content
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1.03M
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int64 4
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|---|---|---|---|
repo = joinpath(homedir(), "ReferenceImages", "gallery")
recordings = joinpath(@__DIR__, "test_recordings")
cp(recordings, repo, force = true)
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module RelayISI
using SpkCore
using Statistics, Random, StatsBase, Optim, ImageFiltering
using LoopVectorization
using ..RelayUtils, ..GLMMetrics, ..GLMFit.Partitions
import ..PredictorSet, ..Predictor, ..GLM, ..cross_validate, ..NullPrior,
..Binomial, ..Logistic, ..wasrelayed
export isi_cross_validate
# ============================================================================ #
function spike_status(ret::Vector{Float64}, lgn::Vector{Float64})
status = wasrelayed(ret, lgn)
# first spike has no isi so we can't make a prediction...
return diff(ret), status[2:end]
end
# ============================================================================ #
function smooth_ef(ef::AbstractVector{<:Real}, sigma::Real, bin_size::Real)
return imfilter(ef, (KernelFactors.IIRGaussian(sigma/bin_size),))
end
# ============================================================================ #
function get_eff(isi::Vector{Float64}, status::AbstractVector{<:Real}, kuse::AbstractVector{<:Integer},
sigma::Real, isibin::Real, isimax::Real)
isi_use = isi[kuse]
status_use = status[kuse]
krel = findall(>(0), status_use)
edges = 0.002:isibin:isimax
all = fit(Histogram, isi_use, edges)
rel = fit(Histogram, isi_use[krel], edges)
eff = Vector{Float64}(undef, length(all.weights))
@turbo thread=8 for k in eachindex(all.weights)
eff[k] = all.weights[k] > 0 ? rel.weights[k] / all.weights[k] : 0.0
end
if sigma > 0.0
out = smooth_ef(eff, sigma, isibin)
else
out = eff
end
# ensure [0,1]
@turbo thread=8 for k in eachindex(out)
out[k] = out[k] < 0.0 ? 0.0 : out[k]
end
return edges, out
end
# ============================================================================ #
@inline function predict(edges::AbstractVector{<:Real}, ef::AbstractVector{T}, isi::Vector{Float64}) where T
return predict!(fill(T(0), length(isi)), edges, ef, isi)
end
# ---------------------------------------------------------------------------- #
function predict!(p::Vector{T}, edges::AbstractVector{<:Real}, ef::AbstractVector{T}, isi::Vector{Float64}) where T<:Real
mu = mean(ef)
Threads.@threads for k in eachindex(p)
@inbounds begin
x = isi[k]
if x >= edges[end]
# for all isi's beyond isimax (edges[end]) predict the mean
# efficacy mu
p[k] = mu
elseif x < edges[1]
p[k] = ef[1]
else
j = findfirst(>(x), edges)
# subtract 1 as we used > above to locate the upper edge of the
# bin that this isi belongs in
p[k] = ef[j-1]
end
end
end
return p
end
# ============================================================================ #
roundn(x::Real, n::Integer) = round(x / 10.0^n) * 10.0^n
# ============================================================================ #
function isi_cross_validate(::Type{T}, ret::AbstractVector{<:Real}, lgn::AbstractVector{<:Real},
isimax::Vector{Float64}, isibin::Real=0.001, nfold::Integer=10) where T <: PerformanceMetric
sigma = roundn.(vcat(0.0, 10 .^ range(log10(0.002), log10(0.03), length=7)), -3)
out = T(nfold)
sig = 0.0
isimx = 0.0
best = worst_value(T)
isi, status = spike_status(ret, lgn)
for row in eachrow([repeat(sigma, inner=length(isimax)) repeat(isimax, outer=length(sigma))])
res = isi_model(T, isi, status, row[1], isibin, row[2], nfold, true)
tmp = mean(res)[1]
if better_than(T, tmp, best)
copy!(out, res)
sig = row[1]
isimx = row[2]
best = tmp
end
end
return out, sig, isimx
end
# ============================================================================ #
# NOTE: we are NOT using the turbo functions from RelayUtils as they do not work
# with ForwardDiff.Dual types [which we need to get the objective gradient from
# ForwardDiff (via Optim) in scale_ef()]; however, Dual numbers work just fine
# with Threads (obviously...)
function logistic!(x::AbstractVector{<:Real})
Threads.@threads for k in eachindex(x)
@inbounds x[k] = 1.0 / (1.0 + exp(-x[k]))
end
return x
end
# ============================================================================ #
# NOTE: this is combined logistic + NEGATIVE likelihood calculation specifically
# for scale_ef() below
function binomial_logistic_nlli!(x::Vector{<:Real}, status::Vector{<:Real})
Threads.@threads for k in eachindex(status)
@inbounds begin
tmp = 1.0 / (1.0 + exp(-x[k]))
x[k] = status[k] > 0 ? log(tmp + eps()) : log(1.0 - (tmp - eps()))
end
end
return -sum(x)
end
# ============================================================================ #
function scale_ef(edges::AbstractVector{<:Real}, ef::Vector{Float64}, isi::Vector{Float64}, status::AbstractVector{<:Real}, isibin::Real)
cache = Dict{DataType, Any}(Float64 => zeros(length(isi)))
N = length(isi)
objective(p::Vector{T}) where T = begin
yp = get!(cache, T) do
Vector{T}(undef, N)
end::Vector{T}
return binomial_logistic_nlli!(predict!(yp, edges, p[1] .+ ef .* p[2], isi), status)
end
x0 = [0.0, 1.0]
# mn = mean(ef)
# mx = maximum(ef)
# x0 = [-mn, 1.0/(mx - mn)]
res = optimize(objective, x0, LBFGS(); autodiff=:forward)
return res.minimizer[1] .+ ef .* res.minimizer[2]
end
# ============================================================================ #
function isi_model(::Type{T}, isi::AbstractVector{<:Real}, status::Vector{Bool},
sigma::Real, isibin::Real, isimax::Real, nfold::Integer, shfl::Bool) where T <: PerformanceMetric
res = T(nfold)
k = 1
for p in ballanced_partition(IndexPartitioner, isi, status, nfold, shfl)
idxtrain = training_set(p)
idxtest = testing_set(p)
edges, ef = get_eff(isi, status, idxtrain, sigma, isibin, isimax)
ef = scale_ef(edges, ef, isi[idxtrain], status[idxtrain], isibin)
pred = logistic!(predict(edges, ef, isi[idxtest]))
eval_and_store!(res, status[idxtest], pred, k)
k += 1
end
return res
end
# ============================================================================ #
end
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] | 2.491525 | 2,596 |
@doc raw"""
Stiefel{n,k,𝔽} <: AbstractEmbeddedManifold{𝔽,DefaultIsometricEmbeddingType}
The Stiefel manifold consists of all $n × k$, $n ≥ k$ unitary matrices, i.e.
````math
\operatorname{St}(n,k) = \bigl\{ p ∈ 𝔽^{n × k}\ \big|\ p^{\mathrm{H}}p = I_k \bigr\},
````
where $𝔽 ∈ \{ℝ, ℂ\}$,
$\cdot^{\mathrm{H}}$ denotes the complex conjugate transpose or Hermitian, and
$I_k ∈ ℝ^{k × k}$ denotes the $k × k$ identity matrix.
The tangent space at a point $p ∈ \mathcal M$ is given by
````math
T_p \mathcal M = \{ X ∈ 𝔽^{n × k} : p^{\mathrm{H}}X + \overline{X^{\mathrm{H}}p} = 0_k\},
````
where $0_k$ is the $k × k$ zero matrix and $\overline{\cdot}$ the (elementwise) complex conjugate.
This manifold is modeled as an embedded manifold to the [`Euclidean`](@ref), i.e.
several functions like the [`inner`](@ref inner(::Euclidean, ::Any...)) product and the
[`zero_vector`](@ref zero_vector(::Euclidean, ::Any...)) are inherited from the embedding.
The manifold is named after
[Eduard L. Stiefel](https://en.wikipedia.org/wiki/Eduard_Stiefel) (1909–1978).
# Constructor
Stiefel(n, k, field = ℝ)
Generate the (real-valued) Stiefel manifold of $n × k$ dimensional orthonormal matrices.
"""
struct Stiefel{n,k,𝔽} <: AbstractEmbeddedManifold{𝔽,DefaultIsometricEmbeddingType} end
Stiefel(n::Int, k::Int, field::AbstractNumbers=ℝ) = Stiefel{n,k,field}()
@doc raw"""
PadeRetraction{m} <: AbstractRetractionMethod
A retraction based on the Padé approximation of order $m$
"""
struct PadeRetraction{m} <: AbstractRetractionMethod end
function PadeRetraction(m::Int)
(m < 1) && error(
"The Padé based retraction is only available for positive orders, not for order $m.",
)
return PadeRetraction{m}()
end
@doc raw"""
CayleyRetraction <: AbstractRetractionMethod
A retraction based on the Cayley transform, which is realized by using the
[`PadeRetraction`](@ref)`{1}`.
"""
const CayleyRetraction = PadeRetraction{1}
function allocation_promotion_function(::Stiefel{n,k,ℂ}, ::Any, ::Tuple) where {n,k}
return complex
end
@doc raw"""
check_point(M::Stiefel, p; kwargs...)
Check whether `p` is a valid point on the [`Stiefel`](@ref) `M`=$\operatorname{St}(n,k)$, i.e. that it has the right
[`AbstractNumbers`](@ref) type and $p^{\mathrm{H}}p$ is (approximately) the identity, where $\cdot^{\mathrm{H}}$ is the
complex conjugate transpose. The settings for approximately can be set with `kwargs...`.
"""
function check_point(M::Stiefel{n,k,𝔽}, p; kwargs...) where {n,k,𝔽}
mpv = invoke(check_point, Tuple{supertype(typeof(M)),typeof(p)}, M, p; kwargs...)
mpv === nothing || return mpv
c = p' * p
if !isapprox(c, one(c); kwargs...)
return DomainError(
norm(c - one(c)),
"The point $(p) does not lie on $(M), because p'p is not the unit matrix.",
)
end
return nothing
end
@doc raw"""
check_vector(M::Stiefel, p, X; kwargs...)
Checks whether `X` is a valid tangent vector at `p` on the [`Stiefel`](@ref)
`M`=$\operatorname{St}(n,k)$, i.e. the [`AbstractNumbers`](@ref) fits and
it (approximately) holds that $p^{\mathrm{H}}X + \overline{X^{\mathrm{H}}p} = 0$,
where $\cdot^{\mathrm{H}}$ denotes the Hermitian and $\overline{\cdot}$ the (elementwise) complex conjugate.
The settings for approximately can be set with `kwargs...`.
"""
function check_vector(M::Stiefel{n,k,𝔽}, p, X; kwargs...) where {n,k,𝔽}
mpv = invoke(
check_vector,
Tuple{supertype(typeof(M)),typeof(p),typeof(X)},
M,
p,
X;
kwargs...,
)
mpv === nothing || return mpv
if !isapprox(p' * X, -conj(X' * p); kwargs...)
return DomainError(
norm(p' * X + conj(X' * p)),
"The matrix $(X) is does not lie in the tangent space of $(p) on the Stiefel manifold of dimension ($(n),$(k)), since p'X + X'p is not the zero matrix.",
)
end
return nothing
end
decorated_manifold(::Stiefel{N,K,𝔽}) where {N,K,𝔽} = Euclidean(N, K; field=𝔽)
@doc raw"""
inverse_retract(M::Stiefel, p, q, ::PolarInverseRetraction)
Compute the inverse retraction based on a singular value decomposition
for two points `p`, `q` on the [`Stiefel`](@ref) manifold `M`.
This follows the folloing approach: From the Polar retraction we know that
````math
\operatorname{retr}_p^{-1}q = qs - t
````
if such a symmetric positive definite $k × k$ matrix exists. Since $qs - t$
is also a tangent vector at $p$ we obtain
````math
p^{\mathrm{H}}qs + s(p^{\mathrm{H}}q)^{\mathrm{H}} + 2I_k = 0,
````
which can either be solved by a Lyapunov approach or a continuous-time
algebraic Riccati equation.
This implementation follows the Lyapunov approach.
"""
inverse_retract(::Stiefel, ::Any, ::Any, ::PolarInverseRetraction)
@doc raw"""
inverse_retract(M::Stiefel, p, q, ::QRInverseRetraction)
Compute the inverse retraction based on a qr decomposition
for two points `p`, `q` on the [`Stiefel`](@ref) manifold `M` and return
the resulting tangent vector in `X`. The computation follows Algorithm 1
in [^KanekoFioriTanaka2013].
[^KanekoFioriTanaka2013]:
> T. Kaneko, S. Fiori, T. Tanaka: "Empirical Arithmetic Averaging over the
> Compact Stiefel AbstractManifold", IEEE Transactions on Signal Processing, 2013,
> doi: [10.1109/TSP.2012.2226167](https://doi.org/10.1109/TSP.2012.2226167).
"""
inverse_retract(::Stiefel, ::Any, ::Any, ::QRInverseRetraction)
function _stiefel_inv_retr_qr_mul_by_r_generic!(::Stiefel{n,k}, X, q, R, A) where {n,k}
@inbounds for i in 1:k
b = zeros(eltype(R), i)
b[i] = 1
b[1:(end - 1)] = -transpose(R[1:(i - 1), 1:(i - 1)]) * A[i, 1:(i - 1)]
R[1:i, i] = A[1:i, 1:i] \ b
end
#TODO: replace with this once it's supported by StaticArrays
#return mul!(X, q, UpperTriangular(R))
return mul!(X, q, R)
end
function _stiefel_inv_retr_qr_mul_by_r!(::Stiefel{n,1}, X, q, A, ::Type) where {n}
@inbounds R = SMatrix{1,1}(inv(A[1, 1]))
return mul!(X, q, R)
end
function _stiefel_inv_retr_qr_mul_by_r!(
M::Stiefel{n,1},
X,
q,
A::StaticArray,
::Type{ElT},
) where {n,ElT}
return invoke(
_stiefel_inv_retr_qr_mul_by_r!,
Tuple{Stiefel{n,1},typeof(X),typeof(q),AbstractArray,typeof(ElT)},
M,
X,
q,
A,
ElT,
)
end
function _stiefel_inv_retr_qr_mul_by_r!(::Stiefel{n,2}, X, q, A, ::Type{ElT}) where {n,ElT}
R11 = inv(A[1, 1])
@inbounds R =
hcat(SA[R11, zero(ElT)], A[SOneTo(2), SOneTo(2)] \ SA[-R11 * A[2, 1], one(ElT)])
#TODO: replace with this once it's supported by StaticArrays
#return mul!(X, q, UpperTriangular(R))
return mul!(X, q, R)
end
function _stiefel_inv_retr_qr_mul_by_r!(
M::Stiefel{n,2},
X,
q,
A::StaticArray,
::Type{ElT},
) where {n,ElT}
return invoke(
_stiefel_inv_retr_qr_mul_by_r!,
Tuple{Stiefel{n,2},typeof(X),typeof(q),AbstractArray,typeof(ElT)},
M,
X,
q,
A,
ElT,
)
end
function _stiefel_inv_retr_qr_mul_by_r!(
M::Stiefel{n,k},
X,
q,
A::StaticArray,
::Type{ElT},
) where {n,k,ElT}
R = zeros(MMatrix{k,k,ElT})
return _stiefel_inv_retr_qr_mul_by_r_generic!(M, X, q, R, A)
end
function _stiefel_inv_retr_qr_mul_by_r!(
M::Stiefel{n,k},
X,
q,
A,
::Type{ElT},
) where {n,k,ElT}
R = zeros(ElT, k, k)
return _stiefel_inv_retr_qr_mul_by_r_generic!(M, X, q, R, A)
end
function inverse_retract!(::Stiefel, X, p, q, ::PolarInverseRetraction)
A = p' * q
H = -2 * one(p' * p)
B = lyap(A, H)
mul!(X, q, B)
X .-= p
return X
end
function inverse_retract!(M::Stiefel{n,k}, X, p, q, ::QRInverseRetraction) where {n,k}
A = p' * q
@boundscheck size(A) === (k, k)
ElT = typeof(one(eltype(p)) * one(eltype(q)))
_stiefel_inv_retr_qr_mul_by_r!(M, X, q, A, ElT)
X .-= p
return X
end
function Base.isapprox(M::Stiefel, p, X, Y; kwargs...)
return isapprox(sqrt(inner(M, p, zero_vector(M, p), X - Y)), 0; kwargs...)
end
Base.isapprox(::Stiefel, p, q; kwargs...) = isapprox(norm(p - q), 0; kwargs...)
@doc raw"""
manifold_dimension(M::Stiefel)
Return the dimension of the [`Stiefel`](@ref) manifold `M`=$\operatorname{St}(n,k,𝔽)$.
The dimension is given by
````math
\begin{aligned}
\dim \mathrm{St}(n, k, ℝ) &= nk - \frac{1}{2}k(k+1)\\
\dim \mathrm{St}(n, k, ℂ) &= 2nk - k^2\\
\dim \mathrm{St}(n, k, ℍ) &= 4nk - k(2k-1)
\end{aligned}
````
"""
manifold_dimension(::Stiefel{n,k,ℝ}) where {n,k} = n * k - div(k * (k + 1), 2)
manifold_dimension(::Stiefel{n,k,ℂ}) where {n,k} = 2 * n * k - k * k
manifold_dimension(::Stiefel{n,k,ℍ}) where {n,k} = 4 * n * k - k * (2k - 1)
@doc raw"""
retract(::Stiefel, p, X, ::CayleyRetraction)
Compute the retraction on the [`Stiefel`](@ref) that is based on the Cayley transform[^Zhu2017].
Using
````math
W_{p,X} = \operatorname{P}_pXp^{\mathrm{H}} - pX^{\mathrm{H}}\operatorname{P_p}
\quad\text{where}
\operatorname{P}_p = I - \frac{1}{2}pp^{\mathrm{H}}
````
the formula reads
````math
\operatorname{retr}_pX = \Bigl(I - \frac{1}{2}W_{p,X}\Bigr)^{-1}\Bigl(I + \frac{1}{2}W_{p,X}\Bigr)p.
````
It is implemented as the case $m=1$ of the [`PadeRetraction`](@ref).
[^Zhu2017]:
> X. Zhu:
> A Riemannian conjugate gradient method for optimizazion on the Stiefel manifold,
> Computational Optimization and Applications 67(1), pp. 73–110, 2017.
> doi [10.1007/s10589-016-9883-4](https://doi.org/10.1007/s10589-016-9883-4).
"""
retract(::Stiefel, ::Any, ::Any, ::CayleyRetraction)
@doc raw"""
retract(M::Stiefel, p, X, ::PadeRetraction{m})
Compute the retraction on the [`Stiefel`](@ref) manifold `M` based on the Padé approximation of order $m$[^ZhuDuan2018].
Let $p_m$ and $q_m$ be defined for any matrix $A ∈ ℝ^{n×x}$ as
````math
p_m(A) = \sum_{k=0}^m \frac{(2m-k)!m!}{(2m)!(m-k)!}\frac{A^k}{k!}
````
and
````math
q_m(A) = \sum_{k=0}^m \frac{(2m-k)!m!}{(2m)!(m-k)!}\frac{(-A)^k}{k!}
````
respectively. Then the Padé approximation (of the matrix exponential $\exp(A)$) reads
````math
r_m(A) = q_m(A)^{-1}p_m(A)
````
Defining further
````math
W_{p,X} = \operatorname{P}_pXp^{\mathrm{H}} - pX^{\mathrm{H}}\operatorname{P_p}
\quad\text{where}
\operatorname{P}_p = I - \frac{1}{2}pp^{\mathrm{H}}
````
the retraction reads
````math
\operatorname{retr}_pX = r_m(W_{p,X})p
````
[^ZhuDuan2018]:
> X. Zhu, C. Duan:
> On matrix exponentials and their approximations related to optimization on the Stiefel manifold,
> Optimizazion Letters 13(5), pp. 1069–1083, 2018.
> doi [10.1007/s11590-018-1341-z](https://doi.org/10.1007/s11590-018-1341-z).
"""
retract(::Stiefel, ::Any, ::Any, ::PadeRetraction)
@doc raw"""
retract(M::Stiefel, p, X, ::PolarRetraction)
Compute the SVD-based retraction [`PolarRetraction`](@ref) on the
[`Stiefel`](@ref) manifold `M`. With $USV = p + X$ the retraction reads
````math
\operatorname{retr}_p X = U\bar{V}^\mathrm{H}.
````
"""
retract(::Stiefel, ::Any, ::Any, ::PolarRetraction)
@doc raw"""
retract(M::Stiefel, p, X, ::QRRetraction)
Compute the QR-based retraction [`QRRetraction`](@ref) on the
[`Stiefel`](@ref) manifold `M`. With $QR = p + X$ the retraction reads
````math
\operatorname{retr}_p X = QD,
````
where $D$ is a $n × k$ matrix with
````math
D = \operatorname{diag}\bigl(\operatorname{sgn}(R_{ii}+0,5)_{i=1}^k \bigr),
````
where $\operatorname{sgn}(p) = \begin{cases}
1 & \text{ for } p > 0,\\
0 & \text{ for } p = 0,\\
-1& \text{ for } p < 0.
\end{cases}$
"""
retract(::Stiefel, ::Any, ::Any, ::QRRetraction)
_qrfac_to_q(qrfac) = Matrix(qrfac.Q)
_qrfac_to_q(qrfac::StaticArrays.QR) = qrfac.Q
function retract!(::Stiefel, q, p, X, ::PadeRetraction{m}) where {m}
Pp = I - 1 // 2 * p * p'
WpX = Pp * X * p' - p * X' * Pp
pm = zeros(eltype(WpX), size(WpX))
qm = zeros(eltype(WpX), size(WpX))
WpXk = similar(WpX)
copyto!(WpXk, factorial(m) / factorial(2 * m) * I) # factorial factor independent of k
for k in 0:m
# incrementally build (2m-k)!/(m-k)!(k)! for k > 0, i.e.
# remove factor (2m-k+1) in the nominator, (m-k+1) in the denominator and multiply by 1/k
WpXk .*= (k == 0 ? 2 : (m - k + 1) / ((2 * m - k + 1) * k))
pm .+= WpXk
if k % 2 == 0
qm .+= WpXk
else
qm .-= WpXk
end
WpXk *= WpX
end
return copyto!(q, (qm \ pm) * p)
end
function retract!(::Stiefel, q, p, X, ::PolarRetraction)
s = svd(p + X)
return mul!(q, s.U, s.Vt)
end
function retract!(::Stiefel, q, p, X, ::QRRetraction)
qrfac = qr(p + X)
d = diag(qrfac.R)
D = Diagonal(sign.(sign.(d .+ 0.5)))
return mul!(q, _qrfac_to_q(qrfac), D)
end
@doc raw"""
representation_size(M::Stiefel)
Returns the representation size of the [`Stiefel`](@ref) `M`=$\operatorname{St}(n,k)$,
i.e. `(n,k)`, which is the matrix dimensions.
"""
@generated representation_size(::Stiefel{n,k}) where {n,k} = (n, k)
Base.show(io::IO, ::CayleyRetraction) = print(io, "CayleyRetraction()")
Base.show(io::IO, ::PadeRetraction{m}) where {m} = print(io, "PadeRetraction($(m))")
Base.show(io::IO, ::Stiefel{n,k,F}) where {n,k,F} = print(io, "Stiefel($(n), $(k), $(F))")
"""
uniform_distribution(M::Stiefel{n,k,ℝ}, p)
Uniform distribution on given (real-valued) [`Stiefel`](@ref) `M`.
Specifically, this is the normalized Haar and Hausdorff measure on `M`.
Generated points will be of similar type as `p`.
The implementation is based on Section 2.5.1 in [^Chikuse2003];
see also Theorem 2.2.1(iii) in [^Chikuse2003].
[^Chikuse2003]:
> Y. Chikuse: "Statistics on Special Manifolds", Springer New York, 2003,
> doi: [10.1007/978-0-387-21540-2](https://doi.org/10.1007/978-0-387-21540-2).
"""
function uniform_distribution(M::Stiefel{n,k,ℝ}, p) where {n,k}
μ = Distributions.Zeros(n, k)
σ = one(eltype(p))
Σ1 = Distributions.PDMats.ScalMat(n, σ)
Σ2 = Distributions.PDMats.ScalMat(k, σ)
d = MatrixNormal(μ, Σ1, Σ2)
return ProjectedPointDistribution(M, d, project!, p)
end
@doc raw"""
vector_transport_direction(::Stiefel, p, X, d, ::DifferentiatedRetractionVectorTransport{CayleyRetraction})
Compute the vector transport given by the differentiated retraction of the [`CayleyRetraction`](@ref), cf. [^Zhu2017] Equation (17).
The formula reads
````math
\operatorname{T}_{p,d}(X) =
\Bigl(I - \frac{1}{2}W_{p,d}\Bigr)^{-1}W_{p,X}\Bigl(I - \frac{1}{2}W_{p,d}\Bigr)^{-1}p,
````
with
````math
W_{p,X} = \operatorname{P}_pXp^{\mathrm{H}} - pX^{\mathrm{H}}\operatorname{P_p}
\quad\text{where}
\operatorname{P}_p = I - \frac{1}{2}pp^{\mathrm{H}}
````
Since this is the differentiated retraction as a vector transport, the result will be in the
tangent space at $q=\operatorname{retr}_p(d)$ using the [`CayleyRetraction`](@ref).
"""
vector_transport_direction(
M::Stiefel,
p,
X,
d,
::DifferentiatedRetractionVectorTransport{CayleyRetraction},
)
@doc raw"""
vector_transport_direction(M::Stiefel, p, X, d, DifferentiatedRetractionVectorTransport{PolarRetraction})
Compute the vector transport by computing the push forward of
[`retract(::Stiefel, ::Any, ::Any, ::PolarRetraction)`](@ref) Section 3.5 of [^Zhu2017]:
```math
T_{p,d}^{\text{Pol}}(X) = q*Λ + (I-qq^{\mathrm{T}})X(1+d^\mathrm{T}d)^{-\frac{1}{2}},
```
where $q = \operatorname{retr}^{\mathrm{Pol}}_p(d)$, and $Λ$ is the unique solution of the Sylvester equation
```math
Λ(I+d^\mathrm{T}d)^{\frac{1}{2}} + (I + d^\mathrm{T}d)^{\frac{1}{2}} = q^\mathrm{T}X - X^\mathrm{T}q
```
"""
vector_transport_direction(
::Stiefel,
::Any,
::Any,
::Any,
::DifferentiatedRetractionVectorTransport{PolarRetraction},
)
@doc raw"""
vector_transport_direction(M::Stiefel, p, X, d, DifferentiatedRetractionVectorTransport{QRRetraction})
Compute the vector transport by computing the push forward of the
[`retract(::Stiefel, ::Any, ::Any, ::QRRetraction)`](@ref),
See [^AbsilMahonySepulchre2008], p. 173, or Section 3.5 of [^Zhu2017].
```math
T_{p,d}^{\text{QR}}(X) = q*\rho_{\mathrm{s}}(q^\mathrm{T}XR^{-1}) + (I-qq^{\mathrm{T}})XR^{-1},
```
where $q = \operatorname{retr}^{\mathrm{QR}}_p(d)$, $R$ is the $R$ factor of the QR
decomposition of $p + d$, and
```math
\bigl( \rho_{\mathrm{s}}(A) \bigr)_{ij}
= \begin{cases}
A_{ij}&\text{ if } i > j\\
0 \text{ if } i = j\\
-A_{ji} \text{ if } i < j.\\
\end{cases}
```
[^AbsilMahonySepulchre2008]:
>Absil, P.-A., Mahony, R. and Sepulchre R.,
> _Optimization Algorithms on Matrix Manifolds_
> Princeton University Press, 2008,
> doi: [10.1515/9781400830244](https://doi.org/10.1515/9781400830244)
> [open access](http://press.princeton.edu/chapters/absil/)
"""
vector_transport_direction(
::Stiefel,
::Any,
::Any,
::Any,
::DifferentiatedRetractionVectorTransport{QRRetraction},
)
function vector_transport_direction!(
::Stiefel,
Y,
p,
X,
d,
::DifferentiatedRetractionVectorTransport{CayleyRetraction},
)
Pp = I - 1 // 2 * p * p'
Wpd = Pp * d * p' - p * d' * Pp
WpX = Pp * X * p' - p * X' * Pp
q1 = I - 1 // 2 * Wpd
return copyto!(Y, (q1 \ WpX) * (q1 \ p))
end
function vector_transport_direction!(
M::Stiefel,
Y,
p,
X,
d,
::DifferentiatedRetractionVectorTransport{PolarRetraction},
)
q = retract(M, p, d, PolarRetraction())
Iddsqrt = sqrt(I + d' * d)
Λ = sylvester(Iddsqrt, Iddsqrt, -q' * X + X' * q)
return copyto!(Y, q * Λ + (X - q * (q' * X)) / Iddsqrt)
end
function vector_transport_direction!(
M::Stiefel,
Y,
p,
X,
d,
::DifferentiatedRetractionVectorTransport{QRRetraction},
)
q = retract(M, p, d, QRRetraction())
rf = UpperTriangular(qr(p + d).R)
Xrf = X / rf
qtXrf = q' * Xrf
return copyto!(
Y,
q * (UpperTriangular(qtXrf) - UpperTriangular(qtXrf)') + Xrf - q * qtXrf,
)
end
@doc raw"""
vector_transport_to(M::Stiefel, p, X, q, DifferentiatedRetractionVectorTransport{PolarRetraction})
Compute the vector transport by computing the push forward of the
[`retract(M::Stiefel, ::Any, ::Any, ::PolarRetraction)`](@ref), see
Section 4 of [^HuangGallivanAbsil2015] or Section 3.5 of [^Zhu2017]:
```math
T_{q\gets p}^{\text{Pol}}(X) = q*Λ + (I-qq^{\mathrm{T}})X(1+d^\mathrm{T}d)^{-\frac{1}{2}},
```
where $d = \bigl( \operatorname{retr}^{\mathrm{Pol}}_p\bigr)^{-1}(q)$,
and $Λ$ is the unique solution of the Sylvester equation
```math
Λ(I+d^\mathrm{T}d)^{\frac{1}{2}} + (I + d^\mathrm{T}d)^{\frac{1}{2}} = q^\mathrm{T}X - X^\mathrm{T}q
```
[^HuangGallivanAbsil2015]:
> Huang, W., Gallivan, K. A., and Absil, P.-A.:
> _A Broyden class of quasi-Newton methods for Riemannian optimization_
> SIAM Journal of Optimization, 2015, Vol. 25, No. 3, pp. 1660–1685
> doi: [10.1137/140955483](https://doi.org/10.1137/140955483)
> pdf: [tech. report](https://www.math.fsu.edu/~whuang2/pdf/RBroydenBasic_techrep.pdf)
"""
vector_transport_to(
::Stiefel,
::Any,
::Any,
::Any,
::DifferentiatedRetractionVectorTransport{PolarRetraction},
)
@doc raw"""
vector_transport_to(M::Stiefel, p, X, q, DifferentiatedRetractionVectorTransport{QRRetraction})
Compute the vector transport by computing the push forward of the
[`retract(M::Stiefel, ::Any, ::Any, ::QRRetraction)`](@ref),
see [^AbsilMahonySepulchre2008], p. 173, or Section 3.5 of [^Zhu2017].
```math
T_{q \gets p}^{\text{QR}}(X) = q*\rho_{\mathrm{s}}(q^\mathrm{T}XR^{-1}) + (I-qq^{\mathrm{T}})XR^{-1},
```
where $d = \bigl(\operatorname{retr}^{\mathrm{QR}}\bigr)^{-1}_p(q)$, $R$ is the $R$ factor of the QR
decomposition of $p+X$, and
```math
\bigl( \rho_{\mathrm{s}}(A) \bigr)_{ij}
= \begin{cases}
A_{ij}&\text{ if } i > j\\
0 \text{ if } i = j\\
-A_{ji} \text{ if } i < j.\\
\end{cases}
```
"""
vector_transport_to(
::Stiefel,
::Any,
::Any,
::Any,
::DifferentiatedRetractionVectorTransport{QRRetraction},
)
@doc raw"""
vector_transport_to(M::Stiefel, p, X, q, ::ProjectionTransport)
Compute a vector transport by projection, i.e. project `X` from the tangent space at `x` by
projection it onto the tangent space at `q`.
"""
vector_transport_to(::Stiefel, ::Any, ::Any, ::Any, ::ProjectionTransport)
function vector_transport_to!(
M::Stiefel,
Y,
p,
X,
q,
::DifferentiatedRetractionVectorTransport{PolarRetraction},
)
d = inverse_retract(M, p, q, PolarInverseRetraction())
Iddsqrt = sqrt(I + d' * d)
Λ = sylvester(Iddsqrt, Iddsqrt, -q' * X + X' * q)
return copyto!(Y, q * Λ + (X - q * (q' * X)) / Iddsqrt)
end
function vector_transport_to!(
M::Stiefel,
Y,
p,
X,
q,
::DifferentiatedRetractionVectorTransport{QRRetraction},
)
d = inverse_retract(M, p, q, QRInverseRetraction())
rf = UpperTriangular(qr(p + d).R)
Xrf = X / rf
qtXrf = q' * Xrf
return copyto!(
Y,
q * (UpperTriangular(qtXrf) - UpperTriangular(qtXrf)') + Xrf - q * qtXrf,
)
end
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] | 2.154904 | 9,748 |
module ReTest
export retest, @testset, not, interpolated
using Distributed
using Base.Threads: nthreads
import Base: ==
using Random: shuffle!, randstring
# from Test:
export Test,
@test, @test_throws, @test_broken, @test_skip,
@test_warn, @test_nowarn,
@test_logs, @test_deprecated,
@inferred,
detect_ambiguities, detect_unbound_args,
GenericString, GenericSet, GenericDict, GenericArray, GenericOrder
using Test: Test,
@test, @test_throws, @test_broken, @test_skip,
@test_warn, @test_nowarn,
@test_logs, @test_deprecated,
@inferred,
detect_ambiguities, detect_unbound_args,
GenericString, GenericSet, GenericDict, GenericArray
if isdefined(Test, :GenericOrder)
using Test: GenericOrder
end
using InlineTest: @testset, InlineTest, TESTED_MODULES, INLINE_TEST
import InlineTest: retest
# * Pattern (pre)
# pre-declaration for use in testset.jl
abstract type Pattern end
function matches end
# * includes
include("utils.jl")
include("testset.jl")
include("hijack.jl")
using .Testset: Testset, Format
# * Pattern
const PatternX = Union{Pattern, Regex, Integer}
struct And <: Pattern
xs::Vector{PatternX}
end
And() = And(PatternX[])
and(xs...) = And(PatternX[make_pattern(x) for x in xs])
==(a::And, b::And) = a.xs == b.xs
struct Or <: Pattern
xs::AbstractArray{<:PatternX}
end
Or() = Or(PatternX[])
or(xs...) = Or(PatternX[make_pattern(x) for x in xs])
==(a::Or, b::Or) = a.xs == b.xs
struct Not <: Pattern
x::PatternX
end
==(a::Not, b::Not) = a.x == b.x
"""
not(pattern)
Create an object suitable for filtering testsets (in the [`retest`](@ref) function),
which "negates" the meaning of `pattern`: a testset matches `not(pattern)`
if and only if it doesn't match `pattern`.
For example `not("a")` matches any testset whose subject doesn't contain `"a"`,
and `not(1:3)` matches all the testsets but the first three of a module.
"""
not(x) = Not(make_pattern(x))
struct Interpolated <: Pattern end
"""
interpolated
Singleton pattern which matches any testset whose description can be interpolated
"statically", i.e. at filtering time before testset are actually run.
Non-inferrable descriptions include those containing interpolated values
which can't be known until run time.
This pattern has an effect closely related to that of the `static` keyword
of [`retest`](@ref), discussed below, which is probably more generally useful.
# Examples
Given these testset:
```julia
@testset "outer" verbose=true begin
@test true
inner = "inner"
@testset "\$inner" begin
@test true
end
end
@testset "other" begin
@test true
end
```
We get:
```julia
julia> retest("other", dry=true)
Main
1| outer
2| "\$(inner)"
3| other
julia> retest("other", dry=false)
Pass
outer | 1
other | 1
Main | 2
julia> retest("other", dry=true, interpolated)
Main
3| other
```
Without `interpolated`, `retest` can't decide at filtering time whether the "inner"
testset will run, so must mark the "outer" testset as having to run. At run
time, "inner" is not run because it doesn't match the pattern, but "outer"
still had to run to determine this. With the `interpolated` pattern, "inner" is
filtered out and `retest` selects only testsets which are statically known to
have to run.
So again, `interpolated` doesn't have the same effect at filtering time (like when
`dry=true`) and at run time.
For example, one can see the list of non-interpolated descriptions as follows with
`dry=true`, but not run them (because everything is interpolated at run time):
```julia
julia> retest(not(interpolated), dry=true)
1| outer
2| "\$(inner)"
julia> retest(not(interpolated), dry=false)
Pass
Main:
outer | 1
```
### `static` keyword
Unlike `interpolated`, the `static` keyword of `retest`, when `true`,
filters out only testsets which can't be proven to have to run at filtering time,
let's call them "undecidable".
It can have sometimes the same effect as when using `interpolated`,
e.g. `retest("other", dry=true, static=true)` and
`retest("other", dry=true, interpolated)` give the same result.
But in some cases we might want to filter out noisy testsets whose
description can't be interpolated, but still include those which are
relevant. For example, assume we want to run testsets `1` and `2`,
while excluding other testsets with uninterpolated descriptions:
```julia
julia> retest(1:2, dry=true, interpolated)
Main
1| outer
julia> retest(1:2, dry=true, static=true)
Main
1| outer
2| "\$(inner)"
```
The solution with `interpolated` is not what we want, as we specifically
want testset `2` to run. Given the filtering specifications (`1:2` here),
the filtering algorithm can determine that `2` should run even though
its description is unknown at this point.
Given a filtering specification, there are three kind of testsets:
* "undecidable" (see above)
* "match": they are known statically to have to run
* "nomatch": they are known statically to not have to run
The default value of the `static` keyword is `nothing`, which means
to run testsets which are not known with certainty to not match,
i.e. "match" and "undecidable" testsets.
As seen above, when `static == true`, only "match" testsets are run.
When `static == false`, the behavior is the opposite: only "undecidable"
testsets are run.
Of course, other combinations involving "nomatch" testsets can be had
by reversing the filtering pattern via [`not`](@ref).
For example, to get the equivalent to the `not(interpolated)` example above,
but with an effect which persists at run time (`dry = false`),
you can use `static = false` together with the match-all regex pattern `r".*"`,
which will mark the `"inner"` testset as "undecidable"
(the algorithm inspects slightly patterns just to recognize the simple
match-all patterns `""` and `r""`, but won't detect that `r".*"` would
match `"\$(inner)"`):
```julia
julia> retest(r".*", static=false, dry=true)
Main
1| outer
2| "\$(inner)"
julia> retest(r".*", static=false, dry=false)
Pass
Main:
outer | 2
inner | 1
```
One example of a rare case where a given testset is not in a single of the
above three categories is as follows:
```julia
@testset "a" begin
x = 2
@testset "b\$(i==1 ? 1 : x)" for i=1:2
@testset "c" begin
# subject is "match" at first iteration and
# "undecidable" at second iteration
@test true
end
end
end
```
One thing to understand is that the "identity" of a testset is determined
by a given occurrence of the `@testset` macro. In the example above,
for either the patterns "b" or "c", the two inner testsets are both
"match" and "undecidable". In this case, the filtering algorithm
selects a testset to run if at least one iteration would lead to
this decision. Here, if `static=true` the first iteration
would run, and if `static=false` the second iteration would run.
This results in the same selection whatever the value of `static` is.
"""
const interpolated = Interpolated()
alwaysmatches(pat::And) = all(alwaysmatches, pat.xs)
alwaysmatches(pat::Or) =
if pat.xs isa AbstractArray{<:Integer}
false # special case for huge unit ranges; locally, this optimization seems
# unnecessary, i.e. alwaysmatches(Or(1:10...0)) is constant time anyway,
# but on CI, the any(...) below takes tooooo long
else
any(alwaysmatches, pat.xs)
end
alwaysmatches(::Not) = false
alwaysmatches(::Interpolated) = false
alwaysmatches(rx::Regex) = isempty(rx.pattern)
alwaysmatches(id::Integer) = false
matches(pat::And, x, id) = all(p -> matches(p, x, id), pat.xs)
matches(pat::Or, x, id) =
if pat.xs isa AbstractUnitRange{<:Integer} && minimum(pat.xs) >= 0
id ∈ pat.xs # this is optimised, i.e. it's not O(n)
else
any(p -> matches(p, x, id), pat.xs)
end
matches(pat::Not, x, id) = !matches(pat.x, x, id)
matches(::Interpolated, x::Union{Missing,String}, id) = x !== missing
matches(rx::Regex, x, _) = occursin(rx, x)
matches(rx::Regex, ::Missing, _) = alwaysmatches(rx) | missing
matches(pat::Integer, _, id) = pat >= 0 ? pat == id : pat != -id
make_pattern(x::PatternX) = x
function make_pattern(str::AbstractString)
neg = false
if startswith(str, '-')
str = chop(str, head=1, tail=0)
if !startswith(str, '-')
neg = true
end
end
rx =
if isempty(str)
r"" # in order to know to match unconditionally
elseif VERSION >= v"1.3"
r""i * str
else
Regex(str, "i")
end
neg ? not(rx) : rx
end
make_pattern(pat::AbstractArray) = Or(PatternX[make_pattern(p) for p in pat])
# special case for optimizing unit-ranges:
make_pattern(pat::AbstractArray{<:Integer}) = Or(pat)
make_pattern(@nospecialize(pat::Tuple)) = And(PatternX[make_pattern(p) for p in pat])
hasinteger(::Regex) = false
hasinteger(::Integer) = true
hasinteger(pat::Union{And,Or}) = any(hasinteger, pat.xs)
hasinteger(pat::Not) = hasinteger(pat.x)
hasinteger(::Interpolated) = false
# * TestsetExpr
Base.@kwdef mutable struct Options
verbose::Bool = false # annotated verbosity
transient_verbose::Bool = false # verbosity for next run
end
mutable struct TestsetExpr
id::Int64 # unique ID per module (64 bits to be on the safe side)
source::LineNumberNode
mod::String # enclosing module
desc::Union{String,Expr}
options::Options
# loops: the original loop expression, if any, but where each `x=...` is
# pulled out into a vector
loops::Union{Vector{Expr},Nothing}
parent::Union{TestsetExpr,Nothing}
children::Vector{TestsetExpr}
strings::Vector{Union{String,Missing}}
# loopvalues & loopiters: when successful in evaluating loop values in resolve!,
# we "flatten" the nested for loops into a single loop, with loopvalues
# containing tuples of values, and loopiters the tuples of variables to which the
# values are assigned
loopvalues::Union{Nothing,Vector{Any}}
loopiters::Union{Nothing,Expr}
hasbroken::Bool
hasbrokenrec::Bool # recursive hasbroken, transiently
run::Bool
descwidth::Int # max width of self and children shown descriptions
body::Expr
TestsetExpr(source, mod, desc, options, loops, parent, children=TestsetExpr[]) =
new(0, source, mod, desc, options, loops, parent, children, String[])
end
isfor(ts::TestsetExpr) = ts.loops !== nothing
isfinal(ts::TestsetExpr) = isempty(ts.children)
# replace unqualified `@testset` by TestsetExpr
function replace_ts(source, mod, x::Expr, parent)
if x.head === :macrocall && x.args[1] === Symbol("@testset")
@assert x.args[2] isa LineNumberNode
ts, hasbroken = parse_ts(source, mod, Tuple(x.args[3:end]), parent)
parent !== nothing && push!(parent.children, ts)
ts, false # hasbroken counts only "proper" @test_broken, not recursive ones
elseif x.head === :macrocall && x.args[1] === Symbol("@test_broken")
x, true
elseif x.head == :call && x.args[1] == :include
path = x.args[end]
sourcepath = dirname(string(source.file))
x.args[end] = path isa AbstractString ?
joinpath(sourcepath, path) :
:(joinpath($sourcepath, $path))
x, false
else
body_br = map(z -> replace_ts(source, mod, z, parent), x.args)
Expr(x.head, first.(body_br)...), any(last.(body_br))
end
end
replace_ts(source, mod, x, _) = x, false
# create a TestsetExpr from @testset's args
function parse_ts(source, mod, args::Tuple, parent=nothing)
local desc
options = Options()
for arg in args[1:end-1]
if arg isa String || Meta.isexpr(arg, :string)
desc = arg
elseif Meta.isexpr(arg, :(=))
arg.args[1] in fieldnames(Options) || error("unsupported @testset option")
# TODO: make that work with non-literals:
setfield!(options, arg.args[1], arg.args[2])
else
error("unsupported @testset")
end
end
body = args[end]
isa(body, Expr) || error("Expected begin/end block or for loop as argument to @testset")
if body.head === :for
tsbody = body.args[2]
loops = body.args[1]
if loops.head == :(=)
loops = Expr[loops]
else
@assert loops.head == :block
@assert all(arg -> Meta.isexpr(arg, :(=)), loops.args)
loops = loops.args
end
if !@isdefined(desc)
v = loops[1].args[1]
desc = Expr(:string, "anonym $(randstring('0':'9')): $v = ", v)
for l = loops[2:end]
v = l.args[1]
push!(desc.args, ", $v = ", v)
end
end
elseif body.head === :block
loops = nothing
tsbody = body
if !@isdefined(desc)
desc = "anonym $(randstring('0':'9'))"
end
else
error("Expected begin/end block or for loop as argument to @testset")
end
ts = TestsetExpr(source, mod, desc, options, loops, parent)
ts.body, ts.hasbroken = replace_ts(source, mod, tsbody, ts)
ts, false # hasbroken counts only "proper" @test_broken, not recursive ones
end
# this function does 3 things by going recursively through nested testsets:
# - update ts.hasbrokenrec to know whether we print the "broken" column
# - compute ts.descwidth, to know the overall alignment of the first vertical bar
# (only needed when verbose is large enough)
# - the most important: sorting out which testsets must be run
# (and in the process, precompute descriptions when possible, and IDs)
#
# Concerning the last point, we have the following alternatives with
# a different compromise, depending on the value of `strict`:
#
# false) as it's probably rare that a Regex matches a given testset but not its
# children (as in r"a$" for the subjects "/a" and "/a/b"), and in order to reduce
# the computational load of resolve!, once a testset is found to have to run,
# its children are automatically assumed to have to run; the correct filtering
# will then happen only for final testsets. The drawback is a risk for
# more compilation than necessary, and wasted runtime while executing
# children testsets.
#
# true) a testset found to have to run doesn't force its children to run.
# The drawback is more exhaustive tree walking and more string churn.
function resolve!(mod::Module, ts::TestsetExpr, pat::Pattern;
# external calls
verbose::Int, id::Int64, strict::Bool,
static::Union{Bool,Nothing},
# only recursive calls
force::Bool=false, shown::Bool=true, depth::Int=0)
strings = empty!(ts.strings)
desc = ts.desc
ts.run = force | (static !== false) & alwaysmatches(pat)
ts.loopvalues = nothing # unnecessary ?
ts.loopiters = nothing
if ts.id != 0
@assert ts.id == id
end
ts.id = id
id += 1
parentstrs = ts.parent === nothing ? [""] : ts.parent.strings
ts.descwidth = 0
ts.options.transient_verbose = shown & ((verbose > 1) | ts.options.verbose)
# TODO: probably no need to eval the descriptions when they won't be shown
# and ts.run == true
descwidth(desc) =
if desc !== missing
textwidth(desc) + 2*depth
else
# set width to a lower bound to reduce misalignment
2*depth + max(6, # give at least 6 spaces for the common case of a unique part
mapreduce(+, ts.desc.args) do part
if part isa String
textwidth(part)
else
4 # give 4 spaces for unknown string part
end
end)
end
function decide(subj)
m = matches(pat, subj, ts.id)
# For the curious, setting `s = something(static, missing)`, there are few
# "formulas" to compute the result without `if`, but using only
# `coalesce, |, &, ==, !=, ===, !==, (a,b) -> a, (a,b) -> b, (a,b) -> !a,
# (a,b) -> !b`. The shortest formulas involve 5 such
# functions `fi` and are of the form
# `f1(f2(s, m), f3(f4(s, m), f5(s, m)))`, there are about a dozen of them
# (with redundancy because of functions symmetry).
# All the solutions have `f1 == (===)`, and the 5 simplest involve
# `(a, b) -> b`, so only 4 fi functions are really needed:
# - coalesce(s == m, m) === s | m
# - (coalesce(s, m) == m) === s | m
# - coalesce(s, m) | !m === m
# - coalesce(s, m) | (s == m) === m
# - (coalesce(s, m) == (s | m)) === m
# Which one is the most understandable?
# cf. the file "misc/decide_formulas.jl" for the brute-force algorithm
if static === false
m === missing
else
coalesce(m, static !== true)
end
end
loops = ts.loops
if loops === nothing || desc isa String
# TODO: maybe, for testset-for and !(desc isa String), still try this branch
# in case the the interpolation can be resolved thanks to a global binding
# (i.e. the description doesn't depend on loop variables)
if !(desc isa String)
# TODO: compute desc only when !ts.run (i.e. it wasn't forced) ?
try
desc = Core.eval(mod, desc)::String
catch
desc = missing
end
end
if shown
ts.descwidth = descwidth(desc)
end
hasmissing = false
for str in parentstrs
!strict && ts.run && break
new = str * "/" * desc # TODO: implement *(::Missing, ::Char) in Base ?
hasmissing |= new === missing # comes either from desc or str
ts.run = ts.run || decide(new)
hasmissing && str === missing ||
push!(strings, new)
end
else # we have a testset-for with description which needs interpolation
xs = ()
loopiters = Expr(:tuple, (arg.args[1] for arg in loops)...)
try
# we need to evaluate roughly the following:
# xsgen = Expr(:comprehension, Expr(:generator, loopiters, loops...))
# but a comprehension expression returns an array, i.e. loop variables
# can't depend on previous ones; the correct way is therefore to
# construct nested generators flattened with a :flatten Expr, or to
# simply construct directly a for-loop as below
xssym = gensym() # to not risk to shadow a global variable on which
# the iteration expression depends
xsgen = quote
let $xssym = []
$(Expr(:for, Expr(:block, loops...),
Expr(:call, Expr(:., :Base, QuoteNode(:push!)),
xssym, loopiters)))
$xssym
end
end
xs = Core.eval(mod, xsgen)
@assert xs isa Vector
ts.loopvalues = xs
ts.loopiters = loopiters
catch
@assert xs == ()
ts.descwidth = shown ? descwidth(missing) : 0
ts.run = ts.run || decide(missing)
end
hasmissing = false
for x in xs # empty loop if eval above threw
descx = eval_desc(mod, ts, x)
if shown
ts.descwidth = max(ts.descwidth, descwidth(descx))
end
if !strict && ts.run
if !shown # no need to compute subsequent descx to update ts.descwidth
break
else
continue
end
end
for str in parentstrs
!strict && ts.run && break
new = str * "/" * descx
hasmissing |= new === missing
ts.run = ts.run || decide(new)
hasmissing && str === missing ||
push!(strings, new)
end
end
end
run = ts.run
ts.hasbrokenrec = ts.hasbroken
for tsc in ts.children
runc, id = resolve!(mod, tsc, pat, force = !strict && ts.run,
shown=shown & ts.options.transient_verbose, static=static,
depth=depth+1, verbose=verbose-1, id=id, strict=strict)
run |= runc
ts.descwidth = max(ts.descwidth, tsc.descwidth)
if tsc.run
ts.hasbrokenrec |= tsc.hasbrokenrec
end
end
if !run || !shown
ts.descwidth = 0
end
ts.run = run
run, id
end
eval_desc(mod, ts, x) =
if ts.desc isa String
ts.desc
else
try
Core.eval(mod, quote
let $(ts.loopiters) = $x
$(ts.desc)
end
end)::String
catch
missing
end
end
# convert a TestsetExpr into an actually runnable testset
function make_ts(ts::TestsetExpr, pat::Pattern, stats, chan)
ts.run || return nothing
if isfinal(ts)
body = ts.body
else
body = make_ts(ts.body, pat, stats, chan)
end
if ts.loops === nothing
quote
@testset $(ts.mod) $(isfinal(ts)) $pat $(ts.id) $(ts.desc) $(ts.options) $stats $chan $body
end
else
c = count(x -> x === nothing, (ts.loopvalues, ts.loopiters))
@assert c == 0 || c == 2
if c == 0
loops = [Expr(:(=), ts.loopiters, ts.loopvalues)]
else
loops = ts.loops
end
quote
@testset $(ts.mod) $(isfinal(ts)) $pat $(ts.id) $(ts.desc) $(ts.options) $stats $chan $loops $body
end
end
end
make_ts(x, pat, _, _) = x
make_ts(ex::Expr, pat, stats, chan) =
Expr(ex.head, map(x -> make_ts(x, pat, stats, chan), ex.args)...)
# convert raw tests from InlineTest into TestsetExpr tests, and handle overwriting
function updatetests!(mod::Module, dup::Bool)
tests, news, map = InlineTest.get_tests(mod)
# work-around lack of ordered-dict
# map: we keep only the latest version of a test at a given location,
# to be Revise-friendly (just an imperfect heuristic)
# unless dup is true; if later on dup is false, we overwrite only
# the last version; should we delete all of the versions in this case?
for (tsargs, source) in news
ts, hasbroken = parse_ts(source, string(mod), tsargs)
idx = get!(map, ts.desc, length(tests) + 1)
if idx == length(tests) + 1
push!(tests, ts)
else
if !dup && !(revise_pkgid() in keys(Base.loaded_modules))
desc = ts.desc isa String ? string('"', ts.desc, '"') : ts.desc
source = string(ts.source.file, ':', ts.source.line)
@warn "duplicate description for @testset, overwriting: $desc at $source"
end
if dup
push!(tests, ts)
map[ts.desc] = length(tests)
else
tests[idx] = ts
end
end
end
empty!(news)
tests
end
revise_pkgid() = Base.PkgId(Base.UUID("295af30f-e4ad-537b-8983-00126c2a3abe"), "Revise")
"accepted types as positional arguments of `retest`"
const ArgType = Union{Module,PatternX,AbstractString,AbstractArray,Tuple,Symbol,
Pair{Module,
<:Union{PatternX,AbstractString,AbstractArray,Tuple}}}
"""
retest(mod..., pattern...;
dry::Bool=false, stats::Bool=false, verbose::Real=true,
[id::Bool], shuffle::Bool=false, recursive::Bool=true,
static::Union{Bool,Nothing}=nothing, dup::Bool=false,
load::Bool=false)
Run tests declared with [`@testset`](@ref) blocks, within modules `mod` if specified,
or within all currently loaded modules otherwise.
When no `pattern`s are specified, all the tests are run.
### Keywords
* If `dry` is `true`, don't actually run the tests, just print the descriptions
of the testsets which would (presumably) run.
* If `stats` is `true`, print some time/memory statistics for each testset.
* If specified, `verbose` must be an integer or `Inf` indicating the nesting level
of testsets whose results must be printed (this is equivalent to adding the
`verbose=true` annotation to corresponding testsets); the default behavior
(`true` or `1`) corresponds to printing the result of top-level testsets.
* If `id` is `true`, a unique (per module) integer ID is printed next to each testset,
which can be used for filtering. The default value of `id` depends on other options.
* If `shuffle` is `true`, shuffle the order in which top-level testsets within
a given module are run, as well as the list of passed modules.
* If `recursive` is `true`, the tests for all the recursive submodules of
the passed modules `mod` are also run.
* The `static` keyword controls testsets filtering: if `true`, only testsets
which are known to match "statically" the passed patterns, i.e. at filtering
time, are run. See docstring of [`interpolated`](@ref) for more details.
* If `dup` is `true`, multiple toplevel testsets can have the same
description. If `false`, only the last testset of a "duplicate group" is
kept. The default is `false` in order to encourage having unique
descriptions (useful for filtering) but also and mostly to play well with
`Revise`. This keyword applies only to newly added testsets since the last
run.
* When `load` is `true`, for each package module `Mod` which is selected, `retest`
attempts to also select a corresponding `Main.ModTests` module with the
same pattern specification, unless such module is already explicitly
passed as an argument. If this test module doesn't already exist,
`retest` attempts first to include into `Main` the corresponding test file
"test/ModTests.jl" which is assumed, if it exists, to define `ModTests`.
### Filtering
It's possible to filter run testsets by specifying one or multiple `pattern`s.
A testset is guaranteed to run only if it "matches" all passed patterns (conjunction).
Even if a testset is run, its nested testsets might not run if they don't match
the patterns.
Moreover if a testset is run, its enclosing testset, if any, also has to run
(although not necessarily exhaustively, i.e. other nested testsets
might be filtered out).
A `pattern` can be a string, a `Regex`, an integer, an array or a tuple.
For a testset to "match" an array, it must match at least one of its elements (disjunction).
To match a tuple, it must match all of its elements (conjunction).
To match an integer, its ID must be equal to this integer (cf. the `id` keyword).
A pattern can also be the "negation" of a pattern, via the [`not`](@ref) function,
which allows to exclude testsets from being run.
As a special case, the negation of an integer can be expressed as its arithmetic
negation, e.g. `not(3)` is equivalent to `-3`.
A pattern can also be the [`interpolated`](@ref) singleton object, cf. its docstring.
### `Regex` filtering
The "subject" of a testset is the concatenation of the subject of its parent `@testset`,
if any, with `"/\$description"` where `description` is the testset's description.
For example:
```julia
@testset "a" begin # subject == "/a"
@testset "b" begin # subject is "/a/b"
end
@testset "c\$i" for i=1:2 # subjects are "/a/c1" & "/a/c2"
end
end
```
When `pattern` isa a `Regex`, a testset is guaranteed to run only when its subject
matches `pattern`.
Moreover, even if a testset matches (e.g. "/a" above with `pattern == r"a\$"`),
its nested testsets might be filtered out if they don't also match
(e.g. "a/b" doesn't match `pattern`).
If a passed `pattern` is a string, then it is wrapped in a `Regex` with the
"case-insensitive" flag, and must match literally the subjects.
This means for example that `"a|b"` will match a subject like `"a|b"` or `"A|B"`,
but not like `"a"` (only in Julia versions >= 1.3; in older versions,
the regex is simply created as `Regex(pattern, "i")`).
As a special case, if a string pattern starts with the `'-'` character,
it's interpreted as the negation of the pattern corresponding to the
string with `'-'` chopped off, e.g. `"-abc"` is equivalent to `not("abc")`.
Unless the string starts with two `'-'` characters, in which case
the first `'-'` is chopped off, e.g. `"--abc"` will match subjects
such as `"123-abc"`. To negate such a pattern, just use `not`,
e.g. `not("--abc")`.
### Per-module patterns
In addition to modules or patterns, positional arguments of `retest` can also be
a pair of the form `mod => pattern`: then `pattern` is used to filter only
testsets from `mod`; if other "standalone" patterns (not attached to a module) are
specified, they also conjunctively apply to `mod`. For example, a call like
`retest(mod1 => 1:3, mod2, "x")` is equivalent to `retest(mod1 => (1:3, "x"), mod2 => "x")`.
If `recursive` is `true`, `pattern` is also applied to all recursive submodules `sub`
of `mod`; if `sub` is also specified as `sub => subpat`, the patterns are merged,
i.e. this is equivalent to specifying `sub => (pattern, subpat)`.
!!! note
this function executes each (top-level) `@testset` block using `eval` *within* the
module in which it was written (e.g. `mod`, when specified).
"""
function retest(@nospecialize(args::ArgType...);
dry::Bool=false,
stats::Bool=false,
shuffle::Bool=false,
group::Bool=true,
verbose::Real=true, # should be @nospecialize, but not supported on old Julia
recursive::Bool=true,
id=nothing,
strict::Bool=true,
dup::Bool=false,
static::Union{Bool,Nothing}=nothing,
load::Bool=false,
)
dry, stats, shuffle, group, verbose, recursive, id, strict, dup, static =
update_keywords(args, dry, stats, shuffle, group, verbose, recursive, id, strict, dup, static)
implicitmodules, modules, verbose = process_args(args; verbose=verbose, shuffle=shuffle,
recursive=recursive, load=load)
overall = length(modules) > 1
root = Testset.ReTestSet("", "Overall", overall=true)
maxidw = Ref{Int}(0) # visual width for showing IDs (Ref for mutability in hack below)
tests_descs_hasbrokens = fetchtests.(modules, verbose, overall, Ref(maxidw);
strict=strict, dup=dup, static=static)
isempty(tests_descs_hasbrokens) &&
throw(ArgumentError("no modules using ReTest could be found"))
alltests = first.(tests_descs_hasbrokens)
descwidth = max(textwidth(root.description),
maximum(x->x[2], tests_descs_hasbrokens))
format = Format(stats, descwidth)
hasbroken = any(last.(tests_descs_hasbrokens))
emptymods = findall(isempty, alltests)
nmodules = length(modules) - length(emptymods)
if nmodules == 0
plural = length(emptymods) > 1 ? "s" : ""
print("No matching tests for module$plural ")
join(stdout,
string.(first.(getindex.((modules,), emptymods))),
", ", " and ")
println('.')
return
end
id = something(id, dry | any(modules) do (mod, pat)
hasinteger(pat)
end)
maxidw[] = id ? maxidw[] : 0
for imod in eachindex(modules)
mod, pat = modules[imod]
tests = alltests[imod]
isempty(tests) && continue
shuffle &&
shuffle!(tests)
if dry
showmod = overall || implicitmodules
if showmod
imod > 1 && verbose > 0 &&
println()
printstyled(mod, '\n', bold=true)
end
foreach(ts -> dryrun(mod, ts, pat, id ? 0 : showmod*2,
verbose=verbose>0, maxidw = id ? maxidw[] : 0), tests)
continue
end
if group && nworkers() > 1
# make test groups according to file names
files = Dict{Symbol, Int}()
n = 1
for ts in tests
k = get!(files, ts.source.file, n)
n += (k == n)
end
sort!(tests, lt = function(s, t)
files[s.source.file] < files[t.source.file]
end)
groups = [1 => tests[1].source.file]
for (ith, ts) in enumerate(tests)
_, file = groups[end]
if ts.source.file != file
push!(groups, ith => ts.source.file)
end
end
todo = fill(true, length(tests))
end
outchan = RemoteChannel(() -> Channel{Union{Nothing,Testset.ReTestSet}}(0))
computechan = nprocs() == 1 ?
Channel{Nothing}(1) : # to not interrupt printer task
nothing
ntests = 0
nprinted = 0
allpass = true
exception = Ref{Exception}()
interrupted = Threads.Atomic{Bool}(false)
module_ts = Testset.ReTestSet("", string(mod) * ':', overall=true)
push!(root.results, module_ts)
many = length(tests) > 1 || isfor(tests[1]) # FIXME: isfor when only one iteration
printlock = ReentrantLock()
previewchan =
if stdout isa Base.TTY && (nthreads() > 1 || nprocs() > 1)
RemoteChannel(() -> Channel{Union{String,Nothing}}(Inf))
# needs to be "remote" in the case nprocs() == 2, as then nworkers() == 1,
# which means the one remote worker will put descriptions on previewchan
# (if nworkers() > 1, descriptions are not put because we can't predict
# the order in which they complete, and then the previewer will
# not show the descriptions, just the spinning wheel)
# channel size: if nworkers() == 1, then 2 would suffice (one for
# the "compilation step", one for @testset execution step, and then
# the printer would empty the channel; but for two workers and more,
# this second step is not done, so the buffer needs a size of at least
# `nworkers()`
else
# otherwise, the previewing doesn't work well, because the worker task
# keeps the thread busy and doesn't yield enough for previewing to be useful
nothing
end
gotprinted = false
align_overflow = 0
function take_latest!(previewchan)
local desc
while isready(previewchan)
# printer/previewer can't take! it, as we locked
desc = take!(previewchan)
end
@isdefined(desc) ? desc : ""
end
previewer = previewchan === nothing ? nothing :
@async try
timer = ['|', '/', '-', '\\']
cursor = 0
desc = ""
finito = false
while !finito && !interrupted[]
lock(printlock) do
newdesc = take_latest!(previewchan)
if newdesc === nothing
finito = true
return # no need to sleep before looping
elseif newdesc != ""
desc = newdesc
cursor = 0
gotprinted = false
elseif gotprinted
desc = ""
gotprinted = false
align_overflow = 0
elseif desc != ""
align = format.desc_align
if nworkers() > 1
description = align >= 3 ? "..." : ""
style = NamedTuple()
elseif startswith(desc, '\0')
description = chop(desc, head=1, tail=0)
style = (color = :light_black, bold=true)
else
description = desc
style = NamedTuple()
end
if isindented(verbose, overall, many)
description = " " * description
end
cursor += 1
# when verbose == 0, we still can print the currently run
# testset, but then its description might be larger than
# `align`, because it was not taken into account for computing
# `align`;
# `align_overflow` computes how many characters do overflow,
# so that the printer can "erase" them later on;
# once we overflow, we don't go back (leftwards) until the
# printer prints
align_overflow =
max(align_overflow, textwidth(description) - align)
printstyled('\r',
rpad("$description", align+align_overflow, " "),
' ',
timer[mod1(cursor, end)];
style...)
end
end
sleep(0.13)
end
catch ex
# TODO: clarify what is the correct thing to do here
if ex isa InterruptException
interrupted[] = true
rethrow()
else
# then there is probably a bug in the previewer code, but it might be fine
# for the worker/printer to continue?
rethrow()
end
end # previewer task
# TODO: move printer task out of worker?
worker = @task begin
printer = @async begin
errored = false
finito = false
print_overall() =
if many || verbose == 0
@assert endswith(module_ts.description, ':')
module_ts.description = chop(module_ts.description, tail=1)
clear_line()
Testset.print_test_results(module_ts, format,
bold=true, hasbroken=hasbroken,
maxidw=maxidw[])
else
nothing
end
# if the previewer overflowed, we must clear the line, otherwise, if
# what we print now isn't as large, leftovers from the previewer
# will be seen
clear_line() = if previewchan !== nothing
# +2: for the final space before spinning wheel and the wheel
print('\r' * ' '^(format.desc_align+align_overflow+2) * '\r')
align_overflow = 0
end
while !finito && !interrupted[]
rts = take!(outchan)
lock(printlock) do
if previewchan !== nothing
desc = take_latest!(previewchan)
if desc === nothing
# keep `nothing` in so that the previewer knows to terminate
put!(previewchan, nothing)
end
end
gotprinted = true
if rts === nothing
errored || print_overall()
finito = true
return
end
errored && return
if verbose > 0 || rts.anynonpass
clear_line()
Testset.print_test_results(
rts, format;
depth = Int(!rts.overall & isindented(verbose, overall, many)),
bold = rts.overall | !many,
hasbroken=hasbroken,
maxidw=maxidw[]
)
end
if rts.anynonpass
print_overall()
println()
Testset.print_test_errors(rts)
errored = true
allpass = false
ndone = length(tests)
end
nprinted += 1
if rts.exception !== nothing
exception[] = rts.exception
end
if nprocs() == 1
put!(computechan, nothing)
end
end
end
end # printer task
ndone = 0
if overall || !many
# + if overall, we print the module as a header, to know where the currently
# printed testsets belong
# + if !many, we won't print the overall afterwads, which would be redundant
# with the only one printed top-level testset
ntests += 1
put!(outchan, module_ts) # printer task will take care of feeding computechan
else
@async put!(computechan, nothing)
end
@sync for wrkr in workers()
@async begin
if nprocs() == 1
take!(computechan)
end
file = nothing
idx = 0
while ndone < length(tests) && !interrupted[]
ndone += 1
if !@isdefined(groups)
ts = tests[ndone]
else
if file === nothing
if isempty(groups)
idx = 1
else
idx, file = popfirst!(groups)
end
end
idx = findnext(todo, idx) # when a wrkr has file==nothing, it might steal an item from group of another
# worker, so in any case we must search for a non-done item
ts = tests[idx]
todo[idx] = false
if idx == length(tests) || file === nothing ||
tests[idx+1].source.file != file
file = nothing
else
idx += 1
end
end
if previewchan !== nothing
desc = ts.desc
desc = desc isa String ?
desc :
join(replace(desc.args) do part
part isa String ?
part :
"?"
end)
desc = "\0" * desc
# even when nworkers() >= 2, we inform the previewer that
# computation is gonna happen, so the wheel can start spinning
put!(previewchan, desc)
end
chan = (out=outchan, compute=computechan, preview=previewchan)
resp = remotecall_fetch(wrkr, mod, ts, pat, chan
) do mod, ts, pat, chan
mts = make_ts(ts, pat, format.stats, chan)
Core.eval(mod, mts)
end
if resp isa Vector
ntests += length(resp)
append!(module_ts.results, resp)
else
ntests += 1
push!(module_ts.results, resp)
end
end
end # wrkr: @async
end # @sync for wrkr...
# TODO: maybe put the following stuff in a finally clause where we schedule worker
# (as part of the mechanism to handle exceptions vs interrupt[])
put!(outchan, nothing)
previewchan !== nothing &&
put!(previewchan, nothing)
wait(printer)
end # worker = @task begin ...
try
if previewchan !== nothing && nthreads() > 1
# we try to keep thread #1 free of heavy work, so that the previewer stays
# responsive
tid = rand(2:nthreads())
thread_pin(worker, UInt16(tid))
else
schedule(worker)
end
wait(worker)
previewer !== nothing &&
wait(previewer)
catch ex
interrupted[] = true
ex isa InterruptException ||
rethrow()
end
@assert interrupted[] || !allpass || nprinted == ntests
if isassigned(exception)
throw(exception[])
end
nmodules > 1 && verbose > 0 &&
println()
end
nmodules > 1 && !dry &&
Testset.print_test_results(root, format, bold=true,
hasbroken=hasbroken, maxidw=maxidw[])
nothing
end
# cf. https://github.com/JuliaLang/julia/issues/34267#issuecomment-573507670
function thread_pin(t::Task, tid::UInt16)
ccall(:jl_set_task_tid, Cvoid, (Any, Cint), t, tid-1)
schedule(t)
return t
end
# hidden feature, shortcuts for passing kwargs to retest
function update_keywords(@nospecialize(args), dry, stats, shuffle, group, verbose,
recursive, id, strict, dup, static)
for arg in args
if arg isa Symbol
for c in string(arg)
c == 'v' && continue # "verbose" ignored, we care only about the value
val = islowercase(c)
c = lowercase(c)
if isnumeric(c)
verbose = parse(Int, c)
elseif c == 'd'
dry = val
elseif c == 's'
stats = val
elseif c == 'h'
shuffle = val
elseif c == 'g'
group = val
elseif c == 'r'
recursive = val
elseif c == 'i'
id = val
elseif c == 't'
strict = val
elseif c == 'u'
dup = val
elseif c == 'c'
static = val
else
error("bad keyword shortcut")
end
end
end
end
dry, stats, shuffle, group, verbose, recursive, id, strict, dup, static
end
function process_args(@nospecialize(args);
# defaults for keywords are added just for process_args to be more
# easily called from test code
# TODO: set defaults in global variables to help stay in sync?
verbose=true, shuffle=false, recursive=true, load::Bool=false)
########## process args
patterns = PatternX[] # list of standalone patterns
modpats = Dict{Module,Any}() # pairs module => pattern
modules = Module[] # ordered list of keys from modpats
loaded_modules = Set{Module}(load ? values(Base.loaded_modules) : ())
toload = Dict{Module,Module}() # package => testmodule
function load_testmod(mod)::Union{Module,Nothing}
mod ∈ loaded_modules || return
mod in keys(toload) && return
stestmod = Symbol(mod, :Tests)
if !isdefined(Main, stestmod)
testfile = joinpath(dirname(pathof(mod)), "..", "test", string(stestmod, ".jl"))
isfile(testfile) || return
Base.include(Main, testfile)
if !isdefined(Main, stestmod)
@warn "test file $testfile loaded but it did not define module $stestmod"
return
end
end
testmod = getfield(Main, stestmod)
if !(testmod isa Module)
@warn "$testmod exists but is not a module"
return
end
toload[mod] = testmod
end
# first we initialize modpats with the given patterns for "module-patterns"
# standalone are added at a later stage, because we want to add them only
# to "root" modules when recursive=true so that they are not checked multiple
# times (once for a given module and once for each of its tested parent modules)
for arg in args
if arg isa Module
# if arg was already seen, it already has pattern And() added, so nothing to do
get!(modpats, arg, And())
arg ∉ modules && push!(modules, arg)
load_testmod(arg)
elseif arg isa Pair{Module}
mod = first(arg)
pat = get!(modpats, mod, And())
push!(pat.xs, make_pattern(last(arg)))
mod ∉ modules && push!(modules, mod)
load_testmod(mod)
elseif arg isa Symbol
# ignored, already processed in update_keywords
else
push!(patterns, make_pattern(arg))
end
end
# register testmods
for (mod, testmod) in toload
testmod in modules && continue
@assert !(testmod in keys(modpats))
modpats[testmod] = deepcopy(modpats[mod])
# TODO: avoid deepcopy? this is currently added as otherwise `patterns`
# might be added multiple times at the end of module processing below
push!(modules, testmod)
end
########## process modules
@assert allunique(modules)
implicitmodules = isempty(modpats)
if implicitmodules || recursive
update_TESTED_MODULES!()
end
if implicitmodules
for mod in @view(TESTED_MODULES[1:end])
# we iterate only on the current state of TESTED_MODULES (hence the use of a
# @view), because we don't need to call load_testmod on newly loaded test modules
load_testmod(mod) # might update TESTED_MODULES with submodules
end
update_TESTED_MODULES!(false)
# TODO: update_TESTED_MODULES!() might need to be called, if a module is
# replaced by itself within a newly loaded test module? We should add a test
@assert isempty(modules)
append!(modules, TESTED_MODULES)
for mod in modules
modpats[mod] = And(patterns)
end
elseif recursive
roots = Module[]
# explore TESTED_MODULES maintaining order to preserve order of appearance of
# in modules/files
for mod in unique!([modules; TESTED_MODULES;])
par = mod
while true
newpar = parentmodule(par)
if newpar == par # no parent in modules was found
mod in modules && push!(roots, mod)
break
end
par = newpar
if par ∈ modules
# we need to attach par's pattern to mod's pattern
# it's not a problem if par's pattern is updated later, as the
# value in modpats is not changed (but rather mutated in-place),
# so mod's pattern will still see the updated pattern of par
if mod in modules
# modpats[mod]::And must not be set to a new value, as submodules
# might already reference it, and the following update must be
# visible to them; so we update the .xs field instead
push!(modpats[mod].xs, modpats[par])
else
push!(modules, mod)
# patterns for mod and par will always be the same, so no need
# to copy; whether par was initially in modules or not, if in a
# subsequent iteration (over mod) an intermediate module `inter` is
# found (mod << inter << par), we know that `inter` was not
# initially in modules, and can therefore also share the same
# pattern, i.e. pattern for mod doesn't need to diverge from
# that of par
modpats[mod] = modpats[par]
end
break
end
end
end
for mod in roots
append!(modpats[mod].xs, patterns)
end
else
for pat in values(modpats)
append!(pat.xs, patterns)
end
end
# remove modules which don't have tests, which can happen when a parent module without
# tests is passed to retest in order to run tests in its submodules
filter!(m -> isdefined(m, INLINE_TEST), modules)
shuffle && shuffle!(modules)
########## process verbose
if !isinteger(verbose) && !isinf(verbose) || signbit(verbose)
throw(ArgumentError("`verbose` must be a non-negative integer or `Inf`"))
end
if verbose > typemax(Int)
verbose = typemax(Int) # can't use `max`, which promotes to Float64 with Inf
end
verbose = Int(verbose)
(implicitmodules=implicitmodules, modules=[mod => modpats[mod] for mod in modules],
verbose=verbose)
end
function update_TESTED_MODULES!(double_check=true)
# TESTED_MODULES might have "duplicate" entries, i.e. modules which were
# "replaced", when one overwrites itself by being redefined; in this case,
# let's just delete older entries. We must also take into account the possibility
# that a module was overwritten, but the new version doesn't have a @testset,
# in which case there won't be a duplicate, but we must still delete the entry.
seen = Set{String}()
for idx in eachindex(TESTED_MODULES)
if is_replaced(TESTED_MODULES[idx])
TESTED_MODULES[idx] = nothing
else
push!(seen, string(TESTED_MODULES[idx]))
end
end
filter!(x -> x !== nothing, TESTED_MODULES)
if double_check
# What is below is obsolete as we now reliably register modules in TESTED_MODULES.
# We still keep it for a while just to check this assumption.
# TODO: delete
#
# TESTED_MODULES is not up-to-date w.r.t. package modules which have
# precompilation, so we have to also look in Base.loaded_modules
for mod in values(Base.loaded_modules)
# exclude modules from Main, which presumably already had a chance to get
# registered in TESTED_MODULES at runtime
mod ∈ (ReTest, Main, Base) && continue # TODO: should exclude stdlibs too
str = string(mod)
if str ∉ seen
push!(seen, str) # probably unnecessary, if str are all unique in this loop
for sub in recsubmodules(mod)
# new version: just check the assumption
nameof(sub) == INLINE_TEST && continue
if isdefined(sub, INLINE_TEST)
@assert sub in TESTED_MODULES
end
# old effective version:
# if isdefined(sub, INLINE_TEST) && sub ∉ TESTED_MODULES
# # sub might be a submodule of a Main-like module mod (e.g. via a
# # REPL "contextual module"), in which case it already got registered
# push!(TESTED_MODULES, sub)
# end
end
end
end
end
@assert all(m -> m isa Module, TESTED_MODULES)
@assert allunique(TESTED_MODULES)
filter!(m -> m ∉ (ReTest, ReTest.ReTestTest), TESTED_MODULES)
end
function fetchtests((mod, pat), verbose, overall, maxidw; static, strict, dup)
tests = updatetests!(mod, dup)
descwidth = 0
hasbroken = false
id = 1
for ts in tests
run, id = resolve!(mod, ts, pat, verbose=verbose, id=id, strict=strict, static=static)
run || continue
descwidth = max(descwidth, ts.descwidth)
hasbroken |= ts.hasbrokenrec
end
maxidw[] = max(maxidw[], ndigits(id-1))
tests = filter(ts -> ts.run, tests)
many = length(tests) > 1
indented = isindented(verbose, overall, many)
if indented
descwidth += 2
end
descwidth = max(descwidth, textwidth(string(mod)) + indented)
tests, descwidth, hasbroken
end
isindented(verbose, overall, many) = (verbose > 0) & (overall | !many)
function dryrun(mod::Module, ts::TestsetExpr, pat::Pattern, align::Int=0, parentsubj=""
; evaldesc=true, repeated=nothing, verbose, maxidw::Int)
ts.run && verbose || return
desc = ts.desc
if ts.loops === nothing
if evaldesc && !(desc isa String)
try
desc = Core.eval(mod, desc)
catch
end
end
subject = nothing
if parentsubj isa String && desc isa String
subject = parentsubj * '/' * desc
if isfinal(ts)
matches(pat, subject, ts.id) || return
end
end
if maxidw > 0 # width (ndigits) of max id; <= 0 means ids not printed
printstyled(lpad(ts.id, maxidw), "| ", color = :light_black, bold=true)
end
printstyled(' '^align, desc, color = desc isa String ? :normal : Base.warn_color())
if repeated !== nothing
printstyled(" (repeated",
repeated == -1 ? ")" : " $repeated times)", '\n',
color=:light_black)
else
println()
end
for tsc in ts.children
dryrun(mod, tsc, pat, align + 2, subject, verbose=ts.options.transient_verbose,
maxidw=maxidw)
end
else
function dryrun_beginend(descx, repeated=nothing)
# avoid repeating ourselves, transform this iteration into a "begin/end" testset
if descx isa Expr
@assert descx.head == :string
descx = Expr(:string, copy(descx.args)...)
replace!(descx.args) do arg
if arg isa String || arg isa Symbol
# TODO: unify with same function in previewer, which sets "?"
# even for symbols
arg
else
@assert arg isa Expr # just to have a chance to discover other possibilities
"?"
end
end
end
beginend = TestsetExpr(ts.source, ts.mod, descx, ts.options, nothing,
ts.parent, ts.children)
beginend.run = true
beginend.id = ts.id
dryrun(mod, beginend, pat, align, parentsubj; evaldesc=false,
repeated=repeated, verbose=verbose, maxidw=maxidw)
end
loopvalues = ts.loopvalues
if loopvalues === nothing
# ts.desc is probably a String (cf. resolve!); if so, don't print repeated
# identitical lines (caveat: if subjects of children would change randomly)
# but still try simply to evaluate the length of the iterator
repeated = -1
if ts.desc isa String
local iterlen
try
iterlen = 1
for loop in ts.loops
iterlen *= Core.eval(mod, :(length($(loop.args[2]))))
end
repeated = iterlen
catch
end
end
dryrun_beginend(ts.desc, repeated)
else
for (i, x) in enumerate(loopvalues)
descx = eval_desc(mod, ts, x)
if descx === missing
# we would usually have `i == 1`, but not in some rare cases;
# once we find an uninterpolated description, we still assume
# for simplicity that all remaining ones will also be uninterpolated,
# so we add the "repeated" annotation
# (it's certainly not worth it to bother being more precise about
# exactly which iterations are uninterpolated)
return dryrun_beginend(ts.desc, length(loopvalues)-i+1)
end
@assert descx !== missing # should be unnecessary, but there was a test below
dryrun_beginend(descx)
end
end
end
end
module ReTestTest
using ..ReTest
@testset "test Test in sub-module" begin
@test 1 == 1
end
end # module ReTestTest
@testset "self test" begin
@assert typeof(@__MODULE__) == Module
@test 1 != 2
retest(ReTestTest)
end
end # module ReTest
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] | 2.134745 | 29,144 |
using SpinDoctor
using Documenter
DocMeta.setdocmeta!(SpinDoctor, :DocTestSetup, :(using SpinDoctor); recursive = true)
makedocs(;
modules = [SpinDoctor],
authors = "Syver Døving Agdestein <syverda@icloud.com> and contributors",
repo = "https://github.com/agdestein/SpinDoctor.jl/blob/{commit}{path}#{line}",
sitename = "SpinDoctor.jl",
format = Documenter.HTML(;
prettyurls = get(ENV, "CI", "false") == "true",
canonical = "https://agdestein.github.io/SpinDoctor.jl",
assets = String[],
),
pages = ["Home" => "index.md"],
)
deploydocs(; repo = "github.com/agdestein/SpinDoctor.jl", devbranch = "main")
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] | 2.437037 | 270 |
module internal
using Logging, DataStructures
include("../structs/DataSet.jl")
include("../functions/Data.jl")
#set sok and parse data type
function set_sok(instance, dat_sori, hol_sori)
key_list = dat_sori ; sok_dict = Dict(dat => [] for dat in dat_sori) ; sok_type = Dict()
for hol in hol_sori
for i in eachindex(key_list)
append!(sok_dict[key_list[i]], hol[i])
end
end
for key in key_list
sok_type[key], sok_dict[key] = Data.parse_data_list(sok_dict[key])["type"], Data.parse_data_list(sok_dict[key])["data"]
end
new_hol = []
for hol in hol_sori
tmp_hol = []
for i in eachindex(key_list)
if sok_type[key_list[i]] == String
append!(tmp_hol, string(hol[i]))
else
append!(tmp_hol, parse(sok_type[key_list[i]], string(hol[i])))
end
end
append!(new_hol, [tmp_hol])
end
instance.hol_sori = new_hol
return [
DataStructures.OrderedDict(key_list[i]=> DataSet.ColumnSet(sok_dict[key_list[i]], i, key_list[i], instance) for i in eachindex(key_list)),
[DataSet.RowSet(DataStructures.OrderedDict(key => sok_dict[key][i] for key in key_list), i, instance) for i in eachindex(hol_sori)]
]
end
#modify
function insert(instance, dat_pos, dat_name, hol_value)
if dat_pos < 1 || dat_pos > length(instance.dat_sori) + 1
Logging.ERROR("insert position is invalid.")
result = false
else
new_dat_sori = insert!(instance.dat_sori, dat_pos, dat_name)
new_hol_sori = [insert!(instance.hol_sori[i], dat_pos, hol_value[i]) for i in eachindex(instance.hol_sori)]
new_sok_dict, new_sok_arr = set_sok(instance, new_dat_sori, new_hol_sori)
instance.dat_sori, instance.hol_sori, instance._sok_dict, instance._sok_arr = new_dat_sori, new_hol_sori, new_sok_dict, new_sok_arr
result = true
end
return result
end
function append(instance, dat_name, hol_value)
return insert(instance, length(instance.dat_sori) + 1, dat_name, hol_value)
end
#simple selection and query
function select(instance, key)
key = string(key)
if !isnull(tryparse(Int, key))
result = instance._sok_arr[parse(Int, key)]
else
result = instance._sok_dict[key]
end
return result
end
function where(instance, where_string, drop_null)
new_dat_sori, new_hol_sori = instance.dat_sori, []
for row_set in instance._sok_arr
where_result = row_set.where(where_string, drop_null = drop_null)
if where_result != nothing
append!(new_hol_sori, [where_result])
end
end
new_instance = Base.deepcopy(instance)
new_sok_dict, new_sok_arr = set_sok(new_instance, new_dat_sori, new_hol_sori)
new_instance.dat_sori, new_instance.hol_sori, new_instance._sok_dict, new_instance._sok_arr = new_dat_sori, new_hol_sori, new_sok_dict, new_sok_arr
new_instance.name = string(new_instance.name, ": ", where_string)
return new_instance
end
function equation(instance, equation_string, inplace)
equation_to = strip(split(equation_string, "=")[1])
new_dat_sori, new_hol_sori = in(equation_to, instance.dat_sori) ? instance.dat_sori : append!(instance.dat_sori, equation_to), []
for row_set in instance._sok_arr
equation_result = row_set.equation(equation_string)
if equation_result != nothing
append!(new_hol_sori, [equation_result])
end
end
if inplace
new_instance = Base.deepcopy(instance)
else
new_instance = instance
end
new_sok_dict, new_sok_arr = set_sok(new_instance, new_dat_sori, new_hol_sori)
new_instance.dat_sori, new_instance.hol_sori, new_instance._sok_dict, new_instance._sok_arr = new_dat_sori, new_hol_sori, new_sok_dict, new_sok_arr
new_instance.name = string(new_instance.name, ": ", equation_string)
if inplace
println(new_instance.to_string())
else
return new_instance
end
end
#descriptive statistics
function describe(instance)
result = DataStructures.OrderedDict()
for dat in instance.dat_sori
result[dat] = instance.select(dat).describe()
end
return result
end
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] | 2.081021 | 2,234 |
if basename(pwd()) == "aoc"
cd("2017/10")
end
struct CircularBuffer
data
end
CircularBuffer(n::Int) = CircularBuffer(collect(0:n-1))
function Base.getindex(buffer::CircularBuffer, index::Integer)
buffer.data[index%length(buffer.data)+1]
end
function Base.getindex(buffer::CircularBuffer, indices::AbstractRange{<:Integer})
collect(buffer[index] for index in indices)
end
function Base.setindex!(buffer::CircularBuffer, value, index::Integer)
buffer.data[index%length(buffer.data)+1] = value
end
function Base.reverse!(buffer::CircularBuffer, start = 0, stop = length(buffer.data) - 1)
for (i, v) in zip(start:stop, reverse(buffer[start:stop]))
buffer[i] = v
end
buffer
end
function loadlengths(filename::AbstractString)
parse.(Int, split(readline(filename), ","))
end
function knothash!(buffer::CircularBuffer, lengths)
position = 0
for (skip, len) in zip(Iterators.countfrom(0), lengths)
reverse!(buffer, position, position + len - 1)
position += skip + len
end
buffer
end
function part1(filename::AbstractString, n = 256)
prod(knothash!(CircularBuffer(n), loadlengths(filename))[0:1])
end
@assert knothash!(CircularBuffer(5), [3, 4, 1, 5])[0:1] == [3, 4]
# part1("input.txt")
function sparsehash!(buffer, input::AbstractString)
lengths = [(Int(c) for c in input)..., 17, 31, 73, 47, 23]
position = 0
for (skip, len) in zip(Iterators.countfrom(0), repeat(lengths, 64))
reverse!(buffer, position, position + len - 1)
position += skip + len
end
buffer
end
sparsehash(input::AbstractString) = sparsehash!(CircularBuffer(256), input)
function densehash(sparsehash::CircularBuffer)
join(map(Iterators.partition(sparsehash.data, 16)) do section
string(reduce(xor, section), base = 16, pad = 2)
end)
end
densehash(input::AbstractString) = densehash(sparsehash(input))
part2(filename::AbstractString) = densehash(readline(filename))
@assert densehash("") == "a2582a3a0e66e6e86e3812dcb672a272"
@assert densehash("AoC 2017") == "33efeb34ea91902bb2f59c9920caa6cd"
@assert densehash("1,2,3") == "3efbe78a8d82f29979031a4aa0b16a9d"
@assert densehash("1,2,4") == "63960835bcdc130f0b66d7ff4f6a5a8e"
# part2("input.txt")
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1,
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2,
636,
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7203,
15414,
13,
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4943,
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] | 2.556186 | 881 |
function NSDEBase.solve!(solution::MovingWindowSolution, problem, solver::MovingWindowSolver)
@↓ u0, (t0, tN) ← tspan = problem
@↓ 𝒫, τ, Δτ = solver
@↓ 𝒢, P = 𝒫
for m = 1:length(solution)
solution[m] = TimeParallelSolution(problem, 𝒫)
@↓ U, T = solution[m]
if m == 1
TimeParallel.coarseguess!(solution[m], problem, u0, t0, t0 + τ, 𝒫)
else
ΔP = trunc(Int, P * Δτ / τ)
N = P - ΔP + 1
for n = 1:length(T)
T[n] = solution[m-1].T[n] + Δτ
end
for n = 1:N
U[n] = solution[m-1].U[ΔP+n]
end
for n = N:P
chunk = 𝒢(problem, U[n], T[n], T[n+1])
U[n+1] = chunk.u[end]
end
end
𝒫(solution[m], problem)
end
solution
end
function NSDEBase.solve(problem, solver::MovingWindowSolver)
solution = MovingWindowSolution(problem, solver)
solve!(solution, problem, solver)
solution
end
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] | 1.731293 | 588 |
@testset "expected_loglik" begin
# Test that the various methods of computing expectations return the same
# result.
rng = MersenneTwister(123456)
q_f = Normal.(zeros(10), ones(10))
likelihoods_to_test = [
ExponentialLikelihood(),
GammaLikelihood(),
PoissonLikelihood(),
GaussianLikelihood(),
]
@testset "testing all analytic implementations" begin
# Test that we're not missing any analytic implementation in `likelihoods_to_test`!
implementation_types = [
(; quadrature=m.sig.types[2], lik=m.sig.types[5]) for
m in methods(ApproximateGPs.expected_loglik)
]
analytic_likelihoods = [
m.lik for m in implementation_types if
m.quadrature == ApproximateGPs.Analytic && m.lik != Any
]
for lik_type in analytic_likelihoods
@test any(lik isa lik_type for lik in likelihoods_to_test)
end
end
@testset "$(nameof(typeof(lik)))" for lik in likelihoods_to_test
methods = [GaussHermite(100), MonteCarlo(1e7)]
def = ApproximateGPs._default_quadrature(lik)
if def isa Analytic
push!(methods, def)
end
y = rand.(rng, lik.(zeros(10)))
results = map(m -> ApproximateGPs.expected_loglik(m, y, q_f, lik), methods)
@test all(x -> isapprox(x, results[end]; atol=1e-6, rtol=1e-3), results)
end
@test ApproximateGPs.expected_loglik(
MonteCarlo(), zeros(10), q_f, GaussianLikelihood()
) isa Real
@test ApproximateGPs.expected_loglik(
GaussHermite(), zeros(10), q_f, GaussianLikelihood()
) isa Real
@test ApproximateGPs._default_quadrature(θ -> Normal(0, θ)) isa GaussHermite
@testset "testing Zygote compatibility with GaussHermite" begin # see issue #82
N = 10
gh = GaussHermite(12)
μs = randn(rng, N)
σs = rand(rng, N)
# Test differentiation with variational parameters
for lik in likelihoods_to_test
y = rand.(rng, lik.(rand.(Normal.(μs, σs))))
gμ, glogσ = Zygote.gradient(μs, log.(σs)) do μ, logσ
ApproximateGPs.expected_loglik(gh, y, Normal.(μ, exp.(logσ)), lik)
end
@test all(isfinite, gμ)
@test all(isfinite, glogσ)
end
# Test differentiation with likelihood parameters
# Test GaussianLikelihood parameter
σ = 1.0
y = randn(rng, N)
glogσ = only(
Zygote.gradient(log(σ)) do x
ApproximateGPs.expected_loglik(
gh, y, Normal.(μs, σs), GaussianLikelihood(exp(x))
)
end,
)
@test isfinite(glogσ)
# Test GammaLikelihood parameter
α = 2.0
y = rand.(rng, Gamma.(α, rand(N)))
glogα = only(
Zygote.gradient(log(α)) do x
ApproximateGPs.expected_loglik(
gh, y, Normal.(μs, σs), GammaLikelihood(exp(x))
)
end,
)
@test isfinite(glogα)
end
end
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1267,
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11,
198,
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1267,
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220,
220,
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2488,
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4743,
519,
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8,
198,
220,
220,
220,
886,
198,
437,
198
] | 2.038083 | 1,523 |
# Autogenerated wrapper script for Arpack_jll for aarch64-apple-darwin
export libarpack
using OpenBLAS_jll
using CompilerSupportLibraries_jll
JLLWrappers.@generate_wrapper_header("Arpack")
JLLWrappers.@declare_library_product(libarpack, "@rpath/libarpack.2.dylib")
function __init__()
JLLWrappers.@generate_init_header(OpenBLAS_jll, CompilerSupportLibraries_jll)
JLLWrappers.@init_library_product(
libarpack,
"lib/libarpack.2.1.0.dylib",
RTLD_LAZY | RTLD_DEEPBIND,
)
JLLWrappers.@generate_init_footer()
end # __init__()
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263,
3419,
198,
437,
220,
1303,
11593,
15003,
834,
3419,
198
] | 2.365546 | 238 |
using Gadfly, RDatasets
set_default_plot_size(6inch, 6inch)
plot(dataset("MASS", "mammals"), x="Body", y="Brain",
label=1, Scale.x_log10, Scale.y_log10, Geom.point, Geom.label)
| [
3500,
20925,
12254,
11,
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292,
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2617,
62,
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8,
198
] | 2.243902 | 82 |
function _precompile_()
ccall(:jl_generating_output, Cint, ()) == 1 || return nothing
Base.precompile(Tuple{typeof(pager),String})
end
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] | 2.6 | 55 |
# This file is a part of BAT.jl, licensed under the MIT License (MIT).
# UltraNest docstrings are reproduced here under MIT License with the kind
# permission of the original author
# Johannes Buchner <johannes.buchner.acad@gmx.com>.
"""
struct ReactiveNestedSampling <: AbstractUltraNestAlgorithm
*Experimental feature, not part of stable public API.*
[UltraNest](https://github.com/JohannesBuchner/UltraNest) reactive nested
sampling algorithm with.
Uses the UltraNest Python package, via
[UltraNest.jl](https://github.com/bat/UltraNest.jl) (and PyCall).
Constructors:
* ```$(FUNCTIONNAME)(; fields...)```
Fields:
$(TYPEDFIELDS)
!!! note
This functionality is only available when the
[UltraNest](https://github.com/bat/UltraNest.jl) package is loaded (e.g. via
`import UltraNest`).
"""
@with_kw struct ReactiveNestedSampling{TR<:AbstractDensityTransformTarget} <: AbstractSamplingAlgorithm
trafo::TR = PriorToUniform()
# "Indicating whether this parameter wraps around (circular parameter)"
# wrapped_params::Array{Bool}
"Test transform and likelihood with this number of random points for errors first. Useful to catch bugs."
num_test_samples::Int = 2
"If efficiency goes down, dynamically draw more points from the region between ndraw_min and ndraw_max. If set to False, few points are sampled at once."
draw_multiple::Bool = true
"Number of logZ estimators and MLFriends region bootstrap rounds."
num_bootstraps::Int = 30
"Minimum number of points to simultaneously propose. Increase this if your likelihood makes vectorization very cheap."
ndraw_min::Int = 128
"Maximum number of points to simultaneously propose. Increase this if your likelihood makes vectorization very cheap. Memory allocation may be slow for extremely high values."
ndraw_max::Int = 65536
"Update region when the volume shrunk by this amount."
update_interval_volume_fraction::Float64 = 0.8
"Update stdout status line every log_interval iterations."
log_interval::Int = -1
"Show integration progress as a status line."
show_status::Bool = true
# "Callback function when region was rebuilt. Allows to show current state of the live points."
# viz_callback::Function = nop_func
"Target evidence uncertainty. This is the std between bootstrapped logz integrators."
dlogz::Float64 = 0.5
"Target posterior uncertainty. This is the Kullback-Leibler divergence in nat between bootstrapped integrators."
dKL::Float64 = 0.5
"Integrate until this fraction of the integral is left in the remainder. Set to a low number (1e-2 … 1e-5) to make sure peaks are discovered. Set to a higher number (0.5) if you know the posterior is simple."
frac_remain::Float64 = 0.01
"Terminate when live point likelihoods are all the same, within Lepsilon tolerance. Increase this when your likelihood function is inaccurate, to avoid unnecessary search."
Lepsilon::Float64 = 0.001
"Target number of effective posterior samples."
min_ess::Int = 400
"maximum number of integration iterations."
max_iters::Int = -1
"Stop after this many likelihood evaluations."
max_ncalls::Int = -1
"The algorithm tries to assess iteratively where more samples are needed. This number limits the number of improvement loops."
max_num_improvement_loops::Int = -1
"Minimum number of live points throughout the run."
min_num_live_points::Int = 400
"Require at least this many live points per detected cluster."
cluster_num_live_points::Int = 40
"z-score used as a threshold for the insertion order test. Set to infinity to disable."
insertion_test_window::Float64 = 10.0
"Number of iterations after which the insertion order test is reset."
insertion_test_zscore_threshold::Float64 = 2.0
end
export ReactiveNestedSampling
function bat_sample_impl(
rng::AbstractRNG,
target::AnyDensityLike,
algorithm::ReactiveNestedSampling
)
density_notrafo = convert(AbstractDensity, target)
shaped_density, trafo = bat_transform(algorithm.trafo, density_notrafo)
vs = varshape(shaped_density)
density = unshaped(shaped_density)
bounds = var_bounds(density)
if !(all(isequal(0), bounds.vol.lo) && all(isequal(1), bounds.vol.hi))
throw(ArgumentError("ReactiveNestedSampling only supports (transformed) densities defined on the unit hypercube"))
end
function vec_ultranest_logpstr(V_rowwise::AbstractMatrix{<:Real})
map(logdensityof(density), nestedview(copy(V_rowwise')))
end
ndims = totalndof(vs)
paramnames = all_active_names(varshape(density_notrafo))
smplr = UltraNest.ultranest.ReactiveNestedSampler(
paramnames, vec_ultranest_logpstr, vectorized = true,
num_test_samples = algorithm.num_test_samples,
draw_multiple = algorithm.draw_multiple,
num_bootstraps = algorithm.num_bootstraps,
ndraw_min = algorithm.ndraw_min,
ndraw_max = algorithm.ndraw_max
)
unest_result = smplr.run(
log_interval = algorithm.log_interval < 0 ? nothing : algorithm.log_interval,
show_status = algorithm.show_status,
#viz_callback = algorithm.# viz_callback,
dlogz = algorithm.dlogz,
dKL = algorithm.dKL,
frac_remain = algorithm.frac_remain,
Lepsilon = algorithm.Lepsilon,
min_ess = algorithm.min_ess,
max_iters = algorithm.max_iters < 0 ? nothing : algorithm.max_iters,
max_ncalls = algorithm.max_ncalls < 0 ? nothing : algorithm.max_ncalls,
max_num_improvement_loops = algorithm.max_num_improvement_loops,
min_num_live_points = algorithm.min_num_live_points,
cluster_num_live_points = algorithm.cluster_num_live_points,
insertion_test_window = algorithm.insertion_test_window,
insertion_test_zscore_threshold = algorithm.insertion_test_zscore_threshold
)
r = convert(Dict{String, Any}, unest_result)
unest_wsamples = convert(Dict{String, Any}, r["weighted_samples"])
v_trafo_us = nestedview(convert(Matrix{Float64}, unest_wsamples["points"]'))
logvals_trafo = convert(Vector{Float64}, unest_wsamples["logl"])
weight = convert(Vector{Float64}, unest_wsamples["weights"])
samples_trafo = DensitySampleVector(vs.(v_trafo_us), logvals_trafo, weight = weight)
samples_notrafo = inv(trafo).(samples_trafo)
uwv_trafo_us = nestedview(convert(Matrix{Float64}, r["samples"]'))
uwlogvals_trafo = map(logdensityof(density), uwv_trafo_us)
uwsamples_trafo = DensitySampleVector(vs.(uwv_trafo_us), uwlogvals_trafo)
uwsamples_notrafo = inv(trafo).(uwsamples_trafo)
logz = convert(BigFloat, r["logz"])::BigFloat
logzerr = convert(BigFloat, r["logzerr"])::BigFloat
logintegral = Measurements.measurement(logz, logzerr)
ess = convert(Float64, r["ess"])
return (
result = samples_notrafo, result_trafo = samples_trafo, trafo = trafo,
uwresult = uwsamples_notrafo, uwresult_trafo = uwsamples_trafo,
logintegral = logintegral, ess = ess,
info = r
)
end
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] | 2.809468 | 2,556 |
using Formatting
using ArgParse, JSON
using NCDatasets
println("""
This program output the forcing file with QFLX_TEMP and QFLX_SALT from WKRSTT and WKRSTS
variables in history files. It also needs the data folder generated by program
main_concat_and_convert_units.jl that has monthly TEMP and SALT profile.
""")
function runOneCmd(cmd)
println(">> ", string(cmd))
run(cmd)
end
function pleaseRun(cmd)
if isa(cmd, Array)
for i = 1:length(cmd)
runOneCmd(cmd[i])
end
else
runOneCmd(cmd)
end
end
function makeTimeFile(output_file, years)
dom = [31.0, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
sum_dom = sum(dom)
sum_dom == 365 || throw(ErrorException("Sum of dom is $(sum_dom) rather than 365."))
_t = zeros(Float64, length(dom))
_bnds = zeros(Float64, 2, length(dom))
for m=1:length(dom)
#bnds[m, 1] = beg of month m
#bnds[m, 2] = end of month m
if m==1
_bnds[1, m] = 0.0
else
_bnds[1, m] = _bnds[2, m-1]
end
_bnds[2, m] = _bnds[1, m] + dom[m]
_t[m] = (_bnds[1, m] + _bnds[2, m]) / 2.0
end
t = zeros(Float64, 12 * years)
bnds = zeros(Float64, 2, 12 * years)
for y = 1:years
i_offset = (y-1)*12
t_offset = (y-1)*sum_dom
t[i_offset+1:i_offset+12] .+= _t .+ t_offset
bnds[:, i_offset+1:i_offset+12] .+= _bnds .+ t_offset
end
Dataset(output_file, "c") do ds
defDim(ds, "time", Inf)
defDim(ds, "d2", 2)
defVar(ds, "time", t, ("time", ), ; attrib = Dict(
"long_name" => "time",
"bounds" => "time_bound",
"calendar" => "noleap",
"units" => "days since 0001-01-01 00:00:00",
))
defVar(ds, "time_bound", bnds, ("d2", "time"), ; attrib = Dict(
"long_name" => "boundaries for time-averaging interval",
"units" => "days since 0001-01-01 00:00:00",
))
end
end
function parse_commandline()
s = ArgParseSettings()
@add_arg_table s begin
"--hist-dir"
help = "Casename"
arg_type = String
required = true
"--data-dir"
help = "Casename"
arg_type = String
required = true
"--year-rng"
help = "Casename"
arg_type = Int64
nargs = 2
required = true
"--output-file"
help = "Casename"
arg_type = String
default = "forcing_cyclic.nc"
end
return parse_args(s)
end
parsed = parse_commandline()
JSON.print(parsed,4)
beg_yr, end_yr = parsed["year-rng"]
println("Beg year: $(beg_yr)")
println("End year: $(end_yr)")
yr_rng_str = format( "{:04d}-{:04d}", beg_yr, end_yr )
yr_rng_eval = format( "{:04d}..{:04d}", beg_yr, end_yr )
output_file = parsed["output-file"]
println("Make time file: tmp_time.nc")
makeTimeFile("tmp_time.nc", 1)
println("Making mean profile")
mkpath("tmp")
for m = 1:12
m_str = format( "{:02d}", m)
pleaseRun(`bash -c "ncra -v WKRSTT,WKRSTS,dz_cT,lat_sT,mask_sT,area_sT -O $(parsed["hist-dir"])/*.h0.*.{$(yr_rng_eval)}-$(m_str).nc tmp/monthly_mean_$(m_str).nc"`)
end
println("Output file : $(output_file)")
pleaseRun(`bash -c "ncrcat -O tmp/monthly_mean_{01..12}.nc $output_file"`)
pleaseRun(`ncks -O -3 $output_file $output_file`)
pleaseRun(`ncrename -d Nx,nlon -d Ny,nlat -d Nz,z_t -v WKRSTT,QFLXT -v WKRSTS,QFLXS $output_file`)
#pleaseRun(`ncap2 -O -s 'QFLX_TEMP=QFLX_TEMP*3996*1026;' $output_file $output_file`)
pleaseRun(`ncks -A -v SALT,z_w_top,z_w_bot $(parsed["data-dir"])/monthly/SALT_monthly.nc $output_file`)
pleaseRun(`ncks -A -v TEMP $(parsed["data-dir"])/monthly/TEMP_monthly.nc $output_file`)
pleaseRun(`ncks -A -v HMXL $(parsed["data-dir"])/monthly/HMXL_monthly.nc $output_file`)
pleaseRun(`ncks -A -v time tmp_time.nc $output_file`)
rm("tmp_time.nc", force=true)
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62,
2435,
13,
10782,
1600,
2700,
28,
7942,
8,
628,
628,
628
] | 1.950938 | 2,079 |
using PyCall
using Base.Test
# Try your first TensorFlow program
# https://github.com/tensorflow/tensorflow
@pyimport tensorflow as tf
hello = tf.constant("Hello, TensorFlow!")
@test isa(hello, PyObject)
sess = tf.Session()
result = sess[:run](hello)
@test isa(result, String)
@test "Hello, TensorFlow!" == result
a = tf.constant(10)
b = tf.constant(32)
result = sess[:run](a[:__add__](b))
@test isa(result, Array{Int32,0})
@test 42 == result[1]
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60,
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] | 2.5625 | 176 |
Threads.nthreads()
a = zeros(12)
Threads.@threads for i = 1:12
a[i] = Threads.threadid()
end
a
@macroexpand Threads.@threads for i = 1:12
a[i] = Threads.threadid()
end
a = rand(10 ^ 6)
@timev begin
for i = 1:length(a)
a[i] = Threads.threadid()
end
end
@timev begin
Threads.@threads for i = 1:length(a)
a[i] = Threads.threadid()
end
end
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] | 1.989583 | 192 |
const Row = Vector{Union{Float64,Missing}}
"""
Matrix <: AbstractMatrix{Float64}
2-d matrix (typically) containing data from an arff file
To read a matrix in from an ARFF file, use
[`loadarff`](@ref). To copy part of an existing matrix, use [`copymatrix`](@ref).
To initialize an empty matrix with a certain size, use [`Matrix(rows, columns)`](@ref)
You can iterate over the rows using a standard for loop:
```julia
for row in matrix
...
end
```
Rows and individual elements may be accessed or set using index notation:
```julia
row1 = matrix[1]
element5 = matrix[1,5]
matrix[1,5] = 1.0
```
"""
struct Matrix <: AbstractMatrix{Union{Float64,Missing}}
rows::Vector{Row}
attr_name::Vector{AbstractString}
str_to_enum::Vector{Dict{AbstractString,Integer}}
enum_to_str::Vector{Dict{Integer,AbstractString}}
datasetname::AbstractString
end
# These functions allow the Matrix to act like a standard julia collection
# iterate over rows using `for row in m ... end`
# get a specific row using `m[1]` or a specific value using `m[1,5]`
Base.iterate(m::Matrix) = iterate(m.rows)
Base.iterate(m::Matrix, state) = iterate(m.rows, state)
Base.HasLength(::Type{Matrix}) = Base.HasShape{2}()
Base.length(m::Matrix) = length(m.rows)
Base.size(m::Matrix) = (rows(m), columns(m))
Base.firstindex(::Matrix) = 1
Base.lastindex(m::Matrix) = lastindex(m.rows)
Base.getindex(m::Matrix, i::Int) = m.rows[i]
Base.getindex(m::Matrix, i::Int, j::Int) = m.rows[i][j]
Base.setindex!(m::Matrix, v::Row, i::Int) = m.rows[i] = v
Base.setindex!(m::Matrix, v::Float64, i::Int, j::Int) = m.rows[i][j] = v
" rows(matrix)"
const rows = Base.length
" columns(matrix)"
columns(m::Matrix) = length(m.attr_name)
" attributename(matrix, column)"
attributename(m::Matrix, col::Integer) = m.attr_name[col]
" setattributename(matrix, column, name)"
setattributename(m::Matrix, col::Integer, name::AbstractString) = m.attr_name[col] = name
"""
attributevalue(matrix, column, value)
Get the string representation of a value in a column
"""
attributevalue(m::Matrix, col::Integer, value) = m.enum_to_str[col][convert(Integer, value)]
"""
valuecount(matrix, column)
If the column is a nominal feature, get the number of different valid values.
If the column is not nominal, 0.
"""
valuecount(m::Matrix, col::Integer) = length(m.enum_to_str[col])
function applytocolumn(f::Function, m::Matrix, col::Integer)
column = collect(skipmissing(m[:, col]))
if isempty(column)
missing
else
f(column)
end
end
" columnmean(matrix, column)"
columnmean(m::Matrix, col::Integer) = applytocolumn(mean, m, col)
" columnminimum(matrix, column)"
columnminimum(m::Matrix, col::Integer) = applytocolumn(minimum, m, col)
" columnmaximum(matrix, column)"
columnmaximum(m::Matrix, col::Integer) = applytocolumn(maximum, m, col)
" mostcommonvalue(matrix, column)"
mostcommonvalue(m::Matrix, col::Integer) = applytocolumn(mostcommonvalue, m, col)
function mostcommonvalue(column)
counts = Dict{Float64,Integer}()
for value in column
counts[value] = get(counts, value, 0) + 1
end
partialsort!(collect(counts), 1, by=x->x[2], rev=true)[1]
end
"""
iscontinuous(matrix, column)
Return true if the specified column in the given matrix is continuous, false if nominal
"""
iscontinuous(m::Matrix, col::Int) = isempty(m.enum_to_str[col])
"""
shuffle!(matrix[, buddy])
Shuffle the rows of the matrix in place. If buddy is passed in, buddy will be
shuffled in place using the same random permutation as matrix.
"""
shuffle!(m::Matrix) = permute!(m, randperm(rows(m)))
function shuffle!(m::Matrix, buddy::Matrix)
perm = randperm(rows(m))
permute!(m, perm)
permute!(buddy, perm)
end
"""
copymatrix(matrix, rows, columns)
Get the specified portion of `matrix` and return it as a new `Matrix`.
`rows` and `columns` should be a range or an array of indices, or
[any other index](https://docs.julialang.org/en/stable/manual/arrays/#man-supported-index-types-1)
supported by standard julia indexing.
This gets a view of the original matrix, and not a copy. If you modify this
matrix, the original matrix will also be modified.
!!! note
The `Matrix` class is 1-indexed. Thus, the values for rows and columns should
be between 1 and the number of rows or columns, inclusively.
"""
function copymatrix(m::Matrix, rows, columns)
data = map(i->m.rows[i][columns], rows)
attr_name = m.attr_name[columns]
str_to_enum = m.str_to_enum[columns]
enum_to_str = m.enum_to_str[columns]
Matrix(data, attr_name, str_to_enum, enum_to_str, m.datasetname)
end
copymatrix(m::Matrix, rows::Integer, columns::Integer) = copymatrix(m, rows:rows, columns:columns)
copymatrix(m::Matrix, rows, columns::Integer) = copymatrix(m, rows, columns:columns)
copymatrix(m::Matrix, rows::Integer, columns) = copymatrix(m, rows:rows, columns)
"""
getrows(matrix, rows)
Get the specified rows from `matrix`, as a `Matrix`. `rows` can be a range or an array of indices, or
[any other index](https://docs.julialang.org/en/stable/manual/arrays/#man-supported-index-types-1)
supported by standard julia indexing.
"""
getrows(m::Matrix, rows) = copymatrix(m, rows, 1:columns(m))
"""
Split
Holds the result of splitting a set of features and labels into a training set and a test set.
Fields are `trainfeatures`, `trainlabels`, `validationfeatures`, `validationlabels`.
"""
struct Split
trainfeatures::Matrix
trainlabels::Matrix
validationfeatures::Matrix
validationlabels::Matrix
end
"""
splitmatrix(features, labels, percenttest)
Split the given matrices into a training set and a validation set.
`percenttest`% of the rows will be put in the validation set and `1-percenttest`%
of the rows are put into the training set. Returns a [`Split`](@ref) object.
"""
function splitmatrix(features::Matrix, labels::Matrix, percenttest::AbstractFloat)
shuffle!(features, labels)
numrows = rows(features)
trainrows = trunc(Int, (1 - percenttest) * numrows)
trainfeatures = getrows(features, 1:trainrows)
trainlabels = getrows(labels, 1:trainrows)
validationfeatures = getrows(features, trainrows+1:numrows)
validationlabels = getrows(labels, trainrows+1:numrows)
Split(trainfeatures, trainlabels, validationfeatures, validationlabels)
end
"""
Matrix(rows, columns)
Create a matrix with the given number of rows and columns.
"""
function Matrix(rows::Integer, columns::Integer)
data = map(_ -> zeros(columns), 1:rows)
attr_name = fill("", columns)
# kinda hacky, but whatever - creates a new (empty) dictionary for each column
str_to_enum = map(_ -> Dict{AbstractString,Integer}(), 1:columns)
enum_to_str = map(_ -> Dict{Integer,AbstractString}(), 1:columns)
Matrix(data, attr_name, str_to_enum, enum_to_str, "")
end
const numbertypes = Set(["REAL", "CONTINUOUS", "INTEGER", "NUMERIC"])
"""
loadarff(filename)
Read an arff file and return a `Matrix`
"""
function loadarff(filename::AbstractString)::Matrix
io = open(filename)
# skip empty lines and comments at the beginning
skipchars(isspace, io; linecomment='%')
# initialize variables
attr_name = Vector{AbstractString}()
str_to_enum = Vector{Dict{AbstractString,Integer}}()
enum_to_str = Vector{Dict{Integer,AbstractString}}()
datasetname = ""
# read attributes - break when you get to the data
while true
line = readline(io)
(isempty(line) || line[1] == '%') && continue
upper = uppercase(line)
if startswith(upper, "@RELATION")
# Everything after Relation is the datasetname
datasetname = split(line; limit=2)[2]
elseif startswith(upper, "@ATTRIBUTE")
# Attribute should have three distinct parts - @Attribute, name, and type
# e.g. "@attribute 'handicapped-infants' { 'n', 'y'}"
# or "@ATTRIBUTE sepallength Continuous"
attribute = split(line, r"\s+"; limit=3, keepempty=false)
ste = Dict{AbstractString,Integer}()
ets = Dict{Integer,AbstractString}()
push!(attr_name, attribute[2])
push!(str_to_enum, ste)
push!(enum_to_str, ets)
# If it's one of the number types, there's no need to do anything with it
if uppercase(attribute[3]) ∉ numbertypes
stripped = strip(attribute[3], ['{', '}', ' '])
values = split(stripped, [' ', ',']; keepempty=false)
foreach(values, Iterators.countfrom(0)) do val, i
ste[val] = i
ets[i] = val
end
end
elseif startswith(upper, "@DATA")
break
end
end
data = Vector{Row}()
mappers = map(dict -> getfloatvalue(dict) ∘ strip, str_to_enum)
while !eof(io)
line = readline(io)
(isempty(line) || line[1] == '%') && continue
row = map((f,x) -> f(x), mappers, split(line, ','))
push!(data, row)
end
close(io)
Matrix(data, attr_name, str_to_enum, enum_to_str, datasetname)
end
function getfloatvalue(dict::Dict{AbstractString,Integer})
function mapvalue(value::AbstractString)
value = strip(value)
# the ordering of these if statements is very intentional
# if "?" is a key in the dictionary, then use the value it maps to there
# if it's not, it's set to missing whether it's a nominal or a continuous feature
if haskey(dict, value)
dict[value]
elseif value == "?"
missing
elseif isempty(dict)
parse(Float64, value)
else
error("Error parsing value $value with dict $dict")
end
end
mapvalue
end
"""
normalize(matrix[, extrema])
Normalizes `matrix` so that all columns have values between 0 and 1.
If `extrema` is included, the matrix is normalized as though the maximum and
minimum value of each column were the values included in extrema.
This allows for two matrices to be normalized using the same ranges.
This method returns a list of extrema that can then be used to normalize another matrix.
(This is typically only useful when you didn't pass the list of extrema in)
"""
normalize(m::Matrix) = normalize(m, vec(extrema(m, 1)))
function normalize(m::Matrix, extrema::Vector{Tuple{Float64,Float64}})
colinds = map(c -> valuecount(m, c) == 0, 1:columns(m)) .== 0
for row in m.rows
map!(row, row, extrema, colinds) do val, ext, real
real ? normalizevalue(val, ext) : val
end
end
extrema
end
function normalizevalue(value, extrema)
min, max = extrema
(value-min)/(max-min)
end
function Base.show(io::IO, m::Matrix)
println(io, "@RELATION ", m.datasetname)
for (name, enum_to_str) in zip(m.attr_name, m.enum_to_str)
println(io, "@ATTRIBUTE ", name, " ", begin
if isempty(enum_to_str)
"CONTINUOUS"
else
"{", join(values(enum_to_str), ", "), "}"
end
end...)
end
println(io, "@DATA")
for row in m.rows
mapped = map(row, m.enum_to_str) do val, map
ismissing(val) ? "?" : isempty(map) ? val : map[val]
end
join(io, mapped, ", ")
println(io)
end
end
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13,
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1262,
262,
976,
16069,
13,
198,
198,
1212,
2446,
5860,
257,
1351,
286,
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] | 2.782313 | 3,822 |
# when new brainflow binaries are released, then this script needs to be re-executed.
using SHA
using Pkg
using Pkg.Artifacts
using Tar
function sha256sum(tarball_path)
return open(tarball_path, "r") do io
return bytes2hex(sha256(io))
end
end
function add_brainflow_artifact!(
url,
artifact_toml_path = "Artifacts.toml",
artifact_name = "brainflow",
)
download_path = "$(tempname())-download.tar"
download(url, download_path)
tar_hash_sha256 = sha256sum(download_path)
brainflow_hash = create_artifact() do artifact_dir
# Pkg.PlatformEngines.unpack() gives errors for some users on Windows
Tar.extract(download_path, artifact_dir)
end
rm(download_path)
Pkg.Artifacts.bind_artifact!(
artifact_toml_path,
artifact_name,
brainflow_hash;
download_info=[(url, tar_hash_sha256)],
force=true,
lazy=true,
)
return brainflow_hash
end
cd(@__DIR__)
include("src/brainflow_url.jl")
add_brainflow_artifact!(brainflow_url())
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] | 2.400452 | 442 |
# Given some model parameters, synthesize and plot the channel maps and
# integrated spectrum
using ArgParse
s = ArgParseSettings()
@add_arg_table s begin
# "--opt1"
# help = "an option with an argument"
# default = 0
"--norad"
help = "Use the image alread here."
action = :store_true
"config"
help = "a YAML configuration file"
required = true
end
parsed_args = parse_args(ARGS, s)
import YAML
config = YAML.load(open(parsed_args["config"]))
using constants
using image
using model
using HDF5
# import PyPlot.plt
# using LaTeXStrings
pp = config["parameters"]
params = ["M_star", "r_c", "T_10", "q", "gamma", "logM_CO", "ksi", "dpc", "incl", "PA", "vel", "mu_RA", "mu_DEC"]
nparam = length(params)
starting_param = Array(Float64, nparam)
for i=1:nparam
starting_param[i] = pp[params[i]][1]
end
# Convert logM_CO to M_CO
starting_param[6] = 10^starting_param[6]
pars = Parameters(starting_param...)
grd = config["grid"]
grid = Grid(grd["nr"], grd["ntheta"], grd["r_in"], grd["r_out"], true)
# read the wavelengths for all 23 channels
fid = h5open(config["data_file"], "r")
lams = read(fid["lams"]) # [μm]
close(fid)
vel = pars.vel # [km/s]
# RADMC conventions for inclination and PA
incl = pars.incl # [deg]
PA = pars.PA # [deg] Position angle runs counter clockwise
npix = config["npix"] # number of pixels
# Doppler shift the dataset wavelength to rest-frame wavelength
beta = vel/c_kms # relativistic Doppler formula
shift_lams = lams .* sqrt((1. - beta) / (1. + beta)) # [microns]
lam0 = cc/230.538e9 * 1e4 # [microns]
write_dust(pars, "", grid)
write_grid("", grid)
write_model(pars, "", grid)
write_lambda(shift_lams)
# Temporarily overwrite to dust mode
cp("radmc3d.inp.dust", "radmc3d.inp")
if !parsed_args["norad"]
run(`radmc3d mctherm`) # next command will do this automatically
run(`radmc3d image incl $incl posang $PA npix $npix lambda $lam0`)
end
# When done, revert to gas only mode
cp("radmc3d.inp.gas", "radmc3d.inp")
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] | 2.537389 | 789 |
@testset "find_traversal & list_traversal" for t in TEST_TREES
for l in list_traversal(t)
res = find_traversal(t, walk(t, l))
@test all(c -> walk(t, c) === walk(t, l), res)
end
@test isempty(find_traversal(t, Leaf(-1)))
@test isempty(find_traversal(t, [t, t]))
@test isempty(find_traversal(t, BinaryVertex(-1, t, t)))
end
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] | 2.093567 | 171 |
using Oceananigans
using Oceananigans.Units
using OceanTurbulenceParameterEstimation
using OceanTurbulenceParameterEstimation: Transformation
using LinearAlgebra, CairoMakie, DataDeps, Distributions
using ElectronDisplay
using Oceananigans.TurbulenceClosures.CATKEVerticalDiffusivities:
CATKEVerticalDiffusivity,
SurfaceTKEFlux,
MixingLength
#####
##### Compile LESbrary
#####
case_path(case) = @datadep_str("four_day_suite_1m/$(case)_instantaneous_statistics.jld2")
Δz = 2
#times = [96hours - 20minutes, 96hours]
times = [12hours, 96hours]
field_names = (:b, :e, :u, :v)
regrid_size = (1, 1, Int(256/Δz))
k₁ = floor(Int, 120 / Δz)
k₂ = ceil(Int, 150 / Δz)
space = SpaceIndices(z=k₁:k₂)
mult = length(space.z) / regrid_size[3]
transformation = (
b = Transformation(; normalization=ZScore()),
u = Transformation(; normalization=ZScore()),
v = Transformation(; normalization=ZScore()),
e = Transformation(; space, normalization=RescaledZScore(mult)),
)
observation_library = Dict()
# Don't optimize u, v for free_convection
observation_library["free_convection"] =
SyntheticObservations(case_path("free_convection"); transformation, times, regrid_size,
field_names = (:b, :e))
# Don't optimize v for non-rotating cases
observation_library["strong_wind_no_rotation"] =
SyntheticObservations(case_path("strong_wind_no_rotation"); transformation, times, regrid_size,
field_names = (:b, :e, :u))
# The rest are standard
for case in ["strong_wind", "strong_wind_weak_cooling", "weak_wind_strong_cooling"]
observation_library[case] = SyntheticObservations(case_path(case); field_names, transformation, times, regrid_size)
end
@show znodes(Center, observation_library["free_convection"].grid)[space.z]
cases = [
"free_convection",
"weak_wind_strong_cooling",
"strong_wind_weak_cooling",
"strong_wind",
"strong_wind_no_rotation",
]
observations = [observation_library[case] for case in cases]
#####
##### Simulation
#####
# Constant Ri, no convection
mixing_length = MixingLength(Cᴬu = 0.0,
Cᴬc = 0.0,
Cᴬe = 0.0,
Cᴸᵇ = 1.36,
Cᴷu⁻ = 0.101,
Cᴷc⁻ = 0.0574,
Cᴷe⁻ = 3.32,
Cᵟu = 0.5,
Cᵟc = 0.5,
Cᵟe = 0.5,
CᴷRiᶜ = 2.0,
Cᴷuʳ = 0.0,
Cᴷcʳ = 0.0,
Cᴷeʳ = 0.0)
surface_TKE_flux = SurfaceTKEFlux(CᵂwΔ=4.74, Cᵂu★=2.76)
catke = CATKEVerticalDiffusivity(; Cᴰ=1.78, mixing_length)
#####
##### Calibration
#####
mass = 0.6
prior_library = Dict()
prior_library[:CᵂwΔ] = ScaledLogitNormal(; bounds=(4, 10)) #, interval=(2, 5), mass)
prior_library[:Cᵂu★] = ScaledLogitNormal(; bounds=(2, 10)) #, interval=(3, 5), mass)
prior_library[:Cᴸᵇ] = ScaledLogitNormal(; bounds=(0, 0.5)) #, interval=(0.1, 2), mass)
prior_library[:Cᴰ] = ScaledLogitNormal(; bounds=(0, 1)) #, interval=(0.5, 2), mass)
prior_library[:Cᴷu⁻] = ScaledLogitNormal(; bounds=(0, 0.1)) #, interval=(0.01, 0.1), mass)
prior_library[:Cᴷc⁻] = ScaledLogitNormal(; bounds=(0, 1)) #, interval=(0.5, 1.0), mass)
prior_library[:Cᴷe⁻] = ScaledLogitNormal(; bounds=(0, 3)) #, interval=(1.5, 3), mass)
prior_library[:Cᴷuʳ] = ScaledLogitNormal(; bounds=(-1, 1))
prior_library[:Cᴷcʳ] = ScaledLogitNormal(; bounds=(-1, 1))
prior_library[:Cᴷeʳ] = ScaledLogitNormal(; bounds=(-1, 1))
prior_library[:CᴷRiᶜ] = ScaledLogitNormal(; bounds=(1, 3))
prior_library[:CᴷRiʷ] = ScaledLogitNormal(; bounds=(0, 0.5))
prior_library[:Cᴬu] = ScaledLogitNormal(; bounds=(0, 0.1))
prior_library[:Cᴬc] = ScaledLogitNormal(; bounds=(0, 2))
prior_library[:Cᴬe] = ScaledLogitNormal(; bounds=(0, 0.1)) #, interval=(1, 3), mass)
prior_library[:Cᵟu] = ScaledLogitNormal(; bounds=(0, 10))
prior_library[:Cᵟc] = ScaledLogitNormal(; bounds=(0, 10))
prior_library[:Cᵟe] = ScaledLogitNormal(; bounds=(0, 10))
# No convective adjustment:
constant_Ri_parameters = (:Cᴰ, :CᵂwΔ, :Cᵂu★, :Cᴸᵇ, :Cᴷu⁻, :Cᴷc⁻, :Cᴷe⁻, :Cᵟu, :Cᵟc, :Cᵟe)
variable_Ri_parameters = (:Cᴷuʳ, :Cᴷcʳ, :Cᴷeʳ, :CᴷRiʷ, :CᴷRiᶜ, :Cᴰ, :Cᴸᵇ, :CᵂwΔ, :Cᵂu★)
convective_adjustment_parameters = (:Cᴬc, :Cᴬe) # Cᴬu
# :Cᴸˢ
parameter_names = (:CᵂwΔ, :Cᵂu★, :Cᴷe⁻, :Cᴸᵇ, :Cᴰ, :Cᴷc⁻, :Cᴷu⁻, :Cᴷuʳ, :Cᴷcʳ, :Cᴷeʳ, :CᴷRiᶜ, :CᴷRiʷ, :Cᴬc, :Cᴬe)
free_parameters = FreeParameters(prior_library, names=parameter_names)
Nensemble = 4000
Δt = 20.0
function build_simulation()
simulation = ensemble_column_model_simulation(observations;
Nensemble,
architecture = GPU(),
tracers = (:b, :e),
closure = catke)
simulation.Δt = Δt
Qᵘ = simulation.model.velocities.u.boundary_conditions.top.condition
Qᵇ = simulation.model.tracers.b.boundary_conditions.top.condition
N² = simulation.model.tracers.b.boundary_conditions.bottom.condition
for (case, obs) in enumerate(observations)
f = obs.metadata.parameters.coriolis_parameter
view(Qᵘ, :, case) .= obs.metadata.parameters.momentum_flux
view(Qᵇ, :, case) .= obs.metadata.parameters.buoyancy_flux
view(N², :, case) .= obs.metadata.parameters.N²_deep
view(simulation.model.coriolis, :, case) .= Ref(FPlane(f=f))
end
return simulation
end
simulation = build_simulation()
calibration = InverseProblem(observations, simulation, free_parameters)
resampler = Resampler(resample_failure_fraction=0.5, acceptable_failure_fraction=1.0)
eki = EnsembleKalmanInversion(calibration; resampler, convergence_rate=0.8)
#####
##### Plot utils
#####
Nt = length(times)
observed_data = []
for observation in observations
time_serieses = observation.field_time_serieses
names = keys(time_serieses)
case_data = NamedTuple(n => interior(getproperty(time_serieses, n)[Nt])[1, 1, :] for n in names)
push!(observed_data, case_data)
end
function get_modeled_case(icase, name, k=1)
model_time_serieses = calibration.time_series_collector.field_time_serieses
field = getproperty(model_time_serieses, name)[Nt]
return view(interior(field), k, icase, :)
end
colorcycle = [:black, :royalblue1, :darkgreen, :lightsalmon, :seagreen, :magenta2, :red4, :khaki1, :darkgreen, :bisque4,
:silver, :lightsalmon, :lightseagreen, :teal, :royalblue1, :darkorchid4]
markercycle = [:rect, :utriangle, :star5, :circle, :cross, :+, :pentagon, :ltriangle, :airplane, :diamond, :star4]
markercycle = repeat(markercycle, inner=2)
function make_axes(fig, row=1, label=nothing)
ax_b = Axis(fig[row, 1], xlabel = "Buoyancy \n[cm s⁻²]", ylabel = "z [m]")
ax_u = Axis(fig[row, 2], xlabel = "x-velocity \n[cm s⁻¹]")
ax_v = Axis(fig[row, 3], xlabel = "y-velocity \n[cm s⁻¹]")
ax_e = Axis(fig[row, 4], xlabel = "Turbulent kinetic energy \n[cm² s⁻²]")
if !isnothing(label)
ax_t = Axis(fig[row, 5])
xlims!(0, 1)
ylims!(0, 1)
hidespines!(ax_t)
hidedecorations!(ax_t)
text!(ax_t, label, justification=:left, align=(:left, :center), position=(0, 0.5))
end
return (ax_b, ax_u, ax_v, ax_e)
end
function plot_fields!(axs, label, color, b, e, u=zeros(size(b)), v=zeros(size(b)); linewidth=2, linestyle=:solid)
grid = first(values(observation_library)).grid
z = znodes(Center, grid)
b, u, v, e = Tuple(Array(f) for f in (b, u, v, e))
for (q, name) in zip((b, u, v, e), ("b", "u", "v", "e"))
any(isnan.(q)) && @warn("NaNs found in $label $(name)!")
end
## Note unit conversions below, eg m s⁻¹ -> cm s⁻¹:cyan
lines!(axs[1], 1e2 * b, z; color, linestyle, label, linewidth)
lines!(axs[2], 1e2 * u, z; color, linestyle, label, linewidth)
lines!(axs[3], 1e2 * v, z; color, linestyle, label, linewidth)
lines!(axs[4], 1e4 * e, z; color, linestyle, label, linewidth)
return nothing
end
function min_max_parameters(summary)
names = keys(summary.ensemble_mean)
Nens = length(summary.parameters)
parameter_matrix = [summary.parameters[k][name] for name in names, k = 1:Nens]
θ_min = minimum(parameter_matrix, dims=2)
θ_max = maximum(parameter_matrix, dims=2)
return θ_min, θ_max
end
function finitefind(a, val, find)
b = deepcopy(a)
b[.!isfinite.(a)] .= val
return find(b)
end
finitefindmin(a) = finitefind(a, Inf, findmin)
finitefindmax(a) = finitefind(a, -Inf, findmax)
function visualize_parameter_evolution(eki)
summaries = eki.iteration_summaries
Niters = length(summaries)
names = eki.inverse_problem.free_parameters.names
θ_mean = NamedTuple(name => map(s -> s.ensemble_mean[name], summaries) for name in names)
k_best(s) = finitefindmin(s.mean_square_errors)[2]
θ_best = NamedTuple(name => map(s -> s.parameters[k_best(s)][name], summaries) for name in names)
θ_min_max = [min_max_parameters(s) for s in summaries]
θ_min = [[θn[1][i] for θn in θ_min_max] for i in 1:length(names)]
θ_max = [[θn[2][i] for θn in θ_min_max] for i in 1:length(names)]
θᵢ = NamedTuple(name => first(θ_mean[name]) for name in names)
Δθ = NamedTuple(name => (θ_mean[name] .- θᵢ[name]) ./ θᵢ[name] for name in names)
iterations = 0:length(summaries)-1
fig = Figure(resolution=(1200, 1200))
ax1 = Axis(fig[1:3, 1], xlabel = "Iteration", ylabel = "Δθ")
for (i, name) in enumerate(names)
label = string(name)
marker = markercycle[i]
color = colorcycle[i]
scatterlines!(ax1, iterations, parent(Δθ[name]); marker, color=(color, 0.8), label, linewidth=4)
end
fig[1:3, 2] = Legend(fig, ax1)
Nparts = 3
Nθpart = floor(Int, length(names) / Nparts)
for p in 1:Nparts
axp = Axis(fig[p+3, 1], xlabel = "Iteration", ylabel = "θ")
if p == Nparts
np = UnitRange((p-1) * Nθpart + 1, length(names))
else
np = UnitRange((p-1) * Nθpart + 1, p * Nθpart)
end
partnames = names[np]
for (n, name) in enumerate(partnames)
i = (p - 1) * Nθpart + n
label = string(name)
marker = markercycle[i]
color = colorcycle[i]
scatterlines!(axp, iterations, parent(θ_mean[name]); marker, color=(color, 0.6), label, linewidth=4)
lines!(axp, iterations, parent(θ_best[name]); color, linewidth=2)
band!(axp, iterations, parent(θ_min[i]), parent(θ_max[i]), color=(color, 0.3))
end
fig[p+3, 2] = Legend(fig, axp)
end
display(fig)
return nothing
end
function plot_latest(eki)
latest_summary = eki.iteration_summaries[end]
min_error, k_min = finitefindmin(latest_summary.mean_square_errors)
max_error, k_max = finitefindmax(latest_summary.mean_square_errors)
fig = Figure(resolution=(1200, 1200))
for (c, case) in enumerate(cases)
label = replace(case, "_" => "\n")
axs = make_axes(fig, c, label)
observed = observed_data[c]
obs = observations[c]
min_error_data = NamedTuple(n => get_modeled_case(c, n, k_min) for n in keys(obs.field_time_serieses))
max_error_data = NamedTuple(n => get_modeled_case(c, n, k_max) for n in keys(obs.field_time_serieses))
plot_fields!(axs, "observed at t = " * prettytime(times[end]), (:gray23, 0.6), observed...; linewidth=4)
plot_fields!(axs, "min", :navy, min_error_data...)
plot_fields!(axs, "max", :orangered3, max_error_data...)
fig[1, 6] = Legend(fig, axs[1])
end
display(fig)
return nothing
end
#####
##### Visualize observations
#####
fig = Figure(resolution=(1200, 1200))
linestyles = [:solid, :dash, :dot, :dashdot, :dashdotdot]
all_axs = []
for (o, observation) in enumerate(observations)
axs_label = replace(cases[o], "_" => "\n")
axs = make_axes(fig, o, axs_label)
append!(all_axs, axs)
for (n, t) in enumerate(times)
linestyle = linestyles[o]
label = "t = " * prettytime(t)
names = keys(observation.field_time_serieses)
data = map(name -> interior(observation.field_time_serieses[name][n])[1, 1, :], names)
plot_fields!(axs, label, colorcycle[n], data...)
end
end
display(fig)
#####
##### Calibrate
#####
# Initial state after 0 iterations
plot_latest(eki)
# Continuously update
for i = 1:20
@info "Iterating..."
start_time = time_ns()
iterate!(eki)
elapsed = 1e-9 * (time_ns() - start_time)
@info string(" done. (", prettytime(elapsed), ")")
@show eki.iteration_summaries[end]
visualize_parameter_evolution(eki)
plot_latest(eki)
end
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] | 2.032238 | 6,452 |
# make this keyworded?
init(::NEqProblem, ::LineSearch, x) = (z=copy(x), d=copy(x), Fx=copy(x), Jx=x*x')
# the bang is just potentially inplace x and state. nonbang copies these
function solve(problem::NEqProblem, x, method::LineSearch=LineSearch(Newton(), Static(1)), options=NEqOptions(), state=init(problem, method, x))
t0 = time()
# Unpack
scheme, linesearch = modelscheme(method), algorithm(method)
# Unpack important objectives
F = problem.R.F
FJ = problem.R.FJ
# Unpack state
z, d, Fx, Jx = state
T = eltype(Fx)
# Set up MeritObjective. This defines the least squares
# objective for the line search.
merit = MeritObjective(problem, F, FJ, Fx, Jx, d)
meritproblem = OptimizationProblem(merit, nothing, Euclidean(0), nothing, mstyle(problem), nothing)
# Evaluate the residual and Jacobian
Fx, Jx = FJ(Fx, Jx, x)
ρF0, ρ2F0 = norm(Fx, Inf), norm(Fx, 2)
stoptol = T(options.f_reltol)*ρF0 + T(options.f_abstol)
if ρF0 < stoptol
return ConvergenceInfo(method, (solution=x, best_residual=Fx, ρF0=ρF0, ρ2F0=ρ2F0, ρs=T(NaN), iter=0, time=time()-t0), options)
end
# Create variable for norms but keep the first ones for printing purposes.
ρs, ρ2F = ρF0, ρ2F0
iter = 1
while iter ≤ options.maxiter
# Shift z into x
if mstyle isa InPlace
x .= z
else
x = copy(z)
end
# Update the search direction
if mstyle isa InPlace
d = scheme.linsolve(d, Jx, -Fx)
else
d = scheme.linsolve(Jx, -Fx)
end
# Need to restrict to static and backtracking here because we don't allow
# for methods that calculate the gradient of the line objective.
#
# For non-linear systems of equations we choose the sum-of-
# squares merit function. Some useful things to remember is:
#
# f(y) = 1/2*|| F(y) ||^2 =>
# ∇_df = -d'*J(x)'*F(x)
#
# where we remember the notation x means the current iterate and y is any
# proposal. This means that if we step in the Newton direction such that d
# is defined by
#
# J(x)*d = -F(x) => -d'*J(x)' = F(x)' =>
# ∇_df = -F(x)'*F(x) = -f(x)*2
#
# φ = LineObjective!(F, ∇fz, z, x, d, fx, dot(∇fx, d))
φ = LineObjective(meritproblem, z, z, x, d, (ρ2F^2)/2, -ρ2F^2)
# Perform line search along d
α, f_α, ls_success = find_steplength(mstyle, linesearch, φ, T(1))
# # Calculate final step vector and update the state
# Step in the direction α*d
z = retract(problem, z, x, d, α)
# Update residual and jacobian
Fx, Jx = FJ(Fx, Jx, z)
# Update 2-norm for line search conditions: ϕ(0) and ϕ'(0)
ρ2F = norm(Fx, 2)
# Update the largest successive change in the iterate
ρs = mapreduce(x->abs(x[1]-x[2]), max, zip(x,z)) # norm(x.-z, Inf)
if ρ2F < stoptol || ρs <= 1e-12
break
end
iter += 1
end
return ConvergenceInfo(method, (solution=z, best_residual=Fx, ρF0=ρF0, ρ2F0=ρ2F0, ρs=ρs, iter=iter, time=time()-t0), options)
end
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] | 2.122274 | 1,513 |
##### deal with nutrients uptake
function gpu_calc_consume_kernel!(ctsdic, ctsdoc, ctsnh4, ctsno3, ctspo4, plank, ac, x, y, z, ΔT)
index = (blockIdx().x - 1) * blockDim().x + threadIdx().x
stride = blockDim().x * gridDim().x
for i = index:stride:size(ac,1)
@inbounds CUDA.@atomic ctsdic[x[i], y[i], z[i]] += (plank.resp[i] - plank.PS[i]) * ΔT * ac[i]
@inbounds CUDA.@atomic ctsdoc[x[i], y[i], z[i]] += (plank.exu[i] - plank.VDOC[i]) * ΔT * ac[i]
@inbounds CUDA.@atomic ctsnh4[x[i], y[i], z[i]] += -plank.VNH4[i] * ΔT * ac[i]
@inbounds CUDA.@atomic ctsno3[x[i], y[i], z[i]] += -plank.VNO3[i] * ΔT * ac[i]
@inbounds CUDA.@atomic ctspo4[x[i], y[i], z[i]] += -plank.VPO4[i] * ΔT * ac[i]
end
return nothing
end
function calc_consume!(ctsdic, ctsdoc, ctsnh4, ctsno3, ctspo4, plank, ac, x, y, z, ΔT, ::GPU)
@cuda threads=256 blocks=ceil(Int, size(ac,1)/256) gpu_calc_consume_kernel!(ctsdic, ctsdoc, ctsnh4,
ctsno3, ctspo4, plank, ac, x, y, z, ΔT)
return nothing
end
function calc_consume!(ctsdic, ctsdoc, ctsnh4, ctsno3, ctspo4, plank, ac, x, y, z, ΔT, ::CPU)
for i in 1:size(ac,1)
@inbounds ctsdic[x[i], y[i], z[i]] += (plank.resp[i] - plank.PS[i]) * ΔT * ac[i]
@inbounds ctsdoc[x[i], y[i], z[i]] += (plank.exu[i] - plank.VDOC[i]) * ΔT * ac[i]
@inbounds ctsnh4[x[i], y[i], z[i]] += -plank.VNH4[i] * ΔT * ac[i]
@inbounds ctsno3[x[i], y[i], z[i]] += -plank.VNO3[i] * ΔT * ac[i]
@inbounds ctspo4[x[i], y[i], z[i]] += -plank.VPO4[i] * ΔT * ac[i]
end
return nothing
end
##### deal with grazed or dead individuals
function gpu_calc_loss_kernel!(ctsdoc, ctspoc, ctsdon, ctspon, ctsdop, ctspop, plank, ac, x, y, z,
loss, lossFracC, lossFracN, lossFracP, R_NC, R_PC)
index = (blockIdx().x - 1) * blockDim().x + threadIdx().x
stride = blockDim().x * gridDim().x
for i = index:stride:size(ac,1)
@inbounds CUDA.@atomic ctsdoc[x[i], y[i], z[i]] += (plank.Bm[i] + plank.Cq[i]) * lossFracC * ac[i] * loss[i]
@inbounds CUDA.@atomic ctsdon[x[i], y[i], z[i]] += (plank.Bm[i]*R_NC + plank.Nq[i]) * lossFracN * ac[i] * loss[i]
@inbounds CUDA.@atomic ctsdop[x[i], y[i], z[i]] += (plank.Bm[i]*R_PC + plank.Pq[i]) * lossFracP * ac[i] * loss[i]
@inbounds CUDA.@atomic ctspoc[x[i], y[i], z[i]] += (plank.Bm[i] + plank.Cq[i]) * (1.0-lossFracC) * ac[i] * loss[i]
@inbounds CUDA.@atomic ctspon[x[i], y[i], z[i]] += (plank.Bm[i]*R_NC + plank.Nq[i]) * (1.0-lossFracN) * ac[i] * loss[i]
@inbounds CUDA.@atomic ctspop[x[i], y[i], z[i]] += (plank.Bm[i]*R_PC + plank.Pq[i]) * (1.0-lossFracP) * ac[i] * loss[i]
end
return nothing
end
function calc_loss!(ctsdoc, ctspoc, ctsdon, ctspon, ctsdop, ctspop, plank, ac, x, y, z,
loss, lossFracC, lossFracN, lossFracP, R_NC, R_PC, ::GPU)
@cuda threads=256 blocks=ceil(Int, size(ac,1)/256) gpu_calc_loss_kernel!(ctsdoc, ctspoc, ctsdon, ctspon,
ctsdop, ctspop, plank, ac, x, y, z, loss,
lossFracC, lossFracN, lossFracP, R_NC, R_PC)
return nothing
end
function calc_loss!(ctsdoc, ctspoc, ctsdon, ctspon, ctsdop, ctspop, plank, ac, x, y, z,
loss, lossFracC, lossFracN, lossFracP, R_NC, R_PC, ::CPU)
for i in 1:size(ac,1)
@inbounds ctsdoc[x[i], y[i], z[i]] += (plank.Bm[i] + plank.Cq[i]) * lossFracC * ac[i] * loss[i]
@inbounds ctsdon[x[i], y[i], z[i]] += (plank.Bm[i]*R_NC + plank.Nq[i]) * lossFracN * ac[i] * loss[i]
@inbounds ctsdop[x[i], y[i], z[i]] += (plank.Bm[i]*R_PC + plank.Pq[i]) * lossFracP * ac[i] * loss[i]
@inbounds ctspoc[x[i], y[i], z[i]] += (plank.Bm[i] + plank.Cq[i]) * (1.0-lossFracC) * ac[i] * loss[i]
@inbounds ctspon[x[i], y[i], z[i]] += (plank.Bm[i]*R_NC + plank.Nq[i]) * (1.0-lossFracN) * ac[i] * loss[i]
@inbounds ctspop[x[i], y[i], z[i]] += (plank.Bm[i]*R_PC + plank.Pq[i]) * (1.0-lossFracP) * ac[i] * loss[i]
end
return nothing
end | [
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] | 1.742367 | 2,391 |
function centering!(mechanism::Mechanism, αaff::T) where T
system = mechanism.system
n = 0
ν = 0.0
νaff = 0.0
for contact in mechanism.contacts
ν, νaff, n = centering!(ν, νaff, n, mechanism, contact, get_entry(system, contact.id), αaff)
end
for joint in mechanism.joints
ν, νaff, n = centering!(ν, νaff, n, mechanism, joint, get_entry(system, joint.id), αaff)
end
ν /= n
νaff /= n
return ν, νaff
end
function centering!(ν, νaff, n, mechanism, contact::ContactConstraint{T,N,Nc,Cs,N½}, vector_entry::Entry, αaff) where {T,N,Nc,Cs,N½}
s = contact.impulses_dual[2]
γ = contact.impulses[2]
Δs = vector_entry.value[1:N½]
Δγ = vector_entry.value[N½ .+ (1:N½)]
ν += dot(s, γ)
νaff += dot(s + αaff * Δs, γ + αaff * Δγ) # plus or minus
n += cone_degree(contact)
return ν, νaff, n
end
function centering!(ν, νaff, n, mechanism, joint::JointConstraint{T,N,Nc}, vector_entry::Entry, αaff) where {T,N,Nc}
for (i, element) in enumerate([joint.translational, joint.rotational])
s, γ = split_impulses(element, joint.impulses[2][joint_impulse_index(joint,i)])
Δs, Δγ = split_impulses(element, vector_entry.value[joint_impulse_index(joint,i)])
ν += dot(s, γ)
νaff += dot(s + αaff * Δs, γ + αaff * Δγ) # plus or minus
n += length(s)
end
return ν, νaff, n
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] | 2.087218 | 665 |
global const ev2K = 1.160451812e4
global const K2eV = 1.0/1.160451812e4
global const kB = 1.38066e-16
"""
part_weight_one(N::Integer)
Equivalent to no weighting. Returns an Array of ones.
"""
function part_weight_one(N::Integer)
return ones(N)
end
"""
part_weight_physical(N::Integer, par::mappingParameters)
Physical weighting function in units of [cm/pix].
"""
function part_weight_physical(N::Integer, par::mappingParameters, x_cgs::Real=3.085678e21)
return ones(N) .* par.pixelSideLength .* x_cgs
end
"""
part_weight_emission(rho::Array{<:Real}, T_K::Array{<:Real})
Emission weighted mapping. Takes density in internal untis and temperature in K and computes weights.
"""
function part_weight_emission(rho::Array{<:Real}, T_K::Array{<:Real})
return @. rho^2 * √(T_K)
end
"""
part_weight_spectroscopic(rho::Array{<:Real}, T_K::Array{<:Real})
Spectroscopic weighted mapping from Mazotta+ 04. Takes density and temperature and computes weights.
"""
function part_weight_spectroscopic(rho::Array{<:Real}, T_K::Array{<:Real})
return @. rho^2 * T_K^(0.75 - 1.5)
end
"""
part_weight_XrayBand(T_K::Array{<:Real}, Emin::Real, Emax::Real)
Computes Xray weighted emission of a defined energy band. Emin and Emax are energies in eV.
"""
function part_weight_XrayBand(T_K::Array{<:Real}, Emin::Real=5.0e4, Emax::Real=1.0e10)
# convert Kelvin to eV
T_eV = T_K .* cgs2eV
@. exp( -Emin / ( kB * T_eV )) - exp( -Emax / ( kB * T_eV ))
end | [
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] | 2.515254 | 590 |
using DataFrames
using NCDatasets
using NetcdfIO
using Test
@testset verbose = true "NetcdfIO Test" begin
@testset "Create" begin
create_nc!("test.nc");
@test true;
create_nc!("test.nc", String["lon", "lat", "ind"], [36, 18, 0]);
@test true;
create_nc!("test.nc", String["lon", "lat", "ind"], [36, 18, Inf]);
@test true;
rm("test.nc"; force=true);
end;
@testset "Add dim" begin
create_nc!("test.nc");
add_nc_dim!("test.nc", "dim1", 0);
@test true;
add_nc_dim!("test.nc", "dim2", 10);
@test true;
@info "Expecting a warning here!";
add_nc_dim!("test.nc", "dim2", 10);
@test true;
add_nc_dim!("test.nc", "dim3", 10.0);
@test true;
add_nc_dim!("test.nc", "dim4", Inf);
@test true;
_dset = Dataset("test.nc", "a");
add_nc_dim!(_dset, "dim5", 0);
@test true;
add_nc_dim!(_dset, "dim6", 10);
@test true;
add_nc_dim!(_dset, "dim7", 10.0);
@test true;
add_nc_dim!(_dset, "dim8", Inf);
@test true;
close(_dset);
rm("test.nc"; force=true);
end;
@testset "Append" begin
create_nc!("test.nc", String["lon", "lat", "ind"], [36, 18, 5]);
_dset = Dataset("test.nc", "a");
append_nc!(_dset, "str", ["A" for i in 1:18], Dict("longname" => "test strings"), ["lat"]);
@test true;
append_nc!(_dset, "lat", collect(1:18), Dict("longname" => "latitude"), ["lat"]);
@test true;
append_nc!(_dset, "lon", collect(1:36), Dict("longname" => "longitude"), ["lon"]; compress=4);
@test true;
append_nc!(_dset, "ind", collect(1:5), Dict("longname" => "index"), ["ind"]);
@test true;
append_nc!(_dset, "d2d", rand(36,18), Dict("longname" => "a 2d dataset"), ["lon", "lat"]);
@test true;
append_nc!(_dset, "d3d", rand(36,18,5), Dict("longname" => "a 3d dataset"), ["lon", "lat", "ind"]);
@test true;
close(_dset);
create_nc!("test.nc", String["lon", "lat", "ind"], [36, 18, 5]);
append_nc!("test.nc", "str", ["A" for i in 1:18], Dict("longname" => "test strings"), ["lat"]);
@test true;
append_nc!("test.nc", "lat", collect(1:18), Dict("longname" => "latitude"), ["lat"]);
@test true;
append_nc!("test.nc", "lon", collect(1:36), Dict("longname" => "longitude"), ["lon"]; compress=4);
@test true;
append_nc!("test.nc", "ind", collect(1:5), Dict("longname" => "index"), ["ind"]);
@test true;
append_nc!("test.nc", "d2d", rand(36,18), Dict("longname" => "a 2d dataset"), ["lon", "lat"]);
@test true;
append_nc!("test.nc", "d3d", rand(36,18,5), Dict("longname" => "a 3d dataset"), ["lon", "lat", "ind"]);
@test true;
rm("test.nc"; force=true);
end;
@testset "Grow" begin
create_nc!("test.nc", String["lon", "lat", "ind"], [36, 18, 0]);
_dset = Dataset("test.nc", "a");
append_nc!(_dset, "lat", collect(1:18), Dict("longname" => "latitude"), ["lat"]);
append_nc!(_dset, "lon", collect(1:36), Dict("longname" => "longitude"), ["lon"]; compress=4);
append_nc!(_dset, "ind", collect(1:5), Dict("longname" => "index"), ["ind"]);
append_nc!(_dset, "d2d", rand(36,5), Dict("longname" => "a 2d dataset"), ["lon", "ind"]);
append_nc!(_dset, "d3d", rand(36,18,5), Dict("longname" => "a 3d dataset"), ["lon", "lat", "ind"]);
grow_nc!(_dset, "ind", 6, true);
@test true;
grow_nc!(_dset, "ind", 6, false);
@test true;
grow_nc!(_dset, "ind", [8,9], true);
@test true;
grow_nc!(_dset, "ind", [10,11], false);
@test true;
grow_nc!(_dset, "d2d", rand(36), true);
@test true;
grow_nc!(_dset, "d2d", rand(36), false);
@test true;
grow_nc!(_dset, "d2d", rand(36,2), true);
@test true;
grow_nc!(_dset, "d2d", rand(36,2), false);
@test true;
grow_nc!(_dset, "d3d", rand(36,18), true);
@test true;
grow_nc!(_dset, "d3d", rand(36,18), false);
@test true;
grow_nc!(_dset, "d3d", rand(36,18, 2), true);
@test true;
grow_nc!(_dset, "d3d", rand(36,18, 2), false);
@test true;
close(_dset);
grow_nc!("test.nc", "ind", 15, true);
@test true;
grow_nc!("test.nc", "d3d", rand(36,18), false);
@test true;
# do not delete the file so as to test other functions
# rm("test.nc"; force=true);
end;
@testset "Info" begin
@test dimname_nc("test.nc") == ["lon", "lat", "ind"];
@test varname_nc("test.nc") == ["lat", "lon", "ind", "d2d", "d3d"];
@test size_nc("test.nc", "d2d") == (2, (36,15));
@test size_nc("test.nc", "d3d") == (3, (36,18,15));
# do not delete the file so as to test other functions
# rm("test.nc"; force=true);
end;
@testset "Read" begin
read_nc(Float32, "test.nc", "d2d");
@test true;
read_nc(Float32, "test.nc", "d2d"; transform=false);
@test true;
read_nc(Float32, "test.nc", "d3d");
@test true;
read_nc(Float32, "test.nc", "d3d"; transform=false);
@test true;
read_nc(Float32, "test.nc", "lat", 1);
@test true;
read_nc(Float32, "test.nc", "lat", 1; transform=false);
@test true;
read_nc(Float32, "test.nc", "d3d", 1);
@test true;
read_nc(Float32, "test.nc", "d3d", 1; transform=false);
@test true;
read_nc(Float32, "test.nc", "d2d", 1, 1);
@test true;
read_nc(Float32, "test.nc", "d2d", 1, 1; transform=false);
@test true;
read_nc(Float32, "test.nc", "d3d", 1, 1);
@test true;
read_nc(Float32, "test.nc", "d3d", 1, 1; transform=false);
@test true;
read_nc(Float32, "test.nc", "d3d", 1, 1, 1);
@test true;
read_nc(Float32, "test.nc", "d3d", 1, 1, 1; transform=false);
@test true;
append_nc!("test.nc", "A", collect(1:15), Dict("longname" => "DataFrame A"), ["ind"]);
append_nc!("test.nc", "B", collect(1:15), Dict("longname" => "DataFrame B"), ["ind"]);
append_nc!("test.nc", "C", collect(1:15), Dict("longname" => "DataFrame C"), ["ind"]);
read_nc("test.nc", ["A", "B", "C"]);
@test true;
read_nc("test.nc", ["A", "B", "C"]; transform=false);
@test true;
rm("test.nc"; force=true);
end;
@testset "Save" begin
data1 = rand(12) .+ 273.15;
data2 = rand(36,18) .+ 273.15;
data3 = rand(36,18,12) .+ 273.15;
save_nc!("data1.nc", "data1", data1, Dict("description" => "Random temperature", "unit" => "K"));
@test true;
save_nc!("data2.nc", "data2", data2, Dict("description" => "Random temperature", "unit" => "K"));
@test true;
save_nc!("data3.nc", "data3", data3, Dict("description" => "Random temperature", "unit" => "K"));
@test true;
_df = DataFrame();
_df[!,"A"] = rand(5);
_df[!,"B"] = rand(5);
_df[!,"C"] = rand(5);
save_nc!("dataf.nc", _df, ["A","B"], [Dict("A" => "Attribute A"), Dict("B" => "Attribute B")]);
@test true;
save_nc!("datag.nc", _df);
@test true;
rm("data1.nc"; force=true);
rm("data2.nc"; force=true);
rm("data3.nc"; force=true);
rm("dataf.nc"; force=true);
rm("datag.nc"; force=true);
end;
end;
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] | 1.966006 | 3,883 |
@testset "DictChain" begin
@testset "Matching keys and value types for get()/set() methods" begin
dc1 = DictChain{Int,String}()
@test_throws KeyError dc1[:NotThere]
@test_throws MethodError (dc1[:NotThere] = 3)
@test_throws MethodError (dc1[:NotThere] = "abc")
@test_throws MethodError (dc1[3] = 3)
dc1[3] = "abc"
@test dc1[3] == "abc"
end
@testset "merging and chaining" begin
dc1 = DictChain{Symbol,String}()
# incompatible dictionary types
@testset "incompatible dictionary types" begin
dc2 = DictChain{Int,String}()
@test_throws MethodError chain(dc1, dc2)
@test_throws MethodError merge(dc1, dc2)
@test_throws MethodError merge!(dc1, dc2)
dc3 = DictChain{Symbol,Int}()
@test_throws MethodError chain(dc1, dc3)
@test_throws MethodError merge(dc1, dc3)
@test_throws MethodError merge!(dc1, dc3)
end
d1 = Dict{Symbol,Int}(:a => 1)
d2 = Dict{Symbol,Int}(:a => 2, :b => 4)
d3 = Dict{Symbol,Int}(:b => 3, :c => 5)
@testset "using constructor" begin
dc = DictChain(d1, d2, d3)
@test typeof(dc) == DictChain{Symbol,Int}
@test (dc[:a], dc[:b], dc[:c]) == (1, 4, 5)
dc = DictChain(d2, d1, d3)
@test (dc[:a], dc[:b], dc[:c]) == (2, 4, 5)
dc = DictChain(DictChain(d3, d1), d2)
@test (dc[:a], dc[:b], dc[:c]) == (1, 3, 5)
end
@testset "using merge()" begin
dc = merge(DictChain(d2, d1), d3)
@test (dc[:a], dc[:b], dc[:c]) == (2, 3, 5)
dc = merge(d2, DictChain(d3, d1))
@test (dc[:a], dc[:b], dc[:c]) == (1, 3, 5)
dc = merge(DictChain(d1, d3), d2)
@test (dc[:a], dc[:b], dc[:c]) == (2, 4, 5)
end
@testset "using merge!()" begin
dc = DictChain{Int,String}()
@test_throws MethodError merge!(dc, d1) # incompatible
dc = DictChain{Symbol,Int}()
merge!(dc, d1)
@test dc[:a] == 1
@test_throws KeyError dc[:b]
merge!(dc, d2)
@test (dc[:a], dc[:b]) == (2, 4)
merge!(dc, d3)
@test (dc[:a], dc[:b], dc[:c]) == (2, 3, 5)
end
@testset "using chain()" begin
dc = chain(chain(d1, d2), d3)
@test typeof(dc) == DictChain{Symbol,Int}
@test (dc[:a], dc[:b], dc[:c]) == (2, 3, 5)
dc = chain(d2, chain(d1, d3))
@test (dc[:a], dc[:b], dc[:c]) == (1, 3, 5)
dc = chain(chain(d3, d1), d2)
@test (dc[:a], dc[:b], dc[:c]) == (2, 4, 5)
dc = chain(d1, d2, d3)
@test (dc[:a], dc[:b], dc[:c]) == (2, 3, 5)
dc = chain(d3, d1, d2)
@test (dc[:a], dc[:b], dc[:c]) == (2, 4, 5)
end
@testset "Check chaining from real use case" begin
# This sequence is used in compare_optimizers.jl:repeated_bboptimize
parameters = BlackBoxOptim.EMPTY_PARAMS
ftol = 1e-5
params = chain(parameters, ParamsDict(:FitnessTolerance => ftol))
@test params[:FitnessTolerance] == ftol
end
end
@testset "converting to Dict" begin
d1 = Dict{Symbol,Int}(:a => 1)
d2 = Dict{Symbol,Int}(:a => 2, :b => 4)
d3 = Dict{Symbol,Int}(:a => 3, :b => 5)
dc = DictChain(d1, d2, d3)
d123 = convert(Dict{Symbol,Int}, dc)
@test typeof(d123) == Dict{Symbol,Int}
@test length(d123) == 2
@test d123[:a] == 1
@test d123[:b] == 4
end
@testset "show()" begin
d1 = Dict{Symbol,Int}(:a => 1)
d2 = Dict{Symbol,Int}(:a => 2, :b => 4)
d3 = Dict{Symbol,Int}(:a => 3, :b => 5)
dc = DictChain(d1, d2, d3)
iob = IOBuffer()
show(iob, dc)
@test replace(String(take!(iob)), ' '=>"") == "BlackBoxOptim.DictChain{Symbol,$Int}[Dict(:a=>1),Dict(:a=>2,:b=>4),Dict(:a=>3,:b=>5)]"
end
@testset "flatten" begin
d1 = Dict{Symbol,Int}(:a => 1)
d2 = Dict{Symbol,Int}(:a => 2, :b => 4)
d3 = Dict{Symbol,Int}(:a => 3, :b => 5)
dc = DictChain(d1, d2, d3)
fd = flatten(dc)
@test fd[:a] == 1
@test fd[:b] == 4
@test sort(collect(keys(fd))) == [:a, :b]
end
end
@testset "Parameters" begin
@testset "When no parameters or key type doesn't match" begin
ps = ParamsDictChain()
@test isa(ps, Parameters)
@test_throws KeyError ps[:NotThere]
@test_throws KeyError ps["Neither there"]
ps[:a] = 1
@test ps[:a] == 1
end
@testset "With one parameter in one set" begin
ps = ParamsDictChain(ParamsDict(:a => 1))
@test isa(ps, Parameters)
@test ps[:a] == 1
@test_throws KeyError ps["a"] # incorrect key
@test_throws KeyError ps[:A]
@test_throws KeyError ps[:B]
end
@testset "With parameters in multiple sets" begin
ps = ParamsDictChain(ParamsDict(:a => 1, :c => 4),
ParamsDict(:a => 2, :b => 3),
ParamsDict(:c => 5))
@test isa(ps, Parameters)
@test ps[:a] == 1
@test ps[:c] == 4
@test ps[:b] == 3
@test_throws KeyError ps[:A]
@test_throws KeyError ps[:B]
end
@testset "Updating parameters after construction" begin
ps = ParamsDictChain(ParamsDict(:a => 1, :c => 4),
ParamsDict(:a => 2, :b => 3),
ParamsDict(:c => 5))
ps[:c] = 6
ps[:b] = 7
@test ps[:a] == 1
@test ps[:c] == 6
@test ps[:b] == 7
end
@testset "Constructing from another parameters object" begin
ps1 = ParamsDictChain(ParamsDict(:a => 1, :c => 4),
ParamsDict(:a => 2, :b => 3))
ps2 = ParamsDictChain(ParamsDict(:a => 5), ps1,
ParamsDict(:c => 6))
@test ps1[:a] == 1
@test ps2[:a] == 5
@test ps2[:c] == 4
end
@testset "Get key without default" begin
ps = ParamsDictChain(ParamsDict(:a => 1, :c => 4),
ParamsDict(:a => 2, :b => 3))
@test get(ps, :a) == 1
@test get(ps, :b) == 3
@test get(ps, :d) == nothing
end
@testset "Get key with default" begin
ps = ParamsDictChain(ParamsDict(:a => 1, :c => 4),
ParamsDict(:a => 2, :b => 3))
@test get(ps, :d, 10) == 10
end
@testset "Merge with Parameters or Dict" begin
ps = ParamsDictChain(ParamsDict(:a => 1, :c => 4),
ParamsDict(:a => 2, :b => 3))
ps2 = chain(ps, ParamsDict(:d => 5, :a => 20))
@test ps2[:d] == 5
@test ps2[:b] == 3
@test ps2[:a] == 20
end
end
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] | 1.779102 | 3,943 |
nz=43; nx=35
ny=randn(nz, nx);
my1=GeoPhyInv.get_rhovxI(ny)
my2=GeoPhyInv.get_rhovzI(ny)
myp1 = randn(size(my1));
myp2 = randn(size(my2));
# spray
nyp=zeros(nz, nx);
GeoPhyInv.grad_modrr_sprayrr!(nyp, myp1, myp2)
GeoPhyInv.grad_modrr_sprayrrvx!(nyp, myp1)
GeoPhyInv.grad_modrr_sprayrrvz!(nyp, myp2)
# dot product test
@test LinearAlgebra.dot(vcat(my1,my2), vcat(myp1,myp2)) ≈ LinearAlgebra.dot(ny, nyp)
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] | 1.769231 | 234 |
using Test
using DFTK
using IntervalArithmetic
include("testcases.jl")
function discretized_hamiltonian(T, testcase)
Ecut = 10 # Hartree
spec = ElementPsp(testcase.atnum, psp=load_psp(testcase.psp))
atoms = [spec => testcase.positions]
# disable symmetry for interval
model = model_DFT(Array{T}(testcase.lattice), atoms, [:lda_x, :lda_c_vwn], symmetry=:off)
# For interval arithmetic to give useful numbers,
# the fft_size should be a power of 2
fft_size = nextpow.(2, determine_grid_size(model.lattice, Ecut))
basis = PlaneWaveBasis(model, Ecut, kgrid=(1, 1, 1), fft_size=fft_size)
ham = Hamiltonian(basis; ρ=guess_density(basis))
end
@testset "Application of an LDA Hamiltonian with Intervals" begin
T = Float64
ham = discretized_hamiltonian(T, silicon)
hamInt = discretized_hamiltonian(Interval{T}, silicon)
hamk = ham.blocks[1]
hamIntk = hamInt.blocks[1]
x = randn(Complex{T}, length(G_vectors(ham.basis.kpoints[1])))
ref = hamk * x
res = hamIntk * Interval.(x)
# Difference between interval arithmetic and normal application less than 1e-10
@test maximum(mid, abs.(res .- ref)) < 1e-10
# Maximal error done by interval arithmetic less than
@test maximum(radius, abs.(res)) < 1e-10
end
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] | 2.611336 | 494 |
#! /usr/bin/env julia
println("Start test 'tests/loadmodel.jl'")
include("../src/DCellC.jl")
using DCellC
model = UNetLike(GreyscaleImage, bn = true)
println("Test loadmodel.jl: Created model successfully")
mname = tempname()
modelsave(mname, model)
println("Test loadmodel.jl: Saved model successfully")
model2 = modelload(mname)
@assert weights(model) == weights(model2)
#@assert state(model) == state(model2)
println("Test loadmodel.jl: Loaded model sucessfully")
println("Test loadmodel.jl completed")
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] | 2.95977 | 174 |
type Arc
initNode::Int
termNode::Int
capacity::Float64
freeflowtime::Float64
flow::Float64
end
Arc(initNode::Int, termNode::Int, capacity::Float64, freeflowtime::Float64) =
Arc(initNode, termNode, capacity, freeflowtime, 0.)
## Solve an inverse tarffic problem over polynomials of degree at most d
## Optionally use a regularizer from the poly kernel
using JuMP
using Gurobi
using Graphs
using Roots
polyEval(coeffs, pt) = sum([coeffs[i] * pt^(i-1) for i = 1:length(coeffs)])
polyEval(coeffs::Array{Float64, 1}, pt) = sum([coeffs[i] * pt^(i-1) for i = 1:length(coeffs)])
bpacost(flow::Float64, capacity::Float64, freeflowtime::Float64) = freeflowtime*(1 + .15 * (flow/capacity)^4)
bpacost(flow::Float64, arc) = bpacost(flow, arc.capacity, arc.freeflowtime)
bpacost(arc::Arc) = bpacost(arc.flow, arc)
function setUpFitting(deg::Int, c::Float64)
m = Model(solver=GurobiSolver(OutputFlag=false))
@defVar(m, coeffs[1:deg+1])
@defVar(m, Calphas[1:deg+1])
#build the graham matrix; cf. Ref. [21] (Regularization Networks and Support Vector Machines), page 47
samples = linspace(0, 1, deg + 1)
k(x,y) = (c + x*y)^deg
K = [ k(x,y) for x = samples, y=samples]
K = convert(Array{Float64, 2}, K)
#assert(rank(K) == deg+1)
C = chol(K + 1e-6* eye(deg+1))
for i=1:deg + 1
@addConstraint(m, polyEval(coeffs, samples[i]) == sum{C[j, i] * Calphas[j], j=1:deg+1})
end
@defVar(m, reg_term >= 0)
reg_term_ = QuadExpr(Calphas[:], Calphas[:], ones(deg+1), AffExpr())
@addConstraint(m, reg_term >= reg_term_)
return m, coeffs, reg_term
end
function fixCoeffs(m, fcoeffs, coeffs)
for (fc, c) in zip(fcoeffs, coeffs[:])
@addConstraint(m, fc == c)
end
end
function addResid(m, coeffs, ys, demands, arcs, scaling)
@defVar(m, resid)
@defVar(m, dual_cost)
@defVar(m, primal_cost)
@addConstraint(m, dual_cost == sum{demands[(s,t)] * (ys[(s,t), t] - ys[(s,t), s]), (s,t)=keys(demands)})
@addConstraint(m, primal_cost == sum{a.flow * a.freeflowtime * polyEval(coeffs, a.flow/a.capacity), a=values(arcs)})
@addConstraint(m, resid >= (dual_cost - primal_cost) / scaling )
@addConstraint(m, resid >= (primal_cost - dual_cost) / scaling )
return resid
end
function addIncreasingCnsts(m, coeffs, arcs; TOL=0.)
sorted_flows = sort([a.flow / a.capacity for a in values(arcs)])
@addConstraint(m, polyEval(coeffs, 0) <= polyEval(coeffs, sorted_flows[1]))
for i = 2:length(sorted_flows)
@addConstraint(m, polyEval(coeffs, sorted_flows[i-1]) <= polyEval(coeffs, sorted_flows[i]) + TOL)
end
@addConstraint(m, coeffs[1] == 1)
end
#equates the total cost of the network to the true total cost
function normalize(m, coeffs, tot_true_cost::Float64, arcs)
@addConstraint(m,
sum{a.freeflowtime * a.flow * polyEval(coeffs, a.flow / a.capacity), a=values(arcs)} == tot_true_cost)
end
function normalize(m, coeffs, scaled_flow::Float64, cost::Float64)
@addConstraint(m, polyEval(coeffs, scaled_flow) == cost)
end
function normalize(m, coeffs, scaled_flows::Array{Float64, 1}, avgCost::Float64)
@addConstraint(m, sum{polyEval(coeffs, f), f=scaled_flows} == avgCost * length(scaled_flows))
end
function addNetworkCnsts(m, coeffs, demands, arcs, numNodes)
@defVar(m, ys[keys(demands), 1:numNodes])
for k = keys(arcs)
a = arcs[k]
rhs = a.freeflowtime * polyEval(coeffs, a.flow/a.capacity)
for od in keys(demands)
@addConstraint(m, ys[od, k[2]] - ys[od, k[1]] <= rhs)
end
end
return ys
end
############
#Read in the demand file
using PyCall
unshift!(PyVector(pyimport("sys")["path"]), "");
@pyimport parameters_julia
out_dir = parameters_julia.out_dir
files_ID = parameters_julia.files_ID
month_w = parameters_julia.month_w
year = parameters_julia.year
instances_ = parameters_julia.instances_ID
instance = readstring(out_dir * "instance_comm.txt")
file = open(out_dir * "data_traffic_assignment_uni-class/" * files_ID * "_trips_" * month_w * "_" * instance * ".txt")
demands = Dict{(Int64,Int64), Float64}()
s = 0
for line in eachline(file)
if contains(line, "Origin")
s = int(split(line)[2])
else
pairs = split(line, ";")
for pair in pairs
if !contains(pair, "\n")
pair_vals = split(pair, ":")
t, demand = int(pair_vals[1]), float(pair_vals[2])
demands[(s,t)] = demand
end
end
end
end
close(file)
############
#read in the arc files
arcs = Dict{(Int, Int), Arc}()
file = open(out_dir * "data_traffic_assignment_uni-class/" * files_ID * "_net_" * month_w * "_" * instance * ".txt")
inHeader=true
for line in eachline(file)
if inHeader
inHeader = !contains(line, "Init node")
continue
end
vals = split(line, )
arcs[(int(vals[1]), int(vals[2]))] = Arc(int(vals[1]), int(vals[2]), float(vals[3]), float(vals[5]))
end
close(file)
#=
###########
#read in the initial flows
file = open("../data_original/SiouxFallsFlow.txt")
ix = 0;
for line in eachline(file)
ix +=1
if ix ==1
continue
end
vals = split(line)
arcs[(int(vals[1]), int(vals[2]))].flow = float(vals[3])
end
close(file)
=#
##########
# Set up demand data and flow data
##########
flow_data = Array(Float64, length(arcs))
flows = Dict{(Int64,Int64), Float64}()
demand_data = Dict{(Int, Int), Array{Float64, 1}}()
numNodes = maximum(map(pair->pair[1], keys(demands)))
g = simple_inclist(numNodes, is_directed=true)
vArcs = Arc[]
for arc in values(arcs)
add_edge!(g, arc.initNode, arc.termNode)
push!(vArcs, arc)
end
for odpair in keys(demands)
if ! haskey(demand_data, odpair)
demand_data[odpair] = [demands[odpair], ]
else
push!(demand_data[odpair], demands[odpair])
end
end
flow_data = [a.flow::Float64 for a in vArcs]
for a in vArcs
flows[(a.initNode, a.termNode)] = a.flow
end
#flows
using JSON
#load node-link incidence
nodeLink = readall("node_link_incidence_Sioux.json");
nodeLink = JSON.parse(nodeLink);
link_label_dict = readall("link_label_dict_Sioux.json");
link_label_dict = JSON.parse(link_label_dict);
link_label_dict["1"]
int(split(link_label_dict["1"], ',')[1]), int(split(link_label_dict["1"], ',')[2])
flows[int(split(link_label_dict["1"], ',')[1]), int(split(link_label_dict["1"], ',')[2])]
#string(1)
function addResid_(m, coeffs, ys, demands_, demands, arcs, scaling)
@defVar(m, resid)
@defVar(m, dual_cost)
@defVar(m, primal_cost)
for (s,t)=keys(demands)
@addConstraint(m, demands_[(s,t)] >= 0)
end
@addConstraint(m, dual_cost == sum{demands_[(s,t)] * (ys[(s,t), t] - ys[(s,t), s]), (s,t)=keys(demands)})
@addConstraint(m, primal_cost == sum{a.flow * a.freeflowtime * polyEval(coeffs, a.flow/a.capacity), a=values(arcs)})
@addConstraint(m, resid >= (dual_cost - primal_cost) / scaling )
@addConstraint(m, resid >= (primal_cost - dual_cost) / scaling )
return resid
end
##########
#Fitting Funcs
##########
function train_cy(lam::Float64, deg::Int, c::Float64, demands, flow_data, arcs; fcoeffs=nothing)
numNodes = maximum(map(pair->pair[1], keys(arcs)))
m, coeffs, reg_term = setUpFitting(deg, c)
addIncreasingCnsts(m, coeffs, arcs, TOL=1e-8) #uses the original obs flows
avgCost = mean( [bpacost(a.flow, a.capacity, 1.0) for a in values(arcs)] )
normalize(m, coeffs, [a.flow / a.capacity for a in values(arcs)], avgCost)
resids = Variable[]
#copy the flow data over to the arcs
for (ix, a) in enumerate(vArcs)
a.flow = flow_data[ix]
end
#Dual Feasibility
ys = addNetworkCnsts(m, coeffs, demands, arcs, numNodes)
#add the residual for this data point
push!(resids, addResid(m, coeffs, ys, demands, arcs, 1e6))
if fcoeffs != nothing
fixCoeffs(m, fcoeffs, coeffs)
end
@setObjective(m, Min, sum{resids[i], i = 1:length(resids)} + lam*reg_term)
solve(m)
return [getValue(coeffs[i]) for i =1:length(coeffs)], getValue(ys), getValue(resids)
end
#nodeLink["0-75"]
#demands
outfile = open("demands_Sioux.json", "w")
JSON.print(outfile, demands)
close(outfile)
##########
#Fitting Funcs
##########
function train_cd(lam::Float64, deg::Int, c::Float64, ys, flow_data, flows, nodeLink, arcs; fcoeffs=nothing)
numNodes = maximum(map(pair->pair[1], keys(arcs)))
m, coeffs, reg_term = setUpFitting(deg, c)
addIncreasingCnsts(m, coeffs, arcs, TOL=1e-8) #uses the original obs flows
avgCost = mean( [bpacost(a.flow, a.capacity, 1.0) for a in values(arcs)] )
normalize(m, coeffs, [a.flow / a.capacity for a in values(arcs)], avgCost)
resids = Variable[]
#copy the flow data over to the arcs
for (ix, a) in enumerate(vArcs)
a.flow = flow_data[ix]
end
for k = keys(arcs)
a = arcs[k]
rhs = a.freeflowtime * polyEval(coeffs, a.flow/a.capacity)
for od in keys(demands)
@addConstraint(m, ys[od, k[2]] - ys[od, k[1]] <= rhs)
end
end
@defVar(m, demands_[keys(demands)])
for (s,t) = keys(demands)
@addConstraint(m, demands_[(s,t)] >= 0)
@addConstraint(m, demands_[(s,t)] - demands[(s,t)] <= demands[(s,t)] * 0.05)
@addConstraint(m, demands[(s,t)] - demands_[(s,t)] <= demands[(s,t)] * 0.05)
end
numLinks = length(flows) # number of links
@defVar(m, odLinkFlow[keys(demands), 1:numLinks])
for (s,t) = keys(demands)
for k = 1:numLinks
@addConstraint(m, odLinkFlow[(s,t), k] >= 0)
end
end
for k = 1:numLinks
linkFlowPartial = sum([odLinkFlow[(s,t), k] for (s,t) = keys(demands)])
key = (int(split(link_label_dict["$(k-1)"], ',')[1]), int(split(link_label_dict["$(k-1)"], ',')[2]))
@addConstraint(m, linkFlowPartial == flows[key])
end
for l = 1:numNodes
for (s,t) = keys(demands)
if s == t
@addConstraint(m, demands_[(s,t)] == 0)
else
odLinkFlowPartial = sum([nodeLink["$(l-1)-$(k-1)"] * odLinkFlow[(s,t), k] for k = 1:numLinks])
if (l == s)
@addConstraint(m, odLinkFlowPartial + demands_[(s,t)] == 0)
elseif (l == t)
@addConstraint(m, odLinkFlowPartial - demands_[(s,t)] == 0)
else
@addConstraint(m, odLinkFlowPartial == 0)
end
end
end
end
#add the residual for this data point
push!(resids, addResid_(m, coeffs, ys, demands_, demands, arcs, 1e6))
if fcoeffs != nothing
fixCoeffs(m, fcoeffs, coeffs)
end
@setObjective(m, Min, sum{resids[i], i = 1:length(resids)} + lam*reg_term)
solve(m)
return [getValue(coeffs[i]) for i =1:length(coeffs)], getValue(demands_), getValue(resids)
end
#demands[(1, 1)]
#include("trafficCval.jl")
coeffs_dict = Dict{(Int64,Float64,Float64),Array{Float64,1}}()
deg = 6
c = 3.41
lam = 1.
demands_0 = copy(demands)
fcoeffs, ys, resids = train_cy(lam, deg, c, demands_0, flow_data, arcs)
coeffs_dict[(deg, c, lam)] = fcoeffs
fcoeffs, demands_, resides_ = train_cd(lam, deg, c, ys, flow_data, flows, nodeLink, arcs)
for (s,t) = keys(demands)
demands_0[(s,t)] = demands_[(s,t)]
end
coeffs_dict[(deg, c, lam)] = fcoeffs
using PyPlot
true_coeffs = [1, 0, 0, 0, .15]
fcoeffs = coeffs_dict[(6, 3.41, 1.)]
xs = linspace(0, 2, 20)
zs_true = map(x->polyEval(true_coeffs, x), xs)
zs = map(x->polyEval(fcoeffs, x), xs)
plot(xs, zs_true, "s-g", label="True")
plot(xs, zs, "^-m", label="deg=6")
legend(loc="upper left",fancybox="true")
grid("on")
xlim(-0.1, 1.6);
ylim(0.9, 2.0);
font1 = ["family"=>"serif","color"=>"darkred","weight"=>"normal","size"=>14]
xlabel("Scaled Flow", fontdict=font1)
ylabel("Scaled Cost", fontdict=font1)
savefig("fitting_Sioux.pdf")
#demands_0
#demands
# based on https://github.com/chkwon/TrafficAssignment.jl
include("load_network_uni-class.jl")
using Graphs
function create_graph(start_node, end_node)
@assert Base.length(start_node)==Base.length(end_node)
no_node = max(maximum(start_node), maximum(end_node))
no_arc = Base.length(start_node)
graph = simple_inclist(no_node)
for i=1:no_arc
add_edge!(graph, start_node[i], end_node[i])
end
return graph
end
function get_vector(state, origin, destination, link_dic)
current = destination
parent = -1
x = zeros(Int, maximum(link_dic))
while parent != origin
parent = state.parents[current]
link_idx = link_dic[parent,current]
if link_idx != 0
x[link_idx] = 1
end
current = parent
end
return x
end
ta_data = load_ta_network("Sioux Falls");
# ta_data.travel_demand;
# unpacking data from ta_data
network_name = ta_data.network_name
number_of_zones = ta_data.number_of_zones
number_of_nodes = ta_data.number_of_nodes
first_thru_node = ta_data.first_thru_node
number_of_links = ta_data.number_of_links
start_node = ta_data.start_node
end_node = ta_data.end_node
capacity = ta_data.capacity
link_length = ta_data.link_length
free_flow_time = ta_data.free_flow_time
B = ta_data.B
power = ta_data.power
speed_limit = ta_data.speed_limit
toll = ta_data.toll
link_type = ta_data.link_type
number_of_zones = ta_data.number_of_zones
total_od_flow = ta_data.total_od_flow
# travel_demand = ta_data.travel_demand
od_pairs = ta_data.od_pairs
toll_factor = ta_data.toll_factor
distance_factor = ta_data.distance_factor
best_objective = ta_data.best_objective
travel_demand = zeros(24, 24)
for (s,t)=keys(demands)
travel_demand[s,t] = demands_0[(s,t)]
end
# preparing a graph
graph = create_graph(start_node, end_node)
link_dic = sparse(start_node, end_node, 1:number_of_links);
function BPR(x)
bpr = similar(x)
for i=1:length(bpr)
bpr[i] = free_flow_time[i] * polyEval(fcoeffs, x[i]/capacity[i])
# bpr[i] = free_flow_time[i] * ( 1.0 + B[i] * (x[i]/capacity[i])^power[i] )
end
return bpr
end
function all_or_nothing(travel_time)
state = []
path = []
x = zeros(size(start_node))
for r=1:size(travel_demand)[1]
# for each origin node r, find shortest paths to all destination nodes
state = dijkstra_shortest_paths(graph, travel_time, r)
for s=1:size(travel_demand)[2]
# for each destination node s, find the shortest-path vector
# load travel demand
x = x + travel_demand[r,s] * get_vector(state, r, s, link_dic)
end
end
return x
end
# Finding a starting feasible solution
travel_time = BPR(zeros(number_of_links))
xl = all_or_nothing(travel_time);
max_iter_no = 1e3
l = 1
#average_excess_cost = 1
tol = 1e-6
while l < max_iter_no
l += 1
xl_old = xl
# Finding yl
travel_time = BPR(xl)
yl = all_or_nothing(travel_time)
assert(yl != xl)
xl = xl + (yl - xl)/l
xl_new = xl
relative_gap = norm(xl_new - xl_old, 1) / norm(xl_new, 1)
# if l % 500 == 0
# print("l = $l------------------------------------------------\n")
# print("relative_gap is $relative_gap\n")
# end
if relative_gap < tol
print("l = $l------------------------------------------------\n")
print("relative_gap is $relative_gap\n")
break
end
end
tapFlows = Dict{(Int64,Int64),Float64}()
for i = 1:length(ta_data.start_node)
key = (ta_data.start_node[i], ta_data.end_node[i])
tapFlows[key] = xl[i]
end
#tapFlows
#flows
#fcoeffs
function sa(x, a) # calculate the partial derivatives of c_a w.r.t. x_a
assert(a <= length(x) && a >= 1)
n = length(fcoeffs)
dcdx = 0
for i=2:n
dcdx += (i-1) * fcoeffs[i] * (x[a]/capacity[a])^(i-2)
end
dcdx *= free_flow_time[a]/capacity[a]
return dcdx
end
x = zeros(size(start_node))
for k = 1:length(x)
key = (int(split(link_label_dict["$(k-1)"], ',')[1]), int(split(link_label_dict["$(k-1)"], ',')[2]))
x[k] = tapFlows[key]
end
x
## Obtain $\left( {\frac{{\partial {c_a}\left( {{g^l}} \right)}}{{\partial {v_a}}};a \in \mathcal{A}} \right)$
saVec = similar(x)
for a = 1:length(x)
saVec[a] = sa(x, a)
end
# saVec[1:5]
#load OD pair-route incidence
odPairRoute = readall("od_pair_route_incidence_Sioux.json");
odPairRoute = JSON.parse(odPairRoute);
#load link-route incidence
linkRoute = readall("link_route_incidence_Sioux.json");
linkRoute = JSON.parse(linkRoute);
#load OD pair labels
odPairLabel = readall("od_pair_label_dict_Sioux.json");
odPairLabel = JSON.parse(odPairLabel);
odPairLabel_ = readall("od_pair_label_dict__Sioux.json");
odPairLabel_ = JSON.parse(odPairLabel_);
# express the demand data as vector (array)
demandsVec = zeros(length(demands))
for i = 1:length(demandsVec)
demandsVec[i] = demands_0[(odPairLabel_["$i"][1], odPairLabel_["$i"][2])]
end
demandsVec[1:5]
# convert the demand data into dictionary
demandsDict = similar(demands)
for key = keys(demands)
demandsDict[key] = demandsVec[odPairLabel["($(key[1]), $(key[2]))"]]
end
# "1-200" in keys(odPairRoute)
numLinks = size(start_node)[1]
numRoutes = length(odPairRoute)
numODpairs = numNodes * (numNodes - 1)
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45,
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1635,
357,
22510,
45,
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352,
8,
198
] | 2.201513 | 7,801 |
include("../src/PersistenceDiagram.jl") | [
17256,
7203,
40720,
10677,
14,
30946,
13274,
18683,
6713,
13,
20362,
4943
] | 3.25 | 12 |
using Trilinos
using Test
using MPI
if Test.get_testset_depth() == 0
MPI.Init()
end
comm = Teuchos.MpiComm(MPI.CComm(MPI.COMM_WORLD))
const n = 20
rowmap0 = Tpetra.Map(n, 0, comm)
@test Tpetra.getIndexBase(rowmap0) == 0
rowmap1 = Tpetra.Map(n, 1, comm)
@test Tpetra.getIndexBase(rowmap1) == 1
if Test.get_testset_depth() == 0
MPI.Finalize()
end
| [
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833,
346,
11996,
198,
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198,
3500,
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40,
198,
198,
361,
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13,
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62,
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2617,
62,
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657,
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220,
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13,
19006,
1096,
3419,
198,
437,
198
] | 2.191358 | 162 |
# ----------------------------------------------------------------------------
# This file was autogenerated by /dev/parse-SeeK.jl.
# It contains data included in the SeeK-path package (github.com/giovannipizzi/seekpath).
# The SeeK-path package is licensed under the MIT license, a copy of which is
# included below.
# ----------------------------------------------------------------------------
const pathsd_3d = Dict(
:hR1 => [[:Γ, :T, :H₂], [:H₀, :L, :Γ, :S₀], [:S₂, :F, :Γ]],
:tI2 => [[:Γ, :X, :P, :N, :Γ, :M, :S], [:S₀, :Γ], [:X, :R], [:G, :M]],
:tI1 => [[:Γ, :X, :M, :Γ, :Z], [:Z₀, :M], [:X, :P, :N, :Γ]],
:cP1 => [[:Γ, :X, :M, :Γ, :R, :X], [:R, :M, :X₁]],
:oC2 => [[:Γ, :Y, :F₀], [:Δ₀, :Γ, :Z, :B₀], [:G₀, :T, :Y], [:Γ, :S, :R, :Z, :T]],
:oI3 => [[:Γ, :X, :F₀], [:Σ₀, :Γ, :Λ₀], [:G₀, :X], [:Γ, :R, :W, :S, :Γ, :T, :W]],
:oP1 => [[:Γ, :X, :S, :Y, :Γ, :Z, :U, :R, :T, :Z], [:X, :U], [:Y, :T], [:S, :R]],
:oA1 => [[:Γ, :Y, :C₀], [:Σ₀, :Γ, :Z, :A₀], [:E₀, :T, :Y], [:Γ, :S, :R, :Z, :T]],
:mC1 => [[:Γ, :C], [:C₂, :Y₂, :Γ, :M₂, :D], [:D₂, :A, :Γ], [:L₂, :Γ, :V₂]],
:oI2 => [[:Γ, :X, :U₂], [:Y₀, :Γ, :Λ₀], [:G₂, :X], [:Γ, :R, :W, :S, :Γ, :T, :W]],
:hP1 => [[:Γ, :M, :K, :Γ, :A, :L, :H, :A], [:L, :M], [:H, :K, :H₂]],
:tP1 => [[:Γ, :X, :M, :Γ, :Z, :R, :A, :Z], [:X, :R], [:M, :A]],
:cI1 => [[:Γ, :H, :N, :Γ, :P, :H], [:P, :N]],
:mC3 => [[:Γ, :A, :I₂], [:I, :M₂, :Γ, :Y], [:L₂, :Γ, :V₂]],
:hP2 => [[:Γ, :M, :K, :Γ, :A, :L, :H, :A], [:L, :M], [:H, :K]],
:hR2 => [[:Γ, :L, :T, :P₀], [:P₂, :Γ, :F]],
:cF1 => [[:Γ, :X, :U], [:K, :Γ, :L, :W, :X, :W₂]],
:oA2 => [[:Γ, :Y, :F₀], [:Δ₀, :Γ, :Z, :B₀], [:G₀, :T, :Y], [:Γ, :S, :R, :Z, :T]],
:cP2 => [[:Γ, :X, :M, :Γ, :R, :X], [:R, :M]],
:aP2 => [[:Γ, :X], [:Y, :Γ, :Z], [:R, :Γ, :T], [:U, :Γ, :V]],
:oF1 => [[:Γ, :Y, :T, :Z, :Γ, :Σ₀], [:U₀, :T], [:Y, :C₀], [:A₀, :Z], [:Γ, :L]],
:oC1 => [[:Γ, :Y, :C₀], [:Σ₀, :Γ, :Z, :A₀], [:E₀, :T, :Y], [:Γ, :S, :R, :Z, :T]],
:aP3 => [[:Γ, :X], [:Y, :Γ, :Z], [:R₂, :Γ, :T₂], [:U₂, :Γ, :V₂]],
:mC2 => [[:Γ, :Y, :M, :A, :Γ], [:L₂, :Γ, :V₂]],
:oI1 => [[:Γ, :X, :F₂], [:Σ₀, :Γ, :Y₀], [:U₀, :X], [:Γ, :R, :W, :S, :Γ, :T, :W]],
:mP1 => [[:Γ, :Z, :D, :B, :Γ, :A, :E, :Z, :C₂, :Y₂, :Γ]],
:cF2 => [[:Γ, :X, :U], [:K, :Γ, :L, :W, :X]],
:oF2 => [[:Γ, :T, :Z, :Y, :Γ, :Λ₀], [:Q₀, :Z], [:T, :G₀], [:H₀, :Y], [:Γ, :L]],
:oF3 => [[:Γ, :Y, :C₀], [:A₀, :Z, :B₀], [:D₀, :T, :G₀], [:H₀, :Y], [:T, :Γ, :Z], [:Γ, :L]],
)
const pointsd_3d = Dict(
:hR1 => [:Γ => :([0, 0, 0]), :T => :([1 / 2, 1 / 2, 1 / 2]), :L => :([1 / 2, 0, 0]), :F => :([1 / 2, 0, 1 / 2]), :S₀ => :([N, -N, 0]), :S₂ => :([1 - N, 0, N]), :H₀ => :([1 / 2, -1 + Y, 1 - Y]), :H₂ => :([Y, 1 - Y, 1 / 2])],
:tI2 => [:Γ => :([0, 0, 0]), :M => :([1 / 2, 1 / 2, -1 / 2]), :X => :([0, 0, 1 / 2]), :P => :([1 / 4, 1 / 4, 1 / 4]), :N => :([0, 1 / 2, 0]), :S₀ => :([-H, H, H]), :S => :([H, 1 - H, -H]), :R => :([-Z, Z, 1 / 2]), :G => :([1 / 2, 1 / 2, -Z])],
:tI1 => [:Γ => :([0, 0, 0]), :M => :([-1 / 2, 1 / 2, 1 / 2]), :X => :([0, 0, 1 / 2]), :P => :([1 / 4, 1 / 4, 1 / 4]), :Z => :([H, H, -H]), :Z₀ => :([-H, 1 - H, H]), :N => :([0, 1 / 2, 0])],
:cP1 => [:Γ => :([0, 0, 0]), :R => :([1 / 2, 1 / 2, 1 / 2]), :M => :([1 / 2, 1 / 2, 0]), :X => :([0, 1 / 2, 0]), :X₁ => :([1 / 2, 0, 0])],
:oC2 => [:Γ => :([0, 0, 0]), :Y => :([1 / 2, 1 / 2, 0]), :T => :([1 / 2, 1 / 2, 1 / 2]), :Z => :([0, 0, 1 / 2]), :S => :([0, 1 / 2, 0]), :R => :([0, 1 / 2, 1 / 2]), :Δ₀ => :([-X, X, 0]), :F₀ => :([X, 1 - X, 0]), :B₀ => :([-X, X, 1 / 2]), :G₀ => :([X, 1 - X, 1 / 2])],
:oI3 => [:Γ => :([0, 0, 0]), :X => :([1 / 2, -1 / 2, 1 / 2]), :S => :([1 / 2, 0, 0]), :R => :([0, 1 / 2, 0]), :T => :([0, 0, 1 / 2]), :W => :([1 / 4, 1 / 4, 1 / 4]), :Σ₀ => :([-Y, Y, Y]), :F₀ => :([Y, -Y, 1 - Y]), :Λ₀ => :([Z, Z, -Z]), :G₀ => :([1 - Z, -Z, Z])],
:oP1 => [:Γ => :([0, 0, 0]), :X => :([1 / 2, 0, 0]), :Z => :([0, 0, 1 / 2]), :U => :([1 / 2, 0, 1 / 2]), :Y => :([0, 1 / 2, 0]), :S => :([1 / 2, 1 / 2, 0]), :T => :([0, 1 / 2, 1 / 2]), :R => :([1 / 2, 1 / 2, 1 / 2])],
:oA1 => [:Γ => :([0, 0, 0]), :Y => :([-1 / 2, 1 / 2, 0]), :T => :([-1 / 2, 1 / 2, 1 / 2]), :Z => :([0, 0, 1 / 2]), :S => :([0, 1 / 2, 0]), :R => :([0, 1 / 2, 1 / 2]), :Σ₀ => :([X, X, 0]), :C₀ => :([-X, 1 - X, 0]), :A₀ => :([X, X, 1 / 2]), :E₀ => :([-X, 1 - X, 1 / 2])],
:mC1 => [:Γ => :([0, 0, 0]), :Y₂ => :([-1 / 2, 1 / 2, 0]), :A => :([0, 0, 1 / 2]), :M₂ => :([-1 / 2, 1 / 2, 1 / 2]), :V₂ => :([0, 1 / 2, 0]), :L₂ => :([0, 1 / 2, 1 / 2]), :C => :([1 - S, 1 - S, 0]), :C₂ => :([-1 + S, S, 0]), :D => :([-1 + P, P, 1 / 2]), :D₂ => :([1 - P, 1 - P, 1 / 2])],
:oI2 => [:Γ => :([0, 0, 0]), :X => :([-1 / 2, 1 / 2, 1 / 2]), :S => :([1 / 2, 0, 0]), :R => :([0, 1 / 2, 0]), :T => :([0, 0, 1 / 2]), :W => :([1 / 4, 1 / 4, 1 / 4]), :Y₀ => :([Z, -Z, Z]), :U₂ => :([-Z, Z, 1 - Z]), :Λ₀ => :([H, H, -H]), :G₂ => :([-H, 1 - H, H])],
:hP1 => [:Γ => :([0, 0, 0]), :A => :([0, 0, 1 / 2]), :K => :([1 / 3, 1 / 3, 0]), :H => :([1 / 3, 1 / 3, 1 / 2]), :H₂ => :([1 / 3, 1 / 3, -1 / 2]), :M => :([1 / 2, 0, 0]), :L => :([1 / 2, 0, 1 / 2])],
:tP1 => [:Γ => :([0, 0, 0]), :Z => :([0, 0, 1 / 2]), :M => :([1 / 2, 1 / 2, 0]), :A => :([1 / 2, 1 / 2, 1 / 2]), :R => :([0, 1 / 2, 1 / 2]), :X => :([0, 1 / 2, 0])],
:cI1 => [:Γ => :([0, 0, 0]), :H => :([1 / 2, -1 / 2, 1 / 2]), :P => :([1 / 4, 1 / 4, 1 / 4]), :N => :([0, 0, 1 / 2])],
:mC3 => [:Γ => :([0, 0, 0]), :Y => :([1 / 2, 1 / 2, 0]), :A => :([0, 0, 1 / 2]), :M₂ => :([-1 / 2, 1 / 2, 1 / 2]), :V₂ => :([0, 1 / 2, 0]), :L₂ => :([0, 1 / 2, 1 / 2]), :I => :([-1 + R, R, 1 / 2]), :I₂ => :([1 - R, 1 - R, 1 / 2])],
:hP2 => [:Γ => :([0, 0, 0]), :A => :([0, 0, 1 / 2]), :K => :([1 / 3, 1 / 3, 0]), :H => :([1 / 3, 1 / 3, 1 / 2]), :M => :([1 / 2, 0, 0]), :L => :([1 / 2, 0, 1 / 2])],
:hR2 => [:Γ => :([0, 0, 0]), :T => :([1 / 2, -1 / 2, 1 / 2]), :P₀ => :([H, -1 + H, H]), :P₂ => :([H, H, H]), :L => :([1 / 2, 0, 0]), :F => :([1 / 2, -1 / 2, 0])],
:cF1 => [:Γ => :([0, 0, 0]), :X => :([1 / 2, 0, 1 / 2]), :L => :([1 / 2, 1 / 2, 1 / 2]), :W => :([1 / 2, 1 / 4, 3 / 4]), :W₂ => :([3 / 4, 1 / 4, 1 / 2]), :K => :([3 / 8, 3 / 8, 3 / 4]), :U => :([5 / 8, 1 / 4, 5 / 8])],
:oA2 => [:Γ => :([0, 0, 0]), :Y => :([1 / 2, 1 / 2, 0]), :T => :([1 / 2, 1 / 2, 1 / 2]), :Z => :([0, 0, 1 / 2]), :S => :([0, 1 / 2, 0]), :R => :([0, 1 / 2, 1 / 2]), :Δ₀ => :([-X, X, 0]), :F₀ => :([X, 1 - X, 0]), :B₀ => :([-X, X, 1 / 2]), :G₀ => :([X, 1 - X, 1 / 2])],
:cP2 => [:Γ => :([0, 0, 0]), :R => :([1 / 2, 1 / 2, 1 / 2]), :M => :([1 / 2, 1 / 2, 0]), :X => :([0, 1 / 2, 0])],
:aP2 => [:Γ => :([0, 0, 0]), :Z => :([0, 0, 1 / 2]), :Y => :([0, 1 / 2, 0]), :X => :([1 / 2, 0, 0]), :V => :([1 / 2, 1 / 2, 0]), :U => :([1 / 2, 0, 1 / 2]), :T => :([0, 1 / 2, 1 / 2]), :R => :([1 / 2, 1 / 2, 1 / 2])],
:oF1 => [:Γ => :([0, 0, 0]), :T => :([1, 1 / 2, 1 / 2]), :Z => :([1 / 2, 1 / 2, 0]), :Y => :([1 / 2, 0, 1 / 2]), :Σ₀ => :([0, H, H]), :U₀ => :([1, 1 - H, 1 - H]), :A₀ => :([1 / 2, 1 / 2 + J, J]), :C₀ => :([1 / 2, 1 / 2 - J, 1 - J]), :L => :([1 / 2, 1 / 2, 1 / 2])],
:oC1 => [:Γ => :([0, 0, 0]), :Y => :([-1 / 2, 1 / 2, 0]), :T => :([-1 / 2, 1 / 2, 1 / 2]), :Z => :([0, 0, 1 / 2]), :S => :([0, 1 / 2, 0]), :R => :([0, 1 / 2, 1 / 2]), :Σ₀ => :([X, X, 0]), :C₀ => :([-X, 1 - X, 0]), :A₀ => :([X, X, 1 / 2]), :E₀ => :([-X, 1 - X, 1 / 2])],
:aP3 => [:Γ => :([0, 0, 0]), :Z => :([0, 0, 1 / 2]), :Y => :([0, 1 / 2, 0]), :X => :([1 / 2, 0, 0]), :V₂ => :([1 / 2, -1 / 2, 0]), :U₂ => :([-1 / 2, 0, 1 / 2]), :T₂ => :([0, -1 / 2, 1 / 2]), :R₂ => :([-1 / 2, -1 / 2, 1 / 2])],
:mC2 => [:Γ => :([0, 0, 0]), :Y => :([1 / 2, 1 / 2, 0]), :A => :([0, 0, 1 / 2]), :M => :([1 / 2, 1 / 2, 1 / 2]), :V₂ => :([0, 1 / 2, 0]), :L₂ => :([0, 1 / 2, 1 / 2])],
:oI1 => [:Γ => :([0, 0, 0]), :X => :([1 / 2, 1 / 2, -1 / 2]), :S => :([1 / 2, 0, 0]), :R => :([0, 1 / 2, 0]), :T => :([0, 0, 1 / 2]), :W => :([1 / 4, 1 / 4, 1 / 4]), :Σ₀ => :([-Z, Z, Z]), :F₂ => :([Z, 1 - Z, -Z]), :Y₀ => :([H, -H, H]), :U₀ => :([1 - H, H, -H])],
:mP1 => [:Γ => :([0, 0, 0]), :Z => :([0, 1 / 2, 0]), :B => :([0, 0, 1 / 2]), :Y₂ => :([-1 / 2, 0, 0]), :C₂ => :([-1 / 2, 1 / 2, 0]), :D => :([0, 1 / 2, 1 / 2]), :A => :([-1 / 2, 0, 1 / 2]), :E => :([-1 / 2, 1 / 2, 1 / 2])],
:cF2 => [:Γ => :([0, 0, 0]), :X => :([1 / 2, 0, 1 / 2]), :L => :([1 / 2, 1 / 2, 1 / 2]), :W => :([1 / 2, 1 / 4, 3 / 4]), :K => :([3 / 8, 3 / 8, 3 / 4]), :U => :([5 / 8, 1 / 4, 5 / 8])],
:oF2 => [:Γ => :([0, 0, 0]), :T => :([0, 1 / 2, 1 / 2]), :Z => :([1 / 2, 1 / 2, 1]), :Y => :([1 / 2, 0, 1 / 2]), :Λ₀ => :([K, K, 0]), :Q₀ => :([1 - K, 1 - K, 1]), :G₀ => :([1 / 2 - J, 1 - J, 1 / 2]), :H₀ => :([1 / 2 + J, J, 1 / 2]), :L => :([1 / 2, 1 / 2, 1 / 2])],
:oF3 => [:Γ => :([0, 0, 0]), :T => :([0, 1 / 2, 1 / 2]), :Z => :([1 / 2, 1 / 2, 0]), :Y => :([1 / 2, 0, 1 / 2]), :A₀ => :([1 / 2, 1 / 2 + H, H]), :C₀ => :([1 / 2, 1 / 2 - H, 1 - H]), :B₀ => :([1 / 2 + K, 1 / 2, K]), :D₀ => :([1 / 2 - K, 1 / 2, 1 - K]), :G₀ => :([P, 1 / 2 + P, 1 / 2]), :H₀ => :([1 - P, 1 / 2 - P, 1 / 2]), :L => :([1 / 2, 1 / 2, 1 / 2])],
)
const paramsd_3d = Dict(
:hR1 => [:D => :((((a * a) / 4) / c) / c), :Y => :(5 / 6 - 2D), :N => :(1 / 3 + D)],
:tI2 => [:H => :((1 + ((a * a) / c) / c) / 4), :Z => :((((a * a) / 2) / c) / c)],
:tI1 => [:H => :((1 + ((c * c) / a) / a) / 4)],
:oC2 => [:X => :((1 + ((b * b) / a) / a) / 4)],
:oI3 => [:Z => :((1 + ((c * c) / b) / b) / 4), :Y => :((1 + ((a * a) / b) / b) / 4), :D => :((((a * a - c * c) / 4) / b) / b), :M => :((((c * c + a * a) / 4) / b) / b)],
:oA1 => [:X => :((1 + ((b * b) / c) / c) / 4)],
:mC1 => [:Z => :((((2 + (a / c) * cosβ) / 4) / sinβ) / sinβ), :H => :(1 / 2 - (2 * Z * c * cosβ) / a), :S => :(3 / 4 - (((((b * b) / 4) / a) / a) / sinβ) / sinβ), :P => :(S - ((3 / 4 - S) * a * cosβ) / c)],
:oI2 => [:Z => :((1 + ((b * b) / a) / a) / 4), :H => :((1 + ((c * c) / a) / a) / 4), :D => :((((c * c - b * b) / 4) / a) / a), :N => :((((b * b + c * c) / 4) / a) / a)],
:mC3 => [:Z => :((((a * a) / b) / b + ((1 + (a / c) * cosβ) / sinβ) / sinβ) / 4), :R => :(1 - ((Z * b * b) / a) / a), :E => :(1 / 2 - (2 * Z * c * cosβ) / a), :F => :(E / 2 + (((a * a) / 4) / b) / b + (((a * c * cosβ) / 2) / b) / b), :U => :(2F - Z), :W => :((((c / 2) / a) / cosβ) * ((1 - 4U) + ((a * a * sinβ * sinβ) / b) / b)), :D => :((-1 / 4 + W / 2) - (Z * c * cosβ) / a)],
:hR2 => [:Z => :(1 / 6 - (((c * c) / 9) / a) / a), :H => :(1 / 2 - 2Z), :N => :(1 / 2 + Z)],
:oA2 => [:X => :((1 + ((c * c) / b) / b) / 4)],
:oF1 => [:J => :(((1 + ((a * a) / b) / b) - ((a * a) / c) / c) / 4), :H => :((1 + ((a * a) / b) / b + ((a * a) / c) / c) / 4)],
:oC1 => [:X => :((1 + ((a * a) / b) / b) / 4)],
:mC2 => [:Z => :((((a * a) / b) / b + ((1 + (a / c) * cosβ) / sinβ) / sinβ) / 4), :M => :((1 + ((a * a) / b) / b) / 4), :D => :((((-a * c * cosβ) / 2) / b) / b), :X => :(1 / 2 - (2 * Z * c * cosβ) / a), :P => :((1 + Z) - 2M), :S => :(X - 2D)],
:oI1 => [:Z => :((1 + ((a * a) / c) / c) / 4), :H => :((1 + ((b * b) / c) / c) / 4), :D => :((((b * b - a * a) / 4) / c) / c), :N => :((((a * a + b * b) / 4) / c) / c)],
:mP1 => [:Y => :((((1 + (a / c) * cosβ) / 2) / sinβ) / sinβ), :N => :(1 / 2 + (Y * c * cosβ) / a)],
:oF2 => [:J => :(((1 + ((c * c) / a) / a) - ((c * c) / b) / b) / 4), :K => :((1 + ((c * c) / a) / a + ((c * c) / b) / b) / 4)],
:oF3 => [:H => :(((1 + ((a * a) / b) / b) - ((a * a) / c) / c) / 4), :K => :(((1 + ((b * b) / a) / a) - ((b * b) / c) / c) / 4), :P => :(((1 + ((c * c) / b) / b) - ((c * c) / a) / a) / 4)],
)
# ------------------ COPY OF GIOVANNIPIZZI/SEEKPATH LICENSE ------------------
# The MIT License (MIT)
#
# Copyright (c), 2016-2018, Giovanni Pizzi, ECOLE POLYTECHNIQUE FEDERALE DE
# LAUSANNE (Theory and Simulation of Materials (THEOS) and National Centre for
# Computational Design and Discovery of Novel Materials (NCCR MARVEL)),
# Switzerland. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
# ---
#
# For a list of open-source software included in this repository, see the file
# open_source_linceses.txt.
# ---------------------------------------------------------------------------- | [
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#############################################################################
# ECOS.jl
# Wrapper around the ECOS solver https://github.com/ifa-ethz/ecos
# See http://github.com/JuliaOpt/ECOS.jl
#############################################################################
# ECOSSolverInterface.jl
# MathProgBase.jl interface for the ECOS.jl solver wrapper
#############################################################################
require(joinpath(Pkg.dir("MathProgBase"),"src","MathProgSolverInterface.jl"))
importall MathProgSolverInterface
#############################################################################
# Define the MPB Solver and Model objects
export ECOSSolver
immutable ECOSSolver <: AbstractMathProgSolver
end
type ECOSMathProgModel <: AbstractMathProgModel
nvar::Int # Number of variables
nineq::Int # Number of inequalities Gx <=_K h
neq::Int # Number of equalities Ax = b
npos::Int # Number of ???
ncones::Int # Number of SO cones
conedims::Vector{Int} # ?
G::SparseMatrixCSC{Float64,Int} # The G matrix (inequalties)
A::SparseMatrixCSC{Float64,Int} # The A matrix (equalities)
c::Vector{Float64} # The objective coeffs (always min)
orig_sense::Symbol # Original objective sense
h::Vector{Float64} # RHS for inequality
b::Vector{Float64} # RHS for equality
# Post-solve
solve_stat::Symbol
obj_val::Float64
primal_sol::Vector{Float64}
fwd_map::Vector{Int} # To reorder solution if we solved
end # using the conic interface
ECOSMathProgModel() = ECOSMathProgModel(0,0,0,0,0,
Int[],
spzeros(0,0),
spzeros(0,0),
Float64[], :Min,
Float64[], Float64[],
:NotSolved, 0.0, Float64[], Int[])
#############################################################################
# Begin implementation of the MPB low-level interface
# Implements
# - model
# - loadproblem!
# - optimize!
# - status
# http://mathprogbasejl.readthedocs.org/en/latest/lowlevel.html
model(s::ECOSSolver) = ECOSMathProgModel()
# Loads the provided problem data to set up the linear programming problem:
# min c'x
# st lb <= Ax <= ub
# l <= x <= u
# where sense = :Min or :Max
function loadproblem!(m::ECOSMathProgModel, A, collb, colub, obj, rowlb, rowub, sense)
(nvar = length(collb)) == length(colub) || error("Unequal lengths for column bounds")
(nrow = length(rowlb)) == length(rowub) || error("Unequal lengths for row bounds")
# Turn variable bounds into constraints
# Inefficient, because keeps allocating memory!
# Would need to batch, get tricky...
for j = 1:nvar
if collb[j] != -Inf
# Variable has lower bound
newrow = zeros(1, nvar)
newrow[j] = -1.0
A = vcat(A, newrow)
rowlb = vcat(rowlb, -Inf)
rowub = vcat(rowub, -collb[j])
nrow += 1
end
if colub[j] != +Inf
# Variable has upper bound
newrow = zeros(1, nvar)
newrow[j] = 1.0
A = vcat(A, newrow)
rowlb = vcat(rowlb, -Inf)
rowub = vcat(rowub, colub[j])
nrow += 1
end
end
eqidx = Int[] # Equality row indicies
ineqidx = Int[] # Inequality row indicies
eqbnd = Float64[] # Bounds for equality rows
ineqbnd = Float64[] # Bounds for inequality row
for it in 1:nrow
# Equality constraint
if rowlb[it] == rowub[it]
push!(eqidx, it)
push!(eqbnd, rowlb[it])
# Range constraint - not supported
elseif rowlb[it] != -Inf && rowub[it] != Inf
error("Ranged constraints unsupported!")
# Less-than constraint
elseif rowlb[it] == -Inf
push!(ineqidx, it)
push!(ineqbnd, rowub[it])
# Greater-than constraint - flip sign so only have <= constraints
else
push!(ineqidx, it)
push!(ineqbnd, -rowlb[it])
A[it,:] *= -1 # flip signs so we have Ax<=b
end
end
m.nvar = nvar # Number of variables
m.nineq = length(ineqidx) # Number of inequalities Gx <=_K h
m.neq = length(eqidx) # Number of equalities Ax = b
m.npos = length(ineqidx) # Number of ???
m.ncones = 0 # Number of SO cones
m.conedims = Int[] # ???
m.G = sparse(A[ineqidx,:]) # The G matrix (inequalties)
m.A = sparse(A[eqidx,:]) # The A matrix (equalities)
m.c = (sense == :Max) ? obj * -1 : obj[:]
# The objective coeffs (always min)
m.orig_sense = sense # Original objective sense
m.h = ineqbnd # RHS for inequality
m.b = eqbnd # RHS for equality
m.fwd_map = [1:nvar] # Identity mapping
end
function optimize!(m::ECOSMathProgModel)
ecos_prob_ptr = setup(
m.nvar, m.nineq, m.neq,
m.npos, m.ncones, m.conedims,
m.G, m.A,
m.c[:], # Seems to modify this
m.h, m.b)
flag = solve(ecos_prob_ptr)
if flag == ECOS_OPTIMAL
m.solve_stat = :Optimal
elseif flag == ECOS_PINF
m.solve_stat = :Infeasible
elseif flag == ECOS_DINF # Dual infeasible = primal unbounded, probably
m.solve_stat = :Unbounded
elseif flag == ECOS_MAXIT
m.solve_stat = :UserLimit
else
m.solve_stat = :Error
end
# Extract solution
ecos_prob = pointer_to_array(ecos_prob_ptr, 1)[1]
m.primal_sol = pointer_to_array(ecos_prob.x, m.nvar)[:]
m.obj_val = dot(m.c, m.primal_sol) * (m.orig_sense == :Max ? -1 : +1)
cleanup(ecos_prob_ptr, 0)
end
status(m::ECOSMathProgModel) = m.solve_stat
getobjval(m::ECOSMathProgModel) = m.obj_val
getsolution(m::ECOSMathProgModel) = m.primal_sol[m.fwd_map]
#############################################################################
# Begin implementation of the MPB conic interface
# Implements
# - loadconicproblem!
# http://mathprogbasejl.readthedocs.org/en/latest/conic.html
function loadconicproblem!(m::ECOSMathProgModel, c, A, b, cones)
# TODO (if it matters): make this more efficient for sparse A
# We don't support SOCRotated, SDP, or Exp*
bad_cones = [:SOCRotated, :SDP, :ExpPrimal, :ExpDual]
for cone_vars in cones
cone_vars[1] in bad_cones && error("Cone type $(cone_vars[1]) not supported")
end
# MathProgBase form ECOS form
# min c'x min c'x
# st A x = b st A x = b
# x in K h - Gx in K
# Expand out the cones info
# The cones can come in any order, so we need to build a mapping
# from the variable indices in the input to the internal ordering
# we will use.
# In the first past we'll just count up the number of variables
# of each type.
num_vars = 0
for (cone_type, idxs) in cones
num_vars += length(idxs)
end
fwd_map = Array(Int, num_vars) # Will be used for SOCs
rev_map = Array(Int, num_vars) # Need to restore sol. vec.
idxcone = Array(Symbol, num_vars) # We'll uses this for non-SOCs
# Now build the mapping
pos = 1
for (cone, idxs) in cones
for i in idxs
fwd_map[i] = pos # fwd_map = orig idx -> internal idx
rev_map[pos] = i # rev_map = internal idx -> orig idx
idxcone[pos] = cone
pos += 1
end
end
# Rearrange data into the internal ordering
ecos_c = c[rev_map]
ecos_A = A[:,rev_map]
ecos_b = b[:]
# For all variables in the :Zero cone, fix at 0 with an
# equality constraint. TODO: Don't even include them
for j = 1:num_vars
idxcone[j] != :Zero && continue
new_row = zeros(1,num_vars)
new_row[j] = 1.0
ecos_A = vcat(ecos_A, new_row)
ecos_b = vcat(ecos_b, 0.0)
end
# Build G matrix
# There will be one row for every :NonNeg and :NonPos cone
# and an additional row for every variable in a :SOC cone
# Or in other words, everything that isn't a :Free or :Zero
# gets a row in G and h
num_G_row = 0
for j = 1:num_vars
idxcone[j] == :Free && continue
idxcone[j] == :Zero && continue
num_G_row += 1
end
ecos_G = zeros(num_G_row,num_vars)
ecos_h = zeros(num_G_row)
# First, handle the :NonNeg, :NonPos cases
num_pos_orth = 0
G_row = 1
for j = 1:num_vars
if idxcone[j] == :NonNeg
ecos_G[G_row,j] = -1.0
G_row += 1
num_pos_orth += 1
elseif idxcone[j] == :NonPos
ecos_G[G_row,j] = +1.0
G_row += 1
num_pos_orth += 1
end
end
@assert G_row == num_pos_orth + 1
# Now handle the SOCs
# The MPB unput form is basically just says a vector of
# variables (y,x) lives in the SOC || x || <= y
# ECOS wants somethings in the form h - Gx in Q so we
# will prove 0 - Ix \in Q
num_SOC_cones = 0
SOC_conedims = Int[]
for (cone, idxs) in cones
cone != :SOC && continue
# Found a new SOC
num_SOC_cones += 1
push!(SOC_conedims, length(idxs))
# Add the entries (carrying on from pos. orthant)
for j in idxs
ecos_G[G_row,fwd_map[j]] = -1.0
G_row += 1
end
end
@assert G_row == num_G_row + 1
# Store in the ECOS structure
m.nvar = num_vars
m.nineq = num_G_row
m.neq = length(ecos_b)
m.npos = num_pos_orth
m.ncones = num_SOC_cones
m.conedims = SOC_conedims
m.G = ecos_G
m.A = ecos_A
m.c = ecos_c
m.orig_sense = :Min
m.h = ecos_h
m.b = ecos_b
m.fwd_map = fwd_map
end | [
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437
] | 2.026734 | 5,162 |
function vertex_input_attribute_descriptions(::Type{T}, binding, formats=Format.(fieldtypes(T))) where {T}
VertexInputAttributeDescription.(
0:fieldcount(T)-1,
binding,
formats,
fieldoffset.(T, 1:fieldcount(T)),
)
end
Vulkan.VertexInputBindingDescription(::Type{T}, binding; input_rate = VERTEX_INPUT_RATE_VERTEX) where {T} =
VertexInputBindingDescription(binding, sizeof(T), input_rate)
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198
] | 2.577381 | 168 |
let
# fitting noisy data to an exponential model
model(x, p) = @. p[1] * exp(-x * p[2])
#model(x,p) = [p[1],100*(p[2]-p[1]^2)]
# some example data
Random.seed!(12345)
xdata = range(0, stop=10, length=50_000)
ydata = model(xdata, [1.0, 2.0]) + 0.01*randn(length(xdata))
p0 = [.5, .5]
function jacobian_model(x,p)
J = Array{Float64}(undef, length(x), length(p))
@. J[:,1] = exp(-x*p[2]) #dmodel/dp[1]
@. @views J[:,2] = -x*p[1]*J[:,1]
J
end
#Generating the "automatic" avv
model_inplace(out, p) = @. out = p[1] * exp(-xdata * p[2])
hessians = Array{Float64}(undef, length(xdata)*length(p0), length(p0))
h! = make_hessian(model_inplace, xdata, p0)
auto_avv! = Avv(h!, length(p0), length(xdata))
# a couple notes on the Avv function:
# - the basic idea is to see the model output as simply a collection of functions: f1...fm
# - then Avv return an array of size m, where each elements corresponds to
# v'H(p)v, with H an n*n Hessian matrix of the m-th function, with n the size of p
function manual_avv!(dir_deriv,p,v)
v1 = v[1]
v2 = v[2]
for i=1:length(xdata)
#compute all the elements of the H matrix
h11 = 0
h12 = (-xdata[i] * exp(-xdata[i] * p[2]))
#h21 = h12
h22 = (xdata[i]^2 * p[1] * exp(-xdata[i] * p[2]))
# manually compute v'Hv. This whole process might seem cumbersome, but
# allocating temporary matrices quickly becomes REALLY expensive and might even
# render the use of geodesic acceleration terribly inefficient
dir_deriv[i] = h11*v1^2 + 2*h12*v1*v2 + h22*v2^2
end
end
curve_fit(model, jacobian_model, xdata, ydata, p0; maxIter=1); #warmup
curve_fit(model, jacobian_model, xdata, ydata, p0; maxIter=1, avv! = manual_avv!,lambda=0, min_step_quality = 0); #lambda = 0 to match Mark's code
curve_fit(model, jacobian_model, xdata, ydata, p0; maxIter=10, avv! = auto_avv!,lambda=0, min_step_quality = 0)
println("--------------\nPerformance of curve_fit vs geo")
println("\t Non-inplace")
fit = @time curve_fit(model, jacobian_model, xdata, ydata, p0; maxIter=1000)
@test fit.converged
println("\t Geodesic")
fit_geo = @time curve_fit(model, jacobian_model, xdata, ydata, p0; maxIter=10, avv! = manual_avv!,lambda=0, min_step_quality = 0)
@test fit_geo.converged
println("\t Geodesic - auto avv!")
fit_geo_auto = @time curve_fit(model, jacobian_model, xdata, ydata, p0; maxIter=10, avv! = auto_avv!,lambda=0, min_step_quality = 0)
@test fit_geo_auto.converged
@test maximum(abs.(fit.param-[1.0, 2.0])) < 1e-1
@test maximum(abs.(fit.param-fit_geo.param)) < 1e-6
@test maximum(abs.(fit.param-fit_geo_auto.param)) < 1e-6
#with noise
yvars = 1e-6*rand(length(xdata))
ydata = model(xdata, [1.0, 2.0]) + sqrt.(yvars) .* randn(length(xdata))
#warm up
curve_fit(model, jacobian_model, xdata, ydata, 1 ./ yvars, p0; maxIter=1)
curve_fit(model, jacobian_model, xdata, ydata, 1 ./ yvars, p0; maxIter=1, avv! = manual_avv!,lambda=0, min_step_quality = 0)
curve_fit(model, jacobian_model, xdata, ydata, 1 ./ yvars, p0; maxIter=1, avv! = auto_avv!,lambda=0, min_step_quality = 0)
println("--------------\nPerformance of curve_fit vs geo with weights")
println("\t Non-inplace")
fit_wt = @time curve_fit(model, jacobian_model, xdata, ydata, 1 ./ yvars, p0; maxIter=100)
@test fit_wt.converged
println("\t Geodesic")
fit_geo_wt = @time curve_fit(model, jacobian_model, xdata, ydata, 1 ./ yvars, p0; maxIter=100, avv! = manual_avv!,lambda=0, min_step_quality = 0)
@test fit_geo_wt.converged
println("\t Geodesic - auto avv!")
fit_geo_auto_wt = @time curve_fit(model, jacobian_model, xdata, ydata, 1 ./ yvars, p0; maxIter=100, avv! = auto_avv!,lambda=0, min_step_quality = 0)
@test fit_geo_auto_wt.converged
@test maximum(abs.(fit_wt.param-[1.0, 2.0])) < 1e-1
@test maximum(abs.(fit_wt.param-fit_geo_wt.param)) < 1e-6
@test maximum(abs.(fit_wt.param-fit_geo_auto_wt.param)) < 1e-6
end
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] | 2.149594 | 1,972 |
using Catalyst
using PathWeightSampling: ComplexSystem
using StaticArrays
using Test
sn = @reaction_network begin
κ, ∅ --> 2L
λ, L --> ∅
end κ λ
rn = @reaction_network begin
ρ, L + R --> L + LR
μ, LR --> R
ξ, R + CheY --> R + CheYp
ν, CheYp --> CheY
end ρ μ ξ ν
xn = @reaction_network begin
δ, CheYp --> CheYp + X
χ, X --> ∅
end δ χ
u0 = SA[10, 30, 0, 50, 0, 0]
dtimes = 0:0.5:10.0
ps = [5.0, 1.0]
pr = [1.0, 4.0, 1.0, 2.0]
px = [1.0, 1.0]
system = ComplexSystem(sn, rn, xn, u0, ps, pr, px, dtimes)
algorithms = [DirectMCEstimate(128), SMCEstimate(128)]
for algorithm in algorithms
result = mutual_information(system, algorithm, num_samples = 5)
for v in result[!, :MutualInformation]
@test length(v) == length(system.dtimes)
end
end | [
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] | 2.137466 | 371 |
function raster_one_to_all(T, V, cfg)::Matrix{T}
# Load the data
rasterdata = load_raster_data(T, V, cfg)
# Get flags
flags = get_raster_flags(cfg)
# Send to main kernel
onetoall_kernel(rasterdata, flags, cfg)
end
function onetoall_kernel(data::RasData{T,V}, flags, cfg)::Matrix{T} where {T,V}
# Data
strengths = data.strengths
included_pairs = data.included_pairs
points_rc = data.points_rc
gmap = data.cellmap
polymap = data.polymap
hbmeta = data.hbmeta
source_map = data.source_map
# Flags
use_variable_strengths = !isempty(strengths)
use_included_pairs = !isempty(included_pairs)
mode = included_pairs.mode == :include ? 0 : 1
one_to_all = flags.is_onetoall
avg_res = flags.avg_res
four_neighbors = flags.four_neighbors
if use_included_pairs
points_unique = included_pairs.point_ids
prune_points!(points_rc, included_pairs.point_ids)
if use_variable_strengths
prune_strengths!(strengths, included_pairs.point_ids)
end
end
# Construct point map
point_map = zeros(V, size(gmap))
f(i, x) = points_rc[i][x]
for x = 1:size(points_rc[1], 1)
point_map[f(1,x), f(2,x)] = f(3, x)
end
points_unique = unique(points_rc[3])
newpoly = create_new_polymap(gmap, polymap, points_rc, 0, 0, point_map)
nodemap = construct_node_map(gmap, newpoly)
a = construct_graph(gmap, nodemap, avg_res, four_neighbors)
cc = connected_components(SimpleWeightedGraph(a))
G = laplacian(a)
csinfo("There are $(size(a, 1)) points and $(length(cc)) connected components")
# source_map = Matrix{eltype(a)}(0, 0)
# ground_map = Matrix{eltype(a)}(0, 0)
s = zeros(eltype(a), size(point_map))
z = deepcopy(s)
cum = initialize_cum_maps(gmap, flags.outputflags.write_max_cur_maps)
point_ids = included_pairs.point_ids
res = zeros(eltype(a), size(points_unique, 1)) |> SharedArray
num_points_to_solve = size(points_unique, 1)
original_point_map = copy(point_map)
unique_point_map = zeros(V, size(gmap))
for i in points_unique
ind = findfirst(x -> x == i, points_rc[3])
unique_point_map[f(1,ind), f(2,ind)] = f(3,ind)
end
# @distributed for i = 1:num_points_to_solve
function f(i)
# copyto!(point_map, original_point_map)
point_map = copy(original_point_map)
str = use_variable_strengths ? strengths[i,2] : 1
csinfo("Solving point $i of $num_points_to_solve")
# copyto!(s, z)
s = copy(z)
n = points_unique[i]
if use_included_pairs
for j = 1:size(point_ids,1)
if i != j && included_pairs.include_pairs[i,j] == mode
exclude = point_ids[j]
map!(x -> x == exclude ? 0 : x, point_map, point_map)
end
end
# polymap = create_new_polymap(gmap, Polymap(polymap), points_rc, point_map = point_map)
newpoly = create_new_polymap(gmap, polymap, points_rc, 0, 0, point_map)
nodemap = construct_node_map(gmap, polymap)
a = construct_graph(gmap, nodemap, avg_res, four_neighbors)
end
# T = eltype(a)
if one_to_all
#source_map = map(x -> x == n ? str : 0, point_map)
source_map = map(x -> x == n ? T(str) : T(0), unique_point_map)
ground_map = map(x -> x == n ? T(0) : T(x), point_map)
map!(x -> x > 0 ? Inf : x, ground_map, ground_map)
else
source_map = map(x -> x != 0 ? T(x) : T(0), point_map)
map!(x -> x == n ? 0 : x, source_map, source_map)
map!(x -> x != 0 ? 1 : x, source_map, source_map)
ground_map = map(x -> x == n ? Inf : T(0), point_map)
end
check_node = nodemap[points_rc[1][i], points_rc[2][i]]
policy = one_to_all ? :rmvgnd : :rmvsrc
sources, grounds, finite_grounds =
_get_sources_and_grounds(source_map, ground_map,
flags, G, nodemap, policy)
advanced_data = AdvancedData(G, cc, nodemap, newpoly, hbmeta,
sources, grounds, source_map, finite_grounds,
check_node, n, gmap)
if one_to_all
# v = advanced(cfg, a, source_map, ground_map; nodemap = nodemap, policy = :rmvgnd,
# check_node = check_node, src = n, polymap = Polymap(newpoly), hbmeta = hbmeta)
v, curr = advanced_kernel(advanced_data, flags, cfg)
else
# v = advanced(cfg, a, source_map, ground_map; nodemap = nodemap, policy = :rmvsrc,
# check_node = check_node, src = n, polymap = Polymap(newpoly), hbmeta = hbmeta)
v, curr = advanced_kernel(advanced_data, flags, cfg)
end
res[i] = v[1]
cum.cum_curr[mycsid()] .+= curr
flags.outputflags.write_max_cur_maps && (cum.max_curr[mycsid()] .= max.(cum.max_curr[mycsid()], curr))
end
pmap(x -> f(x), 1:num_points_to_solve)
if flags.outputflags.write_cur_maps
write_cum_maps(cum, gmap, cfg, hbmeta,
flags.outputflags.write_max_cur_maps,
flags.outputflags.write_cum_cur_map_only)
end
hcat(points_unique, res)
end
function prune_points!(points_rc, point_ids::Vector{V}) where V
rmv = V[]
for (i,p) in enumerate(points_rc[3])
if p in point_ids
continue
else
#for it in 1:3 deleteat!(points_rc[it], i) end
push!(rmv, i)
end
end
for i in 1:3 deleteat!(points_rc[i], rmv) end
end
function prune_strengths!(strengths, point_ids::Vector{V}) where V
pts = strengths[:,1]
l = length(pts)
rmv = V[]
for (i,p) in enumerate(pts)
if !(p in point_ids)
push!(rmv, i)
end
end
rng = collect(1:l)
deleteat!(rng, rmv)
strengths[rng,:]
end
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0,
7,
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8,
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220,
220,
220,
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220,
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886,
198,
220,
220,
220,
886,
198,
220,
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374,
782,
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220,
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12233,
265,
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198,
220,
220,
220,
18929,
58,
81,
782,
11,
47715,
198,
437,
198
] | 2.001657 | 3,018 |
# Note that this script can accept some limited command-line arguments, run
# `julia build_tarballs.jl --help` to see a usage message.
using BinaryBuilder, Pkg
name = "glmnet"
version = v"4.0.2"
# Collection of sources required to complete build
sources = [
GitSource("https://github.com/cran/glmnet.git", "b1a4b50de01e0cd24343959d7cf86452bac17b26")
]
# Bash recipe for building across all platforms
script = raw"""
cd $WORKSPACE/srcdir/glmnet/src
# Add stub for `setpb`, which normally comes from `pb.c` to connect the
# progress meter to R, but we don't need that
echo "
subroutine setpb(val)
return
end
" > pb.f
flags="-fdefault-real-8 -ffixed-form -shared -O3"
if [[ ${target} != *mingw* ]]; then
flags="${flags} -fPIC";
fi
if [[ ${target} != aarch64* ]] && [[ ${target} != arm* ]]; then
flags="${flags} -m${nbits}";
fi
mkdir -p ${libdir}
${FC} ${LDFLAGS} ${flags} glmnet5dpclean.f wls.f pb.f -o ${libdir}/libglmnet.${dlext}
install_license ../DESCRIPTION
"""
# These are the platforms we will build for by default, unless further
# platforms are passed in on the command line
platforms = expand_gfortran_versions(supported_platforms())
# The products that we will ensure are always built
products = [
LibraryProduct("libglmnet", :libglmnet)
]
# Dependencies that must be installed before this package can be built
dependencies = Dependency[
Dependency(PackageSpec(name="CompilerSupportLibraries_jll", uuid="e66e0078-7015-5450-92f7-15fbd957f2ae"))
]
# Build the tarballs, and possibly a `build.jl` as well.
build_tarballs(ARGS, name, version, sources, script, platforms, products, dependencies)
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2,
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11,
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11,
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11,
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] | 2.815385 | 585 |
module UMAP
using Arpack
using Distances
using LinearAlgebra
using LsqFit: curve_fit
using NearestNeighborDescent
using SparseArrays
include("utils.jl")
include("embeddings.jl")
include("umap_.jl")
export umap, UMAP_, transform
end # module
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] | 2.987805 | 82 |
module CompGrids
using ParallelStencil
using ImplicitGlobalGrid
using MPI
using OffsetArrays
@init_parallel_stencil(Threads, Float64, 3);
export backend,
BackendPETSc, BackendParallelStencil, BackendNone,
#
initialize_backend, initialize_grid,
# Boundary topology
# Grids
RegularRectilinearCollocatedGrid, Bounded, Ghosted, Periodic
# Declare the backend we are employing:
include("Backends.jl")
include("PETSc_backend.jl")
include("ParallelStencil_backend.jl")
include("Grids.jl")
# Declare the backend we are employing:
# Define different grid types
end # module
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10706,
3858,
628,
628,
198,
198,
437,
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] | 2.640496 | 242 |
using MathOptInterface
const MOI = MathOptInterface
struct DIRCOLProblemMT{T} <: MOI.AbstractNLPEvaluator
prob::Problem{T,Continuous}
cost::Function
cost_gradient!::Function
solver::DIRCOLSolverMT{T,HermiteSimpson}
jac_struct
part_z::NamedTuple{(:X,:U,:H), NTuple{3,Matrix{Int}}}
p::NTuple{4,Int} # (total constraints, p_colloc, p_custom, p_h)
nG::NTuple{4,Int} # (total constraint jacobian, nG_colloc, nG_custom, nG_h)
end
function DIRCOLProblemMT(prob::Problem{T,Continuous}, solver::DIRCOLSolverMT{T,HermiteSimpson}) where T
n,m,N = size(prob)
p = num_constraints(prob)
p_colloc = num_colloc(prob)
p_custom = sum(p)
p_h = N-2
P = p_colloc + p_custom + p_h
NN = N*(n+m) + (N-1)
nG_colloc = p_colloc*2*(n + m) + p_colloc
nG_custom = sum(p[1:N-1])*(n+m) + p[N]*n
nG_h = (N-2)*2
nG = nG_colloc + nG_custom + nG_h
part_z = create_partition(n,m,1,N,N,N-1)
jac_struct_colloc = collocation_constraint_jacobian_sparsityMT!(prob)
jac_struct_custom = custom_constraint_jacobian_sparsityMT!(prob,p_colloc)
jac_struct_h = h_eq_constraint_sparsityMT!(prob,p_colloc+p_custom)
jac_struct = copy(jac_struct_colloc)
append!(jac_struct,jac_struct_custom)
append!(jac_struct,jac_struct_h)
num_con = (P,p_colloc,p_custom,p_h)
num_jac = (nG, nG_colloc, nG_custom, nG_h)
DIRCOLProblemMT(prob, gen_stage_cost_min_time(prob,solver.opts.R_min_time), gen_stage_cost_gradient_min_time(prob,solver.opts.R_min_time), solver, jac_struct, part_z, num_con, num_jac)
end
MOI.features_available(d::DIRCOLProblemMT) = [:Grad, :Jac]
MOI.initialize(d::DIRCOLProblemMT, features) = nothing
MOI.jacobian_structure(d::DIRCOLProblemMT) = d.jac_struct
MOI.hessian_lagrangian_structure(d::DIRCOLProblemMT) = []
function MOI.eval_objective(d::DIRCOLProblemMT, Z)
X,U,H = unpackMT(Z, d.part_z)
d.cost(X, U, H)
end
function MOI.eval_objective_gradient(d::DIRCOLProblemMT, grad_f, Z)
X,U,H = unpackMT(Z, d.part_z)
d.cost_gradient!(grad_f,X,U,H)
end
function MOI.eval_constraint(d::DIRCOLProblemMT, g, Z)
X,U,H = unpackMT(Z, d.part_z)
P,p_colloc,p_custom,p_h = d.p
g_colloc = view(g, 1:p_colloc)
g_custom = view(g, p_colloc .+ (1:p_custom))
g_h = view(g, (p_colloc+p_custom) .+ (1:p_h))
collocation_constraints!(g_colloc, d.prob, d.solver, X, U, H)
update_constraints!(g_custom, d.prob, d.solver, X, U)
h_eq_constraints!(g_h,d.prob,d.solver,H)
end
function MOI.eval_constraint_jacobian(d::DIRCOLProblemMT, jac, Z)
X,U,H = unpackMT(Z, d.part_z)
n,m = size(d.prob)
P, p_colloc, p_custom, p_h = d.p
nG, nG_colloc, nG_custom, nG_h = d.nG
jac_colloc = view(jac, 1:nG_colloc)
jac_custom = view(jac, nG_colloc .+ (1:nG_custom))
jac_h = view(jac, (nG_colloc+nG_custom) .+ (1:nG_h))
collocation_constraint_jacobian!(jac_colloc, d.prob, d.solver, X, U, H)
constraint_jacobian!(jac_custom, d.prob, d.solver, X, U)
h_eq_constraint_jacobian!(jac_h,d.prob,d.solver,H)
end
MOI.eval_hessian_lagrangian(::DIRCOLProblemMT, H, x, σ, μ) = nothing
function solve_moi(prob::Problem, opts::DIRCOLSolverMTOptions)
prob = copy(prob)
bnds = remove_bounds!(prob)
z_U, z_L, g_U, g_L = get_boundsMT(prob, bnds, opts.h_max, opts.h_min)
n,m,N = size(prob)
NN = (n+m)*N + (N-1)
# Get initial condition
Z0 = PrimalsMT(prob, true)
# Create NLP Block
has_objective = true
dircol = DIRCOLSolverMT(prob, opts)
d = DIRCOLProblemMT(prob, dircol)
nlp_bounds = MOI.NLPBoundsPair.(g_L, g_U)
block_data = MOI.NLPBlockData(nlp_bounds, d, has_objective)
# Create solver
solver = typeof(opts.nlp)(;opts.opts...)
Z = MOI.add_variables(solver, NN)
# Add bound constraints
for i = 1:NN
zi = MOI.SingleVariable(Z[i])
MOI.add_constraint(solver, zi, MOI.LessThan(z_U[i]))
MOI.add_constraint(solver, zi, MOI.GreaterThan(z_L[i]))
MOI.set(solver, MOI.VariablePrimalStart(), Z[i], Z0.Z[i])
end
# Solve the problem
@info "DIRCOL solve using " * String(nameof(parentmodule(typeof(solver))))
MOI.set(solver, MOI.NLPBlock(), block_data)
MOI.set(solver, MOI.ObjectiveSense(), MOI.MIN_SENSE)
MOI.optimize!(solver)
# Get the solution
res = MOI.get(solver, MOI.VariablePrimal(), Z)
res = PrimalsMT(res, d.part_z)
d.solver.Z = res
d.solver.stats[:status] = MOI.get(solver, MOI.TerminationStatus())
# Return the results
return d.solver
end
function solve!(prob::Problem,opts::DIRCOLSolverMTOptions)
dircol = solve_moi(prob, opts)
copyto!(prob.X,dircol.Z.X)
prob.U = [k != prob.N ? [dircol.Z.U[k];sqrt(dircol.Z.H[k])] : dircol.Z.U[k] for k = 1:prob.N]
return dircol
end
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] | 2.012241 | 2,369 |
# ------------------------------------------------------------------
# Licensed under the ISC License. See LICENSE in the project root.
# ------------------------------------------------------------------
"""
UniversalKriging(γ, degree, dim)
UniversalKriging(X, z, γ, degree)
Universal Kriging with variogram model `γ` and polynomial
`degree` on a spatial domain of dimension `dim`.
Optionally, pass the coordinates `X` and values `z`
to the [`fit`](@ref) function.
### Notes
* [`OrdinaryKriging`](@ref) is recovered for 0th degree polynomial
* For non-polynomial mean, see [`ExternalDriftKriging`](@ref)
"""
struct UniversalKriging{G<:Variogram} <: KrigingEstimator
γ::G
degree::Int
dim::Int
exponents::Matrix{Int}
function UniversalKriging{G}(γ, degree, dim) where {G<:Variogram}
@assert degree ≥ 0 "degree must be nonnegative"
@assert dim > 0 "dimension must be positive"
exponents = UKexps(degree, dim)
new(γ, degree, dim, exponents)
end
end
UniversalKriging(γ, degree, dim) = UniversalKriging{typeof(γ)}(γ, degree, dim)
UniversalKriging(X, z, γ, degree) = GeoStatsBase.fit(UniversalKriging(γ, degree, size(X,1)), X, z)
function UKexps(degree::Int, dim::Int)
# multinomial expansion
expmats = [hcat(collect(multiexponents(dim, d))...) for d in 0:degree]
exponents = hcat(expmats...)
# sort expansion for better conditioned Kriging matrices
sorted = sortperm(vec(maximum(exponents, dims=1)), rev=true)
exponents[:,sorted]
end
nconstraints(estimator::UniversalKriging) = size(estimator.exponents, 2)
function set_constraints_lhs!(estimator::UniversalKriging, LHS::AbstractMatrix, X::AbstractMatrix)
exponents = estimator.exponents
nobs = size(X, 2)
nterms = size(exponents, 2)
T = eltype(LHS)
# set polynomial drift blocks
for i=1:nobs, j=1:nterms
LHS[nobs+j,i] = prod(X[:,i].^exponents[:,j])
LHS[i,nobs+j] = LHS[nobs+j,i]
end
# set zero block
LHS[nobs+1:end,nobs+1:end] .= zero(T)
nothing
end
factorize(estimator::UniversalKriging, LHS::AbstractMatrix) = lu(LHS, check=false)
function set_constraints_rhs!(estimator::FittedKriging{E,S},
xₒ::AbstractVector) where {E<:UniversalKriging,S<:KrigingState}
exponents = estimator.estimator.exponents
RHS = estimator.state.RHS
nobs = size(estimator.state.X, 2)
nterms = size(exponents, 2)
for j in 1:nterms
RHS[nobs+j] = prod(xₒ.^exponents[:,j])
end
nothing
end
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] | 2.638321 | 929 |
# *****************************************************************************
# Written by Ritchie Lee, ritchie.lee@sv.cmu.edu
# *****************************************************************************
# Copyright ã 2015, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved. The Reinforcement Learning Encounter Simulator (RLES)
# platform is licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. You
# may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0. Unless required by applicable
# law or agreed to in writing, software distributed under the License is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the specific language
# governing permissions and limitations under the License.
# _____________________________________________________________________________
# Reinforcement Learning Encounter Simulator (RLES) includes the following
# third party software. The SISLES.jl package is licensed under the MIT Expat
# License: Copyright (c) 2014: Youngjun Kim.
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to
# deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
# sell copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED
# "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR
# PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
# *****************************************************************************
module ConfigMCTS
export defineMCTSParams
using AdaptiveStressTesting
function defineMCTSParams(;
d::Int64 = 51,
ec::Float64 = 100.0,
n::Int64 = 1000,
k::Float64 = 0.5,
alpha::Float64 = 0.85,
kp::Float64 = 1.0,
alphap::Float64 = 0.0,
clear_nodes::Bool = true,
maxtime_s::Float64 = realmax(Float64),
rng_seed::UInt64 = UInt64(0)
)
return p = DPWParams(d, ec, n, k, alpha, kp, alphap, clear_nodes, maxtime_s, rng_seed)
end
end #module
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"""
## module Potentials
### Summary
This module implements some basic interatomic potentials in pure Julia, as well
as provides building blocks and prototypes for further implementations
The implementation is done in such a way that they can be used either in "raw"
form or withinabstractframeworks.
### Types
### `evaluate`, `evaluate_d`, `evaluate_dd`, `grad`
### The `@D`, `@DD`, `@GRAD` macros
TODO: write documentation
"""
module Potentials
using JuLIP: Atoms, AbstractAtoms, AbstractCalculator,
JVec, mat, vec, JMat, SVec, vecs, SMat,
positions, set_positions!, fltype_intersect
using JuLIP.Chemistry: atomic_number
using StaticArrays: @SMatrix, SVector
using NeighbourLists
using LinearAlgebra: norm
using SparseArrays: sparse
import JuLIP: energy, forces, cutoff, virial, hessian_pos, hessian,
site_energies, r_sum,
site_energy, site_energy_d,
energy!, forces!, virial!,
alloc_temp, alloc_temp_d, alloc_temp_dd,
read_dict, write_dict, fltype, rfltype
export PairPotential, SitePotential, ZeroSitePotential
# the following are prototypes for internal functions around which IPs are
# defined
function evaluate end
function evaluate_d end
function evaluate_ed end
function evaluate_dd end
function evaluate! end
function evaluate_d! end
function evaluate_dd! end
function precon! end
include("potentials_base.jl")
# * @pot, @D, @DD
# *
"""
`SitePotential`:abstractsupertype for generic site potentials
"""
abstract type SitePotential <: AbstractCalculator end
"""
`SimpleSitePotential`:abstractsupertype for generic site potentials,
ignoring species
"""
abstract type SimpleSitePotential <: SitePotential end
"""
`PairPotential`:abstractsupertype for pair potentials
"""
abstract type PairPotential <: SitePotential end
"""
`SimplePairPotential`:abstractsupertype for pair potentials,
ignoring species
"""
abstract type SimplePairPotential <: PairPotential end
abstract type ExplicitPairPotential <: SimplePairPotential end
# ---- redirect with some convenience functions ----
# redirect allocating -> non-allocating calls
evaluate(V::SitePotential, R, args...) =
evaluate!(alloc_temp(V, length(R)), V, R, args...)
evaluate_d(V::SitePotential, R::AbstractVector{JVec{T}}, args...) where {T} =
evaluate_d!(zeros(JVec{fltype_intersect(V, T)}, length(R)),
alloc_temp_d(V, length(R)),
V, R, args...)
evaluate_dd(V::SitePotential, R::AbstractVector{JVec{T}}, args...) where {T} =
evaluate_dd!(zeros(JMat{fltype_intersect(V, T)}, length(R), length(R)),
alloc_temp_dd(V, length(R)),
V, R, args...)
# ----- interface for SimpleSitePotential
evaluate!(tmp, V::SimpleSitePotential, R, Z, z0) =
evaluate!(tmp, V, R)
evaluate_d!(dEs, tmp, V::SimpleSitePotential, R, Z, z0) =
evaluate_d!(dEs, tmp, V, R)
evaluate_dd!(hEs, tmp, V::SimpleSitePotential, R, Z, z0) =
evaluate_dd!(hEs, tmp, V, R)
precon!(hEs, tmp, V::SimpleSitePotential, R, Z, z0, innerstab) =
precon!(hEs, tmp, V, R, innerstab)
# ------- Neighbourlist related business -------------------
NeighbourLists.sites(at::AbstractAtoms, rcut::AbstractFloat) =
sites(neighbourlist(at, rcut))
NeighbourLists.pairs(at::AbstractAtoms, rcut::AbstractFloat) =
pairs(neighbourlist(at, rcut))
"""
`neigsz!(tmp, nlist::PairList, at::Atoms, i::Integer) -> j, R Z`
requires a temporary storage array `tmp` with fields
`tmp.R, tmp.Z`.
"""
function neigsz!(tmp, nlist::PairList, at::Atoms, i::Integer)
j, R = neigs!(tmp.R, nlist, i)
Z = tmp.Z
for n = 1:length(j)
Z[n] = at.Z[j[n]]
end
return j, R, (@view Z[1:length(j)])
end
function neigsz(nlist::PairList, at::Atoms, i::Integer)
j, R = NeighbourLists.neigs(nlist, i)
return j, R, at.Z[j]
end
# ------------------------------------------------------
"a site potential that just returns zero"
mutable struct ZeroSitePotential <: SitePotential
end
@pot ZeroSitePotential
cutoff(::ZeroSitePotential) = Bool(0)
energy(V::ZeroSitePotential, at::AbstractAtoms{T}; kwargs...) where T = zero(T)
forces(V::ZeroSitePotential, at::AbstractAtoms{T}; kwargs...) where T = zeros(JVec{T}, length(at))
evaluate!(tmp, p::ZeroSitePotential, args...) = Bool(0)
evaluate_d!(dEs, tmp, V::ZeroSitePotential, args...) = fill!(dEs, zero(eltype(dEs)))
evaluate_dd!(hEs, tmp, V::ZeroSitePotential, args...) = fill!(hEs, zero(eltype(hEs)))
# Implementation of a generic site potential
# ================================================
alloc_temp_site(N::Integer, T=Float64) =
( R = zeros(JVec{T}, N),
Z = zeros(AtomicNumber, N), )
alloc_temp(V::SitePotential, at::AbstractAtoms) =
alloc_temp(V, maxneigs(neighbourlist(at, cutoff(V))))
alloc_temp(V::SitePotential, N::Integer) =
( R = zeros(JVecF, N),
Z = zeros(AtomicNumber, N), )
alloc_temp_d(V::SitePotential, at::AbstractAtoms) =
alloc_temp_d(V, maxneigs(neighbourlist(at, cutoff(V))))
alloc_temp_d(V::SitePotential, N::Integer) =
(dV = zeros(JVec{fltype(V)}, N),
R = zeros(JVecF, N),
Z = zeros(AtomicNumber, N), )
alloc_temp_dd(V::SitePotential, args...) = nothing
# -------------- Implementations of energy, forces, virials
# for a generic site potential
energy(V::SitePotential, at::AbstractAtoms; kwargs...) =
energy!(alloc_temp(V, at), V, at; kwargs...)
virial(V::SitePotential, at::AbstractAtoms; kwargs...) =
virial!(alloc_temp_d(V, at), V, at; kwargs...)
forces(V::SitePotential, at::AbstractAtoms; kwargs...) =
forces!(zeros(JVec{fltype_intersect(V, at)}, length(at)),
alloc_temp_d(V, at), V, at; kwargs...)
function energy!(tmp, calc::SitePotential, at::Atoms;
domain=1:length(at))
TFL = fltype_intersect(calc, at)
E = zero(TFL)
nlist = neighbourlist(at, cutoff(calc))
for i in domain
j, R, Z = neigsz!(tmp, nlist, at, i)
E += evaluate!(tmp, calc, R, Z, at.Z[i])
end
return E
end
function forces!(frc, tmp, calc::SitePotential, at::Atoms;
domain=1:length(at), reset=true)
TFL = fltype_intersect(calc, at)
if reset; fill!(frc, zero(eltype(frc))); end
nlist = neighbourlist(at, cutoff(calc))
for i in domain
j, R, Z = neigsz!(tmp, nlist, at, i)
if length(j) > 0
evaluate_d!(tmp.dV, tmp, calc, R, Z, at.Z[i])
for a = 1:length(j)
frc[j[a]] -= tmp.dV[a]
frc[i] += tmp.dV[a]
end
end
end
return frc
end
site_virial(dV::AbstractVector{JVec{T1}}, R::AbstractVector{JVec{T2}}
) where {T1, T2} = (
length(R) > 0 ? (- sum( dVi * Ri' for (dVi, Ri) in zip(dV, R) ))
: zero(JMat{fltype_intersect(T1, T2)})
)
function virial!(tmp, calc::SitePotential, at::Atoms; domain=1:length(at))
TFL = fltype_intersect(calc, at)
nlist = neighbourlist(at, cutoff(calc))
vir = zero(JMat{TFL})
for i in domain
j, R, Z = neigsz!(tmp, nlist, at, i)
if length(j) > 0
evaluate_d!(tmp.dV, tmp, calc, R, Z, at.Z[i])
vir += site_virial(tmp.dV, R)
end
end
return vir
end
function site_energies(V::SitePotential, at::AbstractAtoms; kwargs...)
TFL = fltype_intersect(V, at)
return site_energies!(zeros(TFL, length(at)),
alloc_temp(V, at), V, at; kwargs...)
end
function site_energies!(Es, tmp, V::SitePotential, at::AbstractAtoms;
domain = 1:length(at))
nlist = neighbourlist(at, cutoff(V))
for i in domain
_j, R, Z = neigsz!(tmp, nlist, at, i)
Es[i] = evaluate!(tmp, V, R, Z, at.Z[i])
end
return Es
end
site_energy(V::SitePotential, at::AbstractAtoms, i0::Integer) =
energy(V, at; domain = (i0,))
site_energy_d(V::SitePotential, at::AbstractAtoms, i0::Integer) =
rmul!(forces(V, at; domain = (i0,)), -one(fltype(at)))
# ------------------------------------------------
# specialisation for Pair potentials
include("analyticpotential.jl")
include("cutoffs.jl")
include("pairpotentials.jl")
include("adsite.jl")
include("stillingerweber.jl")
include("splines.jl")
include("eam.jl")
include("onebody.jl")
include("hessians.jl")
include("emt.jl")
end
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] | 2.40104 | 3,461 |
using BinaryBuilder, Pkg
# Read in input `.json` file
json = String(read(ARGS[1]))
buff = IOBuffer(strip(json))
objs = []
while !eof(buff)
push!(objs, BinaryBuilder.JSON.parse(buff))
end
# Merge the multiple outputs into one
merged = BinaryBuilder.merge_json_objects(objs)
BinaryBuilder.cleanup_merged_object!(merged)
# Determine build version
name = merged["name"]
jll_name = string(name, "_jll")
repo_name = string("JuliaBinaryWrappers/", jll_name, ".jl")
@info "Initializing $(repo_name)..."
BinaryBuilder.init_jll_package(name, joinpath(Pkg.devdir(), jll_name), repo_name)
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] | 2.703704 | 216 |
using LinearAlgebra
using DataFrames
using CSV
using Plots
A = CSV.File("../../DATA/hald_ingredients.csv", header = false) |> Tables.matrix
b = CSV.File("../../DATA/hald_heat.csv", header = false) |> Tables.matrix
U, S, V = svd(A)
x = (V*inv(diagm(S))*U'*b) # Solve Ax=b using the SVD
p1 = plot(b, label = "Heat data") # Plot data
plot!(A*x, label = "Regression") # Plot regression
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] | 2.522876 | 153 |
__precompile__()
module OSQP
export OSQPMathProgBaseInterface
# Compatibility stuff
using Compat
using Compat.SparseArrays
using Compat.Iterators
if isfile(joinpath(dirname(@__FILE__), "..", "deps", "deps.jl"))
include("../deps/deps.jl")
else
error("OSQP not properly installed. Please run Pkg.build(\"OSQP\")")
end
function __init__()
# Get version
ver_array = split(version(), ".")
ver_string = string(ver_array[1], ".", ver_array[2], ".", ver_array[3]) # Get string without dev vers
vnum = VersionNumber(ver_string)
depsdir = realpath(joinpath(dirname(@__FILE__), "..", "deps"))
if (vnum.major != 0 && vnum.minor != 2)
error("Current OSQP version installed is $(osqp_version()), but we require version 0.2.*. Delete the contents of the `$depsdir` directory except for the files `build.jl` and `.gitignore`, then rerun Pkg.build(\"OSQP\").")
end
end
include("constants.jl")
include("types.jl")
include("interface.jl")
include("mpbinterface.jl")
include("MathOptInterfaceOSQP.jl")
end # module
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] | 2.701799 | 389 |
# Produces a maximally entangled state as vector or density matrix
export maxEnt, maxEntVec;
""" `vec = maxEnt(d)`
Returns a projector (density matrix) onto the maximally entangled state of a given dimension *d*.
"""
function maxEnt(d::Number)
v = maxEntVec(d);
return (v*v');
end
""" `vec = maxEntVec(d)`
Returns the maximally entangled state of a given dimension *d* as a vector.
"""
function maxEntVec(d::Number)
v = zeros(d^2);
for j = 1:d
v += (1/sqrt(d)) * kron(eVec(d,j),eVec(d,j));
end
return v;
end
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] | 2.60199 | 201 |
"""
WeightedCovMatrix(T = Float64)
Weighted covariance matrix, tracked as a matrix of type `T`.
*After* a call to `cov` the covariance matrix is stored in `o.C`.
# Example:
o = fit!(WeightedCovMatrix(), rand(100, 4), rand(100))
sum(o)
mean(o)
var(o)
std(o)
cov(o)
cor(o)
"""
mutable struct WeightedCovMatrix{T} <: WeightedOnlineStat{VectorOb}
C::Matrix{T}
A::Matrix{T}
b::Vector{T}
W::T
W2::T
n::Int
function WeightedCovMatrix{T}(
C = zeros(T, 0, 0),
A = zeros(T, 0, 0),
b = zeros(T, 0),
W = T(0),
W2 = T(0),
n = 0
) where T
new{T}(C, A, b, W, W2, n)
end
end
function WeightedCovMatrix(
C::Matrix{T},
A::Matrix{T},
b::Vector{T},
W::T,
W2::T,
n::Int
) where T
WeightedCovMatrix{T}(C, A, b, W, W2, n)
end
WeightedCovMatrix(::Type{T}, p::Int=0) where T =
WeightedCovMatrix(zeros(T, p, p), zeros(T, p, p), zeros(T, p), T(0), T(0), 0)
WeightedCovMatrix() = WeightedCovMatrix(Float64)
function _fit!(o::WeightedCovMatrix{T}, x, w) where T
xx = convert(Vector{T}, x)
ww = convert(T, w)
o.n += 1
γ1 = T(1) / o.n
o.W = smooth(o.W, ww, γ1)
o.W2 = smooth(o.W2, ww*ww, γ1)
γ2 = ww / (o.W * o.n)
if isempty(o.A)
p = length(xx)
o.b = zeros(T, p)
o.A = zeros(T, p, p)
o.C = zeros(T, p, p)
end
smooth!(o.b, xx, γ2)
smooth_syr!(o.A, xx, γ2)
end
function _fit!(o::WeightedCovMatrix{T1},
x::AbstractVector{Union{T2, Missing}}, w) where {T1, T2}
if !mapreduce(ismissing, |, x)
xx = convert(Vector{T1}, x)
_fit!(o, xx, w)
end
return o
end
_fit!(o::WeightedCovMatrix, x, w::Missing) = o
function _merge!(o::WeightedCovMatrix{T}, o2::WeightedCovMatrix) where T
o2_A = convert(Matrix{T}, o2.A)
o2_b = convert(Vector{T}, o2.b)
o2_W = convert(T, o2.W)
o2_W2 = convert(T, o2.W2)
if isempty(o.A)
o.C = convert(Matrix{T}, o2.C)
o.A = o2_A
o.b = o2_b
o.W = o2_W
o.W2 = o2_W2
o.n = o2.n
else
n = o.n + o2.n
W = smooth(o.W, o2_W, o2.n / n)
γ = (o2_W * o2.n) / (W * n)
smooth!(o.A, o2_A, γ)
smooth!(o.b, o2_b, γ)
o.n = n
o.W = W
o.W2 = smooth(o.W2, o2_W2, o2.n / o.n)
end
return o
end
nvars(o::WeightedCovMatrix) = size(o.A, 1)
function OnlineStatsBase.value(o::WeightedCovMatrix)
# o.A is only the upper triangle:
# o.C .= o.A .- o.b .* o.b'
@inbounds for i in 1:size(o.A, 1)
for j in 1:i
o.C[j, i] = o.A[j, i] - o.b[i] * o.b[j]
end
end
LinearAlgebra.copytri!(o.C, 'U')
o.C
end
function Statistics.cov(o::WeightedCovMatrix; corrected = false, weight_type = :analytic)
if corrected
if weight_type == :analytic
LinearAlgebra.rmul!(
value(o), 1 / (1 - (o.W2 * nobs(o)) / (weightsum(o) ^ 2))
)
elseif weight_type == :frequency
LinearAlgebra.rmul!(
value(o), 1 / (weightsum(o) - 1) * weightsum(o)
)
elseif weight_type == :probability
error("If you need this, please make a PR or open an issue")
else
throw(ArgumentError("weight type $weight_type not implemented"))
end
else
value(o)
end
end
function Statistics.cor(o::WeightedCovMatrix; kw...)
cov(o; kw...)
v = diag(o.C)
v .= 1 ./ sqrt.(v)
return o.C .* v .* v'
end
Base.sum(o::WeightedCovMatrix) = o.b .* (meanweight(o) * nobs(o))
Statistics.mean(o::WeightedCovMatrix) = copy(o.b)
Statistics.var(o::WeightedCovMatrix; kw...) = diag(cov(o; kw...))
Statistics.std(o::WeightedCovMatrix; kw...) = sqrt.(var(o; kw...))
Base.eltype(o::WeightedCovMatrix{T}) where T = T
Base.copy(o::WeightedCovMatrix) =
WeightedCovMatrix(copy(o.C), copy(o.A), copy(o.b), o.W, o.W2, o.n)
Base.size(x::WeightedCovMatrix, i) = size(x.C, i)
Base.size(x::WeightedCovMatrix) = size(x.C)
function Base.convert(::Type{WeightedCovMatrix{T}}, o::WeightedCovMatrix) where T
WeightedCovMatrix{T}(
convert(Matrix{T}, o.C),
convert(Matrix{T}, o.A),
convert(Vector{T}, o.b),
convert(T, o.W),
convert(T, o.W2),
o.n
)
end
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] | 1.812268 | 2,429 |
export rmap
rmap(f, x) = f(x)
function rmap(f, t::Tuple)
map(x -> rmap(f,x), t)
end
function rmap(f, nt::NamedTuple{N,T}) where {N,T}
NamedTuple{N}(map(x -> rmap(f,x), values(nt)))
end
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] | 1.884615 | 104 |
# ---
# title: 1414. Find the Minimum Number of Fibonacci Numbers Whose Sum Is K
# id: problem1414
# author: Indigo
# date: 2022-2-18
# difficulty: Medium
# categories: Array, Greedy
# link: <https://leetcode.com/problems/find-the-minimum-number-of-fibonacci-numbers-whose-sum-is-k/description/>
# hidden: true
# ---
#
# Given an integer `k`, _return the minimum number of Fibonacci numbers whose
# sum is equal to_`k`. The same Fibonacci number can be used multiple times.
#
# The Fibonacci numbers are defined as:
#
# * `F1 = 1`
# * `F2 = 1`
# * `Fn = Fn-1 + Fn-2` for `n > 2.`
#
# It is guaranteed that for the given constraints we can always find such
# Fibonacci numbers that sum up to `k`.
#
#
#
# **Example 1:**
#
#
#
# Input: k = 7
# Output: 2
# Explanation: The Fibonacci numbers are: 1, 1, 2, 3, 5, 8, 13, ...
# For k = 7 we can use 2 + 5 = 7.
#
# **Example 2:**
#
#
#
# Input: k = 10
# Output: 2
# Explanation: For k = 10 we can use 2 + 8 = 10.
#
#
# **Example 3:**
#
#
#
# Input: k = 19
# Output: 3
# Explanation: For k = 19 we can use 1 + 5 + 13 = 19.
#
#
#
#
# **Constraints:**
#
# * `1 <= k <= 10^9`
#
#
## @lc code=start
using LeetCode
function find_min_fibonacci_numbers(k::Int)
fibs = [0, 1, 1]
while fibs[end] <= k
push!(fibs, fibs[end] + fibs[end - 1])
end
"""
If we need 2 or more fib_i's,
we can always use fib_{i+1} and fib_{i-2} to replace them:
2fib_i = fib_i + fib_{i-1} + fib_{i-2} = fib_{i+1} + fib_{i-2}.
So greedy algorithm can be performed.
"""
pop!(fibs)
res = 0
idx = length(fibs) + 1
while k != 0
idx -= 1
k >= fibs[idx] || continue
k -= fibs[idx]
res += 1
end
return res
end
## @lc code=end
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] | 2.139697 | 859 |
using ConvolutionTools
using Base.Test
import Base.isapprox
function isapprox{T, N}(a::Array{T, N}, b::Array{T, N}, eps=10e-10)
if size(a) != size(b)
return false
end
el_diff = sum(abs(a .- b)) / prod(size(a))
return el_diff < eps
end
function test_pad_const_1d()
a = rand(3)
n = [1]
c = 2.3
res = pad_const(a, n, c)
return length(res) == 5 && res[1] == c && res[end] == c && typeof(res) == typeof(a)
end
function test_pad_const_2d()
a = rand(3, 2)
n = [1, 3]
res = pad_const(a, n)
return size(res) == (5, 8)
end
function test_1d()
a = rand((7))
b = rand((2))
res = ConvolutionTools.conv_valid(a, b)
return ndims(res) == 1 && eltype(res) <: Number
end
function test_2d()
a = rand((6, 7))
b = rand((1, 2))
res = ConvolutionTools.conv_valid(a, b)
return ndims(res) == 2 && eltype(res) <: Number
end
function test_3d()
a = rand((6, 7, 10))
b = rand((1, 2, 3))
res = ConvolutionTools.conv_valid(a, b)
return ndims(res) == 3 && eltype(res) <: Number
end
function test_identity()
a = [i^2 for i in 1:100]
b = [1]
res = ConvolutionTools.conv_valid(a, b)
return all(a .== res)
end
function test_zero()
a = float([i^2 for i in 1:100])
b = float([0])
res = ConvolutionTools.conv_valid(a, b)
return all(res .== zero(eltype(res)))
end
function test_derivative()
n = 10
a = float([0.5 * i^2 for i in 1:n])
b = float([1, 0, -1]) / 2
res = ConvolutionTools.conv_valid(a, b)
ref = float([i for i in 2:(n-1)])
return isapprox(ref, res)
end
function test_integers()
n = 10
a = [i for i in 1:n]
b = [1, 0, -1]
res = ConvolutionTools.conv_valid(a, b)
return all(res .== 2)
end
function test_mixed_input()
a = [i for i in 1:10]
b = float([1, 2, 3])
res1 = ConvolutionTools.conv(a, b)
res2 = ConvolutionTools.conv(b, a)
return isapprox(res1, res2)
end
custom_handler(r::Test.Success) = print(".")
custom_handler(r::Test.Failure) = Test.default_handler(r)
custom_handler(r::Test.Error) = Test.default_handler(r)
Test.with_handler(custom_handler) do
@test test_pad_const_1d()
@test test_pad_const_2d()
@test test_1d()
@test test_2d()
@test test_3d()
@test test_zero()
@test test_identity()
@test test_derivative()
@test test_integers()
@test test_mixed_input()
end
println("")
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35235,
7203,
4943,
628
] | 2.171763 | 1,112 |
@cache mutable struct ABDF2ConstantCache{N,dtType,rate_prototype} <: OrdinaryDiffEqConstantCache
nlsolver::N
eulercache::ImplicitEulerConstantCache
dtₙ₋₁::dtType
fsalfirstprev::rate_prototype
end
function alg_cache(alg::ABDF2,u,rate_prototype,::Type{uEltypeNoUnits},::Type{uBottomEltypeNoUnits},::Type{tTypeNoUnits},
uprev,uprev2,f,t,dt,reltol,p,calck,::Val{false}) where {uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits}
γ, c = 2//3, 1
nlsolver = build_nlsolver(alg,u,uprev,p,t,dt,f,rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,γ,c,Val(false))
eulercache = ImplicitEulerConstantCache(nlsolver)
dtₙ₋₁ = one(dt)
fsalfirstprev = rate_prototype
ABDF2ConstantCache(nlsolver, eulercache, dtₙ₋₁, fsalfirstprev)
end
@cache mutable struct ABDF2Cache{uType,rateType,uNoUnitsType,N,dtType} <: OrdinaryDiffEqMutableCache
uₙ::uType
uₙ₋₁::uType
uₙ₋₂::uType
fsalfirst::rateType
fsalfirstprev::rateType
zₙ₋₁::uType
atmp::uNoUnitsType
nlsolver::N
eulercache::ImplicitEulerCache
dtₙ₋₁::dtType
end
function alg_cache(alg::ABDF2,u,rate_prototype,::Type{uEltypeNoUnits},::Type{uBottomEltypeNoUnits},
::Type{tTypeNoUnits},uprev,uprev2,f,t,dt,reltol,p,calck,::Val{true}) where {uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits}
γ, c = 2//3, 1
nlsolver = build_nlsolver(alg,u,uprev,p,t,dt,f,rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,γ,c,Val(true))
fsalfirst = zero(rate_prototype)
fsalfirstprev = zero(rate_prototype)
atmp = similar(u,uEltypeNoUnits); recursivefill!(atmp,false)
eulercache = ImplicitEulerCache(u,uprev,uprev2,fsalfirst,atmp,nlsolver)
dtₙ₋₁ = one(dt)
zₙ₋₁ = zero(u)
ABDF2Cache(u,uprev,uprev2,fsalfirst,fsalfirstprev,zₙ₋₁,atmp,
nlsolver,eulercache,dtₙ₋₁)
end
# SBDF
@cache mutable struct SBDFConstantCache{rateType,N,uType} <: OrdinaryDiffEqConstantCache
cnt::Int
ark::Bool
k2::rateType
nlsolver::N
uprev2::uType
uprev4::uType
uprev3::uType
k₁::rateType
k₂::rateType
k₃::rateType
du₁::rateType
du₂::rateType
end
@cache mutable struct SBDFCache{uType,rateType,N} <: OrdinaryDiffEqMutableCache
cnt::Int
ark::Bool
u::uType
uprev::uType
fsalfirst::rateType
nlsolver::N
uprev2::uType
uprev3::uType
uprev4::uType
k₁::rateType
k₂::rateType
k₃::rateType
du₁::rateType
du₂::rateType
end
function alg_cache(alg::SBDF,u,rate_prototype,::Type{uEltypeNoUnits},::Type{uBottomEltypeNoUnits},::Type{tTypeNoUnits},uprev,uprev2,f,t,dt,reltol,p,calck,::Val{false}) where {uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits}
γ, c = 1//1, 1
nlsolver = build_nlsolver(alg,u,uprev,p,t,dt,f,rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,γ,c,Val(false))
k2 = rate_prototype
k₁ = rate_prototype; k₂ = rate_prototype; k₃ = rate_prototype
du₁ = rate_prototype; du₂ = rate_prototype
uprev2 = u; uprev3 = u; uprev4 = u
SBDFConstantCache(1,alg.ark,k2,nlsolver,uprev2,uprev3,uprev4,k₁,k₂,k₃,du₁,du₂)
end
function alg_cache(alg::SBDF,u,rate_prototype,::Type{uEltypeNoUnits},::Type{uBottomEltypeNoUnits},::Type{tTypeNoUnits},uprev,uprev2,f,t,dt,reltol,p,calck,::Val{true}) where {uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits}
γ, c = 1//1, 1
nlsolver = build_nlsolver(alg,u,uprev,p,t,dt,f,rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,γ,c,Val(true))
fsalfirst = zero(rate_prototype)
order = alg.order
k₁ = zero(rate_prototype)
k₂ = order >= 3 ? zero(rate_prototype) : k₁
k₃ = order == 4 ? zero(rate_prototype) : k₁
du₁ = zero(rate_prototype)
du₂ = zero(rate_prototype)
uprev2 = zero(u)
uprev3 = order >= 3 ? zero(u) : uprev2
uprev4 = order == 4 ? zero(u) : uprev2
SBDFCache(1,alg.ark,u,uprev,fsalfirst,nlsolver,uprev2,uprev3,uprev4,k₁,k₂,k₃,du₁,du₂)
end
# QNDF1
@cache mutable struct QNDF1ConstantCache{N,coefType,coefType1,coefType2,dtType,uType} <: OrdinaryDiffEqConstantCache
nlsolver::N
D::coefType1
D2::coefType2
R::coefType
U::coefType
uprev2::uType
dtₙ₋₁::dtType
end
@cache mutable struct QNDF1Cache{uType,rateType,coefType,coefType1,coefType2,uNoUnitsType,N,dtType} <: OrdinaryDiffEqMutableCache
uprev2::uType
fsalfirst::rateType
D::coefType1
D2::coefType2
R::coefType
U::coefType
atmp::uNoUnitsType
utilde::uType
nlsolver::N
dtₙ₋₁::dtType
end
function alg_cache(alg::QNDF1,u,rate_prototype,::Type{uEltypeNoUnits},::Type{uBottomEltypeNoUnits},::Type{tTypeNoUnits},uprev,uprev2,f,t,dt,reltol,p,calck,::Val{false}) where {uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits}
γ, c = zero(inv((1-alg.kappa))), 1
nlsolver = build_nlsolver(alg,u,uprev,p,t,dt,f,rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,γ,c,Val(false))
uprev2 = u
dtₙ₋₁ = zero(t)
D = fill(zero(u), 1, 1)
D2 = fill(zero(u), 1, 2)
R = fill(zero(t), 1, 1)
U = fill(zero(t), 1, 1)
U!(1,U)
QNDF1ConstantCache(nlsolver,D,D2,R,U,uprev2,dtₙ₋₁)
end
function alg_cache(alg::QNDF1,u,rate_prototype,::Type{uEltypeNoUnits},::Type{uBottomEltypeNoUnits},::Type{tTypeNoUnits},uprev,uprev2,f,t,dt,reltol,p,calck,::Val{true}) where {uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits}
γ, c = zero(inv((1-alg.kappa))), 1
nlsolver = build_nlsolver(alg,u,uprev,p,t,dt,f,rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,γ,c,Val(true))
fsalfirst = zero(rate_prototype)
D = Array{typeof(u)}(undef, 1, 1)
D2 = Array{typeof(u)}(undef, 1, 2)
R = fill(zero(t), 1, 1)
U = fill(zero(t), 1, 1)
D[1] = zero(u)
D2[1] = zero(u); D2[2] = zero(u)
U!(1,U)
atmp = similar(u,uEltypeNoUnits); recursivefill!(atmp,false)
utilde = zero(u)
uprev2 = zero(u)
dtₙ₋₁ = zero(dt)
QNDF1Cache(uprev2,fsalfirst,D,D2,R,U,atmp,utilde,nlsolver,dtₙ₋₁)
end
# QNDF2
@cache mutable struct QNDF2ConstantCache{N,coefType,coefType1,coefType2,uType,dtType} <: OrdinaryDiffEqConstantCache
nlsolver::N
D::coefType1
D2::coefType2
R::coefType
U::coefType
uprev2::uType
uprev3::uType
dtₙ₋₁::dtType
dtₙ₋₂::dtType
end
@cache mutable struct QNDF2Cache{uType,rateType,coefType,coefType1,coefType2,uNoUnitsType,N,dtType} <: OrdinaryDiffEqMutableCache
uprev2::uType
uprev3::uType
fsalfirst::rateType
D::coefType1
D2::coefType2
R::coefType
U::coefType
atmp::uNoUnitsType
utilde::uType
nlsolver::N
dtₙ₋₁::dtType
dtₙ₋₂::dtType
end
function alg_cache(alg::QNDF2,u,rate_prototype,::Type{uEltypeNoUnits},::Type{uBottomEltypeNoUnits},::Type{tTypeNoUnits},uprev,uprev2,f,t,dt,reltol,p,calck,::Val{false}) where {uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits}
γ, c = zero(inv((1-alg.kappa))), 1
nlsolver = build_nlsolver(alg,u,uprev,p,t,dt,f,rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,γ,c,Val(false))
uprev2 = u
uprev3 = u
dtₙ₋₁ = zero(t)
dtₙ₋₂ = zero(t)
D = fill(zero(u), 1, 2)
D2 = fill(zero(u), 1, 3)
R = fill(zero(t), 2, 2)
U = fill(zero(t), 2, 2)
U!(2,U)
QNDF2ConstantCache(nlsolver,D,D2,R,U,uprev2,uprev3,dtₙ₋₁,dtₙ₋₂)
end
function alg_cache(alg::QNDF2,u,rate_prototype,::Type{uEltypeNoUnits},::Type{uBottomEltypeNoUnits},::Type{tTypeNoUnits},uprev,uprev2,f,t,dt,reltol,p,calck,::Val{true}) where {uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits}
γ, c = zero(inv((1-alg.kappa))), 1
nlsolver = build_nlsolver(alg,u,uprev,p,t,dt,f,rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,γ,c,Val(true))
fsalfirst = zero(rate_prototype)
D = Array{typeof(u)}(undef, 1, 2)
D2 = Array{typeof(u)}(undef, 1, 3)
R = fill(zero(t), 2, 2)
U = fill(zero(t), 2, 2)
D[1] = zero(u); D[2] = zero(u)
D2[1] = zero(u); D2[2] = zero(u); D2[3] = zero(u)
U!(2,U)
atmp = similar(u,uEltypeNoUnits); recursivefill!(atmp,false)
utilde = zero(u)
uprev2 = zero(u)
uprev3 = zero(u)
dtₙ₋₁ = zero(dt)
dtₙ₋₂ = zero(dt)
QNDF2Cache(uprev2,uprev3,fsalfirst,D,D2,R,U,atmp,utilde,nlsolver,dtₙ₋₁,dtₙ₋₂)
end
@cache mutable struct QNDFConstantCache{MO,N,coefType,UType,dtType,EEstType,gammaType} <: OrdinaryDiffEqConstantCache
nlsolver::N
U::UType
D::coefType
prevD::coefType
prevorder::Int
order::Int
max_order::Val{MO}
dtprev::dtType
nconsteps::Int ##Successful Consecutive Step with the same step size
consfailcnt::Int #Consecutive failed steps count
EEst1::EEstType #Error Estimator for k-1 order
EEst2::EEstType #Error Estimator for k+1 order
γₖ::gammaType
end
function alg_cache(alg::QNDF{MO},u,rate_prototype,::Type{uEltypeNoUnits},::Type{uBottomEltypeNoUnits},::Type{tTypeNoUnits},uprev,uprev2,f,t,dt,reltol,p,calck,::Val{false}) where {uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits} where MO
max_order = MO
γ, c = one(eltype(alg.kappa)), 1
nlsolver = build_nlsolver(alg,u,uprev,p,t,dt,f,rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,γ,c,Val(false))
dtprev = one(dt)
D = Matrix{uEltypeNoUnits}(undef, length(u), max_order+2)
recursivefill!(D, zero(uEltypeNoUnits))
prevD = similar(D)
recursivefill!(prevD, zero(uEltypeNoUnits))
EEst1 = tTypeNoUnits(1)
EEst2 = tTypeNoUnits(1)
U = zero(MMatrix{max_order,max_order,tTypeNoUnits})
for r = 1:max_order
U[1,r] = -r
for j = 2:max_order
U[j,r] = U[j-1,r] * ((j-1) - r)/j
end
end
U = SArray(U)
γₖ = SVector(ntuple(k->sum(tTypeNoUnits(1//j) for j in 1:k), Val(max_order)))
QNDFConstantCache(nlsolver, U, D, prevD, 1, 1, Val(max_order), dtprev, 0, 0, EEst1, EEst2, γₖ)
end
@cache mutable struct QNDFCache{MO,UType,RUType,rateType,N,coefType,dtType,EEstType,gammaType,uType,uNoUnitsType} <: OrdinaryDiffEqMutableCache
fsalfirst::rateType
dd::uType
utilde::uType
utildem1::uType
utildep1::uType
ϕ::uType
u₀::uType
nlsolver::N
U::UType
RU::RUType
D::coefType
Dtmp::coefType
tmp2::uType
prevD::coefType
order::Int
prevorder::Int
max_order::Val{MO}
dtprev::dtType
nconsteps::Int ##Successful consecutive step with the same step size
consfailcnt::Int #Consecutive failed steps count
EEst1::EEstType #Error Estimator for k-1 order
EEst2::EEstType #Error Estimator for k+1 order
γₖ::gammaType
atmp::uNoUnitsType
atmpm1::uNoUnitsType
atmpp1::uNoUnitsType
end
function alg_cache(alg::QNDF{MO},u,rate_prototype,::Type{uEltypeNoUnits},::Type{uBottomEltypeNoUnits},::Type{tTypeNoUnits},uprev,uprev2,f,t,dt,reltol,p,calck,::Val{true}) where {uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits} where MO
max_order = MO
γ, c = one(eltype(alg.kappa)), 1
nlsolver = build_nlsolver(alg,u,uprev,p,t,dt,f,rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,γ,c,Val(true))
fsalfirst = zero(rate_prototype)
dd = zero(u)
utilde = zero(u)
utildem1 = zero(u)
utildep1 = zero(u)
ϕ = zero(u)
u₀ = zero(u)
dtprev = one(dt)
D = similar(u, uEltypeNoUnits, length(u), max_order + 2)
recursivefill!(D, zero(uEltypeNoUnits))
Dtmp = similar(D)
recursivefill!(Dtmp, zero(uEltypeNoUnits))
prevD = zero(similar(D))
atmp = zero(similar(u, uEltypeNoUnits))
atmpm1 = zero(similar(u, uEltypeNoUnits))
atmpp1 = zero(similar(u, uEltypeNoUnits))
tmp2 = zero(u)
EEst1 = tTypeNoUnits(1)
EEst2 = tTypeNoUnits(1)
U = zero(MMatrix{max_order,max_order,tTypeNoUnits})
for r = 1:max_order
U[1,r] = -r
for j = 2:max_order
U[j,r] = U[j-1,r] * ((j-1) - r)/j
end
end
U = SArray(U)
RU = Matrix(U)
γₖ = SVector(ntuple(k->sum(tTypeNoUnits(1//j) for j in 1:k), Val(max_order)))
QNDFCache(fsalfirst, dd, utilde, utildem1, utildep1, ϕ, u₀, nlsolver, U, RU, D, Dtmp, tmp2, prevD, 1, 1, Val(max_order), dtprev, 0, 0, EEst1, EEst2, γₖ, atmp, atmpm1, atmpp1)
end
@cache mutable struct MEBDF2Cache{uType,rateType,uNoUnitsType,N} <: OrdinaryDiffEqMutableCache
u::uType
uprev::uType
uprev2::uType
fsalfirst::rateType
z₁::uType
z₂::uType
tmp2::uType
atmp::uNoUnitsType
nlsolver::N
end
function alg_cache(alg::MEBDF2,u,rate_prototype,::Type{uEltypeNoUnits},::Type{uBottomEltypeNoUnits},
::Type{tTypeNoUnits},uprev,uprev2,f,t,dt,reltol,p,calck,::Val{true}) where {uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits}
γ, c = 1, 1
nlsolver = build_nlsolver(alg,u,uprev,p,t,dt,f,rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,γ,c,Val(true))
fsalfirst = zero(rate_prototype)
z₁ = zero(u); z₂ = zero(u); z₃ = zero(u); tmp2 = zero(u)
atmp = similar(u,uEltypeNoUnits); recursivefill!(atmp,false)
MEBDF2Cache(u,uprev,uprev2,fsalfirst,z₁,z₂,tmp2,atmp,nlsolver)
end
mutable struct MEBDF2ConstantCache{N} <: OrdinaryDiffEqConstantCache
nlsolver::N
end
function alg_cache(alg::MEBDF2,u,rate_prototype,::Type{uEltypeNoUnits},::Type{uBottomEltypeNoUnits},
::Type{tTypeNoUnits},uprev,uprev2,f,t,dt,reltol,p,calck,::Val{false}) where {uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits}
γ, c = 1, 1
nlsolver = build_nlsolver(alg,u,uprev,p,t,dt,f,rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,γ,c,Val(false))
MEBDF2ConstantCache(nlsolver)
end
@cache mutable struct FBDFConstantCache{MO,N,tsType,tType,uType,uuType,coeffType,EEstType,rType,wType} <: OrdinaryDiffEqConstantCache
nlsolver::N
ts::tsType
ts_tmp::tsType
t_old::tType
u_history::uuType
order::Int
prev_order::Int
u_corrector::uType
bdf_coeffs::coeffType
max_order::Val{MO}
nconsteps::Int # consecutive success steps
consfailcnt::Int #consecutive failed step counts
terkm2::EEstType
terkm1::EEstType
terk::EEstType
terkp1::EEstType
r::rType
weights::wType
iters_from_event::Int
end
function alg_cache(alg::FBDF{MO},u,rate_prototype,::Type{uEltypeNoUnits},::Type{uBottomEltypeNoUnits},::Type{tTypeNoUnits},uprev,uprev2,f,t,dt,reltol,p,calck,::Val{false}) where {uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits} where MO
γ, c = 1.0, 1.0
max_order = MO
nlsolver = build_nlsolver(alg,u,uprev,p,t,dt,f,rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,γ,c,Val(false))
bdf_coeffs = SA[1 -1 0 0 0 0 ;
3//2 -2 1//2 0 0 0 ;
11//6 -3 3//2 -1//3 0 0 ;
25//12 -4 3 -4//3 1//4 0 ;
137//60 -5 5 -10//3 5//4 -1//5]
ts = zero(Vector{typeof(t)}(undef,max_order+2)) #ts is the successful past points, it will be updated after successful step
ts_tmp = similar(ts)
u_history = zero(Matrix{eltype(u)}(undef,length(u),max_order+2))
order = 1
prev_order = 1
u_corrector = similar(u_history)
recursivefill!(u_corrector,zero(eltype(u)))
recursivefill!(u_history,zero(eltype(u_history)))
terkm2 = tTypeNoUnits(1)
terkm1= tTypeNoUnits(1)
terk= tTypeNoUnits(1)
terkp1 = tTypeNoUnits(1)
r = zero(Vector{typeof(t)}(undef,max_order+2))
weights = zero(Vector{typeof(t)}(undef,max_order+2))
weights[1] = 1
nconsteps = 0
consfailcnt = 0
t_old = zero(t)
iters_from_event = 0
FBDFConstantCache(nlsolver,ts,ts_tmp,t_old,u_history,order,prev_order,u_corrector,bdf_coeffs,Val(5),nconsteps,consfailcnt,terkm2,terkm1,terk,terkp1,r,weights,iters_from_event)
end
@cache mutable struct FBDFCache{MO,N,rateType,uNoUnitsType,tsType,tType,uType,uuType,coeffType,EEstType,rType,wType} <: OrdinaryDiffEqMutableCache
fsalfirst::rateType
nlsolver::N
ts::tsType
ts_tmp::tsType
t_old::tType
u_history::uuType
order::Int
prev_order::Int
u_corrector::uuType
u₀::uType
bdf_coeffs::coeffType
max_order::Val{MO}
nconsteps::Int # consecutive success steps
consfailcnt::Int #consecutive failed step counts
tmp::uType
atmp::uNoUnitsType
terkm2::EEstType
terkm1::EEstType
terk::EEstType #terk corresponds to hᵏyᵏ(tₙ₊₁)
terkp1::EEstType
terk_tmp::uType
terkp1_tmp::uType
r::rType
weights::wType #weights of Lagrangian formula
equi_ts::tsType
iters_from_event::Int
end
function alg_cache(alg::FBDF{MO},u,rate_prototype,::Type{uEltypeNoUnits},::Type{uBottomEltypeNoUnits},::Type{tTypeNoUnits},uprev,uprev2,f,t,dt,reltol,p,calck,::Val{true}) where {MO,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits}
γ, c = 1.0, 1.0
fsalfirst = zero(rate_prototype)
max_order = MO
nlsolver = build_nlsolver(alg,u,uprev,p,t,dt,f,rate_prototype,uEltypeNoUnits,uBottomEltypeNoUnits,tTypeNoUnits,γ,c,Val(true))
bdf_coeffs = SA[1 -1 0 0 0 0 ;
3//2 -2 1//2 0 0 0 ;
11//6 -3 3//2 -1//3 0 0 ;
25//12 -4 3 -4//3 1//4 0 ;
137//60 -5 5 -10//3 5//4 -1//5]
ts = Vector{typeof(t)}(undef,max_order+2) #ts is the successful past points, it will be updated after successful step
u_history = Matrix{eltype(u)}(undef,length(u),max_order+2)
order = 1
prev_order = 1
u_corrector = similar(u_history)
recursivefill!(ts,zero(t))
recursivefill!(u_corrector,zero(eltype(u)))
recursivefill!(u_history,zero(eltype(u_history)))
terkm2 = tTypeNoUnits(1)
terkm1= tTypeNoUnits(1)
terk= tTypeNoUnits(1)
terkp1 = tTypeNoUnits(1)
terk_tmp = similar(u)
terkp1_tmp = similar(u)
r = Vector{typeof(t)}(undef,max_order+2)
weights = Vector{typeof(t)}(undef,max_order+2)
recursivefill!(r,zero(t))
recursivefill!(weights,zero(t))
weights[1] = 1
nconsteps = 0
consfailcnt = 0
t_old = zero(t)
atmp = similar(u, uEltypeNoUnits)
recursivefill!(atmp,zero(uEltypeNoUnits))
u₀ = similar(u)
equi_ts = similar(ts)
tmp = similar(u)
ts_tmp = similar(ts)
iters_from_event = 0
FBDFCache(fsalfirst,nlsolver,ts,ts_tmp,t_old,u_history,order,prev_order,u_corrector,u₀,bdf_coeffs,Val(5),nconsteps,consfailcnt,tmp,atmp,terkm2,terkm1,terk,terkp1,terk_tmp,terkp1_tmp,r,weights,equi_ts,iters_from_event)
end
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912,
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364,
62,
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62,
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437,
198
] | 2.071615 | 8,448 |
using ImageMethodReverb
using Test
using LinearAlgebra
using DelimitedFiles, Random
@testset "ImageMethodReverb" begin
@testset "Image source method" begin
include("test_ism.jl")
end
@testset "equivalence with MATLAB" begin
include("test_julia_vs_matlab.jl")
end
end
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] | 2.831683 | 101 |
# This file was generated, do not modify it.
using CategoricalArrays
v = categorical(["AA", "BB", "CC", "AA", "BB", "CC"])
levels(v)
v = categorical([1, 2, 3, 1, 2, 3, 1, 2, 3], ordered=true)
levels(v)
v[1] < v[2]
v = categorical(["high", "med", "low", "high", "med", "low"], ordered=true)
levels(v)
v[1] < v[2]
levels!(v, ["low", "med", "high"])
v[1] < v[2]
v = categorical(["AA", "BB", missing, "AA", "BB", "CC"]);
levels(v)
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] | 2.172414 | 203 |
#=------------------------------------------------------------------------------
Formatting Routines
------------------------------------------------------------------------------=#
function produce_ssten_from_triangles(file;use_metis=false,lcc=false)
A = MatrixNetworks.readSMAT(file)
(n,m) = size(A)
if(n != m)
println("rectangular")
end
if !issymmetric(A)
A = max.(A,A') #symmetrize for Triangles routine
end
if use_lcc
A, _ = largest_component(A)
end
if use_metis
apply_Metis_permutation!(A)
end
T = collect(MatrixNetworks.triangles(A))
alterfilename = (file,postfix)-> split(file,".smat")[1]*postfix
if use_metis
output_file = alterfilename(file,"_with_metis.ssten")
else
output_file = alterfilename(file,".ssten")
end
open(output_file,"w") do f
write(f,"$(3)\t$(n)\t$(length(T))\n")
for (v_i,v_j,v_k) in T
write(f,"$(v_i)\t$(v_j)\t$(v_k)\t1.0\n")
end
end
end
function load_UnweightedThirdOrderSymTensor(filepath;enforceFormatting = true)
#check path validity
@assert filepath[end-5:end] == ".ssten"
open(filepath) do file
#preallocate from the header line
order, n, m =
[parse(Int,elem) for elem in split(chomp(readline(file)),'\t')]
@assert order == 3
Ti = [ Vector{Tuple{Int,Int}}(undef, 0) for i in 1:n ]
i = 1
@inbounds for line in eachline(file)
entries = split(chomp(line),'\t')
if enforceFormatting
(ti,tj,tk) = sort([parse(Int,elem) for elem in entries[1:end-1]])
else
(ti,tj,tk) = [parse(Int,elem) for elem in entries[1:end-1]]
end
if 0 == ti || 0 == tj || 0 == tk
error("elements must be indexed by 1.")
end
push!(Ti[ti], (tj,tk))
push!(Ti[tj], (ti,tk))
push!(Ti[tk], (ti,tj))
end
sort!.(Ti)
return UnweightedThirdOrderSymTensor(n,Ti)
end
end
"""------------------------------------------------------------------------------
Loads in a ThirdOrderSymTensor from an ssten file. Data specifications can be
found in the 'formatting_specification.info' file the data/ folder. The
enforceFormatting can be used to ensure that the indices are sorted in
increasing order, and any files which have 0's in indices are updated to be
indexed by 1.
------------------------------------------------------------------------------"""
function load_ThirdOrderSymTensor(filepath;enforceFormatting = true)
#check path validity
@assert filepath[end-5:end] == ".ssten"
open(filepath) do file
#preallocate from the header line
order, n, m =
[parse(Int,elem) for elem in split(chomp(readline(file)),'\t')]
@assert order == 3
indices = Array{Int,2}(undef,order,m)
values = Array{Float64,1}(undef,m)
i = 1
@inbounds for line in eachline(file)
entries = split(chomp(line),'\t')
indices[:,i] = [parse(Int,elem) for elem in entries[1:end-1]]
if enforceFormatting
sort!(indices[:,i])
end
values[i] = parse(Float64,entries[end])
i += 1
end
#check for 0 indexing
zero_indexed = false
@inbounds for i in 1:m
if indices[1,i] == 0
zero_indexed = true
break
end
end
if zero_indexed
indices .+= 1
end
return ThirdOrderSymTensor(n,indices,values)
end
end
function write_to_armadillo_raw_ascii_format(X::Array{T,2},output_file::String) where T
open(output_file,"w") do f
for row in eachrow(X)
println(f,join(row," "))
end
end
end
function write_smat(A::SparseMatrixCSC{T,Int},path::String;delimeter::Char=',',kwargs...) where T
@assert path[end-4:end] == ".smat"
open(path,"w") do f
header = join([size(A)...,nnz(A)],delimeter)# ::NTuple{3,Int}
println(f,header)
for (i,j,v)=zip(findnz(A)...)
i -= 1
j -= 1
print(f,i,delimeter,j,delimeter)
println(f,v)
end
end
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] | 2.378695 | 1,624 |
"""
`function construct_fcast_and_hist_dfs(m, cond_type, vars, save_to_table, table_caption,
filename, savedir, forecast_string, include_T_in_df_forecast, use_4q, hist_start,
forecast_end)`
Construct and return two DataFrame objects that respectively contain the forecast and corresponding
history series for a set of specified variables. Alternatively, if `save_to_table` is
specified to be true with `table_caption`, `filename`, and `savedir` also specified, then
`construct_fcast_and_hist_dfs` will save the DataFrames as LaTeX tables in the specified directory.
### Arguments
- `m::AbstractDSGEModel`: The model object that was used for the forecast.
- `cond_type::Symbol`: The conditioning of the forecast.
- `vars::Vector{Symbol}`: The vector of forecasted variables (can be any combination of observables or pseudo-observables) from which to construct the table.
### Keyword Arguments
- `save_to_table::Bool`: Boolean indicator of whether or not to save the table. If set to be true, then one should also specify the `table_caption`, `filename`, and `savedir`.
- `forecast_string::String`: The forecast string (if relevant) for the given forecast.
- `include_T_in_df_forecast`: Whether or not the last historical period value should be included in the forecast table for reference.
- `use_4q::Bool`: Whether to pull the 4q output_var as opposed to the standard quarterly.
- `hist_start::Date`: The date from which to start the historical table.
- `forecast_end::Date`: The date from which to end the forecast table.
"""
function construct_fcast_and_hist_dfs(m::AbstractDSGEModel, cond_type::Symbol,
vars::Vector{Symbol};
save_to_table::Bool = false,
table_caption::String = "",
filename::String = "",
savedir::String = "",
forecast_string::String = "",
include_T_in_df_forecast::Bool = true,
use_4q::Bool = false,
hist_start::Date = quartertodate("2008-Q4"),
forecast_end::Date = iterate_quarters(date_forecast_start(m), 12))
# Assert that if save_to_table is true, there is a caption, filename, and a savedir
if save_to_table
@assert !isempty(table_caption) && !isempty(filename) && !isempty(savedir)
end
if use_4q
mb_histobs = read_mb(m, :full, cond_type, :hist4qobs, forecast_string = forecast_string)
mb_histpseudo = read_mb(m, :full, cond_type, :hist4qpseudo, forecast_string = forecast_string)
mb_forecastobs = read_mb(m, :full, cond_type, :forecast4qobs, forecast_string = forecast_string)
mb_forecastpseudo = read_mb(m, :full, cond_type, :forecast4qpseudo, forecast_string = forecast_string)
mb_histobs = create_q4q4_mb(mb_histobs)
mb_histpseudo = create_q4q4_mb(mb_histpseudo)
mb_forecastobs = create_q4q4_mb(mb_forecastobs)
mb_forecastpseudo = create_q4q4_mb(mb_forecastpseudo)
else
mb_histobs = read_mb(m, :full, cond_type, :histobs, forecast_string = forecast_string)
mb_histpseudo = read_mb(m, :full, cond_type, :histpseudo, forecast_string = forecast_string)
mb_forecastobs = read_mb(m, :full, cond_type, :forecastobs, forecast_string = forecast_string)
mb_forecastpseudo = read_mb(m, :full, cond_type, :forecastpseudo, forecast_string = forecast_string)
end
obs = intersect(vars, m.observables.keys)
pseudo = intersect(vars, m.pseudo_observables.keys)
df_histobs = mb_histobs.means[:, vcat(:date, obs)]
df_histpseudo = mb_histpseudo.means[:, vcat(:date, pseudo)]
hist_start_ind = findfirst(x -> x == hist_start, df_histobs[!, :date])
df_histobs = df_histobs[hist_start_ind:end, :]
df_histpseudo = df_histpseudo[hist_start_ind:end, :]
df_forecastobs = mb_forecastobs.means[:, vcat(:date, obs)]
df_forecastpseudo = mb_forecastpseudo.means[:, vcat(:date, pseudo)]
if use_4q
# If producing 4q figures, then the forecast_end date must be a Q4 date
forecast_end = quartertodate(string(Dates.year(forecast_end))*"-Q4")
end
forecast_end_ind = findfirst(x -> x == forecast_end, df_forecastobs[!, :date])
df_forecastobs = df_forecastobs[1:forecast_end_ind, :]
df_forecastpseudo = df_forecastpseudo[1:forecast_end_ind, :]
if include_T_in_df_forecast
df_forecastobs = append!(DataFrame(df_histobs[end, :]), df_forecastobs)
df_forecastpseudo = append!(DataFrame(df_histpseudo[end, :]), df_forecastpseudo)
end
if isdefined(DataFrames, :innerjoin)
df_forecast = innerjoin(df_forecastobs, df_forecastpseudo, on = :date)
df_hist = innerjoin(df_histobs, df_histpseudo, on = :date)
else
df_forecast = join(df_forecastobs, df_forecastpseudo, on = :date)
df_hist = join(df_histobs, df_histpseudo, on = :date)
end
obs_keys = m.observables.keys
pseudo_keys = m.pseudo_observables.keys
# Constructs mapping from standard key names in observable/pseudo observable means-bands objects
# to actual names of variables and the corresponding inverse mapping (for the purposes
# of creating a mapping of actual names of variables to units)
header_mappings = create_table_header_mappings(m, vars)
units = unit_mappings(m, df_forecast, header_mappings, use_4q = use_4q)
rename!(df_forecast, header_mappings)
rename!(df_hist, header_mappings)
if save_to_table
fcast_table_caption = table_caption*" Forecast"
hist_table_caption = table_caption*" History"
fcast_filename = filename*"_forecast"
hist_filename = filename*"_history"
df_to_table(df_forecast, fcast_table_caption, fcast_filename, savedir, units)
df_to_table(df_hist, hist_table_caption, hist_filename, savedir, units)
else
return df_forecast, df_hist
end
end
# Implement a df_to_table like function that splits up the df/units into
# sub-DataFrames/Dictionaries of 3/4 variables and creates a single LaTeX
# document that has all of the variables in it
# Enforce that the first column of df is a date column named :date
"""
`function df_to_table(df, caption, filename, savedir, units)`
This is the low level function that is called by `construct_fcast_and_hist_dfs` if the
`save_to_table` kwarg for that function is set to be true.
Alternatively, this function can be called directly, provided with the relevant arguments
for naming, labeling, and saving the table.
### Arguments
- `df::DataFrame`: The DataFrame object that is storing the various series.
- `caption::String`: The title of the LaTeX table.
- `filename::String`: The name of the file.
- `savedir::String`: The filepath ending in the lowest level directory that should contain the table.
- `units::OrderedDict{Symbol, String}`: A dictionary that maps the column names of `df` to
the units of that particular series (this calculation is done automatically in if
`save_to_table` is set to be true.
"""
function df_to_table(df::DataFrame, caption::String, filename::String, savedir::String,
units::OrderedDict{Symbol, String})
# Open the TeX file
savedir = savedir[end] == "/" ? savedir : savedir*"/"
if !ispath(savedir)
println("The savedir path provided does not currently exist. Do you want to create the path '"*savedir*"'? y/n")
answer = readline(stdin)
if answer == "y"
mkpath(savedir)
else
error("Create the proper savedir and call df_to_table again.")
end
else
mkpath(savedir)
end
table_out = savedir*filename*".tex"
fid = open(table_out, "w")
write_table_preamble(fid)
function write_single_table(fid::IOStream, df::DataFrame, units::OrderedDict{Symbol, String})
# Write header
n_columns = length(propertynames(df))
col_str = repeat("c", n_columns)
@printf fid "%s%s%s" "\\begin{longtable}{" col_str "}\n"
@printf fid "\\caption{%s}\n" caption
@printf fid "\\\\ \\hline\n"
# Write column names
date_range = Vector(df[:, :date])
column_keys = propertynames(df)
@printf fid "%s " column_keys[1]
for i in 2:n_columns
column_key = string(column_keys[i])
if occursin(r"_", column_key) # if the key has an underscore then replace it with the proper LaTeX syntax
sub_strs = split(column_key, "_")
column_key = sub_strs[1]*"\\_"*sub_strs[2]
end
column_entry = "\\parbox\\{0.3\\linewidth\\}\\{\\centering "*column_key*"\\}"
if i != n_columns
@printf fid "& %s " column_entry
else
@printf fid "& %s \\\\\n" column_entry
end
end
# Write units
for i in 2:n_columns
if i != n_columns
@printf fid "& %s " units[column_keys[i]]
else
@printf fid "& %s\n" units[column_keys[i]]
end
end
@printf fid "\\\\ \\hline\n"
@printf fid "\\endhead\n"
for (i, date) in enumerate(date_range)
for (j, key) in enumerate(column_keys)
if j != length(column_keys)
if key == :date
@printf fid "%s " df[:, key][i]
else
@printf fid "& %.2f " df[:, key][i]
end
else
@printf fid "& %.2f \\\\\n" df[:, key][i]
end
end
end
@printf fid "\\end{longtable}\n"
end
k = 1
for i in 1:3:length(units)
units_keys = i+2 < length(units) ? units.keys[i:i+2] : units.keys[i:end]
df_subset = DataFrame()
units_subset = OrderedDict{Symbol, String}()
df_subset[!, :date] = df[!, :date]
for unit in units_keys
df_subset[!, unit] = df[!, unit]
units_subset[unit] = units[unit]
end
write_single_table(fid, df_subset, units_subset)
if k % 2 == 0
@printf fid "\\clearpage\n" # every two tables, break the page
else
@printf fid "\\vspace*{.5cm}\n"
end
k += 1
end
@printf fid "\\end{document}"
close(fid)
end
# Rename keys in the obs dictionaries
# So that the DataFrame has LaTeX conformant names
function create_table_header_mappings(m::AbstractDSGEModel, vars::Vector{Symbol})
obs_keys = m.observables.keys
header_mappings = OrderedDict{Symbol, Symbol}()
for var in vars
if var in obs_keys
header_mappings[var] = Symbol(m.observable_mappings[var].name)
else
header_mappings[var] = detexify(Symbol(m.pseudo_observable_mappings[var].name))
end
end
return header_mappings
end
# Defining the units for the variables included
function unit_mappings(m::AbstractDSGEModel, df::DataFrame,
header_mappings::OrderedDict{Symbol, Symbol}; use_4q::Bool = false)
units = OrderedDict{Symbol, String}()
quarter = use_4q ? "Q4" : "Q"
obs_keys = m.observables.keys
pseudo_keys = m.pseudo_observables.keys
for key in propertynames(df)
name = key != :date ? header_mappings[key] : continue
if key in obs_keys
if occursin(r"pct_annualized", string(m.observable_mappings[key].rev_transform))
units[name] = "("*quarter*"/"*quarter*") \\% Annualized"
elseif occursin(r"quartertoannual", string(m.observable_mappings[key].rev_transform))
units[name] = quarter
end
elseif key in pseudo_keys
if occursin(r"pct_annualized", string(m.pseudo_observable_mappings[key].rev_transform))
units[name] = "("*quarter*"/"*quarter*") \\% Annualized"
elseif occursin(r"quartertoannual", string(m.pseudo_observable_mappings[key].rev_transform))
units[name] = quarter
elseif occursin(r"identity", string(m.pseudo_observable_mappings[key].rev_transform))
units[name] = quarter
end
end
end
return units
end
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1774,
7448,
343,
290,
869,
47764,
62,
1462,
62,
11487,
757,
19570,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
33480,
6978,
7,
82,
9586,
343,
8,
198,
220,
220,
220,
886,
198,
220,
220,
220,
3084,
62,
448,
796,
7448,
343,
9,
34345,
9,
1911,
16886,
1,
198,
220,
220,
220,
49909,
796,
1280,
7,
11487,
62,
448,
11,
366,
86,
4943,
628,
220,
220,
220,
3551,
62,
11487,
62,
79,
1476,
903,
7,
69,
312,
8,
198,
220,
220,
220,
2163,
3551,
62,
29762,
62,
11487,
7,
69,
312,
3712,
9399,
12124,
11,
47764,
3712,
6601,
19778,
11,
4991,
3712,
35422,
1068,
35,
713,
90,
13940,
23650,
11,
10903,
30072,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
19430,
13639,
198,
220,
220,
220,
220,
220,
220,
220,
299,
62,
28665,
82,
796,
4129,
7,
26745,
14933,
7,
7568,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
951,
62,
2536,
220,
220,
796,
9585,
7203,
66,
1600,
299,
62,
28665,
82,
8,
628,
220,
220,
220,
220,
220,
220,
220,
2488,
37435,
49909,
36521,
82,
4,
82,
4,
82,
1,
366,
6852,
27471,
90,
6511,
11487,
92,
4895,
951,
62,
2536,
366,
32239,
77,
1,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
37435,
49909,
366,
6852,
6888,
1159,
90,
4,
82,
32239,
77,
1,
8305,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
37435,
49909,
366,
13426,
26867,
71,
1370,
59,
77,
1,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
19430,
5721,
3891,
198,
220,
220,
220,
220,
220,
220,
220,
3128,
62,
9521,
796,
20650,
7,
7568,
58,
45299,
1058,
4475,
12962,
628,
220,
220,
220,
220,
220,
220,
220,
5721,
62,
13083,
796,
3119,
14933,
7,
7568,
8,
628,
220,
220,
220,
220,
220,
220,
220,
2488,
37435,
49909,
36521,
82,
366,
5721,
62,
13083,
58,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1312,
287,
362,
25,
77,
62,
28665,
82,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5721,
62,
2539,
796,
4731,
7,
28665,
62,
13083,
58,
72,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
8833,
259,
7,
81,
1,
62,
1600,
5721,
62,
2539,
8,
1303,
611,
262,
1994,
468,
281,
44810,
788,
6330,
340,
351,
262,
1774,
4689,
49568,
15582,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
850,
62,
2536,
82,
796,
6626,
7,
28665,
62,
2539,
11,
45434,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5721,
62,
2539,
796,
850,
62,
2536,
82,
58,
16,
60,
9,
1,
6852,
62,
1,
9,
7266,
62,
2536,
82,
58,
17,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5721,
62,
13000,
796,
366,
6852,
1845,
3524,
6852,
90,
15,
13,
18,
6852,
2815,
413,
5649,
6852,
92,
6852,
90,
6852,
1087,
1586,
366,
9,
28665,
62,
2539,
9,
1,
6852,
36786,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1312,
14512,
299,
62,
28665,
82,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
37435,
49909,
366,
5,
4064,
82,
366,
5721,
62,
13000,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
37435,
49909,
366,
5,
4064,
82,
3467,
13426,
77,
1,
5721,
62,
13000,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
886,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
19430,
4991,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1312,
287,
362,
25,
77,
62,
28665,
82,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1312,
14512,
299,
62,
28665,
82,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
37435,
49909,
366,
5,
4064,
82,
366,
4991,
58,
28665,
62,
13083,
58,
72,
11907,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
37435,
49909,
366,
5,
4064,
82,
59,
77,
1,
4991,
58,
28665,
62,
13083,
58,
72,
11907,
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,
2488,
37435,
49909,
366,
13426,
26867,
71,
1370,
59,
77,
1,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
37435,
49909,
366,
6852,
437,
2256,
59,
77,
1,
628,
220,
220,
220,
220,
220,
220,
220,
329,
357,
72,
11,
3128,
8,
287,
27056,
378,
7,
4475,
62,
9521,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
357,
73,
11,
1994,
8,
287,
27056,
378,
7,
28665,
62,
13083,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
474,
14512,
4129,
7,
28665,
62,
13083,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1994,
6624,
1058,
4475,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
37435,
49909,
36521,
82,
366,
47764,
58,
45299,
1994,
7131,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
37435,
49909,
366,
5,
4064,
13,
17,
69,
366,
47764,
58,
45299,
1994,
7131,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
37435,
49909,
366,
5,
4064,
13,
17,
69,
3467,
13426,
77,
1,
47764,
58,
45299,
1994,
7131,
72,
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,
628,
220,
220,
220,
220,
220,
220,
220,
2488,
37435,
49909,
366,
6852,
437,
90,
6511,
11487,
32239,
77,
1,
198,
220,
220,
220,
886,
628,
220,
220,
220,
479,
796,
352,
198,
220,
220,
220,
329,
1312,
287,
352,
25,
18,
25,
13664,
7,
41667,
8,
198,
220,
220,
220,
220,
220,
220,
220,
4991,
62,
13083,
220,
220,
796,
1312,
10,
17,
1279,
4129,
7,
41667,
8,
5633,
4991,
13,
13083,
58,
72,
25,
72,
10,
17,
60,
1058,
4991,
13,
13083,
58,
72,
25,
437,
60,
198,
220,
220,
220,
220,
220,
220,
220,
47764,
62,
7266,
2617,
220,
220,
220,
796,
6060,
19778,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
4991,
62,
7266,
2617,
796,
14230,
1068,
35,
713,
90,
13940,
23650,
11,
10903,
92,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
47764,
62,
7266,
2617,
58,
28265,
1058,
4475,
60,
796,
47764,
58,
28265,
1058,
4475,
60,
198,
220,
220,
220,
220,
220,
220,
220,
329,
4326,
287,
4991,
62,
13083,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
47764,
62,
7266,
2617,
58,
28265,
4326,
60,
796,
47764,
58,
28265,
4326,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4991,
62,
7266,
2617,
58,
20850,
60,
796,
4991,
58,
20850,
60,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
3551,
62,
29762,
62,
11487,
7,
69,
312,
11,
47764,
62,
7266,
2617,
11,
4991,
62,
7266,
2617,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
479,
4064,
362,
6624,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
37435,
49909,
366,
6852,
2375,
5117,
496,
59,
77,
1,
1303,
790,
734,
8893,
11,
2270,
262,
2443,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2488,
37435,
49909,
366,
6852,
85,
13200,
9,
90,
13,
20,
11215,
32239,
77,
1,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
479,
15853,
352,
198,
220,
220,
220,
886,
628,
220,
220,
220,
2488,
37435,
49909,
366,
6852,
437,
90,
22897,
36786,
628,
220,
220,
220,
1969,
7,
69,
312,
8,
198,
437,
198,
198,
2,
7152,
480,
8251,
287,
262,
10201,
48589,
3166,
198,
2,
1406,
326,
262,
6060,
19778,
468,
4689,
49568,
17216,
415,
3891,
198,
8818,
2251,
62,
11487,
62,
25677,
62,
76,
39242,
7,
76,
3712,
23839,
5258,
38,
3620,
375,
417,
11,
410,
945,
3712,
38469,
90,
13940,
23650,
30072,
198,
220,
220,
220,
10201,
62,
13083,
796,
285,
13,
672,
3168,
2977,
13,
13083,
198,
220,
220,
220,
13639,
62,
76,
39242,
796,
14230,
1068,
35,
713,
90,
13940,
23650,
11,
38357,
92,
3419,
198,
220,
220,
220,
329,
1401,
287,
410,
945,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1401,
287,
10201,
62,
13083,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13639,
62,
76,
39242,
58,
7785,
60,
796,
38357,
7,
76,
13,
672,
3168,
540,
62,
76,
39242,
58,
7785,
4083,
3672,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13639,
62,
76,
39242,
58,
7785,
60,
796,
1062,
1069,
1958,
7,
13940,
23650,
7,
76,
13,
7752,
12003,
62,
672,
3168,
540,
62,
76,
39242,
58,
7785,
4083,
3672,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
198,
220,
220,
220,
1441,
13639,
62,
76,
39242,
198,
437,
198,
198,
2,
2896,
3191,
262,
4991,
329,
262,
9633,
3017,
198,
8818,
4326,
62,
76,
39242,
7,
76,
3712,
23839,
5258,
38,
3620,
375,
417,
11,
47764,
3712,
6601,
19778,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13639,
62,
76,
39242,
3712,
35422,
1068,
35,
713,
90,
13940,
23650,
11,
38357,
19629,
779,
62,
19,
80,
3712,
33,
970,
796,
3991,
8,
198,
220,
220,
220,
4991,
796,
14230,
1068,
35,
713,
90,
13940,
23650,
11,
10903,
92,
3419,
198,
220,
220,
220,
3860,
796,
779,
62,
19,
80,
5633,
366,
48,
19,
1,
1058,
366,
48,
1,
198,
220,
220,
220,
10201,
62,
13083,
796,
285,
13,
672,
3168,
2977,
13,
13083,
198,
220,
220,
220,
24543,
62,
13083,
796,
285,
13,
7752,
12003,
62,
672,
3168,
2977,
13,
13083,
198,
220,
220,
220,
329,
1994,
287,
3119,
14933,
7,
7568,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1438,
796,
1994,
14512,
1058,
4475,
5633,
13639,
62,
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] | 2.270689 | 5,486 |
using Comonicon
using AdvancedHMC: AdvancedHMC
println("Environment variables for testing")
println(ENV)
const DIRECTORY_AdvancedHMC = dirname(dirname(pathof(AdvancedHMC)))
const DIRECTORY_Turing_tests = joinpath(DIRECTORY_AdvancedHMC, "test", "turing")
const GROUP = get(ENV, "AHMC_TEST_GROUP", "AdvancedHMC")
if GROUP == "All" || GROUP == "AdvancedHMC"
using ReTest, CUDA
include("metric.jl")
include("hamiltonian.jl")
include("integrator.jl")
include("trajectory.jl")
include("adaptation.jl")
include("sampler.jl")
include("sampler-vec.jl")
include("demo.jl")
include("models.jl")
include("abstractmcmc.jl")
if CUDA.functional()
include("cuda.jl")
else
@warn "Skipping GPU tests because no GPU available."
end
@main function runtests(patterns...; dry::Bool=false)
retest(patterns...; dry=dry, verbose=Inf)
end
end
if GROUP == "All" || GROUP == "Downstream"
using Pkg
try
# activate separate test environment
Pkg.activate(DIRECTORY_Turing_tests)
Pkg.develop(PackageSpec(; path=DIRECTORY_AdvancedHMC))
Pkg.instantiate()
# make sure that the new environment is considered `using` and `import` statements
# (not added automatically on Julia 1.3, see e.g. PR #209)
if !(joinpath(DIRECTORY_Turing_tests, "Project.toml") in Base.load_path())
pushfirst!(LOAD_PATH, DIRECTORY_Turing_tests)
end
# Avoids conflicting namespaces, e.g. `NUTS` used in Turing.jl's tests
# refers to `Turing.NUTS` not `AdvancedHMC.NUTS`.
include(joinpath("turing", "runtests.jl"))
catch err
err isa Pkg.Resolve.ResolverError || rethrow()
# If we can't resolve that means this is incompatible by SemVer and this is fine
# It means we marked this as a breaking change, so we don't need to worry about
# Mistakenly introducing a breaking change, as we have intentionally made one
@info "Not compatible with this release. No problem." exception = err
end
end
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] | 2.592269 | 802 |
"""
curve_length(f::Function,a::Real,b::Real;rtol=1e-13) -> Float64
Calculates the length of the curve given by `f`, where `f` is function of one parameter, on interval <`a`, `b`> of said parameter.
# Arguments
- `f::Function`: function of the curve to be measured,
- `a::Real`: left-bound of the interval to be integrated on,
- `b::Real`: right-bound of the interval to be integrated on,
- `rtol::Real`: default `rtol = 1e-13`, relative tolerance,
# Keywords
# Returns
- `Float64`: length of the curve defined by the `f` on the interval <`a`, `b`>.
# Throws
"""
function curve_length(f::Function,a::Real,b::Real;rtol=1e-13)
df = x -> ForwardDiff.derivative(f,x)
dr = x -> sqrt(1+df(x)^2)
l,_ = quadgk(dr,a,b;rtol=rtol)
return l
end
"""
curve_fraction(f::Function,a::Real,b::Real,r::Real) -> Float64
Retunrs the position of the fraction of the length from the interval <`a`, `b`>, where the length of the curve is given by one parameter function `f`.
# Arguments
- `f::Function`: function of the curve,
- `a::Real`: left-bound of the interval to be integrated on,
- `b::Real`: right-bound of the interval to be integrated on,
- `r::Real`: fraction of the length of the curve given by `f` from the left,
- `rtol:Real`: default `rtol = 1e-13`, relative tolerance,
# Keywords
# Returns
- `Float64`: position of the fraction of the length from the interval <`a`, `b`>.
# Throws
"""
function curve_fraction(f::Function,a::Real,b::Real,r::Real;rtol=1e-13)
df = x -> ForwardDiff.derivative(f,x)
dr = x -> sqrt(1.0 + df(x)^2)
l,_ = quadgk(dr,a,b;rtol=rtol)
lf = l*r
xo = (a+b)/2
xn = 0.0
eps_n = 1.0
while eps_n > xo*rtol
l,_ = quadgk(dr,a,xo;rtol=rtol)
xn = xo - (l - lf)/dr(xo)
eps_n = abs(xo-xn)
xo = xn
end
return xo
end
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] | 2.384615 | 767 |
@testset "Read GFA" begin
g_test = SimpleDiGraph(7)
add_edge!(g_test, 1, 2)
add_edge!(g_test, 2, 3)
add_edge!(g_test, 3, 4)
add_edge!(g_test, 4, 5)
add_edge!(g_test, 5, 6)
add_edge!(g_test, 7, 2)
w_test = [Weight(7, 10),
Weight(4, 10),
Weight(2, 44),
Weight(3, 2),
Weight(5, 1),
Weight(6, 11),
Weight(1, 2)
]
l_test = [NodeLabel(7, "4\t-", "TACAGGGTGA", ""),
NodeLabel(4, "4\t+", "TACAGGGTGA", ""),
NodeLabel(2, "2\t+", "TTAACTCCATCTTTGAGAAACATTTAATAATGTAATGTGTTTGT", ""),
NodeLabel(3, "3\t+", "CA", ""),
NodeLabel(5, "5\t+", "A", ""),
NodeLabel(6, "6\t+", "TACAGATGCAC", ""),
NodeLabel(1, "1\t+", "AT", "")
]
e_test = [EdgeLabel("2\t+", "3\t+", "0M", ""),
EdgeLabel("4\t-", "2\t+", "0M", ""),
EdgeLabel("1\t+", "2\t+", "0M", ""),
EdgeLabel("5\t+", "6\t+", "0M", ""),
EdgeLabel("4\t+", "5\t+", "0M", ""),
EdgeLabel("3\t+", "4\t+", "0M", "")
]
gfa_result = read_from_gfa("data/gfa_sample_1.gfa");
g = gfa_result.g
w = gfa_result.w
l = gfa_result.l
e = gfa_result.e
@test g == g_test
@test w == w_test
@test e == e_test
@test l == l_test
end
@testset "Read GFA with weight" begin
w_test = [Weight(7, 4),
Weight(4, 4),
Weight(2, 7),
Weight(3, 6),
Weight(5, 11),
Weight(6, 52),
Weight(1, 2)
]
gfa_result = read_from_gfa("data/gfa_sample_1.gfa"; weight_file = "data/weight.csv")
w = gfa_result.w
@test w == w_test
end | [
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] | 1.833939 | 825 |
# This file is a part of Julia. License is MIT: http://julialang.org/license
abstract AbstractSparseArray{Tv,Ti,N} <: AbstractArray{Tv,N}
typealias AbstractSparseVector{Tv,Ti} AbstractSparseArray{Tv,Ti,1}
typealias AbstractSparseMatrix{Tv,Ti} AbstractSparseArray{Tv,Ti,2}
"""
issparse(S)
Returns `true` if `S` is sparse, and `false` otherwise.
"""
issparse(A::AbstractArray) = false
issparse(S::AbstractSparseArray) = true
issparse{T, A<:AbstractSparseMatrix}(S::Symmetric{T, A}) = true
issparse{T, A<:AbstractSparseMatrix}(S::Hermitian{T, A}) = true
issparse{T, A<:AbstractSparseMatrix}(S::LowerTriangular{T, A}) = true
issparse{T, A<:AbstractSparseMatrix}(S::LinAlg.UnitLowerTriangular{T, A}) = true
issparse{T, A<:AbstractSparseMatrix}(S::UpperTriangular{T, A}) = true
issparse{T, A<:AbstractSparseMatrix}(S::LinAlg.UnitUpperTriangular{T, A}) = true
indtype{Tv,Ti}(S::AbstractSparseArray{Tv,Ti}) = Ti
| [
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] | 2.553073 | 358 |
mutable struct VMCgutzwiller
model :: UnitCellQModel
wavefunction :: GutzwillerSlater
total_steps :: Int
n_proposed :: Int
n_accepted :: Int
data :: Vector{Float64}
end
function report(sim::VMCgutzwiller)
println("Finished with ", sim.total_steps)
println("Number of proposed moves = ", sim.n_proposed)
println("Number of accepted moves = ", sim.n_accepted)
n_sites = numofsites(sim.model)
Dict(zip([(i,j) for i=1:n_sites for j=i+1:n_sites],
sim.data./sim.n_accepted))
end
function runVMC(model::UnitCellQModel,
total_steps::Int)
n_sites = numofsites(model)
hmat = generatebdg(model)
symmetrize = eltype(hmat) <: Complex ? Hermitian : Symmetric
fact = eigen(symmetrize(hmat))
n_occupied = div(n_sites, 2)
println(fact.values[n_occupied-2:n_occupied+2])
e_fermi = fact.values[n_occupied]
n=1
while fact.values[n_occupied+n] - e_fermi < 1.e-10
n=n+1
end
states = fact.vectors[:, 1:n_occupied]
wavefunction = random_gutzwiller_half(states)
sim = VMCgutzwiller(model, wavefunction,
total_steps, 0, 0,
zeros(Float64, binomial(n_sites, 2)))
while sim.n_accepted < sim.total_steps
sim.n_proposed +=1
if propose_step!(sim.wavefunction)
sim.n_accepted += 1
corrdata = measure(sim.wavefunction, :ZZ)
sim.data += corrdata
# display progress every 1000 accepted steps
if sim.n_accepted % 1000 == 0
check_and_update_gutzwiller!(sim.wavefunction)
print("*")
end
end
end
println()
report(sim)
end
function measure(wf::GutzwillerSlater, m::Symbol)
n_sites = size(wf.states, 1)
n_operators = binomial(n_sites, 2)
correlations = zeros(Float64, n_operators)
index = 1
for i=1:n_sites, j=i+1:n_sites
ni = wf.configuration[i] - 1/2
nj = wf.configuration[j] - 1/2
correlations[index] = ni * nj
index += 1
end
correlations
end
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] | 2.109328 | 997 |
# Note that this script can accept some limited command-line arguments, run
# `julia build_tarballs.jl --help` to see a usage message.
using BinaryBuilder
name = "H3"
version = v"3.4.2"
# function url2hash(url)
# path = download(url)
# open(io-> bytes2hex(BinaryProvider.sha256(io)), path)
# end
# url2hash("https://github.com/uber/h3/archive/v3.4.2.zip") |> println
sources = [
"https://github.com/uber/h3/archive/v$version.zip" => "c5d024af8f7a852349ffce69fe33b456f96d7c940c4ffbeb872f98318b21e03c",
]
# Bash recipe for building across all platforms
script = """
cd \$WORKSPACE/srcdir/h3-$version/
cat <<EOF > CMakeLists.txt.patch
diff -uNr h3-3.4.2-original/CMakeLists.txt h3-3.4.2/CMakeLists.txt
--- h3-3.4.2-original/CMakeLists.txt 2019-02-23 18:30:30.000000000 +0900
+++ h3-3.4.2/CMakeLists.txt 2019-02-23 17:58:02.000000000 +0900
@@ -53,6 +53,8 @@
project(h3 LANGUAGES C VERSION \\\${H3_VERSION})
+set(CMAKE_C_FLAGS "-std=c99 \\\${CMAKE_C_FLAGS}")
+
set(H3_COMPILE_FLAGS "")
set(H3_LINK_FLAGS "")
if(NOT WIN32)
@@ -495,21 +497,6 @@
add_h3_executable(mkRandGeo src/apps/testapps/mkRandGeo.c \\\${APP_SOURCE_FILES})
add_h3_executable(mkRandGeoBoundary src/apps/testapps/mkRandGeoBoundary.c \\\${APP_SOURCE_FILES})
- # Benchmarks
- add_custom_target(benchmarks)
-
- macro(add_h3_benchmark name srcfile)
- add_h3_executable(\\\${name} \\\${srcfile} \\\${APP_SOURCE_FILES})
- add_custom_target(bench_\\\${name} COMMAND \\\${TEST_WRAPPER} \\\$<TARGET_FILE:\\\${name}>)
- add_dependencies(benchmarks bench_\\\${name})
- endmacro()
-
- add_h3_benchmark(benchmarkH3Api src/apps/benchmarks/benchmarkH3Api.c)
- add_h3_benchmark(benchmarkKRing src/apps/benchmarks/benchmarkKRing.c)
- add_h3_benchmark(benchmarkH3Line src/apps/benchmarks/benchmarkH3Line.c)
- add_h3_benchmark(benchmarkH3SetToLinkedGeo src/apps/benchmarks/benchmarkH3SetToLinkedGeo.c)
- add_h3_benchmark(benchmarkPolyfill src/apps/benchmarks/benchmarkPolyfill.c)
- add_h3_benchmark(benchmarkPolygon src/apps/benchmarks/benchmarkPolygon.c)
endif()
# Installation (https://github.com/forexample/package-example)
EOF
patch -p1 -i CMakeLists.txt.patch
mkdir build
cd build
cmake -DBUILD_SHARED_LIBS=1 -DCMAKE_INSTALL_PREFIX=\$prefix -DCMAKE_TOOLCHAIN_FILE=/opt/\$target/\$target.toolchain ..
make
make install
rm -rf \$WORKSPACE/destdir/bin/{g,h,k}* \$WORKSPACE/destdir/lib/cmake \$WORKSPACE/destdir/logs
ls \$WORKSPACE/destdir/lib
"""
# These are the platforms we will build for by default, unless further
# platforms are passed in on the command line
platforms = supported_platforms()
# The products that we will ensure are always built
products(prefix) = [
LibraryProduct(prefix, "libh3", :libh3),
]
# Dependencies that must be installed before this package can be built
dependencies = [
]
# Build the tarballs, and possibly a `build.jl` as well.
build_tarballs(ARGS, name, version, sources, script, platforms, products, dependencies)
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] | 2.421053 | 1,235 |
using CircuitComponentRounding
using Documenter
DocMeta.setdocmeta!(CircuitComponentRounding, :DocTestSetup, :(using CircuitComponentRounding); recursive=true)
makedocs(;
modules=[CircuitComponentRounding],
authors="KronosTheLate",
repo="https://github.com/KronosTheLate/CircuitComponentRounding.jl/blob/{commit}{path}#{line}",
sitename="CircuitComponentRounding.jl",
format=Documenter.HTML(;
prettyurls=get(ENV, "CI", "false") == "true",
canonical="https://KronosTheLate.github.io/CircuitComponentRounding.jl",
assets=String[],
),
pages=[
"Home" => "index.md",
],
)
deploydocs(;
repo="github.com/KronosTheLate/CircuitComponentRounding.jl",
)
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] | 2.568345 | 278 |
function update_derivs_pos!(state::Vector{Float64}, derivs::Vector{Float64} )
# Input: state = [x,y,vx,vy], a vector of two 2-d positions and velocities for a test particle
# Output: The derivatives of the position are updated in the preallocated array derivs.
@assert length(state) == 4
@assert length(derivs) == 4
v_x = state[3]
v_y = state[4]
derivs[1] = v_x
derivs[2] = v_y
return derivs;
end
function update_derivs_vel!(state::Vector{Float64}, derivs::Vector{Float64} )
# Input: state = [x,y,vx,vy], a vector of two 2-d positions and velocities for a test particle
# Output: The derivatives of the velocity are updated in the preallocated array derivs.
@assert length(state) == 4
@assert length(derivs) == 4
GM = 1.0
r_x = state[1]
r_y = state[2]
r2 = r_x*r_x+r_y*r_y
a = -GM/r2
r = sqrt(r2)
a_x = a * r_x/r
a_y = a * r_y/r
derivs[3] = a_x
derivs[4] = a_y
return derivs;
end
function update_derivs!(state::Vector{Float64}, derivs::Vector{Float64} )
# Input: state = [x,y,vx,vy], a vector of two 2-d positions and velocities for a test particle
# Output: The derivatives are updated in the preallocated array derivs.
update_derivs_vel!(state,derivs)
update_derivs_pos!(state,derivs)
return derivs
end
function advance_leapfrog!(state::Vector{Float64},derivs::Vector{Float64}, dt::Float64; derivs_current::Bool = false)
# Input/Output: state = array of two 2-d positions and velocities for a test particle
# Temporary space: The derivatives are updated in the preallocated array derivs.
# Input: dt is the fixed time step
# Optional param: derivs_current: whether need to calculate derivatives at beginning
@assert length(state) == length(derivs)
if !derivs_current
update_derivs_pos!(state,derivs);
end
state[1] += 0.5*dt*derivs[1]
state[2] += 0.5*dt*derivs[2]
update_derivs_vel!(state,derivs);
state[3] += dt*derivs[3]
state[4] += dt*derivs[4]
update_derivs_pos!(state,derivs);
state[1] += 0.5*dt*derivs[1]
state[2] += 0.5*dt*derivs[2]
end
# Input/Output: state = [x,y,vx,vy], an array of two 2-d positions and velocities for a test particle
# Input: dt is the fixed time step
# Input: duration is the total
function integrate_leapfrog!(state::Vector{Float64}, dt::Float64, duration::Float64; max_num_log::Integer = 100000)
@assert(length(state)==4)
@assert(dt>0.0)
@assert(duration>0.0)
# Preallocate array to hold data log (including initial state)
nsteps = iceil(duration/dt);
nskip = (nsteps<max_num_log) ? 1 : iceil(nsteps/(max_num_log-1))
num_log = iceil(nsteps/nskip)+1
log = Array(Float64,(num_log,length(state)));
# Pre-allocate and pre-compute derivaties
derivs = Array(Float64,4);
update_derivs!(state,derivs);
# Log initial state
log_pos = 1
log[log_pos,:] = deepcopy(state)
n = 0
t = 0.0
while t<duration
# ensure don't integrate for more than duration
dt_tmp = (t+dt<=duration) ? dt : duration-t;
# advance system by one time step
advance_leapfrog!(state,derivs,dt_tmp, derivs_current=true)
t = t + dt_tmp
n = n + 1
if (n%nskip==0) # Log data
log_pos += 1
@assert( log_pos<=length(log) )
@assert( length(log[log_pos,:])==length(state) )
log[log_pos,:] = deepcopy(state)
end
end
return log
end
function calc_error_leapfrog_old(dur::Float64, dt::Float64 = 2pi/200.0)
state = [1.,0.,0.,1.];
integrate_leapfrog!(state,dt,dur*2pi);
dist = state[1]^2+state[2]^2
phase = atan2(state[2],state[1])
offset = sum((state[1:2].-[1.0,0.0]).^2)
return (dist-1.0,phase,offset)
end
function calc_end_distance_leapfrog(dur::Integer, dt::Float64 = 2pi/200.0, state::Vector{Float64} = [1., 0., 0., 1.] )
#state = [1.,0.,0.,1.];
dist_init = sqrt(state[1]^2+state[2]^2)
phase_init = atan2(state[2],state[1])
integrate_leapfrog!(state,dt,dur*2pi);
# Calculate three metrics of the accuracy of the integration
dist = sqrt(state[1]^2+state[2]^2)
phase = atan2(state[2],state[1])
offset = sqrt(sum((state[1:2].-[1.0,0.0]).^2))
return (dist-dist_init,phase-phase_init,offset)
end
using Base.Test
function test_leapfrog(dur::Integer, dt::Float64 = 2pi/200.0)
err = calc_end_distance_leapfrog(dur,dt)
@test_approx_eq_eps(err[1], 0., 1e-6)
@test_approx_eq_eps(err[2], 0., 1e-1)
@test_approx_eq_eps(err[3], 0., 1e-1)
end
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] | 2.295617 | 1,962 |
const Min_Max = NamedTuple{(:min, :max),Tuple{Float64,Float64}}
const From_To_Float = NamedTuple{(:from, :to),Tuple{Float64,Float64}}
const FromTo_ToFrom_Float = NamedTuple{(:from_to, :to_from),Tuple{Float64,Float64}}
"From http://www.pserc.cornell.edu/matpower/MATPOWER-manual.pdf Table B-4"
@enum GeneratorCostModel begin
PIECEWISE_LINEAR = 1
POLYNOMIAL = 2
end
@enum AngleUnit begin
DEGREES
RADIANS
end
@enum BusType begin
ISOLATED
PQ
PV
REF
SLACK
end
@enum LoadModel begin
ConstantImpedance #Z
ConstantCurrent #I
ConstantPower #P
end
"From https://www.eia.gov/survey/form/eia_923/instructions.pdf"
@enum PrimeMovers begin
BA #Energy Storage, Battery
BT #Turbines Used in a Binary Cycle (including those used for geothermal applications)
CA #Combined-Cycle – Steam Part
CC #Combined-Cycle - Aggregated Plant *augmentation of EIA
CE #Energy Storage, Compressed Air
CP #Energy Storage, Concentrated Solar Power
CS #Combined-Cycle Single-Shaft Combustion turbine and steam turbine share a single generator
CT #Combined-Cycle Combustion Turbine Part
ES #Energy Storage, Other (Specify on Schedule 9, Comments)
FC #Fuel Cell
FW #Energy Storage, Flywheel
GT #Combustion (Gas) Turbine (including jet engine design)
HA #Hydrokinetic, Axial Flow Turbine
HB #Hydrokinetic, Wave Buoy
HK #Hydrokinetic, Other
HY #Hydraulic Turbine (including turbines associated with delivery of water by pipeline)
IC #Internal Combustion (diesel, piston, reciprocating) Engine
PS #Energy Storage, Reversible Hydraulic Turbine (Pumped Storage)
OT #Other – Specify on SCHEDULE 9.
ST #Steam Turbine (including nuclear, geothermal and solar steam; does not include combined-cycle turbine)
PVe #Photovoltaic *renaming from EIA PV to PVe to avoid conflict with BusType::PV
WT #Wind Turbine, Onshore
WS #Wind Turbine, Offshore
end
"AER Aggregated Fuel Code From https://www.eia.gov/survey/form/eia_923/instructions.pdf"
@enum ThermalFuels begin
COAL #COL #Anthracite Coal and Bituminous Coal
WASTE_COAL #WOC #Waste/Other Coal (includes anthracite culm, gob, fine coal, lignite waste, waste coal)
DISTILLATE_FUEL_OIL #DFO #Distillate Fuel Oil (Diesel, No. 1, No. 2, and No. 4
WASTE_OIL #WOO #Waste Oil Kerosene and JetFuel Butane, Propane,
PETROLEUM_COKE #PC #Petroleum Coke
RESIDUAL_FUEL_OIL #RFO #Residual Fuel Oil (No. 5, No. 6 Fuel Oils, and Bunker Oil)
NATURAL_GAS #NG #Natural Gas
OTHER_GAS #OOG #Other Gas and blast furnace gas
NUCLEAR #NUC #Nuclear Fission (Uranium, Plutonium, Thorium)
AG_BIPRODUCT #ORW #Agricultural Crop Byproducts/Straw/Energy Crops
MUNICIPAL_WASTE #MLG #Municipal Solid Waste – Biogenic component
WOOD_WASTE #WWW #Wood Waste Liquids excluding Black Liquor (BLQ) (Includes red liquor, sludge wood, spent sulfite liquor, and other wood-based liquids)
GEOTHERMAL #GEO #Geothermal
OTHER #OTH #Other
end
PS_MAX_LOG = parse(Int, get(ENV, "PS_MAX_LOG", "50"))
DEFAULT_BASE_MVA = 100.0
const POWER_SYSTEM_STRUCT_DESCRIPTOR_FILE =
joinpath(dirname(pathof(PowerSystems)), "descriptors", "power_system_structs.json")
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] | 2.650651 | 1,228 |
using Documenter, DocumenterTools
include("DocumenterShowcase.jl")
# The DOCSARGS environment variable can be used to pass additional arguments to make.jl.
# This is useful on CI, if you need to change the behavior of the build slightly but you
# can not change the .travis.yml or make.jl scripts any more (e.g. for a tag build).
if haskey(ENV, "DOCSARGS")
for arg in split(ENV["DOCSARGS"])
(arg in ARGS) || push!(ARGS, arg)
end
end
makedocs(
modules = [Documenter, DocumenterTools, DocumenterShowcase],
format = Documenter.HTML(
# Use clean URLs, unless built as a "local" build
prettyurls = !("local" in ARGS),
canonical = "https://juliadocs.github.io/Documenter.jl/stable/",
assets = ["assets/favicon.ico"],
analytics = "UA-136089579-2",
highlights = ["yaml"],
),
clean = false,
sitename = "Documenter.jl",
authors = "Michael Hatherly, Morten Piibeleht, and contributors.",
linkcheck = !("skiplinks" in ARGS),
pages = [
"Home" => "index.md",
"Manual" => Any[
"Guide" => "man/guide.md",
"man/examples.md",
"man/syntax.md",
"man/doctests.md",
"man/latex.md",
hide("man/hosting.md", [
"man/hosting/walkthrough.md"
]),
"man/other-formats.md",
],
"showcase.md",
"Library" => Any[
"Public" => "lib/public.md",
"Internals" => map(
s -> "lib/internals/$(s)",
sort(readdir(joinpath(@__DIR__, "src/lib/internals")))
),
],
"contributing.md",
],
strict = !("strict=false" in ARGS),
doctest = ("doctest=only" in ARGS) ? :only : true,
)
deploydocs(
repo = "github.com/JuliaDocs/Documenter.jl.git",
target = "build",
)
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220,
7646,
796,
5145,
7203,
301,
2012,
28,
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1,
287,
5923,
14313,
828,
198,
220,
220,
220,
10412,
395,
796,
5855,
4598,
310,
395,
28,
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1,
287,
5923,
14313,
8,
5633,
1058,
8807,
1058,
2081,
11,
198,
8,
198,
198,
2934,
1420,
31628,
7,
198,
220,
220,
220,
29924,
796,
366,
12567,
13,
785,
14,
16980,
544,
23579,
82,
14,
24941,
263,
13,
20362,
13,
18300,
1600,
198,
220,
220,
220,
2496,
796,
366,
11249,
1600,
198,
8,
198
] | 2.125571 | 876 |
# Note that this script can accept some limited command-line arguments, run
# `julia build_tarballs.jl --help` to see a usage message.
using BinaryBuilder
name = "at_spi2_core"
version = v"2.34.0"
# Collection of sources required to build at-spi2-core
sources = [
ArchiveSource("http://ftp.gnome.org/pub/gnome/sources/at-spi2-core/$(version.major).$(version.minor)/at-spi2-core-$(version).tar.xz",
"d629cdbd674e539f8912028512af583990938c7b49e25184c126b00121ef11c6"),
]
# Bash recipe for building across all platforms
script = raw"""
cd $WORKSPACE/srcdir/at-spi2-core-*/
mkdir build && cd build
# Get a local gettext for msgfmt cross-building
apk add gettext
meson .. --cross-file="${MESON_TARGET_TOOLCHAIN}" \
-Dintrospection=no \
-Dx11=yes \
-Dsystemd_user_dir=no
ninja -j${nproc}
ninja install
"""
# These are the platforms we will build for by default, unless further
# platforms are passed in on the command line
platforms = [p for p in supported_platforms() if Sys.islinux(p) || Sys.isfreebsd(p)]
# The products that we will ensure are always built
products = [
LibraryProduct("libatspi", :libatspi),
]
# Dependencies that must be installed before this package can be built
dependencies = [
Dependency("Dbus_jll"),
Dependency("Glib_jll", v"2.59.0"; compat="2.59.0"),
Dependency("Xorg_libXtst_jll"),
]
# Build the tarballs, and possibly a `build.jl` as well.
build_tarballs(ARGS, name, version, sources, script, platforms, products, dependencies)
| [
2,
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62,
73,
297,
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2,
10934,
262,
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257,
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62,
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4237,
11,
4226,
11,
9554,
11,
3186,
11,
20086,
8,
198
] | 2.701252 | 559 |
"""
function blowfish()
A blowfish.
```jldoctest
julia> cowsay("Bloop, bloop", cow=Cowsay.blowfish)
______________
< Bloop, bloop >
--------------
\\
\\
| .
. |L /|
_ . |\\ _| \\--+._/| .
/ ||\\| Y J ) / |/| ./
J |)'( | ` F`.'/
-<| F __ .-<
| / .-'. `. /-. L___
J \\ < \\ | | O\\|.-'
_J \\ .- \\/ O | | \\ |F
'-F -<_. \\ .-' `-' L__
__J _ _. >-' )._. |-'
`-|.' /_. \\_| F
/.- . _.<
/' /.' .' `\\
/L /' |/ _.-'-\\
/'J ___.---'\\|
|\\ .--' V | `. `
|/`. `-. `._)
/ .-.\\
VK \\ ( `\\
`.\\
```
"""
function blowfish(;eyes="oo", tongue=" ", thoughts="\\")
the_cow =
"""
$thoughts
$thoughts
| .
. |L /|
_ . |\\ _| \\--+._/| .
/ ||\\| Y J ) / |/| ./
J |)'( | ` F`.'/
-<| F __ .-<
| / .-'. `. /-. L___
J \\ < \\ | | O\\|.-'
_J \\ .- \\/ O | | \\ |F
'-F -<_. \\ .-' `-' L__
__J _ _. >-' )._. |-'
`-|.' /_. \\_| F
/.- . _.<
/' /.' .' `\\
/L /' |/ _.-'-\\
/'J ___.---'\\|
|\\ .--' V | `. `
|/`. `-. `._)
/ .-.\\
VK \\ ( `\\
`.\\
"""
return the_cow
end
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198,
37811,
198,
7783,
262,
62,
8232,
198,
437,
198
] | 1.505735 | 959 |
function PolynomialNutation(arg0::Vector{jdouble})
return PolynomialNutation((Vector{jdouble},), arg0)
end
function derivative(obj::PolynomialNutation, arg0::RealFieldElement)
return jcall(obj, "derivative", RealFieldElement, (RealFieldElement,), arg0)
end
function derivative(obj::PolynomialNutation, arg0::jdouble)
return jcall(obj, "derivative", jdouble, (jdouble,), arg0)
end
function value(obj::PolynomialNutation, arg0::RealFieldElement)
return jcall(obj, "value", RealFieldElement, (RealFieldElement,), arg0)
end
function value(obj::PolynomialNutation, arg0::jdouble)
return jcall(obj, "value", jdouble, (jdouble,), arg0)
end
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828,
1822,
15,
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198,
437,
628
] | 2.885965 | 228 |
# unit test the bGrad
include("../src/DMD_Util.jl");
include("../src/DMD_Type.jl");
include("../src/DMD_Func.jl");
# Generate DMD Synthetic Data
#------------------------------------------------------------------------------
# dimensions
m = 100; # temporal dimension
n = 100; # spatial dimension
k = 3; # number of modes
T = Float64;
# generate data
X, t, at, Bt = simDMD(m, n, k, T; seed=123456);
# specify the loss
function lossFunc(r)
return sum(abs2, r)
end
#
function lossGrad(r)
conj!(r)
end
#
# Create object
#------------------------------------------------------------------------------
params = DMDParams(k, X, t, lossFunc, lossGrad);
#
# Initialize with true parameter
#------------------------------------------------------------------------------
copyto!(params.a, at);
copyto!(params.B, Bt);
#
# Evaluate bGrad
#------------------------------------------------------------------------------
Random.randn!(params.B);
#
err = aBFunc(params);
#
if err < 1e-6
println("aBFunc: OK");
else
println("aBFunc: Wrong, err: $err");
end
#
id = rand(1:params.n);
#
Random.randn!(params.b[id]);
#
err = abFunc(params, id);
#
if err < 1e-6
println("abFunc: OK");
else
println("abFunc: Wrong, err: $err");
end | [
2,
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25,
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11,
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198,
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] | 3.052239 | 402 |
using Documenter, JuliaDB, IndexedTables
@info "makedocs"
makedocs(
clean = true,
debug = true,
format = Documenter.HTML(),
sitename = "JuliaDB.jl",
pages = [
"index.md",
"basics.md",
"operations.md",
"joins.md",
"onlinestats.md",
"plotting.md",
"missing_values.md",
"out_of_core.md",
"ml.md",
"tutorial.md",
"api.md",
]
)
@info "deploydocs"
deploydocs(
repo = "github.com/JuliaComputing/JuliaDB.jl.git"
)
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544,
11012,
13,
20362,
13,
18300,
1,
198,
8,
198
] | 1.890511 | 274 |
@doc raw"""
ProbabilitySimplex{n} <: AbstractEmbeddedManifold{ℝ,DefaultEmbeddingType}
The (relative interior of) the probability simplex is the set
````math
Δ^n := \biggl\{ p ∈ ℝ^{n+1}\ \big|\ p_i > 0 \text{ for all } i=1,…,n+1,
\text{ and } ⟨\mathbb{1},p⟩ = \sum_{i=1}^{n+1} p_i = 1\biggr\},
````
where $\mathbb{1}=(1,…,1)^{\mathrm{T}}∈ ℝ^{n+1}$ denotes the vector containing only ones.
This set is also called the unit simplex or standard simplex.
The tangent space is given by
````math
T_pΔ^n = \biggl\{ X ∈ ℝ^{n+1}\ \big|\ ⟨\mathbb{1},X⟩ = \sum_{i=1}^{n+1} X_i = 0 \biggr\}
````
The manifold is implemented assuming the Fisher-Rao metric for the multinomial distribution,
which is equivalent to the induced metric from isometrically embedding the probability
simplex in the $n$-sphere of radius 2.
The corresponding diffeomorphism $\varphi: \mathbb Δ^n → \mathcal N$,
where $\mathcal N \subset 2𝕊^n$ is given by $\varphi(p) = 2\sqrt{p}$.
This implementation follows the notation in [^ÅströmPetraSchmitzerSchnörr2017].
[^ÅströmPetraSchmitzerSchnörr2017]:
> F. Åström, S. Petra, B. Schmitzer, C. Schnörr: “Image Labeling by Assignment”,
> Journal of Mathematical Imaging and Vision, 58(2), pp. 221–238, 2017.
> doi: [10.1007/s10851-016-0702-4](https://doi.org/10.1007/s10851-016-0702-4)
> arxiv: [1603.05285](https://arxiv.org/abs/1603.05285).
"""
struct ProbabilitySimplex{n} <: AbstractEmbeddedManifold{ℝ,DefaultEmbeddingType} end
ProbabilitySimplex(n::Int) = ProbabilitySimplex{n}()
"""
SoftmaxRetraction <: AbstractRetractionMethod
Describes a retraction that is based on the softmax function.
"""
struct SoftmaxRetraction <: AbstractRetractionMethod end
"""
SoftmaxInverseRetraction <: AbstractInverseRetractionMethod
Describes an inverse retraction that is based on the softmax function.
"""
struct SoftmaxInverseRetraction <: AbstractInverseRetractionMethod end
"""
FisherRaoMetric <: Metric
The Fisher-Rao metric or Fisher information metric is a particular Riemannian metric which
can be defined on a smooth statistical manifold, i.e., a smooth manifold whose points are
probability measures defined on a common probability space.
See for example the [`ProbabilitySimplex`](@ref).
"""
struct FisherRaoMetric <: Metric end
"""
check_manifold_point(M::ProbabilitySimplex, p; kwargs...)
Check whether `p` is a valid point on the [`ProbabilitySimplex`](@ref) `M`, i.e. is a point in
the embedding with positive entries that sum to one
The tolerance for the last test can be set using the `kwargs...`.
"""
function check_manifold_point(M::ProbabilitySimplex, p; kwargs...)
mpv = invoke(
check_manifold_point,
Tuple{(typeof(get_embedding(M))),typeof(p)},
get_embedding(M),
p;
kwargs...,
)
mpv === nothing || return mpv
if minimum(p) <= 0
return DomainError(
minimum(p),
"The point $(p) does not lie on the $(M) since it has nonpositive entries.",
)
end
if !isapprox(sum(p), 1.0; kwargs...)
return DomainError(
sum(p),
"The point $(p) does not lie on the $(M) since its sum is not 1.",
)
end
return nothing
end
"""
check_tangent_vector(M::ProbabilitySimplex, p, X; check_base_point = true, kwargs... )
Check whether `X` is a tangent vector to `p` on the [`ProbabilitySimplex`](@ref) `M`, i.e.
after [`check_manifold_point`](@ref check_manifold_point(::ProbabilitySimplex, ::Any))`(M,p)`,
`X` has to be of same dimension as `p` and its elements have to sum to one.
The optional parameter `check_base_point` indicates, whether to call
[`check_manifold_point`](@ref check_manifold_point(::ProbabilitySimplex, ::Any)) for `p` or not.
The tolerance for the last test can be set using the `kwargs...`.
"""
function check_tangent_vector(M::ProbabilitySimplex, p, X; check_base_point=true, kwargs...)
if check_base_point
mpe = check_manifold_point(M, p; kwargs...)
mpe === nothing || return mpe
end
mpv = invoke(
check_tangent_vector,
Tuple{typeof(get_embedding(M)),typeof(p),typeof(X)},
get_embedding(M),
p,
X;
check_base_point=false, # already checked above
kwargs...,
)
mpv === nothing || return mpv
if !isapprox(sum(X), 0.0; kwargs...)
return DomainError(
sum(X),
"The vector $(X) is not a tangent vector to $(p) on $(M), since its elements to not sum up to 0.",
)
end
return nothing
end
decorated_manifold(M::ProbabilitySimplex) = Euclidean(representation_size(M)...; field=ℝ)
default_metric_dispatch(::ProbabilitySimplex, ::FisherRaoMetric) = Val(true)
@doc raw"""
distance(M,p,q)
Compute the distance between two points on the [`ProbabilitySimplex`](@ref) `M`.
The formula reads
````math
d_{Δ^n}(p,q) = 2\arccos \biggl( \sum_{i=1}^{n+1} \sqrt{p_i q_i} \biggr)
````
"""
function distance(::ProbabilitySimplex, p, q)
sumsqrt = zero(Base.promote_eltype(p, q))
@inbounds for i in eachindex(p, q)
sumsqrt += sqrt(p[i] * q[i])
end
return 2 * acos(sumsqrt)
end
@doc raw"""
exp(M::ProbabilitySimplex,p,X)
Compute the exponential map on the probability simplex.
````math
\exp_pX = \frac{1}{2}\Bigl(p+\frac{X_p^2}{\lVert X_p \rVert^2}\Bigr)
+ \frac{1}{2}\Bigl(p - \frac{X_p^2}{\lVert X_p \rVert^2}\Bigr)\cos(\lVert X_p\rVert)
+ \frac{1}{\lVert X_p \rVert}\sqrt{p}\sin(\lVert X_p\rVert),
````
where $X_p = \frac{X}{\sqrt{p}}$, with its division meant elementwise, as well as for the
operations $X_p^2$ and $\sqrt{p}$.
"""
exp(::ProbabilitySimplex, ::Any...)
function exp!(::ProbabilitySimplex, q, p, X)
s = sqrt.(p)
Xs = X ./ s ./ 2
θ = norm(Xs)
q .= (cos(θ) .* s .+ usinc(θ) .* Xs) .^ 2
return q
end
@doc raw"""
injectivity_radius(M,p)
compute the injectivity radius on the [`ProbabilitySimplex`](@ref) `M` at the point `p`,
i.e. the distanceradius to a point near/on the boundary, that could be reached by following the
geodesic.
"""
function injectivity_radius(::ProbabilitySimplex{n}, p) where {n}
i = argmin(p)
s = sum(p) - p[i]
return 2 * acos(sqrt(s))
end
function injectivity_radius(M::ProbabilitySimplex, p, ::ExponentialRetraction)
return injectivity_radius(M, p)
end
injectivity_radius(M::ProbabilitySimplex, p, ::SoftmaxRetraction) = injectivity_radius(M, p)
injectivity_radius(M::ProbabilitySimplex) = 0
injectivity_radius(M::ProbabilitySimplex, ::SoftmaxRetraction) = 0
injectivity_radius(M::ProbabilitySimplex, ::ExponentialRetraction) = 0
eval(
quote
@invoke_maker 1 Manifold injectivity_radius(
M::ProbabilitySimplex,
rm::AbstractRetractionMethod,
)
end,
)
@doc raw"""
inner(M::ProbabilitySimplex,p,X,Y)
Compute the inner product of two tangent vectors `X`, `Y` from the tangent space $T_pΔ^n$ at
`p`. The formula reads
````math
g_p(X,Y) = \sum_{i=1}^{n+1}\frac{X_iY_i}{p_i}
````
"""
function inner(::ProbabilitySimplex, p, X, Y)
d = zero(Base.promote_eltype(p, X, Y))
@inbounds for i in eachindex(p, X, Y)
d += X[i] * Y[i] / p[i]
end
return d
end
@doc raw"""
inverse_retract(M::ProbabilitySimplex, p, q, ::SoftmaxInverseRetraction)
Compute a first order approximation by projection. The formula reads
````math
\operatorname{retr}^{-1}_p q = \bigl( I_{n+1} - \frac{1}{n}\mathbb{1}^{n+1,n+1} \bigr)(\log(q)-\log(p))
````
where $\mathbb{1}^{m,n}$ is the size `(m,n)` matrix containing ones, and $\log$ is applied elementwise.
"""
inverse_retract(::ProbabilitySimplex, ::Any, ::Any, ::SoftmaxInverseRetraction)
function inverse_retract!(
::ProbabilitySimplex{n},
X,
p,
q,
::SoftmaxInverseRetraction,
) where {n}
X .= log.(q) .- log.(p)
meanlogdiff = mean(X)
X .-= meanlogdiff
return X
end
@doc raw"""
log(M::ProbabilitySimplex, p, q)
Compute the logarithmic map of `p` and `q` on the [`ProbabilitySimplex`](@ref) `M`.
````math
\log_pq = \frac{d_{Δ^n}(p,q)}{\sqrt{1-⟨\sqrt{p},\sqrt{q}⟩}}(\sqrt{pq} - ⟨\sqrt{p},\sqrt{q}⟩p),
````
where $pq$ and $\sqrt{p}$ is meant elementwise.
"""
log(::ProbabilitySimplex, ::Any...)
function log!(::ProbabilitySimplex, X, p, q)
if p ≈ q
fill!(X, 0)
else
z = sqrt.(p .* q)
s = sum(z)
X .= 2 * acos(s) / sqrt(1 - s^2) .* (z .- s .* p)
end
return X
end
@doc raw"""
manifold_dimension(M::ProbabilitySimplex{n})
Returns the manifold dimension of the probability simplex in $ℝ^{n+1}$, i.e.
````math
\dim_{Δ^n} = n.
````
"""
manifold_dimension(::ProbabilitySimplex{n}) where {n} = n
@doc raw"""
mean(
M::ProbabilitySimplex,
x::AbstractVector,
[w::AbstractWeights,]
method = GeodesicInterpolation();
kwargs...,
)
Compute the Riemannian [`mean`](@ref mean(M::Manifold, args...)) of `x` using
[`GeodesicInterpolation`](@ref).
"""
mean(::ProbabilitySimplex, ::Any...)
function Statistics.mean!(
M::ProbabilitySimplex,
p,
x::AbstractVector,
w::AbstractVector;
kwargs...,
)
return mean!(M, p, x, w, GeodesicInterpolation(); kwargs...)
end
@doc raw"""
project(M::ProbabilitySimplex, p, Y)
project `Y` from the embedding onto the tangent space at `p` on
the [`ProbabilitySimplex`](@ref) `M`. The formula reads
````math
\operatorname{proj}_{Δ^n}(p,Y) = Y - ⟨p,Y⟩p.
````
"""
project(::ProbabilitySimplex, ::Any, ::Any)
function project!(::ProbabilitySimplex, q, p)
if any(x -> x <= 0, p)
throw(
DomainError(
p,
"All coordinates of point from the embedding, that should be projected, must be positive, otherwise the projection is not well defined.",
),
)
end
q .= p ./ sum(p)
return q
end
function project!(::ProbabilitySimplex, X, p, Y)
X .= Y .- sum(Y) .* p
return X
end
@doc raw"""
representation_size(::ProbabilitySimplex{n})
return the representation size of points in the $n$-dimensional probability simplex,
i.e. an array size of `(n+1,)`.
"""
representation_size(::ProbabilitySimplex{n}) where {n} = (n + 1,)
@doc raw"""
retract(M::ProbabilitySimplex, p, X, ::SoftmaxRetraction)
Compute a first order approximation by applying the softmax function. The formula reads
````math
\operatorname{retr}_p X = \frac{p\mathrm{e}^X}{⟨p,\mathrm{e}^X⟩},
````
where multiplication, exponentiation and division are meant elementwise.
"""
retract(::ProbabilitySimplex, ::Any, ::Any, ::SoftmaxRetraction)
function retract!(::ProbabilitySimplex, q, p, X, ::SoftmaxRetraction)
s = zero(eltype(q))
@inbounds for i in eachindex(q, p, X)
q[i] = p[i] * exp(X[i])
s += q[i]
end
q ./= s
return q
end
function Base.show(io::IO, ::ProbabilitySimplex{n}) where {n}
return print(io, "ProbabilitySimplex($(n))")
end
@doc raw"""
zero_tangent_vector(M::ProbabilitySimplex,p)
returns the zero tangent vector in the tangent space of the point `p` from the
[`ProbabilitySimplex`](@ref) `M`, i.e. its representation by the zero vector in the embedding.
"""
zero_tangent_vector(::ProbabilitySimplex, ::Any)
zero_tangent_vector!(M::ProbabilitySimplex, v, p) = fill!(v, 0)
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] | 2.355914 | 4,768 |
# create an index for id6
import Base.ht_keyindex
import Base.ht_keyindex2
function buildindex_iter{T}(val::Vector{T})
index = zeros(Int64, length(val))
for (i,v) in enumerate(val)
dindex = ht_keyindex2(index, v)
end
return index
end
function sumby_index{T,S}(index::Dict{T, Vector{Int64}}, val::Vector{S})
return Dict{T,S}(k => sum(val[index[k]]) for k in keys(index))
end
@time iid4_iter = buildindex_iter(id4); #9
@time sumby_index(iid4_iter, v1); #8
@time sumby_index(iid4_iter, v1); #8
@time iid6_iter = buildindex_iter(id6); #259
@time sumby_index(iid6, v1); # 30.93
@time sumby_index(iid6, v3); #26.7
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] | 2.266187 | 278 |
abstract type AbstractWakeDeflectionModel end
"""
GaussYawDeflection(horizontal_spread_rate, vertical_spread_rate, alpha_star, beta_star)
Container for parameters related to the Gaussian deflection model presented by Bastankhah and Porte-Agel 2016
# Arguments
- `horizontal_spread_rate::Float`: parameter controlling the horizontal spread of the deficit model. Default value is 0.022.
- `vertical_spread_rate::Float`: parameter controlling the vertical spread of the deficit model. Default value is 0.022.
- `alpha_star::Float`: parameter controlling the impact of turbulence intensity on the length of the near wake. Default value is 2.32.
- `beta_star::Float`: parameter controlling the impact of the thrust coefficient on the length of the near wake. Default value is 0.154.
"""
struct GaussYawDeflection{TF} <: AbstractWakeDeflectionModel
horizontal_spread_rate::TF
vertical_spread_rate::TF
alpha_star::TF
beta_star::TF
end
GaussYawDeflection() = GaussYawDeflection(0.022, 0.022, 2.32, 0.154)
"""
GaussYawDeflectionVariableSpread(alpha_star, beta_star, k1, k2, wec_factor)
Container for parameters related to the Gaussian deflection model with yaw presented by Bastankhah and Porte-Agel 2016
# Arguments
- `alpha_star::Float`: parameter controlling the impact of turbulence intensity on the length of the near wake. Default value is 2.32.
- `beta_star::Float`: parameter controlling the impact of the thrust coefficient on the length of the near wake. Default value is 0.154.
- `k1::Float`: first parameter tuning wake spread as based on turbulence intensity
- `k2::Float`: second parameter tuning wake spread as based on turbulence intensity
"""
struct GaussYawVariableSpreadDeflection{TF} <: AbstractWakeDeflectionModel
alpha_star::TF
beta_star::TF
k1::TF
k2::TF
end
GaussYawVariableSpreadDeflection() = GaussYawVariableSpreadDeflection(2.32, 0.154, 0.3837, 0.003678)
GaussYawVariableSpreadDeflection(x, y) = GaussYawVariableSpreadDeflection(x, y, 0.3837, 0.003678)
"""
JiminezYawDeflection(horizontal_spread_rate)
Container for parameters related to the Jiminez deflection model
# Arguments
- `horizontal_spread_rate::Float`: parameter controlling the wake spreading rate and deficit decay. Default value is 0.1
"""
struct JiminezYawDeflection{TF} <: AbstractWakeDeflectionModel
horizontal_spread_rate::TF
end
JiminezYawDeflection() = JiminezYawDeflection(0.1)
"""
MultizoneDeflection(horizontal_spread_rate)
Container for parameters related to the Jiminez deflection model
# Arguments
- `horizontal_spread_rate::Float`: parameter controlling the wake spreading rate and deficit decay. Default value is 0.1
- `ad::Float`:Helps define the horizontal deflection of the wake at 0 deg yaw
- `bd::Float`:Helps define the horizontal deflection of the wake due to downwind distance at 0 deg yaw
"""
struct MultizoneDeflection{TF} <: AbstractWakeDeflectionModel
horizontal_spread_rate::TF
ad::TF
bd::TF
end
MultizoneDeflection() = MultizoneDeflection(0.15, -4.5, -0.01)
"""
wake_deflection_model(locx, locy, locz, turbine_id, turbine_definition::TurbineDefinition, model::JiminezYawDeflection, windfarmstate::SingleWindFarmState)
Calculates the horizontal deflection of the wind turbine wake
Based on:
[1] Jiminez 2010 "Wake defl ection of a wind turbine in yaw"
[2] Gebraad 2014 "Wind plant optimization by yaw control using a parametric wake model"
this version ignores the corrections made to the yaw model for rotor rotation as described in [2] and
[3] Thomas 2017 "Improving the FLORIS wind plant model for compatibility with gradient-based optimization"
"""
function wake_deflection_model(locx, locy, locz, turbine_x, turbine_yaw, turbine_ct, turbine_id, rotor_diameter, turbine_local_ti, model::JiminezYawDeflection)
dx = locx-turbine_x[turbine_id]
yaw = -turbine_yaw[turbine_id] # Jiminez used opposite rotation convention, hence (-) sign
ct = turbine_ct[turbine_id]
diam = rotor_diameter[turbine_id]
kd = model.horizontal_spread_rate
initial_yaw_angle = 0.5*((cos(yaw))^2)*sin(yaw)*ct # [1] eq. 20, [2] eq. 8
# [2] eq. 10
a = 2.0*kd*dx/diam + 1.0
b = initial_yaw_angle*(15.0*a^4+initial_yaw_angle^2)
c = (30.0*kd/diam)*a^5
d = initial_yaw_angle*diam*(15.0 + initial_yaw_angle^2)
e = 30.0*kd
y_deflection = b/c - d/e
return y_deflection
end
"""
wake_deflection_model(locx, locy, locz, turbine_id, turbine_definition::TurbineDefinition, model::MultizoneDeflection, windfarmstate::SingleWindFarmState)
Calculates the horizontal deflection of the wind turbine wake accounting for both yaw and rotational deflection
Based on:
[1] Jiminez 2010 "Wake defl ection of a wind turbine in yaw"
[2] Gebraad 2014 "Wind plant optimization by yaw control using a parametric wake model"
this version ignores the corrections made to the yaw model for rotor rotation as described in [2] and
[3] Thomas 2017 "Improving the FLORIS wind plant model for compatibility with gradient-based optimization"
"""
function wake_deflection_model(locx, locy, locz, turbine_x, turbine_yaw, turbine_ct, turbine_id, rotor_diameter, turbine_local_ti, model::MultizoneDeflection)
dx = locx-turbine_x[turbine_id]
yaw = -turbine_yaw[turbine_id] # Jiminez used opposite rotation convention, hence (-) sign
ct = turbine_ct[turbine_id]
diam = rotor_diameter[turbine_id]
kd = model.horizontal_spread_rate
ad = model.ad
bd = model.bd
initial_yaw_angle = 0.5*((cos(yaw))^2)*sin(yaw)*ct # [1] eq. 20, [2] eq. 8
# [2] eq. 10
a = 2.0*kd*dx/diam + 1.0
b = initial_yaw_angle*(15.0*a^4+initial_yaw_angle^2)
c = (30.0*kd/diam)*a^5
d = initial_yaw_angle*diam*(15.0 + initial_yaw_angle^2)
e = 30.0*kd
# [2] eq. 10, 11, and 12 define deflection
yaw_deflection = b/c - d/e
rotation_deflection = ad + bd*(dx)
y_deflection = -1*(yaw_deflection + rotation_deflection)
return y_deflection
end
function _bpa_theta_0(yaw, ct)
theta0 = (0.3*yaw/cos(yaw))*(1.0-sqrt(1.0-ct*cos(yaw)))
return theta0
end
function _bpa_deflection(diam, ct, yaw, ky, kz, sigmay, sigmaz, theta0, x0)
a = theta0*x0/diam
b = (theta0/14.7)*sqrt(cos(yaw)/(ky*kz*ct))*(2.9-1.3*sqrt(1.0-ct)-ct)
c = (1.6+sqrt(ct))*(1.6*sqrt(8.0*sigmay*sigmaz/(cos(yaw)*diam^2))-ct)
d = (1.6-sqrt(ct))*(1.6*sqrt(8.0*sigmay*sigmaz/(cos(yaw)*diam^2))+ct)
y_deflection = diam*(a+b*log(c/d))
return y_deflection
end
"""
wake_deflection_model(locx, locy, locz, turbine_x, turbine_yaw, turbine_ct, turbine_id, rotor_diameter, turbine_local_ti, model::GaussYawDeflection)
Calculates the horizontal deflection of the wind turbine wake
Based on:
[1] Bastankhah and Porte-Agel 2016 "Experimental and theoretical study of
wind turbine wakes in yawed conditions"
"""
function wake_deflection_model(locx, locy, locz, turbine_x, turbine_yaw, turbine_ct, turbine_id, rotor_diameter, turbine_local_ti, model::GaussYawDeflection)
dx = locx-turbine_x[turbine_id]
yaw = turbine_yaw[turbine_id]
ct = turbine_ct[turbine_id]
diam = rotor_diameter[turbine_id]
ti = turbine_local_ti[turbine_id]
as = model.alpha_star
bs = model.beta_star
ky = model.horizontal_spread_rate
kz = model.vertical_spread_rate
# [1] eqn 6.12
theta0 = _bpa_theta_0(yaw, ct)
# [1] eqn 7.4
x0 = _gauss_yaw_potential_core(diam, yaw, ct, as, ti, bs)
# calculate the discontinuity point of the gauss yaw model
xd = _gauss_yaw_discontinuity(diam, x0, ky, kz, yaw, ct)
# calculate horizontal wake spread (paper eq: 7.2)
sigmay = _gauss_yaw_spread_interpolated(diam, ky, dx, x0, yaw, xd)
# calculate vertical wake spread (paper eq: 7.2)
sigmaz = _gauss_yaw_spread_interpolated(diam, kz, dx, x0, 0.0, xd)
y_deflection = _bpa_deflection(diam, ct, yaw, ky, kz, sigmay, sigmaz, theta0, x0)
return y_deflection
end
"""
wake_deflection_model(oc, turbine_x, turbine_yaw, turbine_ct, turbine_id, rotor_diameter, turbine_local_ti, model::GaussYawVariableSpreadDeflection)
Calculates the horizontal deflection of the wind turbine wake. Varies based on local turbulence intensity.
Based on:
[1] Bastankhah and Porte-Agel 2016 "Experimental and theoretical study of
wind turbine wakes in yawed conditions"
[2] Niayifar and Porte-Agel 2016 "Analytical Modeling of Wind Farms:
A New Approach for Power Prediction"
"""
function wake_deflection_model(locx, locy, locz, turbine_x, turbine_yaw, turbine_ct, turbine_id, rotor_diameter, turbine_local_ti, model::GaussYawVariableSpreadDeflection)
dx = locx-turbine_x[turbine_id]
yaw = turbine_yaw[turbine_id]
ct = turbine_ct[turbine_id]
dt = rotor_diameter[turbine_id]
ti = turbine_local_ti[turbine_id]
as = model.alpha_star
bs = model.beta_star
# [2] calculate wake spread based on local turbulence intensity
ky = kz = _k_star_func(ti, model.k1, model.k2)
# [1] eqn 6.12 initial wake angle
theta0 = _bpa_theta_0(yaw, ct)
# [1] eqn 7.4
x0 = _gauss_yaw_potential_core(dt, yaw, ct, as, ti, bs)
# calculate the discontinuity point of the gauss yaw model
xd = _gauss_yaw_discontinuity(dt, x0, ky, kz, yaw, ct)
# calculate horizontal wake spread (paper eq: 7.2)
sigma_y = _gauss_yaw_spread_interpolated(dt, ky, dx, x0, yaw, xd)
# calculate vertical wake spread (paper eq: 7.2)
sigma_z = _gauss_yaw_spread_interpolated(dt, kz, dx, x0, 0.0, xd)
# finally, calculate deflection
y_deflection = _bpa_deflection(dt, ct, yaw, ky, kz, sigma_y, sigma_z, theta0, x0)
return y_deflection
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download("https://cdn.plot.ly/plotly-2.8.3.min.js", joinpath(@__DIR__, "plotly-2.8.3.min.js"))
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"""
The Accounting Trinity or [CAL]!
[]: Important (somehow) - (it's objective alright)
(): an Optional read , acts as a comment
me: personal words, notes , to add an overall flavor & meaning to the comment itself - as needed
A/C : Account - a fancy way accountants use for any account 'book'
1.Assets k let's start - here goes nothing
2.Capital : an abstract entity me: i.e. bank account value- me: has a Direct Relation with either an 'Asset' or a Liability A/C "Account"
3.Assets: a Tangible object, of Value, has an/a owner(s)
below is the explination, non-academic - based on the Personal View -- for your eyes only,ok
# 3.1 thinking about it - I'm a comment after all oh wait maybe I'm not -- who Am ... I ??? - stop talking this is your brain police stop --or what ? - we'll see
#3.2 Ownership:
the actual Owner: for whom the Account belongs to - usualy 1 or more Seperate entities (persons, companies - or mix of the 2 )
1. One Owner: 1 is the lonliest number, if that's you , kudos to you!
2. A couple, partnerships or best Friends (usually Spoils in, or before the end (whatever that means) you know, as nothing stands still on its own for good- hate to spoil that on you!) - but sb gotta spill the Beans, right ;) -- sb: somebody
3. 3 ot more: fits the bill for family-related members - [Time: for either long-term or short-term]
3.Liabilities: Debt, shares US ()(intl.) - how to say in the UK???(if you're authorized to issue that ), given by other people willingly (hopefully they know what they're doing, right) i.e. Accounts payable A/P , me: the 'pay me back later, okay?' accounts - [either long-term or short-term]
together, they make up the following Golden (brown) Equation:
Assets = Liabilites + Capital
"""
# the Accounting Equation , :
## Assets = Liabilites + Capital
## ---
#existential question the function is already equal to its absolute value -talk about a Self-reference !
#global capital(totalCapital) =abs(totalCapital);
asset(totalAssets) = totalLiabilities # no-abs-allowed #TODO:sanity-check
liability(totalLiabilities) = totalLiabilities # no-abs-allowed #TODO:sanity-check
#nothing, yet - chop chop, gotta go, are you there yet, where you at!
#syntax: incomplete: "module" at /Users/ahmadlutfi/Desktop/src/src/accountingTrinity.jl:3 requires end
#TODO: working input ref ... # DONE!
capital = passmissing(input("please insert total capital")) # insert a value only
liability = passmissing( input("please insert total liability"))#throws(ERROR) : redefinition of Constant Liability #TODO:sanity-check
asset = passmissing(input("please insert total asset"))
print("capital: $capital") # TODO: Ignore Alpha words -- REGEX - maybe later -- I guessed it
#@assert capital >= 0 #doesn;t matter # capital can take both positive & Negative values
#export calcCapital, capital,liability
#=
function calcCapital(totalAssets,totalLiabilities)
asset = abs(totalAssets); liability = abs(totalLiabilites) #edit: I don't recall writing those , conciously -wow
=#
#---
function calcCapital(asset, liability)
#return max(asset, liability) - min(asset, liability) #that's another thing I've been thinking about - to add or not the add -- that is the Question
"""
capital = asset - liability
"""
capital = asset - liability # for some reason i have to check with liability ... #TODO:
return capital # Capital
#---
function calcAsset(capital, liability)
"""
after subtracting all debt (personal debt included)
asset = - liab
capital = asset - liability
"""
# asset = asset < 0 : asset = 0 # if any account is less than 0 , adds it as 0 : Reason: a cadlag process cannot be less than a 0
asset = capital - liability
# capital = asset - liability
return asset # capital
end
function calcLiability(capital, asset)
"""
Liability Could be in a form of bonds
Liability (Debt) = (short-term) Debt + (Long-term) Debt
"""
liability = capital - asset
return liability
end
end
#--- Asset Calculation
#capital Calculation ?
#TODO:calculate Asset = capital - liability #Done!
#"""user inserts totalCapital"""
#TODO:finish
"""
function calcAssets(totalCapital, totalLiabilities)
capital(totalCapital) = tryparse(:Number, input("please insert total Capital")) # capital(totalCapital)
liability(totalLiabilities) = tryparse(:Number, input("please insert total Liabilities")) #abs(totalLiabilities)
#TODO:
return max(capital, liability) - min(capital, liability) #
return capital - liability # = Asset
end
end
"""
"
Capital account v.s liability account
Credit / debit account algebra :
Cr*Cr = Dr
Dr*Dr = Dr
(-): credit
(+): debit
Dr: Debit
Cr: Credit
liability account decrease could be from
paying off Accounts Payable i.e. (-) Accounts Payable A/C [Credit Account] : (-)credit Accounts Payable A/C,
decreasing(-) Accounts Payable A/C - Credit Account #whenever you Credit a (-)credit account, that means you are Debiting (decreasing, here) the Credit Account i.e. Cr*Cr = Dr,
adding(+) more Accounts Recievable (+) Accounts Recievable A/R [Debit Account] : (+)debit, Accounts Recieveble A/C - Debit Account (+)is debit #whenever you Debit a Debit account, means you
i.e. some one is Paying you off Dr*Dr = Dr
(-) Accounts Payable A/C
"
function calcAsset(totalCapital, totalLiabilities)
return totalCapital - totalLiabilites
end
#--- prompt Area
#=
using Parsers
function baseline(s,delimiter=' ')
pieces = split(s, delimiter, keepempty=false)
map(pieces) do piece
parse(Float64, piece)
end
end
read = readline()
#p = Parsers.DELIMITED(" ", ignorerepeated=true);
=#
#--- ....
#= TODO:READ - Someday, ok - getback -- on it
function _sth parse(::Type{T}, s::AbstractString) where T
y = tryparse(T, s)
y === nothing && throw(ArgumentError("Cannot parse as $T: \"$s\""))
return y
end
=#
#--- Checking region ...
# TODO: prompt #Done @ 6:15 pm
function prompt(message="How are you feeling?") #Working: #getback: prompt's 2 methods prompt() & prompt(message)
print(message*"\n")
line = readline() #read # ok # string
# splits on spaces and casts to T
#result = produce(map(x -> parse(T, x), split(line)))
# return #result
return parse(Complex,line) #ok
end #Done ! #Correct #checked #prompt(message)
prompt() #gets lost: reason(type input variable)- i.e. is it Float64 ? --unsure: the most existential questionyou might ask
#oh my handling complex vars is complex (in itself!)
#what type of work around & remedyyou'll do
#don't know what type of input user may enter either string or number
# if number, will it be complex - Problem
#but. let it be a Float64 - no problem!
using Test
global digits= 10
global datatype = nothing
function inputHandling(T)
#--- test Region
for sign in ('-','+'), Im in ("i","j","im"), s1 in (""," "), s2 in (""," "), s3 in (""," "), s4 in (""," ")
for r in (1,0,-1), i in (1,0,-1),
n = Complex(r, sign == '+' ? i : -i)
s = string(s1, r, s2, sign, s3, i, Im, s4)
@test n === parse(Complex{Int}, s)
@test Complex(r) === parse(Complex{Int}, string(s1, r, s2))
@test Complex(0,i) === parse(Complex{Int}, string(s3, i, Im, s4))
for T in (Float64, BigFloat)
nT = parse(Complex{T}, s)
@test nT isa Complex{T}
@test nT == n
@test n == parse(Complex{T}, string(s1, r, ".0", s2, sign, s3, i, ".0", Im, s4))
@test n*parse(T,"1e-3") == parse(Complex{T}, string(s1, r, "e-3", s2, sign, s3, i, "e-3", Im, s4))
end
end
for r in (-1.0,-1e-9,Inf,-Inf,NaN), i in (-1.0,-1e-9,Inf,NaN)
n = Complex(r, sign == '+' ? i : -i)
s = lowercase(string(s1, r, s2, sign, s3, i, Im, s4))
@test n === parse(ComplexF64, s)
@test Complex(r) === parse(ComplexF64, string(s1, r, s2))
@test Complex(0,i) === parse(ComplexF64, string(s3, i, Im, s4))
end
end
end
for T in (Int, Float64), bad in ("3 + 4*im", "3 + 4", "1+2ij", "1im-3im", "++4im")
@test_throws ArgumentError parse(Complex{T}, bad)
end
@test_throws ArgumentError parse(Complex{Int}, "3 + 4.2im")
#test passed
#--- T Input Handling #Re-Opened!
#TODO: sanity check code below
#TODO: isa Useful ?
if(typeof(T) isa Int)
datatype = Int
@inferred parse(Int, "$digits") #throws(error): @inferred not defined
# end
elseif (typeof(T) isa Float64)
datatype = Float64
@inferred parse(Float64, "$digits") #throws(error): @inferred not defined
#end
elseif(typeof(T) isa Complex{Int})
datatype = Complex{Int}
@inferred parse(Complex{Int}, "$digits") #throws(error): @inferred not defined
end
#you're in my head, Always....
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] | 2.857283 | 3,048 |
# Mapping from token string identifiers to enumeration values as used in @K_str
#
# TODO: Unify Tokenize with this approach so we don't need to write these out
# in two places.
const _str_to_kind = let Ts = TzTokens
Dict([
"None" => Ts.NONE
"EndMarker" => Ts.ENDMARKER
"Comment" => Ts.COMMENT
"Whitespace" => Ts.WHITESPACE
"Identifier" => Ts.IDENTIFIER
"@" => Ts.AT_SIGN
"," => Ts.COMMA
";" => Ts.SEMICOLON
"BEGIN_ERRORS" => Ts.begin_errors
# Tokenization errors
"ErrorEofMultiComment" => Ts.EOF_MULTICOMMENT
"ErrorEofChar" => Ts.EOF_CHAR
"ErrorInvalidNumericConstant" => Ts.INVALID_NUMERIC_CONSTANT
"ErrorInvalidOperator" => Ts.INVALID_OPERATOR
"ErrorInvalidInterpolationTerminator" => Ts.INVALID_INTERPOLATION_TERMINATOR
# Generic error
"error" => Ts.ERROR
"END_ERRORS" => Ts.end_errors
"BEGIN_KEYWORDS" => Ts.begin_keywords
"baremodule" => Ts.BAREMODULE
"begin" => Ts.BEGIN
"break" => Ts.BREAK
"catch" => Ts.CATCH
"const" => Ts.CONST
"continue" => Ts.CONTINUE
"do" => Ts.DO
"else" => Ts.ELSE
"elseif" => Ts.ELSEIF
"end" => Ts.END
"export" => Ts.EXPORT
"finally" => Ts.FINALLY
"for" => Ts.FOR
"function" => Ts.FUNCTION
"global" => Ts.GLOBAL
"if" => Ts.IF
"import" => Ts.IMPORT
"let" => Ts.LET
"local" => Ts.LOCAL
"macro" => Ts.MACRO
"module" => Ts.MODULE
"quote" => Ts.QUOTE
"return" => Ts.RETURN
"struct" => Ts.STRUCT
"try" => Ts.TRY
"using" => Ts.USING
"while" => Ts.WHILE
# contextual keywords
"abstract" => Ts.ABSTRACT
"as" => Ts.AS
"doc" => Ts.DOC
"mutable" => Ts.MUTABLE
"outer" => Ts.OUTER
"primitive" => Ts.PRIMITIVE
"type" => Ts.TYPE
"var" => Ts.VAR
"END_KEYWORDS" => Ts.end_keywords
"BEGIN_CSTPARSER" => Ts.begin_cstparser
"nothing" => Ts.NOTHING
"NewlineWs" => Ts.NEWLINE_WS
"END_CSTPARSER" => Ts.end_cstparser
"BEGIN_LITERAL" => Ts.begin_literal
"Integer" => Ts.INTEGER
"BinInt" => Ts.BIN_INT
"HexInt" => Ts.HEX_INT
"OctInt" => Ts.OCT_INT
"Float" => Ts.FLOAT
"String" => Ts.STRING
"Char" => Ts.CHAR
"CmdString" => Ts.CMD
"true" => Ts.TRUE
"false" => Ts.FALSE
"END_LITERAL" => Ts.end_literal
"BEGIN_DELIMITERS" => Ts.begin_delimiters
"[" => Ts.LSQUARE
"]" => Ts.RSQUARE
"{" => Ts.LBRACE
"}" => Ts.RBRACE
"(" => Ts.LPAREN
")" => Ts.RPAREN
"\"" => Ts.DQUOTE
"\"\"\"" => Ts.TRIPLE_DQUOTE
"`" => Ts.BACKTICK
"```" => Ts.TRIPLE_BACKTICK
"END_DELIMITERS" => Ts.end_delimiters
"BEGIN_OPS" => Ts.begin_ops
"..." => Ts.DDDOT
# Level 1
"BEGIN_ASSIGNMENTS" => Ts.begin_assignments
"=" => Ts.EQ
"+=" => Ts.PLUS_EQ
"-=" => Ts.MINUS_EQ
"*=" => Ts.STAR_EQ
"/=" => Ts.FWD_SLASH_EQ
"//=" => Ts.FWDFWD_SLASH_EQ
"|=" => Ts.OR_EQ
"^=" => Ts.CIRCUMFLEX_EQ
"÷=" => Ts.DIVISION_EQ
"%=" => Ts.REM_EQ
"<<=" => Ts.LBITSHIFT_EQ
">>=" => Ts.RBITSHIFT_EQ
">>>=" => Ts.UNSIGNED_BITSHIFT_EQ
"\\=" => Ts.BACKSLASH_EQ
"&=" => Ts.AND_EQ
":=" => Ts.COLON_EQ
"~" => Ts.APPROX
"\$=" => Ts.EX_OR_EQ
"⊻=" => Ts.XOR_EQ
"END_ASSIGNMENTS" => Ts.end_assignments
"BEGIN_PAIRARROW" => Ts.begin_pairarrow
"=>" => Ts.PAIR_ARROW
"END_PAIRARROW" => Ts.end_pairarrow
# Level 2
"BEGIN_CONDITIONAL" => Ts.begin_conditional
"?" => Ts.CONDITIONAL
"END_CONDITIONAL" => Ts.end_conditional
# Level 3
"BEGIN_ARROW" => Ts.begin_arrow
"-->" => Ts.RIGHT_ARROW
"<--" => Ts.LEFT_ARROW
"<-->" => Ts.DOUBLE_ARROW
"←" => Ts.LEFTWARDS_ARROW
"→" => Ts.RIGHTWARDS_ARROW
"↔" => Ts.LEFT_RIGHT_ARROW
"↚" => Ts.LEFTWARDS_ARROW_WITH_STROKE
"↛" => Ts.RIGHTWARDS_ARROW_WITH_STROKE
"↞" => Ts.LEFTWARDS_TWO_HEADED_ARROW
"↠" => Ts.RIGHTWARDS_TWO_HEADED_ARROW
"↢" => Ts.LEFTWARDS_ARROW_WITH_TAIL
"↣" => Ts.RIGHTWARDS_ARROW_WITH_TAIL
"↤" => Ts.LEFTWARDS_ARROW_FROM_BAR
"↦" => Ts.RIGHTWARDS_ARROW_FROM_BAR
"↮" => Ts.LEFT_RIGHT_ARROW_WITH_STROKE
"⇎" => Ts.LEFT_RIGHT_DOUBLE_ARROW_WITH_STROKE
"⇍" => Ts.LEFTWARDS_DOUBLE_ARROW_WITH_STROKE
"⇏" => Ts.RIGHTWARDS_DOUBLE_ARROW_WITH_STROKE
"⇐" => Ts.LEFTWARDS_DOUBLE_ARROW
"⇒" => Ts.RIGHTWARDS_DOUBLE_ARROW
"⇔" => Ts.LEFT_RIGHT_DOUBLE_ARROW
"⇴" => Ts.RIGHT_ARROW_WITH_SMALL_CIRCLE
"⇶" => Ts.THREE_RIGHTWARDS_ARROWS
"⇷" => Ts.LEFTWARDS_ARROW_WITH_VERTICAL_STROKE
"⇸" => Ts.RIGHTWARDS_ARROW_WITH_VERTICAL_STROKE
"⇹" => Ts.LEFT_RIGHT_ARROW_WITH_VERTICAL_STROKE
"⇺" => Ts.LEFTWARDS_ARROW_WITH_DOUBLE_VERTICAL_STROKE
"⇻" => Ts.RIGHTWARDS_ARROW_WITH_DOUBLE_VERTICAL_STROKE
"⇼" => Ts.LEFT_RIGHT_ARROW_WITH_DOUBLE_VERTICAL_STROKE
"⇽" => Ts.LEFTWARDS_OPEN_HEADED_ARROW
"⇾" => Ts.RIGHTWARDS_OPEN_HEADED_ARROW
"⇿" => Ts.LEFT_RIGHT_OPEN_HEADED_ARROW
"⟵" => Ts.LONG_LEFTWARDS_ARROW
"⟶" => Ts.LONG_RIGHTWARDS_ARROW
"⟷" => Ts.LONG_LEFT_RIGHT_ARROW
"⟹" => Ts.LONG_RIGHTWARDS_DOUBLE_ARROW
"⟺" => Ts.LONG_LEFT_RIGHT_DOUBLE_ARROW
"⟻" => Ts.LONG_LEFTWARDS_ARROW_FROM_BAR
"⟼" => Ts.LONG_RIGHTWARDS_ARROW_FROM_BAR
"⟽" => Ts.LONG_LEFTWARDS_DOUBLE_ARROW_FROM_BAR
"⟾" => Ts.LONG_RIGHTWARDS_DOUBLE_ARROW_FROM_BAR
"⟿" => Ts.LONG_RIGHTWARDS_SQUIGGLE_ARROW
"⤀" => Ts.RIGHTWARDS_TWO_HEADED_ARROW_WITH_VERTICAL_STROKE
"⤁" => Ts.RIGHTWARDS_TWO_HEADED_ARROW_WITH_DOUBLE_VERTICAL_STROKE
"⤂" => Ts.LEFTWARDS_DOUBLE_ARROW_WITH_VERTICAL_STROKE
"⤃" => Ts.RIGHTWARDS_DOUBLE_ARROW_WITH_VERTICAL_STROKE
"⤄" => Ts.LEFT_RIGHT_DOUBLE_ARROW_WITH_VERTICAL_STROKE
"⤅" => Ts.RIGHTWARDS_TWO_HEADED_ARROW_FROM_BAR
"⤆" => Ts.LEFTWARDS_DOUBLE_ARROW_FROM_BAR
"⤇" => Ts.RIGHTWARDS_DOUBLE_ARROW_FROM_BAR
"⤌" => Ts.LEFTWARDS_DOUBLE_DASH_ARROW
"⤍" => Ts.RIGHTWARDS_DOUBLE_DASH_ARROW
"⤎" => Ts.LEFTWARDS_TRIPLE_DASH_ARROW
"⤏" => Ts.RIGHTWARDS_TRIPLE_DASH_ARROW
"⤐" => Ts.RIGHTWARDS_TWO_HEADED_TRIPLE_DASH_ARROW
"⤑" => Ts.RIGHTWARDS_ARROW_WITH_DOTTED_STEM
"⤔" => Ts.RIGHTWARDS_ARROW_WITH_TAIL_WITH_VERTICAL_STROKE
"⤕" => Ts.RIGHTWARDS_ARROW_WITH_TAIL_WITH_DOUBLE_VERTICAL_STROKE
"⤖" => Ts.RIGHTWARDS_TWO_HEADED_ARROW_WITH_TAIL
"⤗" => Ts.RIGHTWARDS_TWO_HEADED_ARROW_WITH_TAIL_WITH_VERTICAL_STROKE
"⤘" => Ts.RIGHTWARDS_TWO_HEADED_ARROW_WITH_TAIL_WITH_DOUBLE_VERTICAL_STROKE
"⤝" => Ts.LEFTWARDS_ARROW_TO_BLACK_DIAMOND
"⤞" => Ts.RIGHTWARDS_ARROW_TO_BLACK_DIAMOND
"⤟" => Ts.LEFTWARDS_ARROW_FROM_BAR_TO_BLACK_DIAMOND
"⤠" => Ts.RIGHTWARDS_ARROW_FROM_BAR_TO_BLACK_DIAMOND
"⥄" => Ts.SHORT_RIGHTWARDS_ARROW_ABOVE_LEFTWARDS_ARROW
"⥅" => Ts.RIGHTWARDS_ARROW_WITH_PLUS_BELOW
"⥆" => Ts.LEFTWARDS_ARROW_WITH_PLUS_BELOW
"⥇" => Ts.RIGHTWARDS_ARROW_THROUGH_X
"⥈" => Ts.LEFT_RIGHT_ARROW_THROUGH_SMALL_CIRCLE
"⥊" => Ts.LEFT_BARB_UP_RIGHT_BARB_DOWN_HARPOON
"⥋" => Ts.LEFT_BARB_DOWN_RIGHT_BARB_UP_HARPOON
"⥎" => Ts.LEFT_BARB_UP_RIGHT_BARB_UP_HARPOON
"⥐" => Ts.LEFT_BARB_DOWN_RIGHT_BARB_DOWN_HARPOON
"⥒" => Ts.LEFTWARDS_HARPOON_WITH_BARB_UP_TO_BAR
"⥓" => Ts.RIGHTWARDS_HARPOON_WITH_BARB_UP_TO_BAR
"⥖" => Ts.LEFTWARDS_HARPOON_WITH_BARB_DOWN_TO_BAR
"⥗" => Ts.RIGHTWARDS_HARPOON_WITH_BARB_DOWN_TO_BAR
"⥚" => Ts.LEFTWARDS_HARPOON_WITH_BARB_UP_FROM_BAR
"⥛" => Ts.RIGHTWARDS_HARPOON_WITH_BARB_UP_FROM_BAR
"⥞" => Ts.LEFTWARDS_HARPOON_WITH_BARB_DOWN_FROM_BAR
"⥟" => Ts.RIGHTWARDS_HARPOON_WITH_BARB_DOWN_FROM_BAR
"⥢" => Ts.LEFTWARDS_HARPOON_WITH_BARB_UP_ABOVE_LEFTWARDS_HARPOON_WITH_BARB_DOWN
"⥤" => Ts.RIGHTWARDS_HARPOON_WITH_BARB_UP_ABOVE_RIGHTWARDS_HARPOON_WITH_BARB_DOWN
"⥦" => Ts.LEFTWARDS_HARPOON_WITH_BARB_UP_ABOVE_RIGHTWARDS_HARPOON_WITH_BARB_UP
"⥧" => Ts.LEFTWARDS_HARPOON_WITH_BARB_DOWN_ABOVE_RIGHTWARDS_HARPOON_WITH_BARB_DOWN
"⥨" => Ts.RIGHTWARDS_HARPOON_WITH_BARB_UP_ABOVE_LEFTWARDS_HARPOON_WITH_BARB_UP
"⥩" => Ts.RIGHTWARDS_HARPOON_WITH_BARB_DOWN_ABOVE_LEFTWARDS_HARPOON_WITH_BARB_DOWN
"⥪" => Ts.LEFTWARDS_HARPOON_WITH_BARB_UP_ABOVE_LONG_DASH
"⥫" => Ts.LEFTWARDS_HARPOON_WITH_BARB_DOWN_BELOW_LONG_DASH
"⥬" => Ts.RIGHTWARDS_HARPOON_WITH_BARB_UP_ABOVE_LONG_DASH
"⥭" => Ts.RIGHTWARDS_HARPOON_WITH_BARB_DOWN_BELOW_LONG_DASH
"⥰" => Ts.RIGHT_DOUBLE_ARROW_WITH_ROUNDED_HEAD
"⧴" => Ts.RULE_DELAYED
"⬱" => Ts.THREE_LEFTWARDS_ARROWS
"⬰" => Ts.LEFT_ARROW_WITH_SMALL_CIRCLE
"⬲" => Ts.LEFT_ARROW_WITH_CIRCLED_PLUS
"⬳" => Ts.LONG_LEFTWARDS_SQUIGGLE_ARROW
"⬴" => Ts.LEFTWARDS_TWO_HEADED_ARROW_WITH_VERTICAL_STROKE
"⬵" => Ts.LEFTWARDS_TWO_HEADED_ARROW_WITH_DOUBLE_VERTICAL_STROKE
"⬶" => Ts.LEFTWARDS_TWO_HEADED_ARROW_FROM_BAR
"⬷" => Ts.LEFTWARDS_TWO_HEADED_TRIPLE_DASH_ARROW
"⬸" => Ts.LEFTWARDS_ARROW_WITH_DOTTED_STEM
"⬹" => Ts.LEFTWARDS_ARROW_WITH_TAIL_WITH_VERTICAL_STROKE
"⬺" => Ts.LEFTWARDS_ARROW_WITH_TAIL_WITH_DOUBLE_VERTICAL_STROKE
"⬻" => Ts.LEFTWARDS_TWO_HEADED_ARROW_WITH_TAIL
"⬼" => Ts.LEFTWARDS_TWO_HEADED_ARROW_WITH_TAIL_WITH_VERTICAL_STROKE
"⬽" => Ts.LEFTWARDS_TWO_HEADED_ARROW_WITH_TAIL_WITH_DOUBLE_VERTICAL_STROKE
"⬾" => Ts.LEFTWARDS_ARROW_THROUGH_X
"⬿" => Ts.WAVE_ARROW_POINTING_DIRECTLY_LEFT
"⭀" => Ts.EQUALS_SIGN_ABOVE_LEFTWARDS_ARROW
"⭁" => Ts.REVERSE_TILDE_OPERATOR_ABOVE_LEFTWARDS_ARROW
"⭂" => Ts.LEFTWARDS_ARROW_ABOVE_REVERSE_ALMOST_EQUAL_TO
"⭃" => Ts.RIGHTWARDS_ARROW_THROUGH_GREATER_THAN
"⭄" => Ts.RIGHTWARDS_ARROW_THROUGH_SUPERSET
"⭇" => Ts.REVERSE_TILDE_OPERATOR_ABOVE_RIGHTWARDS_ARROW
"⭈" => Ts.RIGHTWARDS_ARROW_ABOVE_REVERSE_ALMOST_EQUAL_TO
"⭉" => Ts.TILDE_OPERATOR_ABOVE_LEFTWARDS_ARROW
"⭊" => Ts.LEFTWARDS_ARROW_ABOVE_ALMOST_EQUAL_TO
"⭋" => Ts.LEFTWARDS_ARROW_ABOVE_REVERSE_TILDE_OPERATOR
"⭌" => Ts.RIGHTWARDS_ARROW_ABOVE_REVERSE_TILDE_OPERATOR
"￩" => Ts.HALFWIDTH_LEFTWARDS_ARROW
"￫" => Ts.HALFWIDTH_RIGHTWARDS_ARROW
"↻" => Ts.CIRCLE_ARROW_RIGHT
"⇜" => Ts.LEFT_SQUIGGLE_ARROW
"⇝" => Ts.RIGHT_SQUIGGLE_ARROW
"↜" => Ts.LEFT_WAVE_ARROW
"↝" => Ts.RIGHT_WAVE_ARROW
"↩" => Ts.LEFTWARDS_ARROW_WITH_HOOK
"↪" => Ts.RIGHTWARDS_ARROW_WITH_HOOK
"↫" => Ts.LOOP_ARROW_LEFT
"↬" => Ts.LOOP_ARROW_RIGHT
"↼" => Ts.LEFT_HARPOON_UP
"↽" => Ts.LEFT_HARPOON_DOWN
"⇀" => Ts.RIGHT_HARPOON_UP
"⇁" => Ts.RIGHT_HARPOON_DOWN
"⇄" => Ts.RIGHT_LEFT_ARROWS
"⇆" => Ts.LEFT_RIGHT_ARROWS
"⇇" => Ts.LEFT_LEFT_ARROWS
"⇉" => Ts.RIGHT_RIGHT_ARROWS
"⇋" => Ts.LEFT_RIGHT_HARPOONS
"⇌" => Ts.RIGHT_LEFT_HARPOONS
"⇚" => Ts.L_LEFT_ARROW
"⇛" => Ts.R_RIGHT_ARROW
"⇠" => Ts.LEFT_DASH_ARROW
"⇢" => Ts.RIGHT_DASH_ARROW
"↷" => Ts.CURVE_ARROW_RIGHT
"↶" => Ts.CURVE_ARROW_LEFT
"↺" => Ts.CIRCLE_ARROW_LEFT
"END_ARROW" => Ts.end_arrow
# Level 4
"BEGIN_LAZYOR" => Ts.begin_lazyor
"||" => Ts.LAZY_OR
"END_LAZYOR" => Ts.end_lazyor
# Level 5
"BEGIN_LAZYAND" => Ts.begin_lazyand
"&&" => Ts.LAZY_AND
"END_LAZYAND" => Ts.end_lazyand
# Level 6
"BEGIN_COMPARISON" => Ts.begin_comparison
"<:" => Ts.ISSUBTYPE
">:" => Ts.ISSUPERTYPE
">" => Ts.GREATER
"<" => Ts.LESS
">=" => Ts.GREATER_EQ
"≥" => Ts.GREATER_THAN_OR_EQUAL_TO
"<=" => Ts.LESS_EQ
"≤" => Ts.LESS_THAN_OR_EQUAL_TO
"==" => Ts.EQEQ
"===" => Ts.EQEQEQ
"≡" => Ts.IDENTICAL_TO
"!=" => Ts.NOT_EQ
"≠" => Ts.NOT_EQUAL_TO
"!==" => Ts.NOT_IS
"≢" => Ts.NOT_IDENTICAL_TO
"∈" => Ts.ELEMENT_OF
"in" => Ts.IN
"isa" => Ts.ISA
"∉" => Ts.NOT_AN_ELEMENT_OF
"∋" => Ts.CONTAINS_AS_MEMBER
"∌" => Ts.DOES_NOT_CONTAIN_AS_MEMBER
"⊆" => Ts.SUBSET_OF_OR_EQUAL_TO
"⊈" => Ts.NEITHER_A_SUBSET_OF_NOR_EQUAL_TO
"⊂" => Ts.SUBSET_OF
"⊄" => Ts.NOT_A_SUBSET_OF
"⊊" => Ts.SUBSET_OF_WITH_NOT_EQUAL_TO
"∝" => Ts.PROPORTIONAL_TO
"∊" => Ts.SMALL_ELEMENT_OF
"∍" => Ts.SMALL_CONTAINS_AS_MEMBER
"∥" => Ts.PARALLEL_TO
"∦" => Ts.NOT_PARALLEL_TO
"∷" => Ts.PROPORTION
"∺" => Ts.GEOMETRIC_PROPORTION
"∻" => Ts.HOMOTHETIC
"∽" => Ts.REVERSED_TILDE
"∾" => Ts.INVERTED_LAZY_S
"≁" => Ts.NOT_TILDE
"≃" => Ts.ASYMPTOTICALLY_EQUAL_TO
"≄" => Ts.NOT_ASYMPTOTICALLY_EQUAL_TO
"≅" => Ts.APPROXIMATELY_EQUAL_TO
"≆" => Ts.APPROXIMATELY_BUT_NOT_ACTUALLY_EQUAL_TO
"≇" => Ts.NEITHER_APPROXIMATELY_NOR_ACTUALLY_EQUAL_TO
"≈" => Ts.ALMOST_EQUAL_TO
"≉" => Ts.NOT_ALMOST_EQUAL_TO
"≊" => Ts.ALMOST_EQUAL_OR_EQUAL_TO
"≋" => Ts.TRIPLE_TILDE
"≌" => Ts.ALL_EQUAL_TO
"≍" => Ts.EQUIVALENT_TO
"≎" => Ts.GEOMETRICALLY_EQUIVALENT_TO
"≐" => Ts.APPROACHES_THE_LIMIT
"≑" => Ts.GEOMETRICALLY_EQUAL_TO
"≒" => Ts.APPROXIMATELY_EQUAL_TO_OR_THE_IMAGE_OF
"≓" => Ts.IMAGE_OF_OR_APPROXIMATELY_EQUAL_TO
"≔" => Ts.COLON_EQUALS
"≕" => Ts.EQUALS_COLON
"≖" => Ts.RING_IN_EQUAL_TO
"≗" => Ts.RING_EQUAL_TO
"≘" => Ts.CORRESPONDS_TO
"≙" => Ts.ESTIMATES
"≚" => Ts.EQUIANGULAR_TO
"≛" => Ts.STAR_EQUALS
"≜" => Ts.DELTA_EQUAL_TO
"≝" => Ts.EQUAL_TO_BY_DEFINITION
"≞" => Ts.MEASURED_BY
"≟" => Ts.QUESTIONED_EQUAL_TO
"≣" => Ts.STRICTLY_EQUIVALENT_TO
"≦" => Ts.LESS_THAN_OVER_EQUAL_TO
"≧" => Ts.GREATER_THAN_OVER_EQUAL_TO
"≨" => Ts.LESS_THAN_BUT_NOT_EQUAL_TO
"≩" => Ts.GREATER_THAN_BUT_NOT_EQUAL_TO
"≪" => Ts.MUCH_LESS_THAN
"≫" => Ts.MUCH_GREATER_THAN
"≬" => Ts.BETWEEN
"≭" => Ts.NOT_EQUIVALENT_TO
"≮" => Ts.NOT_LESS_THAN
"≯" => Ts.NOT_GREATER_THAN
"≰" => Ts.NEITHER_LESS_THAN_NOR_EQUAL_TO
"≱" => Ts.NEITHER_GREATER_THAN_NOR_EQUAL_TO
"≲" => Ts.LESS_THAN_OR_EQUIVALENT_TO
"≳" => Ts.GREATER_THAN_OR_EQUIVALENT_TO
"≴" => Ts.NEITHER_LESS_THAN_NOR_EQUIVALENT_TO
"≵" => Ts.NEITHER_GREATER_THAN_NOR_EQUIVALENT_TO
"≶" => Ts.LESS_THAN_OR_GREATER_THAN
"≷" => Ts.GREATER_THAN_OR_LESS_THAN
"≸" => Ts.NEITHER_LESS_THAN_NOR_GREATER_THAN
"≹" => Ts.NEITHER_GREATER_THAN_NOR_LESS_THAN
"≺" => Ts.PRECEDES
"≻" => Ts.SUCCEEDS
"≼" => Ts.PRECEDES_OR_EQUAL_TO
"≽" => Ts.SUCCEEDS_OR_EQUAL_TO
"≾" => Ts.PRECEDES_OR_EQUIVALENT_TO
"≿" => Ts.SUCCEEDS_OR_EQUIVALENT_TO
"⊀" => Ts.DOES_NOT_PRECEDE
"⊁" => Ts.DOES_NOT_SUCCEED
"⊃" => Ts.SUPERSET_OF
"⊅" => Ts.NOT_A_SUPERSET_OF
"⊇" => Ts.SUPERSET_OF_OR_EQUAL_TO
"⊉" => Ts.NEITHER_A_SUPERSET_OF_NOR_EQUAL_TO
"⊋" => Ts.SUPERSET_OF_WITH_NOT_EQUAL_TO
"⊏" => Ts.SQUARE_IMAGE_OF
"⊐" => Ts.SQUARE_ORIGINAL_OF
"⊑" => Ts.SQUARE_IMAGE_OF_OR_EQUAL_TO
"⊒" => Ts.SQUARE_ORIGINAL_OF_OR_EQUAL_TO
"⊜" => Ts.CIRCLED_EQUALS
"⊩" => Ts.FORCES
"⊬" => Ts.DOES_NOT_PROVE
"⊮" => Ts.DOES_NOT_FORCE
"⊰" => Ts.PRECEDES_UNDER_RELATION
"⊱" => Ts.SUCCEEDS_UNDER_RELATION
"⊲" => Ts.NORMAL_SUBGROUP_OF
"⊳" => Ts.CONTAINS_AS_NORMAL_SUBGROUP
"⊴" => Ts.NORMAL_SUBGROUP_OF_OR_EQUAL_TO
"⊵" => Ts.CONTAINS_AS_NORMAL_SUBGROUP_OR_EQUAL_TO
"⊶" => Ts.ORIGINAL_OF
"⊷" => Ts.IMAGE_OF
"⋍" => Ts.REVERSED_TILDE_EQUALS
"⋐" => Ts.DOUBLE_SUBSET
"⋑" => Ts.DOUBLE_SUPERSET
"⋕" => Ts.EQUAL_AND_PARALLEL_TO
"⋖" => Ts.LESS_THAN_WITH_DOT
"⋗" => Ts.GREATER_THAN_WITH_DOT
"⋘" => Ts.VERY_MUCH_LESS_THAN
"⋙" => Ts.VERY_MUCH_GREATER_THAN
"⋚" => Ts.LESS_THAN_EQUAL_TO_OR_GREATER_THAN
"⋛" => Ts.GREATER_THAN_EQUAL_TO_OR_LESS_THAN
"⋜" => Ts.EQUAL_TO_OR_LESS_THAN
"⋝" => Ts.EQUAL_TO_OR_GREATER_THAN
"⋞" => Ts.EQUAL_TO_OR_PRECEDES
"⋟" => Ts.EQUAL_TO_OR_SUCCEEDS
"⋠" => Ts.DOES_NOT_PRECEDE_OR_EQUAL
"⋡" => Ts.DOES_NOT_SUCCEED_OR_EQUAL
"⋢" => Ts.NOT_SQUARE_IMAGE_OF_OR_EQUAL_TO
"⋣" => Ts.NOT_SQUARE_ORIGINAL_OF_OR_EQUAL_TO
"⋤" => Ts.SQUARE_IMAGE_OF_OR_NOT_EQUAL_TO
"⋥" => Ts.SQUARE_ORIGINAL_OF_OR_NOT_EQUAL_TO
"⋦" => Ts.LESS_THAN_BUT_NOT_EQUIVALENT_TO
"⋧" => Ts.GREATER_THAN_BUT_NOT_EQUIVALENT_TO
"⋨" => Ts.PRECEDES_BUT_NOT_EQUIVALENT_TO
"⋩" => Ts.SUCCEEDS_BUT_NOT_EQUIVALENT_TO
"⋪" => Ts.NOT_NORMAL_SUBGROUP_OF
"⋫" => Ts.DOES_NOT_CONTAIN_AS_NORMAL_SUBGROUP
"⋬" => Ts.NOT_NORMAL_SUBGROUP_OF_OR_EQUAL_TO
"⋭" => Ts.DOES_NOT_CONTAIN_AS_NORMAL_SUBGROUP_OR_EQUAL
"⋲" => Ts.ELEMENT_OF_WITH_LONG_HORIZONTAL_STROKE
"⋳" => Ts.ELEMENT_OF_WITH_VERTICAL_BAR_AT_END_OF_HORIZONTAL_STROKE
"⋴" => Ts.SMALL_ELEMENT_OF_WITH_VERTICAL_BAR_AT_END_OF_HORIZONTAL_STROKE
"⋵" => Ts.ELEMENT_OF_WITH_DOT_ABOVE
"⋶" => Ts.ELEMENT_OF_WITH_OVERBAR
"⋷" => Ts.SMALL_ELEMENT_OF_WITH_OVERBAR
"⋸" => Ts.ELEMENT_OF_WITH_UNDERBAR
"⋹" => Ts.ELEMENT_OF_WITH_TWO_HORIZONTAL_STROKES
"⋺" => Ts.CONTAINS_WITH_LONG_HORIZONTAL_STROKE
"⋻" => Ts.CONTAINS_WITH_VERTICAL_BAR_AT_END_OF_HORIZONTAL_STROKE
"⋼" => Ts.SMALL_CONTAINS_WITH_VERTICAL_BAR_AT_END_OF_HORIZONTAL_STROKE
"⋽" => Ts.CONTAINS_WITH_OVERBAR
"⋾" => Ts.SMALL_CONTAINS_WITH_OVERBAR
"⋿" => Ts.Z_NOTATION_BAG_MEMBERSHIP
"⟈" => Ts.REVERSE_SOLIDUS_PRECEDING_SUBSET
"⟉" => Ts.SUPERSET_PRECEDING_SOLIDUS
"⟒" => Ts.ELEMENT_OF_OPENING_UPWARDS
"⦷" => Ts.CIRCLED_PARALLEL
"⧀" => Ts.CIRCLED_LESS_THAN
"⧁" => Ts.CIRCLED_GREATER_THAN
"⧡" => Ts.INCREASES_AS
"⧣" => Ts.EQUALS_SIGN_AND_SLANTED_PARALLEL
"⧤" => Ts.EQUALS_SIGN_AND_SLANTED_PARALLEL_WITH_TILDE_ABOVE
"⧥" => Ts.IDENTICAL_TO_AND_SLANTED_PARALLEL
"⩦" => Ts.EQUALS_SIGN_WITH_DOT_BELOW
"⩧" => Ts.IDENTICAL_WITH_DOT_ABOVE
"⩪" => Ts.TILDE_OPERATOR_WITH_DOT_ABOVE
"⩫" => Ts.TILDE_OPERATOR_WITH_RISING_DOTS
"⩬" => Ts.SIMILAR_MINUS_SIMILAR
"⩭" => Ts.CONGRUENT_WITH_DOT_ABOVE
"⩮" => Ts.EQUALS_WITH_ASTERISK
"⩯" => Ts.ALMOST_EQUAL_TO_WITH_CIRCUMFLEX_ACCENT
"⩰" => Ts.APPROXIMATELY_EQUAL_OR_EQUAL_TO
"⩱" => Ts.EQUALS_SIGN_ABOVE_PLUS_SIGN
"⩲" => Ts.PLUS_SIGN_ABOVE_EQUALS_SIGN
"⩳" => Ts.EQUALS_SIGN_ABOVE_TILDE_OPERATOR
"⩴" => Ts.DOUBLE_COLON_EQUAL
"⩵" => Ts.TWO_CONSECUTIVE_EQUALS_SIGNS
"⩶" => Ts.THREE_CONSECUTIVE_EQUALS_SIGNS
"⩷" => Ts.EQUALS_SIGN_WITH_TWO_DOTS_ABOVE_AND_TWO_DOTS_BELOW
"⩸" => Ts.EQUIVALENT_WITH_FOUR_DOTS_ABOVE
"⩹" => Ts.LESS_THAN_WITH_CIRCLE_INSIDE
"⩺" => Ts.GREATER_THAN_WITH_CIRCLE_INSIDE
"⩻" => Ts.LESS_THAN_WITH_QUESTION_MARK_ABOVE
"⩼" => Ts.GREATER_THAN_WITH_QUESTION_MARK_ABOVE
"⩽" => Ts.LESS_THAN_OR_SLANTED_EQUAL_TO
"⩾" => Ts.GREATER_THAN_OR_SLANTED_EQUAL_TO
"⩿" => Ts.LESS_THAN_OR_SLANTED_EQUAL_TO_WITH_DOT_INSIDE
"⪀" => Ts.GREATER_THAN_OR_SLANTED_EQUAL_TO_WITH_DOT_INSIDE
"⪁" => Ts.LESS_THAN_OR_SLANTED_EQUAL_TO_WITH_DOT_ABOVE
"⪂" => Ts.GREATER_THAN_OR_SLANTED_EQUAL_TO_WITH_DOT_ABOVE
"⪃" => Ts.LESS_THAN_OR_SLANTED_EQUAL_TO_WITH_DOT_ABOVE_RIGHT
"⪄" => Ts.GREATER_THAN_OR_SLANTED_EQUAL_TO_WITH_DOT_ABOVE_LEFT
"⪅" => Ts.LESS_THAN_OR_APPROXIMATE
"⪆" => Ts.GREATER_THAN_OR_APPROXIMATE
"⪇" => Ts.LESS_THAN_AND_SINGLE_LINE_NOT_EQUAL_TO
"⪈" => Ts.GREATER_THAN_AND_SINGLE_LINE_NOT_EQUAL_TO
"⪉" => Ts.LESS_THAN_AND_NOT_APPROXIMATE
"⪊" => Ts.GREATER_THAN_AND_NOT_APPROXIMATE
"⪋" => Ts.LESS_THAN_ABOVE_DOUBLE_LINE_EQUAL_ABOVE_GREATER_THAN
"⪌" => Ts.GREATER_THAN_ABOVE_DOUBLE_LINE_EQUAL_ABOVE_LESS_THAN
"⪍" => Ts.LESS_THAN_ABOVE_SIMILAR_OR_EQUAL
"⪎" => Ts.GREATER_THAN_ABOVE_SIMILAR_OR_EQUAL
"⪏" => Ts.LESS_THAN_ABOVE_SIMILAR_ABOVE_GREATER_THAN
"⪐" => Ts.GREATER_THAN_ABOVE_SIMILAR_ABOVE_LESS_THAN
"⪑" => Ts.LESS_THAN_ABOVE_GREATER_THAN_ABOVE_DOUBLE_LINE_EQUAL
"⪒" => Ts.GREATER_THAN_ABOVE_LESS_THAN_ABOVE_DOUBLE_LINE_EQUAL
"⪓" => Ts.LESS_THAN_ABOVE_SLANTED_EQUAL_ABOVE_GREATER_THAN_ABOVE_SLANTED_EQUAL
"⪔" => Ts.GREATER_THAN_ABOVE_SLANTED_EQUAL_ABOVE_LESS_THAN_ABOVE_SLANTED_EQUAL
"⪕" => Ts.SLANTED_EQUAL_TO_OR_LESS_THAN
"⪖" => Ts.SLANTED_EQUAL_TO_OR_GREATER_THAN
"⪗" => Ts.SLANTED_EQUAL_TO_OR_LESS_THAN_WITH_DOT_INSIDE
"⪘" => Ts.SLANTED_EQUAL_TO_OR_GREATER_THAN_WITH_DOT_INSIDE
"⪙" => Ts.DOUBLE_LINE_EQUAL_TO_OR_LESS_THAN
"⪚" => Ts.DOUBLE_LINE_EQUAL_TO_OR_GREATER_THAN
"⪛" => Ts.DOUBLE_LINE_SLANTED_EQUAL_TO_OR_LESS_THAN
"⪜" => Ts.DOUBLE_LINE_SLANTED_EQUAL_TO_OR_GREATER_THAN
"⪝" => Ts.SIMILAR_OR_LESS_THAN
"⪞" => Ts.SIMILAR_OR_GREATER_THAN
"⪟" => Ts.SIMILAR_ABOVE_LESS_THAN_ABOVE_EQUALS_SIGN
"⪠" => Ts.SIMILAR_ABOVE_GREATER_THAN_ABOVE_EQUALS_SIGN
"⪡" => Ts.DOUBLE_NESTED_LESS_THAN
"⪢" => Ts.DOUBLE_NESTED_GREATER_THAN
"⪣" => Ts.DOUBLE_NESTED_LESS_THAN_WITH_UNDERBAR
"⪤" => Ts.GREATER_THAN_OVERLAPPING_LESS_THAN
"⪥" => Ts.GREATER_THAN_BESIDE_LESS_THAN
"⪦" => Ts.LESS_THAN_CLOSED_BY_CURVE
"⪧" => Ts.GREATER_THAN_CLOSED_BY_CURVE
"⪨" => Ts.LESS_THAN_CLOSED_BY_CURVE_ABOVE_SLANTED_EQUAL
"⪩" => Ts.GREATER_THAN_CLOSED_BY_CURVE_ABOVE_SLANTED_EQUAL
"⪪" => Ts.SMALLER_THAN
"⪫" => Ts.LARGER_THAN
"⪬" => Ts.SMALLER_THAN_OR_EQUAL_TO
"⪭" => Ts.LARGER_THAN_OR_EQUAL_TO
"⪮" => Ts.EQUALS_SIGN_WITH_BUMPY_ABOVE
"⪯" => Ts.PRECEDES_ABOVE_SINGLE_LINE_EQUALS_SIGN
"⪰" => Ts.SUCCEEDS_ABOVE_SINGLE_LINE_EQUALS_SIGN
"⪱" => Ts.PRECEDES_ABOVE_SINGLE_LINE_NOT_EQUAL_TO
"⪲" => Ts.SUCCEEDS_ABOVE_SINGLE_LINE_NOT_EQUAL_TO
"⪳" => Ts.PRECEDES_ABOVE_EQUALS_SIGN
"⪴" => Ts.SUCCEEDS_ABOVE_EQUALS_SIGN
"⪵" => Ts.PRECEDES_ABOVE_NOT_EQUAL_TO
"⪶" => Ts.SUCCEEDS_ABOVE_NOT_EQUAL_TO
"⪷" => Ts.PRECEDES_ABOVE_ALMOST_EQUAL_TO
"⪸" => Ts.SUCCEEDS_ABOVE_ALMOST_EQUAL_TO
"⪹" => Ts.PRECEDES_ABOVE_NOT_ALMOST_EQUAL_TO
"⪺" => Ts.SUCCEEDS_ABOVE_NOT_ALMOST_EQUAL_TO
"⪻" => Ts.DOUBLE_PRECEDES
"⪼" => Ts.DOUBLE_SUCCEEDS
"⪽" => Ts.SUBSET_WITH_DOT
"⪾" => Ts.SUPERSET_WITH_DOT
"⪿" => Ts.SUBSET_WITH_PLUS_SIGN_BELOW
"⫀" => Ts.SUPERSET_WITH_PLUS_SIGN_BELOW
"⫁" => Ts.SUBSET_WITH_MULTIPLICATION_SIGN_BELOW
"⫂" => Ts.SUPERSET_WITH_MULTIPLICATION_SIGN_BELOW
"⫃" => Ts.SUBSET_OF_OR_EQUAL_TO_WITH_DOT_ABOVE
"⫄" => Ts.SUPERSET_OF_OR_EQUAL_TO_WITH_DOT_ABOVE
"⫅" => Ts.SUBSET_OF_ABOVE_EQUALS_SIGN
"⫆" => Ts.SUPERSET_OF_ABOVE_EQUALS_SIGN
"⫇" => Ts.SUBSET_OF_ABOVE_TILDE_OPERATOR
"⫈" => Ts.SUPERSET_OF_ABOVE_TILDE_OPERATOR
"⫉" => Ts.SUBSET_OF_ABOVE_ALMOST_EQUAL_TO
"⫊" => Ts.SUPERSET_OF_ABOVE_ALMOST_EQUAL_TO
"⫋" => Ts.SUBSET_OF_ABOVE_NOT_EQUAL_TO
"⫌" => Ts.SUPERSET_OF_ABOVE_NOT_EQUAL_TO
"⫍" => Ts.SQUARE_LEFT_OPEN_BOX_OPERATOR
"⫎" => Ts.SQUARE_RIGHT_OPEN_BOX_OPERATOR
"⫏" => Ts.CLOSED_SUBSET
"⫐" => Ts.CLOSED_SUPERSET
"⫑" => Ts.CLOSED_SUBSET_OR_EQUAL_TO
"⫒" => Ts.CLOSED_SUPERSET_OR_EQUAL_TO
"⫓" => Ts.SUBSET_ABOVE_SUPERSET
"⫔" => Ts.SUPERSET_ABOVE_SUBSET
"⫕" => Ts.SUBSET_ABOVE_SUBSET
"⫖" => Ts.SUPERSET_ABOVE_SUPERSET
"⫗" => Ts.SUPERSET_BESIDE_SUBSET
"⫘" => Ts.SUPERSET_BESIDE_AND_JOINED_BY_DASH_WITH_SUBSET
"⫙" => Ts.ELEMENT_OF_OPENING_DOWNWARDS
"⫷" => Ts.TRIPLE_NESTED_LESS_THAN
"⫸" => Ts.TRIPLE_NESTED_GREATER_THAN
"⫹" => Ts.DOUBLE_LINE_SLANTED_LESS_THAN_OR_EQUAL_TO
"⫺" => Ts.DOUBLE_LINE_SLANTED_GREATER_THAN_OR_EQUAL_TO
"⊢" => Ts.RIGHT_TACK
"⊣" => Ts.LEFT_TACK
# ⫪,⫫ see https://github.com/JuliaLang/julia/issues/39350
"⫪" => Ts.DOUBLE_DOWN_TACK
"⫫" => Ts.DOUBLE_UP_TACK
"⟂" => Ts.PERP
"END_COMPARISON" => Ts.end_comparison
# Level 7
"BEGIN_PIPE" => Ts.begin_pipe
"<|" => Ts.LPIPE
"|>" => Ts.RPIPE
"END_PIPE" => Ts.end_pipe
# Level 8
"BEGIN_COLON" => Ts.begin_colon
":" => Ts.COLON
".." => Ts.DDOT
"…" => Ts.LDOTS
"⁝" => Ts.TRICOLON
"⋮" => Ts.VDOTS
"⋱" => Ts.DDOTS
"⋰" => Ts.ADOTS
"⋯" => Ts.CDOTS
"END_COLON" => Ts.end_colon
# Level 9
"BEGIN_PLUS" => Ts.begin_plus
"\$" => Ts.EX_OR
"+" => Ts.PLUS
"-" => Ts.MINUS
"++" => Ts.PLUSPLUS
"⊕" => Ts.CIRCLED_PLUS
"⊖" => Ts.CIRCLED_MINUS
"⊞" => Ts.SQUARED_PLUS
"⊟" => Ts.SQUARED_MINUS
"|" => Ts.OR
"∪" => Ts.UNION
"∨" => Ts.LOGICAL_OR
"⊔" => Ts.SQUARE_CUP
"±" => Ts.PLUS_MINUS_SIGN
"∓" => Ts.MINUS_OR_PLUS_SIGN
"∔" => Ts.DOT_PLUS
"∸" => Ts.DOT_MINUS
"≂" => Ts.MINUS_TILDE
"≏" => Ts.DIFFERENCE_BETWEEN
"⊎" => Ts.MULTISET_UNION
"⊻" => Ts.XOR
"⊽" => Ts.NOR
"⋎" => Ts.CURLY_LOGICAL_OR
"⋓" => Ts.DOUBLE_UNION
"⧺" => Ts.DOUBLE_PLUS
"⧻" => Ts.TRIPLE_PLUS
"⨈" => Ts.TWO_LOGICAL_OR_OPERATOR
"⨢" => Ts.PLUS_SIGN_WITH_SMALL_CIRCLE_ABOVE
"⨣" => Ts.PLUS_SIGN_WITH_CIRCUMFLEX_ACCENT_ABOVE
"⨤" => Ts.PLUS_SIGN_WITH_TILDE_ABOVE
"⨥" => Ts.PLUS_SIGN_WITH_DOT_BELOW
"⨦" => Ts.PLUS_SIGN_WITH_TILDE_BELOW
"⨧" => Ts.PLUS_SIGN_WITH_SUBSCRIPT_TWO
"⨨" => Ts.PLUS_SIGN_WITH_BLACK_TRIANGLE
"⨩" => Ts.MINUS_SIGN_WITH_COMMA_ABOVE
"⨪" => Ts.MINUS_SIGN_WITH_DOT_BELOW
"⨫" => Ts.MINUS_SIGN_WITH_FALLING_DOTS
"⨬" => Ts.MINUS_SIGN_WITH_RISING_DOTS
"⨭" => Ts.PLUS_SIGN_IN_LEFT_HALF_CIRCLE
"⨮" => Ts.PLUS_SIGN_IN_RIGHT_HALF_CIRCLE
"⨹" => Ts.PLUS_SIGN_IN_TRIANGLE
"⨺" => Ts.MINUS_SIGN_IN_TRIANGLE
"⩁" => Ts.UNION_WITH_MINUS_SIGN
"⩂" => Ts.UNION_WITH_OVERBAR
"⩅" => Ts.UNION_WITH_LOGICAL_OR
"⩊" => Ts.UNION_BESIDE_AND_JOINED_WITH_UNION
"⩌" => Ts.CLOSED_UNION_WITH_SERIFS
"⩏" => Ts.DOUBLE_SQUARE_UNION
"⩐" => Ts.CLOSED_UNION_WITH_SERIFS_AND_SMASH_PRODUCT
"⩒" => Ts.LOGICAL_OR_WITH_DOT_ABOVE
"⩔" => Ts.DOUBLE_LOGICAL_OR
"⩖" => Ts.TWO_INTERSECTING_LOGICAL_OR
"⩗" => Ts.SLOPING_LARGE_OR
"⩛" => Ts.LOGICAL_OR_WITH_MIDDLE_STEM
"⩝" => Ts.LOGICAL_OR_WITH_HORIZONTAL_DASH
"⩡" => Ts.SMALL_VEE_WITH_UNDERBAR
"⩢" => Ts.LOGICAL_OR_WITH_DOUBLE_OVERBAR
"⩣" => Ts.LOGICAL_OR_WITH_DOUBLE_UNDERBAR
"¦" => Ts.BROKEN_BAR
"END_PLUS" => Ts.end_plus
# Level 10
"BEGIN_BITSHIFTS" => Ts.begin_bitshifts
"<<" => Ts.LBITSHIFT
">>" => Ts.RBITSHIFT
">>>" => Ts.UNSIGNED_BITSHIFT
"END_BITSHIFTS" => Ts.end_bitshifts
# Level 11
"BEGIN_TIMES" => Ts.begin_times
"*" => Ts.STAR
"/" => Ts.FWD_SLASH
"÷" => Ts.DIVISION_SIGN
"%" => Ts.REM
"⋅" => Ts.UNICODE_DOT
"∘" => Ts.RING_OPERATOR
"×" => Ts.MULTIPLICATION_SIGN
"\\" => Ts.BACKSLASH
"&" => Ts.AND
"∩" => Ts.INTERSECTION
"∧" => Ts.LOGICAL_AND
"⊗" => Ts.CIRCLED_TIMES
"⊘" => Ts.CIRCLED_DIVISION_SLASH
"⊙" => Ts.CIRCLED_DOT_OPERATOR
"⊚" => Ts.CIRCLED_RING_OPERATOR
"⊛" => Ts.CIRCLED_ASTERISK_OPERATOR
"⊠" => Ts.SQUARED_TIMES
"⊡" => Ts.SQUARED_DOT_OPERATOR
"⊓" => Ts.SQUARE_CAP
"∗" => Ts.ASTERISK_OPERATOR
"∙" => Ts.BULLET_OPERATOR
"∤" => Ts.DOES_NOT_DIVIDE
"⅋" => Ts.TURNED_AMPERSAND
"≀" => Ts.WREATH_PRODUCT
"⊼" => Ts.NAND
"⋄" => Ts.DIAMOND_OPERATOR
"⋆" => Ts.STAR_OPERATOR
"⋇" => Ts.DIVISION_TIMES
"⋉" => Ts.LEFT_NORMAL_FACTOR_SEMIDIRECT_PRODUCT
"⋊" => Ts.RIGHT_NORMAL_FACTOR_SEMIDIRECT_PRODUCT
"⋋" => Ts.LEFT_SEMIDIRECT_PRODUCT
"⋌" => Ts.RIGHT_SEMIDIRECT_PRODUCT
"⋏" => Ts.CURLY_LOGICAL_AND
"⋒" => Ts.DOUBLE_INTERSECTION
"⟑" => Ts.AND_WITH_DOT
"⦸" => Ts.CIRCLED_REVERSE_SOLIDUS
"⦼" => Ts.CIRCLED_ANTICLOCKWISE_ROTATED_DIVISION_SIGN
"⦾" => Ts.CIRCLED_WHITE_BULLET
"⦿" => Ts.CIRCLED_BULLET
"⧶" => Ts.SOLIDUS_WITH_OVERBAR
"⧷" => Ts.REVERSE_SOLIDUS_WITH_HORIZONTAL_STROKE
"⨇" => Ts.TWO_LOGICAL_AND_OPERATOR
"⨰" => Ts.MULTIPLICATION_SIGN_WITH_DOT_ABOVE
"⨱" => Ts.MULTIPLICATION_SIGN_WITH_UNDERBAR
"⨲" => Ts.SEMIDIRECT_PRODUCT_WITH_BOTTOM_CLOSED
"⨳" => Ts.SMASH_PRODUCT
"⨴" => Ts.MULTIPLICATION_SIGN_IN_LEFT_HALF_CIRCLE
"⨵" => Ts.MULTIPLICATION_SIGN_IN_RIGHT_HALF_CIRCLE
"⨶" => Ts.CIRCLED_MULTIPLICATION_SIGN_WITH_CIRCUMFLEX_ACCENT
"⨷" => Ts.MULTIPLICATION_SIGN_IN_DOUBLE_CIRCLE
"⨸" => Ts.CIRCLED_DIVISION_SIGN
"⨻" => Ts.MULTIPLICATION_SIGN_IN_TRIANGLE
"⨼" => Ts.INTERIOR_PRODUCT
"⨽" => Ts.RIGHTHAND_INTERIOR_PRODUCT
"⩀" => Ts.INTERSECTION_WITH_DOT
"⩃" => Ts.INTERSECTION_WITH_OVERBAR
"⩄" => Ts.INTERSECTION_WITH_LOGICAL_AND
"⩋" => Ts.INTERSECTION_BESIDE_AND_JOINED_WITH_INTERSECTION
"⩍" => Ts.CLOSED_INTERSECTION_WITH_SERIFS
"⩎" => Ts.DOUBLE_SQUARE_INTERSECTION
"⩑" => Ts.LOGICAL_AND_WITH_DOT_ABOVE
"⩓" => Ts.DOUBLE_LOGICAL_AND
"⩕" => Ts.TWO_INTERSECTING_LOGICAL_AND
"⩘" => Ts.SLOPING_LARGE_AND
"⩚" => Ts.LOGICAL_AND_WITH_MIDDLE_STEM
"⩜" => Ts.LOGICAL_AND_WITH_HORIZONTAL_DASH
"⩞" => Ts.LOGICAL_AND_WITH_DOUBLE_OVERBAR
"⩟" => Ts.LOGICAL_AND_WITH_UNDERBAR
"⩠" => Ts.LOGICAL_AND_WITH_DOUBLE_UNDERBAR
"⫛" => Ts.TRANSVERSAL_INTERSECTION
"⊍" => Ts.MULTISET_MULTIPLICATION
"▷" => Ts.WHITE_RIGHT_POINTING_TRIANGLE
"⨝" => Ts.JOIN
"⟕" => Ts.LEFT_OUTER_JOIN
"⟖" => Ts.RIGHT_OUTER_JOIN
"⟗" => Ts.FULL_OUTER_JOIN
"⌿" => Ts.NOT_SLASH
"⨟" => Ts.BB_SEMI
"END_TIMES" => Ts.end_times
# Level 12
"BEGIN_RATIONAL" => Ts.begin_rational
"//" => Ts.FWDFWD_SLASH
"END_RATIONAL" => Ts.end_rational
# Level 13
"BEGIN_POWER" => Ts.begin_power
"^" => Ts.CIRCUMFLEX_ACCENT
"↑" => Ts.UPWARDS_ARROW
"↓" => Ts.DOWNWARDS_ARROW
"⇵" => Ts.DOWNWARDS_ARROW_LEFTWARDS_OF_UPWARDS_ARROW
"⟰" => Ts.UPWARDS_QUADRUPLE_ARROW
"⟱" => Ts.DOWNWARDS_QUADRUPLE_ARROW
"⤈" => Ts.DOWNWARDS_ARROW_WITH_HORIZONTAL_STROKE
"⤉" => Ts.UPWARDS_ARROW_WITH_HORIZONTAL_STROKE
"⤊" => Ts.UPWARDS_TRIPLE_ARROW
"⤋" => Ts.DOWNWARDS_TRIPLE_ARROW
"⤒" => Ts.UPWARDS_ARROW_TO_BAR
"⤓" => Ts.DOWNWARDS_ARROW_TO_BAR
"⥉" => Ts.UPWARDS_TWO_HEADED_ARROW_FROM_SMALL_CIRCLE
"⥌" => Ts.UP_BARB_RIGHT_DOWN_BARB_LEFT_HARPOON
"⥍" => Ts.UP_BARB_LEFT_DOWN_BARB_RIGHT_HARPOON
"⥏" => Ts.UP_BARB_RIGHT_DOWN_BARB_RIGHT_HARPOON
"⥑" => Ts.UP_BARB_LEFT_DOWN_BARB_LEFT_HARPOON
"⥔" => Ts.UPWARDS_HARPOON_WITH_BARB_RIGHT_TO_BAR
"⥕" => Ts.DOWNWARDS_HARPOON_WITH_BARB_RIGHT_TO_BAR
"⥘" => Ts.UPWARDS_HARPOON_WITH_BARB_LEFT_TO_BAR
"⥙" => Ts.DOWNWARDS_HARPOON_WITH_BARB_LEFT_TO_BAR
"⥜" => Ts.UPWARDS_HARPOON_WITH_BARB_RIGHT_FROM_BAR
"⥝" => Ts.DOWNWARDS_HARPOON_WITH_BARB_RIGHT_FROM_BAR
"⥠" => Ts.UPWARDS_HARPOON_WITH_BARB_LEFT_FROM_BAR
"⥡" => Ts.DOWNWARDS_HARPOON_WITH_BARB_LEFT_FROM_BAR
"⥣" => Ts.UPWARDS_HARPOON_WITH_BARB_LEFT_BESIDE_UPWARDS_HARPOON_WITH_BARB_RIGHT
"⥥" => Ts.DOWNWARDS_HARPOON_WITH_BARB_LEFT_BESIDE_DOWNWARDS_HARPOON_WITH_BARB_RIGHT
"⥮" => Ts.UPWARDS_HARPOON_WITH_BARB_LEFT_BESIDE_DOWNWARDS_HARPOON_WITH_BARB_RIGHT
"⥯" => Ts.DOWNWARDS_HARPOON_WITH_BARB_LEFT_BESIDE_UPWARDS_HARPOON_WITH_BARB_RIGHT
"￪" => Ts.HALFWIDTH_UPWARDS_ARROW
"￬" => Ts.HALFWIDTH_DOWNWARDS_ARROW
"END_POWER" => Ts.end_power
# Level 14
"BEGIN_DECL" => Ts.begin_decl
"::" => Ts.DECLARATION
"END_DECL" => Ts.end_decl
# Level 15
"BEGIN_WHERE" => Ts.begin_where
"where" => Ts.WHERE
"END_WHERE" => Ts.end_where
# Level 16
"BEGIN_DOT" => Ts.begin_dot
"." => Ts.DOT
"END_DOT" => Ts.end_dot
"!" => Ts.NOT
"'" => Ts.PRIME
".'" => Ts.TRANSPOSE
"->" => Ts.ANON_FUNC
"BEGIN_UNICODE_OPS" => Ts.begin_unicode_ops
"¬" => Ts.NOT_SIGN
"√" => Ts.SQUARE_ROOT
"∛" => Ts.CUBE_ROOT
"∜" => Ts.QUAD_ROOT
"END_UNICODE_OPS" => Ts.end_unicode_ops
"END_OPS" => Ts.end_ops
"BEGIN_PARSER_TOKENS" => Ts.begin_parser_tokens
"TOMBSTONE" => Ts.TOMBSTONE
# Macro names are modelled as a special kind of identifier because the
# @ may not be attached to the macro name in the source (or may not be
# associated with a token at all in the case of implied macro calls
# like CORE_DOC_MACRO_NAME)
"BEGIN_MACRO_NAMES" => Ts.begin_macro_names
"MacroName" => Ts.MACRO_NAME # A macro name identifier
"@." => Ts.DOT_MACRO_NAME # The macro name of @.
"StringMacroName" => Ts.STRING_MACRO_NAME # macname"some_str"
"CmdMacroName" => Ts.CMD_MACRO_NAME # macname`some_str`
"core_@doc" => Ts.CORE_DOC_MACRO_NAME # Core.@doc
"core_@cmd" => Ts.CORE_CMD_MACRO_NAME # Core.@cmd
"core_@int128_str" => Ts.CORE_INT128_STR_MACRO_NAME # Core.@int128_str
"core_@uint128_str" => Ts.CORE_UINT128_STR_MACRO_NAME # Core.@uint128_str
"core_@big_str" => Ts.CORE_BIG_STR_MACRO_NAME # Core.@big_str
"END_MACRO_NAMES" => Ts.end_macro_names
"END_PARSER_TOKENS" => Ts.end_parser_tokens
# Our custom syntax tokens
"BEGIN_SYNTAX_KINDS" => Ts.begin_syntax_kinds
"block" => Ts.BLOCK
"call" => Ts.CALL
"comparison" => Ts.COMPARISON
"curly" => Ts.CURLY
"inert" => Ts.INERT
"string" => Ts.STRING_INTERP
"macrocall" => Ts.MACROCALL
"kw" => Ts.KW # the = in f(a=1)
"parameters" => Ts.PARAMETERS # the list after ; in f(; a=1)
"toplevel" => Ts.TOPLEVEL
"tuple" => Ts.TUPLE
"ref" => Ts.REF
"vect" => Ts.VECT
"braces" => Ts.BRACES
"bracescat" => Ts.BRACESCAT
"hcat" => Ts.HCAT
"vcat" => Ts.VCAT
"ncat" => Ts.NCAT
"typed_hcat" => Ts.TYPED_HCAT
"typed_vcat" => Ts.TYPED_VCAT
"typed_ncat" => Ts.TYPED_NCAT
"row" => Ts.ROW
"nrow" => Ts.NROW
"generator" => Ts.GENERATOR
"filter" => Ts.FILTER
"flatten" => Ts.FLATTEN
"comprehension" => Ts.COMPREHENSION
"typed_comprehension" => Ts.TYPED_COMPREHENSION
"END_SYNTAX_KINDS" => Ts.end_syntax_kinds
])
end
# Mapping from kinds to their unique string representation, if it exists
const _kind_to_str_unique =
Dict{Kind,String}(k=>string(s) for (k,s) in TzTokens.UNICODE_OPS_REVERSE)
for kw in split("""
( [ { } ] ) @ , ; " \"\"\" ` ```
baremodule begin break catch const
continue do else elseif end export finally for
function global if import let local
macro module quote return struct try type using while
as abstract doc mutable outer primitive type var
block call comparison curly string inert macrocall kw parameters
toplevel tuple ref vect braces bracescat hcat
vcat ncat typed_hcat typed_vcat typed_ncat row nrow generator
filter flatten comprehension typed_comprehension
error nothing true false None
""")
_kind_to_str_unique[_str_to_kind[kw]] = kw
end
const _kind_to_str = Dict(s=>k for (k,s) in _str_to_kind)
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13,
437,
62,
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2,
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604,
198,
1,
33,
43312,
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13534,
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35510,
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1,
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1,
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1,
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2200,
28884,
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19807,
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19807,
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10970,
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3955,
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1,
35705,
239,
1,
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5218,
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41132,
19807,
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1,
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1,
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1,
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1340,
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1847,
62,
10468,
62,
17513,
62,
7206,
20032,
17941,
198,
1,
35705,
252,
1,
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220,
220,
5218,
220,
13146,
13,
11682,
1921,
4261,
1961,
62,
17513,
198,
1,
35705,
253,
1,
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220,
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5218,
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13,
35780,
2849,
1961,
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1847,
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10468,
198,
1,
35705,
96,
1,
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5218,
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13,
18601,
18379,
11319,
62,
36,
10917,
3824,
1847,
3525,
62,
10468,
198,
1,
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99,
1,
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5218,
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48481,
62,
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1565,
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62,
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198,
1,
35705,
100,
1,
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5218,
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28934,
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198,
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101,
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5218,
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11929,
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198,
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1,
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5218,
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28934,
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47526,
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11929,
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198,
1,
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103,
1,
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5218,
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42422,
3398,
62,
48481,
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4221,
1565,
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104,
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42422,
3398,
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28934,
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1,
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5218,
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33,
2767,
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1677,
198,
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35705,
255,
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1,
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5218,
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28934,
23261,
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] | 1.640335 | 20,875 |
println("foo!")
println(STDERR, "bar!")
function calculate100(f, a, b)
println("f: $f")
println("a: $a, b: $b")
return f.(linspace(a, b, 100))
end
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] | 2.208333 | 72 |
# Copyright 2016, Iain Dunning, Joey Huchette, Miles Lubin, and contributors
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
#############################################################################
# JuMP
# An algebraic modelling langauge for Julia
# See http://github.com/JuliaOpt/JuMP.jl
#############################################################################
# noswot.jl
#
# Solve a nontrivial MIP problem with user cuts. The jump model is populated
# from an .MPS file by using MathProgBase to construct another model and
# copying the data over to the JuMP model. The problem comes from the
# MIPLIB 3.0 benchmark collection.
#############################################################################
using JuMP
using MathProgBase
using Gurobi
mod = Model(solver=GurobiSolver(PreCrush=1, Cuts=0, Presolve=0, Heuristics=0.0, DisplayInterval=1))
m_internal = MathProgBase.LinearQuadraticModel(GurobiSolver())
## Uncomment the following three lines to solve using CPLEX
# using CPLEX
# mod = Model(solver=CplexSolver(CPX_PARAM_PRELINEAR=0, CPX_PARAM_PREIND=0, CPX_PARAM_ADVIND=0, CPX_PARAM_MIPSEARCH=1,CPX_PARAM_MIPCBREDLP=0))
# m_internal = MathProgBase.LinearQuadraticModel(CplexSolver())
# Load the model from .MPS file
MathProgBase.loadproblem!(m_internal, "data/noswot.mps")
# grab MathProgBase data
c = MathProgBase.getobj(m_internal)
A = MathProgBase.getconstrmatrix(m_internal)
m, n = size(A)
xlb = MathProgBase.getvarLB(m_internal)
xub = MathProgBase.getvarUB(m_internal)
l = MathProgBase.getconstrLB(m_internal)
u = MathProgBase.getconstrUB(m_internal)
vtypes = MathProgBase.getvartype(m_internal)
# populate JuMP model with data from internal model
@variable(mod, x[1:n])
for i in 1:n
setlowerbound(x[i], xlb[i])
setupperbound(x[i], xub[i])
vtypes[i] == 'I' ? mod.colCat[x[i].col] = :Int : nothing # change vartype to integer when appropriate
end
At = A' # transpose to get useful row-wise sparse representation
for i in 1:At.n
@constraint( mod, l[i] <= sum{ At.nzval[idx]*x[At.rowval[idx]], idx = At.colptr[i]:(At.colptr[i+1]-1) } <= u[i] )
end
@objective(mod, Min, sum{ c[i]*x[i], i=1:n })
function mycutgenerator(cb) # valid cuts
x_val = getvalue(x)
println("in callback")
@usercut(cb, x[62]-x[63] <= 0)
@usercut(cb, x[63]-x[64] <= 0)
@usercut(cb, x[64]-x[65] <= 0)
@usercut(cb, 2.08x[52] + 2.98x[62] + 3.47x[72] + 2.24x[82] + 2.08x[92] + 0.25x[51] + 0.25x[61] + 0.25x[71] + 0.25x[81] + 0.25x[91] <= 20.25)
@usercut(cb, 2.08x[54] + 2.98x[64] + 3.47x[74] + 2.24x[84] + 2.08x[94] + 0.25x[53] + 0.25x[63] + 0.25x[73] + 0.25x[83] + 0.25x[93] <= 20.25)
@usercut(cb, 2.08x[56] + 2.98x[66] + 3.4722x[76] + 2.24x[86] + 2.08x[96] + 0.25x[55] + 0.25x[65] + 0.25x[75] + 0.25x[85] + 0.25x[95] <= 20.25)
@usercut(cb, 2.08x[58] + 2.98x[68] + 3.47x[78] + 2.24x[88] + 2.08x[98] + 0.25x[57] + 0.25x[67] + 0.25x[77] + 0.25x[87] + 0.25x[97] <= 20.25)
@usercut(cb, 2.08x[60] + 2.98x[70] + 3.47x[80] + 2.24x[90] + 2.08x[100] + 0.25x[59] + 0.25x[69] + 0.25x[79] + 0.25x[89] + 0.25x[99] <= 16.25)
end # End of callback function
# # Tell JuMP/CPLEX to use our callback function
addcutcallback(mod, mycutgenerator)
stat = solve(mod)
println("Solve status: ", stat)
println("Objective value: ", getobjectivevalue(mod))
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] | 2.357435 | 1,466 |
module DataFitting
using Printf, PrettyTables
using Statistics, Distributions
using DataStructures
using LsqFit
import Base.push!
import Base.show
import Base.ndims
import Base.size
import Base.length
import Base.getindex
import Base.reshape
import Base.propertynames
import Base.getproperty
import Base.getindex
import Base.setindex!
import Base.iterate
export Domain, CartesianDomain, Measures,
Prediction, Model, evaluate, parindex, thaw, freeze, fit!
include("domain.jl")
# ====================================================================
# Parameter
#
mutable struct Parameter
val::Float64
low::Float64 # lower limit value
high::Float64 # upper limit value
step::Float64
free::Bool
Parameter(value::Number) = new(float(value), -Inf, +Inf, NaN, true)
end
# ====================================================================
# A *component* is a generic implementation of a constituent part of a
# model.
#
# A component must inherit `AbstractComponent`, and implement the
# `ceval_data` and `evaluate` methods
abstract type AbstractComponent end
function getparams(comp::AbstractComponent)
params = OrderedDict{Tuple{Symbol,Int}, Parameter}()
for pname in fieldnames(typeof(comp))
par = getfield(comp, pname)
if isa(par, Parameter)
params[(pname, 0)] = par
elseif isa(par, Vector{Parameter})
for i in 1:length(par)
params[(pname, i)] = par[i]
end
end
end
params
end
# ====================================================================
# CompEval: a wrapper for a component evaluated on a specific domain
#
mutable struct CompEval{TDomain <: AbstractDomain, TComp <: AbstractComponent}
domain::TDomain
comp::TComp
params::OrderedDict{Tuple{Symbol,Int}, Parameter}
cdata
counter::Int
lastvalues::Vector{Float64}
eval::Vector{Float64}
ipar::Vector{Int} # handled by Model
function CompEval(domain::AbstractDomain, comp::AbstractComponent)
params = getparams(comp)
(cdata, len) = ceval_data(domain, comp)
return new{typeof(domain), typeof(comp)}(
domain, comp, params, cdata, 0,
fill(NaN, length(params)),
fill(NaN, len), Vector{Int}())
end
end
# This is called to `update` to distinguish it from component's `evaluate`.
update(c::CompEval) = update(c, [par.val for par in values(c.params)])
function update(c::CompEval, pvalues::Vector{Float64})
@assert length(c.params) == length(pvalues)
# Do we actually need a new evaluation?
if (any(c.lastvalues .!= pvalues) || (c.counter == 0))
c.lastvalues .= pvalues
c.counter += 1
@assert all(.!isnan.(pvalues))
evaluate(c, pvalues...)
end
return c.eval
end
# ====================================================================
# Component fall back methods
ceval_data(domain::AbstractDomain, comp::AbstractComponent) =
error("Component " * string(typeof(comp)) * " must implement its own method for `ceval_data`.")
evaluate(c::CompEval{TDomain, TComp}, args...) where {TDomain, TComp} =
error("Component " * string(TComp) * " must implement its own method for `evaluate`.")
# ====================================================================
# Built-in components
#
include("components/ScalarParam.jl")
include("components/FuncWrap.jl")
include("components/OffsetSlope.jl")
include("components/Gaussian.jl")
# ====================================================================
# Parse a user defined structure or dictionary to extract all
# components
function extract_components(things...; prefix="")
out = OrderedDict{Symbol, AbstractComponent}()
for thing in things
#println()
#println("Thing $(typeof(thing)) (prefix = $(prefix))")
if isa(thing, AbstractComponent)
#println("Adding...")
out[Symbol(prefix)] = thing
else
(length(prefix) > 0) && (prefix *= "_")
if isa(thing, AbstractDict)
for (name, v) in thing
#println("Dict: Walk through $name :: $(typeof(v))")
merge!(out, extract_components(v; prefix=prefix * string(name)))
end
elseif isa(thing, Pair)
#println("Pair: $(thing[1]), $(typeof(thing[2]))")
if isa(thing[1], Symbol)
name = thing[1]
v = thing[2]
isa(v, Number) && (v = ScalarParam(v))
if isa(v, AbstractComponent)
merge!(out, extract_components(v; prefix=prefix * string(name)))
end
end
elseif isstructtype(typeof(thing))
for name in fieldnames(typeof(thing))
v = getfield(thing, name)
#println("Structure: Walk through $name :: $(typeof(v))")
merge!(out, extract_components(v; prefix=prefix * string(name)))
end
end
end
end
return out
end
# ====================================================================
# A model prediction suitable to be compared to experimental data
mutable struct Prediction
domain::AbstractDomain
cevals::OrderedDict{Symbol, CompEval}
eval::Vector{Float64}
reduce_with_dict::Bool
reducer::Union{Nothing, Function}
counter::Int
function Prediction(domain::AbstractDomain, things...;
prefix="", reduce=reduce)
comps = extract_components(things...; prefix=prefix)
cevals = OrderedDict{Symbol, CompEval}()
for (name, comp) in comps
cevals[name] = CompEval(domain, comp)
end
out = new(domain, cevals, Vector{Float64}(), false, reduce, 0)
evaluate(out) # TODO: is this correct?
return out
end
end
# Default reducer: add all components
reduce(domain::AbstractDomain, args...) = .+(args...)
# Reduce prediction by combining individual components
function reduce(pred::Prediction)
if pred.reduce_with_dict
d = Dict([(cname, ceval.eval) for (cname, ceval) in pred.cevals])
expr = pred.reducer(pred.domain, d)
else
d = [ceval.eval for (cname, ceval) in pred.cevals]
expr = pred.reducer(pred.domain, d...)
end
if length(pred.eval) == 0
append!(pred.eval, expr)
else
pred.eval .= expr
end
pred.counter += 1
end
function evaluate(pred::Prediction)
for (name, ceval) in pred.cevals
update(ceval)
end
reduce(pred)
end
# ====================================================================
# Global model, actually a collection of `Prediction`s.
mutable struct Model
preds::Vector{Prediction}
comps::OrderedDict{Symbol, AbstractComponent}
cfree::OrderedDict{Symbol, Bool}
params::OrderedDict{Tuple{Symbol, Symbol, Int}, Parameter}
pvalues::Vector{Float64}
actual::Vector{Float64}
buffer::Vector{Float64}
partransform::Function
end
function Model(v::Vector{Prediction})
model = Model(v, OrderedDict{Symbol, AbstractComponent}(),
OrderedDict{Symbol, Bool}(),
OrderedDict{Tuple{Symbol, Symbol, Int}, Parameter}(),
Vector{Float64}(), Vector{Float64}(), Vector{Float64}(), default_partransform)
evaluate(model)
return model
end
Model(p::Prediction) = Model([p])
Model(args...; kw...) = Model(Prediction(args...; kw...))
function evaluate(model::Model)
@assert length(model.preds) >= 1
# Save list of previously free components
cfree = deepcopy(model.cfree)
# Collect components and parameters
empty!(model.comps)
empty!(model.cfree)
empty!(model.params)
for pred in model.preds
for (cname, ceval) in pred.cevals
model.comps[cname] = ceval.comp
model.cfree[cname] = get(cfree, cname, true)
for (pname, par) in ceval.params
cpname = (cname, pname[1], pname[2])
model.params[cpname] = par
end
end
end
# Populate CompEval.ipar and evaluate all predictions
ndata = 0
cpnames = keys(model.params)
for pred in model.preds
for (cname, ceval) in pred.cevals
empty!(ceval.ipar)
for (pname, par) in ceval.params
cpname = (cname, pname[1], pname[2])
push!(ceval.ipar, findfirst(cpnames .== Ref(cpname)))
end
update(ceval)
end
reduce(pred)
ndata += length(pred.eval)
end
model.pvalues = [par.val for par in values(model.params)]
model.actual = deepcopy(model.pvalues)
model.buffer = Vector{Float64}(undef, ndata)
quick_evaluate(model)
return model
end
default_partransform(model::Model, pvalues::Vector{Float64}, actual::Vector{Float64}) = nothing
# This is supposed to be called from `fit!`, not by user
function quick_evaluate(model::Model)
model.actual .= model.pvalues # copy all values by default
model.partransform(model, model.pvalues, model.actual)
for pred in model.preds
for (cname, ceval) in pred.cevals
update(ceval, model.actual[ceval.ipar])
end
end
for pred in model.preds
reduce(pred)
end
nothing
end
function Base.push!(m::Model, p::Prediction)
push!(m.preds, p)
evaluate(model)
return model
end
Base.getindex(m::Model, i::Int) = m.preds[i].eval
Base.getindex(m::Model, cname::Symbol) = m.comps[cname]
parindex(model::Model, cname::Symbol, pname::Symbol, i::Int=0) =
findfirst(keys(model.params) .== Ref((cname, pname, i)))
function freeze(model::Model, cname::Symbol)
@assert cname in keys(model.cfree) "Component $c is not defined"
model.cfree[cname] = false
model
end
function thaw(model::Model, cname::Symbol)
@assert cname in keys(model.cfree) "Component $c is not defined"
model.cfree[cname] = true
model
end
# ====================================================================
# Fit results
#
struct BestFitPar
val::Float64
unc::Float64
free::Bool
calc::Float64 # value after transformation
end
struct BestFitComp
params::OrderedDict{Symbol, Union{BestFitPar, Vector{BestFitPar}}}
BestFitComp() = new(OrderedDict{Symbol, Union{BestFitPar, Vector{BestFitPar}}}())
end
Base.propertynames(comp::BestFitComp) = keys(getfield(comp, :params))
Base.getproperty(comp::BestFitComp, p::Symbol) = getfield(comp, :params)[p]
Base.getindex(comp::BestFitComp, p::Symbol) = getfield(comp, :params)[p]
Base.length(comp::BestFitComp) = length(getfield(comp, :params))
Base.iterate(comp::BestFitComp, args...) = iterate(getfield(comp, :params), args...)
Base.setindex!(comp::BestFitComp, x, p::Symbol) = getfield(comp, :params)[p] = x
struct BestFitResult
comps::OrderedDict{Symbol, BestFitComp}
ndata::Int
dof::Int
cost::Float64
status::Symbol #:Optimal, :NonOptimal, :Warn, :Error
log10testprob::Float64
elapsed::Float64
end
Base.getindex(res::BestFitResult, cname::Symbol) = res.comps[cname]
# ====================================================================
function data1D(model::Model, data::Vector{T}) where T<:AbstractMeasures
out = Vector{Measures_1D}()
for i in 1:length(model.preds)
pred = model.preds[i]
@assert(length(data[i]) == length(pred.eval),
"Length of dataset $i do not match corresponding model prediction.")
push!(out, flatten(data[i], pred.domain))
end
return out
end
function residuals1d(model::Model, data1d::Vector{Measures_1D})
c1 = 1
for i in 1:length(model.preds)
pred = model.preds[i]
c2 = c1 + length(pred.eval) - 1
model.buffer[c1:c2] .= ((pred.eval .- data1d[i].val) ./ data1d[i].unc)
c1 = c2 + 1
end
return model.buffer
end
# ====================================================================
abstract type AbstractMinimizer end
using LsqFit
mutable struct lsqfit <: AbstractMinimizer
end
function minimize(minimizer::lsqfit, func::Function, params::Vector{Parameter})
ndata = length(func(getfield.(params, :val)))
bestfit = LsqFit.curve_fit((dummy, pvalues) -> func(pvalues),
1.:ndata, fill(0., ndata),
getfield.(params, :val),
lower=getfield.(params, :low),
upper=getfield.(params, :high))
status = :NonOptimal
(bestfit.converged) && (status = :Optimal)
error = LsqFit.margin_error(bestfit, 0.6827)
return (status, getfield.(Ref(bestfit), :param), error)
end
macro enable_CMPFit()
return esc(:(
import DataFitting.minimize;
mutable struct cmpfit <: DataFitting.AbstractMinimizer;
config::CMPFit.Config;
cmpfit() = new(CMPFit.Config());
end;
function minimize(minimizer::cmpfit, func::Function, params::Vector{DataFitting.Parameter});
guess = getfield.(params, :val);
low = getfield.(params, :low);
high = getfield.(params, :high);
parinfo = CMPFit.Parinfo(length(guess));
for i in 1:length(guess);
llow = isfinite(low[i]) ? 1 : 0;
lhigh = isfinite(high[i]) ? 1 : 0;
parinfo[i].limited = (llow, lhigh);
parinfo[i].limits = (low[i], high[i]);
end;
bestfit = CMPFit.cmpfit((pvalues) -> func(pvalues),
guess, parinfo=parinfo, config=minimizer.config);
return (:Optimal, getfield.(Ref(bestfit), :param), getfield.(Ref(bestfit), :perror));
end;
))
end
fit!(model::Model, data::T; kw...) where T<:AbstractMeasures =
fit!(model, [data]; kw...)
function fit!(model::Model, data::Vector{T};
minimizer=lsqfit()) where T<:AbstractMeasures
elapsedTime = Base.time_ns()
evaluate(model)
free = Vector{Bool}()
for (cpname, par) in model.params
push!(free, par.free && model.cfree[cpname[1]])
end
ifree = findall(free)
@assert length(ifree) > 0 "No free parameter in the model"
# Flatten empirical data
data1d = data1D(model, data)
# Evaluate normalized residuals starting from free parameter values
function pval2resid(pvalues_free::Vector{Float64})
model.pvalues[ifree] .= pvalues_free # update parameter values
quick_evaluate(model)
return residuals1d(model, data1d)
end
(status, best_val, best_unc) = minimize(minimizer, pval2resid,
collect(values(model.params))[ifree])
model.pvalues[ifree] .= best_val
setfield!.(values(model.params), :val, model.pvalues)
uncerts = fill(NaN, length(model.pvalues))
uncerts[ifree] .= best_unc
# Prepare output
quick_evaluate(model) # ensure best fit values are used
comps = OrderedDict{Symbol, BestFitComp}()
for cname in keys(model.comps)
comps[cname] = BestFitComp()
end
i = 1
for (cpname, par) in model.params
cname = cpname[1]
pname = cpname[2]
parid = cpname[3]
bfpar = BestFitPar(model.pvalues[i], uncerts[i],
(i in ifree), model.actual[i])
if parid == 0
comps[cname][pname] = bfpar
else
if parid == 1
comps[cname][pname] = [bfpar]
else
push!(comps[cname][pname], bfpar)
end
end
i += 1
end
cost = sum(abs2, model.buffer)
dof = length(model.buffer) - length(ifree)
result = BestFitResult(comps, length(model.buffer), dof, cost, status,
logccdf(Chisq(dof), cost) * log10(exp(1)),
float(Base.time_ns() - elapsedTime) / 1.e9)
return result
end
include("show.jl")
end
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4008,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20121,
0,
7,
448,
11,
7925,
62,
5589,
3906,
7,
85,
26,
21231,
28,
40290,
1635,
4731,
7,
3672,
22305,
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220,
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220,
220,
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220,
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220,
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198,
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220,
220,
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220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
1303,
35235,
7203,
47,
958,
25,
29568,
1197,
58,
16,
46570,
29568,
4906,
1659,
7,
1197,
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17,
60,
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220,
220,
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220,
220,
220,
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1438,
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16,
60,
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318,
64,
7,
85,
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220,
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220,
357,
85,
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34529,
283,
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7,
85,
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220,
220,
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220,
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220,
611,
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64,
7,
85,
11,
27741,
21950,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20121,
0,
7,
448,
11,
7925,
62,
5589,
3906,
7,
85,
26,
21231,
28,
40290,
1635,
4731,
7,
3672,
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220,
220,
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2073,
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318,
7249,
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7,
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7,
1197,
4008,
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220,
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329,
1438,
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410,
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1197,
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1303,
35235,
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1273,
5620,
25,
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832,
720,
3672,
7904,
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85,
4008,
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220,
220,
220,
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20121,
0,
7,
448,
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7925,
62,
5589,
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7,
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26,
21231,
28,
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2,
38093,
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317,
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284,
307,
3688,
284,
11992,
1366,
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76,
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2878,
46690,
198,
220,
220,
220,
7386,
3712,
23839,
43961,
198,
220,
220,
220,
269,
1990,
874,
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35422,
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92,
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5418,
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3753,
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5317,
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2163,
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27830,
3712,
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11,
1243,
986,
26,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
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220,
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220,
21231,
2625,
1600,
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28,
445,
7234,
8,
198,
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220,
220,
220,
552,
82,
796,
7925,
62,
5589,
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986,
26,
21231,
28,
40290,
8,
198,
220,
220,
220,
220,
220,
220,
220,
269,
1990,
874,
796,
14230,
1068,
35,
713,
90,
13940,
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11,
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36,
2100,
92,
3419,
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220,
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329,
357,
3672,
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8,
287,
552,
82,
198,
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220,
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220,
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269,
1990,
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36,
2100,
7,
27830,
11,
552,
8,
198,
220,
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220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
503,
796,
649,
7,
27830,
11,
269,
1990,
874,
11,
20650,
90,
43879,
2414,
92,
22784,
3991,
11,
4646,
11,
657,
8,
198,
220,
220,
220,
220,
220,
220,
220,
13446,
7,
448,
8,
220,
1303,
16926,
46,
25,
318,
428,
3376,
30,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
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220,
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198,
2,
15161,
2027,
2189,
25,
751,
477,
6805,
198,
445,
7234,
7,
27830,
3712,
23839,
43961,
11,
26498,
23029,
796,
764,
33747,
22046,
23029,
198,
198,
2,
44048,
17724,
416,
19771,
1981,
6805,
198,
8818,
4646,
7,
28764,
3712,
39156,
2867,
8,
198,
220,
220,
220,
611,
2747,
13,
445,
7234,
62,
4480,
62,
11600,
198,
220,
220,
220,
220,
220,
220,
220,
288,
796,
360,
713,
26933,
7,
66,
3672,
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13,
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329,
357,
66,
3672,
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269,
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287,
2747,
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344,
12786,
12962,
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220,
220,
220,
44052,
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2747,
13,
445,
48915,
7,
28764,
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27830,
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288,
796,
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60,
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44052,
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445,
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27830,
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288,
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886,
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7,
28764,
13,
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6624,
657,
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24443,
0,
7,
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51,
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2558,
5512,
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636,
26084,
687,
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7,
85,
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7,
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31396,
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16,
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17,
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299,
7890,
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31396,
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220,
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220,
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6565,
0,
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31396,
3672,
796,
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66,
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279,
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] | 2.3858 | 6,690 |
using DelimitedFiles, Plots
"""
`StrongField.plot( comps::Array{StrongField.Computation,1}, results::Array{Dict{String,Any},1}, energyScale::String = "atomic", probabilityScaling::String = "linear", dataLabel::String = "StrongFieldData" )`
... generates a graphical representation of the observable (StrongField.SfaEnergyDistribution, StrongField.SfaMomentumDistribution, StrongField.SfaAzimuthalAngularDistribution or StrongField.SfaPolarAngularDistribution) with the results of all computations comps combined in one plot.
- All comps.observable need to be equal!
- The probabilities for all comps need to be at the same grid points (either energies, momenta or angles)
- energyScale determines the scaling of the energy axis either in atomic units (energyScale = "atomic") or in units of hbar*omega (energyScale = "omega").
- The y-axis is scaled either linearly (probabilityScaling = "linear") or logarithmically (probabilityScaling = "log")
"""
function plot( comps::Array{StrongField.Computation,1}, results::Array{Dict{String,Any},1}, energyScale::String = "atomic", probabilityScaling::String = "linear", dataLabel::String = "StrongFieldData" )
#---Check if all observables are equal---
observable = typeof(comps[1].observable)
for comp in comps
if typeof(comp.observable) != observable
error("Unequal types of observables for data given to StrongField.plot().")
return nothing
end
end
#Define colors and linestyles for plot of SfaEnergyDistribution and SfaAngularDistribution
colors = [:black,:blue,:red,:green,:purple]
styles = filter((s->begin
s in Plots.supported_styles()
end), [:solid, :dash, :dot, :dashdot, :dashdotdot])
#---Photoelectron energy spectra---
if observable == StrongField.SfaEnergyDistribution
for k = 1:size(comps,1) #Loop over all comps
#prepare data and rescale the x-axis if neccessary
if energyScale == "atomic"
energyLabel = "E (a.u.)"
energies = results[k]["energy distribution"].energies
elseif energyScale == "omega"
energyLabel = "E/w"
energies = results[k]["energy distribution"].energies / comps[k].beam.omega
end
probabilities = results[k]["energy distribution"].probabilities
gr() #sets the plotting backend to the package "GR"
#set scaling of the y-axis
scaling = :identity
if probabilityScaling == "log"
scaling = :log10
end
#generate the plot
if k == 1 #initial plot generation
Plots.plot(energies, probabilities,
title = "Photoelectron energy spectrum",
xlabel = energyLabel,
ylabel = "P(E)",
xscale = :identity,
yscale = scaling,
framestyle = :box,
legend = :none,
#markershape = :circle,
#markershape = :none,
line = (2,styles[1]),
linecolor = colors[1],
gridlinewidth = 2,
tickfontsize = 10,
labelfontsize = 10,
labelfontfamily = "Latin Modern Roman",
titlefontfamily = "Latin Modern Roman"
)
else #add to the plot already generated for k = 1
Plots.plot!(energies, probabilities,
title = "Photoelectron energy spectrum",
xlabel = energyLabel,
ylabel = "P(E)",
xscale = :identity,
yscale = scaling,
framestyle = :box,
legend = :none,
#markershape = :circle,
#markershape = :none,
line = (2,styles[k]),
linecolor = colors[k],
gridlinewidth = 2,
tickfontsize = 10,
labelfontsize = 10,
labelfontfamily = "Latin Modern Roman",
titlefontfamily = "Latin Modern Roman"
)
end
end
#export the plot as png-file
savefig(dataLabel * "-energy_spectrum.pdf")
#-----------------------------------
#---Photoelectron momentum distributions - DOES NOT WORK YET---
elseif observable == StrongField.SfaMomentumDistribution
#prepare data
#pList = results["momentum distribution"].momenta
#probList = results["momentum distribution"].probabilities
#generate the plot
#pyplot()
#r = range(0,stop=10,length=11)
#theta = range(0,stop=360,length=361)
#f(r,theta) = r^2
#println("$(f.(r,theta'))")
#Plots.plot( heatmap( f.(r,theta'), proj=:polar ) )
#println("$(transpose(probList))")
#Plots.plot( heatmap( pList, transpose(probList), proj=:polar ) )
#imshow(pList,transpose(probList))
#probList = probList[2:-1]
#matplotlib.pyplot.pcolormesh(pList[:,1], pList[:,2], transpose(probList))
contourf( pList[:,1], pList[:,2], probList )
#matplotlib.pyplot.imshow(pList)
#heatmap( pList[:,1], pList[:,2], probList, proj=:polar, legend=true
#heatmap( probList
# )
#p = Plots.plot( #heatmap( pList[:,1], pList[:,2], probList
# Plots.GR.polarheatmap( probList
##heatmap( probList
# )
# )
#export the plot as png-file
#savefig(dataLabel * "-momentum_distribution.pdf")
#-----------------------------------------
#---Photoelectron angular distributions---
elseif observable == StrongField.SfaAzimuthalAngularDistribution || observable == StrongField.SfaPolarAngularDistribution
gr() #sets the plotting backend to the package "GR"
for k = 1:size(comps,1) #Loop over all comps
#prepare data
if observable == StrongField.SfaAzimuthalAngularDistribution
angleList = results[k]["angular distribution"].phis
plotTitle = "Photoelectron angular distribution (azimuthal)"
elseif observable == StrongField.SfaPolarAngularDistribution
angleList = results[k]["angular distribution"].thetas
plotTitle = "Photoelectron angular distribution (polar)"
end
probList = results[k]["angular distribution"].probabilities
lineLabel = ""
if energyScale == "atomic"
lineLabel = string( round( results[k]["angular distribution"].energy, digits = 2) ) * " a.u."
legendTitle = "Energy"
elseif energyScale == "omega"
lineLabel = string( round( results[k]["angular distribution"].energy / comps[k].beam.omega, digits = 1 ) ) * " w"
legendTitle = "Energy"
end
if k == 1 #initial plot generation
GR.polar(angleList, probList)
Plots.plot(angleList,probList,
title = plotTitle,
proj = :polar,
line = (2,styles[1]),
linecolor = colors[1],
labelfontfamily = "Latin Modern Roman",
titlefontfamily = "Latin Modern Roman",
gridlinewidth = 2,
tickfontsize = 10,
labelfontsize = 10,
label = lineLabel,
legendtitle = legendTitle,
legend = true
)
else
Plots.plot!(angleList,probList,
proj = :polar,
line = (2,styles[k]),
linecolor = colors[k],
gridlinewidth = 2,
tickfontsize = 10,
labelfontsize = 10,
label = lineLabel,
labelfontfamily = "Latin Modern Roman",
)
end
end
#export the plot as pdf-file
savefig(dataLabel * "-angular_distribution.pdf")
#-----------------------------------------
#---Not a valid obserable---
else
error("Undefined observable for strong-field computations in StrongField.plot().")
end
#---------------------------
end
"""
`StrongField.exportData( comps::Array{StrongField.Computation,1}, results::Array{Dict{String,Any},1}, dataLabel::String = "StrongFieldData" )`
... exports the results = [Array1 Array2 ...] returned by StrongField.perform with the StrongField computations comps = [computation1 computation2 ...]
into files with name dataLabel-dataType-1.csv, dataLabel-dataType-2.csv, ... where dataType = energy_distribution, azimuthal_angular_distribution, etc.
"""
function exportData( comps::Array{StrongField.Computation,1}, results::Array{Dict{String,Any},1}, dataLabel::String = "StrongFieldData" )
for j = 1:size(results)[1]
w = results[j]
if typeof(comps[j].observable) == StrongField.SfaEnergyDistribution
energyDistribution = w["energy distribution"]
writedlm(dataLabel * "-energy_distribution" * "-" * string(j) * ".csv",hcat(energyDistribution.energies,energyDistribution.probabilities))
elseif typeof(comps[j].observable) == StrongField.SfaMomentumDistribution
angularDistribution = w["momentum distribution"]
writedlm(dataLabel * "-momentum_distribution" * "-" * string(j) * ".csv",hcat(angularDistribution.phis,angularDistribution.probabilities))
elseif typeof(comps[j].observable) == StrongField.SfaAzimuthalAngularDistribution
angularDistribution = w["angular distribution"]
writedlm(dataLabel * "-azimuthal_angular_distribution" * "-" * string(j) * ".csv",hcat(angularDistribution.phis,angularDistribution.probabilities))
elseif typeof(comps[j].observable) == StrongField.SfaPolarAngularDistribution
angularDistribution = w["angular distribution"]
writedlm(dataLabel * "-polar_angular_distribution" * "-" * string(j) * ".csv",hcat(angularDistribution.phis,angularDistribution.probabilities))
end
end
end
#------------------------TEST: Plot the radial wave functions (JAC + hydrogenic)------------------------------------
"""
`StrongField.exportRadialWavefunctions( comps::Array{StrongField.Computation,1}, dataLabel::String = "StrongFieldData", savePlot::Bool = false )`
... exports the radial wave functions (initial state) = [Array1 Array2 ...] that are used in the StrongField computations comps = [computation1 computation2 ...]
into files with name dataLabel-radial_wavefunction-1.csv, dataLabel-radial_wavefunction-2.csv, etc.
- If savePlot == true, the wave functions are also plotted and exported into a single figure dataLabel-radial_wave_function.pdf
"""
function exportRadialWavefunctions( comps::Array{StrongField.Computation,1}, dataLabel::String = "StrongFieldData", savePlot::Bool = false )
minIonizationPotential = Float64
for k = 1:size(comps)[1]
#Extract the initial orbital of the active electron from the many-electron comp.initialLevel and set quantum numbers
initialOrbitals = comps[k].initialLevel.basis.orbitals
#Find highest lying orbital (smallest ionization potential)
defaultSubshell = [sh for (sh,or) in initialOrbitals][1] #This is not nice; must be a better way to simply get a default element from a Dict
o = initialOrbitals[defaultSubshell]
minIonizationPotential = abs(o.energy)
for (subshell,orbital) in initialOrbitals
if abs(orbital.energy) < minIonizationPotential
o = orbital
minIonizationPotential = abs(orbital.energy)
end
end
ls = LevelSymmetry(o.subshell)
n = o.subshell.n; l = Int((ls.J.num+1)/2); j = ls.J.num/2;
if (sign((-1)^l) == -1 && ls.parity == plus::Parity) || (sign((-1)^l) == 1 && ls.parity == minus::Parity)
l = l - 1
end
l = floor(Int,l)
if comps[k].settings.hydrogenic
if comps[k].settings.hydrogenic1s
P = StrongField.HydrogenPnl( o.energy, 1, 0, rGrid.r )
else
P = StrongField.HydrogenPnl( o.energy, n, l, rGrid.r )
end
else
P = o.P
end
writedlm(dataLabel * "-initial_radial_wavefunction" * string(k) * ".csv",hcat(comps[k].grid.r,P))
if savePlot
if k == 1
Plots.plot(comps[k].grid.r, P,
title = "Radial wave functions",
xlabel = "r (a.u.)",
ylabel = "P(r)",
markershape = :circle,
gridlinewidth = 2,
tickfontsize = 10,
labelfontsize = 10,
labelfontfamily = "Latin Modern Roman",
titlefontfamily = "Latin Modern Roman"
)
else
Plots.plot!(comps[k].grid.r, P)
end
end
end
if savePlot
savefig(dataLabel * "-initial_radial_wave_function.pdf")
end
end
#if false
# minIonizationPotential = 0.
#Extract the initial orbital of the active electron from the many-electron comp.initialLevel and set quantum numbers
# initialOrbitals = initialLevel.basis.orbitals
#Find highest lying orbital (smallest ionization potential)
# defaultSubshell = [sh for (sh,or) in initialOrbitals][1] #This is not nice; must be a better way to simply get a default element from a Dict
# o = initialOrbitals[defaultSubshell]
# minIonizationPotential = abs(o.energy)
# for (subshell,orbital) in initialOrbitals
# if abs(orbital.energy) < minIonizationPotential
# global o = orbital
# global minIonizationPotential = abs(orbital.energy)
# end
# end
# ls = LevelSymmetry(o.subshell)
# n = o.subshell.n; l = Int((ls.J.num+1)/2); j = ls.J.num/2;
#
# if (sign((-1)^l) == -1 && ls.parity == plus::Parity) || (sign((-1)^l) == 1 && ls.parity == minus::Parity)
# l = l - 1
# end
# l = floor(Int,l)
#
# hydrogenP = StrongField.HydrogenPnl( o.energy, n, l, rGrid.r )
#
# Plots.plot(rGrid.r, [o.P hydrogenP],
# title = "Radial wave function: n=" * string(n) * ", l=" * string(l) * ", Ip=" * string(round(convertUnits("energy: from atomic to eV", o.energy),digits=2)) * " eV",
# xlabel = "r (a.u.)",
# ylabel = "P(r)",
# markershape = :circle,
# gridlinewidth = 2,
# tickfontsize = 10,
# labelfontsize = 10,
# labelfontfamily = "Latin Modern Roman",
# titlefontfamily = "Latin Modern Roman",
# label = ["JAC" "Hydrogen"]
# )
#
# savefig("radial_wavefunction.pdf")
#
# writedlm(dataName * "-radial_wavefunction.csv",hcat(rGrid.r,o.P,hydrogenP))
#end
##-----------------------------------------------------------------------------------------------------------------------
#if false
#epsilonp=2*omega
#kappa =-1
#lp=0
#nrContinuum = Continuum.gridConsistency(epsilonp, rGrid)
#contSettings = Continuum.Settings(false, nrContinuum)
#contSettings = Continuum.Settings(false, rGrid.NoPoints)
#newiLevel = Basics.generateLevelWithSymmetryReducedBasis(initialLevel, initialLevel.basis.subshells)
#newfLevel = Basics.generateLevelWithSymmetryReducedBasis(finalLevel, newiLevel.basis.subshells)
#newiLevel = Basics.generateLevelWithExtraSubshell(Subshell(101, kappa), newiLevel)
#cOrbital, phase = Continuum.generateOrbitalForLevel(epsilonp, Subshell(101, kappa), newfLevel, nuclearModel, rGrid, contSettings)
#Plots.plot( rGrid.r[1:size(cOrbital.P)[1]], real(cOrbital.P * exp(im*phase)),
# title = "Radial wave function continuum",
# xlabel = "r (a.u.)",
# ylabel = "P(r)",
# markershape = :circle,
# gridlinewidth = 2,
# tickfontsize = 10,
# labelfontsize = 10,
# labelfontfamily = "Latin Modern Roman",
# titlefontfamily = "Latin Modern Roman"
# )
#
# Z=1.0
# cVolkov = StrongField.CoulombVolkovP( epsilonp, lp, Z, rGrid.r )
# Plots.plot!( rGrid.r, real(cVolkov) )
#
# savefig("radial_wavefunction_continuum.pdf")
#end
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8818,
10784,
6601,
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82,
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90,
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13,
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1996,
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11,
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1,
1267,
198,
220,
220,
220,
329,
474,
796,
352,
25,
7857,
7,
43420,
38381,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
266,
796,
2482,
58,
73,
60,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2099,
1659,
7,
785,
862,
58,
73,
4083,
672,
3168,
540,
8,
6624,
13535,
15878,
13,
50,
13331,
28925,
20344,
3890,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2568,
20344,
3890,
796,
266,
14692,
22554,
6082,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1991,
276,
75,
76,
7,
7890,
33986,
1635,
27444,
22554,
62,
17080,
3890,
1,
1635,
366,
21215,
1635,
4731,
7,
73,
8,
1635,
27071,
40664,
1600,
71,
9246,
7,
22554,
20344,
3890,
13,
877,
70,
444,
11,
22554,
20344,
3890,
13,
1676,
65,
5738,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
361,
2099,
1659,
7,
785,
862,
58,
73,
4083,
672,
3168,
540,
8,
6624,
13535,
15878,
13,
50,
13331,
29252,
298,
388,
20344,
3890,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
32558,
20344,
3890,
796,
266,
14692,
32542,
298,
388,
6082,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1991,
276,
75,
76,
7,
7890,
33986,
1635,
27444,
32542,
298,
388,
62,
17080,
3890,
1,
1635,
366,
21215,
1635,
4731,
7,
73,
8,
1635,
27071,
40664,
1600,
71,
9246,
7,
21413,
20344,
3890,
13,
18691,
11,
21413,
20344,
3890,
13,
1676,
65,
5738,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
361,
2099,
1659,
7,
785,
862,
58,
73,
4083,
672,
3168,
540,
8,
6624,
13535,
15878,
13,
50,
13331,
26903,
320,
1071,
282,
13450,
934,
20344,
3890,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
32558,
20344,
3890,
796,
266,
14692,
21413,
6082,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1991,
276,
75,
76,
7,
7890,
33986,
1635,
27444,
1031,
320,
1071,
282,
62,
21413,
62,
17080,
3890,
1,
1635,
366,
21215,
1635,
4731,
7,
73,
8,
1635,
27071,
40664,
1600,
71,
9246,
7,
21413,
20344,
3890,
13,
18691,
11,
21413,
20344,
3890,
13,
1676,
65,
5738,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
361,
2099,
1659,
7,
785,
862,
58,
73,
4083,
672,
3168,
540,
8,
6624,
13535,
15878,
13,
50,
13331,
47,
6192,
13450,
934,
20344,
3890,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
32558,
20344,
3890,
796,
266,
14692,
21413,
6082,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1991,
276,
75,
76,
7,
7890,
33986,
1635,
27444,
79,
6192,
62,
21413,
62,
17080,
3890,
1,
1635,
366,
21215,
1635,
4731,
7,
73,
8,
1635,
27071,
40664,
1600,
71,
9246,
7,
21413,
20344,
3890,
13,
18691,
11,
21413,
20344,
3890,
13,
1676,
65,
5738,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
198,
437,
628,
628,
628,
198,
2,
22369,
51,
6465,
25,
28114,
262,
44503,
6769,
5499,
357,
41,
2246,
1343,
17669,
291,
8,
3880,
650,
198,
37811,
198,
63,
33004,
15878,
13,
39344,
15546,
498,
39709,
12543,
2733,
7,
552,
82,
3712,
19182,
90,
33004,
15878,
13,
5377,
1996,
341,
11,
16,
5512,
1366,
33986,
3712,
10100,
796,
366,
33004,
15878,
6601,
1600,
3613,
43328,
3712,
33,
970,
796,
3991,
1267,
63,
198,
220,
220,
220,
2644,
15319,
262,
44503,
6769,
5499,
357,
36733,
1181,
8,
796,
685,
19182,
16,
15690,
17,
2644,
60,
326,
389,
973,
287,
262,
13535,
15878,
2653,
602,
552,
82,
796,
685,
785,
1996,
341,
16,
29964,
17,
2644,
60,
220,
198,
220,
220,
220,
220,
220,
220,
220,
656,
3696,
351,
1438,
1366,
33986,
12,
6335,
498,
62,
19204,
8818,
12,
16,
13,
40664,
11,
1366,
33986,
12,
6335,
498,
62,
19204,
8818,
12,
17,
13,
40664,
11,
3503,
13,
198,
220,
220,
220,
220,
220,
220,
220,
532,
1002,
3613,
43328,
6624,
2081,
11,
262,
6769,
5499,
389,
635,
37515,
290,
29050,
656,
257,
2060,
3785,
1366,
33986,
12,
6335,
498,
62,
19204,
62,
8818,
13,
12315,
198,
37811,
198,
8818,
10784,
15546,
498,
39709,
12543,
2733,
7,
552,
82,
3712,
19182,
90,
33004,
15878,
13,
5377,
1996,
341,
11,
16,
5512,
1366,
33986,
3712,
10100,
796,
366,
33004,
15878,
6601,
1600,
3613,
43328,
3712,
33,
970,
796,
3991,
1267,
628,
220,
220,
220,
220,
220,
220,
220,
949,
40,
261,
1634,
25396,
1843,
796,
48436,
2414,
628,
220,
220,
220,
220,
220,
220,
220,
329,
479,
796,
352,
25,
7857,
7,
785,
862,
38381,
16,
60,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
11627,
974,
262,
4238,
32362,
286,
262,
4075,
11538,
422,
262,
867,
12,
9509,
1313,
552,
13,
36733,
4971,
290,
900,
14821,
3146,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4238,
5574,
2545,
874,
796,
552,
82,
58,
74,
4083,
36733,
4971,
13,
12093,
271,
13,
42594,
874,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
16742,
4511,
9105,
32362,
357,
17470,
395,
22088,
1634,
2785,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4277,
7004,
29149,
796,
685,
1477,
329,
357,
1477,
11,
273,
8,
287,
4238,
5574,
2545,
874,
7131,
16,
60,
1303,
1212,
318,
407,
3621,
26,
1276,
307,
257,
1365,
835,
284,
2391,
651,
257,
4277,
5002,
422,
257,
360,
713,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
267,
796,
4238,
5574,
2545,
874,
58,
12286,
7004,
29149,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
949,
40,
261,
1634,
25396,
1843,
796,
2352,
7,
78,
13,
22554,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
357,
7266,
29149,
11,
27688,
1287,
8,
287,
4238,
5574,
2545,
874,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
220,
220,
2352,
7,
27688,
1287,
13,
22554,
8,
1279,
949,
40,
261,
1634,
25396,
1843,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
267,
796,
32362,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
949,
40,
261,
1634,
25396,
1843,
796,
2352,
7,
27688,
1287,
13,
22554,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
43979,
796,
5684,
13940,
3020,
11973,
7,
78,
13,
7266,
29149,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
299,
796,
267,
13,
7266,
29149,
13,
77,
26,
220,
220,
220,
220,
220,
300,
796,
2558,
19510,
7278,
13,
41,
13,
22510,
10,
16,
20679,
17,
1776,
220,
220,
220,
474,
796,
43979,
13,
41,
13,
22510,
14,
17,
26,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
220,
357,
12683,
19510,
12,
16,
8,
61,
75,
8,
6624,
532,
16,
11405,
43979,
13,
1845,
414,
6624,
5556,
3712,
47,
6806,
8,
8614,
357,
12683,
19510,
12,
16,
8,
61,
75,
8,
6624,
352,
11405,
43979,
13,
1845,
414,
6624,
20208,
3712,
47,
6806,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
300,
796,
300,
532,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
300,
796,
4314,
7,
5317,
11,
75,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
220,
552,
82,
58,
74,
4083,
33692,
13,
15511,
8648,
291,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
552,
82,
58,
74,
4083,
33692,
13,
15511,
8648,
291,
16,
82,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
350,
796,
13535,
15878,
13,
40436,
8648,
47,
21283,
7,
267,
13,
22554,
11,
352,
11,
657,
11,
374,
41339,
13,
81,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
350,
796,
13535,
15878,
13,
40436,
8648,
47,
21283,
7,
267,
13,
22554,
11,
299,
11,
300,
11,
374,
41339,
13,
81,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
350,
796,
267,
13,
47,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1991,
276,
75,
76,
7,
7890,
33986,
1635,
27444,
36733,
62,
6335,
498,
62,
19204,
8818,
1,
1635,
4731,
7,
74,
8,
1635,
27071,
40664,
1600,
71,
9246,
7,
785,
862,
58,
74,
4083,
25928,
13,
81,
11,
47,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
3613,
43328,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
479,
6624,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1345,
1747,
13,
29487,
7,
785,
862,
58,
74,
4083,
25928,
13,
81,
11,
350,
11,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3670,
796,
366,
15546,
498,
6769,
5499,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
18242,
796,
366,
81,
357,
64,
13,
84,
2014,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
331,
18242,
796,
366,
47,
7,
81,
42501,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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# THIS IS NOT A FULL JAVA WRAPPER!
# Even though the name seems to imply it, it only provides some convenience
# definitions for use in the MOI wrapper.
## Type definitions
# These correspond to Java types (which are directly returned by the functions
# in this binder). In particular, the inheritance defined in Java is not brought
# back to Julia.
const Store = JavaObject{Symbol("org.jacop.core.Store")}
const BooleanVar = JavaObject{Symbol("org.jacop.core.BooleanVar")}
const IntVar = JavaObject{Symbol("org.jacop.core.IntVar")}
const SetVar = JavaObject{Symbol("org.jacop.set.core.SetVar")}
const FloatVar = JavaObject{Symbol("org.jacop.floats.core.FloatVar")}
const CircuitVar = JavaObject{Symbol("org.jacop.constraints.CircuitVar")}
const Constraint = JavaObject{Symbol("org.jacop.constraints.Constraint")}
const LinearInt = JavaObject{Symbol("org.jacop.constraints.LinearInt")}
const Alldifferent = JavaObject{Symbol("org.jacop.constraints.Alldifferent")}
const In = JavaObject{Symbol("org.jacop.constraints.In")}
const XeqC = JavaObject{Symbol("org.jacop.constraints.XeqC")}
# Unions of types to model Java type hierarchy.
const Variable = Union{
BooleanVar,
IntVar,
SetVar,
FloatVar,
CircuitVar,
}
# Add a constraint to a store.
function jacop_add_constraint_to_store(store::Store, constraint::Constraint)
jcall(store, "impose", Nothing, (Constraint,), constraint)
return
end
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] | 3.004228 | 473 |
export Model
"""
abstract type Model end
Abstract type for the representation of a physical model for which calculations can be performed.
Needs to be implemented system-specifically, in particular the following functions need to be defined: (for now)
- the one-particle matrix element, `energy(::Model, ::Orbital)`
- the two-particle matrix element, `w(::Model, ::Orbital, ::Orbital, ::Orbital, ::Orbital)`
"""
abstract type Model end
function energy(m::Model, o::Orbital)
error("missing implementation of energy(::$(typeof(m)), ::$(typeof(o)))")
end
function w(m::Model, o1::Orbital, o2::Orbital, o3::Orbital, o4::Orbital)
error("missing implementation of w(::$(typeof(m)), ::$(typeof(o1)), ::$(typeof(o2)))")
end
@doc raw"""
wminus(::Model, i,j,k,l)
return the difference of the two-particle matrix element with the same but the last two indices transposed
antisymmetric difference of the two-particle matrix elements:
$w^-_{ijkl} = w_{ijkl} - w_{ijlk}$
This is called the antisymmetrized two-particle matrix element.
This is an abbreviation for these terms arising in the
Slater-Condon rules for the calculation of the many-body matrix elements
via the one- and two-particle matrix elements of the underlying single-particle basis.
"""
wminus(m::Model, i,j,k,l) = w(m,i,j,k,l) - w(m,i,j,l,k)
@doc raw"""
Woffdiag_element(::Model, ::Ensemble, ::Orbital, ::Orbital, ::Orbital, ::Orbital)
Return the offdiagonal many body matrix element of the interaction operator
$\frac{1}{4} ( w_{ijkl} - w_{ijlk} ) (\pm \langle\{\tilde{n}\}|\{n\}^{ij}_{kl}\rangle)$
for an excitation given by creating orbitals i,j and annihilating orbitals k, l
as given by the Slater-Condon rules.
"""
function Woffdiag_element(m::Model, e::Ensemble, i::Orbital, j::Orbital, k::Orbital, l::Orbital)
# We sample with the weight of antisymmetrized matrix element but we do not restrict
# the order of indices of our possible kinks. We therefor need an extra factor 1/4 in the weight-function
1/4 * e.λ * wminus(m, i,j,k,l)
end
Woffdiag_element(m::Model, e::Ensemble, kink::T4) = Woffdiag_element(m, e, kink.i, kink.j, kink.k, kink.l)
"""
ΔWoffdiag_element(::Model, e::Ensemble, itr, itr)
return the change in the offdiagonal many body matrix element of the interaction operator
given by adding the kinks in the first iterable and removing the kinks in the second iterable
both arguments `itr` are required to be iterables containing kinks
"""
function ΔWoffdiag_element(m::Model, e::Ensemble, add_kinks, drop_kinks)
if isempty(drop_kinks)
prod(Woffdiag_element(m, e, k) for k in add_kinks)
elseif isempty(add_kinks)
1.0 / prod(Woffdiag_element(m, e, k) for k in drop_kinks)
else
prod(Woffdiag_element(m, e, k) for k in add_kinks) / prod(Woffdiag_element(m, e, k) for k in drop_kinks)
end
end
ΔWoffdiag_element(m::Model, e::Ensemble, add_kinks::Kinks, drop_kinks::Kinks) = ΔWoffdiag_element(m, e, excitations(add_kinks), excitations(drop_kinks))
"""
ΔW_diag(m::Model, i, j, k, l, occ)
change in the diagonal interaction matrix element due to a change in the occupation `occ`
in a periodic interval (τ1,τ2) where no kinks occur
the change in the occupation is assumed to consist in a creation of two orbitals i, j
and in the annihlation of two orbitals k, l
"""
function ΔW_diag(m::Model, i, j, k, l, occ)
@assert (i ∉ occ) & (j ∉ occ) "Calculation of the change in the many-body diagonal interaction matrix element: This function assumes that the first two orbitals\n\t $(i)\n and\n\t $(j) given are the creator orbitals and thus that they are not occupied in the given occupation\n\t $(occ). "
# contributions due to mean field interactions of the annihilated orbitals
Δ = sum( wminus(m, ν,k,k,ν) + wminus(m, ν,l,l,ν) for ν in drop_orbs(occ, [k,l]) )# interactions of mean field with k and l
Δ += wminus(m, k,l,l,k)# interaction between k and l
# contributions due to mean field interactions of the created orbitals
# note: the annihilator orbitals k, l are not in the new occupation
Δ -= sum( wminus(m, ν,i,i,ν) + wminus(m, ν,j,j,ν) for ν in drop_orbs(occ, [k,l]) )# interactions of mean field with i and j
Δ -= wminus(m, i,j,j,i)# interaction between i and j
return Δ
end
"""
ΔW_diag(m::Model, i, j, occ)
change in the diagonal interaction matrix element due to a change in the occupation `occ`
in a periodic interval (τ1,τ2) where no kinks occur
the change in the occupation is assumed to consist in a creation of one orbitals i
and in the annihlation of one orbitals j
"""
function ΔW_diag(m::Model, i, j, occ)
@assert (i ∉ occ) "Calculation of the change in the many-body diagonal interaction matrix element: This function assumes that the first two orbitals\n\t $(i)\n and\n\t $(j) given are the creator orbitals and thus that they are not occupied in the given occupation\n\t $(occ). "
# contributions due to mean field interactions of the annihilated orbitals
Δ = sum( wminus(m, ν,j,j,ν) for ν in drop_orbs(occ, (j,)) )# interactions of mean field with k
# contributions due to mean field interactions of the created orbitals
Δ -= sum( wminus(m, ν,i,i,ν) for ν in drop_orbs(occ, (j,)) )
end
### convention: all ΔX_element represent only the difference in the matrix elements will be used as exp(-Δ) for the weight change
### thus, contributions from the new (proposed) configuration (creators) appear positive (+)
### and the contributions from the old configuration (annihilators) appear negative (-)
"""
ΔT_element(::Model, i,j,k,l)
return the change in the kinetic many body matrix element due
to creating orbitals i, j and annihilating orbitals k, l
"""
ΔT_element(m::Model, i,j,k,l) = energy(m, i) + energy(m, j) - energy(m, k) - energy(m, l)
"""
ΔWdiag_element(::Model, ::Ensemble, ::Configuration, i, j, k, l, τ1, τ2)
Calculate the change in the diagonal interaction many-body matrix element
due to a change in the occupations given by creating two orbitals i and j
and annihilating two orbitals k, l in the interval (τ1, τ2).
This interval may be periodically extended over the bounds (0,1) if τ1 > τ2,
i.e. the change in the occupation is considered for (τ1,1] ∪ [0,τ2) in that case.
We do not need to evaluate the diagonal interaction
between all orbitals in all time-intervalls, but it is sufficient to evaluate
the full diagonal interaction with the occupations at the start of the intervall
and then consider only contributions of orbitals that are changed by kinks in the intervall.
"""
function ΔWdiag_element(m::Model, e::Ensemble, c::Configuration, i, j, k, l, τ1, τ2)# TODO: assuming that i, j are creators and k, l are annihilators. Use Step instead ?
@assert τ1 != τ2 " The diagonal interaction matrix element changes when kinks are added at different times and thus the occupations between the kinks are altered. It has no meaning to calculate this matrix element (or to add kinks) at equal times τ1=$(τ1), τ2=$(τ2). "
# get all kinks between τ1 and τ2
Ks = kinks_from_periodic_interval(c.kinks, τ1, τ2)
# calculate Wdiag with the occupation at the start of the interval
ΔWdiag = ΔW_diag(m, i,j,k,l, occupations_at(c,τ1)) * Δ(τ1,τ2)
if !isempty(Ks)
# calculate contrubutions to ΔWdiag from the orbitals changed by kinks in the intervall:
# add a contribution if an orbital is created and
# remove a contribution if an orbital is annilated
# this is more efficient than calculating the occupation for each consecutive time-interval
# via `occupation(occ,t)` since this function applies all kinks up to t::ImgTime
τs = times_from_periodic_interval(c.kinks, τ1, τ2)# get a time-ordered list of the times of the kinks between τ1 and τ2
ΔWdiag += sum( (ΔW_diag(m, i,j,k,l, creators(Ks[t1])) - ΔW_diag(m, i,j,k,l, annihilators(Ks[t1]))) * Δ(t1,τ2) for t1 in τs)
end
e.λ * ΔWdiag
end
"""
ΔWdiag_element(::Model, ::Ensemble, ::Configuration, i, j, τ1, τ2)
Calculate the change in the diagonal interaction many-body matrix element
due to a change in the occupations given by creating an orbital i
and annihilating an orbital j in the interval (τ1, τ2).
This interval may be periodically extended over the bounds (0,1) if τ1 > τ2,
i.e. the change in the occupation is considered for (τ1,1] ∪ [0,τ2) in that case.
We do not need to evaluate the diagonal interaction
between all orbitals in all time-intervalls, but it is sufficient to evaluate
the full diagonal interaction with the occupations at the start of the intervall
and then consider only contributions of orbitals that are changed by kinks in the intervall.
"""
function ΔWdiag_element(m::Model, e::Ensemble, c::Configuration, i, j, τ1, τ2)# TODO: assuming that i is creator and j is annihilator. Use Step instead ?
@assert τ1 != τ2 " The diagonal interaction matrix element changes when kinks are added at different times and thus the occupations between the kinks are altered. It has no meaning to calculate this matrix element (or to add kinks) at equal times τ1=$(τ1), τ2=$(τ2). "
# get all kinks between τ1 and τ2
Ks = kinks_from_periodic_interval(c.kinks, τ1, τ2)
# calculate Wdiag with the occupation at the start of the interval
ΔWdiag = ΔW_diag(m, i,j, occupations_at(c,τ1)) * Δ(τ1,τ2)
if !isempty(Ks)
# calculate contrubutions to ΔWdiag from the orbitals changed by kinks in the intervall:
# add a contribution if an orbital is created and
# remove a contribution if an orbital is annilated
# this is more efficient than calculating the occupation for each consecutive time-interval
# via `occupation(occ,t)` since this function applies all kinks up to t::ImgTime
τs = times_from_periodic_interval(c.kinks, τ1, τ2)# get a time-ordered list of the times of the kinks between τ1 and τ2
ΔWdiag += sum( (ΔW_diag(m, i,j, creators(Ks[t1])) - ΔW_diag(m, i,j, annihilators(Ks[t1]))) * Δ(t1,τ2) for t1 in τs)
end
e.λ * ΔWdiag
end
@doc raw"""
sign_offdiagonal_product(::Model, ::Configuration)
Return the sign of the product of two-particle terms in the offdiagonal many-body matrix elements.
These are given by function `wminus`, i.e.
$\langle \{\tilde{n}\} | a^{\dagger}_i a^{\dagger}_j a_k a_l | \{n\} \rangle
= \pm ( w_{ijkl} - w_{ijlk} ) \text{ for } \{\tilde{n}\} = \{n\}_{kl}^{ij}$
the term in the braces may be negative and this function returns the product of the
sign of all these contributions $w_{ijkl} - w_{ijlk}$ from all kinks.
The sign $\pm$ is determined from the permutation factor of the orbitals i,j,k,l
and is not calculated here.
used for the calculation the sign of the weight function
"""
function sign_offdiagonal_product(m::Model, c::Configuration)
s = 1
for κ in excitations(c.kinks)
s *= sign(wminus(m, κ.i, κ.j, κ.k, κ.l))
end
return s
end
"""
signum(m::Model, c::Configuration)
Calculate the sign of the `Configuration`'s weight.
"""
signum(m::Model, c::Configuration) = ladder_operator_order_factor(c.kinks)*sign_offdiagonal_product(m, c)
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] | 2.844722 | 3,922 |
using SafeTestsets
using Test
@safetestset vas_sample = "VAS samples with direct" begin
using Random: MersenneTwister
using Fleck: VectorAdditionModel, MarkovDirect, vas_initial, send, vas_input
rng = MersenneTwister(2930472)
vas = VectorAdditionModel(transitions, rates)
sampler = MarkovDirect()
input_process = vas_initial(vas, [1, 1, 0])
for i in 1:10
y = send(vas, input_process)
sample = send(sampler, y)
input_process = vas_input(vas, sample)
end
end
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] | 2.57868 | 197 |
__precompile__(true)
module Sugar
using Matcha, MacroTools, DataStructures
const AllFuncs = Union{Function, Core.Builtin, Core.IntrinsicFunction}
const IntrinsicFuncs = Union{Core.Builtin, Core.IntrinsicFunction}
# All kind of patterns for regex/matcha. Contains also matching isa functions
# include("patterns.jl")
# various tools to replace and work with asts
# TODO figure out what can be used from the great MacroTools
include("ast_tools.jl")
export normalize_ast
# Tools for extracting all kind of representations out of a method/function
include("lambdas.jl")
export slot_vector, get_lambda, clean_typed
# "Sugarcoats" tools to transform the unsightly representation returned by code_typed
# into something sweet and beautiful (namely an Expr tree closer to what you get
# from a macro)
include("sugarcoating.jl")
export remove_goto, sugared
include("show.jl")
# helper to work with methods
include("methods.jl")
include("pointer_tracking.jl")
export LazyMethod, getast!, getfunction, isfunction, istype, dependencies!, @lazymethod
end # module
| [
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] | 3.40836 | 311 |
using tiddlywinks
using Test
@testset "tiddlywinks.jl" begin
@test tiddlywinks.tiddly_greet() == "tiddlywinks is working"
@test tiddlywinks.tiddly_greet() != "hello world"
end
| [
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] | 2.3125 | 80 |
export TabularRandomPolicy
using StatsBase:Weights, sample
"""
TabularRandomPolicy(prob::Array{Float64, 2})
`prob` describes the distribution of actions for each state.
"""
struct TabularRandomPolicy <: AbstractPolicy
prob::Array{Float64,2}
end
(π::TabularRandomPolicy)(s::Int) = sample(Weights(get_prob(π, s)))
(π::TabularRandomPolicy)(obs) = π(get_state(obs))
RLBase.get_prob(π::TabularRandomPolicy, s) = @view π.prob[:, s]
RLBase.get_prob(π::TabularRandomPolicy, s, a) = π.prob[a, s] | [
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] | 2.612565 | 191 |
const COL_NUM_PARTICLES = :num_particles
const COL_NUM_METAINFERENCE = :num_metainference
const COL_PROPOSAL_NAME = :proposal_name
const COL_AIDE_ESTIMATE = :aide_estimate
const COL_AIDE_STDERR = :aide_stderr
const COL_GOLD_STANDARD_NAME = :gold_standard_name
const PRIOR_PROPOSAL_NAME = "prior"
const OPTIMAL_PROPOSAL_NAME = "optimal"
const EXACT_GOLD_STANDARD = "exact"
const APPROXIMATE_GOLD_STANDARD = "approximate"
| [
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] | 2.45614 | 171 |
mutable struct Future <: AbstractFuture
value::Any
value_id::ValueId
mutated::Bool
stale::Bool
function Future(value::Any, value_id::ValueId, mutated::Bool, stale::Bool)
new_future = new(value, value_id, mutated, stale)
# Create finalizer and register
finalizer(new_future) do fut
try
record_request(DestroyRequest(fut.value_id))
catch e
# `record_request` will fail if there isn't any job to add the
# request to. So we just continue silently.
# @warn "Failed to destroy value $(fut.value_id) because job has stopped: $e"
end
end
new_future
end
end
isview(f::F) where F <: AbstractFuture = false | [
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796,
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] | 2.260355 | 338 |
struct ogSAFTParam <: EoSParam
segment::SingleParam{Float64}
sigma::PairParam{Float64}
epsilon::PairParam{Float64}
epsilon_assoc::AssocParam{Float64}
bondvol::AssocParam{Float64}
end
abstract type ogSAFTModel <: SAFTModel end
@newmodel ogSAFT ogSAFTModel ogSAFTParam
export ogSAFT
function ogSAFT(components; idealmodel=BasicIdeal, userlocations=String[], ideal_userlocations=String[], verbose=false)
params = getparams(components, ["SAFT/ogSAFT"]; userlocations=userlocations, verbose=verbose)
segment = params["m"]
k = params["k"]
params["sigma"].values .*= 1E-10
sigma = sigma_LorentzBerthelot(params["sigma"])
epsilon = epsilon_LorentzBerthelot(params["epsilon"], k)
epsilon_assoc = params["epsilon_assoc"]
bondvol = params["bondvol"]
sites = SiteParam(Dict("e" => params["n_e"], "H" => params["n_H"]))
packagedparams = ogSAFTParam(segment, sigma, epsilon, epsilon_assoc, bondvol)
references = ["todo"]
model = ogSAFT(packagedparams, sites, idealmodel; ideal_userlocations=ideal_userlocations, references=references, verbose=verbose)
return model
end
function a_res(model::ogSAFTModel, V, T, z)
return @f(a_seg) + @f(a_chain) + @f(a_assoc)
end
function a_seg(model::ogSAFTModel, V, T, z)
x = z/∑(z)
m = model.params.segment.values
m̄ = ∑(x .* m)
return m̄*(@f(a_hs)+@f(a_disp))
end
function a_chain(model::ogSAFTModel, V, T, z)
x = z/∑(z)
m = model.params.segment.values
return ∑(x[i]*(1-m[i])*log(@f(g_hsij, i, i)) for i ∈ @comps)
end
function d(model::ogSAFTModel, V, T, z, i)
ϵ = model.params.epsilon.diagvalues
σ = model.params.sigma.diagvalues
m = model.params.segment.values
fm = 0.0010477+0.025337*(m[i]-1)/m[i]
f = (1+0.2977T/ϵ[i])/(1+0.33163T/ϵ[i]+fm*(T/ϵ[i])^2)
return σ[i] * f
end
function dx(model::ogSAFTModel, V, T, z)
x = z/∑(z)
m = model.params.segment.values
σ = model.params.sigma.values
ϵ = model.params.epsilon.values
comps = @comps
mx = ∑(x .* m)
σx = (∑(x[i]*x[j]*m[i]*m[j]*σ[i,j]^3 for i ∈ comps for j ∈ comps)/mx^2)^(1/3)
ϵx = (∑(x[i]*x[j]*m[i]*m[j]*σ[i,j]^3*ϵ[i,j] for i ∈ comps for j ∈ comps)/mx^2)/σx^3
fm = 0.0010477+0.025337*(mx-1)/mx
f = (1+0.2977T/ϵx)/(1+0.33163T/ϵx+fm*(T/ϵx)^2)
return σx * f
end
function ζn(model::ogSAFTModel, V, T, z, n)
∑z = ∑(z)
x = z/∑z
m = model.params.segment.values
return N_A*∑z*π/6/V * ∑(x[i]*m[i]*@f(d, i)^n for i ∈ @comps)
end
function η(model::ogSAFTModel, V, T, z)
∑z = ∑(z)
x = z/∑z
m = model.params.segment.values
m̄ = ∑(x .* m)
return N_A*∑z*π/6/V*@f(dx)^3*m̄
end
function g_hsij(model::ogSAFTModel, V, T, z, i, j)
di = @f(d,i)
dj = @f(d,j)
ζ2 = @f(ζn,2)
ζ3 = @f(ζn,3)
return 1/(1-ζ3) + di*dj/(di+dj)*3ζ2/(1-ζ3)^2 + (di*dj/(di+dj))^2*2ζ2^2/(1-ζ3)^3
end
function a_hs(model::ogSAFTModel, V, T, z)
ηx = @f(η)
return (4ηx-3ηx^2)/(1-ηx)^2
end
function a_disp(model::ogSAFTModel, V, T, z)
m = model.params.segment.values
σ = model.params.sigma.values
ϵ = model.params.epsilon.values
x = z/∑(z)
comps = @comps
ϵx = ∑(x[i]*x[j]*m[i]*m[j]*σ[i,j]^3*ϵ[i,j] for i ∈ comps for j ∈ comps)/∑(x[i]*x[j]*m[i]*m[j]*σ[i,j]^3 for i ∈ comps for j ∈ comps)
ηx = @f(η)
ρR = (6/sqrt(2)/π)*ηx
TR = T/ϵx
a_seg1 = ρR*evalpoly(ρR,(-8.5959,-4.5424,-2.1268,10.285))
a_seg2 = ρR*evalpoly(ρR,(-1.9075,9.9724,-22.216,+15.904))
return 1/TR*(a_seg1+a_seg2/TR)
end
## This is an attempt to make Twu et al.'s segment term; does not work yet
# function a_seg(model::ogSAFTModel, V, T, z)
# Bo = [1.31024,-3.80636,-2.37238,-0.798872,0.198761,1.47014,-0.786367,2.19465,5.75429,6.7822,-9.94904,-15.6162,86.643,18.527,9.04755,8.68282]
# Ba = [3.79621,-6.14518,-1.84061,-2.77584,-0.420751,-5.66128,19.2144,-3.33443,33.0305,-5.90766,9.55619,-197.883,-61.2535,77.1802,-6.57983,0.0]
# ω = 0.011
# A = []
# for i ∈ 1:16
# append!(A,Bo[i]+ω*Ba[i])
# end
# m = model.params.segment
# σ = model.params.sigma
# ϵ = model.params.epsilon
# x = z/∑(z[i] for i ∈ @comps)
# mx = ∑(x[i]*m[i] for i ∈ @comps)
# σx = (∑(x[i]*x[j]*m[i]*m[j]*σ[i,j]^3 for i ∈ @comps for j ∈ @comps)/mx^2)^(1/3)
# ϵx = (∑(x[i]*x[j]*m[i]*m[j]*σ[i,j]^3*ϵ[i,j] for i ∈ @comps for j ∈ @comps)/mx^2)/σx^3
#
# ρ = ∑(z)*N_A/V
# # ρR = ρ*mx*σx^3
# ηx = η(model,V, T, z)
# ρR = (6/π)*ηx
# TR = T/ϵx
#
# u_res = (A[2]/TR+2A[3]/TR^2+3A[4]/TR^3+5A[5]/TR^5)*ρR+1/2*A[7]/TR*ρR^2+
# 1/(2*A[16])*(3A[9]/TR^3+4A[10]/TR^4+5A[11]/TR^5)*(1-exp(-A[16]*ρR^2))+
# 1/(2*A[16]^2)*(3A[12]/TR^3+4A[13]/TR^4+5A[14]/TR^5)*(1-(1+A[16]*ρR^2)*exp(-A[16]*ρR^2))+
# 1/5*A[15]/TR*ρR^5
# s_res = -log(ρ*R̄*T)-(A[1]-A[3]/TR^2-2A[4]/TR^3-4A[5]/TR^5)*ρR-1/2*A[6]*ρR^2-1/3*A[8]*ρR^3+
# 1/(2*A[16])*(2A[9]/TR^3+3A[10]/TR^4+4A[11]/TR^5)*(1-exp(-A[16]*ρR^2))+
# 1/(2*A[16]^2)*(2A[12]/TR^3+3A[13]/TR^4+4A[14]/TR^5)*(1-(1+A[16]*ρR^2)*exp(-A[16]*ρR^2))
# a_res = u_res-s_res
# return mx*(a_res)
# end
function a_assoc(model::ogSAFTModel, V, T, z)
x = z/∑(z)
X_ = @f(X)
n = model.allcomponentnsites
return ∑(x[i]*∑(n[i][a] * (log(X_[i][a]) - X_[i][a]/2 + 0.5) for a ∈ @sites(i)) for i ∈ @comps)
end
function X(model::ogSAFTModel, V, T, z)
_1 = one(V+T+first(z))
∑z = ∑(z)
x = z/∑z
ρ = N_A*∑z/V
itermax = 100
dampingfactor = 0.5
error = 1.
tol = model.absolutetolerance
iter = 1
X_ = [[_1 for a ∈ @sites(i)] for i ∈ @comps]
X_old = deepcopy(X_)
while error > tol
iter > itermax && error("X has failed to converge after $itermax iterations")
for i ∈ @comps, a ∈ @sites(i)
rhs = 1/(1+∑(ρ*x[j]*∑(X_old[j][b]*@f(Δ,i,j,a,b) for b ∈ @sites(j)) for j ∈ @comps))
X_[i][a] = (1-dampingfactor)*X_old[i][a] + dampingfactor*rhs
end
error = sqrt(∑(∑((X_[i][a] - X_old[i][a])^2 for a ∈ @sites(i)) for i ∈ @comps))
for i = 1:length(X_)
X_old[i] .= X_[i]
end
iter += 1
end
return X_
end
function Δ(model::ogSAFTModel, V, T, z, i, j, a, b)
ϵ_assoc = model.params.epsilon_assoc.values
κ = model.params.bondvol.values
g = @f(g_hsij,i,j)
return (@f(d,i)+@f(d,j))^3/2^3*g*(exp(ϵ_assoc[i,j][a,b]/T)-1)*κ[i,j][a,b]
end
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] | 1.665188 | 3,832 |
@testset "ReificationDifferentFrom2Indicator: $(fct_type), type $(T)" for fct_type in ["vector of variables", "vector affine function"], T in [Int, Float64]
base_model = if T == Int
IntDifferentFromIndicatorMILPModel{Int}()
elseif T == Float64
FloatDifferentFromIndicatorMILPModel{Float64}()
else
@assert false
end
mock = MOIU.MockOptimizer(base_model)
model = COIB.ReificationDifferentFrom2Indicator{T}(mock)
if T == Int
@test MOI.supports_constraint(model, MOI.VariableIndex, MOI.Integer)
end
@test MOI.supports_constraint(
model,
MOI.ScalarAffineFunction{T},
MOI.EqualTo{T},
)
@test MOIB.supports_bridging_constraint(
model,
MOI.VectorAffineFunction{T},
CP.Reification{CP.DifferentFrom{T}},
)
@test MOIB.supports_bridging_constraint(
model,
MOI.VectorOfVariables,
CP.Reification{CP.DifferentFrom{T}},
)
x, _ = MOI.add_constrained_variable(model, MOI.ZeroOne())
if T == Int
y, _ = MOI.add_constrained_variable(model, MOI.Integer())
elseif T == Float64
y = MOI.add_variable(model)
end
fct = if fct_type == "vector of variables"
MOI.VectorOfVariables([x, y])
elseif fct_type == "vector affine function"
MOIU.vectorize([x, y])
else
@assert false
end
c = MOI.add_constraint(model, fct, CP.Reification(CP.DifferentFrom(zero(T))))
@test MOI.is_valid(model, x)
@test MOI.is_valid(model, y)
@test MOI.is_valid(model, c)
bridge = MOIBC.bridges(model)[MOI.ConstraintIndex{MOI.VectorOfVariables, CP.Reification{CP.DifferentFrom{T}}}(-1)]
@testset "Bridge properties" begin
@test MOIBC.concrete_bridge_type(typeof(bridge), MOI.VectorOfVariables, CP.Reification{CP.DifferentFrom{T}}) == typeof(bridge)
@test MOIB.added_constrained_variable_types(typeof(bridge)) == Tuple{Type}[]
@test MOIB.added_constraint_types(typeof(bridge)) == [
(MOI.VectorAffineFunction{T}, MOI.Indicator{MOI.ACTIVATE_ON_ONE, MOI.EqualTo{T}}),
(MOI.VectorAffineFunction{T}, MOI.Indicator{MOI.ACTIVATE_ON_ZERO, CP.DifferentFrom{T}}),
]
@test MOI.get(bridge, MOI.NumberOfVariables()) == 0
@test MOI.get(bridge, MOI.NumberOfConstraints{MOI.VectorAffineFunction{T}, MOI.Indicator{MOI.ACTIVATE_ON_ONE, MOI.EqualTo{T}}}()) == 1
@test MOI.get(bridge, MOI.NumberOfConstraints{MOI.VectorAffineFunction{T}, MOI.Indicator{MOI.ACTIVATE_ON_ZERO, CP.DifferentFrom{T}}}()) == 1
@test MOI.get(bridge, MOI.ListOfConstraintIndices{MOI.VectorAffineFunction{T}, MOI.Indicator{MOI.ACTIVATE_ON_ONE, MOI.EqualTo{T}}}()) == [bridge.indic_true]
@test MOI.get(bridge, MOI.ListOfConstraintIndices{MOI.VectorAffineFunction{T}, MOI.Indicator{MOI.ACTIVATE_ON_ZERO, CP.DifferentFrom{T}}}()) == [bridge.indic_false]
end
@testset "Constraint: indicator if true" begin
@test MOI.is_valid(model, bridge.indic_true)
f = MOI.get(model, MOI.ConstraintFunction(), bridge.indic_true)
@test length(f.terms) == 2
@test MOI.get(model, MOI.ConstraintSet(), bridge.indic_true) == MOI.Indicator{MOI.ACTIVATE_ON_ONE}(MOI.EqualTo(zero(T)))
@test f.constants == [one(T), zero(T)]
t1 = f.terms[1]
@test t1.output_index == 1
@test t1.scalar_term.coefficient === -one(T)
@test t1.scalar_term.variable == x
t2 = f.terms[2]
@test t2.output_index == 2
@test t2.scalar_term.coefficient === one(T)
@test t2.scalar_term.variable == y
end
@testset "Constraint: indicator if false" begin
@test MOI.is_valid(model, bridge.indic_false)
f = MOI.get(model, MOI.ConstraintFunction(), bridge.indic_false)
@test length(f.terms) == 2
@test MOI.get(model, MOI.ConstraintSet(), bridge.indic_false) == MOI.Indicator{MOI.ACTIVATE_ON_ZERO}(CP.DifferentFrom(zero(T)))
@test f.constants == [one(T), zero(T)]
t1 = f.terms[1]
@test t1.output_index == 1
@test t1.scalar_term.coefficient === -one(T)
@test t1.scalar_term.variable == x
t2 = f.terms[2]
@test t2.output_index == 2
@test t2.scalar_term.coefficient === one(T)
@test t2.scalar_term.variable == y
end
end
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] | 2.165586 | 2,005 |
"""
Now if we pad to the start on the left hand side
and read to the right we see that the bit strings are all uniquely
identifiable. For example if I see 101, I know that represents
#9 and isn't just a subset for something else.
0, 010
4, 1100
5, 1101
6, 011
7, 00
8, 100
9, 101
10, 1110
11, 11110
16, 111110
17, 1111110
18, 1111111
Note however that bits are never processed left to right, they are
processed right to left. So for processing of our data we need to reverse
the bits. Also note that this does not mean swapping the first and last bit
in a byte since the bit strings are of a given length.
So here is a side by side of the original values and what we need.
Orig New Level & Index New as # and hex
0, 010 - 010 L3 - 1 2
4, 1100 - 0011 L4 - 1 3
5, 1101 - 1011 L4 - 2 11 b
6, 011 - 110 L3 - 2 6
7, 00 - 00 L2 - 1 0
8, 100 - 001 L3 - 3 1
9, 101 - 101 L3 - 4 5
10, 1110 - 0111 L4 - 3 7
11, 11110 - 01111 L5 - 1
16, 111110 - 011111 L6 - 1
17, 1111110 - 0111111 L7 - 1
18, 1111111 - 1111111 L7 - 2
"""
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] | 2.276515 | 528 |
### A Pluto.jl notebook ###
# v0.14.3
using Markdown
using InteractiveUtils
# ╔═╡ fae2ba1d-02ab-4cee-8c83-abda1e6a098c
begin
import Pkg
Pkg.activate(mktempdir())
Pkg.add([
Pkg.PackageSpec(name="CSV"),
Pkg.PackageSpec(name="DataFrames")#,
#Pkg.PackageSpec(name="EchelleCCFs")
])
Pkg.develop(name="EchelleCCFs")
using CSV, DataFrames
using EchelleCCFs
end
# ╔═╡ 0cbdbb36-4874-4862-823f-6bf0fbd4e60c
begin
neid_data_path = "/gpfs/group/ebf11/default/ebf11/neid_solar/data/"
proc_version_path = "v0.7develop20210501/solar"
path_start = joinpath(neid_data_path,proc_version_path)
output_path_base = "/gpfs/group/ebf11/default/ebf11/neid_solar/data/outputs2"
proj_dir = "/gpfs/group/ebf11/default/ebf11/neid_solar/code/NeidSolarScripts.jl"
script = joinpath(proj_dir,"examples","calc_order_ccfs_using_continuum_new.jl")
end
# ╔═╡ a62db4ef-c321-4dfe-b167-19c29ce45484
html"""<style>
main {
max-width: 1000px;
}
"""
# ╔═╡ bd273401-b32e-44a6-a490-92575ed05ae9
begin
df = DataFrame(:manifest_filename=>String[], :output_filename=>String[],:num_lines=>Int64[])
for (root, dirs, files) in walkdir(output_path_base)
for file in files
if !occursin(r"manifest\.csv",file) continue end
#println("root = ", root)
#println("dirs = ", dirs)
#println("files = ", files)
manifest_fn = joinpath(root,file)
output_fn = joinpath(root,"daily_ccfs.jld2")
nlines = countlines(manifest_fn)
push!(df, Dict(:manifest_filename=>manifest_fn, :output_filename=>output_fn, :num_lines=>nlines))
end
end
df
end
# ╔═╡ d48ad3e0-14db-47b4-84db-4db10e1f0114
begin
num_threads = 1
min_order = 56
max_order = 108
#line_list_filename = joinpath(ENV["JULIA_DEPOT_PATH"],"dev/EchelleCCFs/data/masks","espresso+neid_mask_97_to_108.mas")
#line_list_filename = joinpath(pkgdir(EchelleCCFs),"data/masks","espresso+neid_mask_97_to_108.mas")
line_list_filename = "/gpfs/group/ebf11/default/ebf11/neid_solar/code/NeidSolarScripts.jl/scripts/linelist_20210208.csv"
sed_filename = joinpath(proj_dir,"data", "neidMaster_HR_SmoothLampSED_20210101.fits")
anchors_filename = "/gpfs/group/ebf11/default/ebf11/neid_solar/code/NeidSolarScripts.jl/scripts/anchors_20210208.jld2"
end
# ╔═╡ 8366a29a-1857-410f-aea3-87049e6248f6
pkgdir(EchelleCCFs)
# ╔═╡ e08192ce-9f0d-4e6f-a4aa-864f093dfcdd
#df[1,:manifest_filename]
df[1,:output_filename]
# ╔═╡ 73d682e9-cfa2-45b5-ba9e-b7cbf157b31d
begin
last_job_id = 0
function gen_job_name(; prefix::String = "auto")
global last_job_id
last_job_id += 1
return prefix * string(last_job_id)
end
end
# ╔═╡ ed421e9d-2d2a-4af6-b832-591f38e092ad
function make_pbs_scr(;proj_dir::String, script::String, input_fn::String, output_fn::String, job_name::String = gen_job_name() )
pbs_scr = """
#!/bin/bash
#PBS -N $job_name
#PBS -l nodes=1:ppn=$num_threads
#PBS -l pmem=4000mb
#PBS -l walltime=2:00:00
###PBS -A ebf11_c_g_vc_default
###PBS -q hprc
#PBS -A cyberlamp
#PBS -l feature=rhel7
#PBS -j oe
#PBS -M ebf11@psu.edu
# Get started
echo Job started on `hostname` at `date`
# Go to the correct place
cd \$PBS_O_WORKDIR
#cd $proj_dir
# Run the job itself
echo ~/julia --project=$proj_dir -t $num_threads $script $input_fn $output_fn --line_list_filename $line_list_filename --sed_filename $sed_filename --orders_to_use=$min_order $max_order --overwrite
~/julia --project=$proj_dir -t $num_threads $script $input_fn $output_fn --line_list_filename $line_list_filename --sed_filename $sed_filename --anchors_filename $anchors_filename --orders_to_use=$min_order $max_order --apply_continuum_normalization --overwrite
# Finish up
echo Job Ended at `date`
"""
return pbs_scr
end
# ╔═╡ b5769171-0b0d-4e48-ae4e-be8c4995b4f2
"output_filename" ∈ names(df)
# ╔═╡ 8a7ff512-39aa-4b76-b3fd-f02ac410c85b
function make_pbs_multi_scr(;proj_dir::String, script::String, df::DataFrame, job_name::String = "auto", num_threads::Integer=1, pmem::String ="8000mb", walltime::String ="23:59:00", min_row::Integer = 1, max_row::Integer = size(df,1) )
@assert "manifest_filename" ∈ names(df)
@assert "output_filename" ∈ names(df)
@assert size(df,1) >=1
pbs_hdr_str = """
#!/bin/bash
#PBS -N $job_name
#PBS -l nodes=1:ppn=$num_threads
#PBS -l pmem=$pmem
#PBS -l walltime=$walltime
###PBS -A ebf11_c_g_vc_default
###PBS -q hprc
#PBS -A cyberlamp
#PBS -l feature=rhel7
#PBS -j oe
#PBS -M ebf11@psu.edu
# Get started
echo Job started on `hostname` at `date`
# Go to the correct place
cd \$PBS_O_WORKDIR
#cd $proj_dir
"""
pbs_str = pbs_hdr_str
for (i,row) in enumerate(eachrow(df))
if !(min_row<=i<=max_row) continue end
input_fn = df[i,"manifest_filename"]
output_fn = df[i,"output_filename"]
pbs_cmd_str = """
# Run the job itself
~/julia --project=$proj_dir -t $num_threads $script $input_fn $output_fn --line_list_filename $line_list_filename --sed_filename $sed_filename --anchors_filename $anchors_filename --orders_to_use=$min_order $max_order --apply_continuum_normalization --overwrite
"""
pbs_str = pbs_str * pbs_cmd_str
# If want to do 2
output2_fn = replace(output_fn,"daily_ccfs"=>"daily_ccfs_norm=sed")
pbs_cmd_str = """
# Run the job itself
~/julia --project=$proj_dir -t $num_threads $script $input_fn $output2_fn --line_list_filename $line_list_filename --sed_filename $sed_filename --anchors_filename $anchors_filename --orders_to_use=$min_order $max_order
"""
pbs_str = pbs_str * pbs_cmd_str
end
pbs_ftr_str = """
# Finish up
echo Job Ended at `date`
"""
pbs_str = pbs_str * pbs_ftr_str
return pbs_str
end
# ╔═╡ 962d7e9e-4fdd-4080-82bf-f0175002ca19
open("submit_calc_ccfs.sh","w") do f_submit
for row in eachrow(df)
if row.num_lines <= 1 continue end
#=
if isfile(joinpath(row.output_dir, "manifest.csv"))
println("# Skipping ", row.output_dir)
continue
end
=#
m = match(r"(\d+)$", dirname(row.manifest_filename))
dir = m.captures[1]
pbs_scr = make_pbs_scr(proj_dir=proj_dir, script=script, input_fn=row.manifest_filename, output_fn=row.output_filename, job_name = "ccfs_"*dir)
#println("Created script: ")
#println(pbs_scr)
#println("Echoing script: ")
open("ccfs_$dir.pbs","w") do f_pbs
print(f_pbs, pbs_scr)
end
println(f_submit, "qsub ccfs_$dir.pbs")
#println(f_submit, "sleep $sleep_interval")
#output = readchomp(pipeline(`echo $pbs_scr`, `qsub`))
#println("output = ")
#println(output)
end
end # submit_calc_ccfs.sh
# ╔═╡ 487c2c86-e56f-4823-be3c-05c076dbbe89
open("submit_calc_ccfs_multi.sh","w") do f_submit
files_per_jobs = 4
for i in reverse(1:files_per_jobs:size(df,1))
#if row.num_lines <= 1 continue end
#=
if isfile(joinpath(row.output_dir, "manifest.csv"))
println("# Skipping ", row.output_dir)
continue
end
=#
#m = match(r"(\d+)$", dirname(row.manifest_filename))
#dir = m.captures[1]
pbs_scr = make_pbs_multi_scr(proj_dir=proj_dir, script=script, df=df, min_row=i, max_row=min(i+files_per_jobs-1,size(df,1)), job_name = "ccfs_"*string(i), num_threads=num_threads)
#println("Created script: ")
#println(pbs_scr)
#println("Echoing script: ")
open("ccfs_" *string(i) * ".pbs","w") do f_pbs
print(f_pbs, pbs_scr)
end
println(f_submit, "qsub ccfs_" *string(i) *".pbs")
#println(f_submit, "sleep $sleep_interval")
#output = readchomp(pipeline(`echo $pbs_scr`, `qsub`))
#println("output = ")
#println(output)
end
end # submit_calc_ccfs.sh|
# ╔═╡ Cell order:
# ╠═fae2ba1d-02ab-4cee-8c83-abda1e6a098c
# ╠═0cbdbb36-4874-4862-823f-6bf0fbd4e60c
# ╠═a62db4ef-c321-4dfe-b167-19c29ce45484
# ╠═bd273401-b32e-44a6-a490-92575ed05ae9
# ╠═d48ad3e0-14db-47b4-84db-4db10e1f0114
# ╠═8366a29a-1857-410f-aea3-87049e6248f6
# ╠═e08192ce-9f0d-4e6f-a4aa-864f093dfcdd
# ╠═73d682e9-cfa2-45b5-ba9e-b7cbf157b31d
# ╠═ed421e9d-2d2a-4af6-b832-591f38e092ad
# ╠═b5769171-0b0d-4e48-ae4e-be8c4995b4f2
# ╠═8a7ff512-39aa-4b76-b3fd-f02ac410c85b
# ╠═962d7e9e-4fdd-4080-82bf-f0175002ca19
# ╠═487c2c86-e56f-4823-be3c-05c076dbbe89
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# array.jl
export ndgrid_array
"""
(xg, yg, ...) = ndgrid_array(v1, v2, ...)
Method to construct a tuple of (dense) `Array`s from a set of vectors.
This tuple can use a lot of memory so should be avoided in general!
It is provided mainly for testing and timing comparisons.
Each input should be an `AbstractVector` of some type.
The corresponding output Array will have the same element type.
This method provides similar functionality as Matlab's `ndarray` function
but is more general because the vectors can be any type.
# Examples
```jldoctest
julia> ndgrid_array(1:3, 1:2)
([1 1; 2 2; 3 3], [1 2; 1 2; 1 2])
julia> ndgrid(1:3, [:a,:b])
([1 1; 2 2; 3 3], [:a :b; :a :b; :a :b])
```
"""
ndgrid_array(vs::AbstractVector...) = Array.(ndgrid(vs...))
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export DefiniteIntegral,DefiniteLineIntegral
abstract CalculusFunctional{S,T} <: Operator{T}
@functional CalculusFunctional
##TODO: Add ConcreteOp
macro calculus_functional(Op)
ConcOp=parse("Concrete"*string(Op))
WrappOp=parse(string(Op)*"Wrapper")
return esc(quote
abstract $Op{SSS,TTT} <: CalculusFunctional{SSS,TTT}
immutable $ConcOp{S,T} <: $Op{S,T}
domainspace::S
end
immutable $WrappOp{BT<:Operator,S<:Space,T} <: $Op{S,T}
func::BT
end
# We expect the operator to be real/complex if the basis is real/complex
$ConcOp(dsp::Space) = $ConcOp{typeof(dsp),eltype(dsp)}(dsp)
$Op() = $Op(UnsetSpace())
$Op(dsp) = $ConcOp(dsp)
$Op(d::Domain) = $Op(Space(d))
promotedomainspace(::$Op,sp::Space) = $Op(sp)
Base.convert{T}(::Type{Operator{T}},Σ::$ConcOp) =
T==eltype(Σ)?Σ:$ConcOp{typeof(Σ.domainspace),T}(Σ.domainspace)
domain(Σ::$ConcOp) = domain(Σ.domainspace)
domainspace(Σ::$ConcOp) = Σ.domainspace
getindex(::$ConcOp{UnsetSpace},kr::Range) =
error("Spaces cannot be inferred for operator")
$WrappOp(op::Operator) =
$WrappOp{typeof(op),typeof(domainspace(op)),eltype(op)}(op)
Base.convert{T}(::Type{Operator{T}},Σ::$WrappOp) =
T==eltype(Σ)?Σ:$WrappOp(convert(Operator{T},Σ.func))
#Wrapper just adds the operator it wraps
getindex(D::$WrappOp,k::Range) = D.func[k]
getindex(D::$WrappOp,k::Integer) = D.func[k]
domainspace(D::$WrappOp) = domainspace(D.func)
bandinds(D::$WrappOp) = bandinds(D.func)
end)
end
@calculus_functional(DefiniteIntegral)
@calculus_functional(DefiniteLineIntegral)
#default implementation
function getindex(B::ConcreteDefiniteIntegral,kr::Range)
S=domainspace(B)
Q=Integral(S)
A=(Evaluation(S,true)-Evaluation(S,false))*Q
A[kr]
end
function getindex(B::ConcreteDefiniteIntegral,kr::Integer)
S=domainspace(B)
Q=Integral(S)
A=(Evaluation(S,true)-Evaluation(S,false))*Q
A[kr]
end
function DefiniteIntegral(sp::Space)
if typeof(canonicaldomain(sp)).name==typeof(domain(sp)).name
ConcreteDefiniteIntegral{typeof(sp),eltype(sp)}(sp)
else
M = Multiplication(fromcanonicalD(sp),setcanonicaldomain(sp))
Op = DefiniteIntegral(rangespace(M))*M
DefiniteIntegralWrapper(SpaceOperator(Op,sp,rangespace(Op)))
end
end
function DefiniteLineIntegral(sp::Space)
if typeof(canonicaldomain(sp)).name==typeof(domain(sp)).name
ConcreteDefiniteLineIntegral{typeof(sp),eltype(sp)}(sp)
else
M = Multiplication(abs(fromcanonicalD(sp)),setcanonicaldomain(sp))
Op = DefiniteLineIntegral(rangespace(M))*M
DefiniteLineIntegralWrapper(SpaceOperator(Op,sp,rangespace(Op)))
end
end
#TODO: Remove SPECIALOPS reimplement
# *{T,D<:DefiniteIntegral,M<:Multiplication}(A::TimesFunctional{T,D,M},b::Fun) = bilinearform(A.op.f,b)
# *{T,D<:DefiniteLineIntegral,M<:Multiplication}(A::TimesFunctional{T,D,M},b::Fun) = linebilinearform(A.op.f,b)
# *{T,D<:Union{DefiniteIntegral,DefiniteLineIntegral},
# M<:Multiplication,V}(A::FunctionalOperator{TimesFunctional{T,D,M},V},b::Fun) =
# Fun(A.func*b)
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#
# Do work here ...
#
v = sin(1.234567) + cos(0.122122)
println("Julia side: I will return a Float64: ", v)
return v
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] | 2.352941 | 51 |
@testset "aggregation" begin
v = rand(5)
@test aggregate(v, mav) ≈ mean(v)
@test aggregate(v, TruePositive()) ≈ sum(v)
@test aggregate(v, rms) ≈ sqrt(mean(v.^2))
λ = rand()
@test aggregate(λ, rms) === λ
@test aggregate(aggregate(v, l2), l2) == aggregate(v, l2)
end
@testset "metadata" begin
measures()
measures(m -> m.target_scitype <: AbstractVector{<:Finite} &&
m.supports_weights)
end
@testset "coverage" begin
# just checking that the traits work not that they're correct
@test orientation(BrierScore()) == :score
@test orientation(auc) == :score
@test orientation(rms) == :loss
@test reports_each_observation(auc) == false
@test is_feature_dependent(auc) == false
@test MLJBase.distribution_type(BrierScore{UnivariateFinite}) ==
UnivariateFinite
end
true
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