_min,a_max;θ=θ_a,N=n_a)
else
a_grid = range(a_min,a_max,length=n_a)
end
# Return
return a_grid
end
# %%
# Generate structure of model objects
@with_kw struct Model
# Parameters
p::Par = Par() # Model paramters in their own structure
# Parameters for Asset Grid
a_max::Float64 = 50 # Default max node of a_grid
θ_a::Float64 = 2.5 # Default Curvature of a_grid
n_a::Int64 = 200 # Default Size of a_grid
n_a_fine::Int64 = 1000 # Default Size of fine grid for interpolation and distribution
# Productivity process
n_ϵ::Int64 = 11 # Default size of discretized grid for productivity as a markov process
log_ϵ = Rouwenhorst95(n_ϵ,p)[1]
Π = Rouwenhorst95(n_ϵ,p)[2]
ϵ_grid = exp.(log_ϵ)
# Prices and aggregates
r::Float64 = 0.05 # Initial guess for rate of return on assets
# Lower bound on Asset Grid (borrowing limit)
a_min = -ϵ_grid[1]/r
# Capital grid
a_grid = Make_A_Grid(n_a,θ_a,a_min,a_max,p) # Grid on assets for model solution
a_grid_fine = Make_A_Grid(n_a_fine,1,a_min,a_max,p) # Fine grid on assets for interpolation
# State matrices
a_mat = repeat(a_grid',n_ϵ,1)
a_mat_fine= repeat(a_grid_fine',n_ϵ,1)
ϵ_mat = repeat(ϵ_grid,1,n_a)
# Value and policy functions
V = Array{Float64}(undef,n_ϵ,n_a) # Value Function
G_ap = Array{Float64}(undef,n_ϵ,n_a) # Policy Function
G_c = Array{Float64}(undef,n_ϵ,n_a) # Policy Function
V_fine = Array{Float64}(undef,n_ϵ,n_a_fine) # Value Function on fine grid
G_ap_fine = Array{Float64}(undef,n_ϵ,n_a_fine) # Policy Function on fine grid
G_c_fine = Array{Float64}(undef,n_ϵ,n_a_fine) # Policy Function on fine grid
# Distribution
Γ = 1/(n_ϵ*n_a_fine)*ones(n_ϵ,n_a_fine) # Distribution (initiliazed to uniform)
# Error in Euler equation
Euler = Array{Float64}(undef,n_ϵ,n_a_fine) # Errors in Euler equation
end
# Allocate model to object M for future calling
M = Model()
# Utility function
function utility(c,p::Par)
if p.γ>1
return (c).^(1-p.γ)/(1-p.γ)
else
return log.(c)
end
end
function d_utility(c,p::Par)
return (c).^(-p.γ)
end
function d_utility_inv(x,p::Par)
return x.^(-1/p.γ)
end
# Get index for histogram method
function get_ind(a_grid,g_a)
n = length(a_grid)
if g_a > maximum(a_grid)
return n
elseif g_a < minimum(a_grid)
return 1
else
return floor(((g_a-minimum(a_grid))/(maximum(a_grid)-minimum(a_grid)))*(n-1)+1)
end
end
# %%
# PFI Fixed Point - Iterate over policy functions for a given guess of the interest rate r
function PFI_Fixed_Point(T::Function,M::Model,G_ap_old=nothing)
# Unpack model structure
@unpack p, n_ϵ, n_a, n_a_fine, θ_a, a_grid, a_grid_fine, r = M
# PFI paramters
@unpack max_iter, dist_tol = p
# Initialize variables for loop
if G_ap_old==nothing
G_ap_old = (1+r)*M.a_mat
end
G_dist = 1 ; # Initialize distance
println(" ")
println("------------------------")
println("PFI - n_ϵ=$n_ϵ, n_a=$n_a - θ_a=$θ_a - r=$r")
for iter=1:max_iter
# Update value function
G_ap_new, G_c = T(Model(M,G_ap=copy(G_ap_old)))
# Update distance and iterations
G_dist = sqrt(norm(G_ap_new-G_ap_old,2))
# Update old function
G_ap_old = G_ap_new
# Report progress every 250 iterations
if mod(iter,250)==0
println(" PFI Loop: iter=$iter, dist=",G_dist)
end
# Check convergence and return results
if G_dist<=dist_tol
println("PFI - n_ϵ=$n_ϵ, n_a=$n_a - θ_a=$θ_a - r=$r")
println("Iterations = $iter and Distance = ",G_dist)
println("------------------------")
println(" ")
# Interpolate to fine grid
G_ap_fine = zeros(n_ϵ,n_a_fine)
G_c_fine = zeros(n_ϵ,n_a_fine)
for i_ϵ=1:n_ϵ
G_ap_ip = ScaledInterpolations(a_grid,G_ap_new[i_ϵ,:] , BSpline(Cubic(Line(OnGrid()))))
G_ap_fine[i_ϵ,:].= G_ap_ip.(collect(a_grid_fine))
G_c_ip = ScaledInterpolations(a_grid,G_c[i_ϵ,:] , BSpline(Cubic(Line(OnGrid()))))
G_c_fine[i_ϵ,:] .= G_c_ip.(collect(a_grid_fine))
end
# Update model
M = Model(M; G_ap=G_ap_new,G_c=G_c,G_ap_fine=G_ap_fine,G_c_fine=G_c_fine)
# Return results
return M
end
end
# If loop ends there was no convergence -> Error!
error("Error in PFI - Solution not found")
end
# Bellman operator - EGM - Iterate on Policy Functions
function T_EGM_G(M::Model)
@unpack p, n_ϵ, n_a, G_ap, r, a_min, Π = M
@unpack β = p
# Define RHS of Euler equation for each (ϵ,a')
# Rows are present ϵ and columns are tomorrow's a in fixed grid
Euler_RHS = β*(1+r)*Π*d_utility( (1+r)*M.a_mat + M.ϵ_mat - G_ap , p )
# Check Monotonicity
if any( Euler_RHS.<0 )
error("RHS must be monotone for EGM to work")
end
# Define consumption from Euler equation
C_endo = max.(d_utility_inv(Euler_RHS,p),p.c_min)
# Define endogenous grid on assets
A_endo = (C_endo .+ M.a_mat - M.ϵ_mat)/(1+r)
# Interpolate functions on exogenous grid
G_c = Array{Float64}(undef,n_ϵ,n_a)
for i_ϵ=1:n_ϵ
# Sort A_endo for interpolation
sort_ind = sortperm(A_endo[i_ϵ,:])
A_aux = A_endo[i_ϵ,:][sort_ind]
C_aux = C_endo[i_ϵ,:][sort_ind]
# Check boundary condition
# Ap(ϵ,a)=a_min for all a<min(A_aux)
# Note that in that case C is linear between a_min and min(A_aux)
if minimum(A_aux)>a_min
a_vec = M.a_grid[M.a_grid.<minimum(A_aux)] # All assets today between a_min and min(A_aux)
A_aux = [a_vec ; A_aux] # Append to existing grid of today's assets
C_aux = [((1+r)*a_vec.+M.ϵ_grid[i_ϵ].-a_min) ; C_aux] # I can know consumption directly because all those assets imply a'=a_min
end
C_ip = Spline1D(A_aux,C_aux;k=1)
G_c[i_ϵ,:] .= C_ip.(M.a_grid) # interpolate consumption to exogenous grid of assets today
#Ap_aux = (1+r)*collect(M.a_grid) .+ M.ϵ_grid[i_ϵ] .- G_c[i_ϵ,:] # Find corresponding assets tomorrow
end
# Update policy function
G_ap .= (1+r)*M.a_mat .+ M.ϵ_mat .- G_c
# Adjust for numerical error
for ind = findall(<=(1e-10),abs.(G_ap.-a_min))
G_ap[ind] = a_min
G_c[ind] = (1+r)*M.a_mat[ind] + M.ϵ_mat[ind] - a_min
end
# Check for borrowing constraint
if any( G_ap.<a_min )
error("Borrowing Constraint Violated")
end
# Return Results
return G_ap, G_c
end
# Value function (given policy function)
function Value_Function(M::Model)
# Unpack model structure
@unpack p, n_ϵ, n_a, n_a_fine, a_grid, a_grid_fine, r, G_ap, G_c, Π = M
@unpack β, dist_tol = p
# Compute value function with policy function iteration
V = zeros(n_ϵ,n_a)
V_new = zeros(n_ϵ,n_a)
V_dist = 1
U_mat = utility(G_c,p)
while V_dist>dist_tol
for i_ϵ=1:n_ϵ
Pr = Π[i_ϵ,:]'
for i_a=1:n_a
ap = G_ap[i_ϵ,i_a]
Vp = zeros(n_ϵ)
for i_ϵp=1:n_ϵ
Vp_ip = ScaledInterpolations(a_grid,V[i_ϵp,:], BSpline(Cubic(Line(OnGrid()))))
Vp[i_ϵp] = Vp_ip(ap)
end
V_new[i_ϵ,i_a] = U_mat[i_ϵ,i_a] + β*(Pr*Vp)
end
end
V_dist = maximum(abs.(V_new./V.-1))
V = V_new
end
# Interpolate to fine grid
V_fine = zeros(n_ϵ,n_a_fine)
for i_ϵ=1:n_ϵ
V_ip = ScaledInterpolations(a_grid,V[i_ϵ,:], BSpline(Cubic(Line(OnGrid()))))
V_fine[i_ϵ,:] .= V_ip.(collect(a_grid_fine))
end
# Update model
M = Model(M; V=V,V_fine=V_fine)
return M
end
# %%
# Compute stationary distribution with Histogram method
# Histogram method
function Histogram_Method_Loop(M::Model,N_H=nothing,Γ_0=nothing)
@unpack a_min, p, n_ϵ, Π, n_a_fine, a_grid_fine, G_ap_fine = M
@unpack Hist_max_iter, Hist_tol = p
println("\n--------------------------------\nBegining Histogram Method with Loops")
# Change max iter
if N_H==nothing
N_H = Hist_max_iter
end
# Initial distribution (uniorm in income shock and assets)
if Γ_0==nothing
Γ_0 = M.Γ
end
# Discretize distribution
H_ind = Array{Int64}(undef,n_ϵ,n_a_fine)
H_weight = Array{Float64}(undef,n_ϵ,n_a_fine)
a_max = maximum(a_grid_fine)
for i_ϵ=1:n_ϵ
for i_a=1:n_a_fine
H_ind[i_ϵ,i_a] = get_ind(a_grid_fine,G_ap_fine[i_ϵ,i_a])
# weight on node corresponding to H_ind
if H_ind[i_ϵ,i_a] == n_a_fine || H_ind[i_ϵ,i_a] == 1 # Mass points above max or below min in the fine grid
H_weight[i_ϵ,i_a] = 1
else
H_weight[i_ϵ,i_a] = 1-(G_ap_fine[i_ϵ,i_a]-a_grid_fine[H_ind[i_ϵ,i_a]])/(a_grid_fine[H_ind[i_ϵ,i_a]+1]-a_grid_fine[H_ind[i_ϵ,i_a]])
end
end
end
#Γ = zeros(n_ϵ,n_a_fine)
## Loop for updating histogram
#H_dist = 1
#for l = 1:N_H
# for i = 1:n_ϵ
# for j = 1:n_a_fine
# # all combinations of (ϵ,a) today that may lead to a_j tomorrow
# index = findall(x->x==j,H_ind)
# index_ = findall(x->x==j,H_ind.+1)
# # from the above, take only ϵ for the income probability transition
# transindex = map(i->i[1], index)
# transindex_ = map(i->i[1], index_)
# # Update the distribution
# Γ[i,j] = Π[i,transindex]'*(H_weight[index].*Γ_0[index])+Π[i,transindex_]'*((-H_weight[index_].+1).*Γ_0[index_])
# end
# end
# # Update distance
# H_dist = maximum(abs.(Γ-Γ_0))
# # Update initial distribution
# Γ_0 .= Γ
# # Report progress
# println(" Histogram Loop: iter=$l, dist=$H_dist")
# #if mod(l,250)==0
# # println(" Histogram Loop: iter=$l, dist=$H_dist")
# #end
# # Check convergence
# if H_dist<Hist_tol
# println("Histogram iteartion converged in iteration $i_H. H_dist=$H_dist\n--------------------------------\n")
# M = Model(M; Γ=Γ)
# return M
# end
#end
Γ = zeros(n_ϵ,n_a_fine)
# Loop for updating histogram
H_dist = 1
for i_H=1:N_H
# Update histogram
Γ = zeros(n_ϵ,n_a_fine)
for i_ϵ=1:n_ϵ # Current ϵ
for i_a=1:n_a_fine # Current a
i_ap = H_ind[i_ϵ,i_a]
ω_ap = H_weight[i_ϵ,i_a]
for i_ϵp=1:n_ϵ # Future ϵ
Γ[i_ϵp,i_ap] = Γ[i_ϵp,i_ap] + ω_ap *Π[i_ϵ,i_ϵp]*Γ_0[i_ϵ,i_a]
if i_ap < n_a_fine
Γ[i_ϵp,i_ap+1] = Γ[i_ϵp,i_ap+1] + (1-ω_ap)*Π[i_ϵ,i_ϵp]*Γ_0[i_ϵ,i_a]
end
end
end
end
# Update distance
H_dist = maximum(abs.(Γ-Γ_0))
# Update initial distribution
Γ_0 .= Γ
# Report progress
if mod(i_H,50)==0
println(" Histogram Loop: iter=$i_H, dist=$H_dist")
end
# Check convergence
if H_dist<Hist_tol
println("Histogram iteartion converged in iteration $i_H. H_dist=$H_dist\n--------------------------------\n")
M = Model(M; Γ=Γ)
return M
end
end
# Return Results
println("Histogram updated for $N_H iteartions. Current H_dist=$H_dist \n--------------------------------\n")
M = Model(M; Γ=Γ_0)
return M
end
# %%
# Do optimization then check market clearing
function market_clearing(r,M::Model)
# Iterate over policy function given r
M = PFI_Fixed_Point(T_EGM_G,Model(r=r))
# Get value function
M = Value_Function(M)
# Find stationary distribution
M = Histogram_Method_Loop(M)
# Check for market clearing
distance = sum(sum(M.G_ap_fine.*M.Γ,dims=2)).^2
println("Squared distance from market clearing=$distance at intertest rate = $r")
return distance
end
# Outer loop to find interest rate that clears market
function S_RCE(M::Model)
@unpack p = M
@unpack β = p
# Maximum interest rate
r_max = 1/β-1
# Minimum interest rate
r_min = 0
# Find equilibrium interest rate
min_result = optimize(x->market_clearing(x,M).^2,r_min,r_max)
# Check result
converged(min_result) || error("Failed to clear asset market in $(iterations(min_result)) iterations")
# Upddate policy function
r = min_result.minimizer
println("Equilibrium found in $(iterations(min_result)) iterations: r=$r")
# Load equilibrium interest rate into model
M = Model(M; r=r)
# Compute policy function, value function and stationary distribution
M = PFI_Fixed_Point(T_EGM_G,M)
M = Value_Function(M)
M = Histogram_Method_Loop(M)
return M
end
# %%
# Function that makes 3-d plots of the value function, policy function and asset distributon
function plot_hugget(M::Model)
# Interpolate the value function, policy function and asset distribution along the income shock dimension
ϵ_grid_fine = range(M.ϵ_grid[1],M.ϵ_grid[end],length=M.n_a_fine)
V_fine3d = zeros(M.n_a_fine,M.n_a_fine)
G_ap_fine3d = zeros(M.n_a_fine,M.n_a_fine)
Γ_fine3d = zeros(M.n_a_fine,M.n_a_fine)
for i in 1:M.n_a_fine
Γ_fine3d[:,i] = Spline1D(M.ϵ_grid,M.Γ[:,i];k=1).(collect(ϵ_grid_fine))
V_fine3d[:,i] = Spline1D(M.ϵ_grid,M.V_fine[:,i];k=1).(collect(ϵ_grid_fine))
G_ap_fine3d[:,i] = Spline1D(M.ϵ_grid,M.G_ap_fine[:,i];k=1).(collect(ϵ_grid_fine))
end
# Normalize distribution such that it sums to one (need because I interpolated)
Γ_fine3d = Γ_fine3d./(sum(sum(Γ_fine3d)))
# Plots of the value function, policy function and asset distributon
gr()
plot(ϵ_grid_fine,M.a_grid_fine,V_fine3d, st=:contour,xlabel="Income",ylabel="Assets",title="Value Function - Hugget") # Surface plot
savefig("./Figures/surface_vf_hugget")
plot(ϵ_grid_fine,M.a_grid_fine,G_ap_fine3d, st=:contour,xlabel="Income",ylabel="assets",title="Asset policy - Hugget") # Surface plot
savefig("./Figures/surface_policy_hugget")
plot(ϵ_grid_fine,M.a_grid_fine,Γ_fine3d, st=:surface,xlabel="Income",ylabel="Assets",title="Distribution - Hugget") # Surface plot
savefig("./Figures/surface_dist_hugget")
end
# %%
# Call PFI
M_hugget = S_RCE(Model(n_a=100,n_ϵ=11))
# Get plots
plot_hugget(M_hugget)
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220,
220,
220,
1303,
220,
220,
220,
44872,
7203,
220,
220,
5590,
21857,
26304,
25,
11629,
43641,
75,
11,
1233,
43641,
39,
62,
17080,
4943,
198,
220,
220,
220,
1303,
220,
220,
220,
1303,
437,
198,
220,
220,
220,
1303,
220,
220,
220,
1303,
6822,
40826,
198,
220,
220,
220,
1303,
220,
220,
220,
611,
367,
62,
17080,
27,
13749,
62,
83,
349,
198,
220,
220,
220,
1303,
220,
220,
220,
220,
220,
220,
220,
44872,
7203,
13749,
21857,
340,
68,
433,
295,
6718,
2004,
287,
24415,
720,
72,
62,
39,
13,
367,
62,
17080,
43641,
39,
62,
17080,
59,
77,
3880,
59,
77,
4943,
198,
220,
220,
220,
1303,
220,
220,
220,
220,
220,
220,
220,
337,
796,
9104,
7,
44,
26,
7377,
241,
28,
138,
241,
8,
198,
220,
220,
220,
1303,
220,
220,
220,
220,
220,
220,
220,
1441,
337,
198,
220,
220,
220,
1303,
220,
220,
220,
886,
198,
220,
220,
220,
1303,
437,
628,
220,
220,
220,
7377,
241,
796,
1976,
27498,
7,
77,
62,
139,
113,
11,
77,
62,
64,
62,
38125,
8,
198,
220,
220,
220,
1303,
26304,
329,
19698,
1554,
21857,
198,
220,
220,
220,
367,
62,
17080,
796,
352,
198,
220,
220,
220,
329,
1312,
62,
39,
28,
16,
25,
45,
62,
39,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
10133,
1554,
21857,
198,
220,
220,
220,
220,
220,
220,
220,
7377,
241,
796,
1976,
27498,
7,
77,
62,
139,
113,
11,
77,
62,
64,
62,
38125,
8,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1312,
62,
139,
113,
28,
16,
25,
77,
62,
139,
113,
1303,
9236,
18074,
113,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1312,
62,
64,
28,
16,
25,
77,
62,
64,
62,
38125,
1303,
9236,
257,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1312,
62,
499,
796,
367,
62,
521,
58,
72,
62,
139,
113,
11,
72,
62,
64,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
18074,
231,
62,
499,
796,
367,
62,
6551,
58,
72,
62,
139,
113,
11,
72,
62,
64,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1312,
62,
139,
113,
79,
28,
16,
25,
77,
62,
139,
113,
1303,
10898,
18074,
113,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7377,
241,
58,
72,
62,
139,
113,
79,
11,
72,
62,
499,
60,
220,
220,
796,
7377,
241,
58,
72,
62,
139,
113,
79,
11,
72,
62,
499,
60,
220,
220,
1343,
220,
220,
220,
18074,
231,
62,
499,
1635,
138,
254,
58,
72,
62,
139,
113,
11,
72,
62,
139,
113,
79,
60,
9,
138,
241,
62,
15,
58,
72,
62,
139,
113,
11,
72,
62,
64,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1312,
62,
499,
1279,
299,
62,
64,
62,
38125,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7377,
241,
58,
72,
62,
139,
113,
79,
11,
72,
62,
499,
10,
16,
60,
796,
7377,
241,
58,
72,
62,
139,
113,
79,
11,
72,
62,
499,
10,
16,
60,
1343,
357,
16,
12,
49535,
62,
499,
27493,
138,
254,
58,
72,
62,
139,
113,
11,
72,
62,
139,
113,
79,
60,
9,
138,
241,
62,
15,
58,
72,
62,
139,
113,
11,
72,
62,
64,
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,
886,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
10133,
5253,
198,
220,
220,
220,
220,
220,
220,
220,
367,
62,
17080,
796,
5415,
7,
8937,
12195,
138,
241,
12,
138,
241,
62,
15,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
10133,
4238,
6082,
198,
220,
220,
220,
220,
220,
220,
220,
7377,
241,
62,
15,
764,
28,
7377,
241,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
6358,
4371,
198,
220,
220,
220,
220,
220,
220,
220,
611,
953,
7,
72,
62,
39,
11,
1120,
8,
855,
15,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
44872,
7203,
220,
220,
5590,
21857,
26304,
25,
11629,
43641,
72,
62,
39,
11,
1233,
43641,
39,
62,
17080,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
6822,
40826,
198,
220,
220,
220,
220,
220,
220,
220,
611,
367,
62,
17080,
27,
13749,
62,
83,
349,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
44872,
7203,
13749,
21857,
340,
68,
433,
295,
6718,
2004,
287,
24415,
720,
72,
62,
39,
13,
367,
62,
17080,
43641,
39,
62,
17080,
59,
77,
3880,
59,
77,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
337,
796,
9104,
7,
44,
26,
7377,
241,
28,
138,
241,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
337,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
628,
220,
220,
220,
1303,
8229,
15691,
198,
220,
220,
220,
44872,
7203,
13749,
21857,
6153,
329,
720,
45,
62,
39,
340,
68,
433,
507,
13,
9236,
367,
62,
17080,
43641,
39,
62,
17080,
3467,
77,
3880,
59,
77,
4943,
198,
220,
220,
220,
337,
796,
9104,
7,
44,
26,
7377,
241,
28,
138,
241,
62,
15,
8,
198,
220,
220,
220,
1441,
337,
198,
437,
198,
198,
2,
43313,
198,
198,
2,
2141,
23989,
788,
2198,
1910,
17304,
198,
8818,
1910,
62,
2375,
1723,
7,
81,
11,
44,
3712,
17633,
8,
198,
220,
220,
220,
1303,
1849,
29993,
378,
625,
2450,
2163,
1813,
374,
198,
220,
220,
220,
337,
796,
350,
11674,
62,
13715,
62,
12727,
7,
51,
62,
7156,
44,
62,
38,
11,
17633,
7,
81,
28,
81,
4008,
198,
220,
220,
220,
1303,
3497,
1988,
2163,
198,
220,
220,
220,
337,
796,
11052,
62,
22203,
7,
44,
8,
198,
220,
220,
220,
1303,
9938,
31607,
6082,
198,
220,
220,
220,
337,
796,
5590,
21857,
62,
17410,
62,
39516,
7,
44,
8,
198,
220,
220,
220,
1303,
6822,
329,
1910,
17304,
198,
220,
220,
220,
5253,
796,
2160,
7,
16345,
7,
44,
13,
38,
62,
499,
62,
38125,
15885,
44,
13,
138,
241,
11,
67,
12078,
28,
17,
29720,
61,
17,
198,
220,
220,
220,
44872,
7203,
22266,
1144,
5253,
422,
1910,
17304,
43641,
30246,
379,
987,
9288,
2494,
796,
720,
81,
4943,
198,
220,
220,
220,
1441,
5253,
198,
437,
198,
198,
2,
35868,
9052,
284,
1064,
1393,
2494,
326,
37526,
1910,
198,
8818,
311,
62,
49,
5222,
7,
44,
3712,
17633,
8,
198,
220,
220,
220,
2488,
403,
8002,
279,
796,
337,
198,
220,
220,
220,
2488,
403,
8002,
27169,
796,
279,
198,
220,
220,
220,
1303,
1849,
40541,
1393,
2494,
198,
220,
220,
220,
374,
62,
9806,
796,
352,
14,
26638,
12,
16,
198,
220,
220,
220,
1303,
26265,
1393,
2494,
198,
220,
220,
220,
374,
62,
1084,
796,
657,
198,
220,
220,
220,
1303,
9938,
29163,
1393,
2494,
198,
220,
220,
220,
949,
62,
20274,
796,
27183,
7,
87,
3784,
10728,
62,
2375,
1723,
7,
87,
11,
44,
737,
61,
17,
11,
81,
62,
1084,
11,
81,
62,
9806,
8,
198,
220,
220,
220,
1303,
1849,
9787,
1255,
198,
220,
220,
220,
6718,
2004,
7,
1084,
62,
20274,
8,
8614,
4049,
7203,
37,
6255,
284,
1598,
11171,
1910,
287,
29568,
2676,
602,
7,
1084,
62,
20274,
4008,
34820,
4943,
198,
220,
220,
220,
1303,
1849,
4933,
1860,
378,
2450,
2163,
198,
220,
220,
220,
374,
220,
796,
949,
62,
20274,
13,
1084,
320,
7509,
198,
220,
220,
220,
44872,
7203,
23588,
24741,
1043,
287,
29568,
2676,
602,
7,
1084,
62,
20274,
4008,
34820,
25,
374,
43641,
81,
4943,
198,
220,
220,
220,
1303,
8778,
29163,
1393,
2494,
656,
2746,
198,
220,
220,
220,
337,
796,
9104,
7,
44,
26,
374,
28,
81,
8,
198,
220,
220,
220,
1303,
3082,
1133,
2450,
2163,
11,
1988,
2163,
290,
31607,
6082,
198,
220,
220,
220,
337,
796,
350,
11674,
62,
13715,
62,
12727,
7,
51,
62,
7156,
44,
62,
38,
11,
44,
8,
198,
220,
220,
220,
337,
796,
11052,
62,
22203,
7,
44,
8,
198,
220,
220,
220,
337,
796,
5590,
21857,
62,
17410,
62,
39516,
7,
44,
8,
198,
220,
220,
220,
1441,
337,
198,
437,
198,
198,
2,
43313,
198,
198,
2,
15553,
326,
1838,
513,
12,
67,
21528,
286,
262,
1988,
2163,
11,
2450,
2163,
290,
11171,
11309,
261,
198,
8818,
7110,
62,
71,
1018,
1136,
7,
44,
3712,
17633,
8,
198,
220,
220,
220,
1303,
4225,
16104,
378,
262,
1988,
2163,
11,
2450,
2163,
290,
11171,
6082,
1863,
262,
3739,
6380,
15793,
198,
220,
220,
220,
18074,
113,
62,
25928,
62,
38125,
796,
2837,
7,
44,
13,
139,
113,
62,
25928,
58,
16,
4357,
44,
13,
139,
113,
62,
25928,
58,
437,
4357,
13664,
28,
44,
13,
77,
62,
64,
62,
38125,
8,
198,
220,
220,
220,
569,
62,
38125,
18,
67,
796,
1976,
27498,
7,
44,
13,
77,
62,
64,
62,
38125,
11,
44,
13,
77,
62,
64,
62,
38125,
8,
198,
220,
220,
220,
402,
62,
499,
62,
38125,
18,
67,
796,
1976,
27498,
7,
44,
13,
77,
62,
64,
62,
38125,
11,
44,
13,
77,
62,
64,
62,
38125,
8,
198,
220,
220,
220,
7377,
241,
62,
38125,
18,
67,
796,
1976,
27498,
7,
44,
13,
77,
62,
64,
62,
38125,
11,
44,
13,
77,
62,
64,
62,
38125,
8,
198,
220,
220,
220,
329,
1312,
287,
352,
25,
44,
13,
77,
62,
64,
62,
38125,
198,
220,
220,
220,
220,
220,
220,
220,
7377,
241,
62,
38125,
18,
67,
58,
45299,
72,
60,
796,
13341,
500,
16,
35,
7,
44,
13,
139,
113,
62,
25928,
11,
44,
13,
138,
241,
58,
45299,
72,
11208,
74,
28,
16,
737,
7,
33327,
7,
139,
113,
62,
25928,
62,
38125,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
569,
62,
38125,
18,
67,
58,
45299,
72,
60,
796,
13341,
500,
16,
35,
7,
44,
13,
139,
113,
62,
25928,
11,
44,
13,
53,
62,
38125,
58,
45299,
72,
11208,
74,
28,
16,
737,
7,
33327,
7,
139,
113,
62,
25928,
62,
38125,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
402,
62,
499,
62,
38125,
18,
67,
58,
45299,
72,
60,
796,
13341,
500,
16,
35,
7,
44,
13,
139,
113,
62,
25928,
11,
44,
13,
38,
62,
499,
62,
38125,
58,
45299,
72,
11208,
74,
28,
16,
737,
7,
33327,
7,
139,
113,
62,
25928,
62,
38125,
4008,
198,
220,
220,
220,
886,
198,
220,
220,
220,
1303,
14435,
1096,
6082,
884,
326,
340,
21784,
284,
530,
357,
31227,
780,
314,
39555,
515,
8,
198,
220,
220,
220,
7377,
241,
62,
38125,
18,
67,
796,
7377,
241,
62,
38125,
18,
67,
19571,
7,
16345,
7,
16345,
7,
138,
241,
62,
38125,
18,
67,
22305,
198,
220,
220,
220,
1303,
1345,
1747,
286,
262,
1988,
2163,
11,
2450,
2163,
290,
11171,
11309,
261,
198,
220,
220,
220,
1036,
3419,
198,
220,
220,
220,
7110,
7,
139,
113,
62,
25928,
62,
38125,
11,
44,
13,
64,
62,
25928,
62,
38125,
11,
53,
62,
38125,
18,
67,
11,
336,
28,
25,
3642,
454,
11,
87,
18242,
2625,
818,
2958,
1600,
2645,
9608,
2625,
8021,
1039,
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] | 2.035016 | 9,567 |
struct HessianNull <: Hessian end
struct HessianD21{H1,H2} <: Hessian
h1::H1
h2::H2
ref::MyRef{Float64}
ref1::MyRef{Float64}
ref2::MyRef{Float64}
end
struct Hessian11{F,H1,H11} <: Hessian
h1::H1
h11::H11
fref::MyRef{Float64}
ref::MyRef{Float64}
function Hessian11(e::Expression1{F,E},d, indexer = nothing) where {F,E}
h1 = Hessian(e.e1,d.d1,indexer)
h11= Hessian(d.d1,d.d1,indexer)
return new{F,typeof(h1),typeof(h11)}(h1,h11,ref(e.e1),ref(d))
end
end
struct Hessian11F1{F,H1,H11,R} <: Hessian
a::R
h1::H1
h11::H11
fref::MyRef{Float64}
ref::MyRef{Float64}
function Hessian11F1(e::Expression2{F,E1,E2}, d,indexer = nothing) where {F,E1 <: Real,E2}
h1 = Hessian(e.e2,d.d1,indexer)
h11= Hessian(d.d1,d.d1,indexer)
return new{F,typeof(h1),typeof(h11),typeof(e.e1)}(e.e1,h1,h11,ref(e.e2),ref(d))
end
end
struct Hessian11F2{F,H1,H11,R} <: Hessian
a::R
h1::H1
h11::H11
fref::MyRef{Float64}
ref::MyRef{Float64}
function Hessian11F2(e::Expression2{F,E1,E2}, d,indexer = nothing) where {F,E1,E2 <: Real}
h1 = Hessian(e.e1,d.d1,indexer)
h11= Hessian(d.d1,d.d1,indexer)
return new{F,typeof(h1),typeof(h11),typeof(e.e2)}(e.e2,h1,h11,ref(e.e1),ref(d))
end
end
struct Hessian02{H11,H12,H21,H22} <: Hessian
h11::H11
h12::H12
h21::H21
h22::H22
ref11::MyRef{Float64}
ref12::MyRef{Float64}
ref21::MyRef{Float64}
ref22::MyRef{Float64}
end
struct Hessian22{F,H1,H2,H11,H12,H21,H22} <: Hessian
h1::H1
h2::H2
h11::H11
h12::H12
h21::H21
h22::H22
fref1::MyRef{Float64}
fref2::MyRef{Float64}
ref1::MyRef{Float64}
ref2::MyRef{Float64}
function Hessian22(e::Expression2{F,E1,E2},d,indexer = nothing) where {F,E1,E2}
h1 = Hessian(e.e1,d.d1,indexer)
h2 = Hessian(e.e2,d.d2,indexer)
h11= Hessian(d.d1,d.d1,indexer)
h12= Hessian(d.d1,d.d2,indexer)
h21= Hessian(d.d2,d.d1,indexer)
h22= Hessian(d.d2,d.d2,indexer)
new{F,typeof(h1),typeof(h2),typeof(h11),typeof(h12),typeof(h21),typeof(h22)}(h1,h2,h11,h12,h21,h22,ref(e.e1),ref(e.e2),ref1(d),ref2(d))
end
end
# struct Hessian20{H1,H2} <: Hessian
# h1::H1
# h2::H2
# ref1::MyRef{Float64}
# ref2::MyRef{Float64}
# end
struct HessianD00 <: Hessian
index1::Int
index2::Int
islower::Bool
end
struct HessianD00S <: Hessian
index::Int
islower::Bool
end
struct HessianD20{H1,H2} <: Hessian
h1::H1
h2::H2
ref1::MyRef{Float64}
ref2::MyRef{Float64}
end
struct HessianD10{H} <: Hessian
h::H
ref::MyRef{Float64}
end
struct HessianD11{H} <: Hessian
h::H
ref1::MyRef{Float64}
ref2::MyRef{Float64}
end
struct Hessian22a{H1,H2} <: Hessian
h1::H1
h2::H2
ref1::MyRef{Float64}
ref2::MyRef{Float64}
end
struct Hessian22m{H1,H2,H12,H21} <: Hessian
h1::H1
h2::H2
h12::H12
h21::H21
ref1::MyRef{Float64}
ref2::MyRef{Float64}
end
struct Hessian11a{H} <: Hessian
h1::H
ref::MyRef{Float64}
end
struct HessianSum{I,H} <: Hessian
inner::I
hs::Vector{H}
end
const HESSIAN_NULL = HessianNull()
@inline function (h::HessianSum{I,H})(z,x,p=nothing,h0=1) where {I,H}
inner(h)(z,x,p,h0)
@simd for i in eachindex(h.hs)
@inbounds h.hs[i](z,x,p,h0)
end
end
@inline function (h::HessianSum{Nothing,H})(z,x,p=nothing,h0=1) where H
@simd for i in eachindex(h.hs)
@inbounds h.hs[i](z,x,p,h0)
end
end
@inline (::HessianNull)(z,x,p=nothing,h0=1) = nothing
@inline function (h::HessianD00)(z,x,p=nothing,h0 = 1)
islower(h) && @inbounds z[index1(h)::Int,index2(h)::Int] += h0
return
end
@inline function (h::HessianD00S)(z,x,p=nothing,h0 = 1)
islower(h) && @inbounds z[index(h)::Int] += h0
return
end
@inline function (h::HessianD10{H})(z,x,p=nothing,h0 = 1) where H
h.h(z,x,p,h0*refval(h))
return
end
@inline function (h::HessianD11{H})(z,x,p=nothing,h0 = 1) where H
h.h(z,x,p,h0*refval1(h)*refval2(h))
return
end
@inline function (h::HessianD21{H1,H2})(z,x,p=nothing,h0=1) where {H1,H2}
h.h1(z,x,p,h0*refval(h)*refval1(h))
h.h2(z,x,p,h0*refval(h)*refval2(h))
return
end
@inline function (h::HessianD20{H1,H2})(z,x,p=nothing,h0=1) where {H1,H2}
h.h1(z,x,p,h0*refval1(h))
h.h2(z,x,p,h0*refval2(h))
end
@inline function (h::Hessian02{H11,H12,H21,H22})(z,x,p=nothing,h0=1) where {H11,H12,H21,H22}
h.h11(z,x,p,h0*refval11(h)*refval21(h))
h.h12(z,x,p,h0*refval11(h)*refval22(h))
h.h21(z,x,p,h0*refval12(h)*refval21(h))
h.h22(z,x,p,h0*refval12(h)*refval22(h))
return
end
@inline function (h::Hessian11a{H})(z,x,p=nothing,h0=1) where {H}
h.h1(z,x,p,h0*refval(h))
return
end
@inline function (h::Hessian22a{H1,H2})(z,x,p=nothing,h0=1) where {H1,H2}
h.h1(z,x,p,h0*refval1(h))
h.h2(z,x,p,h0*refval2(h))
return
end
@inline function (h::Hessian22m{H1,H2,H12})(z,x,p=nothing,h0=1) where {H1,H2,H12}
h.h1(z,x,p,h0*refval1(h))
h.h2(z,x,p,h0*refval2(h))
h.h12(z,x,p,h0)
h.h21(z,x,p,h0)
return
end
Hessian(e::ExpressionSum{E,I1},d::GradientSum{D,I2},indexer = nothing) where {E,D,I1,I2} = HessianSum(Hessian(inner(e),inner(d),indexer),[Hessian(e,d,indexer) for (e,d) in zip(e.es,d.ds)])
Hessian(e::ExpressionSum{E,Nothing},d::GradientSum{D,Nothing},indexer = nothing) where {E,D} = HessianSum(nothing,[Hessian(e,d,indexer) for (e,d) in zip(e.es,d.ds)])
Hessian(e::Variable,::G,indexer) where G <: Gradient = HESSIAN_NULL
Hessian(e::Parameter,::G,indexer) where G <: Gradient = HESSIAN_NULL
Hessian(e::Constant,::G,indexer) where G <: Gradient = HESSIAN_NULL
Hessian(d1::G,d2::GradientNull,indexer) where G <: Gradient = HESSIAN_NULL
Hessian(d1::GradientNull,d2::G,indexer) where G <: Gradient = HESSIAN_NULL
Hessian(d1::GradientNull,d2::GradientNull,indexer) = HESSIAN_NULL
Hessian(d1::Gradient0,d2::Gradient0,indexer)= HessianD00S(index(d1)>=index(d2) ? set_indexer!(indexer,index(d1),index(d2)) : 0,index(d1) >= index(d2))
Hessian(d1::Gradient0,d2::Gradient0,::Nothing) = HessianD00(index(d1),index(d2),index(d1) >= index(d2))
Hessian(d1::Gradient0,d2::G1, indexer = nothing) where G1 <: Union{Gradient1,Gradient2F1,Gradient2F2} = HessianD10(Hessian(d1,d2.d1,indexer),ref(d2))
Hessian(d1::G1,d2::Gradient0, indexer = nothing) where G1 <: Union{Gradient1,Gradient2F1,Gradient2F2} = HessianD10(Hessian(d1.d1,d2,indexer),ref(d1))
Hessian(d1::G1,d2::G2, indexer = nothing) where {G1 <: Union{Gradient1,Gradient2F1,Gradient2F2}, G2 <: Union{Gradient1,Gradient2F1,Gradient2F2}}= HessianD11(Hessian(d1.d1,d2.d1,indexer),ref(d1),ref(d2))
Hessian(d1::G1,d2::G2, indexer = nothing) where {G1 <: Union{Gradient1,Gradient2F1,Gradient2F2}, G2 <: Gradient2} = HessianD21(Hessian(d1.d1,d2.d1,indexer),Hessian(d1.d1,d2.d2,indexer),ref(d1),ref1(d2),ref2(d2))
Hessian(d1::G1,d2::G2, indexer = nothing) where {G1 <: Gradient2, G2 <: Union{Gradient1,Gradient2F1,Gradient2F2}} = HessianD21(Hessian(d1.d1,d2.d1,indexer),Hessian(d1.d2,d2.d1,indexer),ref(d2),ref1(d1),ref2(d1))
Hessian(d1::Gradient0,d2::Gradient2{F,F1,F2}, indexer = nothing) where {F,F1,F2} = HessianD20(Hessian(d1,d2.d1,indexer),Hessian(d1,d2.d2,indexer),ref1(d2),ref2(d2))
Hessian(d1::Gradient2{F,F1,F2},d2::Gradient0, indexer = nothing) where {F,F1,F2} = HessianD20(Hessian(d1.d1,d2,indexer),Hessian(d1.d2,d2,indexer),ref1(d1),ref2(d1))
Hessian(d1::Gradient2{F,F1,F2} where {F,F1,F2},d2::Gradient2{F,F1,F2} where {F,F1,F2}, indexer = nothing) = Hessian02(Hessian(d1.d1,d2.d1,indexer),Hessian(d1.d1,d2.d2,indexer),Hessian(d1.d2,d2.d1,indexer),Hessian(d1.d2,d2.d2,indexer),ref1(d1),ref2(d1),ref1(d2),ref2(d2))
Hessian(e::Expression1{F,E},d, indexer = nothing) where {F,E} = Hessian11(e,d,indexer)
Hessian(e::Expression2{F,E1,E2},d,indexer = nothing) where {F,E1,E2} = Hessian22(e,d,indexer)
Hessian(e::Expression2{F,E1,E2}, d,indexer = nothing) where {F,E1 <: Real,E2} = Hessian11F1(e,d,indexer)
Hessian(e::Expression2{F,E1,E2}, d,indexer = nothing) where {F,E1,E2 <: Real} = Hessian11F2(e,d,indexer)
Hessian(e::Expression2{typeof(*),E1,E2},d,indexer = nothing) where {E1,E2} = Hessian22m(Hessian(e.e1,d.d1,indexer),Hessian(e.e2,d.d2,indexer),Hessian(d.d1,d.d2,indexer),Hessian(d.d2,d.d1,indexer),ref1(d),ref2(d))
Hessian(e::Expression2{F,E1,E2},d,indexer = nothing) where {F<:Union{typeof(+),typeof(-)},E1,E2} = Hessian22a(Hessian(e.e1,d.d1,indexer),Hessian(e.e2,d.d2,indexer),ref1(d),ref2(d))
Hessian(e::Expression2{F,E1,E2}, d,indexer = nothing) where {F<:Union{typeof(+),typeof(-),typeof(*)},E1 <: Real,E2 <: Expression} = Hessian11a(Hessian(e.e2,d.d1,indexer),ref(d))
Hessian(e::Expression2{F,E1,E2}, d,indexer = nothing) where {F<:Union{typeof(+),typeof(-),typeof(*),typeof(/)},E1 <: Expression,E2 <: Real} = Hessian11a(Hessian(e.e1,d.d1,indexer),ref(d))
# performance killer ---------------
function Hessian(d1::GradientSum{D1,I1},d2::GradientSum{D2,I2},indexer = nothing) where {D1,D2,I1,I2}
@warn "This operation is expensive"
hinner = Hessian(inner(d1),d2,indexer)
hs = [Hessian(d,d2,indexer) for d in d1.ds]
@inline function (z,x,p=nothing,h0=1)
hinner(z,x,p,h0)
@simd for i in eachindex(hs)
@inbounds hs[i](z,x,p,h0)
end
end
end
function Hessian(d1::GradientSum{D1,Nothing},d2::GradientSum{D2,I2},indexer = nothing) where {D1,D2,I2}
@warn "This operation is expensive"
hs = [Hessian(d,d2,indexer) for d in d1.ds]
@inline function (z,x,p=nothing,h0=1)
@simd for i in eachindex(hs)
@inbounds hs[i](z,x,p,h0)
end
end
end
function Hessian(d1::GradientSum{D1,I1},d2::GradientSum{D2,Nothing},indexer = nothing) where {D1,D2,I1}
@warn "This operation is expensive"
hinner = Hessian(inner(d1),d2,indexer)
hs = [Hessian(d,d2,indexer) for d in d1.ds]
@inline function (z,x,p=nothing,h0=1)
hinner(z,x,p,h0)
@simd for i in eachindex(hs)
@inbounds hs[i](z,x,p,h0)
end
end
end
function Hessian(d1::GradientSum{D1,Nothing},d2::GradientSum{D2,Nothing},indexer = nothing) where {D1,D2}
@warn "This operation is expensive"
hs = [Hessian(d,d2,indexer) for d in d1.ds]
@inline function (z,x,p=nothing,h0=1)
@simd for i in eachindex(hs)
@inbounds hs[i](z,x,p,h0)
end
end
end
function Hessian(d1::GradientSum{D1,I1},d2::G,indexer = nothing) where {D1,D2,I1,I2,G <: Gradient}
@warn "This operation is expensive"
hinner = Hessian(inner(d1),d2,indexer)
hs = [Hessian(d,d2,indexer) for d in d1.ds]
@inline function (z,x,p=nothing,h0=1)
hinner(z,x,p,h0)
@simd for i in eachindex(hs)
@inbounds hs[i](z,x,p,h0)
end
end
end
function Hessian(d1::GradientSum{D1,Nothing},d2::G,indexer = nothing) where {D1,D2,I2,G <: Gradient}
@warn "This operation is expensive"
hs = [Hessian(d,d2,indexer) for d in d1.ds]
@inline function (z,x,p=nothing,h0=1)
@simd for i in eachindex(hs)
@inbounds hs[i](z,x,p,h0)
end
end
end
function Hessian(d1::G,d2::GradientSum{D2,I2},indexer = nothing) where {G <: Gradient,D2,I2}
@warn "This operation is expensive"
hinner = Hessian(d1,inner(d2),indexer)
hs = [Hessian(d1,d,indexer) for d in d2.ds]
@inline function (z,x,p=nothing,h0=1)
hinner(z,x,p,h0)
@simd for i in eachindex(hs)
@inbounds hs[i](z,x,p,h0)
end
end
end
function Hessian(d1::G,d2::GradientSum{D2,Nothing},indexer = nothing) where {G <: Gradient, D2}
@warn "This operation is expensive"
hs = [Hessian(d1,d,indexer) for d in d2.ds]
@inline function (z,x,p=nothing,h0=1)
@simd for i in eachindex(hs)
@inbounds hs[i](z,x,p,h0)
end
end
end
islower(h) = h.islower
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] | 1.850571 | 6,391 |
"""
Find all types that may be considered ansestors of this type.
For number types it is the same as Julia type hierarchy.
For array types it includes unparametrized versions of types, i.e.:
type_ansestors(Vector{Float64})
# ==> [Array{Float64,1}, Array{T,1}, DenseArray{T,1},
AbstractArray{T,1}, AbstractArray{T,N}, Any]
"""
function type_ansestors(t::Type{T}) where T<:Number
types = Type[]
while t != Any
push!(types, t)
t = supertype(t)
end
push!(types, Any)
return types
end
type_ansestors(t::Type{Vector{T}}) where {T} =
[t, Vector, DenseVector, AbstractVector, AbstractArray, Any]
type_ansestors(t::Type{Matrix{T}}) where {T} =
[t, Matrix, DenseMatrix, AbstractMatrix, AbstractArray, Any]
function bcast_to_call(pex::Expr)
@assert pex.head == :(.)
return Expr(:call, pex.args[1], pex.args[2].args...)
end
deriv_name(z::Symbol, x::Symbol) = Symbol("d$(z)!d$(x)")
split_deriv_name(vname) = Symbol.(split(String(vname), "!"))
function replace_node(g::AbstractExGraph, vname::Symbol, nds::Vector{ExNode})
i = indexof(g, vname)
delete!(g, vname)
insert!(g, nds)
end
function find_related(g::AbstractExGraph, dydx_v::Symbol)
subderivs = Symbol[]
i = 1
name = Symbol("$(dydx_v)__$(i)")
while haskey(g, name)
push!(subderivs, name)
i += 1
name = Symbol("$(dydx_v)__$(i)")
end
return subderivs
end
# (symbolic) derivative size propagation
const DERIV_NAME_PATTERN = r"(d.+)!(d.+)"
function propagate_deriv_size!(g::AbstractExGraph, dd_name::Symbol)
sizes = @get_or_create(g.ctx, :sizes, Dict())
rg = match(DERIV_NAME_PATTERN, String(dd_name))
@assert length(rg.captures) == 2
str_dnames = rg.captures
zname = Symbol(str_dnames[1][2:end])
xname = Symbol(split(str_dnames[2][2:end], "__")[1]) # cut down `__$(i)` part if any
zsize, xsize = (sizes[zname], sizes[xname])
if zsize == :(())
# output var is constant
sizes[dd_name] = xsize
else
sizes[dd_name] = :(($zsize..., $xsize...)) |> simplify
end
end
function propagate_deriv_size!(g::AbstractExGraph)
for nd in g.tape
vname = varname(nd)
if match(DERIV_NAME_PATTERN, String(vname)) != nothing
propagate_deriv_size!(g, vname)
end
end
end
# (numeric) derivative size propagation
function infer_deriv_size!(g::AbstractExGraph, dd_name::Symbol)
rg = match(DERIV_NAME_PATTERN, String(dd_name))
@assert length(rg.captures) == 2
str_dnames = rg.captures
zname = Symbol(str_dnames[1][2:end])
xname = Symbol(split(str_dnames[2][2:end], "__")[1]) # cut down `__$(i)` part if any
# in case z or x haven't been evaluated and their size isn't known yet
evaluate!(g, zname)
evaluate!(g, xname)
sizes = @get_or_create(g.ctx, :rsizes, Dict())
zsize, xsize = (sizes[zname], sizes[xname])
sizes[dd_name] = (zsize..., xsize...)
end
function infer_deriv_size!(g::AbstractExGraph)
for nd in g.tape
vname = varname(nd)
if match(DERIV_NAME_PATTERN, String(vname)) != nothing
infer_deriv_size!(g, vname)
end
end
end
# top type
"The top type describing given data"
top_type(x::AbstractArray{T,N}) where {T,N} = AbstractArray{T,N}
top_type(x::Number) = Number
top_type(::Type{AT}) where {AT <: AbstractArray{T,N}} where {T,N} = AbstractArray{T,N}
top_type(::Type{T}) where {T <: Number} = Number
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] | 2.290514 | 1,518 |
module Menus
using Genie, Stipple, StippleUI, StippleUI.API
import Genie.Renderer.Html: HTMLString, normal_element, register_normal_element
export menu
register_normal_element("q__menu", context = @__MODULE__)
function menu(
fieldname::Union{Symbol,Nothing} = nothing,
args...;
content::Union{String,Vector} = "",
wrap::Function = StippleUI.DEFAULT_WRAPPER,
kwargs...)
wrap() do
q__menu(args...; attributes([:fieldname => fieldname, kwargs...], StippleUI.API.ATTRIBUTES_MAPPINGS)...) do
join(content)
end
end
end
function menu(content::Function,
fieldname::Union{Symbol,Nothing} = nothing,
args...;
wrap::Function = StippleUI.DEFAULT_WRAPPER,
kwargs...)
menu(label, fieldname, args...; wrap = wrap, content = content(), kwargs...)
end
end
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] | 2.310881 | 386 |
<filename>test/test_core_radius.jl
tol = 1e-10
#
# Sanity check.
#
@test size(Planets.mr_table) == (91,42)
@test haskey(Planets.mr_table,:Mearth)
@test haskey(Planets.mr_table,:rocky)
@test haskey(Planets.mr_table,Symbol("5%fe"))
@test haskey(Planets.mr_table,Symbol("5%h2o"))
@test isa(Planets.mr_table[:Mearth][1], Float64)
@test Planets.mr_table[:Mearth][1] == 0.0625
#
# Test grid points, including edge cases.
#
@test abs(core_radius(0.07179, fe=0.0 ) - 0.4888) < tol
@test abs(core_radius(0.07179, h2o=0.0) - 0.4888) < tol
@test abs(core_radius(0.07179, fe=1.0 ) - 0.3733) < tol
@test abs(core_radius(0.07179, fe=0.5 ) - 0.4394) < tol
@test abs(core_radius(0.07179, fe=0.05) - 0.4841) < tol
@test abs(core_radius(0.07179, h2o=.05) - 0.5041) < tol
@test abs(core_radius(0.07179, h2o=1.0) - 0.6588) < tol
@test abs(core_radius(1.0, fe=1.0) - 0.8228) < tol
@test abs(core_radius(1.0, fe=0.0) - 1.0667) < tol
@test abs(core_radius(1.0, fe=.05) - 1.0568) < tol
@test abs(core_radius(1.0, fe=0.1) - 1.0466) < tol
@test abs(core_radius(1.0, fe=0.2) - 1.0260) < tol
@test abs(core_radius(1.0, fe=0.3) - 1.0050) < tol
@test abs(core_radius(1.0, fe=0.4) - 0.9834) < tol
@test abs(core_radius(1.0, fe=.45) - 0.9723) < tol
@test abs(core_radius(1.0, fe=.35) - 0.9943) < tol
@test abs(core_radius(1.0, fe=1.0) - 0.8228) < tol
#
# Test interpolation across columns.
#
@test abs(core_radius(1.0, fe=0.975) - 0.83350) < tol
@test abs(core_radius(1.0, fe=0.025) - 1.06175) < tol
#
# Test interpolation across rows.
#
@test abs(core_radius(1.0359, fe=1.0) - 0.8307) < tol
#
# Test interpolation across columns AND rows.
#
@test abs(core_radius(1.035900, fe =0.975) - 0.841525) < tol
@test abs(core_radius(0.064745, h2o=0.025) - 0.480450) < tol
#
# Test extrapolation of mass.
#
M_small = 0.0625
R_small = core_radius(M_small, fe=0.3)
M_large = 32.0
R_large = core_radius(M_large, fe=0.3)
@test abs(core_radius(0.01, fe=0.3) - R_small * (0.01/M_small)^(1/3.7)) < tol
@test abs(core_radius(35.0, fe=0.3) - R_large * (35.0/M_large)^(1/3.7)) < tol
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] | 1.968839 | 1,059 |
# Various correlation
#
# spearman correlation functions
#
# spearman correlation between two vectors
function cor_spearman(x::AbstractVector, y::AbstractVector)
if any(isnan(x)) || any(isnan(y)) return NaN end
return cor(tiedrank(x), tiedrank(y))
end
# spearman correlation over all pairs of columns of two matrices
function cor_spearman(X::AbstractMatrix, Y::AbstractMatrix)
return cor(mapslices(tiedrank, X, 1), mapslices(tiedrank, Y, 1))
end
function cor_spearman(X::AbstractMatrix, y::AbstractVector)
return cor(mapslices(tiedrank, X, 1), tiedrank(y))
end
function cor_spearman(x::AbstractVector, Y::AbstractMatrix)
return cor(tiedrank(x), mapslices(tiedrank, Y, 1))
end
# spearman correlation over all pairs of columns of a matrix
function cor_spearman(X::AbstractMatrix)
csp = cor(mapslices(tiedrank, X, 1))
nanindex = vec(mapslices(any, isnan(X), 1))
csp[nanindex, :] = NaN
csp[:, nanindex] = NaN
return csp
end
#
# Kendall's rank correlation
#
# Knigh JASA (1966)
function cor_kendall!{T<:Real,S<:Real}(x::AbstractVector{T}, y::AbstractVector{S})
if any(isnan(x)) || any(isnan(y)) return NaN end
n = length(x)
if n != length(y) error("Vectors must have same length") end
# Initial sorting
pm = sortperm(y)
x[:] = x[pm]
y[:] = y[pm]
pm[:] = sortperm(x)
x[:] = x[pm]
# Counting ties in x and y
iT = 1
nT = 0
iU = 1
nU = 0
for i = 2:n
if x[i] == x[i-1]
iT += 1
else
nT += iT*(iT - 1)
iT = 1
end
if y[i] == y[i-1]
iU += 1
else
nU += iU*(iU - 1)
iU = 1
end
end
if iT > 1 nT += iT*(iT - 1) end
nT = div(nT,2)
if iU > 1 nU += iU*(iU - 1) end
nU = div(nU,2)
# Sort y after x
y[:] = y[pm]
# Calculate double ties
iV = 1
nV = 0
jV = 1
for i = 2:n
if x[i] == x[i-1] && y[i] == y[i-1]
iV += 1
else
nV += iV*(iV - 1)
iV = 1
end
end
if iV > 1 nV += iV*(iV - 1) end
nV = div(nV,2)
nD = div(n*(n - 1),2)
return (nD - nT - nU + nV - 2swaps!(y))/sqrt((nD - nT)*(nD - nU))
end
cor_kendall(x::AbstractVector, y::AbstractVector) = cor_kendall!(copy(x), copy(y))
cor_kendall(x::AbstractVector, Y::AbstractMatrix) = [cor_kendall(x, Y[:,i]) for i in 1:size(Y, 2)]
cor_kendall(X::AbstractMatrix, y::AbstractVector) = [cor_kendall(X[:,i], y) for i in 1:size(X, 2)]
cor_kendall(X::AbstractMatrix, Y::AbstractMatrix) = [cor_kendall(X[:,i], Y[:,j]) for i in 1:size(X, 2), j in 1:size(Y, 2)]
function cor_kendall(X::AbstractMatrix)
n = size(X, 2)
C = eye(n)
for j = 2:n
for i = 1:j-1
C[i,j] = cor_kendall!(X[:,i],X[:,j])
C[j,i] = C[i,j]
end
end
return C
end
# Auxilliary functions for Kendall's rank correlation
function swaps!(x::AbstractVector)
n = length(x)
if n == 1 return 0 end
n2 = div(n, 2)
xl = sub(x, 1:n2)
xr = sub(x, n2+1:n)
nsl = swaps!(xl)
nsr = swaps!(xr)
sort!(xl)
sort!(xr)
return nsl + nsr + mswaps(xl,xr)
end
function mswaps(x::AbstractVector, y::AbstractVector)
i = 1
j = 1
nSwaps = 0
n = length(x)
while i <= n && j <= length(y)
if y[j] < x[i]
nSwaps += n - i + 1
j += 1
else
i += 1
end
end
return nSwaps
end
# autocorrelation for range
function autocor(x::AbstractVector, lags::Ranges)
lx = length(x)
if max(lags) > lx error("Autocorrelation distance must be less than sample size") end
mx = mean(x)
sxinv = 1/stdm(x, mx)
xs = Array(typeof(sxinv), lx)
for i = 1:lx
xs[i] = (x[i] - mx)*sxinv
end
acf = Array(typeof(sxinv), length(lags))
for i in 1:length(lags)
acf[i] = dot(xs[1:end - lags[i]], xs[lags[i] + 1:end])/(lx - 1)
end
return acf
end
# autocorrelation at a specific lag
autocor(x::AbstractVector, lags::Real) = autocor(x, lags:lags)[1]
# autocorrelation at a default of zero to 10log10(length(v)) lags
autocor(v::AbstractVector) = autocor(v, 0:min(length(v) - 1, 10log10(length(v))))
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] | 1.989141 | 2,118 |
using ClimateMachine
const clima_dir = dirname(dirname(pathof(ClimateMachine)));
if parse(Bool, get(ENV, "CLIMATEMACHINE_PLOT_EDMF_COMPARISON", "false"))
plot_dir = joinpath(clima_dir, "output", "bomex_edmf", "pycles_comparison")
else
plot_dir = nothing
end
include(joinpath(@__DIR__, "compute_mse.jl"))
#! format: off
best_mse = Dict()
best_mse[:Bomex] = Dict()
best_mse[:Bomex]["ρ"] = 3.4943021267397123e-02
best_mse[:Bomex]["ρu[1]"] = 3.0714039084256679e+03
best_mse[:Bomex]["ρu[2]"] = 1.3375796498101822e-03
best_mse[:Bomex]["moisture.ρq_tot"] = 4.8463531712319707e-02
best_mse[:Bomex]["turbconv.environment.ρatke"] = 6.6626422991098286e+02
best_mse[:Bomex]["turbconv.environment.ρaθ_liq_cv"] = 8.5667099987715503e+01
best_mse[:Bomex]["turbconv.environment.ρaq_tot_cv"] = 1.6439116552012851e+02
best_mse[:Bomex]["turbconv.updraft[1].ρa"] = 7.9577348413012515e+01
best_mse[:Bomex]["turbconv.updraft[1].ρaw"] = 8.4352188057391225e-02
best_mse[:Bomex]["turbconv.updraft[1].ρaθ_liq"] = 9.0101464252959325e+00
best_mse[:Bomex]["turbconv.updraft[1].ρaq_tot"] = 1.0768120864370509e+01
#! format: on
sufficient_mse(computed_mse, best_mse) = computed_mse <= best_mse + eps()
function test_mse(computed_mse, best_mse, key)
mse_not_regressed = sufficient_mse(computed_mse[key], best_mse[key])
@test mse_not_regressed
mse_not_regressed || @show key
end
computed_mse = Dict(
k => compute_mse(
solver_config.dg.grid,
solver_config.dg.balance_law,
time_data,
dons_arr,
data_files[k],
k,
best_mse[k],
plot_dir,
) for k in keys(data_files)
)
@testset "BOMEX EDMF Solution Quality Assurance (QA) tests" begin
#! format: off
test_mse(computed_mse[:Bomex], best_mse[:Bomex], "ρ")
test_mse(computed_mse[:Bomex], best_mse[:Bomex], "ρu[1]")
test_mse(computed_mse[:Bomex], best_mse[:Bomex], "moisture.ρq_tot")
test_mse(computed_mse[:Bomex], best_mse[:Bomex], "turbconv.updraft[1].ρa")
test_mse(computed_mse[:Bomex], best_mse[:Bomex], "turbconv.updraft[1].ρaw")
test_mse(computed_mse[:Bomex], best_mse[:Bomex], "turbconv.updraft[1].ρaθ_liq")
test_mse(computed_mse[:Bomex], best_mse[:Bomex], "turbconv.updraft[1].ρaq_tot")
test_mse(computed_mse[:Bomex], best_mse[:Bomex], "turbconv.environment.ρatke")
test_mse(computed_mse[:Bomex], best_mse[:Bomex], "turbconv.environment.ρaθ_liq_cv")
test_mse(computed_mse[:Bomex], best_mse[:Bomex], "turbconv.environment.ρaq_tot_cv")
#! format: on
end
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] | 1.940541 | 1,295 |
#Controlled HF noise plot (test data reduction)
using InspectDR
using Colors
#==Input constants
===============================================================================#
dfltline = line(color=RGB24(0, 0, 1), width=1, style=:solid)
#==Input data
===============================================================================#
function gennoise(xv, yv; step=.01, npts=20, lo=0, hi=1)
xmax = xv[end]
xnew = xmax+collect(step:step:(npts*step))
ynew = similar(xnew)
v = Float64[lo, hi]
for i in 1:length(ynew)
ynew[i] = v[(i&0x1)+1]
end
append!(xv, xnew)
append!(yv, ynew)
end
function addpts(ynew, xv, yv; step=1.0)
xmax = xv[end]
npts = length(ynew)
xnew = xmax+collect(step:step:(npts*step))
append!(xv, xnew)
append!(yv, ynew)
end
#Generate dataset:
x = Float64[0]
y = Float64[0]
addpts(Float64[1.1,-.1,], x,y) #Force maximum y-extents
addpts(Float64[1,1,1,0,0], x,y)
gennoise(x,y)
addpts(Float64[0,1,1,1,0,0], x,y)
gennoise(x,y)
addpts(Float64[1,1,1,0,0], x,y)
gennoise(x,y)
addpts(Float64[1,1,1,0,0], x,y)
#==Generate plot
===============================================================================#
plot = InspectDR.Plot2D()
wfrm = add(plot, x, y+1)
wfrm.line = dfltline
plot.xres=1000 #Force resolution
a = plot.annotation
a.title = "Sample Plot (λ)"
a.xlabel = "Time (s)"
a.ylabels = ["Signal Voltage (V)"]
gplot = display(InspectDR.GtkDisplay(), plot)
:Test_Complete
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"""
Only packs `A`. Primitively does column-major packing: it packs blocks of `A` into a column-major temporary.
"""
function matmul_st_only_pack_A!(
C::AbstractStridedPointer{T}, A::AbstractStridedPointer, B::AbstractStridedPointer,
α, β, M, K, N, ::StaticFloat64{W₁}, ::StaticFloat64{W₂}, ::StaticFloat64{R₁}, ::StaticFloat64{R₂}
) where {T, W₁, W₂, R₁, R₂}
mᵣ, nᵣ = matmul_params()
((Mblock, Mblock_Mrem, Mremfinal, Mrem, Miter), (Kblock, Kblock_Krem, Krem, Kiter)) =
solve_McKc(T, M, K, N, StaticFloat64{W₁}(), StaticFloat64{W₂}(), StaticFloat64{R₁}(), StaticFloat64{R₂}(), mᵣ)
for ko ∈ CloseOpen(Kiter)
ksize = ifelse(ko < Krem, Kblock_Krem, Kblock)
let A = A, C = C
for mo in CloseOpen(Miter)
msize = ifelse((mo+1) == Miter, Mremfinal, ifelse(mo < Mrem, Mblock_Mrem, Mblock))
# if ko == 0
# loopmul!(C, A, B, α, β, msize, ksize, N)
# else
# loopmul!(C, A, B, α, One(), msize, ksize, N)
# end
if ko == 0
packaloopmul!(C, A, B, α, β, msize, ksize, N)
else
packaloopmul!(C, A, B, α, One(), msize, ksize, N)
end
A = gesp(A, (msize, Zero()))
C = gesp(C, (msize, Zero()))
end
end
A = gesp(A, (Zero(), ksize))
B = gesp(B, (ksize, Zero()))
end
nothing
end
function matmul_st_pack_A_and_B!(
C::AbstractStridedPointer{T}, A::AbstractStridedPointer, B::AbstractStridedPointer, α, β, M, K, N, W₁, W₂, R₁, R₂, tid
) where {T}
mᵣ, nᵣ = matmul_params()
# TODO: if this is nested in other threaded code, use only a piece of BCACHE and make R₂ (and thus L₂ₑ) smaller
(Mblock, Mblock_Mrem, Mremfinal, Mrem, Miter), (Kblock, Kblock_Krem, Krem, Kiter), (Nblock, Nblock_Nrem, Nrem, Niter) =
solve_block_sizes(T, M, K, N, W₁, W₂, R₁, R₂, mᵣ)
bcache = _use_bcache(tid)
L3ptr = Base.unsafe_convert(Ptr{T}, pointer(bcache))
for n ∈ CloseOpen(Niter)
nsize = ifelse(n < Nrem, Nblock_Nrem, Nblock)
let A = A, B = B
for k ∈ CloseOpen(Kiter)
ksize = ifelse(k < Krem, Kblock_Krem, Kblock)
_B = default_zerobased_stridedpointer(L3ptr, (One(),ksize))
unsafe_copyto_avx!(_B, B, ksize, nsize)
let A = A, C = C, B = _B
for m in CloseOpen(Miter)
msize = ifelse((m+1) == Miter, Mremfinal, ifelse(m < Mrem, Mblock_Mrem, Mblock))
if k == 0
packaloopmul!(C, A, B, α, β, msize, ksize, nsize)
else
packaloopmul!(C, A, B, α, One(), msize, ksize, nsize)
end
A = gesp(A, (msize, Zero()))
C = gesp(C, (msize, Zero()))
end
end
A = gesp(A, (Zero(), ksize))
B = gesp(B, (ksize, Zero()))
end
end
B = gesp(B, (Zero(), nsize))
C = gesp(C, (Zero(), nsize))
end
_free_bcache!(bcache)
nothing
end
@inline contiguousstride1(A) = ArrayInterface.contiguous_axis(A) === One()
@inline contiguousstride1(A::AbstractStridedPointer{T,N,1}) where {T,N} = true
# @inline bytestride(A::AbstractArray, i) = VectorizationBase.bytestrides(A)[i]
@inline bytestride(A::AbstractStridedPointer, i) = strides(A)[i]
@inline firstbytestride(A::AbstractStridedPointer) = bytestride(A, One())
@inline function vectormultiple(bytex, ::Type{Tc}, ::Type{Ta}) where {Tc,Ta}
Wc = pick_vector_width(Tc) * static_sizeof(Ta) - One()
iszero(bytex & (VectorizationBase.register_size() - One()))
end
@inline function dontpack(pA::AbstractStridedPointer{Ta}, M, K, ::StaticInt{mc}, ::StaticInt{kc}, ::Type{Tc}) where {mc, kc, Tc, Ta}
(contiguousstride1(pA) &&
((((MᵣW_mul_factor() + StaticInt(5)) * pick_vector_width(Tc)) ≥ M) ||
(vectormultiple(bytestride(pA, StaticInt{2}()), Tc, Ta) && ((M * K) ≤ (mc * kc)) && iszero(reinterpret(Int, pointer(pA)) & (VectorizationBase.register_size() - One())))))
end
@inline function alloc_matmul_product(A::AbstractArray{TA}, B::AbstractArray{TB}) where {TA,TB}
# TODO: if `M` and `N` are statically sized, shouldn't return a `Matrix`.
M, KA = size(A)
KB, N = size(B)
@assert KA == KB "Size mismatch."
Matrix{promote_type(TA,TB)}(undef, M, N), (M, KA, N)
end
@inline function matmul_serial(A::AbstractMatrix, B::AbstractMatrix)
C, (M,K,N) = alloc_matmul_product(A, B)
_matmul_serial!(C, A, B, One(), Zero(), (M,K,N))
return C
end
# These methods must be compile time constant
maybeinline(::Any, ::Any, ::Any, ::Any) = false
function maybeinline(::StaticInt{M}, ::StaticInt{N}, ::Type{T}, ::Val{true}) where {M,N,T}
mᵣ, nᵣ = matmul_params()
static_sizeof(T) * StaticInt{M}() * StaticInt{N}() < StaticInt{176}() * mᵣ * nᵣ
end
function maybeinline(::StaticInt{M}, ::StaticInt{N}, ::Type{T}, ::Val{false}) where {M,N,T}
StaticInt{M}() * static_sizeof(T) ≤ StaticInt{2}() * VectorizationBase.register_size()
end
@inline function matmul_serial!(C::AbstractMatrix{T}, A::AbstractMatrix, B::AbstractMatrix) where {T}
matmul_serial!(C, A, B, One(), Zero(), nothing, ArrayInterface.contiguous_axis(C))
end
@inline function matmul_serial!(C::AbstractMatrix, A::AbstractMatrix, B::AbstractMatrix, α)
matmul_serial!(C, A, B, α, Zero(), nothing, ArrayInterface.contiguous_axis(C))
end
@inline function matmul_serial!(C::AbstractMatrix, A::AbstractMatrix, B::AbstractMatrix, α, β)
matmul_serial!(C, A, B, α, β, nothing, ArrayInterface.contiguous_axis(C))
end
@inline function matmul_serial!(C::AbstractMatrix, A::AbstractMatrix, B::AbstractMatrix, α, β, MKN, ::StaticInt{2})
_matmul_serial!(C', B', A', α, β, nothing)
return C
end
@inline function matmul_serial!(C::AbstractMatrix, A::AbstractMatrix, B::AbstractMatrix, α, β, MKN, ::StaticInt)
_matmul_serial!(C, A, B, α, β, nothing)
return C
end
"""
matmul_serial!(C, A, B[, α = 1, β = 0])
Calculates `C = α * (A * B) + β * C` in place.
A single threaded matrix-matrix-multiply implementation.
Supports dynamically and statically sized arrays.
Organizationally, `matmul_serial!` checks the arrays properties to try and dispatch to an appropriate implementation.
If the arrays are small and statically sized, it will dispatch to an inlined multiply.
Otherwise, based on the array's size, whether they are transposed, and whether the columns are already aligned, it decides to not pack at all, to pack only `A`, or to pack both arrays `A` and `B`.
"""
@inline function _matmul_serial!(
C::AbstractMatrix{T}, A::AbstractMatrix, B::AbstractMatrix, α, β, MKN
) where {T}
M, K, N = MKN === nothing ? matmul_sizes(C, A, B) : MKN
if M * N == 0
return
elseif K == 0
matmul_only_β!(C, β)
return
end
pA = zstridedpointer(A); pB = zstridedpointer(B); pC = zstridedpointer(C);
Cb = preserve_buffer(C); Ab = preserve_buffer(A); Bb = preserve_buffer(B);
Mc, Kc, Nc = block_sizes(T); mᵣ, nᵣ = matmul_params();
GC.@preserve Cb Ab Bb begin
if maybeinline(M, N, T, ArrayInterface.is_column_major(A)) # check MUST be compile-time resolvable
inlineloopmul!(pC, pA, pB, One(), Zero(), M, K, N)
return
elseif (nᵣ ≥ N) || dontpack(pA, M, K, Mc, Kc, T)
loopmul!(pC, pA, pB, α, β, M, K, N)
return
else
matmul_st_pack_dispatcher!(pC, pA, pB, α, β, M, K, N)
return
end
end
end # function
function matmul_only_β!(C::AbstractMatrix{T}, β::StaticInt{0}) where T
@avx for i=1:length(C)
C[i] = zero(T)
end
end
function matmul_only_β!(C::AbstractMatrix{T}, β) where T
@avx for i=1:length(C)
C[i] = β * C[i]
end
end
function matmul_st_pack_dispatcher!(pC::AbstractStridedPointer{T}, pA, pB, α, β, M, K, N) where {T}
Mc, Kc, Nc = block_sizes(T)
if (contiguousstride1(pB) ? (Kc * Nc ≥ K * N) : (firstbytestride(pB) ≤ 1600))
matmul_st_only_pack_A!(pC, pA, pB, α, β, M, K, N, W₁Default(), W₂Default(), R₁Default(), R₂Default())
# elseif notnested !== nothing && notnested
# matmul_st_pack_A_and_B!(pC, pA, pB, α, β, M, K, N, W₁Default(), W₂Default(), R₁Default(), R₂Default(), nothing)
else
matmul_st_pack_A_and_B!(pC, pA, pB, α, β, M, K, N, W₁Default(), W₂Default(), R₁Default(), R₂Default()/Threads.nthreads(), Threads.threadid() - 1)
end
nothing
end
"""
matmul(A, B)
Multiply matrices `A` and `B`.
"""
@inline function matmul(A::AbstractMatrix, B::AbstractMatrix)
C, (M,K,N) = alloc_matmul_product(A, B)
_matmul!(C, A, B, One(), Zero(), nothing, (M,K,N))
return C
end
"""
matmul!(C, A, B[, α, β, max_threads])
Calculates `C = α * A * B + β * C` in place, overwriting the contents of `A`.
It may use up to `max_threads` threads. It will not use threads when nested in other threaded code.
"""
@inline function matmul!(C::AbstractMatrix{T}, A::AbstractMatrix, B::AbstractMatrix) where {T}
matmul!(C, A, B, One(), Zero(), nothing, nothing, ArrayInterface.contiguous_axis(C))
end
@inline function matmul!(C::AbstractMatrix, A::AbstractMatrix, B::AbstractMatrix, α)
matmul!(C, A, B, α, Zero(), nothing, nothing, ArrayInterface.contiguous_axis(C))
end
@inline function matmul!(C::AbstractMatrix, A::AbstractMatrix, B::AbstractMatrix, α, β)
matmul!(C, A, B, α, β, nothing, nothing, ArrayInterface.contiguous_axis(C))
end
@inline function matmul!(C::AbstractMatrix, A::AbstractMatrix, B::AbstractMatrix, α, β, nthread)
matmul!(C, A, B, α, β, nthread, nothing, ArrayInterface.contiguous_axis(C))
end
@inline function matmul!(C::AbstractMatrix, A::AbstractMatrix, B::AbstractMatrix, α, β, nthread, MKN, ::StaticInt{2})
_matmul!(C', B', A', α, β, nthread, MKN)
return C
end
@inline function matmul!(C::AbstractMatrix, A::AbstractMatrix, B::AbstractMatrix, α, β, nthread, MKN, ::StaticInt)
_matmul!(C, A, B, α, β, nthread, MKN)
return C
end
@inline function dontpack(pA::AbstractStridedPointer{Ta}, M, K, ::StaticInt{mc}, ::StaticInt{kc}, ::Type{Tc}, nspawn) where {mc, kc, Tc, Ta}
# TODO: perhaps consider K vs kc by themselves?
(contiguousstride1(pA) && ((M * K) ≤ (mc * kc) * nspawn >>> 1))
end
# passing MKN directly would let osmeone skip the size check.
@inline function _matmul!(C::AbstractMatrix{T}, A, B, α, β, nthread, MKN) where {T}#::Union{Nothing,Tuple{Vararg{Integer,3}}}) where {T}
M, K, N = MKN === nothing ? matmul_sizes(C, A, B) : MKN
if M * N == 0
return
elseif K == 0
matmul_only_β!(C, β)
return
end
W = pick_vector_width(T)
pA = zstridedpointer(A); pB = zstridedpointer(B); pC = zstridedpointer(C);
Cb = preserve_buffer(C); Ab = preserve_buffer(A); Bb = preserve_buffer(B);
mᵣ, nᵣ = matmul_params()
GC.@preserve Cb Ab Bb begin
if maybeinline(M, N, T, ArrayInterface.is_column_major(A)) # check MUST be compile-time resolvable
inlineloopmul!(pC, pA, pB, One(), Zero(), M, K, N)
return
else
(nᵣ ≥ N) && @goto LOOPMUL
if (Sys.ARCH === :x86_64) || (Sys.ARCH === :i686)
(M*K*N < (StaticInt{4_096}() * W)) && @goto LOOPMUL
else
(M*K*N < (StaticInt{32_000}() * W)) && @goto LOOPMUL
end
__matmul!(pC, pA, pB, α, β, M, K, N, nthread)
return
@label LOOPMUL
loopmul!(pC, pA, pB, α, β, M, K, N)
return
end
end
end
# This funciton is sort of a `pun`. It splits aggressively (it does a lot of "splitin'"), which often means it will split-N.
function matmulsplitn!(C::AbstractStridedPointer{T}, A, B, α, β, ::StaticInt{Mc}, M, K, N, nspawn, ::Val{PACK}) where {T, Mc, PACK}
Mᵣ, Nᵣ = matmul_params()
W = pick_vector_width(T)
MᵣW = Mᵣ*W
_Mblocks, Nblocks = divide_blocks(M, cld_fast(N, Nᵣ), nspawn, W)
Mbsize, Mrem, Mremfinal, Mblocks = split_m(M, _Mblocks, W)
# Nblocks = min(N, _Nblocks)
Nbsize, Nrem = divrem_fast(N, Nblocks)
_nspawn = Mblocks * Nblocks
Mbsize_Mrem, Mbsize_ = promote(Mbsize + W, Mbsize)
Nbsize_Nrem, Nbsize_ = promote(Nbsize + One(), Nbsize)
let _A = A, _B = B, _C = C, n = 0, tnum = 0, Nrc = Nblocks - Nrem, Mrc = Mblocks - Mrem, __Mblocks = Mblocks - One()
while true
nsize = ifelse(Nblocks > Nrc, Nbsize_Nrem, Nbsize_); Nblocks -= 1
let _A = _A, _C = _C, __Mblocks = __Mblocks
while __Mblocks != 0
msize = ifelse(__Mblocks ≥ Mrc, Mbsize_Mrem, Mbsize_); __Mblocks -= 1
launch_thread_mul!(_C, _A, _B, α, β, msize, K, nsize, (tnum += 1), Val{PACK}())
_A = gesp(_A, (msize, Zero()))
_C = gesp(_C, (msize, Zero()))
end
if Nblocks != 0
launch_thread_mul!(_C, _A, _B, α, β, Mremfinal, K, nsize, (tnum += 1), Val{PACK}())
else
call_loopmul!(_C, _A, _B, α, β, Mremfinal, K, nsize, Val{PACK}())
waitonmultasks(CloseOpen(One(), _nspawn))
return
end
end
_B = gesp(_B, (Zero(), nsize))
_C = gesp(_C, (Zero(), nsize))
end
end
end
function __matmul!(
C::AbstractStridedPointer{T}, A::AbstractStridedPointer, B::AbstractStridedPointer, α, β, M, K, N, nthread
) where {T}
Mᵣ, Nᵣ = matmul_params()
W = pick_vector_width(T)
Mc, Kc, Nc = block_sizes(T)
MᵣW = Mᵣ*W
# Not taking the fast path
# But maybe we don't want to thread anyway
# Maybe this is nested, or we have ≤ 1 threads
nt = _nthreads()
_nthread = nthread === nothing ? nt : min(nt, nthread)
if _nthread < 2
matmul_st_pack_dispatcher!(C, A, B, α, β, M, K, N)
return
end
# We are threading, but how many threads?
nspawn = if (Sys.ARCH === :x86_64) || (Sys.ARCH === :i686)
clamp(div_fast(M * N, StaticInt{128}() * W), 1, _nthread)
else
clamp(div_fast(M * N, StaticInt{256}() * W), 1, _nthread)
end
# nkern = cld_fast(M * N, MᵣW * Nᵣ)
# Approach:
# Check if we don't want to pack A,
# if not, aggressively subdivide
# if so, check if we don't want to pack B
# if not, check if we want to thread `N` loop anyway
# if so, divide `M` first, then use ratio of desired divisions / divisions along `M` to calc divisions along `N`
# if not, only thread along `M`. These don't need syncing, as we're not packing `B`
# if so, `matmul_pack_A_and_B!`
#
# MᵣW * (MᵣW_mul_factor - One()) # gives a smaller Mc, then
# if 2M/nspawn is less than it, we don't don't `A`
# First check is: do we just want to split aggressively?
mᵣ, nᵣ = matmul_params()
if dontpack(A, M, K, Mc, Kc, T, nspawn) || (W ≥ M) || (nᵣ*((num_cores() ≥ StaticInt(8)) ? max(nspawn,8) : 8) ≥ N)
# `nᵣ*nspawn ≥ N` is needed at the moment to avoid accidentally splitting `N` to be `< nᵣ` while packing
# Should probably handle that with a smarter splitting function...
matmulsplitn!(C, A, B, α, β, Mc, M, K, N, nspawn, Val{false}())
elseif ((nspawn*(W+W) > M) || (contiguousstride1(B) ? (roundtostaticint(Kc * Nc * R₂Default()) ≥ K * N) : (firstbytestride(B) ≤ 1600)))
matmulsplitn!(C, A, B, α, β, Mc, M, K, N, nspawn, Val{true}())
else # TODO: Allow splitting along `N` for `matmul_pack_A_and_B!`
matmul_pack_A_and_B!(C, A, B, α, β, M, K, N, nspawn, W₁Default(), W₂Default(), R₁Default(), R₂Default())
end
nothing
end
# If tasks is [0,1,2,3] (e.g., `CloseOpen(0,4)`), it will wait on `MULTASKS[i]` for `i = [1,2,3]`.
function waitonmultasks(tasks)
for tid ∈ tasks
wait(tid)
end
end
@inline allocref(::StaticInt{N}) where {N} = Ref{NTuple{N,UInt8}}()
function matmul_pack_A_and_B!(
C::AbstractStridedPointer{T}, A::AbstractStridedPointer, B::AbstractStridedPointer, α, β, M, K, N,
tospawn::Int, ::StaticFloat64{W₁}, ::StaticFloat64{W₂}, ::StaticFloat64{R₁}, ::StaticFloat64{R₂}#, ::Val{1}
) where {T,W₁,W₂,R₁,R₂}
W = pick_vector_width(T)
mᵣ, nᵣ = matmul_params()
mᵣW = mᵣ * W
# atomicsync = Ref{NTuple{16,UInt}}()
Mbsize, Mrem, Mremfinal, _to_spawn = split_m(M, tospawn, W) # M is guaranteed to be > W because of `W ≥ M` condition for `jmultsplitn!`...
atomicsync = allocref(StaticInt{2}()*num_cores()*cache_linesize())
p = reinterpret(Ptr{UInt}, Base.unsafe_convert(Ptr{UInt8}, atomicsync))
GC.@preserve atomicsync begin
for i ∈ CloseOpen(2_to_spawn)
_atomic_store!(p + i*cache_linesize(), zero(UInt))
end
Mblock_Mrem, Mblock_ = promote(Mbsize + W, Mbsize)
u_to_spawn = _to_spawn % UInt
tid = 0
bc = _use_bcache()
bc_ptr = Base.unsafe_convert(typeof(pointer(C)), pointer(bc))
last_id = _to_spawn - One()
for m ∈ CloseOpen(last_id) # ...thus the fact that `CloseOpen()` iterates at least once is okay.
Mblock = ifelse(m < Mrem, Mblock_Mrem, Mblock_)
launch_thread_mul!(C, A, B, α, β, Mblock, K, N, p, bc_ptr, m % UInt, u_to_spawn, StaticFloat64{W₁}(),StaticFloat64{W₂}(),StaticFloat64{R₁}(),StaticFloat64{R₂}())
A = gesp(A, (Mblock, Zero()))
C = gesp(C, (Mblock, Zero()))
end
sync_mul!(C, A, B, α, β, Mremfinal, K, N, p, bc_ptr, last_id % UInt, u_to_spawn, StaticFloat64{W₁}(), StaticFloat64{W₂}(), StaticFloat64{R₁}(), StaticFloat64{R₂}())
waitonmultasks(CloseOpen(One(), _to_spawn))
end
_free_bcache!(bc)
return
end
function sync_mul!(
C::AbstractStridedPointer{T}, A::AbstractStridedPointer, B::AbstractStridedPointer, α, β, M, K, N, atomicp::Ptr{UInt}, bc::Ptr, id::UInt, total_ids::UInt,
::StaticFloat64{W₁}, ::StaticFloat64{W₂}, ::StaticFloat64{R₁}, ::StaticFloat64{R₂}
) where {T, W₁, W₂, R₁, R₂}
(Mblock, Mblock_Mrem, Mremfinal, Mrem, Miter), (Kblock, Kblock_Krem, Krem, Kiter), (Nblock, Nblock_Nrem, Nrem, Niter) =
solve_block_sizes(T, M, K, N, StaticFloat64{W₁}(), StaticFloat64{W₂}(), StaticFloat64{R₁}(), StaticFloat64{R₂}(), One())
# atomics = atomicp + 8sizeof(UInt)
sync_iters = zero(UInt)
myp = atomicp + id *cache_linesize()
atomicp -= cache_linesize()
atomics = atomicp + total_ids*cache_linesize()
mys = myp + total_ids*(cache_linesize() % UInt)
Npackb_r_div, Npackb_r_rem = divrem_fast(Nblock_Nrem, total_ids)
Npackb_r_block_rem, Npackb_r_block_ = promote(Npackb_r_div + One(), Npackb_r_div)
Npackb___div, Npackb___rem = divrem_fast(Nblock, total_ids)
Npackb___block_rem, Npackb___block_ = promote(Npackb___div + One(), Npackb___div)
pack_r_offset = Npackb_r_div * id + min(id, Npackb_r_rem)
pack___offset = Npackb___div * id + min(id, Npackb___rem)
pack_r_len = ifelse(id < Npackb_r_rem, Npackb_r_block_rem, Npackb_r_block_)
pack___len = ifelse(id < Npackb___rem, Npackb___block_rem, Npackb___block_)
for n in CloseOpen(Niter)
# Krem
# pack kc x nc block of B
nfull = n < Nrem
nsize = ifelse(nfull, Nblock_Nrem, Nblock)
pack_offset = ifelse(nfull, pack_r_offset, pack___offset)
pack_len = ifelse(nfull, pack_r_len, pack___len)
let A = A, B = B
for k ∈ CloseOpen(Kiter)
ksize = ifelse(k < Krem, Kblock_Krem, Kblock)
_B = default_zerobased_stridedpointer(bc, (One(), ksize))
unsafe_copyto_avx!(gesp(_B, (Zero(), pack_offset)), gesp(B, (Zero(), pack_offset)), ksize, pack_len)
# synchronize before starting the multiplication, to ensure `B` is packed
_mv = _atomic_add!(myp, one(UInt))
sync_iters += one(UInt)
let atomp = atomicp
for _ ∈ CloseOpen(total_ids)
atomp += cache_linesize()
atomp == myp && continue
while _atomic_load(atomp) != sync_iters
pause()
end
end
end
# multiply
let A = A, B = _B, C = C
for m in CloseOpen(Miter)
msize = ifelse((m+1) == Miter, Mremfinal, ifelse(m < Mrem, Mblock_Mrem, Mblock))
if k == 0
packaloopmul!(C, A, B, α, β, msize, ksize, nsize)
else
packaloopmul!(C, A, B, α, One(), msize, ksize, nsize)
end
A = gesp(A, (msize, Zero()))
C = gesp(C, (msize, Zero()))
end
end
A = gesp(A, (Zero(), ksize))
B = gesp(B, (ksize, Zero()))
# synchronize on completion so we wait until every thread is done with `Bpacked` before beginning to overwrite it
_mv = _atomic_add!(mys, one(UInt))
let atoms = atomics
for _ ∈ CloseOpen(total_ids)
atoms += cache_linesize()
atoms == mys && continue
while _atomic_load(atoms) != sync_iters
pause()
end
end
end
end
end
B = gesp(B, (Zero(), nsize))
C = gesp(C, (Zero(), nsize))
end
nothing
end
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] | 2.011629 | 10,835 |
<gh_stars>0
import Base: linspace
# Multidimensional Linspace
function linspace{D, T}(start ::NTuple{D, T}, stop ::NTuple{D, T}, n=50)
zip([linspace(start[d], stop[d], n+1)[1:n] for d in 1:D]...)
end
function flatten{T}(a :: Array{T})
return reshape(a, prod(size(a)))
end
import Iterators
function linpath{D, T <: Number}(points ::Vector{NTuple{D, T}}, n=50)
segments = []
for ip in 2:length(points)
push!(segments, linspace(points[ip-1], points[ip], n))
end
Iterators.chain(segments...)
end
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] | 2.419811 | 212 |
function cpu_reduce_partial_sum(
partial_sum::StructArray
)
return sum(replace_storage(Array, partial_sum), dims=1)
end
function cuda_reduce_partial_sum(
partial_sum::StructArray
)
return CUDA.sum(partial_sum.re, dims=1), CUDA.sum(partial_sum.im, dims=1)
end
function gen_code_replica_kernel!(
code_replica,
codes,
code_frequency,
sampling_frequency,
start_code_phase,
prn,
num_samples,
latest_shift,
code_length
)
# thread_idx goes from 1:2502
# sample_idx converted to -1:2500 -> [-1 0 +1]
thread_idx = 1 + ((blockIdx().x - 1) * blockDim().x + (threadIdx().x - 1))
if thread_idx <= num_samples
@inbounds code_replica[thread_idx] = codes[1+mod(floor(Int32, code_frequency/sampling_frequency * (thread_idx + latest_shift) + start_code_phase), code_length), prn]
end
return nothing
end
function gen_code_replica_strided_kernel!(
code_replica,
codes,
code_frequency,
sampling_frequency,
start_code_phase,
prn,
num_samples,
latest_shift,
code_length
)
# thread_idx goes from 1:2502
# sample_idx converted to -1:2500 -> [-1 0 +1]
stride = blockDim().x * gridDim().x
thread_idx = 1 + ((blockIdx().x - 1) * blockDim().x + (threadIdx().x - 1))
@inbounds for i = thread_idx:stride:num_samples
code_replica[i] = codes[1+mod(floor(Int32, code_frequency/sampling_frequency * (i + latest_shift) + start_code_phase), code_length), prn]
end
return nothing
end
function gen_code_replica_texture_mem_strided_kernel!(
code_replica,
codes, # texture memory codes
code_frequency,
sampling_frequency,
start_code_phase,
prn::Int,
num_samples,
latest_shift,
code_length
)
# thread_idx goes from 1:2502
# sample_idx converted to -1:2500 -> [-1 0 +1]
thread_idx = (blockIdx().x - 1) * blockDim().x + threadIdx().x
if thread_idx <= num_samples
code_replica[thread_idx] = codes[(code_frequency/sampling_frequency * (thread_idx + latest_shift) + start_code_phase) / code_length, prn]
end
return nothing
end
function gen_code_replica_texture_mem_strided_nsat_kernel!(
code_replica,
codes, # texture memory codes
code_frequency,
sampling_frequency,
start_code_phase,
prn,
num_samples,
latest_shift,
code_length
)
# thread_idx goes from 1:2502
# sample_idx converted to -1:2500 -> [-1 0 +1]
sat_idx = blockIdx().y
thread_idx = (blockIdx().x - 1) * blockDim().x + threadIdx().x
if thread_idx <= num_samples
code_replica[thread_idx, sat_idx] = codes[(code_frequency/sampling_frequency * (thread_idx + latest_shift) + start_code_phase) / code_length, prn[sat_idx]]
end
return nothing
end
function gen_code_replica_texture_mem_strided_kernel!(
code_replica,
codes, # texture memory codes
code_frequency,
sampling_frequency,
start_code_phase,
prn,
num_samples,
latest_shift,
code_length
)
# thread_idx goes from 1:2502
# sample_idx converted to -1:2500 -> [-1 0 +1]
thread_idx = 1 + ((blockIdx().x - 1) * blockDim().x + (threadIdx().x - 1))
if thread_idx <= num_samples
code_replica[thread_idx] = codes[(code_frequency/sampling_frequency * (thread_idx + latest_shift) + start_code_phase) / code_length, prn]
end
return nothing
end
function gen_code_replica_texture_mem_kernel!(
code_replica,
codes, # texture memory codes
code_frequency,
sampling_frequency,
start_code_phase,
prn,
num_samples,
latest_shift,
code_length
)
# thread_idx goes from 1:2502
# sample_idx converted to -1:2500 -> [-1 0 +1]
thread_idx = 1 + ((blockIdx().x - 1) * blockDim().x + (threadIdx().x - 1))
if thread_idx <= num_samples
code_replica[thread_idx] = codes[(code_frequency/sampling_frequency * (thread_idx + latest_shift) + start_code_phase) / code_length, prn]
end
return nothing
end
function downconvert_and_correlate_kernel_1330!(
res_re,
res_im,
signal_re,
signal_im,
codes,
code_frequency,
correlator_sample_shifts,
carrier_frequency,
sampling_frequency,
start_code_phase,
carrier_phase,
code_length,
prn,
num_samples,
num_ants,
num_corrs
)
cache = @cuDynamicSharedMem(Float32, (2 * blockDim().x, num_ants, num_corrs))
sample_idx = 1 + ((blockIdx().x - 1) * blockDim().x + (threadIdx().x - 1))
antenna_idx = 1 + ((blockIdx().y - 1) * blockDim().y + (threadIdx().y - 1))
corr_idx = 1 + ((blockIdx().z - 1) * blockDim().z + (threadIdx().z - 1))
iq_offset = blockDim().x
cache_index = threadIdx().x - 1
code_phase = accum_re = accum_im = dw_re = dw_im = carrier_re = carrier_im = 0.0f0
mod_floor_code_phase = Int(0)
if sample_idx <= num_samples && antenna_idx <= num_ants && corr_idx <= num_corrs
# generate carrier
carrier_im, carrier_re = CUDA.sincos(2π * ((sample_idx - 1) * carrier_frequency / sampling_frequency + carrier_phase))
# downconvert with the conjugate of the carrier
dw_re = signal_re[sample_idx, antenna_idx] * carrier_re + signal_im[sample_idx, antenna_idx] * carrier_im
dw_im = signal_im[sample_idx, antenna_idx] * carrier_re - signal_re[sample_idx, antenna_idx] * carrier_im
# calculate the code phase
code_phase = code_frequency / sampling_frequency * ((sample_idx - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase
# wrap the code phase around the code length e.g. phase = 1024 -> modfloorphase = 1
mod_floor_code_phase = 1 + mod(floor(Int32, code_phase), code_length)
# multiply elementwise with the code
accum_re += codes[mod_floor_code_phase, prn] * dw_re
accum_im += codes[mod_floor_code_phase, prn] * dw_im
end
cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] = accum_re
cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] = accum_im
## Reduction
# wait until all the accumulators have done writing the results to the cache
sync_threads()
i::Int = blockDim().x ÷ 2
@inbounds while i != 0
if cache_index < i
cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 0 * iq_offset + i, antenna_idx, corr_idx]
cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 1 * iq_offset + i, antenna_idx, corr_idx]
end
sync_threads()
i ÷= 2
end
if (threadIdx().x - 1) == 0
res_re[blockIdx().x, antenna_idx, corr_idx] += cache[1 + 0 * iq_offset, antenna_idx, corr_idx]
res_im[blockIdx().x, antenna_idx, corr_idx] += cache[1 + 1 * iq_offset, antenna_idx, corr_idx]
end
return nothing
end
function downconvert_and_correlate_kernel_1331!(
res_re,
res_im,
signal_re,
signal_im,
codes,
code_frequency,
correlator_sample_shifts,
carrier_frequency,
sampling_frequency,
start_code_phase,
carrier_phase,
code_length,
prn,
num_samples,
num_ants,
num_corrs
)
cache = @cuDynamicSharedMem(Float32, (2 * blockDim().x, num_ants, num_corrs))
sample_idx = 1 + ((blockIdx().x - 1) * blockDim().x + (threadIdx().x - 1))
antenna_idx = 1 + ((blockIdx().y - 1) * blockDim().y + (threadIdx().y - 1))
corr_idx = 1 + ((blockIdx().z - 1) * blockDim().z + (threadIdx().z - 1))
iq_offset = blockDim().x
cache_index = threadIdx().x - 1
accum_re = accum_im = dw_re = dw_im = carrier_re = carrier_im = 0.0f0
if sample_idx <= num_samples && antenna_idx <= num_ants && corr_idx <= num_corrs
# generate carrier
carrier_im, carrier_re = CUDA.sincos(2π * ((sample_idx - 1) * carrier_frequency / sampling_frequency + carrier_phase))
# downconvert with the conjugate of the carrier
dw_re = signal_re[sample_idx, antenna_idx] * carrier_re + signal_im[sample_idx, antenna_idx] * carrier_im
dw_im = signal_im[sample_idx, antenna_idx] * carrier_re - signal_re[sample_idx, antenna_idx] * carrier_im
# multiply elementwise with the code
accum_re += codes[(code_frequency / sampling_frequency * ((sample_idx - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase) / code_length, prn] * dw_re
accum_im += codes[(code_frequency / sampling_frequency * ((sample_idx - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase) / code_length, prn] * dw_im
end
cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] = accum_re
cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] = accum_im
## Reduction
# wait until all the accumulators have done writing the results to the cache
sync_threads()
i::Int = blockDim().x ÷ 2
@inbounds while i != 0
if cache_index < i
cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 0 * iq_offset + i, antenna_idx, corr_idx]
cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 1 * iq_offset + i, antenna_idx, corr_idx]
end
sync_threads()
i ÷= 2
end
if (threadIdx().x - 1) == 0
res_re[blockIdx().x, antenna_idx, corr_idx] += cache[1 + 0 * iq_offset, antenna_idx, corr_idx]
res_im[blockIdx().x, antenna_idx, corr_idx] += cache[1 + 1 * iq_offset, antenna_idx, corr_idx]
end
return nothing
end
function downconvert_and_correlate_kernel_1431!(
res_re,
res_im,
signal_re,
signal_im,
codes,
code_frequency,
correlator_sample_shifts,
carrier_frequency,
sampling_frequency,
start_code_phase,
carrier_phase,
code_length,
prn,
num_samples,
num_ants,
num_corrs
)
block_dim_x = 2 * blockDim().x # launched with half the grid
sample_idx = 1 + ((blockIdx().x - 1) * block_dim_x + (threadIdx().x - 1))
antenna_idx = 1 + ((blockIdx().y - 1) * blockDim().y + (threadIdx().y - 1))
corr_idx = 1 + ((blockIdx().z - 1) * blockDim().z + (threadIdx().z - 1))
iq_offset = blockDim().x
cache_index = threadIdx().x - 1
# allocate shared memory
cache = @cuDynamicSharedMem(Float32, (2 * blockDim().x, num_ants, num_corrs))
# wipe values
cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] = 0.0f0
cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] = 0.0f0
# define local variables
accum_re_1 = accum_im_1 = dw_re_1 = dw_im_1 = carrier_re_1 = carrier_im_1 = 0.0f0
accum_re_2 = accum_im_2 = dw_re_2 = dw_im_2 = carrier_re_2 = carrier_im_2 = 0.0f0
if sample_idx <= num_samples && antenna_idx <= num_ants && corr_idx <= num_corrs
# generate carrier
carrier_im_1, carrier_re_1 = CUDA.sincos(2π * ((sample_idx - 1) * carrier_frequency / sampling_frequency + carrier_phase))
# downconvert with the conjugate of the carrier
dw_re_1 = signal_re[sample_idx, antenna_idx] * carrier_re_1 + signal_im[sample_idx, antenna_idx] * carrier_im_1
dw_im_1 = signal_im[sample_idx, antenna_idx] * carrier_re_1 - signal_re[sample_idx, antenna_idx] * carrier_im_1
# multiply elementwise with the code
accum_re_1 += codes[(code_frequency / sampling_frequency * ((sample_idx - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase) / code_length, prn] * dw_re_1
accum_im_1 += codes[(code_frequency / sampling_frequency * ((sample_idx - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase) / code_length, prn] * dw_im_1
# write results to shared memory
cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] = accum_re_1
cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] = accum_im_1
if sample_idx + blockDim().x <= num_samples
# generate carrier for the remaining samples
carrier_im_2, carrier_re_2 = CUDA.sincos(2π * ((sample_idx + blockDim().x - 1) * carrier_frequency / sampling_frequency + carrier_phase))
# downconvert with the conjugate of the carrier for the remaining samples
dw_re_2 = signal_re[sample_idx + blockDim().x, antenna_idx] * carrier_re_2 + signal_im[sample_idx + blockDim().x, antenna_idx] * carrier_im_2
dw_im_2 = signal_im[sample_idx + blockDim().x, antenna_idx] * carrier_re_2 - signal_re[sample_idx + blockDim().x, antenna_idx] * carrier_im_2
# multiply elementwise with the code for the remaining samples
accum_re_2 += codes[(code_frequency / sampling_frequency * ((sample_idx + blockDim().x - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase) / code_length, prn] * dw_re_2
accum_im_2 += codes[(code_frequency / sampling_frequency * ((sample_idx + blockDim().x - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase) / code_length, prn] * dw_im_2
# append results to shared memory
cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += accum_re_2
cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += accum_im_2
end
end
## Reduction in shared memory
# wait until all the accumulators have done writing the results to the cache
sync_threads()
i::Int = blockDim().x ÷ 2
@inbounds while i != 0
if cache_index < i
cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 0 * iq_offset + i, antenna_idx, corr_idx]
cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 1 * iq_offset + i, antenna_idx, corr_idx]
end
sync_threads()
i ÷= 2
end
if (threadIdx().x - 1) == 0
res_re[blockIdx().x, antenna_idx, corr_idx] += cache[1 + 0 * iq_offset, antenna_idx, corr_idx]
res_im[blockIdx().x, antenna_idx, corr_idx] += cache[1 + 1 * iq_offset, antenna_idx, corr_idx]
end
return nothing
end
# function downconvert_and_correlate_kernel_3431!(
# res_re,
# res_im,
# signal_re,
# signal_im,
# codes,
# code_frequency,
# correlator_sample_shifts,
# carrier_frequency,
# sampling_frequency,
# start_code_phase,
# carrier_phase,
# code_length,
# prn,
# num_samples,
# num_ants,
# num_corrs
# )
# block_dim_x = 2 * blockDim().x # launched with half the grid
# sample_idx = 1 + ((blockIdx().x - 1) * block_dim_x + (threadIdx().x - 1))
# antenna_idx = 1 + ((blockIdx().y - 1) * blockDim().y + (threadIdx().y - 1))
# corr_idx = 1 + ((blockIdx().z - 1) * blockDim().z + (threadIdx().z - 1))
# iq_offset = blockDim().x
# cache_index = threadIdx().x - 1
# # allocate shared memory
# cache = @cuDynamicSharedMem(Float32, (2 * blockDim().x, num_ants, num_corrs))
# # wipe values
# cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] = 0.0f0
# cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] = 0.0f0
# # define local variables
# accum_re_1 = accum_im_1 = dw_re_1 = dw_im_1 = carrier_re_1 = carrier_im_1 = 0.0f0
# accum_re_2 = accum_im_2 = dw_re_2 = dw_im_2 = carrier_re_2 = carrier_im_2 = 0.0f0
# if sample_idx <= num_samples && antenna_idx <= num_ants && corr_idx <= num_corrs
# # generate carrier
# carrier_im_1, carrier_re_1 = CUDA.sincos(2π * ((sample_idx - 1) * carrier_frequency / sampling_frequency + carrier_phase))
# # downconvert with the conjugate of the carrier
# dw_re_1 = signal_re[sample_idx, antenna_idx] * carrier_re_1 + signal_im[sample_idx, antenna_idx] * carrier_im_1
# dw_im_1 = signal_im[sample_idx, antenna_idx] * carrier_re_1 - signal_re[sample_idx, antenna_idx] * carrier_im_1
# # multiply elementwise with the code
# accum_re_1 += codes[(code_frequency / sampling_frequency * ((sample_idx - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase) / code_length, prn] * dw_re_1
# accum_im_1 += codes[(code_frequency / sampling_frequency * ((sample_idx - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase) / code_length, prn] * dw_im_1
# # write results to shared memory
# cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] = accum_re_1
# cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] = accum_im_1
# if sample_idx + blockDim().x <= num_samples
# # generate carrier for the remaining samples
# carrier_im_2, carrier_re_2 = CUDA.sincos(2π * ((sample_idx + blockDim().x - 1) * carrier_frequency / sampling_frequency + carrier_phase))
# # downconvert with the conjugate of the carrier for the remaining samples
# dw_re_2 = signal_re[sample_idx + blockDim().x, antenna_idx] * carrier_re_2 + signal_im[sample_idx + blockDim().x, antenna_idx] * carrier_im_2
# dw_im_2 = signal_im[sample_idx + blockDim().x, antenna_idx] * carrier_re_2 - signal_re[sample_idx + blockDim().x, antenna_idx] * carrier_im_2
# # multiply elementwise with the code for the remaining samples
# accum_re_2 += codes[(code_frequency / sampling_frequency * ((sample_idx + blockDim().x - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase) / code_length, prn] * dw_re_2
# accum_im_2 += codes[(code_frequency / sampling_frequency * ((sample_idx + blockDim().x - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase) / code_length, prn] * dw_im_2
# # append results to shared memory
# cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += accum_re_2
# cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += accum_im_2
# end
# end
# ## Reduction in shared memory
# # wait until all the accumulators have done writing the results to the cache
# sync_threads()
# i::Int = blockDim().x ÷ 2
# @inbounds while i != 0
# if cache_index < i
# cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 0 * iq_offset + i, antenna_idx, corr_idx]
# cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 1 * iq_offset + i, antenna_idx, corr_idx]
# end
# sync_threads()
# i ÷= 2
# end
# if (threadIdx().x - 1) == 0
# res_re[blockIdx().x, antenna_idx, corr_idx] += cache[1 + 0 * iq_offset, antenna_idx, corr_idx]
# res_im[blockIdx().x, antenna_idx, corr_idx] += cache[1 + 1 * iq_offset, antenna_idx, corr_idx]
# end
# return nothing
# end
function downconvert_and_correlate_kernel_3431!(
partial_sum_re,
partial_sum_im,
carrier_replica_re,
carrier_replica_im,
downconverted_signal_re,
downconverted_signal_im,
signal_re,
signal_im,
code_replica,
correlator_sample_shifts::SVector{NCOR, Int64},
carrier_frequency,
sampling_frequency,
carrier_phase,
num_samples::Int,
num_ants::NumAnts{NANT}
) where {NCOR, NANT}
cache = @cuDynamicSharedMem(Float32, (2 * blockDim().x, NANT, NCOR))
sample_idx = 1 + ((blockIdx().x - 1) * (2 * blockDim().x) + (threadIdx().x - 1)) # double the grid
iq_offset = blockDim().x # indexing offset for complex values I/Q samples
cache_index = threadIdx().x - 1
dw_re_1 = dw_im_1 = carrier_re_1 = carrier_im_1 = 0.0f0
dw_re_2 = dw_im_2 = carrier_re_2 = carrier_im_2 = 0.0f0
@inbounds if sample_idx <= num_samples
# carrier replica generation, sin->im , cos->re
carrier_im_1, carrier_re_1 = CUDA.sincos(2π * ((sample_idx - 1) * carrier_frequency / sampling_frequency + carrier_phase))
# downconversion / carrier wipe off
for antenna_idx = 1:NANT
dw_re_1 = signal_re[sample_idx, antenna_idx] * carrier_re_1 + signal_im[sample_idx, antenna_idx] * carrier_im_1
dw_im_1 = signal_im[sample_idx, antenna_idx] * carrier_re_1 - signal_re[sample_idx, antenna_idx] * carrier_im_1
for corr_idx = 1:NCOR
sample_shift = correlator_sample_shifts[corr_idx] - correlator_sample_shifts[1]
# write to shared memory cache
cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] = code_replica[sample_idx + sample_shift] * dw_re_1
cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] = code_replica[sample_idx + sample_shift] * dw_im_1
end
end
if sample_idx + blockDim().x <= num_samples
# carrier replica generation, sin->im , cos->re
carrier_im_2, carrier_re_2 = CUDA.sincos(2π * ((sample_idx + blockDim().x - 1) * carrier_frequency / sampling_frequency + carrier_phase))
# downconversion / carrier wipe off
for antenna_idx = 1:NANT
dw_re_2 = signal_re[sample_idx + blockDim().x, antenna_idx] * carrier_re_2 + signal_im[sample_idx + blockDim().x, antenna_idx] * carrier_im_2
dw_im_2 = signal_im[sample_idx + blockDim().x, antenna_idx] * carrier_re_2 - signal_re[sample_idx + blockDim().x, antenna_idx] * carrier_im_2
for corr_idx = 1:NCOR
sample_shift = correlator_sample_shifts[corr_idx] - correlator_sample_shifts[1]
# write to shared memory cache
cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += code_replica[sample_idx + blockDim().x + sample_shift] * dw_re_2
cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += code_replica[sample_idx + blockDim().x + sample_shift] * dw_im_2
end
end
end
end
## Partial Reduction
# wait until all the accumulators have done writing the results to the cache
sync_threads()
i::Int = blockDim().x ÷ 2
@inbounds while i != 0
if cache_index < i
for antenna_idx = 1:NANT
for corr_idx = 1:NCOR
cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 0 * iq_offset + i, antenna_idx, corr_idx]
cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 1 * iq_offset + i, antenna_idx, corr_idx]
end
end
end
sync_threads()
i ÷= 2
end
@inbounds if (threadIdx().x - 1) == 0
for antenna_idx = 1:NANT
for corr_idx = 1:NCOR
partial_sum_re[blockIdx().x, antenna_idx, corr_idx] = cache[1 + 0 * iq_offset, antenna_idx, corr_idx]
partial_sum_im[blockIdx().x, antenna_idx, corr_idx] = cache[1 + 1 * iq_offset, antenna_idx, corr_idx]
end
end
end
return nothing
end
function downconvert_and_correlate_kernel_4431!(
accum_re,
accum_im,
carrier_replica_re,
carrier_replica_im,
downconverted_signal_re,
downconverted_signal_im,
signal_re,
signal_im,
code_replica,
correlator_sample_shifts::SVector{NCOR, Int64},
carrier_frequency,
sampling_frequency,
carrier_phase,
num_samples::Int,
num_ants::NumAnts{NANT}
) where {NCOR, NANT}
cache = @cuDynamicSharedMem(Float32, (2 * blockDim().x, NANT, NCOR))
sample_idx = 1 + ((blockIdx().x - 1) * (2 * blockDim().x) + (threadIdx().x - 1)) # double the grid
iq_offset = blockDim().x # indexing offset for complex values I/Q samples
cache_index = threadIdx().x - 1
dw_re_1 = dw_im_1 = carrier_re_1 = carrier_im_1 = 0.0f0
dw_re_2 = dw_im_2 = carrier_re_2 = carrier_im_2 = 0.0f0
@inbounds if sample_idx <= num_samples
# carrier replica generation, sin->im , cos->re
carrier_im_1, carrier_re_1 = CUDA.sincos(2π * ((sample_idx - 1) * carrier_frequency / sampling_frequency + carrier_phase))
# downconversion / carrier wipe off
for antenna_idx = 1:NANT
dw_re_1 = signal_re[sample_idx, antenna_idx] * carrier_re_1 + signal_im[sample_idx, antenna_idx] * carrier_im_1
dw_im_1 = signal_im[sample_idx, antenna_idx] * carrier_re_1 - signal_re[sample_idx, antenna_idx] * carrier_im_1
for corr_idx = 1:NCOR
sample_shift = correlator_sample_shifts[corr_idx] - correlator_sample_shifts[1]
# write to shared memory cache
cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] = code_replica[sample_idx + sample_shift] * dw_re_1
cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] = code_replica[sample_idx + sample_shift] * dw_im_1
end
end
if sample_idx + blockDim().x <= num_samples
# carrier replica generation, sin->im , cos->re
carrier_im_2, carrier_re_2 = CUDA.sincos(2π * ((sample_idx + blockDim().x - 1) * carrier_frequency / sampling_frequency + carrier_phase))
# downconversion / carrier wipe off
for antenna_idx = 1:NANT
dw_re_2 = signal_re[sample_idx + blockDim().x, antenna_idx] * carrier_re_2 + signal_im[sample_idx + blockDim().x, antenna_idx] * carrier_im_2
dw_im_2 = signal_im[sample_idx + blockDim().x, antenna_idx] * carrier_re_2 - signal_re[sample_idx + blockDim().x, antenna_idx] * carrier_im_2
for corr_idx = 1:NCOR
sample_shift = correlator_sample_shifts[corr_idx] - correlator_sample_shifts[1]
# write to shared memory cache
cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += code_replica[sample_idx + blockDim().x + sample_shift] * dw_re_2
cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += code_replica[sample_idx + blockDim().x + sample_shift] * dw_im_2
end
end
end
end
## Partial Reduction
# wait until all the accumulators have done writing the results to the cache
sync_threads()
i::Int = blockDim().x ÷ 2
@inbounds while i != 0
if cache_index < i
for antenna_idx = 1:NANT
for corr_idx = 1:NCOR
cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 0 * iq_offset + i, antenna_idx, corr_idx]
cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 1 * iq_offset + i, antenna_idx, corr_idx]
end
end
end
sync_threads()
i ÷= 2
end
@inbounds if threadIdx().x == 1
for antenna_idx = 1:NANT
for corr_idx = 1:NCOR
CUDA.@atomic accum_re[antenna_idx, corr_idx] += cache[1 + 0 * iq_offset, antenna_idx, corr_idx]
CUDA.@atomic accum_im[antenna_idx, corr_idx] += cache[1 + 1 * iq_offset, antenna_idx, corr_idx]
end
end
end
return nothing
end
function downconvert_and_correlate_kernel_3d_4431!(
accum_re,
accum_im,
signal_re,
signal_im,
code_replica,
correlator_sample_shifts,
carrier_frequency,
sampling_frequency,
carrier_phase,
num_samples::Int,
num_ants::NumAnts{NANT}
) where {NANT}
cache = CuDynamicSharedArray(Float32, (2 * blockDim().x, NANT))
sample_idx = 1 + ((blockIdx().x - 1) * (2 * blockDim().x) + (threadIdx().x - 1)) # double the grid
iq_offset = blockDim().x # indexing offset for complex values I/Q samples
cache_index = threadIdx().x - 1
antenna_idx = threadIdx().y
corr_idx = blockIdx().y
sat_idx = blockIdx().z
dw_re_1 = dw_im_1 = carrier_re_1 = carrier_im_1 = 0.0f0
dw_re_2 = dw_im_2 = carrier_re_2 = carrier_im_2 = 0.0f0
sample_shift = correlator_sample_shifts[corr_idx]
@inbounds if sample_idx <= num_samples
# carrier replica generation, sin->im , cos->re
carrier_im_1, carrier_re_1 = CUDA.sincos(2π * ((sample_idx - 1) * carrier_frequency / sampling_frequency + carrier_phase))
# downconversion / carrier wipe off
for antenna_idx = 1:NANT
dw_re_1 = signal_re[sample_idx, antenna_idx, sat_idx] * carrier_re_1 + signal_im[sample_idx, antenna_idx, sat_idx] * carrier_im_1
dw_im_1 = signal_im[sample_idx, antenna_idx, sat_idx] * carrier_re_1 - signal_re[sample_idx, antenna_idx, sat_idx] * carrier_im_1
# write to shared memory cache
cache[1 + cache_index + 0 * iq_offset, antenna_idx] = code_replica[sample_idx + sample_shift, sat_idx] * dw_re_1
cache[1 + cache_index + 1 * iq_offset, antenna_idx] = code_replica[sample_idx + sample_shift, sat_idx] * dw_im_1
end
if sample_idx + blockDim().x <= num_samples
# carrier replica generation, sin->im , cos->re
carrier_im_2, carrier_re_2 = CUDA.sincos(2π * ((sample_idx + blockDim().x - 1) * carrier_frequency / sampling_frequency + carrier_phase))
# downconversion / carrier wipe off
for antenna_idx = 1:NANT
dw_re_2 = signal_re[sample_idx + blockDim().x, antenna_idx, sat_idx] * carrier_re_2 + signal_im[sample_idx + blockDim().x, antenna_idx, sat_idx] * carrier_im_2
dw_im_2 = signal_im[sample_idx + blockDim().x, antenna_idx, sat_idx] * carrier_re_2 - signal_re[sample_idx + blockDim().x, antenna_idx, sat_idx] * carrier_im_2
# write to shared memory cache
cache[1 + cache_index + 0 * iq_offset, antenna_idx] += code_replica[sample_idx + blockDim().x + sample_shift, sat_idx] * dw_re_2
cache[1 + cache_index + 1 * iq_offset, antenna_idx] += code_replica[sample_idx + blockDim().x + sample_shift, sat_idx] * dw_im_2
end
end
end
## Partial Reduction
# wait until all the accumulators have done writing the results to the cache
sync_threads()
i::Int = blockDim().x ÷ 2
@inbounds while i != 0
if cache_index < i
for antenna_idx = 1:NANT
cache[1 + cache_index + 0 * iq_offset, antenna_idx] += cache[1 + cache_index + 0 * iq_offset + i, antenna_idx]
cache[1 + cache_index + 1 * iq_offset, antenna_idx] += cache[1 + cache_index + 1 * iq_offset + i, antenna_idx]
end
end
sync_threads()
i ÷= 2
end
@inbounds if threadIdx().x == 1
for antenna_idx = 1:NANT
CUDA.@atomic accum_re[antenna_idx, corr_idx, sat_idx] += cache[1 + 0 * iq_offset, antenna_idx]
CUDA.@atomic accum_im[antenna_idx, corr_idx, sat_idx] += cache[1 + 1 * iq_offset, antenna_idx]
end
end
return nothing
end
function downconvert_and_correlate_kernel_5431!(
accum_re,
accum_im,
carrier_replica_re,
carrier_replica_im,
downconverted_signal_re,
downconverted_signal_im,
signal_re,
signal_im,
codes,
code_length,
code_replica,
prn,
correlator_sample_shifts::SVector{NCOR, Int64},
num_of_shifts,
code_frequency,
carrier_frequency,
sampling_frequency,
start_code_phase,
carrier_phase,
num_samples::Int,
num_ants::NumAnts{NANT},
) where {NCOR, NANT}
cache = @cuDynamicSharedMem(Float32, (2 * blockDim().x, NANT, NCOR))
sample_idx = 1 + ((blockIdx().x - 1) * (2 * blockDim().x) + (threadIdx().x - 1))
iq_offset = blockDim().x # indexing offset for complex values I/Q samples
cache_index = threadIdx().x - 1
# local
dw_re_1 = dw_im_1 = carrier_re_1 = carrier_im_1 = 0.0f0
dw_re_2 = dw_im_2 = carrier_re_2 = carrier_im_2 = 0.0f0
# Code replica generation
if sample_idx <= num_samples + num_of_shifts
@inbounds code_replica[sample_idx] = codes[(code_frequency/sampling_frequency * (sample_idx - num_of_shifts) + start_code_phase)/code_length, prn]
if sample_idx + blockDim().x <= num_samples + num_of_shifts
@inbounds code_replica[sample_idx + blockDim().x] = codes[(code_frequency/sampling_frequency * (sample_idx + blockDim().x - num_of_shifts) + start_code_phase)/code_length, prn]
end
end
sync_threads()
@inbounds if sample_idx <= num_samples
# carrier replica generation, sin->im , cos->re
carrier_im_1, carrier_re_1 = CUDA.sincos(2π * ((sample_idx - 1) * carrier_frequency / sampling_frequency + carrier_phase))
# downconversion / carrier wipe off
for antenna_idx = 1:NANT
dw_re_1 = signal_re[sample_idx, antenna_idx] * carrier_re_1 + signal_im[sample_idx, antenna_idx] * carrier_im_1
dw_im_1 = signal_im[sample_idx, antenna_idx] * carrier_re_1 - signal_re[sample_idx, antenna_idx] * carrier_im_1
for corr_idx = 1:NCOR
sample_shift = correlator_sample_shifts[corr_idx] - correlator_sample_shifts[1]
# write to shared memory cache
cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] = code_replica[sample_idx + sample_shift] * dw_re_1
cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] = code_replica[sample_idx + sample_shift] * dw_im_1
end
end
if sample_idx + blockDim().x <= num_samples
# carrier replica generation, sin->im , cos->re
carrier_im_2, carrier_re_2 = CUDA.sincos(2π * ((sample_idx + blockDim().x - 1) * carrier_frequency / sampling_frequency + carrier_phase))
# downconversion / carrier wipe off
for antenna_idx = 1:NANT
dw_re_2 = signal_re[sample_idx + blockDim().x, antenna_idx] * carrier_re_2 + signal_im[sample_idx + blockDim().x, antenna_idx] * carrier_im_2
dw_im_2 = signal_im[sample_idx + blockDim().x, antenna_idx] * carrier_re_2 - signal_re[sample_idx + blockDim().x, antenna_idx] * carrier_im_2
for corr_idx = 1:NCOR
sample_shift = correlator_sample_shifts[corr_idx] - correlator_sample_shifts[1]
# write to shared memory cache
cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += code_replica[sample_idx + blockDim().x + sample_shift] * dw_re_2
cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += code_replica[sample_idx + blockDim().x + sample_shift] * dw_im_2
end
end
end
end
## Partial Reduction
# wait until all the accumulators have done writing the results to the cache
sync_threads()
i::Int = blockDim().x ÷ 2
@inbounds while i != 0
if cache_index < i
for antenna_idx = 1:NANT
for corr_idx = 1:NCOR
cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 0 * iq_offset + i, antenna_idx, corr_idx]
cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 1 * iq_offset + i, antenna_idx, corr_idx]
end
end
end
sync_threads()
i ÷= 2
end
# Last block reduction via atomic add
@inbounds if (threadIdx().x - 1) == 0
for antenna_idx = 1:NANT
for corr_idx = 1:NCOR
CUDA.@atomic accum_re[antenna_idx, corr_idx] += cache[1 + 0 * iq_offset, antenna_idx, corr_idx]
CUDA.@atomic accum_im[antenna_idx, corr_idx] += cache[1 + 1 * iq_offset, antenna_idx, corr_idx]
end
end
end
return nothing
end
function downconvert_and_accumulate_strided_kernel!(
accum_re,
accum_im,
code_replica,
carrier_replica_re,
carrier_replica_im,
downconverted_signal_re,
downconverted_signal_im,
signal_re,
signal_im,
carrier_frequency,
sampling_frequency,
carrier_phase,
num_samples::Int,
num_ants::NumAnts{NANT},
correlator_sample_shifts::SVector{NCOR, Int64}
) where {NANT, NCOR}
stride = blockDim().x * gridDim().x
thread_idx = 1 + ((blockIdx().x - 1) * blockDim().x + (threadIdx().x - 1))
@inbounds for sample_idx = thread_idx:stride:num_samples
# gen carrier replica
carrier_replica_im[sample_idx], carrier_replica_re[sample_idx] = CUDA.sincos(2π * ((sample_idx - 1) * carrier_frequency / sampling_frequency + carrier_phase))
for antenna_idx = 1:NANT
# downconvert
downconverted_signal_re[sample_idx, antenna_idx] = signal_re[sample_idx, antenna_idx] * carrier_replica_re[sample_idx] + signal_im[sample_idx, antenna_idx] * carrier_replica_im[sample_idx]
downconverted_signal_im[sample_idx, antenna_idx] = signal_im[sample_idx, antenna_idx] * carrier_replica_re[sample_idx] - signal_re[sample_idx, antenna_idx] * carrier_replica_im[sample_idx]
for corr_idx = 1:NCOR
# accumulate
shift = correlator_sample_shifts[corr_idx] - correlator_sample_shifts[1]
accum_re[sample_idx, antenna_idx, corr_idx] = downconverted_signal_re[sample_idx, antenna_idx] * code_replica[sample_idx + shift]
accum_im[sample_idx, antenna_idx, corr_idx] = downconverted_signal_im[sample_idx, antenna_idx] * code_replica[sample_idx + shift]
end
end
end
return nothing
end
# KERNEL 1_3_cplx_multi
function kernel_algorithm(
threads_per_block,
blocks_per_grid,
shmem_size,
code_replica,
codes,
code_frequency,
sampling_frequency,
start_code_phase,
prn,
num_samples,
num_of_shifts,
code_length,
partial_sum,
carrier_replica_re,
carrier_replica_im,
downconverted_signal_re,
downconverted_signal_im,
signal_re,
signal_im,
correlator_sample_shifts::SVector{NCOR, Int64},
carrier_frequency,
carrier_phase,
num_ants::NumAnts{NANT},
num_corrs,
algorithm::KernelAlgorithm{1330};
) where {NANT, NCOR}
@cuda threads=threads_per_block[1] blocks=blocks_per_grid shmem=shmem_size[1] downconvert_and_correlate_kernel_1330!(
partial_sum.re,
partial_sum.im,
signal_re,
signal_im,
codes,
code_frequency,
correlator_sample_shifts,
carrier_frequency,
sampling_frequency,
start_code_phase,
carrier_phase,
code_length,
prn,
num_samples,
NANT,
NCOR
)
@cuda threads=512 blocks=1 shmem=shmem_size[2] reduce_cplx_multi_3(
partial_sum.re,
partial_sum.im,
partial_sum.re,
partial_sum.im,
blocks_per_grid,
num_ants,
correlator_sample_shifts
)
end
# KERNEL 1_3_cplx_multi_textmem
function kernel_algorithm(
threads_per_block,
blocks_per_grid,
shmem_size,
code_replica,
codes,
code_frequency,
sampling_frequency,
start_code_phase,
prn,
num_samples,
num_of_shifts,
code_length,
partial_sum,
carrier_replica_re,
carrier_replica_im,
downconverted_signal_re,
downconverted_signal_im,
signal_re,
signal_im,
correlator_sample_shifts::SVector{NCOR, Int64},
carrier_frequency,
carrier_phase,
num_ants::NumAnts{NANT},
num_corrs,
algorithm::KernelAlgorithm{1331};
) where {NANT, NCOR}
NVTX.@range "downconvert_and_correlate_kernel_1331!" begin
@cuda threads=threads_per_block[1] blocks=blocks_per_grid shmem=shmem_size[1] downconvert_and_correlate_kernel_1331!(
partial_sum.re,
partial_sum.im,
signal_re,
signal_im,
codes,
code_frequency,
correlator_sample_shifts,
carrier_frequency,
sampling_frequency,
start_code_phase,
carrier_phase,
code_length,
prn,
num_samples,
NANT,
NCOR
)
end
NVTX.@range "reduce_cplx_multi_3" begin
@cuda threads=threads_per_block[2] blocks=1 shmem=shmem_size[2] reduce_cplx_multi_3(
partial_sum.re,
partial_sum.im,
partial_sum.re,
partial_sum.im,
blocks_per_grid,
num_ants,
correlator_sample_shifts
)
end
end
# KERNEL 1_4_cplx_multi_textmem
# 1431
function kernel_algorithm(
threads_per_block,
blocks_per_grid,
shmem_size,
code_replica,
codes,
code_frequency,
sampling_frequency,
start_code_phase,
prn,
num_samples,
num_of_shifts,
code_length,
partial_sum,
carrier_replica_re,
carrier_replica_im,
downconverted_signal_re,
downconverted_signal_im,
signal_re,
signal_im,
correlator_sample_shifts::SVector{NCOR, Int64},
carrier_frequency,
carrier_phase,
num_ants::NumAnts{NANT},
num_corrs,
algorithm::KernelAlgorithm{1431};
) where {NANT, NCOR}
NVTX.@range "downconvert_and_correlate_kernel_1431!" begin
@cuda threads=threads_per_block[1] blocks=blocks_per_grid shmem=shmem_size[1] downconvert_and_correlate_kernel_1431!(
partial_sum.re,
partial_sum.im,
signal_re,
signal_im,
codes,
code_frequency,
correlator_sample_shifts,
carrier_frequency,
sampling_frequency,
start_code_phase,
carrier_phase,
code_length,
prn,
num_samples,
NANT,
NCOR
)
end
NVTX.@range "reduce_cplx_multi_4" begin
@cuda threads=threads_per_block[2] blocks=1 shmem=shmem_size[2] reduce_cplx_multi_4(
partial_sum.re,
partial_sum.im,
partial_sum.re,
partial_sum.im,
blocks_per_grid,
num_ants,
correlator_sample_shifts
)
end
end
# KERNEL 2_3_cplx_multi
# 2330
function kernel_algorithm(
threads_per_block,
blocks_per_grid,
shmem_size,
code_replica,
codes,
code_frequency,
sampling_frequency,
start_code_phase,
prn,
num_samples,
num_of_shifts,
code_length,
accum_re,
accum_im,
phi_re,
phi_im,
carrier_replica_re,
carrier_replica_im,
downconverted_signal_re,
downconverted_signal_im,
signal_re,
signal_im,
correlator_sample_shifts::SVector{NCOR, Int64},
carrier_frequency,
carrier_phase,
num_ants::NumAnts{NANT},
num_corrs,
algorithm::KernelAlgorithm{2330}
) where {NANT, NCOR}
NVTX.@range "gen_code_replica_kernel!" begin
@cuda threads=threads_per_block[1] blocks=blocks_per_grid[1] gen_code_replica_strided_kernel!(
code_replica,
codes,
code_frequency,
sampling_frequency,
start_code_phase,
prn,
num_samples,
num_of_shifts,
code_length
)
end
NVTX.@range "downconvert_and_accumulate!" begin
@cuda threads=threads_per_block[2] blocks=blocks_per_grid[2] downconvert_and_accumulate_strided_kernel!(
accum_re,
accum_im,
code_replica,
carrier_replica_re,
carrier_replica_im,
downconverted_signal_re,
downconverted_signal_im,
signal_re,
signal_im,
carrier_frequency,
sampling_frequency,
carrier_phase,
num_samples,
num_ants,
correlator_sample_shifts
)
end
NVTX.@range "reduce_cplx_multi_3" begin
@cuda threads=threads_per_block[3] blocks=blocks_per_grid[3] shmem=shmem_size reduce_cplx_multi_3(
phi_re,
phi_im,
accum_re,
accum_im,
num_samples,
num_ants,
correlator_sample_shifts
)
end
# print(Array(phi_re))
NVTX.@range "reduce_cplx_multi_3" begin
@cuda threads=threads_per_block[3] blocks=1 shmem=shmem_size reduce_cplx_multi_3(
phi_re,
phi_im,
phi_re,
phi_im,
blocks_per_grid[3],
num_ants,
correlator_sample_shifts
)
end
# print(Array(phi_re))
end
# KERNEL 2_3_cplx_multi_textmem
# 2331
function kernel_algorithm(
threads_per_block,
blocks_per_grid,
shmem_size,
code_replica,
codes,
code_frequency,
sampling_frequency,
start_code_phase,
prn,
num_samples,
num_of_shifts,
code_length,
accum_re,
accum_im,
phi_re,
phi_im,
carrier_replica_re,
carrier_replica_im,
downconverted_signal_re,
downconverted_signal_im,
signal_re,
signal_im,
correlator_sample_shifts::SVector{NCOR, Int64},
carrier_frequency,
carrier_phase,
num_ants::NumAnts{NANT},
num_corrs,
algorithm::KernelAlgorithm{2331}
) where {NANT, NCOR}
NVTX.@range "gen_code_replica_kernel!" begin
@cuda threads=threads_per_block[1] blocks=blocks_per_grid[1] gen_code_replica_texture_mem_strided_kernel!(
code_replica,
codes,
code_frequency,
sampling_frequency,
start_code_phase,
prn,
num_samples,
num_of_shifts,
code_length
)
end
NVTX.@range "downconvert_and_accumulate!" begin
@cuda threads=threads_per_block[2] blocks=blocks_per_grid[2] downconvert_and_accumulate_strided_kernel!(
accum_re,
accum_im,
code_replica,
carrier_replica_re,
carrier_replica_im,
downconverted_signal_re,
downconverted_signal_im,
signal_re,
signal_im,
carrier_frequency,
sampling_frequency,
carrier_phase,
num_samples,
num_ants,
correlator_sample_shifts
)
end
NVTX.@range "reduce_cplx_multi_3" begin
@cuda threads=threads_per_block[3] blocks=blocks_per_grid[3] shmem=shmem_size reduce_cplx_multi_3(
phi_re,
phi_im,
accum_re,
accum_im,
num_samples,
num_ants,
correlator_sample_shifts
)
end
# print(Array(phi_re))
NVTX.@range "reduce_cplx_multi_3" begin
@cuda threads=threads_per_block[3] blocks=1 shmem=shmem_size reduce_cplx_multi_3(
phi_re,
phi_im,
phi_re,
phi_im,
blocks_per_grid[3],
num_ants,
correlator_sample_shifts
)
end
# print(Array(phi_re))
end
# KERNEL 2_4_cplx_multi
# 2430
function kernel_algorithm(
threads_per_block,
blocks_per_grid,
shmem_size,
code_replica,
codes,
code_frequency,
sampling_frequency,
start_code_phase,
prn,
num_samples,
num_of_shifts,
code_length,
accum_re,
accum_im,
phi_re,
phi_im,
carrier_replica_re,
carrier_replica_im,
downconverted_signal_re,
downconverted_signal_im,
signal_re,
signal_im,
correlator_sample_shifts::SVector{NCOR, Int64},
carrier_frequency,
carrier_phase,
num_ants::NumAnts{NANT},
num_corrs,
algorithm::KernelAlgorithm{2430}
) where {NANT, NCOR}
NVTX.@range "gen_code_replica_kernel!" begin
@cuda threads=threads_per_block[1] blocks=blocks_per_grid[1] gen_code_replica_strided_kernel!(
code_replica,
codes,
code_frequency,
sampling_frequency,
start_code_phase,
prn,
num_samples,
num_of_shifts,
code_length
)
end
NVTX.@range "downconvert_and_accumulate!" begin
@cuda threads=threads_per_block[2] blocks=blocks_per_grid[2] downconvert_and_accumulate_strided_kernel!(
accum_re,
accum_im,
code_replica,
carrier_replica_re,
carrier_replica_im,
downconverted_signal_re,
downconverted_signal_im,
signal_re,
signal_im,
carrier_frequency,
sampling_frequency,
carrier_phase,
num_samples,
num_ants,
correlator_sample_shifts
)
end
NVTX.@range "reduce_cplx_multi_4" begin
@cuda threads=threads_per_block[3] blocks=cld(blocks_per_grid[3], 2) shmem=shmem_size reduce_cplx_multi_4(
phi_re,
phi_im,
accum_re,
accum_im,
num_samples,
num_ants,
correlator_sample_shifts
)
end
# print(Array(phi_re))
NVTX.@range "reduce_cplx_multi_4" begin
@cuda threads=threads_per_block[3] blocks=1 shmem=shmem_size reduce_cplx_multi_4(
phi_re,
phi_im,
phi_re,
phi_im,
blocks_per_grid[3],
num_ants,
correlator_sample_shifts
)
end
# print(Array(phi_re))
end
# KERNEL 2_4_cplx_multi_textmem
# 2431
function kernel_algorithm(
threads_per_block,
blocks_per_grid,
shmem_size,
code_replica,
codes,
code_frequency,
sampling_frequency,
start_code_phase,
prn,
num_samples,
num_of_shifts,
code_length,
accum_re,
accum_im,
phi_re,
phi_im,
carrier_replica_re,
carrier_replica_im,
downconverted_signal_re,
downconverted_signal_im,
signal_re,
signal_im,
correlator_sample_shifts::SVector{NCOR, Int64},
carrier_frequency,
carrier_phase,
num_ants::NumAnts{NANT},
num_corrs,
algorithm::KernelAlgorithm{2431}
) where {NANT, NCOR}
NVTX.@range "gen_code_replica_kernel!" begin
@cuda threads=threads_per_block[1] blocks=blocks_per_grid[1] gen_code_replica_texture_mem_strided_kernel!(
code_replica,
codes,
code_frequency,
sampling_frequency,
start_code_phase,
prn,
num_samples,
num_of_shifts,
code_length
)
end
NVTX.@range "downconvert_and_accumulate!" begin
@cuda threads=threads_per_block[2] blocks=blocks_per_grid[2] downconvert_and_accumulate_strided_kernel!(
accum_re,
accum_im,
code_replica,
carrier_replica_re,
carrier_replica_im,
downconverted_signal_re,
downconverted_signal_im,
signal_re,
signal_im,
carrier_frequency,
sampling_frequency,
carrier_phase,
num_samples,
num_ants,
correlator_sample_shifts
)
end
NVTX.@range "reduce_cplx_multi_4" begin
@cuda threads=threads_per_block[3] blocks=cld(blocks_per_grid[3], 2) shmem=shmem_size reduce_cplx_multi_4(
phi_re,
phi_im,
accum_re,
accum_im,
num_samples,
num_ants,
correlator_sample_shifts
)
end
# print(Array(phi_re))
NVTX.@range "reduce_cplx_multi_4" begin
@cuda threads=threads_per_block[3] blocks=1 shmem=shmem_size reduce_cplx_multi_4(
phi_re,
phi_im,
phi_re,
phi_im,
blocks_per_grid[3],
num_ants,
correlator_sample_shifts
)
end
# print(Array(phi_re))
end
# KERNEL 3_4_cplx_multi_textmem
# 3431
function kernel_algorithm(
threads_per_block,
blocks_per_grid,
shmem_size,
code_replica,
codes,
code_frequency,
sampling_frequency,
start_code_phase,
prn,
num_samples,
num_of_shifts,
code_length,
accum_re,
accum_im,
carrier_replica_re,
carrier_replica_im,
downconverted_signal_re,
downconverted_signal_im,
signal_re,
signal_im,
correlator_sample_shifts::SVector{NCOR, Int64},
carrier_frequency,
carrier_phase,
num_ants::NumAnts{NANT},
num_corrs,
algorithm::KernelAlgorithm{3431}
) where {NANT, NCOR}
NVTX.@range "gen_code_replica_texture_mem_strided_kernel!" begin
@cuda threads=threads_per_block[1] blocks=blocks_per_grid[1] gen_code_replica_texture_mem_strided_kernel!(
code_replica,
codes,
code_frequency,
sampling_frequency,
start_code_phase,
prn,
num_samples,
num_of_shifts,
code_length
)
end
NVTX.@range "downconvert_and_correlate_kernel_3431!" begin
@cuda threads=threads_per_block[2] blocks=blocks_per_grid[2] shmem=shmem_size[1] downconvert_and_correlate_kernel_3431!(
accum_re,
accum_im,
carrier_replica_re,
carrier_replica_im,
downconverted_signal_re,
downconverted_signal_im,
signal_re,
signal_im,
code_replica,
correlator_sample_shifts,
carrier_frequency,
sampling_frequency,
carrier_phase,
num_samples,
num_ants
)
end
NVTX.@range "reduce_cplx_multi_4" begin
@cuda threads=threads_per_block[3] blocks=1 shmem=shmem_size[2] reduce_cplx_multi_4(
accum_re,
accum_im,
accum_re,
accum_im,
blocks_per_grid[2],
num_ants,
correlator_sample_shifts
)
end
end
# KERNEL 4_4_cplx_multi_textmem
# 4431
function kernel_algorithm(
threads_per_block,
blocks_per_grid,
shmem_size,
code_replica,
codes,
code_frequency,
sampling_frequency,
start_code_phase,
prn,
num_samples,
num_of_shifts,
code_length,
accum_re,
accum_im,
carrier_replica_re,
carrier_replica_im,
downconverted_signal_re,
downconverted_signal_im,
signal_re,
signal_im,
correlator_sample_shifts::SVector{NCOR, Int64},
carrier_frequency,
carrier_phase,
num_ants::NumAnts{NANT},
num_corrs,
algorithm::KernelAlgorithm{4431}
) where {NANT, NCOR}
NVTX.@range "gen_code_replica_texture_mem_strided_kernel!" begin
@cuda threads=threads_per_block[1] blocks=blocks_per_grid[1] gen_code_replica_texture_mem_strided_kernel!(
code_replica,
codes,
code_frequency,
sampling_frequency,
start_code_phase,
prn,
num_samples,
num_of_shifts,
code_length
)
end
NVTX.@range "downconvert_and_correlate_kernel_3431!" begin
@cuda threads=threads_per_block[2] blocks=blocks_per_grid[2] shmem=shmem_size[1] downconvert_and_correlate_kernel_4431!(
accum_re,
accum_im,
carrier_replica_re,
carrier_replica_im,
downconverted_signal_re,
downconverted_signal_im,
signal_re,
signal_im,
code_replica,
correlator_sample_shifts,
carrier_frequency,
sampling_frequency,
carrier_phase,
num_samples,
num_ants
)
end
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] | 2.062184 | 26,904 |
using DataFrames
using CSV
cd("/Users/kiristern/Documents/GitHub/SDM/")
predators = CSV.read("data/preds/originals/Nasua_nasua2.csv")
predators = select(predators, [:species, :decimalLatitude, :decimalLongitude, :year])
# select only ursus arctos from imported all predators csv file
#df = predators[predators.species .== "Ursus arctos", :]
p1 = CSV.read("data/prey_class/Diplopoda2.csv")
p1 = select(p1,[:class, :decimalLatitude, :decimalLongitude, :year])
rename!(p1, :class => :species)
#create one dataframe with predators/prey dataframes
pred_prey = vcat(predators, p1)
pred_prey = dropmissing!(pred_prey, [:decimalLatitude, :decimalLongitude, :year])
CSV.write("data/pred_prey/Nasua_nasua.csv", pred_prey)
show(by(pred_prey, :species, :species => length), allcols=true)
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] | 2.596026 | 302 |
function zad35(d, el1, el2)
A = zeros(d,d)
A[:, 1:2:end] .= el1
A[:, 2:2:end] .= el2
return A
end | [
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] | 1.616438 | 73 |
<gh_stars>0
@testset "Database IO" begin
@testset "SQLite" begin
fname = joinpath(testpath, "test.db")
db = SQLite.DB(fname)
write_table!(db, "test1", df)
@test filesize(fname) > 0
df_recovered = DataFrame(read_table(fname, "test1"); copycols=false)
@test df == df_recovered
df_sql = DataFrame(read_sql(db, "select * from test1 where a < 5"); copycols=false)
@test df[df.a .< 5, :] == df_sql
write_table!(fname, "test2", nt)
nt_recovered = read_table(db, "test2")
@test DataFrame(nt) == DataFrame(nt_recovered)
end
@testset "PostgreSQL" begin
# the following tests require a running PostgreSQL database.
# `docker run --rm --detach --name test-libpqjl -e POSTGRES_HOST_AUTH_METHOD=trust -p 5432:5432 postgres`
conn = LibPQ.Connection("dbname=postgres user=postgres")
execute(conn, """CREATE TEMPORARY TABLE test1 (
a integer PRIMARY KEY,
b numeric,
c character varying,
d boolean,
e date,
f character varying
);""")
write_table!(conn, "test1", df)
df_recovered = DataFrame(read_table(conn, "test1"); copycols=false)
@test df == df_recovered
df_sql = DataFrame(read_sql(conn, "select * from test1 where a < 5"); copycols=false)
@test df[df.a .< 5, :] == df_sql
execute(conn, """CREATE TEMPORARY TABLE test2 (
a integer PRIMARY KEY,
b numeric,
c character varying
);""")
write_table!(conn, "test2", nt)
nt_recovered = read_table(conn, "test2")
@test DataFrame(nt) == DataFrame(nt_recovered)
close(conn)
end
end
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# abstract type AbstractRule{T} end
# abstract type ConditionalRule{T} <: AbstractRule{T} end
# struct SimpleRule{T} <: AbstractReplacementRule{T}
# r::T
# priority::Int
# end
# struct UpwindRule{T, T2}
# r::T
# priority::Int
# condition::T2
# end
# struct RuleSet{T, T2}
# derivrules::T
# valrules::T2
# end
# function RuleSet(rules::Vector{T}, conditional_rules::Vector{C}) where {T<:SimpleRule, C<:ConditionalRule}
# priorities = vcat(map(r -> r.priority, rules), map(r -> r.priority, conditional_rules))
# for (i,r) in enumerate(vcat(rules, conditional_rules))
# end
# ModelingToolkit.substitute(expr, rule::AbstractRule{T}) where T = substitute(expr, rule.r)
"""
`interpolate_discrete_param`
Interpolate gridpoints by taking the average of the values of the discrete points, or if the offset is outside the grid, extrapolate the value with dx.
"""
@inline function interpolate_discrete_param(i, s, itap, x, bpc)
return s.grid[x][i+itap]+s.dxs[x]*.5
end
"""
`cartesian_nonlinear_laplacian`
Differential(x)(expr(x)*Differential(x)(u(x)))
Given an internal multiplying expression `expr`, return the correct finite difference equation for the nonlinear laplacian at the location in the grid given by `II`.
The inner derivative is discretized with the half offset centered scheme, giving the derivative at interpolated grid points offset by dx/2 from the regular grid.
The outer derivative is discretized with the centered scheme, giving the nonlinear laplacian at the grid point `II`.
For first order returns something like this:
`d/dx( a du/dx ) ~ (a(x+1/2) * (u[i+1] - u[i]) - a(x-1/2) * (u[i] - u[i-1]) / dx^2`
For 4th order, returns something like this:
```
first_finite_diffs = [a(x-3/2)*finitediff(u, i-3/2),
a(x-1/2)*finitediff(u, i-1/2),
a(x+1/2)*finitediff(u, i+1/2),
a(x+3/2)*finitediff(u, i+3/2)]
dot(central_finite_diff_weights, first_finite_diffs)
```
where `finitediff(u, i)` is the finite difference at the interpolated point `i` in the grid.
And so on.
"""
function cartesian_nonlinear_laplacian(expr, II, derivweights, s::DiscreteSpace{N}, b, depvars, x, u) where N
# Based on the paper https://web.mit.edu/braatzgroup/analysis_of_finite_difference_discretization_schemes_for_diffusion_in_spheres_with_variable_diffusivity.pdf
# See scheme 1, namely the term without the 1/r dependence. See also #354 and #371 in DiffEqOperators, the previous home of this package.
ndims(u,s) == 0 && return Num(0)
jx = j, x = (x2i(s, u, x), x)
@assert II[j] != 1 "The nonlinear laplacian is only defined on the interior of the grid, it is unsupported in boundary conditions."
@assert II[j] != length(s, x) "The nonlinear laplacian is only defined on the interior of the grid, it is unsupported in boundary conditions."
D_inner = derivweights.halfoffsetmap[Differential(x)]
inner_interpolater = derivweights.interpmap[x]
# Get the outer weights and stencil. clip() essentially removes a point from either end of the grid, for this reason this function is only defined on the interior, not in bcs#
cliplen = length(s, x) - 1
outerweights, outerstencil = get_half_offset_weights_and_stencil(D_inner, II-unitindex(N,j), s, b, u, jx, cliplen)
# Get the correct weights and stencils for this II
inner_deriv_weights_and_stencil = [get_half_offset_weights_and_stencil(D_inner, I, s, b, u, jx) for I in outerstencil]
interp_weights_and_stencil = [get_half_offset_weights_and_stencil(inner_interpolater, I, s, b, u, jx) for I in outerstencil]
# map variables to symbolically inerpolated/extrapolated expressions
map_vars_to_interpolated(stencil, weights) = [v => dot(weights, s.discvars[v][stencil]) for v in depvars]
# Map parameters to interpolated values. Using simplistic extrapolation/interpolation for now as grids are uniform
#TODO: make this more efficient
map_params_to_interpolated(stencil, weights) = vcat([x => dot(weights, getindex.((s.grid[x],), getindex.(stencil, (j,))))], [s.x̄[k] => s.grid[s.x̄[k]][II[k]] for k in setdiff(1:N, [j])])
# Take the inner finite difference
inner_difference = [dot(inner_weights, s.discvars[u][inner_stencil]) for (inner_weights, inner_stencil) in inner_deriv_weights_and_stencil]
# Symbolically interpolate the multiplying expression
interpolated_expr = map(interp_weights_and_stencil) do (weights, stencil)
Num(substitute(substitute(expr, map_vars_to_interpolated(stencil, weights)), map_params_to_interpolated(stencil, weights)))
end
# multiply the inner finite difference by the interpolated expression, and finally take the outer finite difference
return dot(outerweights, inner_difference .* interpolated_expr)
end
"""
`spherical_diffusion`
Based on https://web.mit.edu/braatzgroup/analysis_of_finite_difference_discretization_schemes_for_diffusion_in_spheres_with_variable_diffusivity.pdf
See scheme 1 in appendix A. The r = 0 case is treated in a later appendix
"""
function spherical_diffusion(innerexpr, II, derivweights, s, b, depvars, r, u)
# Based on the paper https://web.mit.edu/braatzgroup/analysis_of_finite_difference_discretization_schemes_for_diffusion_in_spheres_with_variable_diffusivity.pdf
D_1 = derivweights.map[Differential(r)]
D_2 = derivweights.map[Differential(r)^2]
#TODO!: Update this to use indvars of the pde
# What to replace parameter x with given I
_rsubs(x, I) = x => s.grid[x][I[s.x2i[x]]]
# Full rules for substituting parameters in the inner expression
rsubs(I) = vcat([v => s.discvars[v][I] for v in depvars], [_rsubs(x, I) for x in s.x̄])
# Discretization func for u
ufunc_u(v, I, x) = s.discvars[v][I]
# 2nd order finite difference in u
exprhere = Num(substitute(innerexpr, rsubs(II)))
# Catch the r ≈ 0 case
if Symbolics.unwrap(substitute(r, _rsubs(r, II))) ≈ 0
D_2_u = central_difference(D_2, II, s, b, (s.x2i[r], r), u, ufunc_u)
return 3exprhere*D_2_u # See appendix B of the paper
end
D_1_u = central_difference(D_1, II, s, b, (s.x2i[r], r), u, ufunc_u)
# See scheme 1 in appendix A of the paper
return exprhere*(D_1_u/Num(substitute(r, _rsubs(r, II))) + cartesian_nonlinear_laplacian(innerexpr, II, derivweights, s, b, depvars, r, u))
end
@inline function generate_cartesian_rules(II, s, depvars, derivweights, pmap, indexmap, terms)
central_ufunc(u, I, x) = s.discvars[u][I]
return reduce(vcat, [reduce(vcat, [[(Differential(x)^d)(u) => central_difference(derivweights.map[Differential(x)^d], Idx(II, s, u, indexmap), s, pmap.map[operation(u)][x], (x2i(s,u,x),x), u, central_ufunc) for d in (let orders = derivweights.orders[x]; orders[iseven.(orders)] end)] for x in params(u, s)]) for u in depvars])
end
@inline function upwind_difference(expr, d::Int, II::CartesianIndex{N}, s::DiscreteSpace{N}, b, depvars, derivweights, (j,x), u, central_ufunc, indexmap) where N
# TODO: Allow derivatives in expr
expr = substitute(expr, valmaps(s, u, depvars, Idx(II, s, depvar(u, s), indexmap), indexmap))
IfElse.ifelse(expr > 0,
expr*upwind_difference(d, II, s, b, derivweights, (j,x), u, central_ufunc, true),
expr*upwind_difference(d, II, s, b, derivweights, (j,x), u, central_ufunc, false))
end
@inline function generate_winding_rules(II, s, depvars, derivweights, pmap, indexmap, terms)
wind_ufunc(v, I, x) = s.discvars[v][I]
# for all independent variables and dependant variables
rules = vcat(#Catch multiplication
reduce(vcat, [reduce(vcat, [[@rule *(~~a, $(Differential(x)^d)(u), ~~b) => upwind_difference(*(~a..., ~b...), d, Idx(II, s, u, indexmap), s, pmap.map[operation(u)][x], depvars, derivweights, (x2i(s,u,x),x), u, wind_ufunc, indexmap) for d in (let orders = derivweights.orders[x]; orders[isodd.(orders)] end)] for x in params(u, s)]) for u in depvars]),
#Catch division and multiplication, see issue #1
reduce(vcat, [reduce(vcat, [[@rule /(*(~~a, $(Differential(x)^d)(u), ~~b), ~c) => upwind_difference(*(~a..., ~b...)/~c, d, Idx(II, s, u, indexmap), s, pmap.map[operation(u)][x], depvars, derivweights, (x2i(s,u,x), x), u, wind_ufunc, indexmap) for d in (let orders = derivweights.orders[x]; orders[isodd.(orders)] end)] for x in params(u, s)]) for u in depvars])
)
wind_rules = []
# wind_exprs = []
for t in terms
for r in rules
if r(t) !== nothing
push!(wind_rules, t => r(t))
end
end
end
return vcat(wind_rules, vec(mapreduce(vcat, depvars) do u
mapreduce(vcat, params(u, s)) do x
j = x2i(s,u,x)
let orders = derivweights.orders[x]
oddorders = orders[isodd.(orders)]
# for all odd orders
if length(oddorders) > 0
map(oddorders) do d
(Differential(x)^d)(u) => upwind_difference(d, Idx(II, s, u, indexmap), s, pmap.map[operation(u)][x], derivweights, (j,x), u, wind_ufunc, true)
end
else
[]
end
end
end
end))
end
@inline function generate_nonlinlap_rules(II, s, depvars, derivweights, pmap, indexmap, terms)
rules = reduce(vcat, [vec([@rule *(~~c, $(Differential(x))(*(~~a, $(Differential(x))(u), ~~b)), ~~d) => *(~c...,cartesian_nonlinear_laplacian(*(a..., b...), Idx(II, s, u, indexmap), derivweights, s, pmap.map[operation(u)][x], depvars, x, u), ~d...) for x in params(u, s)]) for u in depvars])
rules = vcat(rules, reduce(vcat, [vec([@rule $(Differential(x))(*(~~a, $(Differential(x))(u), ~~b)) => cartesian_nonlinear_laplacian(*(a..., b...), Idx(II, s, u, indexmap), derivweights, s, pmap.map[operation(u)][x], depvars, x, u) for x in params(u, s)]) for u in depvars]))
rules = vcat(rules, reduce(vcat, [vec([@rule ($(Differential(x))($(Differential(x))(u)/~a)) => cartesian_nonlinear_laplacian(1/~a, Idx(II, s, u, indexmap), derivweights, s, pmap.map[operation(u)][x], depvars, x, u) for x in params(u, s)]) for u in depvars]))
nonlinlap_rules = []
for t in terms
for r in rules
if r(t) !== nothing
push!(nonlinlap_rules, t => r(t))
end
end
end
return nonlinlap_rules
end
@inline function generate_spherical_diffusion_rules(II, s, depvars, derivweights, pmap, indexmap, terms)
rules = reduce(vcat, [vec([@rule *(~~a, 1/(r^2), ($(Differential(r))(*(~~c, (r^2), ~~d, $(Differential(r))(u), ~~e))), ~~b) => *(~a..., spherical_diffusion(*(~c..., ~d..., ~e...), Idx(II, s, u, indexmap), derivweights, s, pmap.map[operation(u)][r], depvars, r, u), ~b...)
for r in params(u,s)]) for u in depvars])
rules = vcat(rules, reduce(vcat, [vec([@rule /(*(~~a, $(Differential(r))(*(~~c, (r^2), ~~d, $(Differential(r))(u), ~~e)), ~~b), (r^2)) => *(~a..., ~b..., spherical_diffusion(*(~c..., ~d..., ~e...), Idx(II, s, u, indexmap), derivweights, s, pmap.map[operation(u)][r], depvars, r, u))
for r in params(u,s)]) for u in depvars]))
rules = vcat(rules, reduce(vcat, [vec([@rule /(($(Differential(r))(*(~~c, (r^2), ~~d, $(Differential(r))(u), ~~e))), (r^2)) => spherical_diffusion(*(~c..., ~d..., ~e...), Idx(II, s, u, indexmap), derivweights, s, pmap.map[operation(u)][r], depvars, r, u)
for r in params(u, s)]) for u in depvars]))
spherical_diffusion_rules = []
for t in terms
for r in rules
if r(t) !== nothing
push!(spherical_diffusion_rules, t => r(t))
end
end
end
return spherical_diffusion_rules
end
"""
`generate_finite_difference_rules`
Generate a vector of finite difference rules to dictate what to replace variables in the `pde` with at the gridpoint `II`.
Care is taken to make sure that the rules only use points that are actually in the discretized grid by progressively up/downwinding the stencils when the gridpoint `II` is close to the boundary.
There is a genral catch all ruleset that uses the cartesian centered difference scheme for derivatives, and simply the discretized variable at the given gridpoint for particular variables.
There are of course more specific schemes that are used to improve stability/speed/accuracy when particular forms are encountered in the PDE. These rules are applied first to override the general ruleset.
##Currently implemented special cases are as follows:
- Spherical derivatives
- Nonlinear laplacian uses a half offset centered scheme for the inner derivative to improve stability
- Spherical nonlinear laplacian.
- Up/Downwind schemes to be used for odd ordered derivatives multiplied by a coefficient, downwinding when the coefficient is positive, and upwinding when the coefficient is negative.
Please submit an issue if you know of any special cases which impact stability or accuracy that are not implemented, with links to papers and/or code that demonstrates the special case.
"""
function generate_finite_difference_rules(II, s, depvars, pde, derivweights, pmap, indexmap)
terms = split_terms(pde, s.x̄)
# Standard cartesian centered difference scheme
central_deriv_rules_cartesian = generate_cartesian_rules(II, s, depvars, derivweights, pmap, indexmap, terms)
# Nonlinear laplacian scheme
nonlinlap_rules = generate_nonlinlap_rules(II, s, depvars, derivweights, pmap, indexmap, terms)
# Because winding needs to know about multiplying terms, we can't split the terms into additive and multiplicative terms.
winding_rules = generate_winding_rules(II, s, depvars, derivweights, pmap, indexmap, terms)
# Spherical diffusion scheme
spherical_diffusion_rules = generate_spherical_diffusion_rules(II, s, depvars, derivweights, pmap, indexmap, split_additive_terms(pde))
return vcat(vec(spherical_diffusion_rules), vec(nonlinlap_rules), vec(winding_rules), vec(central_deriv_rules_cartesian))
end
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] | 2.510446 | 5,648 |
<filename>src/requires/cairomakie.jl
function savefig_monitoring_results(r::MonitoringResults, symbols=keys(r.results); ext=:pdf)
for s in symbols
savefig_monitoring_results(r, s; ext)
end
return nothing
end
function savefig_monitoring_results(r::MonitoringResults, s::Symbol; ext=:pdf)
times = r.times
values = r.results[s]
title, ylabel = _symbol2title_and_label(s)
ylims = _defaultylims(values)
device_labels = [str for (str, uuid) in r.devices]
f = CairoMakie.Figure(; resolution=(1000, 500))
ax =
f[1, 1] = CairoMakie.Axis(
f; xlabel="Time [s]", ylabel=ylabel, title=title, ylims=ylims
)
CairoMakie.scatterlines!(times, getindex.(values, 1); label=device_labels[1])
for i in 2:length(first(values))
CairoMakie.scatterlines!(times, getindex.(values, i); label=device_labels[i])
end
f[1, 2] = CairoMakie.Legend(f, ax, "Devices"; framevisible=false)
filename =
replace(replace(replace(lowercase(title), " " => "_"), "(" => ""), ")" => "") * "_plot.$(string(ext))"
CairoMakie.save(filename, f)
return nothing
end | [
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] | 2.354772 | 482 |
using .Instantiation
using .ModelElaboration
using Unitful
using .StructuralTransform
using .Synchronous: sample, Clock, previous, hold, positive, positiveChange, positiveEdge
"""
Shortcut for `Variable`
"""
Var(; args...) = Variable(; args...)
"""
Create a floating-point `Variable`
"""
Float(value=nothing; info="", size=nothing, unit=NoUnits, displayUnit=NoUnits,
min=nothing, max=nothing, start=nothing, fixed::Bool=false, nominal=nothing,
variability=continuous,
flow::Bool=false, state::Bool=true) = Variable(variability, Float64, size, value,
unit, displayUnit, min, max, start, fixed, nominal, info, flow, state, general)
"""
Create a boolean `Variable`
"""
Boolean(value=nothing; info="", size=nothing, unit=NoUnits, displayUnit=NoUnits,
min=nothing, max=nothing, start=nothing, fixed::Bool=false, nominal=nothing,
variability=continuous,
flow::Bool=false, state::Bool=true) = Variable(variability, Bool, size, value,
unit, displayUnit, min, max, start, fixed, nominal, info, flow, state, general)
"""
Create an integer `Variable`
"""
Integ(value=nothing; info="", size=nothing, unit=NoUnits, displayUnit=NoUnits,
min=nothing, max=nothing, start=nothing, fixed::Bool=false, nominal=nothing,
variability=continuous,
flow::Bool=false, state::Bool=true) = Variable(variability, Int, size, value,
unit, displayUnit, min, max, start, fixed, nominal, info, flow, state, general)
"""
Create a string `Variable`
"""
Str(value=nothing; info="", size=nothing, unit=NoUnits, displayUnit=NoUnits,
min=nothing, max=nothing, start=nothing, fixed::Bool=false, nominal=nothing,
variability=continuous,
flow::Bool=false, state::Bool=true) = Variable(variability, String, size, value,
unit, displayUnit, min, max, start, fixed, nominal, info, flow, state, general)
#=
Float(; args...) = Var(T=Float64; args...)
Float0(; args...) = Float(size=(); args...)
Boolean(; args...) = Var(T=Bool; args...)
Boolean0(; args...) = Boolean(size=(); args...)
Integ(; args...) = Var(T=Int; args...)
Integ0(; args...) = Integ(size=(); args...)
Str(; args...) = Var(T=String; args...)
Str0(; args...) = Str(size=(); args...)
=#
"""
Create a `Variable` with `parameter` variability, meaning it
is an input variable that is constant with time
"""
Parameter(; args...) = Variable(variability=parameter; args...)
Parameter(value; args...) = Variable(variability=parameter, value=value; args...)
"""
Shortcut for `Parameter`
"""
Par(; args...) = Variable(variability=parameter; args...)
Par(value; args...) = Variable(variability=parameter, value=value; args...)
"""
A value meant to be filled in later
"""
const undefined = nothing
| [
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796,
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] | 3.03491 | 888 |
@testset "Joint Life" begin
@testset "Joint Last Survivor unknown status" begin
@testset "ALMCR §9.4" begin
ℓ₁ = [43302,42854,42081,41351,40050]
ℓ₂ = [47260,47040,46755,46500,46227]
ps₁ = ℓ₁ ./ ℓ₁[1]
qs₁ = [1 - ps₁[t] / ps₁[t - 1] for t in 2:5 ]
ps₂ = ℓ₂ ./ ℓ₂[1]
qs₂ = [1 - ps₂[t] / ps₂[t - 1] for t in 2:5 ]
m1 = UltimateMortality(qs₁, start_age=65)
m2 = UltimateMortality(qs₂, start_age=60)
@test decrement(m1, 65, 66) == 1 - ℓ₁[2] / ℓ₁[1]
@test decrement(m2, 60, 61) == 1 - ℓ₂[2] / ℓ₂[1]
l1 = SingleLife(mort = m1, issue_age = 65)
l2 = SingleLife(mort = m2, issue_age = 60)
jl = JointLife(lives=(l1, l2), contingency = LastSurvivor(), joint_assumption=Frasier())
@test isapprox( survival(jl, 2) , 0.9997, atol = 1e-4)
ins = LifeContingency(jl,Yields.Constant(0.05))
ins_l1 = LifeContingency(jl.lives[1],Yields.Constant(0.05))
ins_l2 = LifeContingency(jl.lives[2],Yields.Constant(0.05))
# problem 9.1.f
@test isapprox( present_value(AnnuityDue(ins,n=5)), 4.5437, atol = 1e-4)
@test isapprox( present_value(AnnuityDue(ins)), 4.5437, atol = 1e-4)
end
@testset "CIA tables" begin
m1 = MortalityTables.table("1986-92 CIA – Male Smoker, ANB")
m2 = MortalityTables.table("1986-92 CIA – Female Nonsmoker, ANB")
l1 = SingleLife(mort = m1.ultimate, issue_age = 40)
l2 = SingleLife(mort = m2.ultimate, issue_age = 37)
jl = JointLife(lives=(l1, l2), contingency=LastSurvivor(), joint_assumption=Frasier())
@testset "independent lives" begin
for time in 1:40
tpx = survival(l1,time)
tpy = survival(l2,time)
@test survival(jl, time) == tpx + tpy - tpx * tpy
end
end
q_annual = [0.00000141120,0.00000478349,0.00000921100,0.00001508953,0.00002255325,0.00003179413,0.00004327035,0.00005782855,0.00007524178,0.00009695039,0.00012370408,0.00015606419,0.00019612090,0.00024529620,0.00030502659,0.00037768543,0.00046587280,0.00057325907,0.00070238876,0.00085952824,0.00104786439,0.00127420204,0.00154640464,0.00187374100,0.00226186101,0.00272457482,0.00327193743,0.00391984214,0.00468084563,0.00557778027,0.00662135901,0.00783684326,0.00924475551,0.01086696400,0.01272977389,0.01485150445,0.01727277853,0.02000566587,0.02308982834,0.02654184882,0.03039819991,0.03469299691,0.03944737905,0.04469931521,0.05048294677,0.05683522818,0.06379854263,0.07142629169,0.07976664920,0.08888579420,0.09884411368,0.10971505011,0.12159455247,0.13456410066,0.14873755448,0.16421406941,0.18111392905,0.19954137714,0.21962070363,0.24144744481,0.26515968560,0.29099967654,0.31901974549,0.35111309929,0.39733960046,0.47004540325,0.58255000000,0.74852030000,1.00000000000]
q_cumulative = [0.00000141120,0.00000619469,0.00001540563,0.00003049492,0.00005304748,0.00008483992,0.00012810660,0.00018592774,0.00026115553,0.00035808060,0.00048174038,0.00063772939,0.00083372522,0.00107881691,0.00138351443,0.00176067732,0.00222572986,0.00279771301,0.00349813669,0.00435465818,0.00539795948,0.00666528343,0.00820138084,0.01005975458,0.01229886182,0.01498992747,0.01821281880,0.02206126957,0.02663884979,0.03206804441,0.03847706939,0.04601237389,0.05483175625,0.06510286552,0.07700389465,0.09071177542,0.10641770955,0.12429441828,0.14451430984,0.16722048170,0.19253547997,0.22054884408,0.25129614928,0.28476269870,0.32086998531,0.35946849466,0.40033347120,0.44316542761,0.48758225561,0.53312891378,0.57927637250,0.62543608637,0.67098101782,0.71525516126,0.75760741222,0.79741168545,0.83410325108,0.86720651682,0.89637071504,0.92139174110,0.94223548231,0.95904493828,0.97211041164,0.98190281145,0.98909354112,0.99422007198,0.99758716905,0.99939322200,1.00000000000]
@testset "precalced vectors" begin
for time in 1:40
@test isapprox(decrement(jl,time), q_cumulative[time], atol = 1e-6)
end
for time in 1:40
q′ = 1 - survival(jl, time) / survival(jl, time-1)
@test isapprox(q′, q_annual[time], atol = 1e-6)
end
for time in 1:40
@test isapprox(survival(jl, time), 1 - q_cumulative[time], atol = 1e-6)
end
end
end
end
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<gh_stars>0
using Test, Printf
t1 = @elapsed using Tullio
@info @sprintf("Loading Tullio took %.1f seconds", t1)
@info "Testing with $(Threads.nthreads()) threads"
if Threads.nthreads() > 1 # use threading even on small arrays
Tullio.BLOCK[] = 32
Tullio.TILE[] = 32
end
#===== stuff =====#
t2 = time()
@testset "parsing all the things" begin include("parsing.jl") end
@testset "tests from Einsum.jl" begin include("einsum.jl") end
@info @sprintf("Basic tests took %.1f seconds", time()-t2)
@testset "internal pieces" begin include("utils.jl") end
@testset "matrix multiplication" begin
# size 200 is big enough to test block_halves even with MINIBLOCK = 64^3
@testset "size $N, elements $T" for N in [2, 20, 200], T in [1:99, Float32, Float64, ComplexF64]
for f in [identity, adjoint]
A = f(rand(T, N,N));
B = f(rand(T, N,N));
@test A * B ≈ @tullio C[i,k] := A[i,j] * B[j,k]
end
if N < 200
X = rand(T, N,N+1);
Y = rand(T, N+1,N+2);
Z = rand(T, N+2,N+1);
@test X * Y * Z ≈ @tullio C[a,d] := X[a,b] * Y[b,c] * Z[c,d]
end
end
@testset "@allocated" begin
m!(C,A,B) = @tullio C[i,k] = A[i,j] * B[j,k] threads=false
C1, A1, B1 = rand(4,4), rand(4,4), rand(4,4)
@allocated m!(C1, A1, B1)
@test 0 == @allocated m!(C1, A1, B1)
end
end
#===== Tracker =====#
t3 = time()
using Tracker
GRAD = :Tracker
_gradient(x...) = Tracker.gradient(x...)
@tullio grad=Base
@testset "gradients: Tracker + DiffRules" begin include("gradients.jl") end
@tullio grad=Dual
@testset "gradients: Tracker + ForwardDiff" begin include("gradients.jl") end
@info @sprintf("Tracker tests took %.1f seconds", time()-t3)
#===== KernelAbstractions =====#
t4 = time()
using KernelAbstractions
@testset "KernelAbstractions + gradients" begin
A = (rand(3,4));
B = (rand(4,5));
@tullio C[i,k] := A[i,j] * B[j,k] threads=false # verbose=2
@test C ≈ A * B
@tullio threads=false # else KernelAbstractions CPU kernels not used
include("gradients.jl")
@tullio threads=true
for sy in Tullio.SYMBOLS
@test !isdefined(@__MODULE__, sy)
end
end
using CUDA
if CUDA.has_cuda_gpu()
@info "===== found a GPU, starting CUDA tests ====="
@testset "===== CUDA tests on GPU =====" begin
include("cuda.jl")
end
end
@info @sprintf("KernelAbstractions tests took %.1f seconds", time()-t4)
@tullio cuda=false
#===== Zygote =====#
t5 = time()
using Zygote
GRAD = :Zygote
_gradient(x...) = Zygote.gradient(x...)
@tullio grad=Base
@testset "gradients: Zygote + DiffRules" begin include("gradients.jl") end
@tullio grad=Dual
@testset "gradients: Zygote + ForwardDiff" begin include("gradients.jl") end
@tullio grad=Base
if VERSION >= v"1.4" # mysterious failures on 1.3
@testset "complex gradients with Zygote" begin
x0 = [1,2,3] .+ [5im, 0, -11im]
# y0 = rand(Int8,3) .+ im .* rand(Int8,3) .+ 0.0
@testset "analytic" begin
g1 = _gradient(x -> real(sum(x)), x0)[1]
g1i = _gradient(x -> imag(sum(x)), x0)[1]
@test g1 ≈ _gradient(x -> real(@tullio y := x[i]), x0)[1]
@test g1i ≈ _gradient(x -> imag(@tullio y := x[i]), x0)[1]
g2 = _gradient(x -> real(sum(exp, x)), x0)[1]
g2i = _gradient(x -> imag(sum(exp, x)), x0)[1]
@test g2 ≈ _gradient(x -> real(@tullio y := exp(x[i])), x0)[1]
@test g2i ≈ _gradient(x -> imag(@tullio y := exp(x[i])), x0)[1]
g3 = _gradient(x -> real(sum(1 ./ (x.+im).^2)), x0)[1]
g3i = _gradient(x -> imag(sum(1 ./ (x.+im).^2)), x0)[1]
@test g3 ≈ _gradient(x -> real(@tullio y := 1/(x[i] + im)^2), x0)[1]
@test g3 ≈ _gradient(x -> real(@tullio y := inv(x[i] + im)^2), x0)[1]
@test g3i ≈ _gradient(x -> imag(@tullio y := 1/(x[i] + im)^2), x0)[1]
@test g3i ≈ _gradient(x -> imag(@tullio y := inv(x[i] + im)^2), x0)[1]
# with finaliser
g7 = _gradient(x -> real(sum(sqrt.(sum(exp.(x), dims=2)))), x0 .+ x0')[1]
g7i = _gradient(x -> imag(sum(sqrt.(sum(exp.(x), dims=2)))), x0 .+ x0')[1]
@test g7 ≈ _gradient(x -> real(sum(@tullio y[i] := sqrt <| exp(x[i,j]) )), x0 .+ x0')[1]
@test g7i ≈ _gradient(x -> imag(sum(@tullio y[i] := sqrt <| exp(x[i,j]) )), x0 .+ x0')[1]
end
@testset "non-analytic" begin
g4 = _gradient(x -> real(sum(x * x')), x0)[1]
g4i = _gradient(x -> imag(sum(x * x')), x0)[1] # zero!
@test_broken g4 ≈ _gradient(x -> real(@tullio y := x[i] * conj(x[j])), x0)[1]
@test_broken g4i ≈ _gradient(x -> imag(@tullio y := x[i] * conj(x[j])), x0)[1]
@test_broken g4 ≈ _gradient(x -> real(@tullio y := x[i] * adjoint(x[j])), x0)[1]
@test_broken g4i ≈ _gradient(x -> imag(@tullio y := x[i] * adjoint(x[j])), x0)[1]
g5 = _gradient(x -> real(sum(abs2.(x .+ 2 .+ im))), x0)[1]
g5i = _gradient(x -> imag(sum(abs2.(x .+ 2 .+ im))), x0)[1] # zero!
@test_broken g5 ≈ _gradient(x -> real(@tullio y := abs2(x[i] + 2 + im)), x0)[1]
@test_broken g5i ≈ _gradient(x -> real(@tullio y := abs2(x[i] + 2 + im)), x0)[1]
g6 = _gradient(x -> real(sum(abs.(x.^3))), x0)[1]
g6i = _gradient(x -> imag(sum(abs.(x.^3))), x0)[1] # zero!
@test_broken g6 ≈ _gradient(x -> real(@tullio y := abs(x[i]^3)), x0)[1]
@test_broken g6i ≈ _gradient(x -> real(@tullio y := abs(x[i]^3)), x0)[1]
end
end
end # VERSION
@info @sprintf("Zygote tests took %.1f seconds", time()-t5)
#===== ReverseDiff =====#
#=
t6 = time()
using ReverseDiff
GRAD = :ReverseDiff
_gradient(x...) = ReverseDiff.gradient(x...) # ??
@tullio grad=Base
@testset "gradients: ReverseDiff + DiffRules" begin include("gradients.jl") end
@tullio grad=Dual
@testset "gradients: ReverseDiff + ForwardDiff" begin include("gradients.jl") end
@info @sprintf("ReverseDiff tests took %.1f seconds", time()-t6)
=#
#===== Yota =====#
#=
t7 = time()
using Yota
GRAD = :Yota
_gradient(x...) = Yota.grad(x...)[2]
@tullio grad=Base
@testset "gradients: Yota + DiffRules" begin include("gradients.jl") end
@tullio grad=Dual
@testset "gradients: Yota + ForwardDiff" begin include("gradients.jl") end
@info @sprintf("Yota tests took %.1f seconds", time()-t7)
=#
#===== LoopVectorization =====#
#=
t8 = time()
using LoopVectorization
using LoopVectorization.VectorizationBase: SVec, Mask, prevpow2
sv = SVec{4,Int}(1,2,3,4) # SVec{4,Int64}<1, 2, 3, 4>
ms = Mask(0x03) # Mask{8,Bool}<1, 1, 0, 0, 0, 0, 0, 0>
@test Tullio.onlyone(ms, 0) == Mask(0x02)
@test Tullio.onlyone(ms, sv) == Mask(0x00)
@test Tullio.onlyone(ms, zero(sv)) == Mask(0x02)
GRAD = :Tracker
_gradient(x...) = Tracker.gradient(x...)
@tullio grad=Base
@testset "gradients: Tracker + DiffRules + LoopVectorization" begin include("gradients.jl") end
@tullio grad=Dual
@testset "gradients: Tracker + ForwardDiff + LoopVectorization" begin include("gradients.jl") end
GRAD = :Zygote
_gradient(x...) = Zygote.gradient(x...)
@tullio grad=Base
@testset "gradients: Zygote + LoopVectorization" begin include("gradients.jl") end
@testset "parsing + LoopVectorization" begin include("parsing.jl") end
@info @sprintf("LoopVectorization tests took %.1f seconds", time()-t8)
=#
#===== TensorOperations =====#
t9 = time()
using TensorOperations
using Tracker
GRAD = :Tracker
_gradient(x...) = Tracker.gradient(x...)
@tullio grad=Base
@testset "gradients: Tracker + TensorOperations" begin include("gradients.jl") end
using Zygote
GRAD = :Zygote
_gradient(x...) = Zygote.gradient(x...)
@tullio grad=Base
@testset "gradients: Zygote + TensorOperations" begin include("gradients.jl") end
@testset "complex gradients with TensorOperations" begin
x0 = [1 2; 3 4] .+ [5im 0; 7im -8im]
@testset "analytic" begin
g1 = _gradient(x -> real(sum(x * x)), x0)[1]
g1i = _gradient(x -> imag(sum(x * x)), x0)[1]
@test g1 ≈ _gradient(x -> real(sum(@tullio y[i,j] := x[i,k] * x[k,j])), x0)[1]
@test g1i ≈ _gradient(x -> imag(sum(@tullio y[i,j] := x[i,k] * x[k,j])), x0)[1]
end
@testset "non-analytic" begin
g2 = _gradient(x -> real(sum(x * x')), x0)[1]
g2i = _gradient(x -> imag(sum(x * x')), x0)[1] # zero
@test_broken g2 ≈ _gradient(x -> real(sum(@tullio y[i,j] := x[i,k] * conj(x[j,k]))), x0)[1]
@test_broken g2i ≈ _gradient(x -> imag(sum(@tullio y[i,j] := x[i,k] * conj(x[j,k]))), x0)[1]
end
end
@testset "parsing + TensorOperations" begin include("parsing.jl") end # testing correct fallback
@info @sprintf("TensorOperations tests took %.1f seconds", time()-t9)
#===== done! =====#
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] | 2.150298 | 4,032 |
using LispSyntax
using Test
#-------------------------------------------------------------------------------
# Setup
macro incr(x)
esc(quote
$x = $x + 1
$x
end)
end
#-------------------------------------------------------------------------------
@testset "Reader" begin
@test LispSyntax.read("1.1f") == 1.1f0
@test LispSyntax.read("1.2f") == 1.2f0
@test LispSyntax.read("2f") == 2f0
@test LispSyntax.read("3.0d") == 3.0
@test LispSyntax.read("4") == 4
@test LispSyntax.read("\\u2312") == '\u2312'
@test LispSyntax.read("\\040") == ' '
@test LispSyntax.read("\\c") == 'c'
@test LispSyntax.read("\"test\"") == "test"
@test LispSyntax.read("true") == true
@test LispSyntax.read("false") == false
@test LispSyntax.read("test") == :test
@test LispSyntax.read("()") == sx()
@test LispSyntax.read("(1.1f)") == sx(1.1f0)
@test LispSyntax.read("(1.1f 2.2f)") == sx(1.1f0, 2.2f0)
@test LispSyntax.read("(+ 1.1f 2)") == sx(:+, 1.1f0, 2)
@test LispSyntax.read("(this (+ 1.1f 2))") == sx(:this, sx(:+, 1.1f0, 2))
@test LispSyntax.read("(this (+ 1.1f 2) )") == sx(:this, sx(:+, 1.1f0, 2))
@test LispSyntax.read("#{1 2 3 4}") == Set([1, 2, 3, 4])
@test LispSyntax.read("""#{
1 2
3 4
}""") == Set([1, 2, 3, 4])
@test LispSyntax.read("{a 2 b 3}") == Dict(:a => 2, :b => 3)
@test LispSyntax.read("""{
a 2
b 3
}""") == Dict(:a => 2, :b => 3)
@test LispSyntax.read("[1 2 3 4]") == sx(1, 2, 3, 4)
@test LispSyntax.read("""[
1 2
3 4
]""") == sx(1, 2, 3, 4)
@test LispSyntax.read("[]") == sx()
@test LispSyntax.read("[1]") == sx(1)
@test LispSyntax.read("'test") == sx(:quote, :test)
@test LispSyntax.read("`test") == sx(:quasi, :test)
@test LispSyntax.read("~test") == sx(:splice, :test)
@test LispSyntax.read("~@(1 2 3)") == sx(:splice_seq, sx(1, 2, 3))
@test LispSyntax.read("`~test") == sx(:quasi, sx(:splice, :test))
@test desx(sx(:splice_seq, sx(1, 2, 3))) == Any[ :splice_seq, [1, 2, 3] ]
@test desx(sx(:splice_seq, sx(1, 2, sx(3)))) == Any[ :splice_seq, Any[ 1, 2, [3] ] ]
@test LispSyntax.read("""(defn multiline
[x]
(+ x 1))""") == sx(:defn, :multiline, sx(:x), sx(:+, :x, 1))
@test LispSyntax.read("""
(defn f1 [n]
(if (< n 2)
1
(+ (f1 (- n 1))
(f1 (- n 2)))))
""") == sx(:defn, :f1, sx(:n),
sx(:if, sx(:<, :n, 2),
1,
sx(:+, sx(:f1, sx(:-, :n, 1)), sx(:f1, sx(:-, :n, 2)))))
assign_reader_dispatch(:sx, x -> sx(x.vector...))
assign_reader_dispatch(:hash, x -> Dict(x.vector[i] => x.vector[i+1] for i = 1:2:length(x.vector)))
@test LispSyntax.read("#sx[a b c]") == sx(:a, :b, :c)
@test LispSyntax.read("#sx [ 1 2 3 ]") == sx(1, 2, 3)
end
#-------------------------------------------------------------------------------
@testset "Code generation" begin
@test codegen(desx(LispSyntax.read("(if true a)"))) == :(true && a)
@test codegen(desx(LispSyntax.read("(if true a b)"))) == :(true ? a : b)
@test codegen(desx(LispSyntax.read("(call)"))) == :(call())
@test codegen(desx(LispSyntax.read("(call a)"))) == :(call(a))
@test codegen(desx(LispSyntax.read("(call a b)"))) == :(call(a,b))
@test codegen(desx(LispSyntax.read("(call a b c)"))) == :(call(a,b,c))
@test codegen(desx(LispSyntax.read("(lambda (x) (call x))"))) == Base.remove_linenums!(:(function (x) call(x) end))
@test codegen(desx(LispSyntax.read("(def x 3)"))) == :(global x = 3)
@test codegen(desx(LispSyntax.read("(def x (+ 3 1))"))) == :(global x = 3 + 1)
construct_sexpr = LispSyntax.construct_sexpr
@test codegen(desx(LispSyntax.read("test"))) == :test
@test codegen(desx(LispSyntax.read("'test"))) == QuoteNode(:test)
@test codegen(desx(LispSyntax.read("'(1 2)"))) == :($construct_sexpr(1, 2))
@test codegen(desx(LispSyntax.read("'(1 x)"))) == :($construct_sexpr(1, :x))
@test codegen(desx(LispSyntax.read("'(1 (1 2))"))) == :($construct_sexpr(1, $construct_sexpr(1, 2)))
@test codegen(desx(LispSyntax.read("'(1 (test x))"))) == :($construct_sexpr(1, $construct_sexpr(:test, :x)))
@test codegen(desx(LispSyntax.read("(call 1 '2)"))) == :(call(1,2))
end
#-------------------------------------------------------------------------------
@testset "Scope and variables" begin
x = 10
@test lisp"x" == 10
let
# In clojure, def affects global bindings
lisp"(def w (+ 3 1))"
end
let
@test w == 4
end
end
#-------------------------------------------------------------------------------
@testset "Quoting and splicing" begin
x = 10
@test lisp"`~x" == 10
@test lisp"'test" == :test
@test lisp"'(1 2)" == Any[1, 2]
@test lisp"'(1 x)" == Any[1, :x]
@test lisp"'(1 (1 2))" == Any[1, Any[1, 2]]
@test lisp"'(1 (test x))" == Any[1, Any[:test, :x]]
@test lisp"`(test ~x)" == Any[ :test, 10 ]
@test lisp"`(~x ~x)" == Any[ 10, 10 ]
global y = Any[ 1, 2 ]
@test lisp"`(~x ~@y)" == Any[ 10, 1, 2 ]
@test lisp"`(~x ~y)" == Any[ 10, Any[1, 2] ]
@test lisp"`(10 ~(+ 10 x))" == Any[10, 20]
@test lisp"(quote (+ 1 2))" == Any[:+, 1, 2]
end
#-------------------------------------------------------------------------------
@testset "Functions" begin
lisp"(defn xxx [a b] (+ a b))"
@test lisp"(xxx 1 2)" == 3
global z = 10
lisp"(defn yyy [a] (+ a z))"
@test lisp"(yyy 1)" == 11
@test lisp"(yyy z)" == 20
# recursion
lisp"(defn fib [a] (if (< a 2) a (+ (fib (- a 1)) (fib (- a 2)))))"
@test lisp"(fib 2)" == 1
@test lisp"(fib 4)" == 3
@test lisp"(fib 30)" == 832040
@test lisp"(fib 40)" == 102334155
# Note this version is slow due to the non-const global binding fib2
lisp"(def fib2 (lambda [a] (if (< a 2) a (+ (fib2 (- a 1)) (fib2 (- a 2))))))"
@test lisp"(fib2 2)" == 1
@test lisp"(fib2 4)" == 3
@test lisp"(fib2 30)" == 832040
lisp"(defn dostuff [a] (@incr a) (@incr a) (@incr a))"
@test lisp"(dostuff 3)" == 6
@test lisp"(dostuff 6)" == 9
lisp"(def dostuff2 (lambda [a] (@incr a) (@incr a) (@incr a)))"
@test lisp"(dostuff2 3)" == 6
@test lisp"(dostuff2 6)" == 9
lisp"(def dostuff3 (fn [a] (@incr a) (@incr a) (@incr a)))"
@test lisp"(dostuff3 3)" == 6
@test lisp"(dostuff3 6)" == 9
@test lisp"((lambda [x] (+ x 1)) 5)" == 6
@test lisp"#{1 2 z}" == Set([1, 2, 10])
@test lisp"{1 2 2 z}" == Dict(1 => 2, 2 => 10)
@test lisp"#sx[+ 1 2]" == 3
@test lisp"#hash['+ 1 '- z]" == Dict(:+ => 1, :- => 10)
end
#-------------------------------------------------------------------------------
# Macros. Note that LispSyntax currently uses the Julia macro system, which
# includes the hygenic-by-default hygiene rules. This is very different from
# Clojure style explicit hygiene.
lisp"(defmacro fapply [f a] (esc `(~f ~a)))"
fcount = 0
lisp"(defmacro fapply_trace [f a] (esc `(do (global fcount) (@incr fcount) (~f ~a))))"
@testset "Macros" begin
lisp"(defn fact [a] (if (< a 1) 1 (* a (fact (- a 1)))))"
@test @fapply(fib2, 2) == 1
@test @fapply(fact, 3 + 1) == 24
@test lisp"(@fapply fib2 2)" == 1
@test lisp"(@fapply fact (+ 3 1))" == 24
@test @fapply_trace(fib2, 2) == 1
@test fcount == 1
@test @fapply_trace(fact, 3 + 1) == 24
@test fcount == 2
end
#-------------------------------------------------------------------------------
@testset "Loops" begin
number = 0
output = 0
lisp"(while (< number 2) (@incr number) (@incr output))"
@test number == 2
@test output == 2
r = output
lisp"(for [i (: 1 10)] (@incr r))"
@test r == 12
r = 0
lisp"(for [i (: 1 10) j (: 1 10)] (@incr r))"
@test r == 100
end
#-------------------------------------------------------------------------------
@testset "Let and do" begin
number = 2
r = 100
output = 2
@test lisp"(let [x 10] x)" == 10
@test lisp"(let [x 10 y 20] (+ x y))" == 30
@test lisp"(let [x 10 y 20 z 20] (+ x y z))" == 50
@test lisp"(let [x 10 y 20 z 20] (+ x y z number))" == 52
@test lisp"(let [x 10 y 20 z 20 number 10] (+ x y z number))" == 60
@test lisp"(let [x 10 y 20 z 20] (- (+ x y z number) output))" == 50
lisp"(do (@incr r) (@incr number))"
@test number == 3
@test r == 101
end
#-------------------------------------------------------------------------------
lisp"(import ParserCombinator)"
@testset "Module import" begin
@test lisp"(@E_str \"S\")" == E"S"
end
#-------------------------------------------------------------------------------
@testset "Include from file" begin
# Return value is value of last expression
@test include_lisp(@__MODULE__, "lisp.clj") == 100
# Test objects defined in lisp.clj
@test func_in_clj_file(1, 2) == "x = 1; y = 2"
@test func_in_clj_file(10, 20) == "x = 10; y = 20"
@test some_global === 1.23f0
@test !isdefined(@__MODULE__, :not_a_global)
end
#-------------------------------------------------------------------------------
# Bug reports
@testset "Bug reports" begin
@test lisp"""(def game_map (Dict
(=> 'living_room
'((you are in the living room
of a wizards house - there is a wizard
snoring loudly on the couch -)
(west door garden)
(upstairs stairway attic)))))""" ==
Dict(:living_room =>
Any[ Any[ :you, :are, :in, :the, :living, :room, :of, :a, :wizards, :house, :-,
:there, :is, :a, :wizard, :snoring, :loudly, :on, :the, :couch, :- ],
Any[ :west, :door, :garden ],
Any[ :upstairs, :stairway, :attic ] ])
end
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4377,
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301,
958,
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1058,
1078,
291,
2361,
33761,
198,
437,
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] | 2.148413 | 4,757 |
abstract type Topology <: Component end
| [
397,
8709,
2099,
5849,
1435,
1279,
25,
35100,
886,
628
] | 4.1 | 10 |
<gh_stars>1-10
################################################################################
# # DESCRIPTION
# Wrapper for Python Paraview functions.
# # AUTHORSHIP
# * Author : <NAME>
# * Email : <EMAIL>
# * Created : Nov 2017
# * License : MIT License
################################################################################
# Wrap VTKtools_paraview.py
@pyimport imp
(file, filename, data) = imp.find_module("VTKtools_paraview", [module_path])
parapy = imp.load_module("VTKtools_paraview", file, filename, data)
| [
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] | 3.388889 | 162 |
@testset "Orders in absolute number fields" begin
println("NfOrd.jl")
@time include("NfOrd/NfOrd.jl")
println("Elem.jl")
@time include("NfOrd/Elem.jl")
println("Ideal.jl")
@time include("NfOrd/Ideal.jl")
println("FracIdl.jl")
@time include("NfOrd/FracIdl.jl")
println("ResidueRing.jl")
@time include("NfOrd/ResidueRing.jl")
println("Clgp.jl")
@time include("NfOrd/Clgp.jl")
println("RayClassGroup.jl")
@time include("NfOrd/RayClassGroup.jl")
println("ResidueRingMultGrp.jl")
@time include("NfOrd/ResidueRingMultGrp.jl")
println("Overorders.jl")
@time include("NfOrd/Overorders.jl")
println("LinearAlgebra.jl")
@time include("NfOrd/LinearAlgebra.jl")
#println("PicardGroup.jl")
#@time include("NfOrd/PicardGroup.jl")
end
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] | 2.475728 | 309 |
<reponame>lemauee/RoME.jl<filename>src/variables/deprecated/Point3D.jl
"""
$(TYPEDEF)
XYZ Euclidean manifold variable node softtype.
Example
-------
```julia
p3 = Point3()
```
"""
struct Point3 <: IncrementalInference.InferenceVariable end
getDimension(::Point3) = 3
getManifolds(::Point3) = (:Euclid,:Euclid,:Euclid)
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] | 2.476923 | 130 |
<gh_stars>10-100
@testset "537.complex-number-multiplication.jl" begin
@test complex_number_multiply("1+1i", "1+1i") == "0+2i"
@test complex_number_multiply("1+-1i", "1+-1i") == "0+-2i"
end | [
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<filename>src/NSFVector.jl
mutable struct NSFVecCoefs
A::Matrix{Float64}
B::Matrix{Float64}
C::Matrix{Float64}
T::Union{Matrix{Float64},Nothing}
NSFVecCoefs() = new()
NSFVecCoefs(A,B,C,T) = new(A,B,C,T)
end
mutable struct NSFVector
size::Int
moments::Int
scales::Int
coefs::Vector{NSFVecCoefs}
end
function NSFVector_decomp(size::Int,
moments::Int,
scales::Int,
X::Matrix{Float64}
)::NSFVector
if size%(1<<scales) != 0
throw("size not divisible by 2^scales")
end
H,G = get_qmf(moments)
Y = NSFVector(size,moments,scales,Vector{NSFVecCoefs}(undef,scales))
T = X
for j in 1:scales
A,B,C,T = dwtmat(size,T,moments,H,G)
Y.coefs[j] = NSFVecCoefs(A,B,C,nothing)
size ÷= 2
end
Y.coefs[scales].T = T
return Y
end
function NSFVector_reconst(X::NSFVector)::Matrix{Float64}
n = X.size÷(2^X.scales)
H,G = get_qmf(X.moments)
T = X.coefs[X.scales].T
for j in X.scales:-1:1
T = idwtmat(n,X.moments,H,G,X.coefs[j].A,X.coefs[j].B,X.coefs[j].C,T)
n *= 2
end
return T
end
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] | 1.825321 | 624 |
<filename>src/Surrogates.jl<gh_stars>0
module Surrogates
using LinearAlgebra
using Distributions
using Requires
abstract type AbstractSurrogate <: Function end
include("utils.jl")
include("Radials.jl")
include("Kriging.jl")
include("Sampling.jl")
include("Optimization.jl")
include("Lobachevsky.jl")
include("LinearSurrogate.jl")
include("InverseDistanceSurrogate.jl")
include("SecondOrderPolynomialSurrogate.jl")
function __init__()
@require Stheno="8188c328-b5d6-583d-959b-9690869a5511" begin
include("SthenoKriging.jl")
end
end
include("RandomForestSurrogate.jl")
include("NeuralSurrogate.jl")
include("Wendland.jl")
include("MOE.jl") #rewrite gaussian mixture with own algorithm to fix deps issue
include("VariableFidelity.jl")
include("PolynomialChaos.jl")
include("Earth.jl")
include("GEK.jl")
current_surrogates = ["Kriging","LinearSurrogate","LobachevskySurrogate","NeuralSurrogate",
"RadialBasis","RandomForestSurrogate","SecondOrderPolynomialSurrogate",
"Wendland","GEK","PolynomialChaosSurrogate"]
#Radial structure:
function RadialBasisStructure(;radial_function,scale_factor,sparse)
return (name = "RadialBasis", radial_function = radial_function, scale_factor = scale_factor, sparse = sparse)
end
#Kriging structure:
function KrigingStructure(;p,theta)
return (name = "Kriging", p = p, theta = theta)
end
function GEKStructure(;p,theta)
return (name = "GEK", p = p, theta = theta)
end
#Linear structure
function LinearStructure()
return (name = "LinearSurrogate")
end
#InverseDistance structure
function InverseDistanceStructure(;p)
return (name = "InverseDistanceSurrogate", p = p)
end
#Lobachevsky structure
function LobachevskyStructure(;alpha,n,sparse)
return (name = "LobachevskySurrogate", alpha = alpha, n = n, sparse = sparse)
end
#Neural structure
function NeuralStructure(;model,loss,opt,n_echos)
return (name ="NeuralSurrogate", model = model ,loss = loss,opt = opt,n_echos = n_echos)
end
#Random forest structure
function RandomForestStructure(;num_round)
return (name = "RandomForestSurrogate", num_round = num_round)
end
#Second order poly structure
function SecondOrderPolynomialStructure()
return (name = "SecondOrderPolynomialSurrogate")
end
#Wendland structure
function WendlandStructure(; eps, maxiters, tol)
return (name = "Wendland", eps = eps, maxiters = maxiters, tol = tol)
end
#Polychaos structure
function PolyChaosStructure(; op)
return (name = "PolynomialChaosSurrogate", op = op)
end
export current_surrogates
export RadialBasisStructure, KrigingStructure, LinearStructure, InverseDistanceStructure
export LobachevskyStructure, NeuralStructure, RandomForestStructure, SecondOrderPolynomialStructure
export WendlandStructure
export AbstractSurrogate, SamplingAlgorithm
export Kriging, RadialBasis, add_point!, current_estimate, std_error_at_point
export linearRadial,cubicRadial,multiquadricRadial,thinplateRadial
export sample, GridSample, UniformSample, SobolSample, LatinHypercubeSample, LowDiscrepancySample
export RandomSample, KroneckerSample, GoldenSample
export SRBF,LCBS,EI,DYCORS,SOP,EGO,RTEA,SMB,surrogate_optimize
export LobachevskySurrogate, lobachevsky_integral, lobachevsky_integrate_dimension
export LinearSurrogate
export RandomForestSurrogate
export SVMSurrogate
export NeuralSurrogate
export InverseDistanceSurrogate
export SecondOrderPolynomialSurrogate
export SthenoKriging
export Wendland
export RadialBasisStructure, KrigingStructure, LinearStructure, InverseDistanceStructure
export LobachevskyStructure, NeuralStructure, RandomForestStructure, SecondOrderPolynomialStructure
export WendlandStructure
#export MOE
export VariableFidelitySurrogate
export PolynomialChaosSurrogate
export EarthSurrogate
export GEK
end
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] | 2.925385 | 1,300 |
<filename>contents/bogo_sort/code/julia/bogo.jl
using Random
function is_sorted(a::Vector{Float64})
for i = 1:length(a)-1
if (a[i] > a[i + 1])
return false
end
end
return true
end
function bogo_sort(a::Vector{Float64})
while(!is_sorted(a))
shuffle!(a)
end
end
function main()
a = [1, 3, 2, 4]
bogo_sort(a)
println(a)
end
main()
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220,
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220,
220,
220,
44872,
7,
64,
8,
198,
437,
198,
198,
12417,
3419,
198
] | 1.960784 | 204 |
"""
UniversalFallback
The `UniversalFallback` can be applied on a [`MathOptInterface.ModelLike`](@ref)
`model` to create the model `UniversalFallback(model)` supporting *any*
constraint and attribute. This allows to have a specialized implementation in
`model` for performance critical constraints and attributes while still
supporting other attributes with a small performance penalty. Note that `model`
is unaware of constraints and attributes stored by `UniversalFallback` so this
is not appropriate if `model` is an optimizer (for this reason,
[`MathOptInterface.optimize!`](@ref) has not been implemented). In that case,
optimizer bridges should be used instead.
"""
mutable struct UniversalFallback{MT} <: MOI.ModelLike
model::MT
objective::Union{MOI.AbstractScalarFunction, Nothing}
constraints::OrderedDict{Tuple{DataType, DataType}, OrderedDict} # See https://github.com/jump-dev/JuMP.jl/issues/1152 and https://github.com/jump-dev/JuMP.jl/issues/2238
nextconstraintid::Int64
con_to_name::Dict{CI, String}
name_to_con::Union{Dict{String, MOI.ConstraintIndex}, Nothing}
optattr::Dict{MOI.AbstractOptimizerAttribute, Any}
modattr::Dict{MOI.AbstractModelAttribute, Any}
varattr::Dict{MOI.AbstractVariableAttribute, Dict{VI, Any}}
conattr::Dict{MOI.AbstractConstraintAttribute, Dict{CI, Any}}
function UniversalFallback{MT}(model::MOI.ModelLike) where {MT}
new{typeof(model)}(model,
nothing,
OrderedDict{Tuple{DataType, DataType}, OrderedDict}(),
0,
Dict{CI, String}(),
nothing,
Dict{MOI.AbstractOptimizerAttribute, Any}(),
Dict{MOI.AbstractModelAttribute, Any}(),
Dict{MOI.AbstractVariableAttribute, Dict{VI, Any}}(),
Dict{MOI.AbstractConstraintAttribute, Dict{CI, Any}}())
end
end
UniversalFallback(model::MOI.ModelLike) = UniversalFallback{typeof(model)}(model)
function Base.show(io::IO, U::UniversalFallback)
s(n) = n == 1 ? "" : "s"
indent = " "^get(io, :indent, 0)
MOIU.print_with_acronym(io, summary(U))
!(U.objective === nothing) && print(io, "\n$(indent)with objective")
for (attr, name) in ( (U.constraints, "constraint"),
(U.optattr, "optimizer attribute"),
(U.modattr, "model attribute"),
(U.varattr, "variable attribute"),
(U.conattr, "constraint attribute") )
n = length(attr)
if n > 0
print(io, "\n$(indent)with $n $name$(s(n))")
end
end
print(io, "\n$(indent)fallback for ")
show(IOContext(io, :indent => get(io, :indent, 0)+2), U.model)
end
function MOI.is_empty(uf::UniversalFallback)
return MOI.is_empty(uf.model) && uf.objective === nothing && isempty(uf.constraints) &&
isempty(uf.modattr) && isempty(uf.varattr) && isempty(uf.conattr)
end
function MOI.empty!(uf::UniversalFallback)
MOI.empty!(uf.model)
uf.objective = nothing
empty!(uf.constraints)
uf.nextconstraintid = 0
empty!(uf.con_to_name)
uf.name_to_con = nothing
empty!(uf.modattr)
empty!(uf.varattr)
empty!(uf.conattr)
end
function MOI.copy_to(uf::UniversalFallback, src::MOI.ModelLike; kws...)
MOIU.automatic_copy_to(uf, src; kws...)
end
function supports_default_copy_to(uf::UniversalFallback, copy_names::Bool)
return supports_default_copy_to(uf.model, copy_names)
end
# References
MOI.is_valid(uf::UniversalFallback, idx::VI) = MOI.is_valid(uf.model, idx)
function MOI.is_valid(uf::UniversalFallback, idx::CI{F, S}) where {F, S}
if MOI.supports_constraint(uf.model, F, S)
MOI.is_valid(uf.model, idx)
else
haskey(uf.constraints, (F, S)) && haskey(uf.constraints[(F, S)], idx)
end
end
function MOI.delete(uf::UniversalFallback, ci::CI{F, S}) where {F, S}
if MOI.supports_constraint(uf.model, F, S)
MOI.delete(uf.model, ci)
else
if !MOI.is_valid(uf, ci)
throw(MOI.InvalidIndex(ci))
end
delete!(uf.constraints[(F, S)], ci)
delete!(uf.con_to_name, ci)
uf.name_to_con = nothing
end
for d in values(uf.conattr)
delete!(d, ci)
end
end
function _remove_variable(uf::UniversalFallback,
constraints::OrderedDict{<:CI{MOI.SingleVariable}}, vi::VI)
to_delete = keytype(constraints)[]
for (ci, constraint) in constraints
f::MOI.SingleVariable = constraint[1]
if f.variable == vi
push!(to_delete, ci)
end
end
MOI.delete(uf, to_delete)
end
function _remove_variable(uf::UniversalFallback,
constraints::OrderedDict{CI{MOI.VectorOfVariables, S}},
vi::VI) where S
to_delete = keytype(constraints)[]
for (ci, constraint) in constraints
f::MOI.VectorOfVariables, s = constraint
if vi in f.variables
if length(f.variables) > 1
if MOI.supports_dimension_update(S)
constraints[ci] = remove_variable(f, s, vi)
else
throw_delete_variable_in_vov(vi)
end
else
push!(to_delete, ci)
end
end
end
MOI.delete(uf, to_delete)
end
function _remove_variable(::UniversalFallback, constraints::OrderedDict{<:CI}, vi::VI)
for (ci, constraint) in constraints
f, s = constraint
constraints[ci] = remove_variable(f, s, vi)
end
end
function _remove_vector_of_variables(
uf::UniversalFallback, constraints::OrderedDict{<:CI{MOI.VectorOfVariables}},
vis::Vector{VI}
)
to_delete = keytype(constraints)[]
for (ci, constraint) in constraints
f::MOI.VectorOfVariables = constraint[1]
if vis == f.variables
push!(to_delete, ci)
end
end
MOI.delete(uf, to_delete)
end
function _remove_vector_of_variables(
::UniversalFallback, ::OrderedDict{<:CI}, ::Vector{VI})
end
function MOI.delete(uf::UniversalFallback, vi::VI)
MOI.delete(uf.model, vi)
for d in values(uf.varattr)
delete!(d, vi)
end
if uf.objective !== nothing
uf.objective = remove_variable(uf.objective, vi)
end
for (_, constraints) in uf.constraints
_remove_variable(uf, constraints, vi)
end
end
function MOI.delete(uf::UniversalFallback, vis::Vector{VI})
MOI.delete(uf.model, vis)
for d in values(uf.varattr)
for vi in vis
delete!(d, vi)
end
end
if uf.objective !== nothing
uf.objective = remove_variable(uf.objective, vis)
end
for (_, constraints) in uf.constraints
_remove_vector_of_variables(uf, constraints, vis)
for vi in vis
_remove_variable(uf, constraints, vi)
end
end
end
# Attributes
_get(uf, attr::MOI.AbstractOptimizerAttribute) = uf.optattr[attr]
_get(uf, attr::MOI.AbstractModelAttribute) = uf.modattr[attr]
function _get(uf, attr::MOI.AbstractVariableAttribute, vi::VI)
attribute_dict = get(uf.varattr, attr, nothing)
if attribute_dict === nothing
# It means the attribute is not set to any variable so in particular, it
# is not set for `vi`
return nothing
end
return get(attribute_dict, vi, nothing)
end
function _get(uf, attr::MOI.AbstractConstraintAttribute, ci::CI)
attribute_dict = get(uf.conattr, attr, nothing)
if attribute_dict === nothing
# It means the attribute is not set to any constraint so in particular,
# it is not set for `ci`
return nothing
end
return get(attribute_dict, ci, nothing)
end
function _get(uf, attr::MOI.CanonicalConstraintFunction, ci::MOI.ConstraintIndex)
return MOI.get_fallback(uf, attr, ci)
func = MOI.get(uf, MOI.ConstraintFunction(), ci)
if is_canonical(func)
return func
else
return canonical(func)
end
end
function MOI.get(uf::UniversalFallback,
attr::Union{MOI.AbstractOptimizerAttribute,
MOI.AbstractModelAttribute})
if !MOI.is_copyable(attr) || MOI.supports(uf.model, attr)
MOI.get(uf.model, attr)
else
_get(uf, attr)
end
end
function MOI.get(uf::UniversalFallback,
attr::MOI.AbstractConstraintAttribute,
idx::MOI.ConstraintIndex{F, S}) where {F, S}
if MOI.supports_constraint(uf.model, F, S) &&
(!MOI.is_copyable(attr) || MOI.supports(uf.model, attr, typeof(idx)))
MOI.get(uf.model, attr, idx)
else
_get(uf, attr, idx)
end
end
function MOI.get(uf::UniversalFallback,
attr::MOI.AbstractVariableAttribute, idx::MOI.VariableIndex)
if !MOI.is_copyable(attr) || MOI.supports(uf.model, attr, typeof(idx))
MOI.get(uf.model, attr, idx)
else
_get(uf, attr, idx)
end
end
function MOI.get(uf::UniversalFallback,
attr::MOI.NumberOfConstraints{F, S}) where {F, S}
if MOI.supports_constraint(uf.model, F, S)
return MOI.get(uf.model, attr)
else
return length(get(uf.constraints, (F, S), OrderedDict{CI{F, S}, Tuple{F, S}}()))
end
end
function MOI.get(uf::UniversalFallback,
listattr::MOI.ListOfConstraintIndices{F, S}) where {F, S}
if MOI.supports_constraint(uf.model, F, S)
MOI.get(uf.model, listattr)
else
collect(keys(get(uf.constraints, (F, S), OrderedDict{CI{F, S}, Tuple{F, S}}())))
end
end
function MOI.get(uf::UniversalFallback, listattr::MOI.ListOfConstraints)
list = MOI.get(uf.model, listattr)
for (FS, constraints) in uf.constraints
if !isempty(constraints)
push!(list, FS)
end
end
list
end
function MOI.get(uf::UniversalFallback, listattr::MOI.ListOfOptimizerAttributesSet)
list = MOI.get(uf.model, listattr)
for attr in keys(uf.optattr)
push!(list, attr)
end
list
end
function MOI.get(uf::UniversalFallback, listattr::MOI.ListOfModelAttributesSet)
list = MOI.get(uf.model, listattr)
if uf.objective !== nothing
push!(list, MOI.ObjectiveFunction{typeof(uf.objective)}())
end
for attr in keys(uf.modattr)
push!(list, attr)
end
list
end
function MOI.get(uf::UniversalFallback, listattr::MOI.ListOfVariableAttributesSet)
list = MOI.get(uf.model, listattr)
for attr in keys(uf.varattr)
push!(list, attr)
end
list
end
function MOI.get(uf::UniversalFallback, listattr::MOI.ListOfConstraintAttributesSet{F, S}) where {F, S}
list = MOI.get(uf.model, listattr)
for attr in keys(uf.conattr)
push!(list, attr)
end
return list
end
# Objective
function MOI.set(uf::UniversalFallback, attr::MOI.ObjectiveSense,
sense::MOI.OptimizationSense) where T
if sense == MOI.FEASIBILITY_SENSE
uf.objective = nothing
end
MOI.set(uf.model, attr, sense)
end
function MOI.get(uf::UniversalFallback,
attr::MOI.ObjectiveFunctionType)
if uf.objective === nothing
return MOI.get(uf.model, attr)
else
return typeof(uf.objective)
end
end
function MOI.get(uf::UniversalFallback,
attr::MOI.ObjectiveFunction{F})::F where F
if uf.objective === nothing
return MOI.get(uf.model, attr)
else
return uf.objective
end
end
function MOI.set(uf::UniversalFallback,
attr::MOI.ObjectiveFunction,
func::MOI.AbstractScalarFunction)
if MOI.supports(uf.model, attr)
MOI.set(uf.model, attr, func)
# Clear any fallback objective
uf.objective = nothing
else
uf.objective = copy(func)
# Clear any `model` objective
sense = MOI.get(uf.model, MOI.ObjectiveSense())
MOI.set(uf.model, MOI.ObjectiveSense(), MOI.FEASIBILITY_SENSE)
MOI.set(uf.model, MOI.ObjectiveSense(), sense)
end
end
function MOI.modify(uf::UniversalFallback, obj::MOI.ObjectiveFunction, change::MOI.AbstractFunctionModification) where F
if uf.objective === nothing
MOI.modify(uf.model, obj, change)
else
uf.objective = modify_function(uf.objective, change)
end
end
# Name
# The names of constraints not supported by `uf.model` need to be handled
function MOI.set(uf::UniversalFallback, attr::MOI.ConstraintName, ci::CI{F, S}, name::String) where {F, S}
if MOI.supports_constraint(uf.model, F, S)
MOI.set(uf.model, attr, ci, name)
else
uf.con_to_name[ci] = name
uf.name_to_con = nothing # Invalidate the name map.
end
return
end
function MOI.get(uf::UniversalFallback, attr::MOI.ConstraintName, ci::CI{F, S}) where {F, S}
if MOI.supports_constraint(uf.model, F, S)
return MOI.get(uf.model, attr, ci)
else
return get(uf.con_to_name, ci, EMPTYSTRING)
end
end
MOI.get(uf::UniversalFallback, ::Type{VI}, name::String) = MOI.get(uf.model, VI, name)
check_type_and_multiple_names(::Type, ::Nothing, ::Nothing, name) = nothing
check_type_and_multiple_names(::Type{T}, value::T, ::Nothing, name) where T = value
check_type_and_multiple_names(::Type, ::Any, ::Nothing, name) where T = nothing
check_type_and_multiple_names(::Type{T}, ::Nothing, value::T, name) where T = value
check_type_and_multiple_names(::Type, ::Nothing, ::Any, name) where T = nothing
function check_type_and_multiple_names(T::Type, ::Any, ::Any, name)
throw_multiple_name_error(T, name)
end
function MOI.get(uf::UniversalFallback, ::Type{CI{F, S}}, name::String) where {F, S}
if uf.name_to_con === nothing
uf.name_to_con = build_name_to_con_map(uf.con_to_name)
end
if MOI.supports_constraint(uf.model, F, S)
ci = MOI.get(uf.model, CI{F, S}, name)
else
# There is no `F`-in-`S` constraint in `b.model`, `ci` is only queried
# to check for duplicate names.
ci = MOI.get(uf.model, CI, name)
end
ci_uf = get(uf.name_to_con, name, nothing)
throw_if_multiple_with_name(ci_uf, name)
return check_type_and_multiple_names(CI{F, S}, ci_uf, ci, name)
end
function MOI.get(uf::UniversalFallback, ::Type{CI}, name::String)
if uf.name_to_con === nothing
uf.name_to_con = build_name_to_con_map(uf.con_to_name)
end
ci_uf = get(uf.name_to_con, name, nothing)
throw_if_multiple_with_name(ci_uf, name)
return check_type_and_multiple_names(
CI, ci_uf, MOI.get(uf.model, CI, name), name)
end
_set(uf, attr::MOI.AbstractOptimizerAttribute, value) = uf.optattr[attr] = value
_set(uf, attr::MOI.AbstractModelAttribute, value) = uf.modattr[attr] = value
function _set(uf, attr::MOI.AbstractVariableAttribute, vi::VI, value)
if !haskey(uf.varattr, attr)
uf.varattr[attr] = Dict{VI, Any}()
end
uf.varattr[attr][vi] = value
end
function _set(uf, attr::MOI.AbstractConstraintAttribute, ci::CI, value)
if !haskey(uf.conattr, attr)
uf.conattr[attr] = Dict{CI, Any}()
end
uf.conattr[attr][ci] = value
end
MOI.supports(::UniversalFallback, ::Union{MOI.AbstractModelAttribute, MOI.AbstractOptimizerAttribute}) = true
function MOI.set(uf::UniversalFallback, attr::Union{MOI.AbstractOptimizerAttribute, MOI.AbstractModelAttribute}, value)
if MOI.supports(uf.model, attr)
return MOI.set(uf.model, attr, value)
else
return _set(uf, attr, value)
end
end
MOI.supports(::UniversalFallback, ::Union{MOI.AbstractVariableAttribute, MOI.AbstractConstraintAttribute}, ::Type{<:MOI.Index}) = true
function MOI.set(uf::UniversalFallback, attr::MOI.AbstractVariableAttribute, idx::VI, value)
if MOI.supports(uf.model, attr, typeof(idx))
return MOI.set(uf.model, attr, idx, value)
else
return _set(uf, attr, idx, value)
end
end
function MOI.set(uf::UniversalFallback, attr::MOI.AbstractConstraintAttribute, idx::CI{F, S}, value) where {F, S}
if MOI.supports_constraint(uf.model, F, S) && MOI.supports(uf.model, attr, CI{F, S})
return MOI.set(uf.model, attr, idx, value)
else
return _set(uf, attr, idx, value)
end
end
# Constraints
MOI.supports_constraint(uf::UniversalFallback, ::Type{F}, ::Type{S}) where {F<:MOI.AbstractFunction, S<:MOI.AbstractSet} = true
function _new_constraint_index(uf, f::MOI.SingleVariable, s::MOI.AbstractScalarSet)
return CI{MOI.SingleVariable, typeof(s)}(f.variable.value)
end
function _new_constraint_index(uf, f::MOI.AbstractFunction, s::MOI.AbstractSet)
uf.nextconstraintid += 1
return CI{typeof(f), typeof(s)}(uf.nextconstraintid)
end
function MOI.add_constraint(uf::UniversalFallback, f::MOI.AbstractFunction, s::MOI.AbstractSet)
F = typeof(f)
S = typeof(s)
if MOI.supports_constraint(uf.model, F, S)
return MOI.add_constraint(uf.model, f, s)
else
constraints = get!(uf.constraints, (F, S)) do
OrderedDict{CI{F, S}, Tuple{F, S}}()
end::OrderedDict{CI{F, S}, Tuple{F, S}}
ci = _new_constraint_index(uf, canonical(f), copy(s))
constraints[ci] = (f, s)
return ci
end
end
function MOI.modify(uf::UniversalFallback, ci::CI{F, S}, change::MOI.AbstractFunctionModification) where {F, S}
if MOI.supports_constraint(uf.model, F, S)
MOI.modify(uf.model, ci, change)
else
(f, s) = uf.constraints[(F, S)][ci]
uf.constraints[(F, S)][ci] = (modify_function(f, change), s)
end
end
function MOI.get(uf::UniversalFallback, attr::MOI.ConstraintFunction, ci::CI{F, S}) where {F, S}
if MOI.supports_constraint(uf.model, F, S)
MOI.get(uf.model, attr, ci)
else
MOI.throw_if_not_valid(uf, ci)
uf.constraints[(F, S)][ci][1]
end
end
function MOI.get(uf::UniversalFallback, attr::MOI.ConstraintSet, ci::CI{F, S}) where {F, S}
if MOI.supports_constraint(uf.model, F, S)
MOI.get(uf.model, attr, ci)
else
MOI.throw_if_not_valid(uf, ci)
uf.constraints[(F, S)][ci][2]
end
end
function MOI.set(uf::UniversalFallback, ::MOI.ConstraintFunction, ci::CI{F,S}, func::F) where {F, S}
if MOI.supports_constraint(uf.model, F, S)
MOI.set(uf.model, MOI.ConstraintFunction(), ci, func)
else
MOI.throw_if_not_valid(uf, ci)
if F == MOI.SingleVariable
throw(MOI.SettingSingleVariableFunctionNotAllowed())
end
(_, s) = uf.constraints[(F, S)][ci]
uf.constraints[(F, S)][ci] = (func, s)
end
end
function MOI.set(uf::UniversalFallback, ::MOI.ConstraintSet, ci::CI{F,S}, set::S) where {F, S}
if MOI.supports_constraint(uf.model, F, S)
MOI.set(uf.model, MOI.ConstraintSet(), ci, set)
else
MOI.throw_if_not_valid(uf, ci)
(f, _) = uf.constraints[(F, S)][ci]
uf.constraints[(F, S)][ci] = (f, set)
end
end
# Variables
MOI.add_variable(uf::UniversalFallback) = MOI.add_variable(uf.model)
MOI.add_variables(uf::UniversalFallback, n) = MOI.add_variables(uf.model, n)
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] | 2.187314 | 8,734 |
<filename>test/dynamics_constraints.jl
model = Dynamics.Cartpole()
prob = Problems.Cartpole()[1]
bnd = prob.constraints.constraints[1]
n,m = size(prob)
N = prob.N
rollout!(prob)
Z = prob.Z
vals = [@SVector zeros(model.n) for k = 1:N-1]
∇c = [SizedMatrix{n,2n+m}(zeros(n,2n+m)) for k = 1:N-1]
dyn_con = DynamicsConstraint{RK3}(model, N)
@test TO.width(dyn_con) == 2n+m
evaluate!(vals, dyn_con, Z)
jacobian!(∇c, dyn_con, Z)
@test (@allocated evaluate!(vals, dyn_con, Z)) == 0
@test (@allocated jacobian!(∇c, dyn_con, Z)) == 0
con_rk3 = ConstraintVals(dyn_con, 1:N-1)
evaluate!(con_rk3, Z)
jacobian!(con_rk3, Z)
TO.max_violation!(con_rk3)
maximum(con_rk3.c_max)
@test (@allocated evaluate!(con_rk3, Z)) == 0
@test (@allocated jacobian!(con_rk3, Z)) == 0
∇c = [zeros(SizedMatrix{n,2n+2m}) for k = 1:N-1]
dyn_con = DynamicsConstraint{HermiteSimpson}(model, N)
@test TO.width(dyn_con) == 2(n+m)
evaluate!(vals, dyn_con, Z)
jacobian!(∇c, dyn_con, Z)
@test (@allocated evaluate!(vals, dyn_con, Z)) == 0
@test (@allocated jacobian!(∇c, dyn_con, Z)) == 0
con_hs = ConstraintVals(dyn_con, 1:N-1)
evaluate!(con_hs, Z)
jacobian!(con_hs, Z)
@test (@allocated evaluate!(con_hs, Z)) == 0
@test (@allocated jacobian!(con_hs, Z)) == 0
# Test default
dyn_con = DynamicsConstraint(model, N)
@test integration(dyn_con) == RK3 == TO.DEFAULT_Q
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] | 2.08805 | 636 |
module MixedModels
using BlockArrays
using BlockDiagonals
using CategoricalArrays
using Distributions
using GLM
using LinearAlgebra
using NamedArrays
using NLopt
using Random
using ProgressMeter
using Showoff
using SparseArrays
using StaticArrays
using Statistics
using StatsBase
using StatsModels
using Tables
using TypedTables
using LinearAlgebra: BlasFloat, BlasReal, HermOrSym, PosDefException, copytri!
using Printf: @printf, @sprintf
using GLM: Link, canonicallink
using StatsFuns: log2π
import Base: *
import GLM: dispersion, dispersion_parameter
import NLopt: Opt
import StatsBase: fit, fit!
export @formula,
Bernoulli,
Binomial,
Block,
BlockedSparse,
DummyCoding,
EffectsCoding,
Gamma,
GeneralizedLinearMixedModel,
HelmertCoding,
InverseGaussian,
InverseLink,
LinearMixedModel,
LogitLink,
LogLink,
MixedModel,
Normal,
OptSummary,
Poisson,
RaggedArray,
RandomEffectsTerm,
ReMat,
UniformBlockDiagonal,
VarCorr,
aic,
aicc,
bic,
coef,
coefnames,
coeftable,
cond,
describeblocks,
condVar,
deviance,
dispersion,
dispersion_parameter,
dof,
dof_residual,
fit,
fit!,
fitted,
fixef,
fulldummy,
fnames,
GHnorm,
loglikelihood,
lowerbd,
nblocks,
nobs,
objective,
parametricbootstrap,
pirls!,
predict,
pwrss,
ranef,
refit!,
residuals,
response,
shortestCovInt,
sdest,
setθ!,
simulate!,
sparse,
statscholesky,
std,
stderror,
updateL!,
varest,
vcov,
zerocorr,
zerocorr!
import Base: ==, *
abstract type MixedModel{T} <: StatsModels.RegressionModel end # model with fixed and random effects
include("utilities.jl")
include("arraytypes.jl")
include("varcorr.jl")
include("femat.jl")
include("remat.jl")
include("optsummary.jl")
include("randomeffectsterm.jl")
include("linearmixedmodel.jl")
include("gausshermite.jl")
include("generalizedlinearmixedmodel.jl")
include("mixed.jl")
include("linalg/statschol.jl")
include("linalg/cholUnblocked.jl")
include("linalg/rankUpdate.jl")
include("linalg/logdet.jl")
include("linalg.jl")
include("simulate.jl")
end # module
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] | 2.147986 | 1,142 |
<filename>examples/imagelike/contourf.jl
using GLVisualize, GeometryTypes
if !isdefined(:runtests)
window = glscreen()
timesignal = loop(linspace(0f0,1f0,360))
end
description = """
Simple animated contour plot. You need to press Ctrl to move the camera.
Most 2D camera's use the Ctrl modifier to make editing easier.
"""
# use the performance tips to speed this up
# (http://docs.julialang.org/en/release-0.4/manual/performance-tips/)
# the array is 512x512 after all
const N = 256
const range = linspace(-5f0, 5f0, N)
const data = zeros(Intensity{Float32}, N, N)
function contour_inner(i, x, y)
Intensity{Float32}(sin(1.3*x*i)*cos(0.9*y)+cos(.8*x)*sin(1.9*y)+cos(y*.2*x))
end
function contourdata(t)
for i=1:size(data, 1)
for j=1:size(data, 2)
@inbounds data[i,j] = contour_inner(t, range[i], range[j])
end
end
data
end
renderable = visualize(map(contourdata, timesignal), color_norm=Vec2f0(-3, 3))
_view(renderable, window, camera=:orthographic_pixel)
if !isdefined(:runtests)
renderloop(window)
end
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] | 2.417234 | 441 |
using Test
using Statistics
using LinearAlgebra
using Random
using EnhancedGJK
using EnhancedGJK: projection_weights, projection_weights_reference
using CoordinateTransformations: IdentityTransformation, Translation
using StaticArrays: SVector
import GeometryTypes
const gt = GeometryTypes
using FileIO
const mesh_dir = joinpath(dirname(@__FILE__), "meshes")
@testset "reference distance" begin
mesh = load(joinpath(mesh_dir, "base_link.obj"))
for x in range(0.05, stop=1, length=10)
for y in range(-1, stop=1, length=10)
for z in range(-1, stop=1, length=10)
point = SVector(x, y, z)
gjk_dist = gjk(mesh, point)
simple_dist = ReferenceDistance.signed_distance(mesh, point)
@test isapprox(gjk_dist.signed_distance, simple_dist, atol=2e-4)
end
end
end
end
@testset "reference interior distance" begin
verts = [gt.Point(1., -1, 1), gt.Point(1., 1, 1), gt.Point(-1., 1, 1), gt.Point(-1., -1, 1),
gt.Point(1., -1, -1), gt.Point(1., 1, -1), gt.Point(-1., 1, -1), gt.Point(-1., -1, -1)]
ft = gt.Face{3, Int}
faces = [ft(1, 2, 5), ft(6, 5, 2),
ft(2, 3, 6), ft(7, 6, 3),
ft(3, 4, 7), ft(8, 7, 4),
ft(4, 1, 8), ft(5, 8, 1),
ft(2, 1, 3), ft(4, 3, 1),
ft(5, 6, 7), ft(7, 8, 5)]
mesh = gt.HomogenousMesh(verts, faces)
ReferenceDistance.signed_distance(mesh, SVector(0, 0, 0))
for x in range(-1, stop=1, length=10)
for y in range(-1, stop=1, length=10)
for z in range(-1, stop=1, length=10)
point = SVector(x, y, z)
expected = -min(1 - abs(x), 1 - abs(y), 1 - abs(z))
actual = ReferenceDistance.signed_distance(mesh, point)
@test isapprox(expected, actual, atol=1e-15)
end
end
end
end
@testset "table" begin
width = 0.5
thickness = 0.05
surface_points = Vector{SVector{3, Float64}}()
for z in [-thickness, thickness]
for x in [-width, width]
for y in [-width, width]
push!(surface_points, SVector(x, y, z))
end
end
end
geometry = SVector{length(surface_points)}(surface_points)
point = zeros(SVector{3, Float64})
for x in range(-width, stop=width, length=21)
for y in range(-width, stop=width, length=21)
z = 0.1
@test isapprox(gjk(geometry,
point,
IdentityTransformation(),
Translation(SVector(x, y, z))).signed_distance, 0.05)
z = 0.06
@test isapprox(gjk(geometry,
point,
IdentityTransformation(),
Translation(SVector(x, y, z))).signed_distance,
0.01,
atol=1e-12)
z = 0.05
@test isapprox(gjk(geometry,
point,
IdentityTransformation(),
Translation(SVector(x, y, z))).signed_distance,
0.0,
atol=1e-12)
end
end
end
@testset "johnson distance subalgorithm" begin
include("johnson_distance.jl")
end
@testset "simplex distance" begin
simplex = SVector{3}(SVector{2, Float64}[[1., 0], [2., 0], [1., 1]])
pt = SVector(0., 0)
cache = CollisionCache(simplex, pt);
result = gjk!(cache, IdentityTransformation(), IdentityTransformation())
@test isapprox(result.signed_distance, 1.0)
@test isapprox(result.closest_point_in_body.a, [1.0, 0.0])
@test isapprox(result.closest_point_in_body.b, [0.0, 0.0])
end
@testset "mesh to mesh" begin
mesh = load(joinpath(mesh_dir, "r_foot_chull.obj"))
dx = 1.0
foot_length = 0.172786 + 0.090933
cache = CollisionCache(mesh, mesh)
result = gjk!(cache, IdentityTransformation(), Translation(SVector(dx, 0, 0)))
@test isapprox(result.signed_distance, dx - foot_length, atol=1e-3)
cache = CollisionCache(mesh, mesh)
result = gjk!(cache, Translation(SVector(dx, 0, 0)), IdentityTransformation())
@test isapprox(result.signed_distance, dx - foot_length, atol=1e-3)
cache = CollisionCache(mesh, mesh)
expected_penetration = 0.01
result = gjk!(cache, IdentityTransformation(), Translation(foot_length - expected_penetration, 0, 0))
# TODO: penetration distance is inconsistent and inaccurate
@test result.signed_distance < 0
end
@testset "neighbor mesh to mesh" begin
mesh = NeighborMesh(load(joinpath(mesh_dir, "r_foot_chull.obj")))
dx = 1.0
foot_length = 0.172786 + 0.090933
cache = CollisionCache(mesh, mesh)
result = gjk!(cache, IdentityTransformation(), Translation(SVector(dx, 0, 0)))
@test isapprox(result.signed_distance, dx - foot_length, atol=1e-3)
cache = CollisionCache(mesh, mesh)
result = gjk!(cache, Translation(SVector(dx, 0, 0)), IdentityTransformation())
@test isapprox(result.signed_distance, dx - foot_length, atol=1e-3)
cache = CollisionCache(mesh, mesh)
expected_penetration = 0.01
result = gjk!(cache, IdentityTransformation(), Translation(foot_length - expected_penetration, 0, 0))
# TODO: penetration distance is inconsistent and inaccurate
@test result.signed_distance < 0
end
@testset "geometry types" begin
# Adapted from
# https://github.com/JuliaGeometry/GeometryTypes.jl/blob/master/test/gjk.jl
@testset "gjk examples" begin
c1 = gt.Simplex(gt.Vec(-1.))
c2 = gt.Simplex(gt.Vec(4.))
cache = CollisionCache(c1, c2)
result = gjk!(cache, IdentityTransformation(), IdentityTransformation())
@test isapprox(result.signed_distance, 5.0)
c1 = gt.Simplex(gt.Vec(-1.,0,0))
c2 = gt.Simplex(gt.Vec(4.,0,0))
cache = CollisionCache(c1, c2)
result = gjk!(cache, IdentityTransformation(), IdentityTransformation())
@test isapprox(result.signed_distance, 5.0)
c1 = gt.FlexibleConvexHull([gt.Vec(0.,0), gt.Vec(0.,1), gt.Vec(1.,0),gt.Vec(1.,1)])
c2 = gt.Simplex(gt.Vec(4.,0.5))
cache = CollisionCache(c1, c2)
result = gjk!(cache, IdentityTransformation(), IdentityTransformation())
@test isapprox(result.signed_distance, 3.0)
pt1 = gt.Vec(1,2,3.)
pt2 = gt.Vec(3,4,5.)
cache = CollisionCache(pt1, pt2)
result = gjk!(cache, IdentityTransformation(), IdentityTransformation())
@test isapprox(result.signed_distance, norm(pt1 - pt2))
end
@testset "gjk intersecting lines" begin
c1 = gt.Simplex(gt.Vec(1,1.), gt.Vec(1, 2.))
@test gjk(c1, c1).signed_distance == 0.
c2 = gt.Simplex(gt.Vec(1,1.), gt.Vec(10, 2.))
@test gjk(c1, c2).signed_distance == 0.
end
end
@testset "benchmarks" begin
include("../perf/runbenchmarks.jl")
end
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] | 2.126126 | 3,219 |
<gh_stars>1-10
using MathOptInterface
const MOI = MathOptInterface
# Some tests are excluded because UniversalFallback accepts absolutely
# everything.
MOI.Test.runtests(
MOI.Utilities.MockOptimizer(
MOI.Utilities.UniversalFallback(MOI.Utilities.Model{Float64}()),
),
MOI.Test.Config(),
exclude = [
"test_model_ScalarFunctionConstantNotZero",
"test_model_copy_to_UnsupportedAttribute",
"test_model_copy_to_UnsupportedConstraint",
"test_model_supports_constraint_ScalarAffineFunction_EqualTo",
"test_model_supports_constraint_VariableIndex_EqualTo",
"test_model_supports_constraint_VectorOfVariables_Nonnegatives",
],
warn_unsupported = true,
)
# Run the previously excluded tests, this time without UniversalFallback.
MOI.Test.runtests(
MOI.Utilities.MockOptimizer(
MOI.Utilities.Model{Float64}(),
scalar_function_constant_non_zero = true,
),
MOI.Test.Config(),
include = [
"test_model_ScalarFunctionConstantNotZero",
"test_model_copy_to_UnsupportedAttribute",
"test_model_copy_to_UnsupportedConstraint",
"test_model_supports_constraint_ScalarAffineFunction_EqualTo",
"test_model_supports_constraint_VariableIndex_EqualTo",
"test_model_supports_constraint_VectorOfVariables_Nonnegatives",
],
)
# Test for Issue #1757
MOI.Test.test_model_ScalarFunctionConstantNotZero(
MOI.Utilities.MockOptimizer(
MOI.Utilities.Model{Float64}(),
scalar_function_constant_non_zero = false,
),
MOI.Test.Config(exclude = Any[MOI.ConstraintFunction]),
)
# Test exclude_tests_after. This should work despite no methods being added for IncompleteOptimizer
# because every test should get skipped.
struct IncompleteOptimizer <: MOI.AbstractOptimizer end
MOI.Test.runtests(
IncompleteOptimizer(),
MOI.Test.Config();
exclude_tests_after = v"0.0.1",
)
# Non-Float64 tests
MOI.Test.runtests(
MOI.Utilities.MockOptimizer(
MOI.Utilities.UniversalFallback(MOI.Utilities.Model{BigFloat}()),
BigFloat,
),
MOI.Test.Config(BigFloat),
exclude = [
# ========================= Expected failures ==========================
# UniversalFallback supports these tests.
"test_model_copy_to_UnsupportedAttribute",
"test_model_copy_to_UnsupportedConstraint",
"test_model_supports_constraint_ScalarAffineFunction_EqualTo",
"test_model_supports_constraint_VariableIndex_EqualTo",
"test_model_supports_constraint_VectorOfVariables_Nonnegatives",
],
)
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] | 2.475332 | 1,054 |
<filename>src/functions/utils/generate_file_template.jl
function generate_file_template(parent_folder, file_type, file_name="", file_params=Dict())
file_suffix = "jl"
if contains(file_type, ".")
file_type, file_suffix = map(s -> String(s), split(file_type, "."; limit=2))
end
if file_name == "" ; file_name = file_type ; end
template_path = "$(dirname(@__FILE__))/../../../templates/$parent_folder/$file_type.$file_suffix"
template = readstring(template_path)
template_dictionary = Dict()
template_dictionary["app"] = replace(rsplit(pwd(), "/"; limit=2)[2], ".jl", "")
template_dictionary["user"] = LibGit2.getconfig("github.user", "")
template_dictionary["name"] = file_name
template_dictionary["title"] = title(file_name)
template_dictionary["class"] = get_class_name(file_name)
template_dictionary["params"] = join(file_params, ", ")
template_dictionary["fields"] = join(file_params, "\n ")
render(template, template_dictionary)
end
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] | 2.884956 | 339 |
using Plots, Test, Colors
const PLOTS_DEFAULTS = Dict(:theme => :wong2, :fontfamily => :palantino)
Plots.__init__()
@testset "Loading theme" begin
pl = plot(1:5)
@test pl[1][1][:seriescolor] == RGBA(colorant"black")
@test Plots.guidefont(pl[1][:xaxis]).family == "palantino"
end
empty!(PLOTS_DEFAULTS)
Plots.__init__()
@testset "Legend defaults" begin
p = plot()
@test p[1][:legend_font_family] == "sans-serif"
@test p[1][:legend_font_pointsize] == 8
@test p[1][:legend_font_halign] == :hcenter
@test p[1][:legend_font_valign] == :vcenter
@test p[1][:legend_font_rotation] == 0.0
@test p[1][:legend_font_color] == RGB{Colors.N0f8}(0.0, 0.0, 0.0)
@test p[1][:legend_position] == :best
@test p[1][:legend_title] == nothing
@test p[1][:legend_title_font_family] == "sans-serif"
@test p[1][:legend_title_font_pointsize] == 11
@test p[1][:legend_title_font_halign] == :hcenter
@test p[1][:legend_title_font_valign] == :vcenter
@test p[1][:legend_title_font_rotation] == 0.0
@test p[1][:legend_title_font_color] == RGB{Colors.N0f8}(0.0, 0.0, 0.0)
@test p[1][:legend_background_color] == RGBA{Float64}(1.0, 1.0, 1.0, 1.0)
@test p[1][:legend_foreground_color] == RGB{Colors.N0f8}(0.0, 0.0, 0.0)
end # testset
@testset "Legend API" begin
p = plot(;
legendfontfamily = "serif",
legendfontsize = 12,
legendfonthalign = :left,
legendfontvalign = :top,
legendfontrotation = 1,
legendfontcolor = :red,
legend = :outertopleft,
legendtitle = "The legend",
legendtitlefontfamily = "helvetica",
legendtitlefontsize = 3,
legendtitlefonthalign = :right,
legendtitlefontvalign = :bottom,
legendtitlefontrotation = -5.2,
legendtitlefontcolor = :blue,
background_color_legend = :cyan,
foreground_color_legend = :green,
)
@test p[1][:legend_font_family] == "serif"
@test p[1][:legend_font_pointsize] == 12
@test p[1][:legend_font_halign] == :left
@test p[1][:legend_font_valign] == :top
@test p[1][:legend_font_rotation] == 1.0
@test p[1][:legend_font_color] == :red
@test p[1][:legend_position] == :outertopleft
@test p[1][:legend_title] == "The legend"
@test p[1][:legend_title_font_family] == "helvetica"
@test p[1][:legend_title_font_pointsize] == 3
@test p[1][:legend_title_font_halign] == :right
@test p[1][:legend_title_font_valign] == :bottom
@test p[1][:legend_title_font_rotation] == -5.2
@test p[1][:legend_title_font_color] == :blue
@test p[1][:legend_background_color] == RGBA{Float64}(0.0, 1.0, 1.0, 1.0)
@test p[1][:legend_foreground_color] == RGBA{Float64}(0.0, 0.5019607843137255, 0.0, 1.0)
#setting whole font
sp = plot(
1:5,
legendfont = font(12),
legend_font_halign = :left,
foreground_color_subplot = :red,
)[1]
@test Plots.legendfont(sp).pointsize == 12
@test Plots.legendfont(sp).halign == :left
# match mechanism
@test sp[:legend_font_color] == sp[:foreground_color_subplot]
@test Plots.legendfont(sp).color == sp[:foreground_color_subplot]
end # testset
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] | 2.123923 | 1,509 |
<filename>test/test_float.jl
using Push
using Test
cfg_path = joinpath(dirname(@__FILE__), "configuration/float.cfg")
cfg = Push.load_configuration(cfg_path)
# FLOAT.*
s = Push.run("(1.0 FLOAT.*)", cfg)
@test s.float == [1.0]
s = Push.run("(2.5 3.0 50.0 0.1 FLOAT.*)", cfg)
@test s.float == [2.5, 3.0, 5.0]
s = Push.run("(2.5 3.0 -50.0 10.0 FLOAT.*)", cfg)
@test s.float == [2.5, 3.0, -500.0]
# FLOAT.+
s = Push.run("(1.0 FLOAT.+)", cfg)
@test s.float == [1.0]
s = Push.run("(7.0 3.0 2.5 10.25 FLOAT.+)", cfg)
@test s.float == [7.0, 3.0, 12.75]
s = Push.run("(7.0 3.0 2.75 -10.25 FLOAT.+)", cfg)
@test s.float == [7.0, 3.0, -7.5]
# FLOAT.-
s = Push.run("(1.0 FLOAT.-)", cfg)
@test s.float == [1.0]
s = Push.run("(7.0 3.0 10.75 2.25 FLOAT.-)", cfg)
@test s.float == [7.0, 3.0, 8.5]
s = Push.run("(7.0 3.0 10.0 25.0 FLOAT.-)", cfg)
@test s.float == [7.0, 3.0, -15.0]
# FLOAT./
s = Push.run("(1.0 FLOAT./)", cfg)
@test s.float == [1.0]
s = Push.run("(7.0 3.0 10.0 2.0 FLOAT./)", cfg)
@test s.float == [7.0, 3.0, 5.0]
s = Push.run("(7.0 3.0 -10.0 4.0 FLOAT./)", cfg)
@test s.float == [7.0, 3.0, -2.5]
s = Push.run("(7.0 3.0 -10.0 0.0 FLOAT./)", cfg)
@test s.float == [7.0, 3.0, -10.0, 0.0]
# FLOAT.<
s = Push.run("(1.0 FLOAT.<)", cfg)
@test s.float == [1.0] && isempty(s.boolean)
s = Push.run("(100.0 900.0 2.0 3.0 FLOAT.<)", cfg)
@test s.float == [100.0, 900.0] && s.boolean == [true]
s = Push.run("(100.0 900.0 5.0 -10.0 FLOAT.<)", cfg)
@test s.float == [100.0, 900.0] && s.boolean == [false]
s = Push.run("(100.0 900.0 5.0 5.0 FLOAT.<)", cfg)
@test s.float == [100.0, 900.0] && s.boolean == [false]
# FLOAT.=
s = Push.run("(1.0 FLOAT.=)", cfg)
@test s.float == [1.0] && isempty(s.boolean)
s = Push.run("(100.0 900.0 -10.0 90.0 FLOAT.=)", cfg)
@test s.float == [100.0, 900.0] && s.boolean == [false]
s = Push.run("(100.0 900.0 -10.0 -10.0 FLOAT.=)", cfg)
@test s.float == [100.0, 900.0] && s.boolean == [true]
# FLOAT.>
s = Push.run("(1.0 FLOAT.>)", cfg)
@test s.float == [1.0] && isempty(s.boolean)
s = Push.run("(100.0 900.0 2.0 3.0 FLOAT.>)", cfg)
@test s.float == [100.0, 900.0] && s.boolean == [false]
s = Push.run("(100.0 900.0 5.0 -10.0 FLOAT.>)", cfg)
@test s.float == [100.0, 900.0] && s.boolean == [true]
s = Push.run("(100.0 900.0 5.0 5.0 FLOAT.>)", cfg)
@test s.float == [100.0, 900.0] && s.boolean == [false]
# FLOAT.DUP
s = Push.run("(10.0 20.0 30.0 FLOAT.DUP)", cfg)
@test s.float == [10.0, 20.0, 30.0, 30.0]
# FLOAT.FLUSH
s = Push.run("(10.0 20.0 30.0 FLOAT.FLUSH)", cfg)
@test s.float == []
# FLOAT.FROMBOOLEAN
s = Push.run("(10.0 20.0 TRUE FALSE FLOAT.FROMBOOLEAN)", cfg)
@test s.float == [10.0, 20.0, 0.0] && s.boolean == [true]
s = Push.run("(10.0 20.0 FALSE TRUE FLOAT.FROMBOOLEAN)", cfg)
@test s.float == [10.0, 20.0, 1.0] && s.boolean == [false]
# FLOAT.FROMINTEGER
s = Push.run("(89 FLOAT.FROMINTEGER)", cfg)
@test s.integer == [] && s.float == [89.0]
# FLOAT.MAX
s = Push.run("(10.0 10.0 FLOAT.MAX)", cfg)
@test s.float == [10.0]
s = Push.run("(20.0 10.0 FLOAT.MAX)", cfg)
@test s.float == [20.0]
s = Push.run("(10.0 20.0 FLOAT.MAX)", cfg)
@test s.float == [20.0]
# FLOAT.MIN
s = Push.run("(10.0 10.0 FLOAT.MIN)", cfg)
@test s.float == [10.0]
s = Push.run("(20.0 10.0 FLOAT.MIN)", cfg)
@test s.float == [10.0]
s = Push.run("(10.0 20.0 FLOAT.MIN)", cfg)
@test s.float == [10.0]
# FLOAT.POP
s = Push.run("(10.0 20.0 30.0 FLOAT.POP)", cfg)
@test s.float == [10.0, 20.0]
# FLOAT.ROT
s = Push.run("(1.0 FLOAT.ROT)", cfg)
@test s.float == [1.0]
s = Push.run("(0.0 10.0 20.0 30.0 FLOAT.ROT)", cfg)
@test s.float == [0.0, 30.0, 10.0, 20.0]
# FLOAT.SHOVE
s = Push.run("(1.0 FLOAT.SHOVE)", cfg)
@test s.float == [1.0]
s = Push.run("(2.0 0 FLOAT.SHOVE)", cfg)
@test s.float == [2.0]
s = Push.run("(2.0 9000 FLOAT.SHOVE)", cfg)
@test s.float == [2.0]
s = Push.run("(1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 900.0 -20 FLOAT.SHOVE)", cfg)
@test s.float == [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,900.0]
s = Push.run("(1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 900.0 0 FLOAT.SHOVE)", cfg)
@test s.float == [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 900.0]
s = Push.run("(1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 900.0 1 FLOAT.SHOVE)", cfg)
@test s.float == [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 900.0, 8.0]
s = Push.run("(1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 900.0 2 FLOAT.SHOVE)", cfg)
@test s.float == [1.0,2.0,3.0,4.0,5.0,6.0,900.0,7.0,8.0]
s = Push.run("(1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 900.0 9 FLOAT.SHOVE)", cfg)
@test s.float == [900.0,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0]
s = Push.run("(1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 900.0 1000 FLOAT.SHOVE)", cfg)
@test s.float == [900.0,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0]
# FLOAT.STACKDEPTH
s = Push.run("(FLOAT.STACKDEPTH)", cfg)
@test s.integer == [0]
s = Push.run("(1 20.0 30.0 40.0 FLOAT.STACKDEPTH)", cfg)
@test s.integer == [1, 3] && s.float == [20.0, 30.0, 40.0]
# FLOAT.SWAP
s = Push.run("(1.0 FLOAT.SWAP)", cfg)
@test s.float == [1.0]
s = Push.run("(1.0 2.0 3.0 4.0 5.0 6.0 FLOAT.SWAP)", cfg)
@test s.float == [1.0,2.0,3.0,4.0,6.0,5.0]
# FLOAT.YANK
s = Push.run("(1.0 FLOAT.YANK)", cfg)
@test s.float == [1.0]
s = Push.run("(10.0 0 FLOAT.YANK)", cfg)
@test s.float == [10.0]
s = Push.run("(10.0 20.0 30.0 40.0 50.0 -90 FLOAT.YANK)", cfg)
@test s.float == [10.0, 20.0, 30.0, 40.0, 50.0]
s = Push.run("(10.0 20.0 30.0 40.0 50.0 0 FLOAT.YANK)", cfg)
@test s.float == [10.0, 20.0, 30.0, 40.0, 50.0]
s = Push.run("(10.0 20.0 30.0 40.0 50.0 1 FLOAT.YANK)", cfg)
@test s.float == [10.0, 20.0, 30.0, 50.0, 40.0]
s = Push.run("(10.0 20.0 30.0 40.0 50.0 2 FLOAT.YANK)", cfg)
@test s.float == [10.0, 20.0, 40.0, 50.0, 30.0]
s = Push.run("(10.0 20.0 30.0 40.0 50.0 5 FLOAT.YANK)", cfg)
@test s.float == [20.0, 30.0, 40.0, 50.0, 10.0]
s = Push.run("(10.0 20.0 30.0 40.0 50.0 987 FLOAT.YANK)", cfg)
@test s.float == [20.0, 30.0, 40.0, 50.0, 10.0]
# FLOAT.YANKDUP
s = Push.run("(1.0 FLOAT.YANKDUP)", cfg)
@test s.float == [1.0]
s = Push.run("(10.0 20.0 30.0 40.0 50.0 0 FLOAT.YANKDUP)", cfg)
@test s.float == [10.0, 20.0, 30.0, 40.0, 50.0, 50.0]
s = Push.run("(10.0 20.0 30.0 40.0 50.0 1 FLOAT.YANKDUP)", cfg)
@test s.float == [10.0, 20.0, 30.0, 40.0, 50.0, 40.0]
s = Push.run("(10.0 20.0 30.0 40.0 50.0 2 FLOAT.YANKDUP)", cfg)
@test s.float == [10.0, 20.0, 30.0, 40.0, 50.0, 30.0]
s = Push.run("(10.0 20.0 30.0 40.0 50.0 3 FLOAT.YANKDUP)", cfg)
@test s.float == [10.0, 20.0, 30.0, 40.0, 50.0, 20.0]
s = Push.run("(10.0 20.0 30.0 40.0 50.0 4 FLOAT.YANKDUP)", cfg)
@test s.float == [10.0, 20.0, 30.0, 40.0, 50.0, 10.0]
s = Push.run("(10.0 20.0 30.0 40.0 50.0 987 FLOAT.YANKDUP)", cfg)
@test s.float == [10.0, 20.0, 30.0, 40.0, 50.0, 10.0]
s = Push.run("(10.0 20.0 30.0 40.0 50.0 -987 FLOAT.YANKDUP)", cfg)
@test s.float == [10.0, 20.0, 30.0, 40.0, 50.0, 50.0]
# FLOAT.TAN
s = Push.run("(90.0 FLOAT.TAN)", cfg)
@test s.float == Float32[tan(90.0)]
# FLOAT.COS
s = Push.run("(90.0 FLOAT.COS)", cfg)
@test s.float == Float32[cos(90.0)]
# FLOAT.SIN
s = Push.run("(90.0 FLOAT.SIN)", cfg)
@test s.float == Float32[sin(90.0)]
# FLOAT.DEFINE
s = Push.run("(X FLOAT.DEFINE X)", cfg)
@test isempty(s.float) && s.name == [:X, :X]
s = Push.run("(3.0 X FLOAT.DEFINE X)", cfg)
@test isempty(s.name) && s.float == [3.0]
# FLOAT.RAND
s = Push.run("(FLOAT.RAND)", cfg)
@test length(s.float) == 1
# FLOAT.%
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] | 1.793335 | 4,021 |
<reponame>JJMinton/TensorBoardLogger.jl
import .Plots: Plots
function Base.convert(t::Type{PngImage}, plot::Plots.Plot)
pb = PipeBuffer()
show(pb, MIME("image/png"), plot)
return PngImage(pb)
end
preprocess(name, plot::Plots.Plot, data) = preprocess(name, convert(PngImage, plot), data)
preprocess(name, plots::AbstractArray{<:Plots.Plot}, data) = begin
for (i, plot)=enumerate(plots)
preprocess(name*"/$i", plot, data)
end
return data
end
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# This file contains utilities to facilities the use of
# DataFrames.jl for generating benchmark settings
# The idea is to benchmark different "features" and obtain
# "measurements". Dependent features are supported.
using FileIO, DataFrames;
@doc """
generate_all_settings( features_dict, dependent_features = nothing )
Return a DataFrame, the columns of which correspond to every possible
combination of feature values.
## Arguments
* `features_dict` is a Dict or NamedTuple. The keys must be Strings or
Symbols and provide a feature name. This is also the column name in the
returned DataFrame. The values must be Vectors of possible feature values.
E.g. if feature `"x"` can take values `[1,2,3]` provide an entry
`"x" => [1,2,3]`.
* `dependent_features` (optional). A Vector of Dicts or NamedTuples, one
for each **dependent** feature. A dependent feature depends on some features
from `features_dict`; we call these **arguments**.
Each `dependent_feature` is defined by an entry in `dependent_features` with
keys `"name"`, `"depends_on"` and `"values"`.
The value of `"values"` must either be a function that takes as keyword arguments
the arguments and returns a vector of possible values.\n
It can also be a Dict/NamedTuple with keys being Tuples corresponding to all
possible value combinations of features in `depends_on` (in the order given there).
## Example
```jldoctest
features = Dict(
:X => [1,],
:Y => [1,2,]
);
dep_features = [
Dict(
"name" => :Z,
"depends_on" => [:X, :Y],
"values" => function(;X,Y) fill( X+Y, X+Y ) end
),
];
s = generate_all_settings(features, dep_features)
# output
5×3 DataFrame
Row │ Y X Z
│ Int64 Int64 Int64
─────┼─────────────────────
1 │ 1 1 2
2 │ 1 1 2
3 │ 2 1 3
4 │ 2 1 3
5 │ 2 1 3
```
"""
function generate_all_settings( features_dict :: Union{Dict, NamedTuple},
dependent_features :: Union{Nothing,Vector{D}} where D<:Union{Dict, NamedTuple} = nothing
)
# turn entries of `features_dict` into Dict with key=feature name and
# value = single column DataFrame of values
simple_dfs = Dict{Union{String},DataFrame}()
for (feature, feature_values) ∈ pairs(features_dict)
simple_dfs[string(feature)] = DataFrame( feature => feature_values )
end
# combine to first settings DataFrame
# rows = all possible combinations of feature values from `features_dict`
settings_df = crossjoin( (simple_dfs[string(k)] for k in keys(features_dict))... )
if !isnothing( dependent_features )
for dependent_feature ∈ dependent_features
# for each possible value combination of feature values of
# features in `dependent_feature["depends_on"]` put a dataframe
# into `dependent_dfs`; these are later stacked vertically
dependent_dfs = DataFrame[];
if length( dependent_feature["depends_on"] ) > 1
input_args = crossjoin(
(simple_dfs[ string(dependency) ]
for dependency ∈ dependent_feature["depends_on"] )...
);
else
input_args = simple_dfs[ string(dependent_feature["depends_on"][1]) ]
end
for arg_row = eachrow( input_args )
# a 1-col DF with colname=dependent_feature["name"] and values=f(;arg_row...)
row_df = DataFrame(
dependent_feature["name"] => let dp_vals = dependent_feature["values"];
if isa(dp_vals, Function)
dp_vals(; arg_row... )
elseif isa( dp_vals, Union{Dict,NamedTuple} )
dp_vals[ Tuple(arg_row) ];
else
error(
"Field `values` of dependent feature $(dependent_features["name"])
must be a function with kwargs or a Dict/NamedTuple with keys corresponding
to all possible value combinations of $(dependent_feature["depends_on"])."
);
end#if
end#let
);
# add redundant columns containing input_args information
# and colnames for joining with settings_df
push!( dependent_dfs, crossjoin( DataFrame(arg_row), row_df ) )
end
settings_df = innerjoin( settings_df, vcat( dependent_dfs... );
on = dependent_feature["depends_on"]
);
end
end
return settings_df;
end
#=
features = Dict(
:X => [1,],
:Y => [1,2,]
);
dep_features = [
Dict(
"name" => :Z,
"depends_on" => [:X, :Y],
"values" => function(;X,Y) fill( X+Y, X+Y ) end
),
];
s = generate_all_settings(features, dep_features)
=#
function feature_names( features_dict :: Union{Dict, NamedTuple},
dependent_features :: Union{Nothing,Vector{D}} where D<:Union{Dict, NamedTuple} = nothing
)
return [
string.(collect( keys( features_dict ) ));
[ string(dep_feature["name"]) for dep_feature ∈ dependent_features ]
]
end
#%%
function unpack_feature( feature_name :: String, some_dict )
feature_symbol = Symbol( feature_name );
feature_symbol_symbol = :(Symbol( $feature_name ));
@eval $feature_symbol = getindex($some_dict, $feature_symbol_symbol);
end
#%%
@doc """
add_observation_columns!(df, observations ::Union{Dict, NamedTuple} )
Add one or several new empty column(s) to DataFrame `df` or overwrite if exists.
Columns are specified by `observations` where each key gives a column name and
each value the corresponding data type.
Each column will have then have this data type in union with `Nothing`.
"""
function add_observation_columns!(df :: DataFrame, observations ::Union{Dict, NamedTuple} )
n_rows = size(df, 1);
for (obs_name, obs_type) ∈ pairs(observations)
df[!, string(obs_name)] = Vector{Union{obs_type,Missings.Missing}}(Missings.missing, n_rows)
end
nothing
end
#=
observations = (;
:ω => Float64,
)
add_observation_columns!( s, observations );
=#
#%%
function load_previous_results( filename; result_key = "results" )
file_data = load( filename );
if !haskey( file_data, result_key )
error("Cannot retrieve previous data.");
end
return file_data[result_key];
end
function scan_feature_values( df :: DataFrame, feature_name :: Union{Symbol, String})
if feature_name in names(df)
return unique( df[ !, feature_name ] );
else
@warn "No column $(feature_name) found in DataFrame."
return []
end
end
#%%
function scan_dependent_feature_values( df :: DataFrame, feature_name :: Union{Symbol, String},
depends_on :: Union{S, Vector{S}} where S<:Union{Symbol, String}
)
feature_name = string(feature_name);
if feature_name in names(df)
dep_args = isa( depends_on, Union{Symbol, String} ) ? [ string( depends_on ), ] : string.(depends_on);
new_dep_dict = Dict()
for sub_df in groupby( df, dep_args )
new_dep_dict[ Tuple( sub_df[1, dep_args]) ] = unique( sub_df[:, feature_name ] );
end
return new_dep_dict
else
@warn "No column $(feature_name) found in DataFrame."
return []
end
end
function scan_dependent_feature_values(df :: DataFrame, feature :: Union{Dict, NamedTuple})
return scan_dependent_feature_values( feature["name"], feature["depends_on"] )
end
#=
z_vals = scan_dependent_feature_values( s, "Z", ["X","Y"])
=#
#%%
function find_observation_rows( df :: DataFrame,
observations :: Vector{<:Symbol} = Symbol[]
)
return findall(
.!(vec(
prod( hcat( ( ismissing.( df[:,obs] ) for obs in observations)... ), dims = 2 )
))
);
end
find_observation_rows(df :: DataFrame, obs :: Union{Dict, NamedTuple}) = find_observation_rows( df, collect(keys(obs)) )
function fill_from_partial_results!(target :: DataFrame, source :: DataFrame,
feature_names :: Vector{ S } where S<:Union{Symbol, String},
observations :: Union{Nothing, Union{Dict, NamedTuple} } = nothing,
required_observations :: Union{Nothing, Union{Dict, NamedTuple} } = nothing;
)
if isnothing( observations )
observations = intersect( setdiff( names(target), string.(feature_names) ), names( source ) )
else
observations = collect(keys(observations));
end
if !isnothing( observations )
if isnothing( required_observations )
required_observations = observations;
end
investigated_row_indices = Int[];
non_missing_indices = find_observation_rows( source, required_observations );
for non_missing_index in non_missing_indices
src_row = source[ non_missing_index, : ];
for target_row ∈ eachrow( target )
if Tuple(target_row[feature_names]) == Tuple(src_row[feature_names])
target_row[observations] = src_row[observations];
push!( investigated_row_indices, rownumber(target_row) )
# break here if we expect/assume unique features
end
end
end
to_do_row_indices = setdiff( 1 : size(target,1), investigated_row_indices )
println("Filled in data from $(length(investigated_row_indices)) rows.")
return investigated_row_indices, to_do_row_indices
end
return nothing
end
function fill_from_partial_results!( target :: DataFrame, source :: DataFrame,
features_dict :: Union{Dict, NamedTuple},
dependent_features :: Union{Nothing,Vector{D}} where D<:Union{Dict, NamedTuple} = nothing,
observations :: Union{Nothing, Union{Dict, NamedTuple} } = nothing
)
feature_names = collect(keys( features_dict ));
if !isnothing( dependent_features )
push!( feature_names, [dep_feat["name"] for dep_feat ∈ dependent_features]... )
end
return fill_from_partial_results!( target, source, feature_names, observations)
end
#=
new_df = DataFrame( X=[1,1,2,2], Y=[1,2,1,2] )
add_observation_columns!( new_df, (; :Z => Int,))
src = DataFrame( X = [1,2], Y= [2,2], Z = [10,11])
fill_from_partial_results!( new_df, src, ["X", "Y"] )
=#
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220,
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10612,
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] | 2.331605 | 4,629 |
<filename>src/RepeatingStructures/Multilens/HexTilings.jl
# MIT license
# Copyright (c) Microsoft Corporation. All rights reserved.
# See LICENSE in the project root for full license information.
# All of the objects can be displayed with Vis.draw. Example:
# Vis.draw(hex4RGB())
function clustercolors(lattice)
elements = Repeat.clusterelements(lattice)
colors = Vector{String}(undef, length(elements))
for (i, element) in pairs(elements)
colors[i] = pointcolor(element, lattice)
end
return colors
end
export clustercolors
function hex3RGB()
lattice = hex3()
colors = clustercolors(lattice)
names = ["R","G","B"]
properties = DataFrames.DataFrame(Color=colors, Name=names)
return Repeat.ClusterWithProperties(hex3(), properties)
end
export hex3RGB
function hex4RGB()
lattice = hex4()
colors = clustercolors(lattice)
names = ["R","G","B","W"]
properties = DataFrames.DataFrame(Color=colors, Name=names)
return Repeat.ClusterWithProperties(hex4(), properties)
end
export hex4RGB
function hex7RGB()
lattice = hex7()
colors = clustercolors(lattice)
names = ["R1","G1","B1","W","R2","G2","B2"]
properties = DataFrames.DataFrame(Color=colors, Name=names)
return Repeat.ClusterWithProperties(hex7(), properties)
end
export hex7RGB
function hex9RGB()
lattice = hex9()
colors = clustercolors(lattice)
names = ["R-1","G-1","B-1","R0","G0","B0","R1","G1","B1"]
properties = DataFrames.DataFrame(Color=colors, Name=names)
return Repeat.ClusterWithProperties(hex9(), properties)
end
export hex9RGB
function hex12RGB()
lattice = hex12()
colors = clustercolors(lattice)
names = [
"G3","B0",
"B2","R1","G2",
"R0","G1","B1","R3",
"B3","R2","G0"
]
properties = DataFrames.DataFrame(Color=colors, Name=names)
return Repeat.ClusterWithProperties(hex12(), properties)
end
export hex12RGB
function hex19RGB()
lattice = hex19()
colors = clustercolors(lattice)
names = [
"W",
"B0","G1","R2","B3","G4","R5",
"G0","B1","R1","G2","B2","R3","G3","B4","R4","G5","B5","R0"
]
properties = DataFrames.DataFrame(Color=colors, Name=names)
# properties = DataFrames.DataFrame(Color = colors)
return Repeat.ClusterWithProperties(lattice, properties)
end
export hex19RGB
function hex19fov1()
lattice = hex19()
offset = 1
colornames = ["R","G","B"]
colors = ["red","green","blue"]
fov1 = [8,19,18,17,7,2]
fov2 = [16,15,14,13,5,6]
fov3 = [12,11,10,9,3,4]
allfov = vcat([0],fov1,fov2,fov3)
names = [x -> string("fov",x) for x in 1:6]
allnames = Vector{String}(undef,19)
allcolors = Vector{String}(undef,19)
for (index,fov) in pairs((fov1,fov2,fov3))
for i in eachindex(fov)
println("$index $i")
latticeindex = (index-1)*length(fov1) + i + offset
allcolors[allfov[latticeindex]] = colors[index]
allnames[allfov[latticeindex]] = string("fov",string(index))
end
end
allcolors[offset] = "white"
allnames[offset] = "W"
properties = DataFrames.DataFrame(Color=allcolors, Name=allnames)
# properties = DataFrames.DataFrame(Color = colors)
return Repeat.ClusterWithProperties(lattice, properties)
end
export hex19fov1
function hex19fov2()
lattice = hex19()
colors = clustercolors(lattice)
names = [string("fov",x÷3) for x in 1:18]
names = vcat("W",names)
properties = DataFrames.DataFrame(Color=colors, Name=names)
# properties = DataFrames.DataFrame(Color = colors)
return Repeat.ClusterWithProperties(lattice, properties)
end
export hex19fov2 | [
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] | 2.373724 | 1,568 |
module includes
import Random
import REPL
import TOML
using Dates
"""
note: works on mac & windows
safe_path credits to: the Julia Programming Team (from [Pkg.jl/src/manifest.jl](https://github.com/JuliaLang/Pkg.jl/blob/master/src/manifest.jl)):
(ordered as-is by github.com) [10 members]:
<NAME> @KristofferC
<NAME> @IanButterworth #PRO
<NAME> @00vareladavid
<NAME> @timholy #PRO [neuroscientist, professor, julia tutor]
<NAME> @GunnarFarneback
<NAME> @fredrikekre #PRO [postdoc]
<NAME> @DilumAluthge
<NAME> @tpapp
<NAME> @devmotion #PRO
<NAME> @aviatesk
"""
# turn /-paths into \-paths on Windows
"""safe_path
source:
https://github.com/JuliaLang/Pkg.jl/blob/b4da4946735fe4c7c6bb3e2bc16af95b4e76e487/src/manifest.jl#L57
helps in resolving uris' slashes for windows users
"""
function safe_path(path::String)
if Sys.iswindows() && !isabspath(path)
path = joinpath(split(path, "/")...) # what if: if we can do all urls as "\": thus we'll only Modify for windows users while other OSs can communicate too worry-free -newly standardized
end
return path
end
paths = ["functions", "functions/Generators", "Distributions", "NeuralNet/Networks", "src/DataStructures/Arrays/Array.jl", "tests"] #Idea: all string paths in here # used in a for loop - in a custom iterable function #parallizable
#TODO: Iterate all locations in paths
#include(safe_path(".")) #why refeing to thyself?
#ERROR:PermissionDenied #TODO: find a solution on windows pc to solve previlages in julia
include(safe_path("functions"))
include(safe_path("functions/Generators"))
include(safe_path("Distributions"))
include(safe_path("NeuralNet/Networks"))
include(safe_path("tests"))
include(safe_path("src/constants.jl"))
include(safe_path("src/DataStructures/Arrays/Array.jl"))
"""
to be called upon path error
"""
#= #systemError: Permission Denied (on Windows)#TODO: check common # solution? #nice try !, needs further checking to see why it defaults to last catch
function pathRevise(path::String) # TRY TO FIX (possible) url typos
strings = split(path, "/")
try
strings[1] = Upper(strings[1])#Gatcha#1: try Uppercasing the first letter #(input is a miniscule, while actual directory is Majiscule)
path = joinpath(strings...)
catch
end
#=for i in enumerate length(strings)
#TODO: do minor changes to path - i.e. uppercase other words #TODO: how to spot others , first? [Hint: names are CamelToed!] #possible-fix #available
end=#
end
paths = ["functions", "functions/Generators", "Distributions", "NeuralNet/Networks", "src/DataStructures/Arrays/Array.jl", "tests"]
#TODO: Iterate all locations in paths
#
try
#include(safe_path(".")) #why refeing to thyself?
include(safe_path("functions"))
include(safe_path("functions/Generators"))
include(safe_path("Distributions"))
include(safe_path("NeuralNet/Networks"))
include(safe_path("tests"))
include(safe_path("src/constants.jl"))
include(safe_path("src/DataStructures/Arrays/Array.jl"))
catch
try
strings = split(path, "/")
n = length(strings)
for i in enumerate(n)
strings[1] = Upper(strings[1])#try Uppercasing the first letter
path = joinpath(strings...)
end
catch
println("path does not exist")
end
end
end
=#
"""
DEPOT_PATH
A stack of "depot" locations where the package manager, as well as Julia's code loading mechanisms, look for package registries, installed packages, named
environments, repo clones, cached compiled package images, and configuration files. By default it includes:
1. ~/.julia where ~ is the user home as appropriate on the system;
2. an architecture-specific shared system directory, e.g. /usr/local/share/julia;
3. an architecture-independent shared system directory, e.g. /usr/share/julia.
So DEPOT_PATH might be:
[joinpath(homedir(), ".julia"), "/usr/local/share/julia", "/usr/share/julia"]
The first entry is the "user depot" and should be writable by and owned by the current user. The user depot is where: registries are cloned, new package
versions are installed, named environments are created and updated, package repos are cloned, newly compiled package image files are saved, log files are
written, development packages are checked out by default, and global configuration data is saved. Later entries in the depot path are treated as read-only
and are appropriate for registries, packages, etc. installed and managed by system administrators.
DEPOT_PATH is populated based on the JULIA_DEPOT_PATH environment variable if set.
DEPOT_PATH contents
=====================
Each entry in DEPOT_PATH is a path to a directory which contains subdirectories used by Julia for various purposes. Here is an overview of some of the
subdirectories that may exist in a depot:
• clones: Contains full clones of package repos. Maintained by Pkg.jl and used as a cache.
• compiled: Contains precompiled *.ji files for packages. Maintained by Julia.
• dev: Default directory for Pkg.develop. Maintained by Pkg.jl and the user. <--- ok
• environments: Default package environments. For instance the global environment for a specific julia version. Maintained by Pkg.jl.
• logs: Contains logs of Pkg and REPL operations. Maintained by Pkg.jl and Julia.
• packages: Contains packages, some of which were explicitly installed and some which are implicit dependencies. Maintained by Pkg.jl.
• registries: Contains package registries. By default only General. Maintained by Pkg.jl.
See also: JULIA_DEPOT_PATH, and Code Loading.
"""
depots() = Base.DEPOT_PATH
logdir(depot = depots1()) = joinpath(depot, "logs")
devdir(depot = depots1()) = get(ENV, "JULIA_PKG_DEVDIR", joinpath(depot, "dev"))
envdir(depot = depots1()) = joinpath(depot, "environments")
const UPDATED_REGISTRY_THIS_SESSION = Ref(false)
const OFFLINE_MODE = Ref(false)
function depots1()
d = depots()
isempty(d) && Pkg.Types.pkgerror("no depots found in DEPOT_PATH")
return d[1]
end
function pkg_server()
server = get(ENV, "JULIA_PKG_SERVER", "https://pkg.julialang.org")
isempty(server) && return nothing
startswith(server, r"\w+://") || (server = "https://$server")
return rstrip(server, '/')
end
#--- For globally overriding in e.g. tests
const DEFAULT_IO = Ref{Union{IO,Nothing}}(nothing)
stderr_f() = something(DEFAULT_IO[], stderr)
stdout_f() = something(DEFAULT_IO[], stdout)
const PREV_ENV_PATH = Ref{String}("") #a goo rule of thumb
can_fancyprint(io::IO) = (io isa Base.TTY) && (get(ENV, "CI", nothing) != "true") # fancyprint: <interesting>
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] | 2.865628 | 2,374 |
function plot(tb::TBModel; plothopping::Bool=false)
figure()
if plothopping
x_cncts = Vector{Float64}[]
y_cncts = similar(x_cncts)
for site in eachindex(tb.neighbors)
for neighbor in tb.neighbors[site]
push!(x_cncts, tb.pos[[site,neighbor],1])
push!(y_cncts, tb.pos[[site,neighbor],2])
end
end
# faster to collect all the connections in one array than to issue multiple calls to
# PyPlot for each connection
plot(hcat(x_cncts...), hcat(y_cncts...), "-", color="gray")
# TODO: Avoid plotting same connection twice...
end
plot(tb.pos[:,1], tb.pos[:,2], ".k")
axis("equal")
end | [
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] | 2.332061 | 262 |
module TestConversions
using Compat, Compat.Test, DataFrames, Compat.InteractiveUtils
using DataStructures: OrderedDict, SortedDict
const ≅ = isequal
df = DataFrame()
df[:A] = 1:5
df[:B] = [:A, :B, :C, :D, :E]
@test isa(convert(Array, df), Matrix{Any})
@test isa(convert(Array{Any}, df), Matrix{Any})
df = DataFrame()
df[:A] = 1:5
df[:B] = 1.0:5.0
@test isa(convert(Array, df), Matrix{Float64})
@test isa(convert(Array{Any}, df), Matrix{Any})
@test isa(convert(Array{Float64}, df), Matrix{Float64})
df = DataFrame()
df[:A] = Vector{Union{Float64, Missing}}(1.0:5.0)
df[:B] = Vector{Union{Float64, Missing}}(1.0:5.0)
a = convert(Array, df)
aa = convert(Array{Any}, df)
ai = convert(Array{Int}, df)
@test isa(a, Matrix{Union{Float64, Missing}})
@test a == convert(Array, convert(Array{Union{Float64, Missing}}, df))
@test a == convert(Matrix, df)
@test isa(aa, Matrix{Any})
@test aa == convert(Matrix{Any}, df)
@test isa(ai, Matrix{Int})
@test ai == convert(Matrix{Int}, df)
df[1,1] = missing
@test_throws ErrorException convert(Array{Float64}, df)
na = convert(Array{Union{Float64, Missing}}, df)
naa = convert(Array{Union{Any, Missing}}, df)
nai = convert(Array{Union{Int, Missing}}, df)
@test isa(na, Matrix{Union{Float64, Missing}})
@test na ≅ convert(Matrix, df)
@test isa(naa, Matrix{Union{Any, Missing}})
@test naa ≅ convert(Matrix{Union{Any, Missing}}, df)
@test isa(nai, Matrix{Union{Int, Missing}})
@test nai ≅ convert(Matrix{Union{Int, Missing}}, df)
a = Union{Float64, Missing}[1.0,2.0]
b = Union{Float64, Missing}[-0.1,3]
c = Union{Float64, Missing}[-3.1,7]
di = Dict("a"=>a, "b"=>b, "c"=>c)
df = convert(DataFrame, di)
@test isa(df, DataFrame)
@test names(df) == Symbol[x for x in sort(collect(keys(di)))]
@test df[:a] == a
@test df[:b] == b
@test df[:c] == c
od = OrderedDict("c"=>c, "a"=>a, "b"=>b)
df = convert(DataFrame,od)
@test isa(df, DataFrame)
@test names(df) == Symbol[x for x in keys(od)]
@test df[:a] == a
@test df[:b] == b
@test df[:c] == c
sd = SortedDict("c"=>c, "a"=>a, "b"=>b)
df = convert(DataFrame,sd)
@test isa(df, DataFrame)
@test names(df) == Symbol[x for x in keys(sd)]
@test df[:a] == a
@test df[:b] == b
@test df[:c] == c
a = [1.0]
di = Dict("a"=>a, "b"=>b, "c"=>c)
@test_throws DimensionMismatch convert(DataFrame,di)
end
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] | 2.160547 | 1,171 |
# Autogenerated wrapper script for SOLAR_jll for armv7l-linux-gnueabihf-cxx03
export solar
JLLWrappers.@generate_wrapper_header("SOLAR")
JLLWrappers.@declare_executable_product(solar)
function __init__()
JLLWrappers.@generate_init_header()
JLLWrappers.@init_executable_product(
solar,
"bin/solar",
)
JLLWrappers.@generate_init_footer()
end # __init__()
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] | 2.337349 | 166 |