content
stringlengths 5
1.03M
| input_ids
sequencelengths 4
823k
| ratio_char_token
float64 0.4
12.5
| token_count
int64 4
823k
|
|---|---|---|---|
"""
newtonroot(f[, f']; x0 = 0.8, tolerance = 1.e-13, maxiter = 1000)
This function computes the local root of a univariate smooth function `f(x)` using the Newton's Method. This method starts with a `x_0` guess, a function `f(x)` and the first-derivative `f'(x)`. The algorithm follows: `` x^{n+1} = x^n - f(x^n)/f'(x^n) `` until `` |x^{n+1} - x^n|`` is below the tolerance level.
...
# Arguments
- `f(x)::Function`: the function you are trying to solve.
- `f'(x)::Function`: the first derivative of the function you are trying to solve. If it is not given the program estimate `f'(x)` using ForwardDiff.
- `x0::Float64`: the initial value for the algorithm. The default value is 0.8.
- `tolerance::Float64` : the minimum tolerance for the algorithm to converge. The default value is 1.0e-13.
- `maxiter::Int64`: the maximum number of iterations allowed. The default value is 1000.
---
# Examples
```julia-repl
julia> f(x) = (x - 2)^2
f (generic function with 1 method)
julia> df(x) = 2(x - 2)
df (generic function with 1 method)
julia> newtonroot(f,df)
(value = 1.9999999999999318, normdiff = 6.816769371198461e-14, iter = 45)
julia> a = newtonroot(f)[1]
1.9999999999999318
julia> newtonroot(f)[1] ≈ 2
true
```
...
"""
function newtonroot(f, f_prime; x0=0.8, tolerance=1.0E-13, maxiter=1000)
# Initial values
v_old = x0
normdiff = Inf
iter = 1
# Running the algorithm
while (normdiff > tolerance) && (iter < maxiter)
if !(f_prime(v_old) == 0)
v_new = v_old - (f(v_old) / f_prime(v_old))
normdiff = norm(v_new - v_old)
# replace and continue
v_old = v_new
iter = iter + 1
else
return(value = nothing, normdiff = nothing, iter = nothing)
end
end
if (iter < maxiter)
return(value = v_old, normdiff = normdiff, iter = iter)
else
return(value = nothing, normdiff = nothing, iter = nothing)
end
end
function newtonroot(f; x0=0.8, tolerance=1.0E-13, maxiter=1000)
# Initial values
v_old = x0
normdiff = Inf
iter = 1
# Taking the derivative
D(f) = x -> ForwardDiff.derivative(f, x)
f_prime = D(f)
# Running the algorithm
while (normdiff > tolerance) && (iter < maxiter)
if !(f_prime(v_old) == 0)
v_new = v_old - (f(v_old) / f_prime(v_old))
normdiff = norm(v_new - v_old)
# replace and continue
v_old = v_new
iter = iter + 1
else
return(value = nothing, normdiff = nothing, iter = nothing)
end
end
if (iter < maxiter)
return(value = v_old, normdiff = normdiff, iter = iter)
else
return(value = nothing, normdiff = nothing, iter = nothing)
end
end
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] | 2.302135 | 1,218 |
#=win.jl - replay the game fromstart to finish, at every step checking if the game
is won. If the game is won, print ”B” for Black winning, and ”W” for white winning. If black resigned,
print ”R”. If white resigned, print ”r”. If the game is on, print ”?”. If the game is a draw, print
”D”.
Accepts 1 command line argument,<filename> => database
=#
include("board.jl")
include("dParse.jl")
module win
using BM
using SQLite
function winner()
database = ARGS[1] #/path/to/database/file {string}
db = SQLite.DB(database) #Opens the database gamefile
board = BM.startGame("standard")
state = board.state
res = "?"
maxMove = parse(get(SQLite.query(db, """SELECT max(move_number) from moves;""")[1,1]))
for x in 1:maxMove #iterates through each row of the database
dataMove = SQLite.query(db, """SELECT move_number, move_type, targetx, targety,sourcex,sourcey FROM moves WHERE "move_number" = '$x'""")
move_type = get(dataMove[1,2])
if !isnull(dataMove[1,3])
targetx = parse(get(dataMove[1,3]))
targety = parse(get(dataMove[1,4]))
end
#Case 1: Resigned
if move_type == "resign"
if iseven(x) == true
res="r"
else #x is odd
res="R"
end
#Case 2: Win
#Game is won on the move that captures a king
#I implemented win where, if the target X Y location of the current move is a king, it means the king is captpured so the other team wins
elseif ( get(state,(targetx,targety),('x','x'))[1] == 'k') #uses function from spuare.jl
if (get(state,(targetx,targety),('x','x'))[2] == 'b')
res = "W"
else #board[targetx][targety].team == "w"
res = "B"
end
end
if !(isnull(dataMove[1,5])) #type:move
sourcex=parse(get(dataMove[1,5]))
sourcey=parse(get(dataMove[1,6]))
state[(targetx,targety)] = deepcopy(state[(sourcex,sourcey)]) #Updates the board before next move
delete!(state,(sourcex,sourcey))
end
end
return res
end #End function
res = winner()
println(res)
end
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411,
796,
8464,
3419,
198,
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7,
411,
8,
198,
437,
198
] | 2.482165 | 813 |
module BuildBasis
export buildbasisfunc
using TypesParticles, TypesBasis
@doc raw"""
buildbasisfunction(atomicsystem::SystemOfAtoms,basis::Basis)
Returns atom-centered basis functions and number of electrons.
Description: This function will take the system information and create the basis functions
that are atomically centered. Each basis function will contain the parameters for the Gaussian
functions that when linearly combined make up the basis function. Note that the actual basis
function is not yet constructed here, i.e., we are not taking the summation of Gaussians.
Depends on datatypes in TypesParticles.jl and TypesBasis.jl
""" function buildbasisfunc(atomicsystem::SystemOfAtoms,basis::Basis)
natoms = atomicsystem.natoms;
nelectrons = 0;
#Closed-shell system only
for i=1:natoms
nelectrons += atomicsystem.atoms[i].Z
end
nbasis = basis.nbasisfunc;
basisfunc = Array{GaussOrbitals,2}(undef,natoms,nbasis);
#Assign each atom-centered basis functions
#ie = 1; #total electron counter
for a=1:natoms
#For each gaussian store 5 values: c,alpha,xo,yo,zo
#For Z=1 n-basis per atom is 1 so basisfunc length equal num atoms.
for j=1:nbasis
#6-31 type basis set
if basis.basisfunc[j].info == "6-31"
basisfunc[a,j] = GaussOrbitals(atomicsystem.atoms[a],basis.basisfunc[j])
end
end # nbasis
end # atomicsystem.atoms
return basisfunc,nelectrons
end #buildbasisfunction
end #BuildBasis
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] | 2.674699 | 581 |
module JellyFish
using Libdl: find_library
export jaro_winkler, jaro_distance, hamming_distance,
levenshtein_distance, damerau_levenshtein_distance, soundex, metaphone,
match_rating_codex, match_rating_comparison, nysiis
const _jellyfishlib = find_library(["jellyfish"], [joinpath(@__DIR__, "..", "deps")])
macro _calls1s2type(func, returntype, s1, s2)
:(ccall(($(esc(func)), _jellyfishlib), $(esc(returntype)), (Ptr{Cchar}, Ptr{Cchar}), $(esc(s1)), $(esc(s2))))
end
function jaro_winkler(s1::String, s2::String)
ccall((:jaro_winkler, _jellyfishlib), Cdouble, (Ptr{Cchar}, Ptr{Cchar}, Bool), s1, s2, false)
end
function jaro_distance(s1::String, s2::String)
@_calls1s2type(:jaro_distance, Cdouble, s1, s2)
end
function hamming_distance(s1::String, s2::String)
convert(Int, @_calls1s2type(:hamming_distance, Csize_t, s1, s2))
end
function levenshtein_distance(s1::String, s2::String)
@_calls1s2type(:levenshtein_distance, Cint, s1, s2)
end
function damerau_levenshtein_distance(s1::String, s2::String)
@_calls1s2type(:damerau_levenshtein_distance, Cint, s1, s2)
end
# for some reason doesn't work:
function _callstr(func::Symbol, s::String)
r = ccall((func, _jellyfishlib), Ptr{Cchar}, (Ptr{Cchar},), s)
unsafe_string(r)
end
function soundex(s::String)
# _callstr(:soundex, s)
r = ccall((:soundex, _jellyfishlib), Ptr{Cchar}, (Ptr{Cchar},), s)
unsafe_string(r)
end
function metaphone(s::String)
# _callstr(:metaphone, s)
r = ccall((:metaphone, _jellyfishlib), Ptr{Cchar}, (Ptr{Cchar},), s)
unsafe_string(r)
end
function match_rating_codex(s::String)
# _callstr(:match_rating_codex, s)
r = ccall((:match_rating_codex, _jellyfishlib), Ptr{Cchar}, (Ptr{Cchar},), s)
unsafe_string(r)
end
function match_rating_comparison(s1::String, s2::String)
@_calls1s2type(:match_rating_comparison, Bool, s1, s2)
end
function nysiis(s::String)
# _callstr(:nysiis, s)
r = ccall((:nysiis, _jellyfishlib), Ptr{Cchar}, (Ptr{Cchar},), s)
unsafe_string(r)
end
end
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] | 2.232123 | 909 |
import Pkg; Pkg.activate("../../")
println("Loading libraries ...")
include("PreProcess.jl")
import CSV
import DataFrames; const DF = DataFrames
println("Finished loading libraries.")
data_dir = "../data"
path_to_data = "$(data_dir)/cb_transformed.csv"
function separatesamples(Y)
sample_ids = sort(unique(Y.sample_id))
return Matrix.(DF.select(Y[Y.sample_id .== i, :], DF.Not(:sample_id))
for i in sample_ids)
end
# Read transformed CB data into Y as a data frame.
# Replace `missing` with `NaN`.
Y = coalesce.(CSV.read(path_to_data), NaN)
markernames = filter(x -> x != :sample_id, names(Y))
y = separatesamples(Y)
isgoodmarker = PreProcess.preprocess!(y, maxNanOrNegProp=.9,
maxPosProp=.9, rowThresh=-6.0)
includedmarkers = Symbol[]
excludedmarkers = Symbol[]
for j in 1:length(isgoodmarker)
marker = markernames[j]
if isgoodmarker[j]
append!(includedmarkers, [marker])
else
append!(excludedmarkers, [marker])
end
end
# Concatenate y and convert to data frame
Y_reduced = DF.DataFrame(vcat(y...), includedmarkers)
# Convert NaN to missing
Y_reduced = ifelse.(isnan.(Y_reduced), missing, Y_reduced)
# Append a column for sample_id
Y_reduced[!, :sample_id] = vcat([fill(i, size(y[i], 1))
for i in 1:length(y)]...)
# Path to reduced data
path_to_reduced_data = "$(data_dir)/cb_transformed_reduced.csv"
# Write reduced data to disk.
println("Writing reduced-and-transformed CB data to: ")
println(path_to_reduced_data)
CSV.write(path_to_reduced_data, Y_reduced)
println("Done!")
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] | 2.465331 | 649 |
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# formats: ipynb,jl:light
# text_representation:
# extension: .jl
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.4.2
# kernelspec:
# display_name: Julia 1.4.0
# language: julia
# name: julia-1.4
# ---
# # Strong Landau Damping
#
# Electrostatic example of strong Landau damping
# $$
# f(x,v) =\frac{1}{2\pi\sigma^2} \exp
# \Big( - \frac{v_1^2 + v_2^2}{2\sigma^2} \Big)
# ( 1+\alpha \cos(k x)),
# $$
#
using ProgressMeter, Plots
using CSV, Dates, FFTW
using GEMPIC
using Sobol
using Random
using Distributions
using JLD2
using FileIO
function main_sample!( pg :: ParticleGroup{1,1},
ps :: ParticleSampler,
df :: GEMPIC.CosSumGaussianSpin,
mesh :: Mesh )
s = zeros( pg.n_spin )
theta = 0.0
phi = 0.0
n_rnds = 0
if df.params.n_gaussians > 1
n_rnds = 1
end
δ = zeros(df.params.n_gaussians)
for i_v=1:df.params.n_gaussians
δ[i_v] = sum(df.params.δ[1:i_v])
end
n_rnds += 2
rdn = zeros(3)
if ps.sampling_type == :sobol
rng_sobol = SobolSeq(1)
end
rng_random = MersenneTwister(ps.seed)
d = Normal()
for i_part = 1:(pg.n_particles)
if ps.sampling_type == :sobol
x = mesh.xmin[1] + Sobol.next!(rng_sobol)[1] * mesh.Lx[1]
else
x = mesh.xmin[1] + rand(rng_random) * mesh.Lx[1]
end
v = rand(rng_random, d)
# For multiple Gaussian, draw which one to take
rnd_no = rdn[3]
i_gauss = 1
while( rnd_no > δ[i_gauss] )
i_gauss += 1
end
v = v * df.params.σ[i_gauss] + df.params.μ[i_gauss]
#for a peaked initial condition in the s direction
s = [0, 0, 1]
#for a uniformly distributed initial condition on the sphere
# for tt = 1:10
# s[1] = randn()
# s[2] = randn()
# s[3] = randn()
# if norm(s) > 10^(-4)
# break
# end
# end
# s .= s./norm(s)
# Set weight according to value of perturbation
w = GEMPIC.eval_x_density(df, x) * prod(mesh.Lx)
# Copy the generated numbers to the particle
GEMPIC.set_x(pg, i_part, x[1])
GEMPIC.set_v(pg, i_part, v)
GEMPIC.set_spin(pg, i_part, 1, s[1])
GEMPIC.set_spin(pg, i_part, 2, s[2])
GEMPIC.set_spin(pg, i_part, 3, s[3])
# Set weights.
GEMPIC.set_weights(pg, i_part, w)
end
end
# +
function run( steps :: Int64)
σ, μ = 0.02, 0.0
kx, α = 1.004355, 0.001
xmin, xmax = 0, 2π/kx
nx = 32 #NC 1024
n_particles = 1000 #NC 100000
mesh = Mesh( xmin, xmax, nx)
spline_degree = 3
df = GEMPIC.CosSumGaussianSpin([[kx]], [α], [[σ]], [[μ]] )
particle_group2 = ParticleGroup{1,1}( n_particles, n_spin=3)
sampler = ParticleSampler{1,1}( :sobol, n_particles)
main_sample!(particle_group2, sampler, df, mesh)
particle_group = ParticleGroup{1,1}( n_particles, n_spin=3)
for i_part = 1:n_particles
x = zeros( 1 )
v = zeros( 1 )
s = zeros( 3 )
w = zeros( 1 )
x = GEMPIC.get_x(particle_group2, i_part)
v = GEMPIC.get_v(particle_group2, i_part)
s1 = GEMPIC.get_spin(particle_group2, i_part, 1)
s2 = GEMPIC.get_spin(particle_group2, i_part, 2)
s3 = GEMPIC.get_spin(particle_group2, i_part, 3)
w = GEMPIC.get_weights(particle_group2, i_part)
GEMPIC.set_x(particle_group, i_part, x[1])
GEMPIC.set_v(particle_group, i_part, v[1])
GEMPIC.set_spin(particle_group, i_part, 1, s1)
GEMPIC.set_spin(particle_group, i_part, 2, s2)
GEMPIC.set_spin(particle_group, i_part, 3, s3)
GEMPIC.set_weights(particle_group, i_part, w[1])
end
kernel_smoother2 = ParticleMeshCoupling( mesh, n_particles, spline_degree-2, :galerkin)
kernel_smoother1 = ParticleMeshCoupling( mesh, n_particles, spline_degree-1, :galerkin)
kernel_smoother0 = ParticleMeshCoupling( mesh, n_particles, spline_degree, :galerkin)
rho = zeros(Float64, nx)
efield_poisson = zeros(Float64, nx)
# Init!ialize the field solver
maxwell_solver = Maxwell1DFEM(mesh, spline_degree)
# efield by Poisson
solve_poisson!( efield_poisson, particle_group, kernel_smoother0, maxwell_solver, rho )
# Initialize the arrays for the spline coefficients of the fields
k0 = 12.0523
E0 = 10
ww = 12.104827940833333
#Test 1 with only Ey and Ay (and with HH=0)
Ey(x) = E0*cos(k0*x)
Ez(x) = 0.0*E0*sin(k0*x)
Ay(x) = -E0/ww*sin(k0*x)
Az(x) = 0.0*E0/ww*cos(k0*x)
efield_dofs = [efield_poisson, zeros(Float64, nx), zeros(Float64, nx)]
afield_dofs = [zeros(Float64, nx), zeros(Float64, nx)]
l2projection!( efield_dofs[2], maxwell_solver, Ey, spline_degree)
l2projection!( efield_dofs[3], maxwell_solver, Ez, spline_degree)
l2projection!( afield_dofs[1], maxwell_solver, Ay, spline_degree)
l2projection!( afield_dofs[2], maxwell_solver, Az, spline_degree)
propagator = HamiltonianSplittingSpin( maxwell_solver,
kernel_smoother0,
kernel_smoother1,
kernel_smoother2,
particle_group,
efield_dofs,
afield_dofs)
efield_dofs_n = propagator.e_dofs
thdiag = TimeHistoryDiagnosticsSpin( particle_group, maxwell_solver,
kernel_smoother0, kernel_smoother1 );
# mode = zeros(ComplexF64,steps,nx)
th_modes = Vector{ComplexF64}[]
elec_tmp = zeros(Float64,nx)
Δt = 0.002
@showprogress 1 for jstep = 1:steps # loop over time
# Strang splitting
strang_splitting!(propagator, Δt, 1)
solve_poisson!( efield_poisson, particle_group,
kernel_smoother0, maxwell_solver, rho)
write_step!(thdiag, jstep * Δt, spline_degree,
efield_dofs, afield_dofs,
efield_dofs_n, efield_poisson, propagator)
#diagnostics
#store particles at some specific times
if (jstep % 1000 == 0)
GEMPIC.save( "save_particles", jstep, particle_group)
end
#Fourier modes of the longitudinal electric field
for i = 1:nx
elec_tmp[i] = GEMPIC.evaluate(thdiag.kernel_smoother_1, (i-1)*propagator.delta_x[1], efield_dofs[1])
end
push!(th_modes,fft(elec_tmp))
end
thdiag.data, th_modes
end
# +
thdiag, th_modes = run(100) # choose number of steps
CSV.write("thdiag-$(now()).csv", thdiag)
file="th_modes"
#datafile = @sprintf("%s.jld2", file)
FileIO.save("th_modes.jld2", Dict("modes" => th_modes))
# -
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] | 1.873458 | 3,730 |
#=
Modela diferentes tipos de anemômetros
=#
"""
HWSensor(I, E, R, T)
Condições de operação do sensor de um anemômetro de fio quente.
"""
struct HWSensor
"Corrente passando no sensor"
I::Float64
"Tenão no sensor"
E::Float64
"Resistência do sensor"
R::Float64
"Temmperatura do sensor"
T::Float64
"Potência dissipada no sensor"
P::Float64
HWSensor(I, E, R, T) = new(I, E, R, T, I*E)
end
"""
const_current(mA, R::Thermistor, U=5.0, d=3.0, Ta=20.0, Pa=101.3, dterr=1e-6, nmax=20000)
Modela um anemômetro trabalhando a corrente constante. Não simula circuitos elétricos.
Utiliza um esquema iterativo.
Argumentos:
* `mA` Corrente em mA
* `R` Objeto que modela o termistor
* `U` Velocidade em m/s
* `d` Diâmetro em mm
* `Ta` Temperatura ao longe em °C
* `Pa` Pressão atmosférica em kPa
* `dterr` Erro admissível para convergência da temperatura.
* `nmax` Número máximo de iterações.
```jldoctest
julia> R = Thermistor(5e3, 3200, 20)
THW.Thermistor(5000.0, 3200.0, 293.15)
julia> const_current(20, R, 5.0, 2.0)
THW.HWSensor(0.02, 9.395209322307162, 469.7604661153581, 101.07772779650266, 0.18790418644614323)
julia> const_current(20, R, 1.0, 2.0)
THW.HWSensor(0.02, 5.872996120670518, 293.64980603352586, 122.8352988426391, 0.11745992241341036)
julia> const_current(20, R, 10.0, 2.0)
THW.HWSensor(0.02, 11.70131737284239, 585.0658686421194, 91.71177677466494, 0.2340263474568478)
julia> const_current(20, R, 10.0, 10.0)
THW.HWSensor(0.02, 44.971641701710844, 2248.582085085542, 43.15632417996514, 0.8994328340342169)
```
"""
function const_current(mA, R::Thermistor, U=5.0, d=3.0, Ta=20.0, Pa=101.3, dterr=1e-6, nmax=20000)
I = mA/1000
D = d/1000
r = D/2
A = 4π*r^2
Tw = Ta + 0.01
i = 0
P = 0.0
for i = 1:nmax
h = hconvect(U, D, Tw, Ta, Pa)
#println(h)
P = R(Tw)*I^2
Tw1 = P/(h*A) + Ta
dt = Tw1-Tw
if abs(dt) < dterr
Tw = Tw1
break
end
Tw = Tw + 0.4*dt
end
res = R(Tw)
E = res*I
return HWSensor(I, E, res, Tw)
end
"""
const_temperature(Tw, R::Thermistor, U=5.0, d=3.0, Ta=20.0, Pa=101.3)
Modela um anemômetro trabalhando a temperatura constante. Não simula circuitos elétricos.
Argumentos:
* `Tw` Temperatura de operação do sensor
* `R` Objeto que modela o termistor
* `U` Velocidade em m/s
* `d` Diâmetro em mm
* `Ta` Temperatura ao longe em °C
* `Pa` Pressão atmosférica em kPa
```jldoctest
julia> R = Thermistor(5e3, 3200, 20)
THW.Thermistor(5000.0, 3200.0, 293.15)
julia> const_temperature(100.0, R, 5.0, 2.0)
THW.HWSensor(0.019627341883449854, 9.45069202339691, 481.50646580247894, 100.0, 0.18549196337840362)
julia> const_temperature(100.0, R, 1.0, 2.0)
THW.HWSensor(0.01383013241982859, 6.65929818305195, 481.50646580247894, 100.0, 0.09209897569473241)
julia> const_temperature(100.0, R, 10.0, 2.0)
THW.HWSensor(0.023241402160834233, 11.190885414757389, 481.50646580247894, 100.0, 0.26009186846019067)
julia> const_temperature(100.0, R, 20.0, 2.0)
THW.HWSensor(0.027741857167247384, 13.357883599398457, 481.50646580247894, 100.0, 0.3705724988712284)
julia> const_temperature(100.0, R, 20.0, 3.0)
THW.HWSensor(0.037797691428912955, 18.19983281542853, 481.50646580247894, 100.0, 0.6879116648153717)
```
"""
function const_temperature(Tw, R::Thermistor, U=5.0, d=3.0, Ta=20.0, Pa=101.3)
D = d/1000
r = D/2
A = 4π*r^2
Rw = R(Tw)
h = hconvect(U, D, Tw, Ta, Pa)
i = sqrt(h*A*(Tw-Ta)/Rw)
return HWSensor(i, Rw*i, Rw, Tw)
end
function const_temperature_cyl(Tw, R::AbstractResistor, U=5.0, d=5.0, l=3, Ta=20.0, Pa=101.325)
D = d/1e6
L = l / 1000
r = D/2
A = L * π * D
Rw = R(Tw)
h = hconvectcyl(U, D, Tw, Ta, Pa)
i = sqrt(h*A*(Tw-Ta)/Rw)
return i*1000
end
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] | 1.905206 | 2,036 |
function avgfilter1d_constprop_setup(n, w = 3)
xs = rand(SVector{w,UInt8}, n)
ys = zero(xs)
return (; ys, xs)
end
@inline function avg_at(xs0, i, N)
@static if USE_ALIASSCOPE
xs = Const(xs0)
else
xs = xs0
end
s = zero(eltype(xs))
for k in 1:N
s = s .+ @inbounds xs[i + k - 1]
end
return s .÷ N
end
@inline function avgfilter1d_constprop_seq!(ys, xs, N = 4)
@assert axes(ys) == axes(xs)
for i0 in 0:length(xs) - N
i = firstindex(xs) + i0
@inbounds ys[i] = avg_at(xs, i, N)
end
return ys
end
@inline function avgfilter1d_constprop_threads!(ys, xs, N = 4)
@assert axes(ys) == axes(xs)
Threads.@threads for i0 in 0:length(xs) - N
i = firstindex(xs) + i0
@inbounds ys[i] = avg_at(xs, i, N)
end
return ys
end
# Using `@inline` to nudge that the 3-arg method sees the constant 4 set by the
# "outer" 2-arg function.
@inline function avgfilter1d_constprop_tapir_dac!(ys, xs, N = 4)
@assert axes(ys) == axes(xs)
Tapir.@par dac for i0 in 0:length(xs) - N
i = firstindex(xs) + i0
@inbounds ys[i] = avg_at(xs, i, N)
@grainsize 131072
end
return ys
end
@inline function avgfilter1d_constprop_tapir_seq!(ys, xs, N = 4)
@assert axes(ys) == axes(xs)
Tapir.@par seq for i0 in 0:length(xs) - N
i = firstindex(xs) + i0
@inbounds ys[i] = avg_at(xs, i, N)
@grainsize 131072
end
return ys
end
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] | 1.970784 | 753 |
srand(1)
a = mapweight(grid2(20),x->1/(rand(1)[1]));
la = lap(a)
n = size(la)[1]
b = randn(n)
b = b - mean(b);
d = diag(la)
pre = (x)->(x ./ d)
x = pcg(la,b,pre,maxits=2000,maxtime=1,verbose=true);
x = Laplacians.pcgBLAS(la,b,pre,maxits=2000,maxtime=1,verbose=true);
x = Laplacians.pcgSlow(la,b,pre,maxits=2000,maxtime=1,verbose=true);
x = pcg(la,b,pre,maxits=2000,verbose=true);
x = Laplacians.pcgBLAS(la,b,pre,maxits=2000,verbose=true);
x = Laplacians.pcgSlow(la,b,pre,maxits=2000,verbose=true);
x = Laplacians.pcgBLAS(la,b,pre,tol=1e-2);
x = Laplacians.pcgSlow(la,b,pre,tol=1e-2);
x = pcg(la,b,pre,tol=1e-2);
x = cg(la,b,verbose=true,maxtime=1,maxits=1000);
x = Laplacians.cgBLAS(la,b,verbose=true,maxtime=1,maxits=1000);
x = Laplacians.cgSlow(la,b,verbose=true,maxtime=1,maxits=1000);
x = cg(la,b,verbose=true,maxits=2000);
x = Laplacians.cgBLAS(la,b,verbose=true,maxits=2000);
x = Laplacians.cgSlow(la,b,verbose=true,maxits=2000);
x = cg(la,b,verbose=true,tol=0.5);
x = Laplacians.cgBLAS(la,b,verbose=true,tol=0.5);
x = Laplacians.cgSlow(la,b,verbose=true,tol=0.5);
n = 100
a = wtedChimera(n,3)
la = lap(a)
f = cgSolver(la,verbose=false)
b = randn(n)
b = b - mean(b)
x = f(b)
norm(la*x-b)
x = f(b,maxits=15,verbose=true, maxtime=1)
d = diagm(1./diag(la))
pre = x -> d*x
x = pcg(la, b, diagm(diag(la)),maxits=10)
x = pcg(la, b, pre,maxits=10,verbose=true)
f = pcgSolver(la, diagm(diag(la)) ,maxits=10,verbose=true)
x = f(b)
f = pcgSolver(la,pre,maxits=10,verbose=false)
x = f(b,verbose=true, maxits=1000, maxtime = 2)
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] | 1.853688 | 827 |
using Plots
using Colors
pyplot()
include("N_plots.jl")
# Model and Objective
N = 51
model, obj = Dynamics.dubinscar_parallelpark
obj_uncon = UnconstrainedObjective(obj)
opts = SolverOptions()
opts.verbose = false
opts.cost_tolerance = 1e-6
opts.cost_tolerance_intermediate = 1e-5
opts.constraint_tolerance = 1e-4
opts.resolve_feasible = false
opts.outer_loop_update_type = :default
opts.penalty_scaling = 200
opts.penalty_initial = .1
opts.R_infeasible = 20
dircol_options = Dict("tol"=>opts.cost_tolerance,"constr_viol_tol"=>opts.constraint_tolerance)
#### UNCONSTRAINED #####
solver = Solver(model, obj_uncon, N=N, opts=opts)
n,m,N = get_sizes(solver)
U0 = ones(m,N)
X0 = line_trajectory(solver)
X0_rollout = rollout(solver,U0)
res_i, stats_i = solve(solver,U0)
stats_i["iterations"]
stats_i["runtime"]
evals(solver,:f) / stats_i["iterations"]
res_p, stats_p = solve_dircol(solver,X0_rollout,U0)
stats_p["iterations"]
evals(solver,:f)/stats_p["iterations"]
stats_p["runtime"]
Ns = [101,201,301,401]
group = "parallelpark/unconstrained"
run_step_size_comparison(model, obj_uncon, U0, group, Ns, opts=opts, integrations=[:rk3,:ipopt],benchmark=true)
plot_stat("runtime",group,legend=:bottomright,["rk3","ipopt"],title="Unconstrained Parallel Park")
plot_stat("iterations",group,legend=:bottom,["rk3","ipopt"],title="Unconstrained Parallel Park")
plot_stat("error",group,yscale=:log10,legend=:right,["rk3","ipopt"],title="Unconstrained Parallel Park")
plot_stat("std",group,yscale=:log10,legend=:right,["rk3","ipopt"],title="Unconstrained Parallel Park")
#### CONSTRAINED ####
solver = Solver(model, obj, N=N, opts=opts)
n,m,N = get_sizes(solver)
U0 = ones(m,N)
X0 = line_trajectory(solver)
X0_rollout = rollout(solver,U0)
res_i, stats_i = solve(solver,U0)
res_p, stats_p = solve_dircol(solver,X0_rollout,U0)
stats_i["iterations"]
stats_i["runtime"]
evals(solver,:f) / stats_i["iterations"]
stats_p["iterations"]
evals(solver,:f)/stats_p["iterations"]
stats_p["runtime"]
Ns = [101,201,301,401]
group = "parallelpark/constrained"
run_step_size_comparison(model, obj, U0, group, Ns, opts=opts, integrations=[:rk3,:ipopt],benchmark=true)
plot_stat("runtime",group,legend=:bottomright,["rk3","ipopt",""],title="Constrained Parallel Park",color=[:blue :darkorange2])
savefig(joinpath(IMAGE_DIR,"ppark_runtime.eps"))
plot_stat("iterations",group,legend=:bottom,["rk3","ipopt"],title="Constrained Parallel Park",color=[:blue :darkorange2])
savefig(joinpath(IMAGE_DIR,"ppark_iterations.eps"))
plot_stat("error",group,yscale=:log10,legend=:right,["rk3","ipopt"],title="Constrained Parallel Park")
Plots.eps(joinpath(IMAGE_DIR,"ppark_runtime"))
# Constraint vs time (Newton tail)
# using TrajectoryOptimization: gen_usrfun_newton, NewtonVars, gen_newton_functions, newton_projection
# t_start = time_ns()
# V_ = newton_projection(solver,res_i,eps=1e-8,verbose=false)
# res_ = ConstrainedVectorResults(solver,V_.Z.X,V_.Z.U)
# backwardpass!(res_,solver)
# rollout!(res_,solver,0.0)
# max_violation(res_)
# t_newton = float(time_ns()-t_start)/1e9
#
# t_i = 0.469548 # from running @btime
# t_p = 0.659831 # from running @btime
# t_2 = 11.325 # from running @btime
# time_i = collect(range(0,stop=t_i,length=stats_i["iterations"]))
# time_p = range(0,stop=t_p,length=stats_p["iterations"])
# p = plot(time_p,stats_p["c_max"][2:end],yscale=:log10,label="Ipopt",color=:blue,width=2,
# markershape=:circle,markerstrokecolor=:blue)
# c_max = [stats_i["c_max"]; max_violation(res_)]
# push!(time_i,t_i+t_newton)
# plot!(time_i,c_max,label="ALTRO*",color=:green,width=2,markershape=:circle,markerstrokecolor=:green,
# xlabel="runtime (s)",ylabel="max constraint violation")
# plot_vertical_lines!(p,[t_i])
# time_2 = range(0,step=time_i[2]-time_i[1],length=stats_2["iterations"])
# plot!(time_2,stats_2["c_max"],label="ALTRO",width=2,color=:darkorange2,
# markershape=:circle,markerstrokecolor=:darkorange2,xlim=[0,1.5],size=(500,250))
# savefig(p,joinpath(IMAGE_DIR,"ppark_newton.eps"))
# Constraint vs Iteration (Newton tail)
using TrajectoryOptimization: gen_usrfun_newton, NewtonVars, gen_newton_functions, newton_projection
t_start = time_ns()
solver.opts.iterations = 25
solver.opts.constraint_tolerance = 1e-2
res_i, stats_i = solve(solver,U0)
max_violation(res_i)
plot(stats_i["c_max"],yscale=:log10)
V_ = newton_projection(solver,res_i,eps=1e-8,verbose=false)
res_ = ConstrainedVectorResults(solver,V_.Z.X,V_.Z.U)
backwardpass!(res_,solver)
rollout!(res_,solver,0.0)
max_violation(res_)
t_newton = float(time_ns()-t_start)/1e9
t_i = 0.469548 # from running @btime
t_p = 0.659831 # from running @btime
t_2 = 11.325 # from running @btime
time_i = collect(range(0,stop=t_i,length=stats_i["iterations"]))
time_p = range(0,stop=t_p,length=stats_p["iterations"])
p = plot(time_p,stats_p["c_max"][2:end],yscale=:log10,label="Ipopt",color=:blue,width=2,
markershape=:circle,markerstrokecolor=:blue)
c_max = [stats_i["c_max"]; max_violation(res_)]
push!(time_i,t_i+t_newton)
plot!(time_i,c_max,label="ALTRO*",color=:green,width=2,markershape=:circle,markerstrokecolor=:green,
xlabel="runtime (s)",ylabel="max constraint violation")
plot_vertical_lines!(p,[t_i])
time_2 = range(0,step=time_i[2]-time_i[1],length=stats_2["iterations"])
plot!(time_2,stats_2["c_max"],label="ALTRO",width=2,color=:darkorange2,
markershape=:circle,markerstrokecolor=:darkorange2,xlim=[0,1.5],size=(500,250))
savefig(p,joinpath(IMAGE_DIR,"ppark_newton.eps"))
# #### INFEASIBLE ####
# opts = SolverOptions()
# opts.verbose = false
# opts.cost_tolerance = 1e-6
# opts.cost_tolerance_intermediate = 1e-3
# opts.cost_tolerance_infeasible = 1e-4
# opts.constraint_tolerance = 1e-4
# opts.resolve_feasible = false
# opts.outer_loop_update_type = :default
# opts.use_nesterov = true
# opts.penalty_scaling = 200
# opts.penalty_initial = 10
# opts.R_infeasible = 10
# opts.square_root = true
# opts.constraint_decrease_ratio = 0.25
# opts.penalty_update_frequency = 2
#
# solver = Solver(model, obj, N=101, opts=opts)
# n,m,N = get_sizes(solver)
# U0 = ones(m,N)
# X0 = line_trajectory(solver)
# X0_rollout = rollout(solver,U0)
#
# solver.opts.verbose = false
# solver.opts.resolve_feasible = true
# solver.opts.cost_tolerance_infeasible = 1e-5
# @time res_i, stats_i = solve(solver,X0,U0)
# stats_i["iterations"]
# res_i.U[1]
# stats_i["runtime"]
# stats_i["iterations (infeasible)"]
# @time res_p, stats_p = solve_dircol(solver,X0_rollout,U0)
# # res_s, stats_s = solve_dircol(solver,X0_rollout,U0,nlp=:snopt)
#
# # import TrajectoryOptimization: _solve, get_feasible_trajectory
# # results = copy(res_i)
# # solver.state.infeasible = true
# # results_feasible = get_feasible_trajectory(results, solver)
# # results_feasible.λ_prev[1]
#
# # solver.state.infeasible
# # res, stats = _solve(solver,to_array(results_feasible.U),prevResults=results_feasible); stats["iterations"]
# # res, stats = _solve(solver,to_array(results_feasible.U)); stats["iterations"]
# # res, stats = _solve(solver,to_array(results_feasible.U),λ=results_feasible.λ); stats["iterations"]
# # res, stats = _solve(solver,to_array(results_feasible.U),λ=results_feasible.λ,μ=results_feasible.μ); stats["iterations"]
# #
#
# constraint_plot(solver,X0,U0)
#
# Ns = [101,201,301,401]
# group = "parallelpark/infeasible"
# run_step_size_comparison(model, obj, U0, group, Ns, opts=opts, integrations=[:rk3,:ipopt],benchmark=true, infeasible=true)
# plot_stat("runtime",group,legend=:bottomright,["rk3","ipopt"],title="Constrained Parallel Park (infeasible)",ylim=[0,1.5])
# plot_stat("iterations",group,legend=:bottom,["rk3","ipopt"],title="Constrained Parallel Park (infeasible)")
# plot_stat("error",group,yscale=:log10,legend=:right,["rk3","ipopt"],title="Constrained Parallel Park (infeasible)")
#
#
#
# Combined Plot
group = "parallelpark/constrained"
Ns, data = load_data("runtime","ipopt","parallelpark/constrained")
Ns, err = load_data("std","ipopt","parallelpark/constrained")
p1 = plot(Ns,data,yerr=err,label="DIRCOL",color=:blue,marker=:circle,markerstrokecolor=:blue,ylabel="runtime",markersize=6)
Ns, data = load_data("runtime","rk3","parallelpark/constrained")
Ns, err = load_data("std","rk3","parallelpark/constrained")
plot!(Ns,data,yerr=err,label="ALTRO",color=:darkorange2,marker=:circle,markerstrokecolor=:darkorange2,markersize=6,ylim=(0,1.9))
Ns, data = load_data("runtime","rk3","parallelpark/infeasible")
Ns, err = load_data("std","rk3","parallelpark/infeasible")
plot!(Ns,data,yerr=err,label="ALTRO (inf)",color=:darkorange2,style=:dash,
marker=:utriangle,markerstrokecolor=:darkorange2,markersize=8,
title="Constrained",titlefontsize=10)
Ns, err = load_data("std","rk3","parallelpark/infeasible")
Ns, data = load_data("runtime",["rk3","ipopt"],"parallelpark/unconstrained")
Ns, err = load_data("std",["rk3","ipopt"],"parallelpark/unconstrained")
p2 = plot(Ns,data[2],yerr=err[2],color=:blue,style=:dot,label="DIRCOL", width=1.5,
marker=:square,markerstrokecolor=:blue,legend=:topleft,markersize=4,ylim=ylims(p1))
plot!(Ns,data[1],yerr=err[1],color=:darkorange2,style=:dot,label="ALTRO",width=1.5,
marker=:square,markerstrokecolor=:darkorange2,markersize=4,
title="Unconstrained",titlefontsize=10)
plot(p1,p2,layout=(1,2),size=(500,300),dpi=400,xlabel="Knot points",ylabel="Runtime")
savefig(joinpath(IMAGE_DIR,"ppark_runtime.png"))
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] | 2.35891 | 3,962 |
@inline function generate_compat_entry(v::VersionNumber)::String
if v.major == 0 && v.minor == 0
return "0.0.$(v.patch)"
else
return "$(v.major).$(v.minor)"
end
end
# The `_remove_trailing_zeros` function is based on
# the `compat_version` function from:
# https://github.com/invenia/PkgTemplates.jl/blob/master/src/plugins/project_file.jl
@inline function _remove_trailing_zeros(v::VersionNumber)::String
if v.patch == 0
if v.minor == 0
if v.major == 0 # v.major is 0, v.minor is 0, v.patch is 0
throw(DomainError("0.0.0 is not a valid input"))
else # v.major is nonzero and v.minor is 0 and v.patch is 0
return "$(v.major)"
end
else # v.minor is nonzero, v.patch is 0
return "$(v.major).$(v.minor)"
end
else # v.patch is nonzero
return "$(v.major).$(v.minor).$(v.patch)"
end
end
# @inline function MajorMinorVersion(version::VersionNumber)
# major = version.major::Base.VInt
# minor = version.minor::Base.VInt
# result = MajorMinorVersion(major, minor)
# return result
# end
# @inline MajorMinorVersion(::Nothing) = nothing
# @inline function VersionNumber(x::MajorMinorVersion)
# major = x.major::Base.VInt
# minor = x.minor::Base.VInt
# result = VersionNumber(x, y)::VersionNumber
# return result
# end
# @inline function Base.isless(a::MajorMinorVersion, b::MajorMinorVersion)
# a_versionnumber = VersionNumber(a)::VersionNumber
# b_versionnumber = VersionNumber(b)::VersionNumber
# result = Base.isless(a_versionnumber, b_versionnumber)
# return result
# end
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318,
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7,
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25,
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15057,
198,
2,
220,
220,
220,
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1255,
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13,
271,
1203,
7,
64,
62,
9641,
17618,
11,
275,
62,
9641,
17618,
8,
198,
2,
220,
220,
220,
220,
1441,
1255,
198,
2,
886,
198
] | 2.383738 | 701 |
module Orekit
import Dates
using Pkg.Artifacts
using JavaCall
using Reexport
function init()
JavaCall.addClassPath(joinpath(artifact"jars", "orekit-artifacts-10.2", "*.jar"))
JavaCall.init()
end
include(joinpath(@__DIR__, "..", "gen", "OrekitWrapper", "OrekitWrapper.jl"))
include(joinpath(@__DIR__, "..", "gen", "HipparchusWrapper", "HipparchusWrapper.jl"))
include("Time.jl")
@reexport using .Time
end # module
| [
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437,
1303,
8265,
198
] | 2.625767 | 163 |
# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
struct DotDict
inner::Dict
end
DotDict() = DotDict(Dict())
function Base.setproperty!(d::DotDict, key::Symbol, value)
setindex!(getfield(d, :inner), value, key)
end
function Base.getproperty(d::DotDict, key::Symbol)
(key == :inner ? getfield(d, :inner) : d.inner[key])
end
function Base.getindex(d::DotDict, key::Int64)
d.inner[Symbol(key)]
end
function Base.getindex(d::DotDict, key::Symbol)
d.inner[key]
end
function Base.keys(d::DotDict)
keys(d.inner)
end
function Base.values(d::DotDict)
values(d.inner)
end
function Base.iterate(d::DotDict)
iterate(values(d.inner))
end
function Base.iterate(d::DotDict, v::Int64)
iterate(values(d.inner), v)
end
function Base.length(d::DotDict)
length(values(d.inner))
end
function Base.show(io::IO, d::DotDict)
print(io, "DotDict with $(length(keys(d.inner))) entries:\n")
count = 0
for k in keys(d.inner)
count += 1
if count > 10
print(io, " ...\n")
break
end
print(io, " :$(k) => $(d.inner[k])\n")
end
end
function recursive_to_dot_dict(el)
if typeof(el) == Dict{String, Any}
return DotDict(Dict(Symbol(k) => recursive_to_dot_dict(el[k]) for k in keys(el)))
else
return el
end
end
export recursive_to_dot_dict | [
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] | 2.293413 | 668 |
# packaged version of align.jl. Run align_prep.jl or prep_full.jl first.
include("../src/NMRCalibrate.jl")
import .NMRCalibrate
import NLopt
import PlotlyJS
using Plots; plotly()
PyPlot.close("all")
fig_num = 1
Random.seed!(25)
PyPlot.matplotlib["rcParams"][:update](["font.size" => 22, "font.family" => "serif"])
save_BSON_flag = true
N_d = sum( NMRCalibrate.getNd(Bs[n]) for n = 1:length(Bs) )
N_β = sum( NMRCalibrate.getNβ(κs_β_DOFs[n], Bs[n]) for n = 1:length(Bs) )
shift_lb = -ones(N_d)
shift_ub = ones(N_d)
Es = collect( NMRSpectraSimulator.καFIDModelType(Bs[i]) for i = 1:length(Bs) )
a_setp, b_setp, minxs,
rets = NMRCalibrate.setupitpab(0.1, 10, 0.7; optim_algorithm = :LN_BOBYQA)
#
w = ones(Float64, length(As))
#LS_inds = 1:length(U_cost)
loop_range = 1:length(cost_inds_set)
println("Timing:")
@time obj_funcs, minfs, minxs, rets = NMRCalibrate.aligncompoundκ(y,
U_y,
P_y,
As,
Bs,
Es,
fs,
SW,
Δsys_cs,
a_setp, b_setp, κs_β_DOFs, κs_β_orderings,
shift_lb,
shift_ub,
cost_inds_set;
loop_range = loop_range,
w = w,
N_starts = 100,
#N_starts = 500, # ethanol region 1.
local_optim_algorithm = NLopt.LN_BOBYQA,
#xtol_rel = 1e-3,
xtol_rel = 1e-9,
maxeval = 50,
maxtime = Inf,
β_optim_algorithm = :GN_DIRECT_L,
κ_lb_default = 1e-2,
κ_ub_default = 1e2,
β_max_iters = 500,
#β_max_iters = 1000,
β_xtol_rel = 1e-9,
β_ftol_rel = 1e-9,
β_maxtime = Inf);
dummy = 1
if save_BSON_flag
save_path = joinpath(project_folder, "alignment_results.bson")
BSON.bson(save_path, region_min_dist = region_min_dist,
minfs = minfs,
minxs = minxs,
rets = rets)
end
function plotregion(P, U, q_U, P_y, y, P_cost, y_cost, display_threshold_factor, display_reduction_factor,
save_folder, title_string, file_name;
save_plot_flag = true,
display_plot_flag = true,
canvas_size = (1000, 400))
# # reduce the plotting positions for low signal regions. Otherwise the plot store size will be too large, and the time to load the plot will be long.
# inds, _ = NMRSpectraSimulator.prunelowsignalentries(q_U, display_threshold_factor, display_reduction_factor)
# P_display = P[inds]
# U_display = U[inds]
# q_U_display = q_U[inds]
P_display = P
U_display = U
q_U_display = q_U
# plot.
isdir(save_folder) || mkpath(save_folder)
plots_save_path = joinpath(save_folder, file_name)
#title_string = "$(project_name) alignment results, region $(r), real part"
plot_obj = Plots.plot( P_display,
real.(q_U_display),
title = title_string,
label = "model",
seriestype = :line,
ticks = :native,
xlims = (P_display[1],P_display[end]),
hover = P_display,
linewidth = 4,
xlabel = "ppm",
ylabel = "real part of spectrum",
xflip = true,
size = canvas_size)
Plots.plot!(plot_obj, P_y, real.(y), label = "full data",
seriestype = :line,
linestyle = :dot,
xflip = true,
linewidth = 4)
Plots.plot!(plot_obj, P_cost, real.(y_cost), label = "fit data",
markershape = :circle,
seriestype = :scatter,
xflip = true)
if save_plot_flag
Plots.savefig(plot_obj, plots_save_path)
end
if display_plot_flag
display(plot_obj)
end
return nothing
end
function plotalignmentresults(As, Es, w, save_folder,
P_y, y,
region_min_dist,
obj_funcs, minfs, minxs, rets,
display_reduction_factor, display_threshold_factor,
cost_inds_set, loop_range;
canvas_size = (1000, 400),
display_flag = false,
save_plot_flag = true,
N_viz = 50000)
U = LinRange(u_min, u_max, N_viz)
P = hz2ppmfunc.(U)
U_rad = U .* (2*π)
for r in loop_range
y_cost = y[cost_inds_set[r]]
P_cost = P_y[cost_inds_set[r]]
q2 = uu->NMRSpectraSimulator.evalitpproxymixture(uu, As, Es; w = w)
obj_funcs[r](minxs[r])
# # debug.
# N_κ, N_κ_singlets = NMRCalibrate.countκ(Es)
# N_κ_vars = N_κ + N_κ_singlets
# NMRCalibrate.parseκ!(Es, ones(N_κ_vars))
# #
q_U = q2.(U_rad)
file_name = "results_real_$(r).html"
title_string = "$(project_name) alignment results, region $(r), real part"
plotregion(P, U, q_U, P_y, y, P_cost, y_cost, display_threshold_factor, display_reduction_factor,
save_folder, title_string, file_name;
save_plot_flag = save_plot_flag,
display_plot_flag = display_flag,
canvas_size = canvas_size)
end
return nothing
end
#### visualize.
# minxs[1][1] = 0.000
# minxs[2][1] = 0.000
display_reduction_factor = 100
display_threshold_factor = 0.05/10
if "L-Isoleucine" in molecule_names
display_reduction_factor = 1
display_threshold_factor = 0.001/10
end
save_plot_flag = true
display_flag = true
plotalignmentresults(As, Es, w, project_folder,
P_y, y,
region_min_dist,
obj_funcs, minfs, minxs, rets,
display_reduction_factor, display_threshold_factor,
cost_inds_set, loop_range;
canvas_size = (1000, 400),
display_flag = display_flag,
save_plot_flag = save_plot_flag,
N_viz = 50000)
for r in loop_range
println("region $(r):")
println("objective: $(minfs[r]), return status: $(rets)")
println("shift variable:")
display(minxs)
println()
end
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] | 2.121601 | 2,574 |
using Swagger
using Test
# set the current julia executable to be used henceforth
("JULIA" in keys(ENV)) || (ENV["JULIA"] = joinpath(Sys.BINDIR, "julia"))
const gencmd = joinpath(dirname(@__FILE__()), "petstore", "generate.sh")
println("Generating petstore using $gencmd")
run(`$gencmd`)
println("Running petstore tests...")
include("petstore/runtests.jl")
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] | 2.834646 | 127 |
# ---
# title: 765. Couples Holding Hands
# id: problem765
# author: Indigo
# date: 2021-02-14
# difficulty: Hard
# categories: Greedy, Union Find, Graph
# link: <https://leetcode.com/problems/couples-holding-hands/description/>
# hidden: true
# ---
#
# N couples sit in 2N seats arranged in a row and want to hold hands. We want to
# know the minimum number of swaps so that every couple is sitting side by side.
# A _swap_ consists of choosing **any** two people, then they stand up and
# switch seats.
#
# The people and seats are represented by an integer from `0` to `2N-1`, the
# couples are numbered in order, the first couple being `(0, 1)`, the second
# couple being `(2, 3)`, and so on with the last couple being `(2N-2, 2N-1)`.
#
# The couples' initial seating is given by `row[i]` being the value of the
# person who is initially sitting in the i-th seat.
#
# **Example 1:**
#
#
#
# Input: row = [0, 2, 1, 3]
# Output: 1
# Explanation: We only need to swap the second (row[1]) and third (row[2]) person.
#
#
# **Example 2:**
#
#
#
# Input: row = [3, 2, 0, 1]
# Output: 0
# Explanation: All couples are already seated side by side.
#
#
# **Note:**
#
# 1. `len(row)` is even and in the range of `[4, 60]`.
# 2. `row` is guaranteed to be a permutation of `0...len(row)-1`.
#
#
## @lc code=start
using LeetCode
function min_swaps_couples(row::Vector{Int})
len, ps, res = length(row), length(row) ÷ 2, 0
grp = [Set{Int}() for i in 1:ps]
for i in 1:2:len
l, r = row[i] ÷ 2 + 1, row[i + 1] ÷ 2 + 1
(l != r) && (push!(grp[l], r); push!(grp[r], l))
end
visited = fill(false, ps)
q = Queue{Int}()
for i in 1:ps
visited[i] && continue
enqueue!(q, i)
visited[i] = true
cnt = 0
while !isempty(q)
frt = dequeue!(q)
cnt += 1
for neighbor in grp[frt]
visited[neighbor] && continue
enqueue!(q, neighbor)
visited[neighbor] = true
end
end
res += cnt - 1
end
res
end
## @lc code=end
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2235,
2488,
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28,
437,
198
] | 2.233402 | 964 |
# NOTE: These tests are not part of the active test suite, because they require Distributions.jl.
# DO NOT INCORPORATE INTO runtests.jl.
using Distributions
using LightGraphs
using StatsBase
using Test
import Random
import Base: -
import LightGraphs: randbn
import StatsBase: SummaryStats
function -(s::SummaryStats, t::SummaryStats)
return SummaryStats(s.mean - t.mean,
s.min - t.min,
s.q25 - t.q25,
s.median - t.median,
s.q75 - t.q75,
s.max - t.max)
end
function binomial_test(n, p, s)
drand = rand(Binomial(n, p), s)
lrand = Int64[randbn(n, p) for i in 1:s]
ds = @show summarystats(drand)
ls = @show summarystats(lrand)
dσ = @show std(drand)
lσ = @show std(lrand)
summarydiff = @show ds - ls
@test abs(summarydiff.mean) / ds.mean < .10
@test abs(summarydiff.median) / ds.median < .10
@test abs(summarydiff.q25) / ds.q25 < .10
@test abs(summarydiff.q75) / ds.q75 < .10
@show dσ - lσ
@test abs(dσ - lσ) / dσ < .10
end
seed!(1234)
n = 10000
p = 0.3
s = 100000
for (n, p, s) in [(100, 0.3, 1000), (1000, 0.8, 1000), (10000, 0.25, 1000)]
binomial_test(n, p, s)
end
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] | 2.239006 | 523 |
module Classical
using LinearAlgebra
#using StaticArrays
"""
visco()
Implements a power-law dependence for the viscosity ν on the surface density Σ,
ν = ν₀(Σ/Σ₀)ᵝ
following Grätz, Seiß & Spahn (2018).
"""
function visco(Σrel,β)
THRESH = 0.01 # Thresholding allows us to use β<0, which would otherwise lead to infinities.
threshold(Σ) = Σ > THRESH ? Σ : THRESH
Σr = threshold.(Σrel)
return Diagonal(Σr.^β)
end
"""
Potentially confusing terminology: a planetary particulate disk is called a "ring",
not a disk.
"""
mutable struct ring
# α::Float64
# Δx::Float64
# Δt::Float64
# xi, xf ::Float64
N::Integer
β::Float64
ΔtMAX::Float64
# Defined on nodes:
X_::Array{Float64,1}
ΔX_::Array{Float64,1}
U_::Array{Float64,1}
# Defined on cells:
x_::Array{Float64,1}
σ_::Array{Float64,1}
Gᵐ::Any # Tridiagonal
# μ_::Array{Float64,1}
# νᵐ::Any
end
function ring(N=5;β=0,xf=3.0,xi=0.01)#10*xf/N)
xtemp_ = collect(range(xi,xf,length=N+1))
X_ = xtemp_[2:end]
U_ = 1.0 ./ X_.^4
ΔX_ = diff(xtemp_)
x_ = 0.5*(xtemp_[1:end-1]+xtemp_[2:end]) # midpoints
σ_ = ones(N)
νᵐ = visco(σ_,β)
Mᵐ = Diagonal(ΔX_)
Mⁱᵐ= Diagonal(1.0./ΔX_)
Kdiag = [1.0 ./ diff(x_)..., 0.0]
Ksuper = -Kdiag[1:end-1]
Ksub = zeros(N-1)
Kᵐ = Tridiagonal(Ksub,Kdiag,Ksuper)
Dᵐ = Tridiagonal(ones(N-1),-ones(N),zeros(N-1))
Gᵐ = 3 * Dᵐ * Kᵐ * νᵐ
μ_ = Mᵐ * σ_ # mass (per azimuthal length) in annulus
ΔtVN = 0.5 * minimum(diff(X_).^2 ./ diag(νᵐ)[1:end-1] )
ΔtADV = minimum(ΔX_ ./ U_)
ΔtMAX = min(ΔtVN,ΔtADV)
return ring(N,β,ΔtMAX,X_,ΔX_,U_,x_,σ_,Gᵐ)#,μ_)
end
function adjust!(r::ring)
νᵐ = visco(r.σ_,r.β)
Kdiag = [1.0 ./ diff(r.x_)..., 0.0]
Ksuper = -Kdiag[1:end-1]
Ksub = zeros(r.N-1)
Kᵐ = Tridiagonal(Ksub,Kdiag,Ksuper)
Dᵐ = Tridiagonal(ones(r.N-1),-ones(r.N),zeros(r.N-1))
Gᵐ = 3 * Dᵐ * Kᵐ * νᵐ
r.Gᵐ = Gᵐ
end
function advect!(r::ring;Δt=r.ΔtMAX)
σ = r.σ_; U = r.U_;; ΔX = r.ΔX_
σ[2:end] += Δt .* (σ[1:end-1] .* U[1:end-1] ./ ΔX[2:end] )
σ[1:end] -= Δt .* (σ[1:end] .* U[1:end] ./ ΔX[1:end] )
end
function diffuse!(r::ring;Δt=r.ΔtMAX,α=0.5)
Gᵐ = r.Gᵐ
N = r.N
Aᵐ = I(N) - α * Δt * Gᵐ
Bᵐ = I(N) + (1-α) * Δt * Gᵐ
# Solve:
r.σ_ = Aᵐ \ (Bᵐ * r.σ_)
# r.σ_[1] = 0.0
r.σ_[end] = 1.0
end
function iterate!(r::ring;Δt=r.ΔtMAX,α=0.5)
adjust!(r)
diffuse!(r;Δt=Δt,α=α)
advect!(r;Δt=Δt)
end
end#module
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] | 1.706203 | 1,467 |
let
x = zeros(150, 150)
carts = []
mutable struct Cart
dir::Int
coord::Tuple{Int, Int}
offset::Tuple{Int, Int}
end
#9
d = Dict('^' => (0,1), 'v' => (0,-1), '<' => (-1,0), '>' => (1,0))
mdirs = [(x, y) -> (y, -x), (x, y) -> (x, y), (x, y) -> (-y, x)]
for (j, line) in eachline("day13.e") |> enumerate
for (i, val) in split(line, "") |> enumerate
val[1] in ['/' '\\' '+' '-' '|'] && (x[i,j] = 1)
val[1] in ['^' 'v' '<' '>'] && push!(carts, Cart(3, (i, j), d[val[1]]))
end
end;
while true;
sort!(carts, by = x -> x.coord[2])
for c in carts; @info c
hasint = sum(x[max(1, c.coord[1] - 1): min(150, c.coord[1] + 1), max(1, c.coord[2] - 1): min(150, c.coord[2] + 1)]) > 3
if hasint
c.dir += 1; c.dir > 3 && (c.dir = 1)
c.offset = mdirs[c.dir](c.offset...)
else
nc = c.coord .+ c.offset;
if any(nc .> 150) || any(nc .< 1) || x[nc...] == 0; @info "here"
for (z, i) in enumerate(mdirs);
ncc = c.coord .+ i(c.offset...);
(any(ncc .> 150) || any(ncc .< 1)) && continue
x[ncc...] == 1 && (c.offset = i(c.offset...))
end
end
end
c.coord = c.coord .+ c.offset
for cc in carts
all(c.coord .== cc.coord) && c.offset != cc.offset && error(c.coord)
end
end
end
end
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220,
220,
886,
198
] | 1.794969 | 795 |
export permanent
"""
permanent(A)
Compute the permanent of the square matrix `A`. Works best if `A`
is relatively sparse. For a dense matrix, this will be slow.
"""
function permanent(A::Matrix{T}) where {T<:Number}
r, c = size(A)
if r != c
error("Matrix must be square")
end
if r == 0
return zero(T)
end
allow = [findall((x) -> x != 0, A[i, :]) for i = 1:r]
PG = PermGen(allow)
try
first(PG) # make sure PG is not empty
return sum(prod(A[i, p[i]] for i = 1:r) for p in PG)
catch
return zero(T)
end
end
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] | 2.290698 | 258 |
#############################################################################
#Copyright (c) 2016 Charles Le Losq
#
#The MIT License (MIT)
#
#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.
#
#############################################################################
function rameau(paths::Tuple,switches::Tuple;input_properties=('\t',0),prediction_coef=[0.0059;0.0005],temperature=23.0,laser=532.0,lb_break=1600.,hb_start=2600.,basetype="gcvspline")
# some function definition
calibration_model(x, p) = p[1].*x
baseline_lfmodel(x, p) = p[1].*x + p[2].*x.^2 + p[3].*x.^3
if switches[1] == "internal"
scale = 1
liste=readcsv(paths[1], skipstart=1)
names = liste[:,1]
water = Array{Float64}(liste[:,3])
smo_lf = Array{Float64}(liste[:,4])
smo_hf = Array{Float64}(liste[:,5])
roi = ones(Int((size(liste,2)-5)/2),2,size(liste,1))
for j = 1:size(liste,1)
roi[:,1,j] = (liste[j,6:2:end])
roi[:,2,j] = (liste[j,7:2:end])
end
rws = ones(size(liste,1),3)
for i = 1:size(liste,1)
spectra = readdlm(string(paths[2],names[i]),input_properties[1],skipstart=input_properties[2])
if spectra[end,1] < spectra[1,1]
spectra = flipdim(spectra,1)
end
x = spectra[:,1]
spectra[:,2] = spectra[:,2] - minimum(spectra[:,2])
y = spectra[:,2]./maximum(spectra[:,2]).*scale + 0.1# area normalisation, and we avoid any 0
ratio_bkg = minimum(y)/maximum(y) # to keep a record of the ratio of maximum signal intensity over minimum background intensity
#### PRELIMINARY STEP: FIRST WE GRAB THE GOOD SIGNAL IN THE ROI
interest_index::Array{Int64} = find(roi[1,1,i] .<= x[:,1] .<= roi[1,2,i])
if size(roi)[1] > 1
for j = 2:size(roi)[1]
interest_index = vcat(interest_index, find(roi[j,1,i] .<= x[:,1] .<= roi[j,2,i]))
end
end
interest_x = x[interest_index,1]
interest_y = y[interest_index,1]
if switches[3] == "yes" # if user asks for the experimental baseline
if switches[4] == "yes"
x, y_long, ~ = tlcorrection([x[:] y[:]],temperature,laser) # Long correction
y_long=y_long./maximum(y_long).*scale
### THE FOLLOWING IS PURELY EXPERIMENTAL
lb = roi[:,1,i]
hb = roi[:,2,i]
roi_hf = [lb[hb.>hb_start] hb[hb.>hb_start]]
roi_lf = [lb[hb.<lb_break] hb[hb.<lb_break]]
y_hf = y_long[x.>hb_start]
y_lf = y_long[x.<lb_break]
# A first linear baseline in signals between ~1300 and ~ 2000 cm-1
#y_calc_hf, baseline_hf = baseline(x,y_long,[2600. 3100.;3775. 3900.],"gcvspline",p=smo_hf[i])
#y_calc_hf, baseline_hf = baseline(x,y_long,[roi_hf[end-1,1] roi_hf[end-1,2];roi_hf[end,1] roi_hf[end,2]],"poly",p=3.0)
y_calc_hf, baseline_hf = baseline(x,y_long,[3000. 3100;3750. 3800.],"poly",p=1.0)
x_fit = [x[0 .< x .< 150];x[1250. .< x .< 1500]]
y_fit = [y_long[0 .< x .< 150];y_long[1250. .< x .< 1500.]]
#sss = size(x_fit,1)
#mod = Model(solver=IpoptSolver())
#@variable(mod, k1 >= 0.0,start=1.0)
#@variable(mod, k2 >= 0.0, start = 1.0)
#@NLexpression(mod, base[k=1:sss],k1*x_fit[k] + k2*x_fit[k]*x_fit[k])
#@NLobjective(mod,Min,sum{(base[k] - y_fit[k])^2,k=sss})
#status = solve(mod)
#p1 = getvalue(k1)
#p2 = getvalue(k2)
#fit_bas = curve_fit(baseline_lfmodel, , [y_long[0 .< x .< 100];y_long[1250. .< x .< 1500.]], [0.5,0.5,0.5])
#coef_bas = fit_bas.param
#baseline_lf = baseline_lfmodel(x,[p1;p2])
#y_calc_lf = y_long - baseline_lf
y_calc_lf, baseline_lf = baseline(x,y_long,[0. 150;1250. 1600.],"poly",p=3.0)
#y_calc_lf, baseline_lf = baseline(x,y_long,roi[:,:,i],basetype,p=smo_lf[i])
y_calc2 = [y_calc_lf[x.<=1600.];y_calc_hf[x.>1600.]]
bas2 = [baseline_lf[x.<=1600.];baseline_hf[x.>1600.]]
#y_calc2 = y_calc2[:,1]./trapz(x[:],y_calc2[:,1]).*scale # area normalisation
figure(figsize=(20,20))
suptitle("Baseline sub. with Long corr., sp. $(names[i])")
subplot(3,2,(1,2))
plot(x,y,"black",label="Raw sp.")
plot(x,y_long,"blue",label="Long corr. sp.")
scatter(interest_x,y_long[interest_index,1],s=10.,color="red",label="ROI") # the ROI signals after first baseline and long correction
plot(x,bas2,"red",label="Baseline")
legend(loc="best",fancybox="true")
subplot(3,2,3)
plot(x[x.<1500],y_long[x.<1500],"blue",label="Long corr. sp.")
scatter(interest_x[interest_x.<1500],y_long[interest_index[interest_x.<1500],1],s=10.,color="red",label="ROI") # the ROI signals after first baseline and long correction
plot(x[x.<1500],bas2[x.<1500],"red",label="Baseline")
xlim(0,1500)
legend(loc="best",fancybox="true")
subplot(3,2,4)
plot(x[x.>2800],y_long[x.>2800],"blue",label="Long corr. sp.")
scatter(interest_x[interest_x.>2800],y_long[interest_index[interest_x.>2800],1],s=10.,color="red",label="ROI") # the ROI signals after first baseline and long correction
plot(x[x.>2800],bas2[x.>2800],"red",label="Baseline")
xlim(2800,4000)
legend(loc="best",fancybox="true")
subplot(3,2,(5,6))
plot(x,y_calc2,"cyan",label="Final sp.")
xlabel(L"Raman shift, cm$^{-1}$")
ylabel("Intensity, area normalised")
legend(loc="best",fancybox="true")
else
# ALSO FULLY EXPERIMENTAL!!!! IT DOES NOT WORK!
y_hf = y[x.>hb_start]
y_lf = y[x.<lb_break]
# A first linear baseline in signals between ~1300 and ~ 2000 cm-1
y_calc_hf, baseline_hf = baseline(x,y,[2600. 2900.;3775. 3900.],"poly",p=3.0)
#A first linear baseline in signals between ~1300 and ~ 2000 cm-1
y_calc1_part1, baseline1 = baseline(x,y,[1300 1800.],"poly",p=1.0)
# we get a mean signal for background below 1300 cm-1
X_low_lf = x[y .== minimum(y[100 .<x.<1400])]
b_low_lf = mean(y[X_low_lf[1]-10 .< x .< X_low_lf[1]+10])
baseline1[x.<1250.] = b_low_lf
y_calc1_part1 = y - baseline1
bas2 = [baseline1[x.<lb_break];baseline_hf[x.>lb_break]]
y_calc2 = [y_calc1_part1[x.<lb_break];y_calc_hf[x.>lb_break]]
figure()
plot(x,y,color="black")
plot(x,baseline_hf,color="red")
plot(x,y_calc2,color="blue")
end
else # only a single baseline treatment is asked
# we test if thre Long correction is asked, and react accordingly for baseline substraction
if switches[4] == "yes"
x, y_long, ~ = tlcorrection([x[:] y[:]],temperature,laser) # Long correction
y_long=y_long./maximum(y_long).*scale
y_calc2, bas2 = baseline(x,y_long,roi[:,:,i],basetype,p=smo_lf[i])
y_calc2 = y_calc2[:,1]./trapz(x[:],y_calc2[:,1]).*(scale./10) # area normalisation
figure(figsize=(20,20))
suptitle("LL2012 method, sp. $(names[i])")
subplot(3,2,(1,2))
plot(x,y,"black",label="Raw sp.")
plot(x,y_long,"blue",label="Long corr. sp.")
scatter(interest_x,y_long[interest_index,1],s=10.,color="red",label="ROI") # the ROI signals after first baseline and long correction
plot(x,bas2,"red",label="Baseline")
legend(loc="best",fancybox="true")
xlabel(L"Raman shift, cm$^{-1}$",fontsize=18,fontname="Arial")
ylabel("Intensity,a. u.",fontsize=18,fontname="Arial")
subplot(3,2,3)
plot(x[x.<1500],y_long[x.<1500],"blue",label="Long corr. sp.")
scatter(interest_x[interest_x.<1500],y_long[interest_index[interest_x.<1500],1],s=10.,color="red",label="ROI") # the ROI signals after first baseline and long correction
plot(x[x.<1500],bas2[x.<1500],"red",label="Baseline")
xlim(0,1500)
legend(loc="best",fancybox="true")
xlabel(L"Raman shift, cm$^{-1}$",fontsize=18,fontname="Arial")
ylabel("Intensity,a. u.",fontsize=18,fontname="Arial")
subplot(3,2,4)
plot(x[x.>2800],y_long[x.>2800],"blue",label="Long corr. sp.")
scatter(interest_x[interest_x.>2800],y_long[interest_index[interest_x.>2800],1],s=10.,color="red",label="ROI") # the ROI signals after first baseline and long correction
plot(x[x.>2800],bas2[x.>2800],"red",label="Baseline")
xlim(2600,4000)
legend(loc="best",fancybox="true")
xlabel(L"Raman shift, cm$^{-1}$",fontsize=18,fontname="Arial")
ylabel("Intensity,a. u.",fontsize=18,fontname="Arial")
subplot(3,2,(5,6))
plot(x,y_calc2,"cyan",label="Final sp.")
xlabel(L"Raman shift, cm$^{-1}$")
ylabel("Intensity, area normalised")
legend(loc="best",fancybox="true")
xlabel(L"Raman shift, cm$^{-1}$",fontsize=18,fontname="Arial")
ylabel("Intensity,a. u.",fontsize=18,fontname="Arial")
else
y_calc2, bas2 = baseline(x,y,roi[:,:,i],basetype,p=smo_lf[i])
y_calc2 = y_calc2[:,1]./trapz(x[:],y_calc2[:,1]).*scale # area normalisation
figure()
plot(x,y.*10,"black",label="Raw sp.")
scatter(interest_x,interest_y.*10,s=10.,color="red",label="ROI")
plot(x,bas2.*10,"red",label="Baseline")
plot(x,y_calc2,"cyan",label="Final sp.")
xlabel(L"Raman shift, cm$^{-1}$")
ylabel("Intensity, area normalised")
title("Baseline sub., NO Long corr.")
legend(loc="best",fancybox="true")
end
end
# saving the corrected spectrum
writecsv(string(paths[3],names[i]),[x y_calc2 bas2])
#Saving the generated figure (specified directory should exist)
savefig(string(paths[4],"baseline_",names[i],".pdf"))
close()
if switches[3] == "yes"
# calc=ulating the areas under silicate and water bands
if switches[4] == "yes"
As = (trapz(x[150 .<x.<600],y_calc2[150 .<x.<600])+trapz(x[1000 .<x.<1250],y_calc2[1000 .<x.<1250]))
Aw = trapz(x[3100 .< x .<3750],y_calc2[3100 .< x .<3750])
else
#idx1a = find(y[400 .<x.<600] .== maximum(y[400 .<x.<600]))
#idx1b = find(y[600 .<x.<1000] .== minimum(y[600 .<x.<1000]))
#idx2a = find( y[900 .<x.<1250] .== maximum(y[900 .<x.<1250]))
#idx2b = find( y[1000 .<x.<1250] .== minimum(y[1000 .<x.<1250]))
#idx3a = find( y[3000 .<x.<3700] .== maximum(y[3000 .<x.<3700]))
#idx3b = find( y[3700 .<x.<3800] .== minimum(y[3700 .<x.<3800]))
#As = mean(y[(idx1a[1]-4):(idx1a[1]+4),1]) - mean(y[(idx1b[1]-4):(idx1b[1]+4),1]) + mean(y[(idx2a[1]-4):(idx2a[1]+4),1]) - mean(y[(idx2b[1]-4):(idx2b[1]+4),1])
#Aw = mean(y[idx3a[1]-4 : idx3a[1]+4,1]) - mean(y[idx3b[1]-4 : idx3b[1]+4,1])
As = maximum(y[400 .<x.<600]) - minimum(y[600 .<x.<1000]) + maximum(y[900 .<x.<1250]) - minimum(y[1000 .<x.<1250])
Aw = maximum(y[3000 .<x.<3700]) - minimum(y[3700 .<x.<3800])
end
else
# calc=ulating the areas under silicate and water bands
As = trapz(x[150 .< x .<1300],y_calc2[150 .< x .<1300])
Aw = trapz(x[2800 .< x .<3750],y_calc2[2800 .< x .<3750])
end
# recording them
rws[i,1] = As;
rws[i,2] = Aw;
rws[i,3] = Aw./(As);
println("Spectrum $(names[i]), the rws is $(rws[i,3])")
end
if switches[2] == "yes"
fit = curve_fit(calibration_model, rws[:,3], water[:]./(100.-water[:]), [0.5])
coef = fit.param
sigma = estimate_errors(fit, 0.95)
rws_calibration = collect(0:0.01:round(maximum(rws[:,3]),2))
water_ratio_calibration = calibration_model(rws_calibration,coef)
water_compare = 100.*calibration_model(rws[:,3],coef)./(calibration_model(rws[:,3],coef)+1) # eq. 3 Le Losq et al. (2012)
rmse_calibration = sqrt(1./(size(rws,1)-1).*sum((water_compare-water).^2))
rmse_ratio_ws = rmse_calibration/100*(rmse_calibration/(100-rmse_calibration)+1)
figure()
scatter(rws[:,3],water[:]./(100.-water[:]))
plot(rws_calibration,water_ratio_calibration,color="red",linewidth=2.0)
plot(rws_calibration,water_ratio_calibration+rmse_ratio_ws,color="red",linestyle="--",linewidth=1.0)
plot(rws_calibration,water_ratio_calibration-rmse_ratio_ws,color="red",linestyle="--",linewidth=1.0)
plot(rws_calibration,water_ratio_calibration,color="red")
xlim(0,maximum(rws_calibration)+1./4*maximum(rws_calibration))
ylim(0,maximum(water_ratio_calibration)+1./4*maximum(water_ratio_calibration))
xlabel(L"A$_{water}$/A$_{silicates}$, area ratio",fontsize=18,fontname="Arial")
ylabel("Water/Glass, weight ratio",fontsize=18,fontname="Arial")
annotate(L"R$_{ws}$=",xy=(0.3,0.9),xycoords="axes fraction",fontsize=18,fontname="Arial",horizontalalignment="center")
annotate("$(round(coef[1],5)) +/- $(round(sigma[1],5))",xy=(0.3,0.8),xycoords="axes fraction",fontsize=18,fontname="Arial",horizontalalignment="center")
annotate("Standard deviation\n= $(round(rmse_calibration,2)) wt%",xy=(0.7,0.3),xycoords="axes fraction",horizontalalignment="center",fontsize=18,fontname="Arial")
savefig(paths[6])
writecsv(string(paths[5]), ["spectrum" "product" "Rws" "Water input" "Water Raman";liste[:,1] liste[:,2] rws[:,3] water water_compare])
#return [rws[:,3] water water_compare]
else
water_predicted = 100.*(rws[:,3].*prediction_coef[1]./(rws[:,3].*prediction_coef[1]+1))
water_predicted_high = 100.*(rws[:,3].*sum(prediction_coef)./(rws[:,3].*sum(prediction_coef)+1))
writecsv(string(paths[5]), ["spectrum" "product" "Rws" "Water Raman" "Error";liste[:,1] liste[:,2] rws[:,3] water_predicted water_predicted_high-water_predicted])
#return [rws[:,3] water_predicted]
end
elseif switches[1] == "external"
liste=readcsv(paths[1], skipstart=1)
reference = liste[:,1]
# reference product names are in first column for record
reference_density = liste[:,3]
reference_water = liste[:,4]
sample = liste[:,5]
# sample product names are in column 6 for record
sample_density = liste[:,7]
water = ones(size(liste,1),1) # the array to record water content
for i = 1:size(liste,1)
#importing data
reference_sp = readdlm(string(paths[2],reference[i]),input_properties[1],skipstart=input_properties[2])
sample_sp = readdlm(string(paths[2],sample[i]),input_properties[1],skipstart=input_properties[2])
if reference_sp[end,1] < reference_sp[1,1]
reference_sp = flipdim(reference_sp,1)
end
if sample_sp[end,1] < sample_sp[1,1]
sample_sp = flipdim(sample_sp,1)
end
# temperature - excitation line correction with the Hehlen formula
x_ref, reference_sp_long, ~ = tlcorrection(reference_sp,temperature,laser,correction="hehlen",normalisation="no",density=reference_density[i])
x_sp, sample_sp_long, ~ = tlcorrection(sample_sp,temperature,laser,correction="hehlen",normalisation="no",density=sample_density[i])
# Linear baseline subtraction
roi_hf = [3000. 3100.; 3800. 3900.] # the region where the basline is constrained
reference_sp_corr2, reference_baseline = baseline(x_ref,reference_sp_long,roi_hf,"poly",p=1.0)
sample_sp_corr2, sample_baseline = baseline(x_sp,sample_sp_long,roi_hf,"poly",p=1.0)
# Area calculation between 3100 and 3800 cm-1
Area_reference = trapz(x_ref[3100. .<x_ref[:,1].<3800.],reference_sp_corr2[3100. .<x_ref[:,1].<3800.])
Area_sample = trapz(x_sp[3100. .<x_sp[:,1].<3800.],sample_sp_corr2[3100. .<x_sp[:,1].<3800.])
# With the intensity
Intensity_reference = maximum(reference_sp_corr2[3100. .<x_ref[:,1].<3800])
Intensity_sample = maximum(sample_sp_corr2[3100. .<x_ref[:,1].<3800])
# Water calculation
water[i] = (reference_water[i]./1.8*reference_density[i]) .*Area_sample./Area_reference # water is in mol/L
#water[i] = (reference_water[i]./1.8*reference_density[i]) .*Intensity_sample./Intensity_reference # water is in mol/L
water[i] = water[i] *1.8./sample_density[i] # water converted in wt%
figure(figsize=(7.5,15))
subplot(311)
title("External calibration: $(round(water[i],2)) wt% water,\n sp. $(sample[i]) with ref. $(reference[i]) ")
plot(reference_sp[:,1],reference_sp[:,2],color="black",label="Reference")
plot(sample_sp[:,1],sample_sp[:,2],color="blue",label="Sample")
xlabel(L"Raman shift, cm$^{-1}$",fontsize=18,fontname="Arial")
legend(loc="best",frameon=false)
subplot(312)
plot(x_ref[:],reference_sp_long[:],color="black",label=L"T-$\nu$ corrected reference")
plot(x_ref[:],reference_baseline,color="grey",label="Reference baseline")
plot(x_sp[:],sample_sp_long[:],color="blue",label=L"T-$\nu$ corrected sample")
plot(x_sp[:],sample_baseline,color="cyan",label="Sample baseline")
xlabel(L"Raman shift, cm$^{-1}$",fontsize=18,fontname="Arial")
ylabel("Normalized intensity, a. u.",fontsize=18,fontname="Arial")
legend(loc="best",frameon=false)
subplot(313)
plot(x_ref,reference_sp_corr2,color="black",label="Ref. spectrum")
plot(x_sp,sample_sp_corr2,color="blue",label="Sample spectrum")
xlabel(L"Raman shift, cm$^{-1}$",fontsize=18,fontname="Arial")
legend(loc="best",frameon=false)
tight_layout()
# saving the water content
writecsv(string(paths[5]), ["Spectrum" "Sample Name" "Estimated water content";liste[:,5] liste[:,6] water])
#Saving the generated figure (specified directory should exist)
savefig(string(paths[4],"Internal_Std_",sample[i],".pdf"))
end
else # or error message
error("the first switch indicates if you want to use an internal or external calibration mode. Please choose between both.")
end
end
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] | 2.141722 | 8,559 |
using NPZ
using JuMP
using Ipopt
using LinearAlgebra
using .Threads
using WoodburyMatrices
using Statistics
using UnicodePlots
function main(B)
X1 = npzread("Kernels/G1.npy")
X2 = npzread("Kernels/G2.npy")
X3 = npzread("Kernels/G3.npy")
nfactor = npzread("Testing/nfactor.npy")
n = size(X3, 1)
ndas = size(X1, 1)
T = Float64
ϵ = 1.0
λ = 100.0
α = 100.0
X = [X3 zeros(T, size(X3));
zeros(T, size(X3)) X3;
nfactor*X1 nfactor*X2]
Xn = maximum(sqrt.(sum(X.^2, dims=2)))
#Xn = sqrt.(sum(X.^2, dims=2)) #normalize all rows to be 1?
X ./= Xn
v, V = eigen(X'*X + ϵ*I)
#eigtol = 5#quantile(v, 0.99)
n_eigs = 5#sum(v.>=eigtol)
Γ = V[:, (end-n_eigs+1):end]
μ_init = ones(n) ./ n .* B
μn = sum(μ_init)/B
μ_init ./= μn
U_init = copy(Γ)
function Ml(μ, X, ϵ)
M = ϵ*I + X'*Diagonal(μ)*X
Mc = cholesky(Hermitian(M))
return (M, Mc)
end
Ml(μ) = Ml(μ, X, ϵ)
function subproblem_sμ(μ_init, u, X, λ, B, maxiter=1000)
function f(μ, u, λ)
M, Mc = Ml(μ)
return -logdet(M) + λ*tr(u'*(Mc\u))
end
f(μ) = f(μ, u, λ)
jump_wrapper_f(x...) = f(vcat([x[i] for i in 1:length(x)], [x[i] for i in 1:length(x)], ones(T, ndas)))
function gi(i, Mc, μ, u, λ)
ld_term = @views -X[i,:]'*(Mc\X[i,:])
tmp_A = SymWoodbury(Mc, X[i,:], -μ[i])
tmp_b = @views tmp_A \ X[i,:]
#tmp_1 = (u'*tmp_b)^2
tmp_1 = sum((u'*tmp_b).^2) # think that this should be correct for tr(U'MU) for matrix U but need to check
tmp_d = @views X[i,:]'*tmp_b
tmp_den = (1+μ[i]*tmp_d)^2
eig_term = ((1+μ[i]*tmp_d)*tmp_1 - μ[i]*tmp_1*tmp_d) / tmp_den
return ld_term - λ*eig_term #apparently need a -ve sign here, work out why
end
function g!(gvec, μ, X, u, λ, n)
M, Mc = Ml(μ)
@threads for i = 1:n
g1 = gi(i, Mc, μ, u, λ) # in respect to x component
g2 = gi(i+n, Mc, μ, u, λ) # in respect to y component
gvec[i] = g1 + g2
end
end
g!(gvec, μ) = g!(gvec, μ, X, u, λ, n)
jump_wrapper_g!(gvec, x...) = g!(gvec, vcat([x[i] for i in 1:length(x)], [x[i] for i in 1:length(x)], ones(T, ndas)))
model = Model(optimizer_with_attributes(Ipopt.Optimizer, "max_iter"=>maxiter, "tol"=>1e-4/B))
JuMP.register(model, :f, n, jump_wrapper_f, jump_wrapper_g!)
@variable(model, 0.0<=x[1:n]<= 1.0)
set_start_value.(x, μ_init)
@NLconstraint(model, +(x...) <= B)
@NLobjective(model, Min, f(x...))
optimize!(model)
return (value.(x), vcat(value.(x), value.(x), ones(T, ndas)), objective_value(model))
end
function subproblem_sO(u_init, μ, γ, X, λ, α, maxiter=1000)
_, Mc = Ml(μ)
f(u) = α*sum((u.-γ).^2) + λ*tr(u'*(Mc\u))
function g!(gvec, u)
gvec .= 2*α*(u-γ) + 2*λ*(Mc\u)
end
jump_wrapper_f(x...) = f([x[i] for i in 1:length(x)])
jump_wrapper_g!(gvec, x...) = g!(gvec, [x[i] for i in 1:length(x)])
sumsquares(x...) = sum(x[i]^2 for i in 1:length(x))
function sumsquares_grad!(gvec, x...)
for i in 1:length(x)
gvec[i] = 2*x[i]
end
end
model = Model(optimizer_with_attributes(Ipopt.Optimizer, "max_iter"=>maxiter))
JuMP.register(model, :f, length(γ), jump_wrapper_f, jump_wrapper_g!)
JuMP.register(model, :sumsquares, length(γ), sumsquares, sumsquares_grad!)
@variable(model, x[1:length(γ)])
set_start_value.(x, u_init)
@NLconstraint(model, sumsquares(x...) == 1)
@NLobjective(model, Min, f(x...))
optimize!(model)
return (value.(x), objective_value(model))
end
function subproblem_sO(U_init, μ, Γ, X, λ, α, m, n, maxiter=1000)
_, Mc = Ml(μ)
f(U) = α*sum((U.-Γ).^2) + λ*tr(U'*(Mc\U))
function g!(gvec, U, m, n)
@threads for i = 1:n
@views gvec[((i-1)*m+1):i*m] .= 2*α*(U[1:m, i].-Γ[1:m, i]) .+ 2*λ*(Mc\U[1:m, i])
end
end
L = LinearIndices((m, n))
jump_wrapper_f(x...) = f([x[L[i,j]] for i in 1:m, j in 1:n])
jump_wrapper_g!(gvec, x...) = g!(gvec, [x[L[i,j]] for i in 1:m, j in 1:n], m, n)
sumprod(x...) = sum(x[i]*x[i+m] for i in 1:m)
function sumprod_grad!(gvec, x...)
for i in 1:m
gvec[i] = x[i+m]
gvec[i+m] = x[i]
end
end
model = Model(optimizer_with_attributes(Ipopt.Optimizer, "max_iter"=>maxiter))
JuMP.register(model, :f, m*n, jump_wrapper_f, jump_wrapper_g!)
JuMP.register(model, :sumprod, 2*m, sumprod, sumprod_grad!)
@variable(model, x[1:m, 1:n])
set_start_value.(x, U_init)
for i = 1:n
for j = i:n
x_i = x[1:m, i]
x_j = x[1:m, j]
@NLconstraint(model, sumprod(x_i..., x_j...) == ((i==j) ? 1 : 0)) end
end
@NLobjective(model, Min, f(x...))
optimize!(model)
return (value.(x), objective_value(model)) # want to have all of the x here as a matrix
end
function optimize_spectral(μ_init, u_init, iters, γ, X, λ, α, B)
μ = copy(μ_init)
u = copy(u_init)
ob1l = Float64[]
ob2l = Float64[]
for iter in 1:iters
μ, μl, ob1 = subproblem_sμ(μ, u, X, λ, B)
u, ob2 = subproblem_sO(u, μl, γ, X, λ, α)
push!(ob1l, ob1)
push!(ob2l, ob2)
end
μ, μl, ob1 = subproblem_sμ(μ, u, X, λ, B)
push!(ob1l, ob1)
return (μ, u, ob1l, ob2l)
end
function optimize_spectral(μ_init, U_init, iters, Γ, X, λ, α, B, m, n)
μ = copy(μ_init)
U = copy(U_init)
ob1l = Float64[]
ob2l = Float64[]
for iter in 1:iters
μ, μl, ob1 = subproblem_sμ(μ, U, X, λ, B)
U, ob2 = subproblem_sO(U, μl, Γ, X, λ, α, m, n)
push!(ob1l, ob1)
push!(ob2l, ob2)
end
μ, μl, ob1 = subproblem_sμ(μ, U, X, λ, B)
push!(ob1l, ob1)
return (μ, U, ob1l, ob2l)
end
μ, U_res, ob1l, ob2l = optimize_spectral(μ_init, U_init, 5, Γ, X, λ, α, B, size(Γ)...)
M, _ = Ml(vcat(μ,μ, ones(ndas)))
vμ, Vμ = eigen(M)
npzwrite("SpectralResults/res_spectral_$B.npy", μ)
lineplot(ob1l)
lineplot(ob2l)
end
for i in [4,8,16,32,64,128,256]
main(i)
end
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] | 1.677277 | 4,084 |
using OED
using jInv.Utils
using jInv.ForwardShare
using Base.Test
using jInv.Mesh
n = 100
nrhs = 5
A = sdiag(1./linspace(1,n,n).^4)
A = A[1:2:end,:]
N = size(A,1)
x = rand(n,nrhs); x[4:7,:] = 1;
D = A*x + 1e-8* randn(N,nrhs)/1e3
OEDparamA = OEDTypeAParam(A,D,zeros(0,0))
# setup OED type B param
r = 20;
l = 2;
Mimg = getRegularMesh([-1 1 -1 1.],[10,10])
Mgrid = getRegularMesh([-1.2 2.4 -1.6 1.4],[20,20])
OEDparamB = getOEDTypeBParam(r,l,x,Mimg,Mgrid,noiseLevel=0.)
OEDparamC = getOEDTypeCParam(r,l,x,Mimg,Mgrid,noiseLevel=0.)
L = 1e-1*speye(n)
pForA = UnconstrainedOEDParam(OEDparamA,L,[],[])
pForB = UnconstrainedOEDParam(OEDparamB,L,[],[])
pForC = UnconstrainedOEDParam(OEDparamC,0*L,[],[])
pFors = (pForA,pForB,pForC)
println("\n\t---test OED with unconstrained lower-level problem---")
for k=1:3
display(pFors[k])
np = getNumberOfDesignParameters(pFors[k].Design)
p0 = rand(np)
println("\t\tcheck reconstruction")
xr, = getData(p0,pFors[k])
println("\t\t\trel.error: $(norm(xr-x)/norm(x))")
print("\t\tcheckDerivative...")
pass, = checkDerivative(p0,pFors[k])
@test pass
print("passed!\n")
print("\t\tadjoint test...")
(nw,nv) = getSensMatSize(pFors[k])
w = rand(nw)
v = rand(nv)
t1 = dot(w,getSensMatVec(v,p0,pFors[k]))
t2 = dot(v,getSensTMatVec(w,p0,pFors[k]))
@test norm(t1-t2)/norm(t1)<1e-10
print("passed!\n")
end
println("\t---least squares IP: sensitivities OK ---")
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] | 1.981894 | 718 |
This is the JLL package corresponding to the `ReSHOP.jl` Julia package.
The Julia code is based on the one produced by `BinaryBuilder.jl` and has been manually edited.
| [
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using Persa
using Test
using DatasetsCF
using ModelBasedCF
dataset = DatasetsCF.MovieLens()
@testset "Random Model Tests" begin
model = ModelBasedCF.RandomModel(dataset)
Persa.train!(model, dataset)
@test !isnan(model[1,1])
end
@testset "Factorization Matrix Models Tests" begin
@testset "Baseline Tests" begin
model = ModelBasedCF.Baseline(dataset)
Persa.train!(model, dataset, max_epochs = 1)
@test !isnan(model[1,1])
end
@testset "RSVD Tests" begin
model = ModelBasedCF.RSVD(dataset, 1)
Persa.train!(model, dataset, max_epochs = 1)
@test !isnan(model[1,1])
end
@testset "IRSVD Tests" begin
model = ModelBasedCF.IRSVD(dataset, 1)
Persa.train!(model, dataset, max_epochs = 1)
@test !isnan(model[1,1])
end
end | [
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] | 2.247283 | 368 |
using DiskArrays
using DiskArrays: ReshapedDiskArray, PermutedDiskArray
using Test
#Define a data structure that can be used for testing
struct _DiskArray{T,N,A<:AbstractArray{T,N}} <: AbstractDiskArray{T,N}
getindex_count::Ref{Int}
setindex_count::Ref{Int}
parent::A
chunksize::NTuple{N,Int}
end
_DiskArray(a;chunksize=size(a)) = _DiskArray(Ref(0),Ref(0),a,chunksize)
Base.size(a::_DiskArray) = size(a.parent)
DiskArrays.haschunks(::_DiskArray) = DiskArrays.Chunked()
DiskArrays.eachchunk(a::_DiskArray) = DiskArrays.GridChunks(a,a.chunksize)
getindex_count(a::_DiskArray) = a.getindex_count[]
setindex_count(a::_DiskArray) = a.setindex_count[]
trueparent(a::_DiskArray) = a.parent
getindex_count(a::ReshapedDiskArray) = getindex_count(a.parent)
setindex_count(a::ReshapedDiskArray) = setindex_count(a.parent)
trueparent(a::ReshapedDiskArray) = trueparent(a.parent)
getindex_count(a::PermutedDiskArray) = getindex_count(a.a.parent)
setindex_count(a::PermutedDiskArray) = setindex_count(a.a.parent)
trueparent(a::PermutedDiskArray{T,N,<:PermutedDimsArray{T,N,perm,iperm}}) where {T,N,perm,iperm} = permutedims(trueparent(a.a.parent),perm)
function DiskArrays.readblock!(a::_DiskArray,aout,i::AbstractUnitRange...)
ndims(a) == length(i) || error("Number of indices is not correct")
all(r->isa(r,AbstractUnitRange),i) || error("Not all indices are unit ranges")
#println("reading from indices ", join(string.(i)," "))
a.getindex_count[] += 1
aout .= a.parent[i...]
end
function DiskArrays.writeblock!(a::_DiskArray,v,i::AbstractUnitRange...)
ndims(a) == length(i) || error("Number of indices is not correct")
all(r->isa(r,AbstractUnitRange),i) || error("Not all indices are unit ranges")
#println("Writing to indices ", join(string.(i)," "))
a.setindex_count[] += 1
view(a.parent,i...) .= v
end
function test_getindex(a)
@test a[2,3,1] == 10
@test a[2,3] == 10
@test a[2,3,1,1] == 10
@test a[:,1] == [1, 2, 3, 4]
@test a[1:2, 1:2,1,1] == [1 5; 2 6]
@test a[2:2:4,1:2:5] == [2 10 18; 4 12 20]
@test a[end:-1:1,1,1] == [4,3,2,1]
@test a[[1,3,4],[1,3],1] == [1 9; 3 11; 4 12]
# Test bitmask indexing
m = falses(4,5,1)
m[2,:,1] .= true
@test a[m] == [2,6,10,14,18]
# Test linear indexing
@test a[11:15] == 11:15
@test a[20:-1:9] == 20:-1:9
@test a[[3, 5, 8]] == [3, 5, 8]
@test a[2:4:14] == [2,6,10,14]
#Test that readblock was called exactly onces for every getindex
@test getindex_count(a) == 13
end
function test_setindex(a)
a[1,1,1] = 1
a[1,2] = 2
a[1,3,1,1] = 3
a[2,:] = [1, 2, 3, 4, 5]
a[3, 3:4,1,1] = [3,4]
# Test bitmask indexing
m = falses(4,5,1)
m[4,:,1] .= true
a[m] = [10,11,12,13,14]
#Test that readblock was called exactly onces for every getindex
@test setindex_count(a) == 6
@test trueparent(a)[1,1:3,1] == [1,2,3]
@test trueparent(a)[2,:,1] == [1,2,3,4,5]
@test trueparent(a)[3,3:4,1] == [3,4]
@test trueparent(a)[4,:,1] == [10,11,12,13,14]
a[1:2:4,1:2:5,1] = [1 2 3; 5 6 7]
@test trueparent(a)[1:2:4,1:2:5,1] == [1 2 3; 5 6 7]
@test setindex_count(a) == 7
a[[2,4],1:2,1] = [1 2; 5 6]
@test trueparent(a)[[2,4],1:2,1] == [1 2; 5 6]
@test setindex_count(a) == 8
end
function test_view(a)
v = view(a,2:3,2:4,1)
v[1:2,1] = [1,2]
v[1:2,2:3] = [4 4; 4 4]
@test v[1:2,1] == [1,2]
@test v[1:2,2:3] == [4 4; 4 4]
@test trueparent(a)[2:3,2] == [1,2]
@test trueparent(a)[2:3,3:4] == [4 4; 4 4]
@test getindex_count(a) == 2
@test setindex_count(a) == 2
end
function test_reductions(af)
data = rand(10,20,2)
for f in (minimum,maximum,sum,
(i,args...;kwargs...)->all(j->j>0.1,i,args...;kwargs...),
(i,args...;kwargs...)->any(j->j<0.1,i,args...;kwargs...),
(i,args...;kwargs...)->mapreduce(x->2*x,+,i,args...;kwargs...))
a = af(data)
@test isapprox(f(a),f(data))
@test getindex_count(a) <= 10
#And test reduction along dimensions
a = _DiskArray(data,chunksize=(5,4,2))
@test all(isapprox.(f(a,dims=2),f(data,dims=2)))
#The minimum and maximum functions do some initialization, which will increase
#the number of reads
@test f in (minimum, maximum) || getindex_count(a) <= 12
a = _DiskArray(data,chunksize=(5,4,2))
@test all(isapprox.(f(a,dims=(1,3)),f(data,dims=(1,3))))
@test f in (minimum, maximum) || getindex_count(a) <= 12
end
end
function test_broadcast(a_disk1)
a_disk2 = _DiskArray(rand(1:10,1,9), chunksize=(1,3))
a_mem = reshape(1:2,1,1,2);
s = a_disk1 .+ a_disk2 .* Ref(2) ./ (2,)
#Test lazy broadcasting
@test s isa DiskArrays.BroadcastDiskArray
@test s === DiskArrays.BroadcastDiskArray(s.bc)
@test getindex_count(a_disk1)==0
@test setindex_count(a_disk1)==0
@test getindex_count(a_disk2)==0
@test setindex_count(a_disk2)==0
@test size(s)==(10,9,2)
@test eltype(s) == Float64
#Lets merge another broadcast
s2 = s ./ a_mem
@test s isa DiskArrays.BroadcastDiskArray
@test getindex_count(a_disk1)==0
@test getindex_count(a_disk2)==0
@test size(s)==(10,9,2)
@test eltype(s) == Float64
#And now do the computation with Array as a sink
aout = zeros(10,9,2)
aout .= s2 .* 2 ./ Ref(2)
#Test if the result is correct
@test aout == (trueparent(a_disk1) .+ trueparent(a_disk2))./a_mem
@test getindex_count(a_disk1)==6
@test getindex_count(a_disk2)==6
#Now use another DiskArray as the output
aout = _DiskArray(zeros(10,9,2),chunksize=(5,3,2))
aout .= s ./ a_mem
@test trueparent(aout) == (trueparent(a_disk1) .+ trueparent(a_disk2))./a_mem
@test setindex_count(aout)==6
@test getindex_count(a_disk1)==12
@test getindex_count(a_disk2)==12
#Test reduction of broadcasted expression
r = sum(s2, dims=(1,2))
@test all(isapprox.(sum((trueparent(a_disk1) .+ trueparent(a_disk2))./a_mem,dims=(1,2)),r))
@test getindex_count(a_disk1)==18
@test getindex_count(a_disk2)==18
end
@testset "Index interpretation" begin
import DiskArrays: DimsDropper, Reshaper
a = zeros(3,3,1)
@test interpret_indices_disk(a, (:,2,:)) == ((Base.OneTo(3), 2:2, Base.OneTo(1)), DimsDropper{Tuple{Int}}((2,)))
@test interpret_indices_disk(a, (1,2,:)) == ((1:1, 2:2, Base.OneTo(1)), DimsDropper{Tuple{Int,Int}}((1, 2)))
@test interpret_indices_disk(a, (1,2,2,1)) == ((1:1, 2:2, 2:2), DimsDropper{Tuple{Int,Int,Int}}((1, 2, 3)))
@test interpret_indices_disk(a, (1,2,2,1)) == ((1:1, 2:2, 2:2), DimsDropper{Tuple{Int,Int,Int}}((1, 2, 3)))
@test interpret_indices_disk(a, (:,1:2)) == ((Base.OneTo(3), 1:2, 1:1), DimsDropper{Tuple{Int}}((3,)))
@test interpret_indices_disk(a, (:,)) == ((Base.OneTo(3), Base.OneTo(3), Base.OneTo(1)), DiskArrays.Reshaper{Int}(9))
end
@testset "AbstractDiskArray getindex" begin
a = _DiskArray(reshape(1:20,4,5,1))
test_getindex(a)
end
@testset "AbstractDiskArray setindex" begin
a = _DiskArray(zeros(Int,4,5,1))
test_setindex(a)
end
@testset "Zerodimensional" begin
a = _DiskArray(zeros(Int))
@test a[] == 0
@test a[1] == 0
a[] = 5
@test a[] == 5
a[1] = 6
@test a[] == 6
end
@testset "Views" begin
a = _DiskArray(zeros(Int,4,5,1))
test_view(a)
end
# The remaing tests only work for Julia >= 1.3
if VERSION >= v"1.3.0"
import Statistics: mean
@testset "Reductions" begin
a = data -> _DiskArray(data,chunksize=(5,4,2))
test_reductions(a)
end
@testset "Broadcast" begin
a_disk1 = _DiskArray(rand(10,9,2), chunksize=(5,3,2))
test_broadcast(a_disk1)
end
@testset "Broadcast with length 1 final dim" begin
a_disk1 = _DiskArray(rand(10,9,1), chunksize=(5,3,1))
a_disk2 = _DiskArray(rand(1:10,1,9), chunksize=(1,3))
s = a_disk1 .+ a_disk2
@test DiskArrays.eachchunk(s) isa DiskArrays.GridChunks{3}
@test size(collect(s)) == (10, 9, 1)
end
@testset "Reshape" begin
a = reshape(_DiskArray(reshape(1:20,4,5)),4,5,1)
test_getindex(a)
a = reshape(_DiskArray(zeros(Int,4,5)),4,5,1)
test_setindex(a)
a = reshape(_DiskArray(zeros(Int,4,5)),4,5,1)
test_view(a)
a = data -> reshape(_DiskArray(data,chunksize=(5,4,2)),10,20,2,1)
test_reductions(a)
a = reshape(_DiskArray(reshape(1:20,4,5)),4,5,1)
@test ReshapedDiskArray(a.parent, a.keepdim, a.newsize) === a
end
import Base.PermutedDimsArrays.invperm
@testset "Permutedims" begin
p = (3,1,2)
ip = invperm(p)
a = permutedims(_DiskArray(permutedims(reshape(1:20,4,5,1),ip)),p)
test_getindex(a)
a = permutedims(_DiskArray(zeros(Int,5,1,4)),p)
test_setindex(a)
a = permutedims(_DiskArray(zeros(Int,5,1,4)),p)
test_view(a)
a = data -> permutedims(_DiskArray(permutedims(data,ip),chunksize=(4,2,5)),p)
test_reductions(a)
a_disk1 = permutedims(_DiskArray(rand(9,2,10), chunksize=(3,2,5)),p)
test_broadcast(a_disk1)
@test PermutedDiskArray(a_disk1.a) === a_disk1
end
@testset "Unchunked String arrays" begin
a = reshape(1:200000,200,1000)
b = string.(a)
c = collect(Union{Int,Missing},a)
DiskArrays.default_chunk_size[] = 100
DiskArrays.fallback_element_size[] = 100
@test DiskArrays.estimate_chunksize(a) == (200,1000)
@test DiskArrays.eachchunk(a) == DiskArrays.GridChunks(a,(200,1000))
@test DiskArrays.estimate_chunksize(b) == (200,1000)
@test DiskArrays.eachchunk(b) == DiskArrays.GridChunks(b,(200,1000))
@test DiskArrays.estimate_chunksize(c) == (200,1000)
@test DiskArrays.eachchunk(c) == DiskArrays.GridChunks(c,(200,1000))
DiskArrays.default_chunk_size[] = 1
@test DiskArrays.estimate_chunksize(a) == (200,625)
@test DiskArrays.eachchunk(a) == DiskArrays.GridChunks(a,(200,625))
@test DiskArrays.estimate_chunksize(b) == (200,50)
@test DiskArrays.eachchunk(b) == DiskArrays.GridChunks(b,(200,50))
@test DiskArrays.estimate_chunksize(c) == (200,625)
@test DiskArrays.eachchunk(c) == DiskArrays.GridChunks(c,(200,625))
DiskArrays.fallback_element_size[] = 1000
@test DiskArrays.estimate_chunksize(a) == (200,625)
@test DiskArrays.eachchunk(a) == DiskArrays.GridChunks(a,(200,625))
@test DiskArrays.estimate_chunksize(b) == (200,5)
@test DiskArrays.eachchunk(b) == DiskArrays.GridChunks(b,(200,5))
@test DiskArrays.estimate_chunksize(c) == (200,625)
@test DiskArrays.eachchunk(c) == DiskArrays.GridChunks(c,(200,625))
end
end
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] | 2.221954 | 4,555 |
function reliability(t::Vector{Float64}, Φ::Matrix{Float64}, dists::Dict)
P = zeros(size(t))
for index in CartesianIndices(Φ)
p = ones(size(t))
for k = 1:length(size(Φ))
mk = size(Φ)[k] - 1
lk = index[k] - 1
Fk = dists[k]
p .*=
binomial(mk, lk) .* (cdf.(Fk, t) .^ (mk - lk)) .* ((1 .- cdf.(Fk, t)) .^ lk)
end
P .+= Φ[index] .* p
end
return P
end
function reliability(
t::Vector{Float64},
Φ::Matrix{Float64},
types::Dict{Int64,Array{Int64,1}},
failures::Matrix{Float64},
)
idx = map_failures_to_type(failures, types)
Φ_f = map_types_to_signature(idx, Φ, types)
sort!(failures, dims = 2)
P = zeros(size(t))
for (r_i, r) ∈ enumerate(eachrow(failures))
@inbounds P[t.<r[1]] .+= 1
for c_i ∈ 1:length(r)-1
if Φ_f[r_i, c_i] === 0.0
break
end
@inbounds P[r[c_i].<=t.<r[c_i+1]] .+= Φ_f[r_i, c_i]
end
if Φ_f[r_i, end] > 0.0
@inbounds P[t.>=r[end]] .+= Φ_f[r_i, end]
end
end
return P ./ size(failures, 1)
end
function map_failures_to_type(f, types::Dict{Int64,Array{Int64,1}})
type_map = Dict{Int64,Int64}()
for n ∈ vcat(values(types)...)
for (type, components) ∈ types
n ∈ components && (type_map[n] = type; break)
end
end
idx = zeros(size(f))
for i ∈ 1:size(f, 1)
@views idx[i, :] = map(x -> type_map[x], sortperm(f[i, :]))
end
return Int.(idx)
end
function map_types_to_signature(idx, Φ, types)
F = ones(Int64, size(idx, 1), size(idx, 2), length(types))
for (type, components) ∈ types
F[:, :, type] += length(components) .- cumsum(idx .== type, dims = 2)
end
Φ_f = zeros(size(idx))
for j = 1:size(F, 2)
for i = 1:size(F, 1)
Φ_f[i, j] = Φ[(F[i, j, :])...]
end
end
return Φ_f
end
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] | 1.775382 | 1,113 |
# ============================================================
# Structs of Initial & Boundary Conditions
# ============================================================
"""
mutable struct IB{T} <: AbstractCondition
fw::Function
bc::T
end
Initial & boundary conditions with no distribution function
"""
mutable struct IB{T<:Union{Function,AbstractArray}} <: AbstractCondition
fw::Function
bc::T
end
function IB(fw, gas::Scalar)
γ = gas.a
bc = function (args...)
w = fw(args...)
return ifelse(γ == 0, conserve_prim(w), conserve_prim(w, γ))
end
return IB{typeof(bc)}(fw, bc)
end
function IB(fw, gas::AbstractGas)
bc = function (args...)
w = fw(args...)
prim = begin
if ndims(w) == 1
conserve_prim(w, gas.γ)
else
mixture_conserve_prim(w, gas.γ)
end
end
return prim
end
return IB{typeof(bc)}(fw, bc)
end
"""
mutable struct IB1F{T} <: AbstractCondition
fw::Function
ff::Function
bc::T
end
Initial & boundary condition with 1 distribution function
"""
mutable struct IB1F{T} <: AbstractCondition
fw::Function
ff::Function
bc::T
end
function IB1F(fw, vs::AbstractVelocitySpace, gas::AbstractProperty)
bc = function (args...)
w = fw(args...)
prim = begin
if ndims(w) == 1
conserve_prim(w, gas.γ)
else
mixture_conserve_prim(w, gas.γ)
end
end
return prim
end
ff = function (args...)
prim = bc(args...)
M = begin
if !isdefined(vs, :v)
if ndims(prim) == 1
maxwellian(vs.u, prim)
else
mixture_maxwellian(vs.u, prim)
end
elseif !isdefined(vs, :w)
if ndims(prim) == 1
maxwellian(vs.u, vs.v, prim)
else
mixture_maxwellian(vs.u, vs.v, prim)
end
else
if ndims(prim) == 1
maxwellian(vs.u, vs.v, vs.w, prim)
else
mixture_maxwellian(vs.u, vs.v, vs.w, prim)
end
end
end
return M
end
return IB1F{typeof(bc)}(fw, ff, bc)
end
"""
mutable struct IB2F{T} <: AbstractCondition
fw::Function
ff::Function
bc::T
end
Initial & boundary condition with 2 distribution functions
"""
mutable struct IB2F{T} <: AbstractCondition
fw::Function
ff::Function
bc::T
end
function IB2F(fw, vs::AbstractVelocitySpace, gas::AbstractProperty)
bc = function (args...)
w = fw(args...)
prim = begin
if ndims(w) == 1
conserve_prim(w, gas.γ)
else
mixture_conserve_prim(w, gas.γ)
end
end
return prim
end
ff = function (args...)
prim = bc(args...)
if !isdefined(vs, :v)
if ndims(prim) == 1
H = maxwellian(vs.u, prim)
B = H .* gas.K / 2 / prim[end]
else
H = mixture_maxwellian(vs.u, prim)
B = zero(H)
for j in axes(B, 2)
B[:, j] = H[:, j] * gas.K / (2.0 * prim[end, j])
end
end
elseif !isdefined(vs, :w)
if ndims(prim) == 1
H = maxwellian(vs.u, vs.v, prim)
B = H .* gas.K / 2 / prim[end]
else
H = mixture_maxwellian(vs.u, vs.v, prim)
B = zero(H)
for j in axes(B, 3)
B[:, :, j] = H[:, :, j] * gas.K / (2.0 * prim[end, j])
end
end
else
if ndims(prim) == 1
H = maxwellian(vs.u, vs.v, vs.w, prim)
B = H .* gas.K / 2 / prim[end]
else
H = mixture_maxwellian(vs.u, vs.v, vs.w, prim)
B = zero(H)
for j in axes(B, 4)
B[:, :, :, j] = H[:, :, :, j] * gas.K / (2.0 * prim[end, j])
end
end
end
return H, B
end
return IB2F{typeof(bc)}(fw, ff, bc)
end
"""
mutable struct IB3F{T} <: AbstractCondition
fw::Function
ff::Function
fE::Function
fB::Function
fL::Function
bc::T
end
Initial & boundary condition with 3 distribution functions
"""
mutable struct IB3F{T} <: AbstractCondition
fw::Function
ff::Function
fE::Function
fB::Function
fL::Function
bc::T
end
"""
mutable struct IB4F{T} <: AbstractCondition
fw::Function
ff::Function
fE::Function
fB::Function
fL::Function
bc::T
end
Initial & boundary condition with 4 distribution functions
"""
mutable struct IB4F{T} <: AbstractCondition
fw::Function
ff::Function
fE::Function
fB::Function
fL::Function
bc::T
end
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] | 1.855596 | 2,770 |
using Test
using PolynomialBasis
using ImplicitDomainQuadrature
using CartesianMesh
# using Revise
using HDGElasticity
function allapprox(v1,v2)
return all(v1 .≈ v2)
end
function allequal(v1,v2)
return all(v1 .== v2)
end
function allapprox(v1,v2,atol)
@assert length(v1) == length(v2)
return all([isapprox(v1[i],v2[i],atol=atol) for i = 1:length(v1)])
end
function plane_distance_function(coords,n,x0)
return [n'*(coords[:,idx]-x0) for idx in 1:size(coords)[2]]
end
facequads = [[1,2],[],[1,2,3],[]]
solverid = HDGElasticity.face_to_solverid(facequads)
@test allequal(solverid,[1,0,2,0])
vbasis = TensorProductBasis(2,1)
mesh = UniformMesh([0.,0.],[1.,1.],[2,1])
coords = HDGElasticity.nodal_coordinates(mesh,vbasis)
NF = HDGElasticity.number_of_basis_functions(vbasis)
coeffs = reshape(plane_distance_function(coords,[1.,0.],[0.4,0.]),NF,:)
poly = InterpolatingPolynomial(1,vbasis)
dgmesh = HDGElasticity.DGMesh(mesh,coeffs,poly)
ufs = HDGElasticity.UniformFunctionSpace(dgmesh,1,4,coeffs,poly)
l1,m1 = (1.,2.)
l2,m2 = (2.,3.)
D1 = sqrt(HDGElasticity.plane_strain_voigt_hooke_matrix_2d(l1,m1))
D2 = sqrt(HDGElasticity.plane_strain_voigt_hooke_matrix_2d(l2,m2))
stabilization = 1.0
cellmap = HDGElasticity.CellMap(dgmesh.domain[1])
solveridx = HDGElasticity.cell_to_solver_index(dgmesh.cellsign)
testsolveridx = [3 1
4 0]
@test allequal(solveridx,testsolveridx)
cellsolvers = HDGElasticity.CellSolvers(dgmesh,ufs,D1,D2,stabilization)
@test length(cellsolvers.localsolvers) == 4
@test allapprox(cellsolvers.stiffness[1],D1)
@test allapprox(cellsolvers.stiffness[2],D2)
@test cellsolvers.stabilization ≈ stabilization
update!(ufs.imap,ufs.icoeffs[1,1])
LL1 = HDGElasticity.local_operator(ufs.vbasis,ufs.vquads[1,1],ufs.fquads[1,1],
dgmesh.facemaps,ufs.iquad,ufs.imap,-ufs.inormals[1],D1,stabilization,cellmap)
fLH1 = HDGElasticity.local_hybrid_operator(ufs.vbasis,ufs.sbasis,ufs.fquads[1,1],
dgmesh.facemaps,ufs.fnormals,D1,stabilization,cellmap)
iLH1 = HDGElasticity.local_hybrid_operator_on_interface(ufs.vbasis,ufs.sbasis,
ufs.iquad,ufs.imap,-ufs.inormals[1],D1,stabilization,cellmap)
LH1 = [hcat(fLH1...) iLH1]
@test allapprox(cellsolvers[1,1].LL,LL1)
@test allapprox(cellsolvers[1,1].LH,LH1)
@test allequal(cellsolvers[1,1].facetosolverid,[1,2,3,0,4])
LL2 = HDGElasticity.local_operator(ufs.vbasis,ufs.vquads[2,1],ufs.fquads[2,1],
dgmesh.facemaps,ufs.iquad,ufs.imap,ufs.inormals[1],D2,stabilization,cellmap)
fLH2 = HDGElasticity.local_hybrid_operator(ufs.vbasis,ufs.sbasis,ufs.fquads[2,1],
dgmesh.facemaps,ufs.fnormals,D2,stabilization,cellmap)
iLH2 = HDGElasticity.local_hybrid_operator_on_interface(ufs.vbasis,ufs.sbasis,
ufs.iquad,ufs.imap,ufs.inormals[1],D2,stabilization,cellmap)
LH2 = [hcat(fLH2...) iLH2]
@test allapprox(cellsolvers[2,1].LL,LL2)
@test allapprox(cellsolvers[2,1].LH,LH2)
@test allapprox(cellsolvers[2,1].facetosolverid,[1,0,2,3,4])
uLL1 = HDGElasticity.local_operator(ufs.vbasis,ufs.vtpq,ufs.ftpq,
dgmesh.facemaps,D1,stabilization,cellmap)
ufLH1 = HDGElasticity.local_hybrid_operator(ufs.vbasis,ufs.sbasis,ufs.ftpq,
dgmesh.facemaps,ufs.fnormals,D1,stabilization,cellmap)
uLH1 = hcat(ufLH1...)
@test allapprox(cellsolvers.localsolvers[1].LL,uLL1)
@test allapprox(cellsolvers.localsolvers[1].LH,uLH1)
@test allequal(cellsolvers.localsolvers[1].facetosolverid,[1,2,3,4])
uLL2 = HDGElasticity.local_operator(ufs.vbasis,ufs.vtpq,ufs.ftpq,
dgmesh.facemaps,D2,stabilization,cellmap)
ufLH2 = HDGElasticity.local_hybrid_operator(ufs.vbasis,ufs.sbasis,ufs.ftpq,
dgmesh.facemaps,ufs.fnormals,D2,stabilization,cellmap)
uLH2 = hcat(ufLH2...)
@test allapprox(cellsolvers.localsolvers[2].LL,uLL2)
@test allapprox(cellsolvers.localsolvers[2].LH,uLH2)
@test allequal(cellsolvers.localsolvers[2].facetosolverid,[1,2,3,4])
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] | 2.028132 | 1,884 |
using Printf
"""
Represents the time of day.
fields: hour, minute, second
"""
struct MyTime
hour
minute
second
end
# Exercise 16-1
function printtime(t::MyTime)
@printf("%02d:%02d:%02d", t.hour, t.minute, t.second)
end
t = MyTime(23, 10, 58)
# printtime(t)
# Exercise 16-2
function isafter(t1::MyTime, t2::MyTime)
return (t1.hour, t1.minute, t1.second) > (t2.hour, t2.minute, t2.second)
end
# Exercise 16-3
function increment!(time, seconds)
time.second += seconds
dmin, time.second = divrem(time.second, 60)
time.minute += dmin
dhour, time.minute = divrem(time.minute, 60)
time.hour += dhour
time.hour %= 24
nothing
end
# Exercise 16-4
function increment(time, seconds)
nsecond = time.second + seconds
dmin, nsecond = divrem(nsecond, 60)
nminute = time.minute + dmin
dhour, nminute = divrem(nminute, 60)
nhour = time.hour + dhour
nhour %= 24
return MyTime(nhour, nminute, nsecond)
end
# Exercise 16-5
function timetoint(time)
minutes = time.hour * 60 + time.minute
seconds = minutes * 60 + time.second
end
function inttotime(seconds)
(minutes, second) = divrem(seconds, 60)
hour, minute = divrem(minutes, 60)
MyTime(hour, minute, second)
end
function increment(time, seconds)
return inttotime(timetoint(time) + seconds)
end
# Exercise 16-6
function multime(time::MyTime, x::Number)
return inttotime(timetoint(time) * x)
end
# println(multime(t, 3))
function timepermile(time::MyTime, dist::Number)
return multime(time, 1/dist)
end
# println(timepermile(t, 3))
# Exercise 16-7
using Dates
td = today()
println(td)
println(dayofweek(td))
println(dayname(td))
function age(bday)
td = today()
td - bday
dyear = year(td) - year(bday)
dmonth = month(td) - month(bday)
dday = day(td) - day(bday)
if dmonth < 0
dyear -= 1
elseif dmonth == 0 && dday < 0
dyear -= 1
end
println("Age: $dyear")
next_bday = bday + Year(dyear + 1)
println("Next birthday: $next_bday")
nextdiff = DateTime(next_bday) - now()
timeleft = canonicalize(Dates.CompoundPeriod(nextdiff))
println("Time to next birthday: $timeleft")
end
# age(Date(1995, 11, 02))
function doubleday(d1, d2)
@assert(d2 > d1, "d2 should be later than d1")
return d2 - d1 + d2
end
d1 = Date(2000, 1, 2)
d2 = Date(2005, 6, 24)
# println(doubleday(d1, d2))
function n_day(d1, d2, n)
@assert(d2 > d1, "d2 should be later than d1")
diff = d2 - d1
age1, age2 = diff.value, 0
while age2 * n != age1
if age2 * n > age1
error("Impossible combination of inputs")
end
age1 += 1
age2 += 1
end
return d1 + Day(age1)
end
# println(n_day(d1, d2, 2))
# println(n_day(d1, d2, 3))
# println(n_day(d1, d2, 6)) | [
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] | 2.311422 | 1,217 |
######### StanSample example ###########
using StanSample, Test
bernoulli_model = "
data {
int<lower=1> N;
array[N] int<lower=0,upper=1> y;
}
parameters {
real<lower=0,upper=1> theta;
}
model {
theta ~ beta(1,1);
y ~ bernoulli(theta);
}
";
bernoulli_data = [
Dict("N" => 10, "y" => [0, 1, 0, 1, 0, 0, 0, 0, 0, 1]),
Dict("N" => 10, "y" => [0, 1, 0, 0, 1, 0, 0, 0, 0, 1]),
Dict("N" => 10, "y" => [0, 1, 0, 1, 0, 0, 0, 0, 1, 0]),
Dict("N" => 10, "y" => [0, 0, 0, 1, 0, 0, 1, 0, 0, 1]),
]
sm = SampleModel("bernoulli", bernoulli_model)
rc = stan_sample(sm; data=bernoulli_data, delta=0.85, num_threads=1)
if success(rc)
# Fetch the same output in the `sdf` ChainDataFrame
sdf = read_summary(sm)
@test sdf[sdf.parameters .== :theta, :mean][1] ≈ 0.33 rtol=0.05
end | [
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] | 2.056848 | 387 |
using Core.Compiler: AbstractInterpreter, CodeInstance, MethodInstance, WorldView, NativeInterpreter
using InteractiveUtils
function infer_function(interp, tt)
# Find all methods that are applicable to these types
mthds = _methods_by_ftype(tt, -1, typemax(UInt))
if mthds === false || length(mthds) != 1
error("Unable to find single applicable method for $tt")
end
mtypes, msp, m = mthds[1]
# Grab the appropriate method instance for these types
mi = Core.Compiler.specialize_method(m, mtypes, msp)
# Construct InferenceResult to hold the result,
result = Core.Compiler.InferenceResult(mi)
# Create an InferenceState to begin inference, give it a world that is always newest
world = Core.Compiler.get_world_counter()
frame = Core.Compiler.InferenceState(result, #=cached=# true, interp)
# Run type inference on this frame. Because the interpreter is embedded
# within this InferenceResult, we don't need to pass the interpreter in.
Core.Compiler.typeinf(interp, frame)
# Give the result back
return (mi, result)
end
struct ExtractingInterpreter <: Core.Compiler.AbstractInterpreter
code::Dict{MethodInstance, CodeInstance}
native_interpreter::NativeInterpreter
msgs::Vector{Tuple{MethodInstance, Int, String}}
optimize::Bool
end
ExtractingInterpreter(;optimize=false) = ExtractingInterpreter(
Dict{MethodInstance, Any}(),
NativeInterpreter(),
Vector{Tuple{MethodInstance, Int, String}}(),
optimize
)
import Core.Compiler: InferenceParams, OptimizationParams, get_world_counter,
get_inference_cache, code_cache,
WorldView, lock_mi_inference, unlock_mi_inference, InferenceState
InferenceParams(ei::ExtractingInterpreter) = InferenceParams(ei.native_interpreter)
OptimizationParams(ei::ExtractingInterpreter) = OptimizationParams(ei.native_interpreter)
get_world_counter(ei::ExtractingInterpreter) = get_world_counter(ei.native_interpreter)
get_inference_cache(ei::ExtractingInterpreter) = get_inference_cache(ei.native_interpreter)
# No need to do any locking since we're not putting our results into the runtime cache
lock_mi_inference(ei::ExtractingInterpreter, mi::MethodInstance) = nothing
unlock_mi_inference(ei::ExtractingInterpreter, mi::MethodInstance) = nothing
code_cache(ei::ExtractingInterpreter) = ei.code
Core.Compiler.get(a::Dict, b, c) = Base.get(a,b,c)
Core.Compiler.get(a::WorldView{<:Dict}, b, c) = Base.get(a.cache,b,c)
Core.Compiler.haskey(a::Dict, b) = Base.haskey(a, b)
Core.Compiler.haskey(a::WorldView{<:Dict}, b) =
Core.Compiler.haskey(a.cache, b)
Core.Compiler.setindex!(a::Dict, b, c) = setindex!(a, b, c)
Core.Compiler.may_optimize(ei::ExtractingInterpreter) = ei.optimize
Core.Compiler.may_compress(ei::ExtractingInterpreter) = false
Core.Compiler.may_discard_trees(ei::ExtractingInterpreter) = false
function Core.Compiler.add_remark!(ei::ExtractingInterpreter, sv::InferenceState, msg)
@show msg
push!(ei.msgs, (sv.linfo, sv.currpc, msg))
end
macro code_typed_nocache(ex0...)
esc(:(@InteractiveUtils.code_typed interp=$(ExtractingInterpreter)() $(ex0...)))
end
export @code_typed_nocache
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] | 2.773205 | 1,142 |
function bob(stimulus)
# 1) Checar si la cadena esta vacia
# 2) Checar si la cadena tiene un signo de interrogacion (?)
# 3) Checar si la cadena es toda en mayusculas
# 4) Cualquier otra cosa regresa "Whatever."
nospace = strip(stimulus)
if isempty(nospace)
return "Fine. Be that way!"
elseif uppercase(stimulus) == stimulus
if endswith(stimulus, "?")
return "Calm down, I know what I'm doing!"
else
return "Whoa, chill out!"
end
else
if endswith(stimulus, "?")
return "Sure."
end
end
return "Whatever."
end
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] | 2.194444 | 288 |
using Distributions
using Test, Random
@test rand(Bernoulli()) isa Bool
@test rand(Bernoulli(), 10) isa Vector{Bool}
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] | 2.878049 | 41 |
A = [1 2 ; 3 4]
B = [10, 10]
sum!(B, A)
sum!(A, B)
using ReverseDiffSource
Nn = [20,10,1]
function ann(l1, l2, x1)
x2 = l1 * x1
x2 = max(0., x2) #relu
x3 = l2 * x2
x3 = (max(0., x3[1]))^2 #relu
end
# ls = Array[ nn.ws...]
ls = init(Nn)
dann = rdiff(ann, (ls..., ones(20) ))
out = dann(ls..., ones(20) )
nn = S.NN((ls[1], ls[2]), (S.Relu(), S.Relu()))
ts2 = S.cycle(nn, ones(20), zeros(1))
out[2]
ts2.dws[1]
out[3]
ts2.dws[2]
ts2.ds[end]
out[3] ./ ts2.dws[2]
r1 = S.calc(nn, ones(20))[1] # 1.16217
r2 = ann(ls..., ones(20))
r1-r2
ls[1][9,1] += 1e-10
(ann(ls..., ones(20)) - r1) * 1e10 # -0.8600
nn.ws[1][9,1] += 1e-10
(S.calc(nn, ones(20)) - r2) * 1e10 # -0.8600
reload("SimpleNeuralNets"); S=SimpleNeuralNets
nn = S.NN([10,10,10,10,1], [ S.Tanh(), S.Tanh()] )
ts = S.TrainState(nn, 10)
size(nn)
size(ts)
ts2 = S.cycle(nn, ones(20,10), ones(5,10))
S.cycle!(ts, nn, ones(10,10), ones(1,10))
S.depth(nn)
s0 = S.calc(nn, rand(20))
copy!(s0.ss[1], ones(20))
S.compatible(nn, s0)
S.calc!(nn, s0)
s0 = S.calc(nn, rand(20,10))
copy!(s0.ss[1], ones(20))
S.compatible(nn, s0)
S.calc!(nn, s0)
a = (1,2,3)
typeof(a)
a[1]
a[1] = 2
nn = S.NN((randn(1,1),), (S.Relu(),))
S.calc(nn, [1.])
nn = S.NN((randn(1,1),), (S.SoftRelu(),))
S.calc(nn, [1.])
########### ANN3 #################################"
import Bokeh
# using ReverseDiffSource
reload("SimpleNeuralNets"); s = SimpleNeuralNets
f(x) = exp(-(x-1)^2/5)*cos(x)*sign(x)
xs = collect(-5:0.001:5)
X = Float64[ x for x in xs ]'
Y = Float64[ f(x) for x in xs ]'
nn = s.NN([1,10,10,10,1], [ s.Relu(), s.Tanh()] )
ts = s.TrainingSet(X, Y)
any(ts.weights .!= 1.0)
λ0, μ = 1e-3, 0.9
s.sgd(nn, ts, λ0, μ, freq=200, steps=1000)
ts2 = s.TrainingSet(X, Y, SqEuclidean(), rand(size(X,2)))
s.sgd(nn, ts2, λ0, μ, freq=200, steps=1000)
s.colwise(a,b,c)
length(a.weights)
px = s.calc(nn, X)
sum(s.colwise(ts2.metric, px, ts2.Y))
λ0, μ = 1e-3, 0.9
s.sgd(nn, λ0, μ, X, Y, freq=200, steps=10000)
λ0, μ = 1e-5, 0.999
s.sgd(nn, λ0, μ, X, Y, freq=200, steps=10000)
Bokeh.hold(true)
Bokeh.plot(f, -5:5, "k-")
px = collect(-5:0.01:5)
py = s.calc(nn, px')'
Bokeh.plot(px, py, "b-")
Bokeh.showplot("/tmp/plot.html")
Bokeh.hold(false)
nn = s.NN([1,10,10,10,1], [ s.SoftRelu(), s.Tanh()] )
λ0, μ = 1e-3, 0.9
s.sgd(nn, λ0, μ, X, Y, freq=200, steps=10000)
λ0, μ = 1e-3, 0.
s.sgd(nn, λ0, μ, X, Y, freq=1000, steps=10000)
Bokeh.hold(true)
Bokeh.plot(f, -5:5, "k-")
px = collect(-4:0.1:4)
py = s.calc(nn, px')'
Bokeh.plot(px, py, "b-")
Bokeh.showplot("/tmp/plot.html")
Bokeh.hold(false) | [
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] | 1.674276 | 1,796 |
import Blosc: compress, decompress
function parallel_density_grid{T}(C::Matrix{T}, width::T, d::Int, σ::T, ϵ::T = 1e-4, num_workers::Int = nworkers())
num_points = size(C, 2)
num_points_per_worker = fill(fld(num_points, num_workers), num_workers)
num_points_per_worker[1:mod(num_points, num_workers)] += 1
start_indices = unshift!(cumsum(num_points_per_worker), 0) .+ 1
pop!(start_indices)
params = map(zip(start_indices, num_points_per_worker)) do r
C[:, range(r[1], r[2])], width, d, σ, ϵ
end
#ρs_compressed = pmap(params, pids = collect(take(workers(), num_workers))) do p
ρs_compressed = pmap(params) do p
compress(density_grid(p...), level = 9, shuffle = true, itemsize = sizeof(T))
end
ρ = mapreduce(.+, zeros(T, d^3), ρs_compressed) do ρ_compressed
decompress(T, ρ_compressed)
end
return reshape(ρ, d, d, d)
end
function density_grid{T}(C::Matrix{T}, width::T, d::Int, σ::T, ϵ::T= 1e-4)
hw = width / 2
ρ = zeros(T, d, d, d)
num_points = size(C, 2)
r_max_sq = max(0, -σ^2 * log(ϵ))
r_max = sqrt(r_max_sq)
b = floor(Int, d * r_max / width) + 1
g = collect(linspace(-hw, hw, d))
@inbounds for i in 1:num_points
ix = floor(Int, d * (C[1, i] + hw) / width) + 1
iy = floor(Int, d * (C[2, i] + hw) / width) + 1
iz = floor(Int, d * (C[3, i] + hw) / width) + 1
x_min = max(1, ix - b)
y_min = max(1, iy - b)
z_min = max(1, iz - b)
x_max = min(d, ix + b)
y_max = min(d, iy + b)
z_max = min(d, iz + b)
for jx in x_min:x_max, jy in y_min:y_max, jz in z_min:z_max
r_sq = (C[1, i] - g[jx])^2 + (C[2, i] - g[jy])^2 + (C[3, i] - g[jz])^2
if r_sq < r_max_sq
ρ[jx, jy, jz] += exp(-r_sq / σ^2)
end
end
end
return ρ
end
function density_grid{T}(x::Array{T, 3}, y::Array{T, 3}, z::Array{T, 3}, C::Matrix{T}, σ::T, ϵ::T = 1e-4)
D = size(x)
ρ = zeros(T, D)
num_points = size(C, 2)
R = CartesianRange(size(ρ))
I_min, I_max = first(R), last(R)
x_spread = x[I_max] - x[I_min]
y_spread = y[I_max] - y[I_min]
z_spread = z[I_max] - z[I_min]
r_max_sq = max(0, -σ^2 * log(ϵ))
r_max = sqrt(r_max_sq)
dx = floor(Int, D[1] * r_max / x_spread) + 1
dy = floor(Int, D[2] * r_max / y_spread) + 1
dz = floor(Int, D[3] * r_max / z_spread) + 1
B = CartesianIndex((dx, dy, dz))
@inbounds for i in 1:num_points
ix = floor(Int, D[1] * (C[1, i] - x[I_min]) / x_spread) + 1
iy = floor(Int, D[2] * (C[2, i] - y[I_min]) / y_spread) + 1
iz = floor(Int, D[3] * (C[3, i] - z[I_min]) / z_spread) + 1
I = CartesianIndex((ix, iy, iz))
for J in CartesianRange(max(I_min, I - B), min(I_max, I + B))
r_sq = (C[1, i] - x[J])^2 + (C[2, i] - y[J])^2 + (C[3, i] - z[J])^2
if r_sq < r_max_sq
ρ[J] += exp(-r_sq / σ^2)
end
end
end
return ρ
end
function density_grid_stats{T}(width::T, d::Int, σ::T, ϵ::T)
r_max = sqrt(max(0, -σ^2 * log(ϵ)))
b = floor(Int, r_max / (width / d)) + 1
num_blocks = (2b + 1)^3
println("Max radius: $(r_max)")
println("Blocks per dimension: $b")
println("Total blocks: $(num_blocks)")
flush(STDOUT)
end
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struct Involute{T} <: JointType{T}
i::SVector{3, T}
j::SVector{3, T}
k::SVector{3, T}
ρ::T
ρ_inv::T
function Involute{T}(b_axis::AbstractVector,n_axis::AbstractVector,ρ::T) where {T}
j, k = map(axis -> normalize(SVector{3}(axis)), (n_axis, b_axis))
@assert isapprox(k ⋅ j, 0; atol = 100 * eps(T))
@assert ρ > 0
new{T}(j × k, j, k, ρ, inv(ρ))
end
end
@propagate_inbounds function joint_transform(jt::Involute, frame_after::CartesianFrame3D, frame_before::CartesianFrame3D, q::AbstractVector)
rot = RotMatrix(AngleAxis(jt.ρ_inv*q[1],jt.k[1],jt.k[2],jt.k[3],false))
trans = rot*(jt.ρ*jt.j+q[1]*jt.i)
Transform3D(frame_after,frame_before,rot,trans)
end
@propagate_inbounds function joint_twist(jt::Involute, frame_after::CartesianFrame3D, frame_before::CartesianFrame3D,
q::AbstractVector, v::AbstractVector)
angular = jt.ρ_inv*jt.k*v[1]
linear = jt.ρ_inv*q[1]*jt.j*v[1]
Twist(frame_after, frame_before, frame_after, angular, linear)
end
@propagate_inbounds function joint_spacial_acceleration(jt::Involute, frame_after::CartesianFrame3D, frame_before::CartesianFrame3D,
q::AbstractVector, v::AbstractVector, vd::AbstractVector)
S = promote_eltype(jt, q, v, vd)
angular = jt.ρ_inv*jt.k*vd[1]
linear = (jt.ρ_inv*q[1]*jt.j)*vd[1] + (jt.j-jt.ρ_inv*q[1]*jt.i)*jt.ρ_inv*v[1]^2
SpatialAcceleration{S}(frame_after, frame_before, frame_after, angular, linear)
end
@propagate_inbounds function bias_acceleration(jt::Involute, frame_after::CartesianFrame3D, frame_before::CartesianFrame3D,
q::AbstractVector, v::AbstractVector)
S = promote_eltype(jt, q, v)
linear = zero(SVector{3,S})
angular = (jt.j-jt.ρ_inv*q[1]*jt.i)*jt.ρ_inv*v[1]^2
SpatialAcceleration{S}(frame_after, frame_before, frame_after, angular,linear)
end
@propagate_inbounds function joint_torque!(τ::AbstractVector, jt::Involute, q::AbstractVector, joint_wrench::Wrench)
R = RotMatrix(AngleAxis(-jt.ρ_inv*q[1],jt.k[1],jt.k[2],jt.k[3],false))
τ[1] = jt.ρ_inv*q[1]*jt.j'*linear(joint_wrench) + ρ_inv*k'*angular(joint_wrench)
nothing
end
@inline function motion_subspace(jt::Involute, frame_after::CartesianFrame3D, frame_before::CartesianFrame3D,
q::AbstractVector)
S = promote_eltype(jt, q)
angular = hcat(zero(SMatrix{3,2,S}),jt.k)
linear = hcat(zero(SVector{3,S}),jt.j,zero(SVector{3,S}))
GeometricJacobian(frame_after, frame_before, frame_after, angular, linear)
end
@inline function constraint_wrench_subspace(jt::Involute, joint_transform::Transform3D)
S = promote_eltype(jt, joint_transform)
angular = hcat(jt.i, jt.j, zero(SVector{3, S}))
linear = hcat(jt.i, zero(SVector{3, S}), jt.k )
WrenchMatrix(joint_transform.from, angular, linear)
end | [
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] | 2.192926 | 1,244 |
@testset "DataMatrix" begin
a = @data [1.0, 2.0, 3.0]
v_a = [1.0, 2.0, 3.0]
b = @data eye(3, 3)
m_b = eye(3, 3)
#
# Transposes
#
@test all(a' .== v_a')
@test all(a'' .== v_a'')
@test all(b' .== m_b')
@test all(b'' .== m_b'')
#
# DataVector * DataMatrix
#
# TODO: Get indexing for b[1, :] to work
# @test all(a * b[1, :] .== v_a * m_b[1, :])
#
# DataMatrix * DataVector
#
@test all(b * a .== m_b * v_a)
@test all(convert(Array, b * a) .== m_b * v_a)
#
# DataMatrix * DataMatrix
#
@test all(b * b .== m_b * m_b)
#
# DataVector * DataMatrix w/ missings
#
b[1, 1] = missing
res = a * b[1:1, :]
@test all(ismissing.(res[:, 1]))
@test all(.!(ismissing.(res[:, 2])))
@test all(.!(ismissing.(res[:, 3])))
res = a * b[2:2, :]
@test all(.!(ismissing.(res)))
#
# DataMatrix w missings * DataVector
#
res = b * a
@test ismissing.(res[1])
@test .!(ismissing.(res[2]))
@test .!(ismissing.(res[3]))
#
# DataMatrix * DataMatrix
#
res = b * b
# 3x3 Float64 DataMatrix:
# missing missing missing
# missing 1.0 0.0
# missing 0.0 1.0
@test ismissing.(res[1, 1])
@test ismissing.(res[1, 2])
@test ismissing.(res[1, 3])
@test ismissing.(res[2, 1])
@test .!(ismissing.(res[2, 2]))
@test .!(ismissing.(res[2, 3]))
@test ismissing.(res[3, 1])
@test .!(ismissing.(res[3, 2]))
@test .!(ismissing.(res[3, 3]))
res = b * @data eye(3)
# 3x3 Float64 DataMatrix:
# missing missing missing
# 0.0 1.0 0.0
# 0.0 0.0 1.0
@test ismissing.(res[1, 1])
@test ismissing.(res[1, 2])
@test ismissing.(res[1, 3])
@test .!(ismissing.(res[2, 1]))
@test .!(ismissing.(res[2, 2]))
@test .!(ismissing.(res[2, 3]))
@test .!(ismissing.(res[3, 1]))
@test .!(ismissing.(res[3, 2]))
@test .!(ismissing.(res[3, 3]))
res = (@data eye(3)) * b
# julia> dataeye(3) * b
# 3x3 Float64 DataMatrix:
# missing 0.0 0.0
# missing 1.0 0.0
# missing 0.0 1.0
@test ismissing.(res[1, 1])
@test .!(ismissing.(res[1, 2]))
@test .!(ismissing.(res[1, 3]))
@test ismissing.(res[2, 1])
@test .!(ismissing.(res[2, 2]))
@test .!(ismissing.(res[2, 3]))
@test ismissing.(res[3, 1])
@test .!(ismissing.(res[3, 2]))
@test .!(ismissing.(res[3, 3]))
# Test row operations
dm = @data eye(6, 2)
# rowmeans(dm)
# Test column operations
dm = @data eye(6, 2)
# colmeans(dm)
# Test linear algebra
du, dd, dv = svd((@data eye(3, 3)))
u, d, v = svd(eye(3, 3))
@test all(du .== u)
@test all(dd .== d)
@test all(dv .== v)
# Test elementary functions
dm = -(@data eye(5, 5))
@test all(abs(dm) .== eye(5, 5))
end
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] | 1.916223 | 1,504 |
immutable PersistentQueue{T}
in::AbstractList{T}
out::AbstractList{T}
length::Int
PersistentQueue(in::AbstractList{T}, out::AbstractList{T}, length::Int) =
new(in, out, length)
PersistentQueue() = new(EmptyList{T}(), EmptyList{T}(), 0)
end
PersistentQueue{T}(v::AbstractVector{T}) =
PersistentQueue{T}(EmptyList{T}(), reverse(PersistentList(v)), length(v))
queue = PersistentQueue
Base.length(q::PersistentQueue) = q.length
Base.isempty(q::PersistentQueue) = is(q.length, 0)
peek(q::PersistentQueue) = isempty(q.out) ? head(reverse(q.in)) : head(q.out)
pop{T}(q::PersistentQueue{T}) =
if isempty(q.out)
PersistentQueue{T}(EmptyList{T}(), tail(reverse(q.in)), length(q) - 1)
else
PersistentQueue{T}(q.in, tail(q.out), length(q) - 1)
end
enq{T}(q::PersistentQueue{T}, val) =
if isempty(q.in) && isempty(q.out)
PersistentQueue{T}(q.in, val..EmptyList{T}(), 1)
else
PersistentQueue{T}(val..q.in, q.out, length(q) + 1)
end
Base.start(q::PersistentQueue) = (q.in, q.out)
Base.done{T}(::PersistentQueue{T}, state::(@compat Tuple{EmptyList{T}, EmptyList{T}})) = true
Base.done(::PersistentQueue, state) = false
function Base.next{T}(::PersistentQueue{T}, state::(@compat Tuple{AbstractList{T},
PersistentList{T}}))
in, out = state
(head(out), (in, tail(out)))
end
function Base.next{T}(q::PersistentQueue{T}, state::(@compat Tuple{PersistentList{T},
EmptyList{T}}))
in, out = state
next(q, (EmptyList{T}(), reverse(in)))
end
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] | 2.111675 | 788 |
# m5.3.As.jl
using Pkg, DrWatson
using MonteCarloMeasurements
using StanSample
using StatisticalRethinking
# ### snippet 5.1
df = CSV.read(sr_datadir("WaffleDivorce.csv"), DataFrame);
scale!(df, [:Marriage, :MedianAgeMarriage, :Divorce])
# Define the Stan language model
stan5_3_A = "
data {
int N;
vector[N] divorce_s;
vector[N] marriage_s;
vector[N] medianagemarriage_s;
}
parameters {
real a;
real bA;
real bM;
real aM;
real bAM;
real<lower=0> sigma;
real<lower=0> sigma_M;
}
model {
// A -> D <- M
vector[N] mu = a + bA * medianagemarriage_s + bM * marriage_s;
a ~ normal( 0 , 0.2 );
bA ~ normal( 0 , 0.5 );
bM ~ normal( 0 , 0.5 );
sigma ~ exponential( 1 );
divorce_s ~ normal( mu , sigma );
// A -> M
vector[N] mu_M = aM + bAM * medianagemarriage_s;
aM ~ normal( 0 , 0.2 );
bAM ~ normal( 0 , 0.5 );
sigma_M ~ exponential( 1 );
marriage_s ~ normal( mu_M , sigma_M );
}
";
# Define the SampleModel
m5_3_As = SampleModel("m5.3_A", stan5_3_A);
# Input data
m5_3_A_data = Dict(
"N" => size(df, 1),
"divorce_s" => df[:, :Divorce_s],
"marriage_s" => df[:, :Marriage_s],
"medianagemarriage_s" => df[:, :MedianAgeMarriage_s]
);
# Sample using cmdstan's sampling option
rc5_3_As = stan_sample(m5_3_As, data=m5_3_A_data);
if success(rc5_3_As)
# Describe the draws
part5_3_As = read_samples(m5_3_As, :particles)
part5_3_As |> display
end
# End of m5.3.As.jl | [
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] | 2.122333 | 703 |
"""
plot(net::HybridNetwork, method::Symbol)
Plot a network using R graphics.
`method` should be `:R` (actually, any symbol would do, for now!).
optional arguments, shared with the Gadfly-based plot function:
- useEdgeLength: if true, the tree edges and major hybrid edges are
drawn proportionally to their length. Minor hybrid edges are not, however.
Note that edge lengths in coalescent units may scale very poorly with time.
- showTipLabel: if true, taxon labels are shown. You may need to zoom out to see them.
- showNodeNumber: if true, nodes are labelled with the number used internally.
- showEdgeLength: if true, edges are labelled with their length (above)
- showGamma: if true, hybrid edges are labelled with their heritability (below)
- edgeColor: color for tree edges. black by default.
- majorHybridEdgeColor: color for major hybrid edges
- minorHybridEdgeColor: color for minor hybrid edges
- showEdgeNumber: if true, edges are labelled with the number used internally.
- showIntNodeLabel: if true, internal nodes are labelled with their names.
Useful for hybrid nodes, which do have tags like 'H1'.
- edgeLabel: dataframe with two columns: the first with edge numbers, the second with labels
(like bootstrap values) to annotate edges. empty by default.
- nodeLabel: dataframe with two columns: the first with node numbers, the second with labels
(like bootstrap values for hybrid relationships) to annotate nodes. empty by default.
optional arguments specific to this function:
- xlim, ylim: array of 2 values
- tipOffset: to offset tip labels
Note that `plot` actually modifies some (minor) attributes of the network,
as it calls `directEdges!`, `preorder!` and `cladewiseorder!`.
If hybrid edges cross tree and major edges, you may choose to rotate some tree
edges to eliminate crossing edges, using `rotate!`
(in [`PhyloNetworks`](http://crsl4.github.io/PhyloNetworks.jl/latest/lib/public/#PhyloNetworks.rotate!)).
**Alternative**: a tree or network can be exported with [`sexp`](@ref)
and then displayed with R's "plot" and all its options.
"""
function plot(net::HybridNetwork, method::Symbol; useEdgeLength=false::Bool,
mainTree=false::Bool, showTipLabel=true::Bool, showNodeNumber=false::Bool,
showEdgeLength=false::Bool, showGamma=false::Bool,
edgeColor="black"::String,
majorHybridEdgeColor="deepskyblue4"::String,
minorHybridEdgeColor="deepskyblue"::String,
showEdgeNumber=false::Bool, showIntNodeLabel=true::Bool,
edgeLabel=DataFrame()::DataFrame, nodeLabel=DataFrame()::DataFrame,
xlim=Float64[]::Array{Float64,1}, ylim=Float64[]::Array{Float64,1},
tipOffset=0.0::Float64, tipcex=1.0::Float64)
(edge_xB, edge_xE, edge_yB, edge_yE, node_x, node_y, node_yB, node_yE,
xmin, xmax, ymin, ymax) = getEdgeNodeCoordinates(net, useEdgeLength)
labelnodes, nodeLabel = checkNodeDataFrame(net, nodeLabel)
ndf = prepareNodeDataFrame(net, nodeLabel, showNodeNumber,
showIntNodeLabel, labelnodes, node_x, node_y)
if (showTipLabel || showNodeNumber || showIntNodeLabel || labelnodes)
expfac = 0.1 # force 10% more space to show tip/node/root name
expfacy = 0.5 # additive expansion for y axis
xmin -= (xmax-xmin)*expfac
xmax += (xmax-xmin)*expfac
ymin -= expfacy
ymax += expfacy
end
xmax += tipOffset
if length(xlim)==2
xmin=xlim[1]; xmax=xlim[2]
end
if length(ylim)==2
ymin=ylim[1]; ymax=ylim[2]
end
leaves = [n.leaf for n in net.node]
eCol = fill(edgeColor, length(net.edge))
eCol[ [ e.hybrid for e in net.edge] ] .= majorHybridEdgeColor
eCol[ [!e.isMajor for e in net.edge] ] .= minorHybridEdgeColor
R"""
plot($(node_x[leaves]), $(node_y[leaves]), type='n',
xlim=c($xmin,$xmax), ylim=c($ymin,$ymax),
axes=FALSE, xlab='', ylab='')
segments($edge_xB, $edge_yB, $edge_xE, $edge_yE, col=$eCol)
segments($node_x, $node_yB, $node_x, $node_yE, col=$edgeColor)
"""
if showTipLabel
R"text"(node_x[leaves] .+ tipOffset, node_y[leaves],
tipLabels(net), adj=0, font=3, cex=tipcex)
end
if showIntNodeLabel
R"text"(ndf[.!ndf[!,:lea],:x], ndf[.!ndf[!,:lea],:y],
ndf[.!ndf[!,:lea],:name], font=3, cex=tipcex, adj=[.5,0])
end
if showNodeNumber
R"text"(ndf[!,:x], ndf[!,:y], ndf[!,:num], adj=1)
end
if labelnodes
R"text"(ndf[!,:x], ndf[!,:y], ndf[!,:lab], adj=1)
end
labeledges, edf = prepareEdgeDataFrame(net, edgeLabel, mainTree,
edge_xB, edge_xE, edge_yB, edge_yE)
if labeledges
R"text"(edf[!,:x], edf[!,:y], edf[!,:lab], adj=[.5,0])
end
if showEdgeLength
R"text"(edf[!,:x], edf[!,:y], edf[!,:len], adj=[.5,1.])
end
if (showGamma && net.numHybrids>0)
im = edf[!,:hyb] .& edf[!,:min]
iM = edf[!,:hyb] .& .!edf[!,:min]
R"text"(edf[im,:x], edf[im,:y], edf[im,:gam],
adj=[.5,1], col=minorHybridEdgeColor)
R"text"(edf[iM,:x], edf[iM,:y], edf[iM,:gam],
adj=[.5,1], col=majorHybridEdgeColor)
end
if showEdgeNumber
R"text"(edf[!,:x], edf[!,:y], edf[!,:num], adj=[.5,0])
end
return (xmin, xmax, ymin, ymax, node_x, node_y, node_yB, node_yE,
edge_xB, edge_xE, edge_yB, edge_yE, ndf, edf)
end
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88,
33,
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62,
87,
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11,
951,
43641,
68,
5216,
8,
198,
220,
220,
220,
17894,
16763,
17440,
62,
87,
11,
720,
17440,
62,
88,
33,
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720,
17440,
62,
87,
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720,
17440,
62,
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951,
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10258,
8,
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220,
220,
37227,
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220,
220,
220,
611,
905,
28434,
33986,
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220,
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8171,
17822,
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7,
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28,
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269,
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220,
220,
886,
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220,
220,
220,
15494,
3212,
11,
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69,
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8335,
37021,
6601,
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7,
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11,
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33986,
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1388,
27660,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5743,
62,
87,
33,
11,
5743,
62,
87,
36,
11,
5743,
62,
88,
33,
11,
5743,
62,
88,
36,
8,
198,
220,
220,
220,
611,
15494,
3212,
198,
220,
220,
220,
220,
220,
371,
1,
5239,
18109,
276,
69,
58,
28265,
25,
87,
4357,
1225,
69,
58,
28265,
25,
88,
4357,
1225,
69,
58,
28265,
25,
23912,
4357,
9224,
41888,
13,
20,
11,
15,
12962,
198,
220,
220,
220,
886,
198,
220,
220,
220,
611,
905,
37021,
24539,
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220,
220,
220,
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220,
371,
1,
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1225,
69,
58,
28265,
25,
11925,
4357,
9224,
41888,
13,
20,
11,
16,
8183,
8,
198,
220,
220,
220,
886,
198,
220,
220,
220,
611,
357,
12860,
34777,
2611,
11405,
2010,
13,
22510,
21217,
40637,
29,
15,
8,
198,
220,
220,
220,
220,
220,
545,
796,
1225,
69,
58,
28265,
25,
12114,
65,
60,
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5,
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69,
58,
28265,
25,
1084,
60,
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220,
220,
220,
220,
220,
1312,
44,
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69,
58,
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25,
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65,
60,
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5,
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0,
276,
69,
58,
28265,
25,
1084,
60,
198,
220,
220,
220,
220,
220,
371,
1,
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69,
58,
320,
11,
25,
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69,
58,
320,
11,
25,
88,
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1225,
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320,
11,
25,
28483,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9224,
41888,
13,
20,
11,
16,
4357,
951,
28,
1084,
273,
21217,
10236,
37021,
10258,
8,
198,
220,
220,
220,
220,
220,
371,
1,
5239,
18109,
276,
69,
58,
72,
44,
11,
25,
87,
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1225,
69,
58,
72,
44,
11,
25,
88,
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1225,
69,
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44,
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4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9224,
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13,
20,
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88,
4357,
1225,
69,
58,
28265,
25,
22510,
4357,
9224,
41888,
13,
20,
11,
15,
12962,
198,
220,
220,
220,
886,
198,
220,
220,
220,
1441,
357,
87,
1084,
11,
2124,
9806,
11,
331,
1084,
11,
331,
9806,
11,
10139,
62,
87,
11,
10139,
62,
88,
11,
10139,
62,
88,
33,
11,
10139,
62,
88,
36,
11,
198,
220,
220,
220,
220,
220,
5743,
62,
87,
33,
11,
5743,
62,
87,
36,
11,
5743,
62,
88,
33,
11,
5743,
62,
88,
36,
11,
299,
7568,
11,
1225,
69,
8,
198,
437,
198
] | 2.436588 | 2,192 |
using EVQueues, PGFPlots
lambda=120.0;
mu=1.0;
gamma=0.5;
#C=80.0;
C=60.0;
Tfinal=100.0;
edf = ev_edf(lambda,mu,gamma,Tfinal,C,snapshots=[Tfinal])
llf = ev_llf(lambda,mu,gamma,Tfinal,C,snapshots=[Tfinal])
llr = ev_llr(lambda,mu,gamma,Tfinal,C,snapshots=[Tfinal])
#compute_statistics!(sim)
fig = Axis([
Plots.Linear([ev.requestedEnergy for ev in edf.EVs][6000:10:end],[ev.requestedEnergy-ev.departureWorkload for ev in edf.EVs][6000:10:end], style="solid,only marks=true,blue", legendentry="EDF"),
Plots.Linear([ev.requestedEnergy for ev in llf.EVs][6000:10:end],[ev.requestedEnergy-ev.departureWorkload for ev in llf.EVs][6000:10:end], style="solid,only marks=true,red", legendentry="LLF"),
Plots.Linear([ev.requestedEnergy for ev in llr.EVs][6000:10:end],[ev.requestedEnergy-ev.departureWorkload for ev in llr.EVs][6000:10:end], style="solid,only marks=true,green", legendentry="LLR"),
],
legendPos="north west", xlabel="Requested Service (\$S\$)", ylabel="Attained Service (\$S_r\$)", xmin=0, xmax=3, ymin=0, ymax=3, width="0.7\\columnwidth", height="0.4\\columnwidth"
);
save("/tmp/sigma_compare.tex",fig,include_preamble=false)
| [
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8696,
15681,
947,
11,
350,
21713,
3646,
1747,
198,
198,
50033,
28,
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26,
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30300,
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220,
220,
220,
220,
220,
220,
1345,
1747,
13,
14993,
451,
26933,
1990,
13,
25927,
276,
28925,
329,
819,
287,
1225,
69,
13,
20114,
82,
7131,
43434,
25,
940,
25,
437,
38430,
1990,
13,
25927,
276,
28925,
12,
1990,
13,
10378,
433,
495,
12468,
2220,
329,
819,
287,
1225,
69,
13,
20114,
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43434,
25,
940,
25,
437,
4357,
3918,
2625,
39390,
11,
8807,
8849,
28,
7942,
11,
17585,
1600,
8177,
13000,
2625,
1961,
37,
12340,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1345,
1747,
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14993,
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26933,
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25927,
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28925,
329,
819,
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276,
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16589,
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220,
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276,
4809,
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59,
3,
50,
59,
3,
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331,
18242,
2625,
8086,
1328,
4809,
357,
59,
3,
50,
62,
81,
59,
3,
42501,
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1084,
28,
15,
11,
2124,
9806,
28,
18,
11,
331,
1084,
28,
15,
11,
331,
9806,
28,
18,
11,
9647,
2625,
15,
13,
22,
6852,
28665,
10394,
1600,
6001,
2625,
15,
13,
19,
6852,
28665,
10394,
1,
198,
220,
220,
220,
5619,
198,
198,
21928,
7203,
14,
22065,
14,
82,
13495,
62,
5589,
533,
13,
16886,
1600,
5647,
11,
17256,
62,
79,
1476,
903,
28,
9562,
8,
198
] | 2.233886 | 543 |
#=
https://dtai.cs.kuleuven.be/problog/tutorial/basic/03_dice.html
"""
Let’s consider an infinite number of dice, which we roll one after the other until we see a
six for the first time. What is the probability of stopping after n dice? The first die is always rolled,
those with higher numbers D are only rolled if the previous roll did not stop the process.
"""
Cf dice_6_throws.jl
~/blog/rolling_dice4.blog
~/psi/rolling_dice4.psi
~/webppl/rolling_dice4.wppl
Theoretical values (1:50)
1:0.16666666666666666
2:0.1388888888888889
3:0.11574074074074076
4:0.09645061728395063
5:0.08037551440329219
6:0.06697959533607684
7:0.05581632944673069
8:0.04651360787227558
9:0.038761339893562986
10:0.032301116577969156
11:0.02691759714830763
12:0.022431330956923026
13:0.018692775797435855
14:0.015577313164529882
15:0.012981094303774901
16:0.010817578586479085
17:0.009014648822065905
18:0.0075122073517215875
19:0.006260172793101323
20:0.005216810660917769
21:0.0043473422174314744
22:0.003622785181192896
23:0.0030189876509940797
24:0.002515823042495067
25:0.002096519202079222
26:0.0017470993350660188
27:0.0014559161125550157
28:0.0012132634271291797
29:0.0010110528559409832
30:0.0008425440466174861
31:0.0007021200388479051
32:0.0005851000323732542
33:0.0004875833603110452
34:0.00040631946692587105
35:0.00033859955577155923
36:0.0002821662964762994
37:0.00023513858039691615
38:0.00019594881699743015
39:0.0001632906808311918
40:0.0001360755673593265
41:0.00011339630613277208
42:9.449692177731006e-5
43:7.874743481442506e-5
44:6.562286234535421e-5
45:5.468571862112852e-5
46:4.5571432184273776e-5
47:3.797619348689481e-5
48:3.164682790574568e-5
49:2.63723565881214e-5
50:2.19769638234345e-5
The values found by this model (run_model(0))
1.00000 => 6631 (0.165775)
2.00000 => 5661 (0.141525)
3.00000 => 4593 (0.114825)
4.00000 => 3867 (0.096675)
5.00000 => 3215 (0.080375)
6.00000 => 2700 (0.067500)
7.00000 => 2210 (0.055250)
8.00000 => 1845 (0.046125)
9.00000 => 1515 (0.037875)
10.00000 => 1282 (0.032050)
11.00000 => 1137 (0.028425)
12.00000 => 881 (0.022025)
13.00000 => 728 (0.018200)
14.00000 => 567 (0.014175)
15.00000 => 505 (0.012625)
16.00000 => 459 (0.011475)
17.00000 => 364 (0.009100)
18.00000 => 301 (0.007525)
19.00000 => 245 (0.006125)
20.00000 => 225 (0.005625)
22.00000 => 163 (0.004075)
21.00000 => 154 (0.003850)
23.00000 => 126 (0.003150)
24.00000 => 117 (0.002925)
25.00000 => 83 (0.002075)
26.00000 => 65 (0.001625)
27.00000 => 54 (0.001350)
30.00000 => 48 (0.001200)
28.00000 => 45 (0.001125)
29.00000 => 36 (0.000900)
31.00000 => 36 (0.000900)
32.00000 => 27 (0.000675)
34.00000 => 22 (0.000550)
33.00000 => 20 (0.000500)
35.00000 => 12 (0.000300)
37.00000 => 9 (0.000225)
38.00000 => 7 (0.000175)
39.00000 => 6 (0.000150)
42.00000 => 5 (0.000125)
40.00000 => 5 (0.000125)
36.00000 => 5 (0.000125)
46.00000 => 4 (0.000100)
41.00000 => 4 (0.000100)
44.00000 => 3 (0.000075)
47.00000 => 3 (0.000075)
48.00000 => 3 (0.000075)
57.00000 => 2 (0.000050)
49.00000 => 1 (0.000025)
45.00000 => 1 (0.000025)
50.00000 => 1 (0.000025)
43.00000 => 1 (0.000025)
72.00000 => 1 (0.000025)
=#
using Turing, StatsPlots, DataFrames
include("jl_utils.jl")
# Closed form of the probability
function theoretical_prob(n)
return (1/6.0) * ((5/6.0)^(n-1))
end
# For val = 0: show the full distribution of the length (len)
# otherwise show the probability of len == val
@model function dice_6_throws(val=0)
max_len = 100
function throws(a)
if length(a) > max_len
return a
end
# For some reason, this don't work: it just generates the same number repeatedly.
# t ~ DiscreteUniform(1,6)
t = rand(DiscreteUniform(1,6)) # This works.
# If we see a 6 then we stop
if t == 6
return vcat(a,t)
else
return throws(vcat(a,t))
end
end
len ~ DiscreteUniform(0,max_len)
# a = throws(TArray{Int64}(undef,0))
a = throws(Int64[])
len = length(a)
if val == 0
return len
else
return len == val
end
end
function run_model(len=0)
model = dice_6_throws(len)
num_chains = 4
# chains = sample(model, Prior(), 10_000)
# chains = sample(model, MH(), MCMCThreads(), 100_000, num_chains)
chains = sample(model, MH(), MCMCThreads(), 10_000, num_chains)
# chains = sample(model, MH(), 10_000)
# chains = sample(model, MH(), 1_000)
# chains = sample(model, PG(15), MCMCThreads(), 1_000, num_chains)
# chains = sample(model, SMC(1000), MCMCThreads(), 10_000, num_chains)
# chains = sample(model, SMC(1000), 10_000)
# chains = sample(model, IS(), 10_000)
#
# display(chains)
# show_var_dist_pct(chains,:len,1000)
println("prob return value:")
genq = generated_quantities(model, chains)
show_var_dist_pct(genq,1000)
if len > 0
println("\ntheoretical_prob($len): ", theoretical_prob(len))
end
end
for val in 0:10
println("\nval:$val")
run_model(val)
end
println("\nval:25")
run_model(25)
println("\nTheoretical values for 1:50")
for i in 1:50
println("$i:", theoretical_prob(i))
end
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] | 2.033073 | 2,691 |
using Gurobi
using Mosek
# train adversarial method with kernel using constraint generation
function train_adv_kernel_cg(X::Matrix, y::Vector, C::Real=1.0,
kernel::Symbol=:linear, kernel_params::Vector=[];
perturb::Real=0.0, tol::Real=1e-6, psdtol::Real=1e-6, obj_reltol::Real=0.0,
log::Real=0, n_thread::Int=0, solver::Symbol=:gurobi, verbose::Bool=true)
n = length(y)
# add one
X1 = [ones(n) X]' # transpose
m = size(X1, 1)
# number of class
n_c = maximum(y)
n_f = n_c * m # number of features
alpha = zeros(0)
# array of tuple
constraints = Tuple{Integer, Vector}[]
# prepare saved vars
idmi = map(i -> idi(m, i), collect(1:n_c))
# kernel
kernel_func = linear_kernel
if kernel == :gaussian
kernel_func = gaussian_kernel
elseif kernel == :polynomial
kernel_func = polynomial_kernel
end
# precompute kernel
K = [( i >= j ? kernel_func(X1[:,i], X1[:,j], kernel_params...) : 0.0 )::Float64 for i=1:n, j=1:n]
K = [( i >= j ? K[i,j] : K[j,i] )::Float64 for i=1:n, j=1:n]
if solver == :gurobi
# gurobi solver
# gurobi environtment
env = Gurobi.Env()
# Method : 0=primal simplex, 1=dual simplex, 2=barrier ; default for QP: barrier
# Threads : default = 0 (use all threads)
setparams!(env, PSDTol=psdtol, LogToConsole=log, Method=2, Threads=n_thread)
elseif solver == :mosek
# mosek environtment
env = makeenv()
end
# params for Gurobi
Q = zeros(0,0)
nu = zeros(0)
A = spzeros(n, 0) # sparse matrix
b = ones(n) * C
Q_prev = zeros(0,0)
nu_prev = zeros(0)
A_prev = spzeros(n, 0) # sparse matrix
# save lambda dot psi_i
n_lps = n * n_c
LPsi = zeros(n_lps, 0)
LPsi_prev = zeros(n_lps, 0)
iter = 0
dual_obj = 0.0
dual_obj_prev = 0.0
while true
iter += 1
if verbose
println("Iteration : ", iter)
tic();
end
# previous constraints
const_prev = copy(constraints)
# previous alpha
alpha_prev = copy(alpha)
## add to constraints
const_added = Tuple{Integer, Vector}[]
# find constraint for each sample
for i=1:n
psis = psi_list_dual(alpha, LPsi, i, n_c)
psis_id, val = best_psis(psis) # most violated constraints
# current xi_i
id_i = find(x -> x[1] == i, constraints)
xi_i_list = map(x -> x[2], constraints[id_i])
max_xi_i = 0
for j = 1:length(xi_i_list)
a = calc_const(psis::Vector, xi_i_list[j])
if a > max_xi_i
max_xi_i = a
end
end
if val > max_xi_i
cs = (i, sort!(psis_id))
if findfirst(constraints .== cs) == 0
push!(constraints, cs)
push!(const_added, cs)
end
end
end
# if no constraints added
if length(const_added) == 0
break
end
n_const = length(constraints)
#### Start QP ###
if verbose
println(">> Start QP")
toc();
tic();
end
n_prev = length(const_prev)
n_added = length(const_added)
# init alpha with previous iteration
alpha = zeros(n_const)
alpha[1:n_prev] = alpha_prev
Q_aug = [ ( calc_dot((const_prev[i][1], const_prev[i][2], const_added[j][1], const_added[j][2]), K, y) )::Float64
for i=1:n_prev, j=1:n_added]
Q_aug_diag = [
( i >= j ? calc_dot((const_added[i][1], const_added[i][2], const_added[j][1], const_added[j][2]), K, y) : 0.0 )::Float64
for i=1:n_added, j=1:n_added]
Q_aug_diag = [ (i >= j ? Q_aug_diag[i,j] : Q_aug_diag[j,i] )::Float64 for i=1:n_added, j=1:n_added ]
Q = [
( (i <= n_prev && j <= n_prev) ? Q_prev[i, j] : ( i <= n_prev ? Q_aug[i, j-n_prev] :
( j <= n_prev ? Q_aug[j, i-n_prev] : Q_aug_diag[i-n_prev, j-n_prev] ) ) )::Float64
for i=1:n_const, j=1:n_const]
nu_aug = [ ( calc_cconst(const_added[i][2]) )::Float64 for i=1:n_added ]
nu = [ ( i <= n_prev ? nu_prev[i] : nu_aug[i-n_prev] )::Float64 for i=1:n_const]
A_aug = spzeros(n, n_added)
for j = 1:n_added
A_aug[const_added[j][1], j] = 1.0
end
A = [A_prev A_aug]
# update LPsi
LPsi_aug = [ ( calc_dot((ceil(Int64, i/n_c), [(i%n_c==0)? n_c:i%n_c], const_added[j][1], const_added[j][2]), K, y) )::Float64
for i=1:n_lps, j=1:n_added]
LPsi = [LPsi_prev LPsi_aug]
## add perturbation
for i=1:n_const
Q[i,i] = Q[i,i] + perturb
end
if verbose
toc();
tic();
end
if solver == :gurobi
if verbose println(">> Optim :: Gurobi") end
## init model
model = gurobi_model(env,
sense = :minimize,
H = Q,
f = -nu,
A = A,
b = b,
lb = zeros(n_const)
)
# Print the model to check correctness
# print(model)
# Solve with Gurobi
Gurobi.optimize(model)
if verbose
toc();
println("<< End QP")
end
dual_obj = -get_objval(model)
# Solution
if verbose println("Objective value: ", dual_obj) end
# get alpha
alpha = get_solution(model)
# println("alpha = ", alpha)
#
# println("constraints = ", constraints)
if verbose println("n constraints = ", length(constraints)) end
### end QP ###
elseif solver == :mosek
if verbose println(">> Optim :: Mosek") end
task = maketask(env)
# set params
putintparam(task, Mosek.MSK_IPAR_LOG, 1)
putintparam(task, Mosek.MSK_IPAR_LOG_CHECK_CONVEXITY, 1)
putdouparam(task, Mosek.MSK_DPAR_CHECK_CONVEXITY_REL_TOL, psdtol)
# variables
appendvars(task, n_const)
# bound on var
for i::Int32 = 1:n_const
putbound(task, Mosek.MSK_ACC_VAR, i, Mosek.MSK_BK_RA, 0.0, C)
end
# objective
putobjsense(task, Mosek.MSK_OBJECTIVE_SENSE_MINIMIZE)
qi = zeros(Int32, (n_const * (n_const+1)) ÷ 2 )
qj = zeros(Int32, (n_const * (n_const+1)) ÷ 2 )
qv = zeros(Float64, (n_const * (n_const+1)) ÷ 2 )
ix = 1
for j::Int32 = 1:n_const
for i::Int32 = j:n_const
qi[ix] = i
qj[ix] = j
qv[ix] = Q[i,j]
ix += 1
end
end
putqobj(task, qi, qj, qv)
putclist(task, collect(1:n_const), -nu)
# constraints
## sparse array
appendcons(task, n)
for i::Int32 = 1:n_const
id_nz = A[:,i].nzind
putacol(task, i, id_nz, ones(length(id_nz)))
end
for i::Int32 = 1:n
putbound(task, Mosek.MSK_ACC_CON, i, Mosek.MSK_BK_RA, 0.0, C)
end
if verbose
toc(); tic();
end
Mosek.optimize(task)
if verbose
toc();
println("<< End QP")
end
# Solution
dual_obj, _ = getsolutioninfo(task, Mosek.MSK_SOL_ITR)
if verbose println("Objective value: ", -dual_obj) end
# get alpha
alpha = getxx(task, Mosek.MSK_SOL_ITR)
if verbose println("n constraints = ", length(constraints)) end
end
if obj_reltol > 0.0
if (dual_obj - dual_obj_prev) / dual_obj_prev < obj_reltol && iter > 1
if verbose
println((dual_obj - dual_obj_prev) / dual_obj_prev)
println(">> Iteration STOPPED | Objective relative tolerance : ", obj_reltol)
end
break
end
end
if verbose println() end
Q_prev = Q
nu_prev = nu
A_prev = A
LPsi_prev = LPsi
dual_obj_prev = dual_obj
end
# finalizing losses
gv_aug = zeros(n)
gv_01 = zeros(n)
l_adv = zeros(n)
l_01 = zeros(n)
for i=1:n
psis = psi_list_dual(alpha, LPsi, i, n_c)
psis_id, val = best_psis(psis) # most violated constraints
n_ps = length(psis_id)
gv_aug[i] = val
# compute probs
p_hat = zeros(n_c)
p_check = zeros(n_c)
for j=1:n_c
if j in psis_id
p_hat[j] = ( (n_ps-1.0)*psis[j] - sum(psis[psis_id[psis_id .!= j]]) + 1.0 ) / n_ps
p_check[j] = 1.0 / n_ps
else
p_hat[j] = 0.0
p_check[j] = 0.0
end
end
C01 = 1 - eye(n_c) # 01 loss matrix
v = p_hat' * C01 * p_check # the result is vector size 1 not a number
gv_01[i] = v[1] #
## training loss
l_adv[i] = 1.0 - p_hat[y[i]]
l_01[i] = 1.0 - round(Int, indmax(p_hat) == y[i])
end
game_value_01 = mean(gv_01)
game_value_augmented = mean(gv_aug)
# create model
adv_model = KernelMultiAdversarialModel(kernel, kernel_params, alpha, constraints, n_c, game_value_01, game_value_augmented, mean(l_adv), mean(l_01))
return adv_model::KernelMultiAdversarialModel
end
function predict_adv_kernel(model::KernelMultiAdversarialModel, X_test::Matrix, X_train::Matrix, y_train::Vector)
alpha = model.alpha
n_c = model.n_class
n = size(X_test, 1)
constraints = model.constraints
n_const = length(alpha)
X1 = [ones(n) X_test]' # transpose
m = size(X1, 1)
# training data
n_tr = size(X_train, 1)
X1_tr = [ones(n_tr) X_train]' # transpose
# kernel
kernel = model.kernel
kernel_params = model.kernel_params
# kernel function
kernel_func = linear_kernel
if kernel == :gaussian
kernel_func = gaussian_kernel
elseif kernel == :polynomial
kernel_func = polynomial_kernel
end
# compute Kernel
K = [ kernel_func(X1_tr[:,i], X1[:,j], kernel_params...)::Float64 for i=1:n_tr, j=1:n]
prob = zeros(n, n_c)
pred = zeros(n)
for i=1:n
psis = zeros(n_c)
for j = 1:n_const
psis -= alpha[j] * calc_dotlphi( (constraints[j][1], constraints[j][2], i), K, y_train, n_c)
end
psis_id, val = best_psis(psis) # most violated constraints
n_ps = length(psis_id)
for j=1:n_c
if j in psis_id
prob[i,j] = ( (n_ps-1.0)*psis[j] - sum(psis[psis_id[psis_id .!= j]]) + 1.0 ) / n_ps
else
prob[i,j] = 0.0
end
end
pred[i] = indmax(psis)
end
return prob::Matrix{Float64}, pred::Vector{Float64}
end
function test_adv_kernel(model::KernelMultiAdversarialModel, X_test::Matrix, y_test::Vector, X_train::Matrix, y_train::Vector)
n = size(X_test, 1)
y_prob, y_pred = predict_adv_kernel(model, X_test, X_train, y_train)
# calculate testing loss
losses = zeros(n)
losses01 = zeros(n)
for i=1:n
losses[i] = 1.0 - y_prob[i, y_test[i]]
losses01[i] = 1.0 - round(Int, y_pred[i] == y_test[i])
end
loss = sum(losses) / n
loss01 = sum(losses01) / n
return loss::Float64, losses::Vector{Float64}, loss01::Float64, losses01::Vector{Float64},
y_prob::Matrix{Float64}, y_pred::Vector{Float64}
end
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] | 2.02935 | 5,213 |
## ----------------------------------------------------------------------------
# proj 2D
let
method = ME_MAX_POL
biom_ider = iJR.BIOMASS_IDER
ps_pool = Dict()
for exp in EXPS
datfile = ME_INDEX[method, :DFILE, exp]
dat = deserialize(datfile)
model = dat[:model]
for Hd_ider in FLX_IDERS
# 2D Projection
p = plot(;title = string("Heerden2013, exp", exp),
xlabel = string(biom_ider), ylabel = string(Hd_ider),
legend = :right
)
proj = DAT[method, :ep, :proj, Hd_ider, exp]
ChP.plot_projection2D!(p, proj; l = 50)
# bounds
lb, ub = DAT[method, :bounds, :flx, Hd_ider, exp]
hline!(p, [lb]; lw = 3,
label = "fva lb",
color = :blue, ls = :solid
)
hline!(p, [ub]; lw = 3,
label = "fva ub",
color = :red, ls = :solid
)
# EXPERIMENTAL FLXS
exp_biom = DAT[method, :Hd, :flx, biom_ider, exp]
exp_exch = DAT[method, :Hd, :flx, Hd_ider, exp]
scatter!(p, [exp_biom], [exp_exch];
m = 8, color = :red, label = "exp"
)
# MAXENT FLXS
ep_biom = DAT[method, :ep, :flx, biom_ider, exp]
ep_biom_err = DAT[method, :eperr, :flx, biom_ider, exp]
ep_exch = DAT[method, :ep, :flx, Hd_ider, exp]
ep_exch_err = DAT[method, :eperr, :flx, Hd_ider, exp]
scatter!(p, [ep_biom], [ep_exch];
xerr = [ep_biom_err], yerr = [ep_exch_err],
m = 8, color = :blue, label = "maxent"
)
# mysavefig(p, "polytope"; Hd_ider, exp, method)
ps_pool[(exp, Hd_ider)] = deepcopy(p)
end
end
# collect
for exp in EXPS
ps = Plots.Plot[ps_pool[(exp, Hd_ider)] for Hd_ider in FLX_IDERS]
mysavefig(ps, "polytope"; exp, method)
end
for Hd_ider in FLX_IDERS
ps = Plots.Plot[ps_pool[(exp, Hd_ider)] for exp in EXPS]
mysavefig(ps, "polytope"; Hd_ider, method)
end
end | [
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] | 1.750794 | 1,260 |
using Base.Iterators
abstract type Part1 end
abstract type Part2 end
function update!(::Type{Part1}, grid::AbstractMatrix{Char})
previous = copy(grid)
updated = false
for j in 1:size(grid, 2), i in 1:size(grid, 1)
middle = previous[i, j]
switch = true
numoccupied = 0
for (δj, δi) in product([-1, 0, 1], [-1, 0, 1])
δi == δj == 0 && continue
try
neighbor = previous[i + δi, j + δj]
if neighbor == '#'
middle == 'L' && (switch = false)
middle == '#' && (numoccupied += 1)
end
catch
end
end
middle == 'L' && switch && (grid[i, j] = '#'; updated = true)
middle == '#' && numoccupied ≥ 4 && (grid[i, j] = 'L'; updated = true)
end
updated
end
const Site = Tuple{Int, Int}
function visibility(init::AbstractMatrix{Char})
N, M = size(init)
visible = Dict{Site, Vector{Site}}()
for j in 1:M, i in 1:N
if init[i, j] != '.'
for (δj, δi) in product([-1, 0, 1], [-1, 0, 1])
δi == δj == 0 && continue
u, v = i + δi, j + δj
try
while init[u, v] == '.'
u += δi
v += δj
end
if haskey(visible, (i, j))
push!(visible[(i, j)], (u, v))
else
visible[(i, j)] = [(u, v)]
end
catch
end
end
end
end
visible
end
function update!(::Type{Part2}, grid::AbstractMatrix{Char}, visible)
previous = copy(grid)
updated = false
for (i, j) in keys(visible)
if previous[i, j] == 'L'
switch = true
for (u, v) in visible[(i, j)]
if previous[u, v] == '#'
switch = false
end
end
switch && (grid[i, j] = '#'; updated = true)
elseif previous[i, j] == '#'
numoccupied = 0
for (u, v) in visible[(i, j)]
if previous[u, v] == '#'
numoccupied += 1
end
end
numoccupied ≥ 5 && (grid[i, j] = 'L'; updated = true)
end
end
updated
end
function part1(grid)
while update!(Part1, grid) end
sum(grid .== '#')
end
function part2(grid)
visible = visibility(grid)
while update!(Part2, grid, visible) end
sum(grid .== '#')
end
let init = hcat(collect.(readlines("input.txt"))...)
@info "Solution" part1(init) part2(init)
end
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] | 1.782034 | 1,514 |
set_global_client()
@testset "Pipeline - Basic" begin
pipe = Pipeline()
@test length(pipe.resp) == 0
for _ in 1:1000
lrange("nothing", 0, -1; client=pipe)
end
@test length(pipe.resp) == 1000
result = execute(pipe)
@test result == fill([], 1000)
@test length(pipe.resp) == 0
end
@testset "Pipeline - Do Block" begin
result = pipeline() do pipe
for _ in 1:1000
lrange("nothing", 0, -1; client=pipe)
end
end
@test result == fill([], 1000)
end
@testset "Pipeline - MULTI/EXEC" begin
no_filter_result = pipeline(; filter_multi_exec=false) do pipe
multi(; client=pipe)
for _ in 1:1000
lrange("nothing", 0, -1; client=pipe)
end
exec(; client=pipe)
end
@test length(no_filter_result) == 1002
@test no_filter_result[1] == "OK"
@test no_filter_result[2:length(no_filter_result)-1] == fill("QUEUED", 1000)
@test no_filter_result[end] == fill([], 1000)
filter_result = pipeline(; filter_multi_exec=true) do pipe
multi_exec(; client=pipe) do
for _ in 1:1000
lrange("nothing", 0, -1; client=pipe)
end
end
end
@test length(filter_result) == 1
@test filter_result[1] == fill([], 1000)
end
flushall() | [
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] | 2.213198 | 591 |
#== # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#
# Description
#
# Tests related to IAU-2006 precession-nutation algorithm.
#
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#
# References
#
# [1] Vallado, D. A (2013). Fundamentals of Astrodynamics and Applications.
# Microcosm Press, Hawthorn, CA, USA.
#
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ==#
# File: ./src/transformations/precession_nutation_iau2006.jl
# ==========================================================
# Function precession_nutation_iau2006
# ------------------------------------
################################################################################
# Test Results
################################################################################
#
# Scenario 01
# ===========
#
# Example 3-14: Performing an IAU-2000 reduction [1, p. 220]
#
# According to this example, using JD_TT = 2453101.828154745, one gets:
#
# X = 80.531880"
# Y = 7.273921"
# s = -0.003027"
#
################################################################################
@testset "Function precession_nutation_iau2006" begin
JD_TT = 2453101.828_154_745
X,Y,s = precession_nutation_iau2006(JD_TT)
@test X*180/pi*3600 ≈ 80.531880 atol = 5e-5
@test Y*180/pi*3600 ≈ 7.273921 atol = 5e-5
@test s*180/pi*3600 ≈ -0.003027 atol = 1e-6
end
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### A Pluto.jl notebook ###
# v0.18.4
using Markdown
using InteractiveUtils
# ╔═╡ 3e14817e-85f8-4d5b-8a8f-075d1799ed27
using AbstractTrees, PlutoUI, Random, Wordlegames
# ╔═╡ ed649770-a53a-11ec-0434-1bbbc561f8cb
# hideall
title = "Wordle games as a tree";
# ╔═╡ 021364a8-f40b-49fe-9a02-406c0044c9ed
"""
+++
title = "$title"
+++
""" |> Base.Text
# ╔═╡ c49e1040-67c8-4386-a6ce-bbbeace8bc90
md"""
# $title
As described in the previous tutorial, strategies such as maximizing the entropy or minimizing the expected pool size for the next stage can be used to select guesses automatically in Wordle or related games.
When doing so the possible games can be represented in a data structure called a [tree](https://en.wikipedia.org/wiki/Tree_(data_structure)).
Some of the terminology used with these structures is based on concepts of a family tree.
First attach some packages that will be used
"""
# ╔═╡ cec480d3-7348-4d14-9628-007893713a07
md"and create an instance of `wordle` where the guesses are chosen to maximize the entropy, which is the default criterion."
# ╔═╡ 9586bcbd-16aa-4598-8faa-8220755566e6
begin
datadir = joinpath(pkgdir(Wordlegames), "data")
wordle = GamePool(collect(readlines(joinpath(datadir, "Wordletargets.txt"))))
end;
# ╔═╡ ddc457c3-f207-4ed1-8a8a-7f1c4cfdaed7
md"Finally, we create a tree from the games for a random selection of 25 targets."
# ╔═╡ 52ed4682-a0d7-44cf-b878-27f1cf9cc91d
gametree25 = tree(wordle, Random.seed!(1234321), 25);
# ╔═╡ 86d0bb82-ed8f-4b03-b90c-31c55e556bae
md"""
## The AbstractTrees package
The [AbstractTrees](https://github.com/JuliaCollections/AbstractTrees.jl) package provides many methods for working with tree data structures.
One of the most useful is `print_tree` which, as the name suggests, prints the tree in a special format.
(Because the content for these tutorials is generated as [Pluto](https://github.com/fonsp/Pluto.jl.git) notebooks, we need to wrap the call to `print_tree` in `with_terminal() do ... end` to have the output displayed.
Outside of Pluto this is not necessary.
"""
# ╔═╡ 30ba17b1-9c0c-4fd6-9f40-3c3c1c986e93
with_terminal() do
print_tree(gametree25; maxdepth=8)
end
# ╔═╡ 384abc84-3fda-4ba3-b52e-2d46052b8ce6
md"""
Each guess in a game constitutes a `"node"` in the tree.
The initial guess in any of the games is `"raise"`, which is the `"root"` node for the tree.
A node can have zero or more `"children"` which are its immediate descendents.
The nodes in this tree are each a `GameNode` struct with a `"children"` field.
"""
# ╔═╡ 0c24b1ca-b36a-46f6-9da7-e5c3e3670d29
typeof(gametree25)
# ╔═╡ 68f9375f-31af-4916-933f-fb41905819f8
fieldnames(GameNode)
# ╔═╡ e1735528-469e-4a17-8b8a-d95dca03f46c
length(gametree25.children) # number of children of the root node
# ╔═╡ 112b0452-7ae4-49b4-bbdd-54fb00ff8bdb
md"""
The `score` field of a `GameNode` is similar to the elements of the `guesses` field of a `GamePool` object but with one important difference.
In a `GameNode` the `score` and `sc` fields are the score that will produce the guess, as opposed to the score for the guess,
That is, the first child of `"raise"` is `"pilot"` which is the next guess in a game in which `"raise"` returns a score of `"🟫🟫🟨🟫🟫"` as tiles or 9 as a decimal number.
"""
# ╔═╡ f8f1521c-fbbb-4648-af9c-0a15c7e787ff
first(gametree25.children).score
# ╔═╡ 1dd5c9fb-820f-41c6-b796-494d68479f1c
md"""
The 16 children of the root node from these 25 games are
"""
# ╔═╡ 8302e26a-53ae-4f9f-9579-26a1c839c865
[child.score.guess for child in gametree25.children]
# ╔═╡ 7f4a7f14-fe77-43fa-b2dd-37ba70cf9da3
md"""
Some of these children have many descendents.
When generating the tree the children of a node are ordered according to the size of the tree rooted at that node.
Because a subtree is exactly the same type of structure as a tree, we can print a subtree with `print_tree`.
"""
# ╔═╡ 088ca1f6-44c3-4d0d-a68b-c71881813410
with_terminal() do
print_tree(first(gametree25.children))
end
# ╔═╡ 785fe89b-4008-437a-ac73-2bdaa26e7cf6
md"""
We see that the size of the tree rooted at `"pilot"` is 10.
"""
# ╔═╡ 8d83a193-aed2-4c1d-8bc5-614a81d16548
md"""
The "leaves" of a tree are the terminal nodes, i.e. the nodes that do not have children.
"""
# ╔═╡ 576d0904-3f3e-49e8-9ce7-ccdd8767bf1b
[leaf.score.guess for leaf in Leaves(gametree25)]
# ╔═╡ 144a9d04-f971-48d9-ace4-dd0156f591c2
md"""
It happens in this case that all of the targets that generated the tree are leaves in this tree, but that is not necessarily the case.
"""
# ╔═╡ 105a0522-729a-49ea-b0a5-62b868ba5e45
length(collect(Leaves(gametree25)))
# ╔═╡ efcb3473-6ea1-459c-9995-32b0a407a2f4
md"""
## Creating the tree structure
As a language Julia gets high marks for "composability" - the ability to adapt one package to use concepts from another package.
The use of generic functions and multiple dispatch is central to this enhanced compositibility.
All that is necessary to use many of the functions in `AbstractTrees.jl` on the trees created from a collection of games using a particular `GamePool` is to define the `GameNode` struct, the method of generating the tree, and methods for `AbstractTrees.children`, `AbstractTrees.nodetype` and `AbstractTrees.printnode`
"""
# ╔═╡ 00000000-0000-0000-0000-000000000001
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git-tree-sha1 = "4c463de78d2f3f9447b695e241eba43cc945f866"
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deps = ["Artifacts", "Libdl", "OpenBLAS_jll"]
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] | 1.986087 | 8,553 |
@doc doc"Abstract Boolean Types: {{true},{false}.{true,false}}" ->
immutable AbstractBool <: Domain{Bool}
v::UInt8
AbstractBool(v::UInt8) = (@assert v == 0x1 || v == 0x2 || v== 0x3; new(v))
end
const t = AbstractBool(0x1)
const f = AbstractBool(0x2)
const tf = AbstractBool(0x3)
promote_rule(::Type{Bool}, ::Type{AbstractBool}) = AbstractBool
convert(::Type{AbstractBool}, b::Bool) = if b t else f end
## AbstractBool Set Operations
## ===========================
subsumes(x::AbstractBool, y::AbstractBool) = x === tf || x === y
subsumes(x::AbstractBool, y::Bool) = subsumes(x,convert(AbstractBool, y))
isintersect(x::AbstractBool, y::AbstractBool) = !((x === t && y === f) || (x === f && y === t))
isintersect(x::AbstractBool, y::Bool) = isintersect(x,convert(AbstractBool, y))
isintersect(x::Bool, y::AbstractBool) = isintersect(convert(AbstractBool, x),y)
isrelational(::Type{AbstractBool}) = false
isabstract(c::Type{Bool}, a::Type{AbstractBool}) = true
isequal(x::AbstractBool, y::AbstractBool) = x.v == y.v
domaineq(x::AbstractBool, y::AbstractBool) = x.v == y.v
⊔(a::AbstractBool) = a
⊔(a::AbstractBool, b::AbstractBool) = a === b ? a : tf
⊔(a::Bool, b::AbstractBool) = ⊔(convert(AbstractBool,a),b)
⊔(a::AbstractBool, b::Bool) = ⊔(a,convert(AbstractBool,b))
⊔(a::Bool, b::Bool) = a === b ? convert(AbstractBool,a) : tf
## =========================
## Lifted Boolean Arithmetic
function !(b::AbstractBool)
if b === t
f
elseif b === f
t
elseif b === tf
tf
end
end
(==)(x::AbstractBool, y::AbstractBool) =
x === tf || y === tf ? tf : x === t && y === t || x === f && y === f
function (==)(x::AbstractBool, y::AbstractBool)
if x === tf || y === tf tf
elseif x === t && y === t t
elseif x === f && y === f t
else f
end
end
(==)(x::AbstractBool, y::Bool) = (==)(promote(x,y)...)
(==)(y::Bool,x::AbstractBool) = (==)(promote(y,x)...)
function (|)(x::AbstractBool, y::AbstractBool)
if x === t || y === t t
elseif x === tf || y === tf tf
else f
end
end
|(x::AbstractBool, y::Bool) = |(x,convert(AbstractBool,y))
|(y::Bool, x::AbstractBool) = |(convert(AbstractBool,y), x)
function (&)(x::AbstractBool, y::AbstractBool)
if x === f || y === f f
elseif x === tf || y === tf tf
else t
end
end
(&)(x::AbstractBool, y::Bool) = x & convert(AbstractBool, y)
(&)(y::Bool, x::AbstractBool) = convert(AbstractBool, y) & x
# When condition is TF we need to evaluate both branches
# and merge with ⊔
function ifelse(c::AbstractBool, x, y)
if c === t
x
elseif c === f
y
elseif c === tf
⊔(x,y)
end
end
## Printing
## ========
string(x::AbstractBool) = ["{true}","{false}","{true,false}"][x.v]
print(io::IO, x::AbstractBool) = print(io, string(x))
show(io::IO, x::AbstractBool) = print(io, string(x))
showcompact(io::IO, x::AbstractBool) = print(io, string(x))
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"""
ArcSwift()
Arc-Swift transition system for dependency parsing.
Described in [Qi & Manning 2017](https://nlp.stanford.edu/pubs/qi2017arcswift.pdf).
"""
struct ArcSwift <: AbstractTransitionSystem end
initconfig(::ArcSwift, graph::DependencyTree) = ArcSwiftConfig(graph)
initconfig(::ArcSwift, words) = ArcSwiftConfig(words)
transition_space(::ArcSwift, labels=[]; max_k=5) =
isempty(labels) ? [LeftArc.(1:max_k)..., RightArc.(1:max_k)..., Shift()] :
[[LeftArc(k, l) for k in 1:max_k for l in labels]...,
[RightArc(k, l) for k in 1:max_k for l in labels]...,
Shift()]
projective_only(::ArcSwift) = true
struct ArcSwiftConfig <: AbstractParserConfiguration
σ::Vector{Int}
β::Vector{Int}
A::Vector{Token}
end
function ArcSwiftConfig(words)
σ = [0]
β = collect(1:length(words))
A = Token.(words)
ArcSwiftConfig(σ, β, A)
end
function ArcSwiftConfig(gold::DependencyTree)
σ = [0]
β = collect(1:length(gold))
A = [Token(t, head=-1) for t in gold]
ArcSwiftConfig(σ, β, A)
end
stack(cfg::ArcSwiftConfig) = cfg.σ
buffer(cfg::ArcSwiftConfig) = cfg.β
tokens(cfg::ArcSwiftConfig) = cfg.A
tokens(cfg::ArcSwiftConfig, is) = [token(cfg, i) for i in is if 0 <= i <= length(cfg.A)]
function leftarc(cfg::ArcSwiftConfig, k::Int, args...; kwargs...)
# Assert a head-dependent relation between the word at the front
# of the input buffer and the word at the top of the stack; pop
# the stack.
i = length(cfg.σ) - k + 1
s, b = cfg.σ[i], cfg.β[1]
A = copy(cfg.A)
if s > 0
A[s] = Token(A[s]; head=b, kwargs...)
end
ArcSwiftConfig(cfg.σ[1:i-1], cfg.β, A)
end
function rightarc(cfg::ArcSwiftConfig, k::Int, args...; kwargs...)
# Assert a head-dependent relation between the word on the top of
# the σ and the word at front of the input buffer; shift the
# word at the front of the input buffer to the stack.
i = length(cfg.σ) - k + 1
s, b = cfg.σ[i], cfg.β[1]
A = copy(cfg.A)
A[b] = Token(A[b]; head=s, kwargs...)
ArcSwiftConfig([cfg.σ[1:i] ; b], cfg.β[2:end], A)
end
function shift(cfg::ArcSwiftConfig)
# Remove the word from the front of the input buffer and push it
# onto the stack.
ArcSwiftConfig([cfg.σ ; cfg.β[1]], cfg.β[2:end], cfg.A)
end
isfinal(cfg::ArcSwiftConfig) =
all(t -> all(h >= 0 for h in t.head), cfg.A) #&& length(cfg.σ) > 0 && length(cfg.β) > 0
"""
static_oracle(cfg::ArcSwiftConfig, tree, arc)
Oracle function for arc-swift dependency parsing.
Described in [Qi & Manning 2017](https://nlp.stanford.edu/pubs/qi2017arcswift.pdf).
"""
function static_oracle(cfg::ArcSwiftConfig, gold, arc=untyped)
S = length(cfg.σ)
if S >= 1 && length(cfg.β) >= 1
b = cfg.β[1]
for k in 1:S
i = S - k + 1
s = cfg.σ[i]
has_arc(gold, b, s) && return LeftArc(k, arc(gold[s])...)
has_arc(gold, s, b) && return RightArc(k, arc(gold[b])...)
end
end
return Shift()
end
==(cfg1::ArcSwiftConfig, cfg2::ArcSwiftConfig) =
cfg1.σ == cfg2.σ && cfg1.β == cfg2.β && cfg1.A == cfg2.A
# TODO
function possible_transitions(cfg::ArcSwiftConfig, graph::DependencyTree, arc=untyped)
TransitionOperator[static_oracle(cfg, graph, arc)]
end
function Base.show(io::IO, c::ArcSwiftConfig)
A = join(["$i $(t.form) $(t.head)" for (i,t) in enumerate(tokens(c))], ", ")
print(io, "ArcSwiftConfig($(c.σ),$(c.β),$A)")
end
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] | 2.282508 | 1,515 |
include("SSA.jl")
function addSim(sys::System, tauVar::TauVar, n::Int64)
ens = newEnsemble(sys, n)
npoints = length(sys.data.dd)
if sys.routine.ssa == :Direct
for i in 2:npoints
if i == 2 && sys.routine.shoot
reShoot!(sys, ens, i-1)
end
_sim!(sys, ens, i)
for k in eachindex(ens)
for xx in ens[k].x
push!(ens[k].xa,xx)
end
push!(ens[k].ta,sys.times[i])
end
end
elseif sys.routine.ssa == :Tau
for i in 2:npoints
if i == 2 && sys.routine.shoot
reShoot!(sys, ens, i-1)
end
_sim!(sys, ens, i, tauVar)
for k in eachindex(ens)
for xx in ens[k].x
push!(ens[k].xa,xx)
end
push!(ens[k].ta,sys.times[i])
end
end
end
ens
end
function sim(sys::System, θ::Vector{Float64}, σ2::Vector{Float64}, n::Int64)
sys.state.θ = θ
sys.state.σ2 = diagm(σ2)
tauVar = preAllocTauVar(sys)
ens = Ensemble()
append!(ens, addSim(sys, tauVar, n))
ens
end
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] | 1.662031 | 719 |
using CSV
using LinearAlgebra: dot, I
using CairoMakie
using naumer_Dimensionality_2022
const Σ = [1.0 0.1 0.1;
0.1 1.0 0.1;
0.1 0.1 1.0]
const v = [1.0/sqrt(3), 1.0/sqrt(3), 1.0/sqrt(3)]
const σ² = 1
f(x) = abs2(dot(v, Σ * x)) / (dot(x, Σ * x) + σ² * dot(x, x))
# Helper function to compute extra coordinate
function getZ(x,y)
if abs2(x) + abs2(y) > 1 - 10*eps()
return NaN
end
return sqrt(1 - abs2(x) - abs2(y))
end
function rewardPositive(x,y)
return f([x,y,getZ(x,y)])
end
function rewardNegative(x,y)
return f([x,y,-getZ(x,y)])
end
state = zeros(3,100)
state[3,1] = 1
for i in 2:100
state[:,i] .= actiongradientstep_1DApprox(state[:,i-1], v, Σ, σ², 0.1)
end
rewards = zeros(100)
for i in 1:100
rewards[i] = f(state[:,i])
end
# Sample Circle
x = -1.1:0.01:1.1
y = -1.1:0.01:1.1
noto_sans = "./resources/NotoSans-Regular.ttf"
noto_sans_bold = "./resources/NotoSans-Bold.ttf"
tickfontsize = 26
labelfontsize = 28
basewidth = 2
fig = Figure(font=noto_sans, resolution=(1000,500), figure_padding=40)
ax1 = Axis( fig[1,1],
xticklabelsize=tickfontsize,
yticklabelsize=tickfontsize,
yticklabelpad=2,
xlabel="x", ylabel = "y",
xlabelsize=labelfontsize,
ylabelsize=labelfontsize,
titlesize=labelfontsize,
ratio=AxisAspect(1))
hm = heatmap!(ax1,x,y,rewardPositive)
lines!(ax1, state[1,:], state[2,:], color=:black, linewidth=3)
ax2 = Axis( fig[1,3],
xticklabelsize=tickfontsize,
yticklabelsize=tickfontsize,
yticklabelpad=2,
xlabel="Iteration", ylabel = "Objective",
xlabelsize=labelfontsize,
ylabelsize=labelfontsize,
titlesize=labelfontsize,
ratio=AxisAspect(1),
xminorticksvisible = true,
xminorgridvisible = true,
xminorticks = IntervalsBetween(10),
yminorticksvisible = true,
yminorgridvisible = true,
yminorticks = IntervalsBetween(5))
lines!(ax2,1:100, rewards, color=:black, width=2)
xlims!(ax2,(0,100))
Colorbar(fig[1, 2], hm, ticklabelsize=tickfontsize)
colgap!(fig.layout,2,80)
Label(fig[1,1,TopLeft()], "A", font=noto_sans_bold, textsize = 36, halign=:left, valign=:bottom, padding=(25,0,10,0))
Label(fig[1,3,TopLeft()], "B", font=noto_sans_bold, textsize = 36, halign=:left, valign=:bottom, padding=(25,0,10,0))
save("out/OptDescent.pdf",fig)
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] | 1.926549 | 1,307 |
export load_model, get_device, seisdata2torch, slide, detect, trigger_onset
export get_picks, load_test_data
"""
load_model(phase;device=nothing)
Load generalized-phase-detection model onto device.
# Arguments
- `phase::String`: Seismic phase to load - "P" or "S".
# Keywords
- `device::Union{PyObject,Nothing}`: Pytorch device - specify "gpu" or "cpu".
"""
function load_model(phase::String;device::Union{PyObject,Nothing}=nothing)
# check phase input
if uppercase(phase) ∉ ["P","S"]
error("Only available models are 'P' and 'S'.")
end
# get correct model
model_dir = joinpath(dirname(@__FILE__),"..","deps","downloads")
modelP = joinpath(model_dir,"model_P.pt")
modelS = joinpath(model_dir,"model_S.pt")
model_file = uppercase(phase) == "P" ? modelP : modelS
# load model
if isa(device,Nothing)
device = get_device()
end
model = UNet(3,1).to(device)
checkpoint = torch.load(model_file, map_location=device)
# load state dictionary
state_dict = Dict()
for key in keys(checkpoint["model_state_dict"])
if occursin("tracked",key)
continue
end
state_dict[key] = checkpoint["model_state_dict"][key]
end
model.load_state_dict(state_dict)
model.eval()
return model
end
"""
get_device()
Returns current PyTorch device.
"""
get_device() = torch.device(torch.cuda.is_available() ? "cuda" : "cpu")
"""
seisdata2torch(S)
Convert 3 component `SeisData` struct `S` into Torch tensor.
# Arguments
- `S::SeisData`: 3-component `SeisData` for detection.
- `window_samples::Int`: Number of samples per detection window.
"""
function seisdata2torch(S::SeisData,window_samples::Int)
# check only 3 channels in SeisChannel
@assert S.n == 3
# create sliding windows
X = cat([slide(S[ii].x,window_samples) for ii = 1:S.n]...,dims=3)
X ./= maximum(abs.(X),dims=(1,3))
return torch.from_numpy(X).float().permute(1,2,0)
end
"""
slide(A,window_samples)
Split vector `A` into non-overlapping windows of length `window_samples`.
# Arguments
- `S::SeisData`: 3-component `SeisData` for detection.
- `window_samples::Int`: Number of samples per detection window.
"""
function slide(A::AbstractVector, window_samples::Int)
N = size(A,1)
if N % window_samples == 0
return Array(reshape(A,window_samples,N ÷ window_samples))
else
return Array(reshape(A[1 : N - N % window_samples], window_samples, N ÷ window_samples))
end
end
"""
detect(X,model,batch_size;device=nothing)
Run generalized-phase-detection on tensor `X` in batches of size `batch_size`.
# Arguments
- `X::PyObject`: Torch tensor.
- `model::PyObject`: Torch `UNet` model.
- `batch_size::Int`: Number of windows per detection. Specifying too large a
batch size will cause the GPU to run out of VRAM.
# Keywords
- `device::Union{PyObject,Nothing}`: Pytorch device - specify "gpu" or "cpu".
"""
function detect(X,model,batch_size::Int;device::Union{PyObject,Nothing}=nothing)
# use default device
if isa(device,Nothing)
device = get_device()
end
# run predictions in batches
Y_pred = zeros(Float32, X.size(2),X.size(0))
for ii in 1:batch_size:size(Y_pred,2)
i_start = ii
i_stop = min(size(Y_pred,2),ii+batch_size-1)
X_test = get(X,i_start-1:i_stop-1)
X_test = X_test.to(device)
@pywith torch.no_grad() begin
out = model(X_test)
Y_pred[:,i_start:i_stop] .= out.cpu().permute(2,0,1).numpy()[:,:,1]
end
GC.gc(false)
end
return Y_pred
end
"""
trigger_onset(charfct, thresh1, thresh2; max_len=1e99, max_len_delete=false)
Calculate trigger on and off times.
Given `thresh1` and `thresh2` calculate trigger on and off times from
characteristic function.
# Arguments
- `charfct::AbstractArray`: Characteristic function of e.g. STA/LTA trigger.
- `thresh1::Real`: Value above which trigger (of characteristic function)
is activated (higher threshold).
- `thresh2::Real`: Value below which trigger (of characteristic function)
is deactivated (lower threshold).
# Keywords
- `max_len::Int`: Maximum length of triggered event in samples. A new
event will be triggered as soon as the signal reaches again above thresh1.
- `max_len_delete::Bool`: Do not write events longer than max_len into report file.
"""
function trigger_onset(charfct::AbstractArray, thresh1::Real, thresh2::Real;
max_len::Int=10^10, max_len_delete::Bool=false,
)
ind1 = findall(charfct .> thresh1)
if length(ind1) == 0
return []
end
ind2 = findall(charfct .> thresh2)
on = [ind1[1]]
off = [-1]
# determine the indices where charfct falls below off-threshold
ind2_ = Array{Bool}(undef,length(ind2))
ind2_[1:end-1] .= diff(ind2) .> 1
# last occurence is missed by diff, add it manually
ind2_[end] = true
append!(off,ind2[ind2_])
append!(on,ind1[findall(diff(ind1) .> 1) .+ 1])
# include last pick if trigger is on or drop it
if max_len_delete
# drop it
append!(off,max_len)
append!(on,on[end])
else
# include it
append!(off,ind2[end])
end
pick = []
while on[end] > off[1]
while on[1] <= off[1]
deleteat!(on,1)
end
while off[1] < on[1]
deleteat!(off,1)
end
if off[1] - on[1] > max_len
if max_len_delete
deleteat!(on,1)
continue
end
prepend!(off,on[1] + max_len)
end
push!(pick,[on[1],off[1]])
end
return permutedims(hcat(pick...))
end
"""
get_picks(S,probs,thresh1,thresh2;min_trig_dur=0.)
Get picks from detection probabilities `probs` from SeisData `S`.
# Arguments
- `S::SeisData`: 3-component `SeisData` for detection.
- `probs::AbstractArray`: Array of probabilities of phase detection per window.
- `thresh1::Real`: Probability (0-1) above which trigger is activated (higher threshold).
- `thresh2::Real`: Probability (0-1) below which trigger is deactivated (lower threshold).
# Keywords
`min_trig_dur::Real`: Minimum duration (in seconds) of trigger.
"""
function get_picks(
S::SeisData,
probs::AbstractArray,
thresh1::Real,
thresh2::Real;
min_trig_dur::Real=0.,
)
@assert 0. < thresh2 < thresh1 <= 1.
tt = collect(0:length(probs)-1) ./ S.fs[1]
picks = Array{DateTime}(undef,0)
trigs = trigger_onset(vcat(probs...),thresh1,thresh2)
for ii in 1:size(trigs,1)
if trigs[ii,1] == trigs[ii,2]
continue
end
# check trigger duration
trig_dur = tt[trigs[ii,2]] - tt[trigs[ii,1]]
if trig_dur < min_trig_dur
continue
end
pick = argmax(probs[trigs[ii,1]:trigs[ii,2]]) + trigs[ii,1]
tstamp = u2d(S.t[1][1,2] * 1e-6+ tt[pick])
push!(picks,tstamp)
end
return picks, tt
end
"""
load_test_data()
Load 3-component seismic data from the 2016 Mw 5.2 Anza, California sequence.
"""
function load_test_data()
sacfiles = joinpath(dirname(@__FILE__),"..","deps","downloads","*.SAC")
return read_data("sac",sacfiles)
end
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] | 2.350408 | 3,065 |
#=
https://github.com/JuliaArrays/EndpointRanges.jl
by Tim Holy
[this has minor edits for v0.7-DEV]
=#
module EndpointRanges
#using Compat
import Base: +, -, *, /, ÷, %
using Base: ViewIndex, tail, indices1
export ibegin, iend
abstract type Endpoint end
struct IBegin <: Endpoint end
struct IEnd <: Endpoint end
const ibegin = IBegin()
const iend = IEnd()
(::IBegin)(b::Integer, e::Integer) = b
(::IEnd )(b::Integer, e::Integer) = e
(::IBegin)(r::AbstractRange) = first(r)
(::IEnd )(r::AbstractRange) = last(r)
struct IndexFunction{F<:Function} <: Endpoint
index::F
end
(f::IndexFunction)(r::AbstractRange) = f.index(r)
for op in (:+, :-)
@eval $op(x::Endpoint) = IndexFunction(r->x(r))
end
for op in (:+, :-, :*, :/, :÷, :%)
@eval $op(x::Endpoint, y::Endpoint) = IndexFunction(r->$op(x(r), y(r)))
@eval $op(x::Endpoint, y::Number) = IndexFunction(r->$op(x(r), y))
@eval $op(x::Number, y::Endpoint) = IndexFunction(r->$op(x, y(r)))
end
# deliberately not <: AbstractUnitRange{Int}
abstract type EndpointRange{T} end
struct EndpointUnitRange{F<:Union{Int,Endpoint},L<:Union{Int,Endpoint}} <: EndpointRange{Int}
start::F
stop::L
end
struct EndpointStepRange{F<:Union{Int,Endpoint},L<:Union{Int,Endpoint}} <: EndpointRange{Int}
start::F
step::Int
stop::L
end
(r::EndpointUnitRange)(s::AbstractRange) = r.start(s):r.stop(s)
(r::EndpointUnitRange{Int,E})(s::AbstractRange) where {E<:Endpoint} = r.start:r.stop(s)
(r::EndpointUnitRange{E,Int})(s::AbstractRange) where {E<:Endpoint} = r.start(s):r.stop
(r::EndpointStepRange)(s::AbstractRange) = r.start(s):r.step:r.stop(s)
(r::EndpointStepRange{Int,E})(s::AbstractRange) where {E<:Endpoint} = r.start:r.step:r.stop(s)
(r::EndpointStepRange{E,Int})(s::AbstractRange) where {E<:Endpoint} = r.start(s):r.step:r.stop
Base.colon(start::Endpoint, stop::Endpoint) = EndpointUnitRange(start, stop)
Base.colon(start::Endpoint, stop::Int) = EndpointUnitRange(start, stop)
Base.colon(start::Int, stop::Endpoint) = EndpointUnitRange(start, stop)
Base.colon(start::Endpoint, step::Int, stop::Endpoint) = EndpointStepRange(start, step, stop)
Base.colon(start::Endpoint, step::Int, stop::Int) = EndpointStepRange(start, step, stop)
Base.colon(start::Int, step::Int, stop::Endpoint) = EndpointStepRange(start, step, stop)
function Base.getindex(r::UnitRange, s::EndpointRange)
getindex(r, newindex(indices1(r), s))
end
function Base.getindex(r::AbstractUnitRange, s::EndpointRange)
getindex(r, newindex(indices1(r), s))
end
function Base.getindex(r::StepRange, s::EndpointRange)
getindex(r, newindex(indices1(r), s))
end
function Base.getindex(r::StepRangeLen, s::EndpointRange)
getindex(r, newindex(indices1(r), s))
end
function Base.getindex(r::LinRange, s::EndpointRange)
getindex(r, newindex(indices1(r), s))
end
@inline function Base.to_indices(A, inds, I::Tuple{Union{Endpoint, EndpointRange}, Vararg{Any}})
(newindex(inds[1], I[1]), to_indices(A, Base._maybetail(inds), Base.tail(I))...)
end
@inline newindices(indsA, inds) = (newindex(indsA[1], inds[1]), newindices(tail(indsA), tail(inds))...)
newindices(::Tuple{}, ::Tuple{}) = ()
newindex(indA, i::Union{Real, AbstractArray, Colon}) = i
newindex(indA, i::EndpointRange) = i(indA)
newindex(indA, i::IBegin) = first(indA)
newindex(indA, i::IEnd) = last(indA)
end # module
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] | 2.446221 | 1,376 |
lines = readlines("input.txt")
re = r"(\w+)"
function main()
#=
Solve using frequency analysis (i.e.: for digits 0 to 9, we see segment 'a' 8 times)
For 1: we need c and f, i.e. 8 + 9 = 17. for 7, a + c + f = 8 + 8 + 9 = 25
This works, because the distribution for the number of times each segment lights
up for digits 0 to 9 is unique
=#
segmentmap = Dict(
0 => collect("abcefg"),
1 => collect("cf"),
2 => collect("acdeg"),
3 => collect("acdfg"),
4 => collect("bcdf"),
5 => collect("abdfg"),
6 => collect("abdefg"),
7 => collect("acf"),
8 => collect("abcdefg"),
9 => collect("abcdfg")
)
freq = X -> Dict([(x, count(==(x), X)) for x = unique(X)])
X = freq(reduce(vcat, values(segmentmap)))
numbers = Dict([(i,sum([X[c] for c = set])) for (i,set) = pairs(segmentmap)])
inverse = Dict(value => key for (key, value) = numbers)
sumdigits = N -> sum(n * 10^e for (n,e) = zip(N, length(N)-1:-1:0))
Σ = 0
for line = lines
input, output = split(line, '|')
signals = [m.match for m = eachmatch(re, input)]
X = freq(reduce(vcat, collect.(signals)))
Y = [m.match for m = eachmatch(re, output)]
Σ_Y = map(s -> sum([X[c] for c = collect(s)]), Y)
N = map(n -> inverse[n], Σ_Y)
Σ += sumdigits(N)
println(sumdigits(N))
end
println("====")
println(Σ)
end
main()
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] | 2.153959 | 682 |
########################################################################################################
## Details
# This file implements the MAP estimation proceedure for UQ inference problems.
# Note, we convert the named tuple p.x into a vector for use in GalacticOptim.
# For the conversion, we use TransformVariables.
#########################################################################################################
function MAP(p :: ArbitraryDistribution{T}) where T<: AbstractFloat
# Setup optimization utilities
trans, x0 = namedtp_to_vec(p.x)
f = distribution_wrapper(p, trans)
params = p.p
# Define problem
g = OptimizationFunction(f, GalacticOptim.AutoForwardDiff())
prob = OptimizationProblem(g, x0, p.p)
# Solve
sol = solve(prob, BFGS())
# return solution
xf = sol.u
p.x = transform(trans, xf)
return p
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] | 3.501946 | 257 |
################################################################################
#
# Parent
#
################################################################################
parent(a::AlgAssElem) = a.parent
################################################################################
#
# Special elements
#
################################################################################
zero(A::AlgAss) = A()
one(A::AlgAss) = A(A.one)
################################################################################
#
# Unary operations
#
################################################################################
function -(a::AlgAssElem{T}) where {T}
coeffs = [ -a.coeffs[i] for i = 1:dim(parent(a)) ]
return AlgAssElem{T}(parent(a), coeffs)
end
################################################################################
#
# Binary operations
#
################################################################################
function +(a::AlgAssElem{T}, b::AlgAssElem{T}) where {T}
parent(a) != parent(b) && error("Parents don't match.")
coeffs = Array{T, 1}(dim(parent(a)))
for i = 1:dim(parent(a))
coeffs[i] = a.coeffs[i] + b.coeffs[i]
end
return AlgAssElem{T}(parent(a), coeffs)
end
function -(a::AlgAssElem{T}, b::AlgAssElem{T}) where {T}
parent(a) != parent(b) && error("Parents don't match.")
coeffs = Array{T, 1}(dim(parent(a)))
for i = 1:dim(parent(a))
coeffs[i] = a.coeffs[i] - b.coeffs[i]
end
return AlgAssElem{T}(parent(a), coeffs)
end
function *(a::AlgAssElem{T}, b::AlgAssElem{T}) where {T}
parent(a) != parent(b) && error("Parents don't match.")
A = parent(a)
n = dim(A)
c = A()
t = base_ring(A)()
for i = 1:n
if iszero(a.coeffs[i])
continue
end
for j = 1:n
t = a.coeffs[i]*b.coeffs[j]
if iszero(t)
continue
end
for k = 1:n
c.coeffs[k] += A.mult_table[i, j, k]*t
end
end
end
return c
end
function mul!(c::AlgAssElem{T}, a::AlgAssElem{T}, b::AlgAssElem{T}) where {T}
A = parent(a)
n = dim(A)
t = base_ring(A)()
s = base_ring(A)()
if c === a || c === b
z = parent(a)()
mul!(z, a, b)
return z
end
for k in 1:n
c.coeffs[k] = zero!(c.coeffs[k])
end
for i = 1:n
if iszero(a.coeffs[i])
continue
end
for j = 1:n
t = a.coeffs[i]*b.coeffs[j]
if iszero(t)
continue
end
for k = 1:n
s = mul!(s, A.mult_table[i, j, k], t)
c.coeffs[k] = add!(c.coeffs[k], c.coeffs[k], s)
#c.coeffs[k] += A.mult_table[i, j, k]*t
end
end
end
#@assert c == a * b
return c
end
################################################################################
#
# Ad hoc operations
#
################################################################################
function *(a::AlgAssElem{T}, b::T) where { T <: RingElem }
return AlgAssElem{T}(parent(a), a.coeffs.*b)
end
*(b::T, a::AlgAssElem{T}) where { T <: RingElem } = a*b
*(a::AlgAssElem{T}, b::Union{Integer, fmpz}) where {T} = a*base_ring(parent(a))(b)
*(b::Union{Integer, fmpz}, a::AlgAssElem{T}) where {T} = a*b
dot(a::AlgAssElem{T}, b::T) where {T} = a*b
dot(b::T, a::AlgAssElem{T}) where {T} = b*a
dot(a::AlgAssElem{T}, b::Union{Integer, fmpz}) where {T} = a*b
dot(b::Union{Integer, fmpz}, a::AlgAssElem{T}) where {T} = b*a
################################################################################
#
# Exponentiation
#
################################################################################
function ^(a::AlgAssElem, b::Int)
if b == 0
return one(parent(a))
elseif b == 1
return deepcopy(a)
else
if b < 0
error("Not implemented yet")
#a = inv(a)
#b = -b
end
bit = ~((~UInt(0)) >> 1)
while (UInt(bit) & b) == 0
bit >>= 1
end
z = deepcopy(a)
bit >>= 1
while bit != 0
z = mul!(z, z, z)
if (UInt(bit) & b) != 0
z = mul!(z, z, a)
end
bit >>= 1
end
return z
end
end
function ^(a::AlgAssElem, b::fmpz)
if nbits(b) < 64
return a^Int(b)
end
if b < 0
error("Not implemented yet")
elseif b == 0
return one(parent(a))
elseif b == 1
return deepcopy(a)
elseif mod(b, 2) == 0
c = a^(div(b, 2))
return c*c
elseif mod(b, 2) == 1
return a^(b - 1)*a
end
end
################################################################################
#
# Parent object overloading
#
################################################################################
(A::AlgAss{T})() where {T} = AlgAssElem{T}(A)
function (A::AlgAss{T})(c::Array{T, 1}) where {T}
length(c) != dim(A) && error("Dimensions don't match.")
return AlgAssElem{T}(A, c)
end
function Base.getindex(A::AlgAss{T}, i::Int) where {T}
(i < 1 || i > dim(A)) && error("Index must be in range $(1:dim(A))")
n = dim(A)
v = Vector{T}(n)
for j in 1:n
v[j] = zero(base_ring(A))
end
v[i] = one(base_ring(A))
return A(v)
end
################################################################################
#
# String I/O
#
################################################################################
function show(io::IO, a::AlgAssElem)
print(io, "Element of ")
print(io, parent(a))
print(io, " with coefficients ")
print(io, a.coeffs)
end
################################################################################
#
# Deepcopy
#
################################################################################
function Base.deepcopy_internal(a::AlgAssElem{T}, dict::ObjectIdDict) where {T}
b = parent(a)()
for x in fieldnames(a)
if x != :parent && isdefined(a, x)
setfield!(b, x, Base.deepcopy_internal(getfield(a, x), dict))
end
end
return b
end
################################################################################
#
# Equality
#
################################################################################
function ==(a::AlgAssElem{T}, b::AlgAssElem{T}) where {T}
parent(a) != parent(b) && return false
return a.coeffs == b.coeffs
end
################################################################################
#
# Minpoly
#
################################################################################
function Generic.minpoly(a::AlgAssElem)
M = representation_matrix(a)
R = PolynomialRing(base_ring(parent(a)), "x", cached=false)[1]
return minpoly(R, M)
end
################################################################################
#
# Representation matrix
#
################################################################################
function elem_to_mat_row!(M::MatElem{T}, i::Int, a::AlgAssElem{T}) where T
for c = 1:cols(M)
M[i, c] = deepcopy(a.coeffs[c])
end
return nothing
end
function elem_from_mat_row(A::AlgAss{T}, M::MatElem{T}, i::Int) where T
a = A()
for c = 1:cols(M)
a.coeffs[c] = deepcopy(M[i, c])
end
return a
end
function representation_matrix(a::AlgAssElem, action::Symbol=:left)
A = parent(a)
M = zero_matrix(base_ring(A), dim(A), dim(A))
if action==:left
for i = 1:dim(A)
if iszero(a.coeffs[i])
continue
end
for j = 1:dim(A)
for k = 1:dim(A)
M[j, k] += a.coeffs[i]*A.mult_table[i, j, k]
end
end
end
elseif action==:right
for i = 1:dim(A)
if iszero(a.coeffs[i])
continue
end
for j = 1:dim(A)
for k = 1:dim(A)
M[j, k] += a.coeffs[i]*A.mult_table[j, i, k]
end
end
end
else
error("Not yet implemented")
end
return M
end
################################################################################
#
# isone/iszero
#
################################################################################
isone(a::AlgAssElem) = a == one(parent(a))
iszero(a::AlgAssElem) = all(i -> i == 0, a.coeffs)
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7,
64,
3712,
2348,
70,
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36,
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8,
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530,
7,
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7,
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36,
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8,
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7,
72,
4613,
1312,
6624,
657,
11,
257,
13,
1073,
14822,
82,
8,
198
] | 2.56984 | 3,057 |
using Test
using MathOptInterface
const MOI = MathOptInterface
const MOIU = MOI.Utilities
@test sprint(MOIU.print_with_acronym, "MathOptInterface") == "MOI"
@test sprint(MOIU.print_with_acronym, "MathOptInterface.MathOptInterface") ==
"MOI.MOI"
@test sprint(
MOIU.print_with_acronym,
"MathOptInterface.Utilities.MathOptInterface",
) == "MOIU.MOI"
@test sprint(MOIU.print_with_acronym, "MathOptInterfaceXXBridges") ==
"MOIXXBridges"
@test sprint(MOIU.print_with_acronym, "MathOptInterface.BridgesXX") == "MOIBXX"
@test sprint(MOIU.print_with_acronym, "MathOptInterface.Test.x") == "MOIT.x"
@test sprint(MOIU.print_with_acronym, "MathOptInterface.x.Test") == "MOI.x.Test"
@test sprint(MOIU.print_with_acronym, "MathOptInterface.Utilities.Test") ==
"MOIU.Test"
@test sprint(MOIU.print_with_acronym, "MathOptInterface.Utilities.Test") ==
"MOIU.Test"
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] | 2.504274 | 351 |
export PyPtr, PyObject, Py_NULL, METH_O, METH_NOARGS, METH_FASTCALL
export Py_ssize_t, Addr
export PyMethodDef
export Py_LE, Py_LT, Py_NE, Py_EQ, Py_GE, Py_GT
const Py_ssize_t = Cssize_t
const Py_hash_t = Py_ssize_t
const Addr = UInt64
# Ref: https://github.com/python/cpython/blob/3.8/Include/object.h#L104
struct PyObject
ob_refcnt::Py_ssize_t
ob_type::Ptr{PyObject}
end
const PyPtr = Ptr{PyObject}
const Py_NULL = reinterpret(PyPtr, C_NULL)
const METH_VARARGS = 0x0001
const METH_KEYWORDS = 0x0002
const METH_NOARGS = 0x0004
const METH_O = 0x0008
const METH_FASTCALL = 0x0080
# Ref: https://github.com/python/cpython/blob/3.8/Include/methodobject.h#L51
mutable struct PyMethodDef
ml_name::Cstring
ml_meth::Ptr{Nothing}
ml_flags::Cint
ml_doc::Cstring # may be NULL
end
# Ref: https://github.com/python/cpython/blob/3.8/Include/descrobject.h#L11
struct PyGetSetDef
name::Ptr{UInt8}
get::Ptr{Cvoid}
set::Ptr{Cvoid} # may be NULL for read-only members
doc::Ptr{UInt8} # may be NULL
closure::Ptr{Cvoid} # pass-through thunk, may be NULL
end
# (o, o) -> p
# struct binaryfunc
# unbox::Ptr{Nothing}
# end
# function (f::binaryfunc)(o1::PyPtr, o2::PyPtr)
# ccall(f.unbox, PyPtr, (PyPtr, PyPtr), o1, o2)
# end
# DIO_ExceptCode(::binaryfunc) = Py_NULL
# (o) -> p
# struct unaryfunc
# unbox::Ptr{Nothing}
# end
# function (f::unaryfunc)(o)
# ccall(f.unbox, PyPtr, (PyPtr,), o)
# end
# DIO_ExceptCode(::unaryfunc) = Py_NULL
# # (o, o, o) -> p
# struct ternaryfunc
# unbox::Ptr{Nothing}
# end
# function (f::ternaryfunc)(o1::PyPtr, o2::PyPtr, o3::PyPtr)
# ccall(f.unbox, PyPtr, (PyPtr, PyPtr, PyPtr), o1, o2, o3)
# end
# DIO_ExceptCode(::ternaryfunc) = Py_NULL
const binaryfunc = Ptr{Nothing}
const unaryfunc = Ptr{Nothing}
const ternaryfunc = Ptr{Nothing}
# Ref: https://github.com/python/cpython/blob/3.8/Include/cpython/object.h#L95
struct PyNumberMethods
nb_add::binaryfunc
nb_subtract::binaryfunc
nb_multiply::binaryfunc
nb_remainder::binaryfunc
nb_divmod::binaryfunc
nb_power::ternaryfunc
nb_negative::unaryfunc
nb_positive::unaryfunc
nb_absolute::unaryfunc
nb_bool::Ptr{Nothing}
nb_invert::unaryfunc
nb_lshift::binaryfunc
nb_rshift::binaryfunc
nb_and::binaryfunc
nb_xor::binaryfunc
nb_or::binaryfunc
nb_int::unaryfunc
nb_reserved::Ptr{Nothing}
nb_float::unaryfunc
nb_inplace_add::binaryfunc
nb_inplace_subtract::binaryfunc
nb_inplace_multiply::binaryfunc
nb_inplace_remainder::binaryfunc
nb_inplace_power::ternaryfunc
nb_inplace_lshift::binaryfunc
nb_inplace_rshift::binaryfunc
nb_inplace_and::binaryfunc
nb_inplace_xor::binaryfunc
nb_inplace_or::binaryfunc
nb_floor_divide::binaryfunc
nb_true_divide::binaryfunc
nb_inplace_floor_divide::binaryfunc
nb_inplace_true_divide::binaryfunc
nb_index::unaryfunc
nb_matrix_multiply::binaryfunc
nb_inplace_matrix_multiply::binaryfunc
end
struct PyMemberDef
name::Ptr{UInt8}
typ::Cint
offset::Int # warning: was Cint for Python <= 2.4
flags::Cint
doc::Ptr{UInt8}
end
# Ref: https://github.com/python/cpython/blob/3.8/Include/cpython/object.h#L177
mutable struct PyTypeObject
# PyObject_HEAD (for non-Py_TRACE_REFS build):
ob_refcnt::Py_ssize_t
ob_type::PyPtr
ob_size::Py_ssize_t # PyObject_VAR_HEAD
# PyTypeObject fields:
tp_name::Ptr{UInt8} # required, should be in format "<module>.<name>"
# warning: these two were Cint for Python <= 2.4
tp_basicsize::Int # required, = sizeof(instance)
tp_itemsize::Int
tp_dealloc::Ptr{Cvoid}
tp_vectorcall_offset :: Addr
tp_getattr::Ptr{Cvoid}
tp_setattr::Ptr{Cvoid}
tp_as_async::Ptr{Cvoid}
tp_repr::Ptr{Cvoid}
tp_as_number::Ptr{PyNumberMethods}
tp_as_sequence::Ptr{Cvoid}
tp_as_mapping::Ptr{Cvoid}
tp_hash::Ptr{Cvoid}
tp_call::Ptr{Cvoid}
tp_str::Ptr{Cvoid}
tp_getattro::Ptr{Cvoid}
tp_setattro::Ptr{Cvoid}
tp_as_buffer::Ptr{Cvoid}
tp_flags::Clong # Required, should default to Py_TPFLAGS_DEFAULT
tp_doc::Ptr{UInt8} # normally set in example code, but may be NULL
tp_traverse::Ptr{Cvoid}
tp_clear::Ptr{Cvoid}
tp_richcompare::Ptr{Cvoid}
tp_weaklistoffset::Int
# added in Python 2.2:
tp_iter::Ptr{Cvoid}
tp_iternext::Ptr{Cvoid}
tp_methods::Ptr{PyMethodDef}
tp_members::Ptr{PyMemberDef}
tp_getset::Ptr{PyGetSetDef}
tp_base::Ptr{Cvoid}
tp_dict::PyPtr
tp_descr_get::Ptr{Cvoid}
tp_descr_set::Ptr{Cvoid}
tp_dictoffset::Int
tp_init::Ptr{Cvoid}
tp_alloc::Ptr{Cvoid}
tp_new::Ptr{Cvoid}
tp_free::Ptr{Cvoid}
tp_is_gc::Ptr{Cvoid}
tp_bases::PyPtr
tp_mro::PyPtr
tp_cache::PyPtr
tp_subclasses::PyPtr
tp_weaklist::PyPtr
tp_del::Ptr{Cvoid}
# added in Python 2.6:
tp_version_tag::Cuint
# only used for COUNT_ALLOCS builds of Python
tp_allocs::Int
tp_frees::Int
tp_maxalloc::Int
tp_prev::Ptr{Cvoid}
tp_next::Ptr{Cvoid}
end
const Py_LT = Cint(0)
const Py_LE = Cint(1)
const Py_EQ = Cint(2)
const Py_NE = Cint(3)
const Py_GT = Cint(4)
const Py_GE = Cint(5)
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] | 2.14092 | 2,434 |
# Author: Gabrio Rizzuti, grizzuti3@gatech.edu
# Date: September 2020
using InvertibleNetworks, LinearAlgebra, Test, Statistics, Random
# Random seed
Random.seed!(1);
###############################################################################
# Initialization
# Input
nx = 28
ny = 28*2
nc = 4
n_hidden = 64
batchsize = 5
X = rand(Float32, nx, ny, nc, batchsize)
# Layers and initialization
logdet = true
N1 = CouplingLayerHINT(nc, n_hidden; permute="full", logdet=logdet)
N2 = ActNorm(nc; logdet=logdet)
N3 = CouplingLayerHINT(nc, n_hidden; permute="full", logdet=logdet)
N = Composition(N1, N2, N3)
###############################################################################
# Test coherency of composition and manual composition
Y = copy(X)
Y, l3 = N3.forward(Y)
Y, l2 = N2.forward(Y)
Y, l1 = N1.forward(Y)
l = l1+l2+l3
Y_, l_ = N.forward(X)
@test isapprox(Y, Y_; rtol=1f-3)
@test isapprox(l, l_; rtol=1f-3)
###############################################################################
# Test coherency of composition and ∘
# Composition with ∘
N_ = N1∘N2∘N3
Y, l = N.forward(X)
Y_, l_ = N_.forward(X)
@test isapprox(Y, Y_; rtol=1f-3)
@test isapprox(l, l_; rtol=1f-3)
###############################################################################
# Test invertibility
@test isapprox(X, N.inverse(N.forward(X)[1]); rtol=1f-3)
@test isapprox(X, N.forward(N.inverse(X))[1]; rtol=1f-3)
###############################################################################
# Gradient Test
function loss(N, X, Y)
# Forward pass
Y_, lgdet = N.forward(X)
# Residual and function value
ΔY = Y_ - Y
f = 0.5f0/batchsize*norm(ΔY)^2f0-lgdet
# Back propagation
ΔX, _ = N.backward(ΔY./batchsize, Y_)
return f, ΔX, deepcopy(get_grads(N))
end
# Initializing nets
N1 = CouplingLayerHINT(nc, n_hidden; permute="full", logdet=logdet)
N2 = ActNorm(nc; logdet=logdet)
N3 = CouplingLayerHINT(nc, n_hidden; permute="full", logdet=logdet)
N0 = Composition(N1, N2, N3); N0.forward(rand(Float32, nx, ny, nc, batchsize))
θ0 = deepcopy(get_params(N0))
N1 = CouplingLayerHINT(nc, n_hidden; permute="full", logdet=logdet)
N2 = ActNorm(nc; logdet=logdet)
N3 = CouplingLayerHINT(nc, n_hidden; permute="full", logdet=logdet)
N = Composition(N1, N2, N3); N.forward(rand(Float32, nx, ny, nc, batchsize))
θ = deepcopy(get_params(N))
# Inputs
X = rand(Float32, nx, ny, nc, batchsize)
X0 = rand(Float32, nx, ny, nc, batchsize)
dX = rand(Float32, nx, ny, nc, batchsize)
Y = rand(Float32, nx, ny, nc, batchsize)
dθ = θ-θ0
for i = 1:length(dθ)
(norm(θ0[i].data) != 0f0) && (dθ[i].data .*= norm(θ0[i].data)/norm(dθ[i].data))
end
# Gradient test (input)
set_params!(N0, θ0)
f0, ΔX, Δθ = loss(N0, X, Y)
h = 0.1f0
maxiter = 5
err1 = zeros(Float32, maxiter)
err2 = zeros(Float32, maxiter)
print("\nGradient test sequential network: input\n")
for j=1:maxiter
f = loss(N0, X+h*dX, Y)[1]
err1[j] = abs(f - f0)
err2[j] = abs(f - f0 - h*dot(dX, ΔX))
print(err1[j], "; ", err2[j], "\n")
global h /= 2f0
end
@test isapprox(err1[end] / (err1[1]/2^(maxiter-1)), 1f0; atol=1f1)
@test isapprox(err2[end] / (err2[1]/4^(maxiter-1)), 1f0; atol=1f1)
# Gradient test (parameters)
h = 0.1f0
maxiter = 5
err3 = zeros(Float32, maxiter)
err4 = zeros(Float32, maxiter)
print("\nGradient test sequential network: parameters\n")
for j=1:maxiter
set_params!(N0, θ0+h*dθ)
f = loss(N0, X, Y)[1]
err3[j] = abs(f - f0)
err4[j] = abs(f - f0 - h*dot(dθ, Δθ))
print(err3[j], "; ", err4[j], "\n")
global h /= 2f0
end
@test isapprox(err3[end] / (err3[1]/2^(maxiter-1)), 1f0; atol=1f1)
@test isapprox(err4[end] / (err4[1]/4^(maxiter-1)), 1f0; atol=1f1)
###################################################################################################
# Jacobian-related tests
# Gradient test
# Initializing nets
N1 = CouplingLayerHINT(nc, n_hidden; permute="full", logdet=logdet)
N2 = ActNorm(nc; logdet=logdet)
N3 = CouplingLayerHINT(nc, n_hidden; permute="full", logdet=logdet)
N0 = Composition(N1, N2, N3); N0.forward(rand(Float32, nx, ny, nc, batchsize))
θ0 = deepcopy(get_params(N0))
N1 = CouplingLayerHINT(nc, n_hidden; permute="full", logdet=logdet)
N2 = ActNorm(nc; logdet=logdet)
N3 = CouplingLayerHINT(nc, n_hidden; permute="full", logdet=logdet)
N = Composition(N1, N2, N3); N.forward(rand(Float32, nx, ny, nc, batchsize))
θ = deepcopy(get_params(N))
X = randn(Float32, nx, ny, nc, batchsize)
# Perturbation (normalized)
dθ = θ-θ0; dθ .*= norm.(θ0)./(norm.(dθ).+1f-10)
dX = randn(Float32, nx, ny, nc, batchsize); dX *= norm(X)/norm(dX)
# Jacobian eval
dY, Y, _, _ = N.jacobian(dX, dθ, X)
# Test
print("\nJacobian test\n")
h = 0.1f0
maxiter = 5
err5 = zeros(Float32, maxiter)
err6 = zeros(Float32, maxiter)
for j=1:maxiter
set_params!(N, θ+h*dθ)
Y_loc, _ = N.forward(X+h*dX)
err5[j] = norm(Y_loc - Y)
err6[j] = norm(Y_loc - Y - h*dY)
print(err5[j], "; ", err6[j], "\n")
global h = h/2f0
end
@test isapprox(err5[end] / (err5[1]/2^(maxiter-1)), 1f0; atol=1f1)
@test isapprox(err6[end] / (err6[1]/4^(maxiter-1)), 1f0; atol=1f1)
# Adjoint test
set_params!(N, θ)
dY, Y, _, _ = N.jacobian(dX, dθ, X)
dY_ = randn(Float32, size(dY))
dX_, dθ_, _, _ = N.adjointJacobian(dY_, Y)
a = dot(dY, dY_)
b = dot(dX, dX_)+dot(dθ, dθ_)
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] | 2.178586 | 2,447 |
# introduce embedding between matrices (vectors) of different dimensions
"""
mat_embed(X, dim; zero_pos = -1, tpin = "her", tpout = "her")
This function embeds X into new dimensions dim, with the Cartesian
order of the entries are preserved. Fill in zeros at zero_pos if necessary.
# Arguments
- `X`: a Hermitian or rectangular matrice
- `dim`: target dimension, array-like [Nnew,Mnew]
- `zero_pos`: an array of user specify Cartesian position for zeros. if
it is -1, then use randomly generated position.
- `tpin`: the type of input matrix, valid options are "her" and "rec".
- `tpout`: the type of ouput matrix, valid options are "her" and "rec"
# Outputs
- `Xnew`: a matrix of dimension dim and the type tpin. The entries
Xnew are from X, with possible extra zeros at zero_pos,
"""
function mat_embed(X, dim; zero_pos = -1, tpin = "her", tpout = "her")
# get the original dimension
X = X[:,:]
N, M = size(X)
# get the target dimension
Nnew, Mnew = dim
# check the input dimension if mat is Hermitian
if (tpin == "her") & (N != M)
error("Input dimension is not for a Hermitian matrix!")
end
# reshape X into vector according to type in
if tpin == "her"
in_size = N*(N + 1)/2
Xvec = [X[i,j] for i=1:N for j=i:N]
elseif tpin == "rec"
in_size = N*M
Xvec = vec(X)
end
# check whether there is enough room in the target shape
if tpout == "her"
out_size = Int((Nnew - 1)*Nnew/2)
elseif tpout == "rec"
out_size = Nnew*Mnew
end
if out_size < in_size
error("The target dimension is too small!")
end
# if the position of zeros are not preset, generate one
if zero_pos == -1
zero_pos = sample(1:out_size, out_size - in_size, replace=false)
end
# Construct Xnewvec
Xnewvec = zeros(out_size)
Xnewvec[[i for i in 1: out_size if ~(i in zero_pos)]] = Xvec
# reshape Xnewvec to Xnew
if tpout == "her"
k = 0
Xnew = [i < j ? (k += 1; Xnewvec[k]) : 0 for i = 1: Nnew, j = 1: Mnew]
Xnew = Xnew + Xnew'
elseif tpout == "rec"
Xnew = reshape(Xnewvec, (Nnew, Mnew))
end
return convert(Array{typeof(X[1,1]), 2}, Xnew)
end | [
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] | 2.162544 | 1,132 |
using BayesianMixtures
using Statistics
n_total = 1000 # total number of MCMC sweeps to run
# _____________________________________________________________________________________
# Check samplers on univariate data
n = 50
x = randn(n)
for mode_ in ["Normal","NormalNonoptimized"]
for model_type_ in ["MFM","DPM"]
# Run MCMC sampler
options = BayesianMixtures.options(mode_,model_type_,x,n_total)
result = BayesianMixtures.run_sampler(options)
end
end
# _____________________________________________________________________________________
# Check samplers on multivariate data
d = 3
n = 50
x = randn(n,d)
x = (x .- mean(x,dims=1))./std(x,dims=1; corrected=false)
x = [x[i,:] for i=1:n]
for mode_ in ["MVN","MVNaaC","MVNaaN","MVNaaRJ"]
for model_type_ in ["MFM","DPM"]
if model_type_=="DPM" && mode_=="MVNaaRJ"; continue; end
# Run MCMC sampler
options = BayesianMixtures.options(mode_,model_type_,x,n_total)
result = BayesianMixtures.run_sampler(options)
end
end
# _____________________________________________________________________________________
# Check other functions using univariate data
# Settings
mode = "Normal"
model_type = "MFM"
n = 100
B = BayesianMixtures
# Generate data and run sampler
x = randn(n)
options = B.options(mode,model_type,x,n_total)
result = B.run_sampler(options)
# Save and load
B.save_result("temp.jld",result)
result2 = B.load_result("temp.jld")
B.t_running(result)
B.t_posterior(result)
B.k_posterior(result)
B.plot_density_estimate(result)
k,a,w,theta = B.sample_mixture_parameters(result,50)
loglik = B.log_likelihoods(x,w,theta,mode)
B.open_figure(1)
B.traceplot(result.t); P.pause(0.5); P.clf()
B.traceplot_timewise(result,1); P.pause(0.5); P.clf()
B.plot_t_running(result); P.pause(0.5); P.clf()
B.plot_autocorrelation(result,20); P.pause(0.5); P.clf()
B.plot_t_posterior(result); P.pause(0.5); P.clf()
B.plot_t_posterior_average([result]); P.pause(0.5); P.clf()
B.plot_k_posterior(result); P.pause(0.5); P.clf()
B.plot_k_posterior_average([result]); P.pause(0.5); P.clf()
B.plot_similarity_matrix(result); P.pause(0.5); P.clf()
B.plot_clusters(x,result.z[:,end]); P.pause(0.5); P.clf()
B.rug_plot(x); P.pause(0.5); P.clf()
B.plot_density_estimate(result); P.pause(0.5); P.clf()
# _____________________________________________________________________________________
# Check other functions using multivariate data
# Settings
mode = "MVN"
model_type = "MFM"
d = 2
n = 100
B = BayesianMixtures
# Generate data and run sampler
x = randn(n,d)
x = (x .- mean(x,dims=1))./std(x,dims=1; corrected=false)
x = [x[i,:] for i=1:n]
options = B.options(mode,model_type,x,n_total)
result = B.run_sampler(options)
# Save and load
B.save_result("temp.jld",result)
result2 = B.load_result("temp.jld")
B.t_running(result)
B.t_posterior(result)
B.k_posterior(result)
B.plot_density_estimate(result)
k,a,w,theta = B.sample_mixture_parameters(result,50)
loglik = B.log_likelihoods(x,w,theta,mode)
B.open_figure(1)
B.traceplot(result.t); P.pause(0.5); P.clf()
B.traceplot_timewise(result,1); P.pause(0.5); P.clf()
B.plot_t_running(result); P.pause(0.5); P.clf()
B.plot_autocorrelation(result,20); P.pause(0.5); P.clf()
B.plot_t_posterior(result); P.pause(0.5); P.clf()
B.plot_t_posterior_average([result]); P.pause(0.5); P.clf()
B.plot_k_posterior(result); P.pause(0.5); P.clf()
B.plot_k_posterior_average([result]); P.pause(0.5); P.clf()
B.plot_similarity_matrix(result); P.pause(0.5); P.clf()
B.plot_clusters(x,result.z[:,end]); P.pause(0.5); P.clf()
B.plot_density_estimate(result); P.pause(0.5); P.clf()
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] | 2.428281 | 1,478 |
function Kprec( pw_grid::PWGrid, psi::Array{ComplexF64,2} )
Ncol = size(psi)[2]
G2 = pw_grid.G2
Npoints = pw_grid.Npoints
Kpsi = zeros( ComplexF64, size(psi) )
for ic = 1:Ncol
for ip = 1:Npoints
Kpsi[ip,ic] = psi[ip,ic] / ( 1.0 + G2[ip] )
end
end
return Kpsi
end
function Kprec(psi)
return psi
end
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] | 1.839378 | 193 |
#!/usr/bin/env julia
# test polygon simplification
using Luxor
using Test
using Random
Random.seed!(42)
function sinecurves()
gsave()
translate(100, 100)
setline(0.25)
sethue(0, 0, 0)
x_vals = collect(0:pi/180: 40pi)
# generate array of Points
plist = [Point(d * 2pi , -sin(d) * cos(20 * d) * 8 * sin(d/10)) for d in x_vals]
prettypoly(plist, :stroke, () -> (gsave(); randomhue(); circle(O, 0.5, :stroke); grestore()))
text("original " * string(length(plist)) * " vertices" , 0, -15)
for detail in [0.01, 0.05, 0.1, 0.2, 0.5, 0.75, 1.0, 2.0]
translate(0, 50)
simplified = simplify(plist, detail)
prettypoly(simplified, :stroke, () -> (gsave(); randomhue(); circle(O, 0.5, :stroke); grestore()))
text("detail $(detail), " * string(length(simplified)) * " vertices" , 0, -15)
end
grestore()
end
function test(pagewidth, pageheight)
translate(100, pageheight/2)
g = GridRect(O, 0, 60)
setline(0.25)
sethue(0, 0, 0)
x_vals = collect(0:pi/100: 4pi)
polyline = [Point(d * 20pi, 5 * -sin(d) * cos(12 * d) * 8 * sin(d/10)) for d in x_vals]
poly(polyline, :stroke)
prettypoly(polyline)
text("original " * string(length(polyline)) * " vertices" , 0, -30)
for detail in [0.01, 0.05, 0.075, 0.1, 0.2, 0.5, 0.75, 1.0, 2.0]
gsave()
translate(nextgridpoint(g))
polysimple = simplify(polyline, detail)
prettypoly(polysimple, :stroke, () -> (gsave(); randomhue(); circle(O, 2, :stroke); grestore()))
text(" detail ($detail), " * string(length(polysimple)) * " vertices" , 0, -20)
grestore()
end
end
function simplify_poly(fname)
pagewidth = 1190.0 # points
pageheight = 1684.0 # points
Drawing(pagewidth, pageheight, fname)
sinecurves()
test(pagewidth, pageheight)
@test finish() == true
println("...finished test: output in $(fname)")
end
function simplify_square()
poly = Point[Point(-1.0, 0.0), Point(-1.0, -1.0), Point(0.0, -1.0), Point(1.0, -1.0), Point(1.0, 0.0), Point(1.0, 1.0), Point(0.0, 1.0), Point(-1.0, 1.0)]
poly = simplify(poly, 0.01)
@test poly == Point[Point(-1.0, 0.0), Point(-1.0, -1.0), Point(1.0, -1.0), Point(1.0, 1.0), Point(-1.0, 1.0)]
end
simplify_poly("simplify-poly.pdf")
simplify_square()
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] | 2.189266 | 1,062 |
using RBNF
using MLStyle
using PrettyPrint
@as_record Token
@as_record Struct_mainprogram
@as_record Struct_ifstmt
@as_record Struct_gate
@as_record Struct_gatedecl
@as_record Struct_decl
@as_record Struct_barrier_ids
@as_record Struct_reset
@as_record Struct_measure
@as_record Struct_iduop
@as_record Struct_u
@as_record Struct_cx
@as_record Struct_h
@as_record Struct_x
@as_record Struct_y
@as_record Struct_z
@as_record Struct_s
@as_record Struct_sdg
@as_record Struct_t
@as_record Struct_tdg
@as_record Struct_rx
@as_record Struct_ry
@as_record Struct_rz
@as_record Struct_ch
@as_record Struct_crx
@as_record Struct_cry
@as_record Struct_crz
@as_record Struct_idlist
@as_record Struct_mixedlist
@as_record Struct_argument
@as_record Struct_explist
@as_record Struct_pi
@as_record Struct_fnexp
@as_record Struct_neg
@as_record Struct_bin
function rec(ctx_tokens)
function app(op, args...)
args = map(rec, args)
op = Symbol(op)
:($op($(args...)))
end
@match ctx_tokens begin
Struct_pi(_) => Base.pi
Token{:id}(str=str) => Symbol(str)
Token{:nnreal}(str=str) => parse(Float64, str)
Token{:nninteger}(str=str) => parse(Int64, str)
Struct_neg(value=value) => :(-$(rec(value)))
Struct_bin(l=l, op=Token(str=op), r=r) => app(op, l, r)
Struct_idlist(hd=Token(str=hd), tl=nothing) => [Symbol(hd)]
Struct_idlist(hd=Token(str=hd), tl=tl) => [Symbol(hd), rec(tl)...]
Struct_explist(hd=hd, tl=nothing) => [rec(hd)]
Struct_explist(hd=hd, tl=tl) => [rec(hd), rec(tl)...]
Struct_mixedlist(hd=hd, tl=nothing) => [rec(hd)]
Struct_mixedlist(hd=hd, tl=tl) => [rec(hd), rec(tl)...]
Struct_argument(id=Token(str=id), arg=nothing) => Symbol(id)
Struct_argument(id=Token(str=id), arg=Token(str=int)) =>
let ind = parse(Int, int) + 1 # due to julia 1-based index
:($(Symbol(id))[$ind])
end
Struct_fnexp(fn = Token(str=fn), arg=arg) =>
let fn = @match fn begin
"sin" => sin
"cos" => cos
"tan" => tan
"exp" => exp
"ln" => log
"sqrt"=> sqrt
_ => error("not impl yet")
end
app(fn, arg)
end
_ => nothing
end
end
function trans_reg(ctx_tokens, qregs)
function app(op, args...)
args = map(rec, args)
op = Symbol(op)
:($op($(args...)))
end
@match ctx_tokens begin
Struct_decl(
regtype = Token(str=regtype),
id = Token(str=id),
int = Token(str = n)
) =>
let id = Symbol(id),
n = parse(Int, n)
return :($id = qreg($n); push!($qregs, $id))
end
Struct_mainprogram(
prog = stmts
) =>
let stmts = trans_reg.(stmts, qregs)
stmts
end
_ => nothing
end
end
function trans_gates(ctx_tokens, qasm_cgc, N)
function app(op, args...)
args = map(rec, args)
op = Symbol(op)
:($op($(args...)))
end
@match ctx_tokens begin
Struct_iduop(gate_name = Token(str=gate_name), args=nothing, outs=outs) =>
let refs = rec(outs),
gate_name = Symbol("custom_gate_"*gate_name)
:($gate_name(nothing, $(refs...); qasm_cgc=$qasm_cgc, N=$N))
end
Struct_iduop(gate_name = Token(str=gate_name), args=exprlist, outs=outs) =>
let refs = rec(outs),
exprs = Expr(:tuple, rec(exprlist)...),
gate_name = Symbol("custom_gate_"*gate_name)
:($gate_name($exprs, $(refs...); qasm_cgc=$qasm_cgc, N=$N))
end
Struct_cx(out1=out1, out2=out2) =>
let ref1 = rec(out1),
ref2 = rec(out2)
:(append!($qasm_cgc, [circuit_gate(($ref2), X, ($ref1))]))
end
Struct_ch(out1=out1, out2=out2) =>
let ref1 = rec(out1),
ref2 = rec(out2)
:(append!($qasm_cgc, [circuit_gate(($ref2), HadamardGate(), ($ref1))]))
end
Struct_u(in1=in1, in2=in2, in3=in3, out=out) =>
let (a, b, c) = map(rec, (in1, in2, in3)),
ref = :($(rec(out))[1])
:(append!($qasm_cgc, [circuit_gate(($ref), RzGate($a)),
circuit_gate(($ref), RyGate($b)),
circuit_gate(($ref), RzGate($c)),]))
end
Struct_x(out=out) =>
let ref = :($(rec(out)))
:(append!($qasm_cgc, [circuit_gate(($ref), X)]))
end
Struct_y(out=out) =>
let ref = :($(rec(out)))
:(append!($qasm_cgc, [circuit_gate(($ref), Y)]))
end
Struct_z(out=out) =>
let ref = :($(rec(out)))
:(append!($qasm_cgc, [circuit_gate(($ref), Z)]))
end
Struct_h(out=out) =>
let ref = :($(rec(out)))
:(append!($qasm_cgc, [circuit_gate(($ref), HadamardGate())]))
end
Struct_t(out=out) =>
let ref = :($(rec(out)))
:(append!($qasm_cgc, [circuit_gate(($ref), TGate())]))
end
Struct_tdg(out=out) =>
let ref = :($(rec(out)))
:(append!($qasm_cgc, [circuit_gate(($ref), TdagGate())]))
end
Struct_s(out=out) =>
let ref = :($(rec(out)))
:(append!($qasm_cgc, [circuit_gate(($ref), SGate())]))
end
Struct_sdg(out=out) =>
let ref = :($(rec(out)))
:(append!($qasm_cgc, [circuit_gate(($ref), SdagGate())]))
end
Struct_rx(in=in, out=out) =>
let ref = :($(rec(out))),
arg = :($(rec(in)))
:(append!($qasm_cgc, [circuit_gate(($ref), RxGate($arg))]))
end
Struct_ry(in=in, out=out) =>
let ref = :($(rec(out))),
arg = :($(rec(in)))
:(append!($qasm_cgc, [circuit_gate(($ref), RyGate($arg))]))
end
Struct_rz(in=in, out=out) =>
let ref = :($(rec(out))),
arg = :($(rec(in)))
:(append!($qasm_cgc, [circuit_gate(($ref), RzGate($arg))]))
end
Struct_crx(in=in, out1=out1, out2=out2) =>
let out = :($(rec(out2))),
cntrl = :($(rec(out1))),
arg = :($(rec(in)))
:(append!($qasm_cgc, [circuit_gate(($out), RxGate($arg), ($cntrl))]))
end
Struct_cry(in=in, out1=out1, out2=out2) =>
let out = :($(rec(out2))),
cntrl = :($(rec(out1))),
arg = :($(rec(in)))
:(append!($qasm_cgc, [circuit_gate(($out), RyGate($arg), ($cntrl))]))
end
Struct_crz(in=in, out1=out1, out2=out2) =>
let out = :($(rec(out2))),
cntrl = :($(rec(out1))),
arg = :($(rec(in)))
:(append!($qasm_cgc, [circuit_gate(($out), RzGate($arg), ($cntrl))]))
end
Struct_gate(
decl = Struct_gatedecl(
id=Token(str=fid),
args=args,
outs=outs
),
goplist=goplist
) =>
let out_ids :: Vector{Symbol} = rec(outs),
fid = Symbol("custom_gate_"*fid),
args = rec(args),
goplist = trans_gates.(goplist, Ref(qasm_cgc), Ref(N))
if isnothing(goplist)
goplist=[]
end
if isnothing(args)
args=[]
end
quote
function $fid(($(args...), ), $(out_ids...); qasm_cgc=$qasm_cgc, N=$N)
$(goplist...)
end
end
end
# Struct_ifstmt(l=Token(str=l), r=r, gate_name=Token(str=gate_name), args=nothing, outs=outs) =>
# let l = Symbol(l),
# r = rec(r),
# refs = rec(outs),
# gate_name = Symbol("custom_gate_"*gate_name)
# :($gate_name(nothing, reg_check($l,$r), $(refs...); qasm_cgc=$qasm_cgc, N=$N))
# end
# Struct_ifstmt(l=Token(str=l), r=r, gate_name=Token(str=gate_name), args=exprlist, outs=outs) =>
# let l = Symbol(l),
# r = rec(r),
# refs = rec(outs),
# exprs = Expr(:tuple, rec(exprlist)...),
# gate_name = Symbol("custom_gate_"*gate_name)
# :($gate_name($exprs, reg_check($l,$r), $(refs...); qasm_cgc=$qasm_cgc, N=$N))
# end
Struct_mainprogram(
prog = stmts
) =>
let stmts = trans_gates.(stmts, qasm_cgc, N)
stmts
end
_ => nothing
end
end
function transform_qasm(ctx_tokens)
qregs = Qaintessent.QRegister[]
reg_declr = trans_reg(ctx_tokens, Ref(qregs))
eval.(reg_declr)
qasm_cgc = Circuit(qregs...)
N = num_wires(qasm_cgc)
gates_declr = trans_gates(ctx_tokens, Ref(qasm_cgc), Ref(N))
eval.(gates_declr)
qasm_cgc
end
"""
qasm2cgc(txt::String)
converts OpenQASM 2.0 text to Circuit{N} object
"""
function qasm2cgc(txt::String)
qasmlex = lex(txt)
qasmparse = parse_qasm(qasmlex)
qasm_cgc = transform_qasm(qasmparse)
end
function Base.:(==)(dcl1::Struct_gate, dcl2::Struct_gate)
dcl1.decl == dcl2.decl && dcl1.goplist == dcl2.goplist
end
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4868,
6624,
288,
565,
17,
13,
70,
404,
4868,
198,
437,
198
] | 1.755901 | 5,592 |
using Pkg if isfile("Project.toml") && isfile("Manifest.toml") Pkg.activate(".") end | [
3500,
350,
10025,
611,
318,
7753,
7203,
16775,
13,
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75,
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11405,
318,
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13,
39532,
75,
4943,
350,
10025,
13,
39022,
7203,
19570,
886
] | 2.896552 | 29 |
export time_modeling
# Setup time-domain linear or nonlinear foward and adjoint modeling and interface to devito
function time_modeling(model_full::Model, srcGeometry, srcData, recGeometry, recData, dm, op::Char, mode::Int64, options)
# Load full geometry for out-of-core geometry containers
recGeometry = Geometry(recGeometry)
srcGeometry = Geometry(srcGeometry)
# Reutrn directly for J*0
if op=='J' && mode == 1
if norm(dm) == 0 && options.return_array == false
return judiVector(recGeometry, zeros(Float32, recGeometry.nt[1], length(recGeometry.xloc[1])))
elseif norm(dm) == 0 && options.return_array == true
return vec(zeros(Float32, recGeometry.nt[1], length(recGeometry.xloc[1])))
end
end
# limit model to area with sources/receivers
if options.limit_m == true
model = deepcopy(model_full)
model, dm = limit_model_to_receiver_area(srcGeometry, recGeometry, model, options.buffer_size; pert=dm)
else
model = model_full
end
# Set up Python model structure
modelPy = devito_model(model, options; dm=dm)
# Load shot record if stored on disk
typeof(recData) == SegyIO.SeisCon && (recData = convert(Array{Float32,2}, recData[1].data))
# Remove receivers outside the modeling domain (otherwise leads to segmentation faults)
recGeometry, recData = remove_out_of_bounds_receivers(recGeometry, recData, model)
# Devito interface
argout = devito_interface(modelPy, model, srcGeometry, srcData, recGeometry, recData, dm, options)
# Extend gradient back to original model size
if op=='J' && mode==-1 && options.limit_m==true
argout = extend_gradient(model_full, model, argout)
end
return argout
end
# Function instance without options
time_modeling(model::Model, srcGeometry::Geometry, srcData, recGeometry::Geometry, recData, perturbation, srcnum::Int64, op::Char, mode::Int64) =
time_modeling(model, srcGeometry, srcData, recGeometry, recData, perturbation, srcnum, op, mode, Options())
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198
] | 2.753333 | 750 |
type BayesTreeNodeData
frontalIDs::Vector{Int}
conditIDs::Vector{Int}
inmsgIDs::Vector{Int}
potIDs::Vector{Int} # this is likely redundant TODO -- remove
potentials::Vector{Int}
cliqAssocMat::Array{Bool,2}
cliqMsgMat::Array{Bool,2}
directvarIDs::Vector{Int}
directFrtlMsgIDs::Vector{Int}
msgskipIDs::Vector{Int}
itervarIDs::Vector{Int}
directPriorMsgIDs::Vector{Int}
debug
debugDwn
BayesTreeNodeData() = new()
BayesTreeNodeData(x...) = new(x[1],x[2],x[3],x[4],x[5],x[6],x[7],x[8],x[9],x[10],x[11],x[12],x[13],x[14])
end
# TODO -- this should be a constructor
function emptyBTNodeData()
BayesTreeNodeData(Int[],Int[],Int[],
Int[],Int[],Array{Bool}(0,0),
Array{Bool}(0,0),Int[],Int[],
Int[],Int[],Int[],
nothing, nothing)
end
# BayesTree declarations
type BayesTree
bt
btid::Int
cliques::Dict{Int,Graphs.ExVertex}
frontals::Dict{String,Int}
end
function emptyBayesTree()
bt = BayesTree(Graphs.inclist(Graphs.ExVertex,is_directed=true),
0,
Dict{Int,Graphs.ExVertex}(),
#[],
Dict{AbstractString, Int}())
return bt
end
# create a new clique
function addClique!(bt::BayesTree, fg::FactorGraph, varID::Int, condIDs::Array{Int}=Int[])
bt.btid += 1
clq = Graphs.add_vertex!(bt.bt, ExVertex(bt.btid,string("Clique",bt.btid)))
bt.cliques[bt.btid] = clq
clq.attributes["label"] = ""
# Specific data container
clq.attributes["data"] = emptyBTNodeData()
appendClique!(bt, bt.btid, fg, varID, condIDs)
return clq
end
# generate the label for particular clique -- graphviz drawing
function makeCliqueLabel(fgl::FactorGraph, bt::BayesTree, clqID::Int)
clq = bt.cliques[clqID]
flbl = ""
clbl = ""
for fr in clq.attributes["data"].frontalIDs
flbl = string(flbl,localapi.getvertex(fgl,fr).attributes["label"], ",") #fgl.v[fr].
end
for cond in clq.attributes["data"].conditIDs
clbl = string(clbl, localapi.getvertex(fgl,cond).attributes["label"], ",") # fgl.v[cond].
end
clq.attributes["label"] = string(flbl, ": ", clbl)
end
# add a conditional ID to clique
function appendConditional(bt::BayesTree, clqID::Int, condIDs::Array{Int,1})
clq = bt.cliques[clqID]
clq.attributes["data"].conditIDs = union(clq.attributes["data"].conditIDs, condIDs)
end
# Add a new frontal variable to clique
function appendClique!(bt::BayesTree, clqID::Int, fg::FactorGraph, varID::Int, condIDs::Array{Int,1}=Int[])
clq = bt.cliques[clqID]
var = localapi.getvertex(fg, varID) # fg.v[varID]
# add frontal variable
push!(clq.attributes["data"].frontalIDs,varID)
# total dictionary of frontals for easy access
bt.frontals[var.attributes["label"]] = clqID#bt.btid
appendConditional(bt, clqID, condIDs)
makeCliqueLabel(fg, bt, clqID)
nothing
end
# instantiate a new child clique in the tree
function newChildClique!(bt::BayesTree, fg::FactorGraph, CpID::Int, varID::Int, Sepj::Array{Int,1})
chclq = addClique!(bt, fg, varID, Sepj)
parent = bt.cliques[CpID]
# Staying with Graphs.jl for tree in first stage
edge = Graphs.make_edge(bt.bt, parent, chclq)
Graphs.add_edge!(bt.bt, edge)
return chclq
end
# post order tree traversal and build potential functions
function findCliqueFromFrontal(bt::BayesTree, frtlID::Int)
for cliqPair in bt.cliques
id = cliqPair[1]
cliq = cliqPair[2]
for frtl in cliq.attributes["data"].frontalIDs
if frtl == frtlID
return cliq
end
end
end
error("Clique with desired frontal ID not found")
end
# eliminate a variable for new
function newPotential(tree::BayesTree, fg::FactorGraph, var::Int, prevVar::Int, p::Array{Int,1})
firvert = localapi.getvertex(fg,var)
if (length(getData(firvert).separator) == 0)
# warn("newPotential -- sep length is 0")
if (length(tree.cliques) == 0)
addClique!(tree, fg, var)
else
appendClique!(tree, 1, fg, var) # add to root
end
else
Sj = getData(firvert).separator
# find parent clique Cp that containts the first eliminated variable of Sj as frontal
firstelim = 99999999999
for s in Sj
temp = findfirst(p, s)
if (temp < firstelim)
firstelim = temp
end
end
felbl = localapi.getvertex(fg, p[firstelim]).attributes["label"]
CpID = tree.frontals[felbl]
# look to add this conditional to the tree
unFC = union(tree.cliques[CpID].attributes["data"].frontalIDs, tree.cliques[CpID].attributes["data"].conditIDs)
if (sort(unFC) == sort(Sj))
appendClique!(tree, CpID, fg, var)
else
newChildClique!(tree, fg, CpID, var, Sj)
end
end
end
# build the whole tree in batch format
function buildTree!(tree::BayesTree, fg::FactorGraph, p::Array{Int,1})
rp = flipdim(p,1)
prevVar = 0
for var in rp
newPotential(tree, fg, var, prevVar, p)
prevVar = var
end
end
## Find batch belief propagation solution
function prepBatchTree!(fg::FactorGraph; ordering::Symbol=:qr,drawpdf::Bool=false)
p = IncrementalInference.getEliminationOrder(fg, ordering=ordering)
println()
fge = deepcopy(fg)
println("Building Bayes net...")
buildBayesNet!(fge, p)
tree = emptyBayesTree()
buildTree!(tree, fge, p)
println("Bayes Net")
# sleep(0.1)
#fid = open("bn.dot","w+")
#write(fid,to_dot(fge.bn))
#close(fid)
# Michael reference -- x2->x1, x2->x3, x2->x4, x2->l1, x4->x3, l1->x3, l1->x4
println("Bayes Tree")
if drawpdf
fid = open("bt.dot","w+")
write(fid,to_dot(tree.bt))
close(fid)
run(`dot bt.dot -Tpdf -o bt.pdf`)
end
# GraphViz.Graph(to_dot(tree.bt))
#Michael reference 3sig -- x2l1x4x3 x1|x2
println("Find potential functions for each clique")
cliq = tree.cliques[1] # start at the root
buildCliquePotentials(fg, tree, cliq); # fg does not have the marginals as fge does
# now update all factor graph vertices used for this tree
for v in vertices(fg.g)
dlapi.updatevertex!(fg, v)
end
return tree
end
function resetData!(vdata::VariableNodeData)
vdata.eliminated = false
vdata.BayesNetOutVertIDs = Int[]
vdata.BayesNetVertID = 0
vdata.separator = Int[]
nothing
end
function resetData!(vdata::FunctionNodeData)
vdata.eliminated = false
vdata.potentialused = false
nothing
end
function resetFactorGraphNewTree!(fg::FactorGraph)
for v in vertices(fg.g)
resetData!(getData(v))
localapi.updatevertex!(fg, v)
end
nothing
end
function wipeBuildNewTree!(fg::FactorGraph; ordering=:qr,drawpdf=false)
resetFactorGraphNewTree!(fg);
return prepBatchTree!(fg, ordering=ordering, drawpdf=drawpdf);
end
function whichCliq{T <: AbstractString}(bt::BayesTree, frt::T)
bt.cliques[bt.frontals[frt]]
end
whichCliq(bt::BayesTree, frt::Symbol) = whichCliq(bt, string(frt))
function appendUseFcts!(usefcts, lblid::Int, fct::Graphs.ExVertex, fid::Int)
for tp in usefcts
if tp == fct.index
return nothing
end
end
tpl = fct.index
push!(usefcts, tpl )
nothing
end
function getCliquePotentials!(fg::FactorGraph, bt::BayesTree, cliq::Graphs.ExVertex)
frtl = cliq.attributes["data"].frontalIDs
cond = cliq.attributes["data"].conditIDs
allids = [frtl;cond]
alldimIDs = Int[]
for fid in frtl
alldimIDs = [alldimIDs; getData(localapi.getvertex(fg,fid)).dimIDs]
end
for cid in cond
alldimIDs = [alldimIDs; getData(localapi.getvertex(fg,cid)).dimIDs]
end
for fid in frtl
usefcts = []
for fct in localapi.outneighbors(fg, localapi.getvertex(fg,fid))
if getData(fct).potentialused!=true
loutn = localapi.outneighbors(fg, fct)
if length(loutn)==1
appendUseFcts!(usefcts, fg.IDs[Symbol(loutn[1].label)], fct, fid)
# TODO -- make update vertex call
fct.attributes["data"].potentialused = true
localapi.updatevertex!(fg, fct)
end
for sepSearch in loutn
sslbl = Symbol(sepSearch.label)
if (fg.IDs[sslbl] == fid)
continue # skip the fid itself
end
sea = findmin(abs.(allids-fg.IDs[sslbl]))
if sea[1]==0.0
appendUseFcts!(usefcts, fg.IDs[sslbl], fct, fid)
# usefcts = [usefcts;(fg.IDs[sslbl], fct, fid)]
fct.attributes["data"].potentialused = true #fct.attributes["potentialused"] = true
localapi.updatevertex!(fg, fct)
end
end
end
end
cliq.attributes["data"].potentials=union(cliq.attributes["data"].potentials,usefcts)
end
return nothing
end
function getCliquePotentials!(fg::FactorGraph, bt::BayesTree, chkcliq::Int)
getCliquePotentials!(fg, bt.cliques[chkcliq])
end
function cliqPotentialIDs(cliq::Graphs.ExVertex)
potIDs = Int[]
for idfct in cliq.attributes["data"].potentials
push!(potIDs,idfct)
end
return potIDs
end
function collectSeparators(bt::BayesTree, cliq::Graphs.ExVertex)
allseps = Int[]
for child in out_neighbors(cliq, bt.bt)#tree
allseps = [allseps; child.attributes["data"].conditIDs]
end
return allseps
end
function compCliqAssocMatrices!(fgl::FactorGraph, bt::BayesTree, cliq::Graphs.ExVertex)
frtl = cliq.attributes["data"].frontalIDs
cond = cliq.attributes["data"].conditIDs
inmsgIDs = collectSeparators(bt, cliq)
potIDs = cliqPotentialIDs(cliq)
# Construct associations matrix here
# matrix has variables are columns, and messages/constraints as rows
cols = [frtl;cond]
cliq.attributes["data"].inmsgIDs = inmsgIDs
cliq.attributes["data"].potIDs = potIDs
cliqAssocMat = Array{Bool,2}(length(potIDs), length(cols))
cliqMsgMat = Array{Bool,2}(length(inmsgIDs), length(cols))
fill!(cliqAssocMat, false)
fill!(cliqMsgMat, false)
for j in 1:length(cols)
for i in 1:length(inmsgIDs)
if cols[j] == inmsgIDs[i]
cliqMsgMat[i,j] = true
end
end
for i in 1:length(potIDs)
idfct = cliq.attributes["data"].potentials[i]
if idfct == potIDs[i] # sanity check on clique potentials ordering
for vertidx in getData(getVert(fgl, idfct, api=localapi)).fncargvID
# for vertidx in getData(getVertNode(fgl, idfct)).fncargvID
if vertidx == cols[j]
cliqAssocMat[i,j] = true
end
end
else
prtslperr("compCliqAssocMatrices! -- potential ID ordering was lost")
end
end
end
cliq.attributes["data"].cliqAssocMat = cliqAssocMat
cliq.attributes["data"].cliqMsgMat = cliqMsgMat
nothing
end
function getCliqAssocMat(cliq::Graphs.ExVertex)
cliq.attributes["data"].cliqAssocMat
end
function getCliqMsgMat(cliq::Graphs.ExVertex)
cliq.attributes["data"].cliqMsgMat
end
function getCliqMat(cliq::Graphs.ExVertex; showmsg=true)
assocMat = getCliqAssocMat(cliq)
msgMat = getCliqMsgMat(cliq)
mat = showmsg ? [assocMat;msgMat] : assocMat
return mat
end
# function spyCliqMat(cliq::Graphs.ExVertex; showmsg=true)
# mat = deepcopy(getCliqMat(cliq, showmsg=showmsg))
# # TODO -- add improved visualization here, iter vs skip
# mat = map(Float64, mat)*2.0-1.0
# numlcl = size(getCliqAssocMat(cliq),1)
# mat[(numlcl+1):end,:] *= 0.9
# mat[(numlcl+1):end,:] -= 0.1
# numfrtl1 = floor(Int,length(cliq.attributes["data"].frontalIDs)+1)
# mat[:,numfrtl1:end] *= 0.9
# mat[:,numfrtl1:end] -= 0.1
# @show cliq.attributes["data"].itervarIDs
# @show cliq.attributes["data"].directvarIDs
# @show cliq.attributes["data"].msgskipIDs
# @show cliq.attributes["data"].directFrtlMsgIDs
# @show cliq.attributes["data"].directPriorMsgIDs
# sp = Gadfly.spy(mat)
# push!(sp.guides, Gadfly.Guide.title("$(cliq.attributes["label"]) || $(cliq.attributes["data"].frontalIDs) :$(cliq.attributes["data"].conditIDs)"))
# push!(sp.guides, Gadfly.Guide.xlabel("fmcmcs $(cliq.attributes["data"].itervarIDs)"))
# push!(sp.guides, Gadfly.Guide.ylabel("lcl=$(numlcl) || msg=$(size(getCliqMsgMat(cliq),1))" ))
# return sp
# end
# function spyCliqMat(bt::BayesTree, lbl::Symbol; showmsg=true)
# spyCliqMat(whichCliq(bt,lbl), showmsg=showmsg)
# end
function countSkips(bt::BayesTree)
skps = 0
for cliq in bt.cliques
m = getCliqMat(cliq[2])
mi = map(Int,m)
skps += sum(map(Int,sum(mi,1) .== 1))
end
return skps
end
function skipThroughMsgsIDs(cliq::Graphs.ExVertex)
numfrtl1 = floor(Int,length(cliq.attributes["data"].frontalIDs)+1)
condAssocMat = cliq.attributes["data"].cliqAssocMat[:,numfrtl1:end]
condMsgMat = cliq.attributes["data"].cliqMsgMat[:,numfrtl1:end]
mat = [condAssocMat;condMsgMat];
mab = sum(map(Int,mat),1) .== 1
mabM = sum(map(Int,condMsgMat),1) .== 1
mab = mab .& mabM
# rang = 1:size(condMsgMat,2)
msgidx = cliq.attributes["data"].conditIDs[vec(collect(mab))]
return msgidx
end
function directPriorMsgIDs(cliq::Graphs.ExVertex)
frtl = cliq.attributes["data"].frontalIDs
cond = cliq.attributes["data"].conditIDs
cols = [frtl;cond]
mat = getCliqMat(cliq, showmsg=true)
singr = sum(map(Int,mat),2) .== 1
rerows = collect(1:length(singr))
b = vec(collect(singr))
rerows2 = rerows[b]
sumsrAc = sum(map(Int,mat[rerows2,:]),1)
sumc = sum(map(Int,mat),1)
pmSkipCols = (sumsrAc - sumc) .== 0
return cols[vec(collect(pmSkipCols))]
end
function directFrtlMsgIDs(cliq::Graphs.ExVertex)
numfrtl = length(cliq.attributes["data"].frontalIDs)
frntAssocMat = cliq.attributes["data"].cliqAssocMat[:,1:numfrtl]
frtlMsgMat = cliq.attributes["data"].cliqMsgMat[:,1:numfrtl]
mat = [frntAssocMat; frtlMsgMat];
mab = sum(map(Int,mat),1) .== 1
mabM = sum(map(Int,frtlMsgMat),1) .== 1
mab = mab .& mabM
return cliq.attributes["data"].frontalIDs[vec(collect(mab))]
end
function directAssignmentIDs(cliq::Graphs.ExVertex)
# NOTE -- old version been included in iterated variable stack
assocMat = cliq.attributes["data"].cliqAssocMat
msgMat = cliq.attributes["data"].cliqMsgMat
mat = [assocMat;msgMat];
mab = sum(map(Int,mat),1) .== 1
mabA = sum(map(Int,assocMat),1) .== 1
mab = mab .& mabA
frtl = cliq.attributes["data"].frontalIDs
cond = cliq.attributes["data"].conditIDs
cols = [frtl;cond]
return cols[vec(collect(mab))]
# also calculate how which are conditionals
end
function mcmcIterationIDs(cliq::Graphs.ExVertex)
assocMat = cliq.attributes["data"].cliqAssocMat
msgMat = cliq.attributes["data"].cliqMsgMat
mat = [assocMat;msgMat];
sum(sum(map(Int,mat),1)) == 0 ? error("mcmcIterationIDs -- unaccounted variables") : nothing
mab = 1 .< sum(map(Int,mat),1)
frtl = cliq.attributes["data"].frontalIDs
cond = cliq.attributes["data"].conditIDs
cols = [frtl;cond]
# dev code
# prioritize prior'ed variables, then high degree next
# get all prior type rows, should be prioritized
# TODO -- maybe sort according to decreasing degree
# singleperrows = 1 .== sum(map(Int,mat),2)
# singleperrows = collect(singleperrows)
# prioredvarsum = sum(map(Int,mat[singleperrows]),1)
# prioredperm = sortperm(prioredvarsum, rev=true) # decreasing count
#end dev code
# final output
return cols[vec(collect(mab))]
end
function setCliqMCIDs!(cliq::Graphs.ExVertex)
cliq.attributes["data"].directPriorMsgIDs = directPriorMsgIDs(cliq)
# NOTE -- combined larger iter group
cliq.attributes["data"].directvarIDs = directAssignmentIDs(cliq)
# cliq.attributes["data"].itervarIDs = mcmcIterationIDs(cliq)
# NOTE -- fix direct vs itervar issue, DirectVarIDs against Iters should also Iter
usset = union(mcmcIterationIDs(cliq), directAssignmentIDs(cliq))
cliq.attributes["data"].itervarIDs = setdiff(usset, cliq.attributes["data"].directPriorMsgIDs)
cliq.attributes["data"].msgskipIDs = skipThroughMsgsIDs(cliq)
cliq.attributes["data"].directFrtlMsgIDs = directFrtlMsgIDs(cliq)
nothing
end
# post order tree traversal and build potential functions
function buildCliquePotentials(fg::FactorGraph, bt::BayesTree, cliq::Graphs.ExVertex)
for child in out_neighbors(cliq, bt.bt)#tree
buildCliquePotentials(fg, bt, child)
end
println("Get potentials $(cliq.attributes["label"])");
getCliquePotentials!(fg, bt, cliq);
compCliqAssocMatrices!(fg, bt, cliq);
setCliqMCIDs!(cliq);
nothing
end
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2147,
198,
437,
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] | 2.293324 | 7,190 |
function intersects_geometry(m::AbstractAccretionGeometry{T}, line_element) where {T}
if in_nearby_region(m, line_element)
return has_intersect(m, line_element)
end
false
end
in_nearby_region(d::AbstractAccretionGeometry{T}, line_element) where {T} =
error("Not implemented for $(typeof(d))")
has_intersect(d::AbstractAccretionGeometry{T}, line_element) where {T} =
error("Not implemented for $(typeof(d))")
# Jiménez, Segura, Feito. Computation Geometry 43 (2010) 474-492
function jsr_algorithm(V₁::T, V₂::T, V₃::T, Q₁::V, Q₂::V; ϵ = 1e-8) where {T,V}
A = Q₁ .- V₃
B = V₁ .- V₃
C = V₂ .- V₃
W₁ = B × C
w = A ⋅ W₁
if w > ϵ
D = Q₂ .- V₃
s = D ⋅ W₁
s > ϵ && return false
W₂ = A × D
t = W₂ ⋅ C
t < -ϵ && return false
u = -W₂ ⋅ B
u < -ϵ && return false
w < s + t + u && return false
elseif w < -ϵ
return false
else # w == 0
D = Q₂ .- V₃
s = D ⋅ W₁
if s > ϵ
return false
elseif s < -ϵ
W₂ = D × A
t = W₂ ⋅ C
t > ϵ && return false
u = -W₂ ⋅ B
u > ϵ && return false
-s > t + u && return false
else
return false
end
end
true
end
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] | 1.72346 | 763 |
module Sodium
typealias size_t Csize_t
typealias uint32_t Uint32
typealias uint8_t Uint8
include("ls_exports_h.jl")
include("ls_common_h.jl")
include("ls_sodium_h.jl")
include("ls_defines_h.jl")
end
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] | 2.376471 | 85 |
using Flux, Flux.Tracker
using NeuralProcesses, NeuralProcesses.Experiment
using Distributions
using Plots
using Random
using PyCall
@pyimport pickle
function save(path, obj)
out = open(path, "w")
pickle.dump(obj, out)
close(out)
end
convcnp = NeuralProcesses.untrack(best_model("models/convcnp/loglik/sawtooth.bson"))
convnp = NeuralProcesses.untrack(best_model("models/convnp/loglik/sawtooth.bson"))
p = Sawtooth()
xt = collect(range(-2, 2, length=600))
yt = reshape(rand(p(xt, 1e-10)), :)
inds = sort(randperm(length(xt))[1:7])
xc = xt[inds]
yc = yt[inds]
save("sawtooth/data.pickle", Dict(
"xc" => xc,
"yc" => yc,
"xt" => xt,
"yt" => yt
))
xc = reshape(xc, :, 1, 1)
yc = reshape(yc, :, 1, 1)
xt = reshape(xt, :, 1, 1)
yt = reshape(yt, :, 1, 1)
# Embedding:
xz = convcnp.encoder.disc(xc, xt, margin=16f0)
xz, z = code(convcnp.encoder.coder[1], xc, yc, xz)
inds = reshape((xz .>= -2) .& (xz .<= 2), :)
plot(xz[inds, 1, 1], z[inds, 1, 1])
save("sawtooth/embedding.pickle", Dict(
"xz" => xz[inds, 1, 1],
"z1" => z[inds, 1, 1],
"z2" => z[inds, 2, 1]
))
xz, z = code(convcnp.encoder.coder[2], xz, z, xz)
z = NeuralProcesses.sample(z, num_samples=1)
# CNN:
xz, z = code(convcnp.decoder[1], xz, z, xt)
save("sawtooth/embedding_cnn.pickle", Dict(
"xz" => xz[inds, 1, 1],
"z1" => z[inds, 1, 1],
"z2" => z[inds, 2, 1]
))
xz, z = code(convcnp.decoder[2], xz, z, xt)
xz, z = code(convcnp.decoder[3], xz, z, xt)
xc = reshape(xc, :)
yc = reshape(yc, :)
xt = reshape(xt, :)
yt = reshape(yt, :)
μ = reshape(mean(z), :)
σ = reshape(std(z), :)
# Prediction:
save("sawtooth/pred.pickle", Dict(
"xt" => xt,
"mu" => μ,
"std" => σ
))
# Also predict with ConvNP:
_, _, _, samples = predict(convnp, xc, yc, xt; margin=16f0)
save("sawtooth/pred_convnp.pickle", Dict(
"xt" => xt,
"samples" => samples
))
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] | 2.008593 | 931 |
__precompile__()
module Configs
include("utils.jl")
using JSON, Memoize
export getconfig,
setconfig!,
hasconfig,
initconfig
configs = nothing
const configs_defaultorder = [
"default.json",
"custom-environment-variables.json"
]
function initconfig(;deployment_key = "DEPLOYMENT", configs_directory = "configs")::NamedTuple
global configs = Dict{String, Any}()
configs_order = copy(configs_defaultorder)
configs_directory = parseenvkey("CONFIGS_DIRECTORY", configs_directory)
deployment_key = parseenvkey("DEPLOYMENT_KEY", deployment_key)
configs_files = getfiles(configs_directory)
deployment = parseenvkey(deployment_key, false)
deployment != false && insert!(configs_order, 2, lowercase(deployment) * ".json")
filter!((file)-> file in configs_files, configs_order)
for file in configs_order
open(joinpath(configs_directory, file), "r") do filepath
file_content = String(read(filepath))
newtree = JSON.parse(file_content)
if file === "custom-environment-variables.json"
parsecustomenv!(newtree)
end
override!(configs, newtree)
end
end
(; configs_directory = configs_directory, deployment_key = deployment_key, configs_order = configs_order)
end
@memoize Dict{Tuple{String}, Any} function getconfig(path::String = "")
global configs
configs === nothing && initconfig()
configs isa Dict && (configs = makeimmutable(configs))
path === "" && return configs
epath = Meta.parse("configs.$path")
try
eval(epath)
catch err
throw(Configserror("no such config: " * path))
end
end
function setconfig!(path::String, value::String)
try
value = JSON.parse(value)
catch err
end
value = pathtodict(path, value)
setconfig!(value)
end
function setconfig!(path::String, value)
value = pathtodict(path, value)
setconfig!(value)
end
function setconfig!(path::String, value::Union{Tuple, AbstractArray, NamedTuple, Dict})
value = json(value) |> JSON.parse
value = pathtodict(path, value)
setconfig!(value)
end
function setconfig!(value::Dict)
configs === nothing && initconfig()
configs isa NamedTuple && throw(Configserror("""config is immutable. Please set all values before calling "get" """))
override!(configs, value)
end
function hasconfig(path::String)::Bool
path === "" && throw(Configserror("a path is required to query a config"))
configs === nothing && initconfig()
subpaths = split(path, ".")
ref = configs
for i in eachindex(subpaths)
subpath = Symbol(subpaths[i])
if haskey(ref, subpath)
length(subpaths) === i && return true
ref = ref[subpath]
else
return false
end
end
end
end
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220,
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220,
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220,
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220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
198,
437,
198
] | 2.220588 | 1,428 |
"""
function cudaAdapt_(code)
Function that adapts vector declarations and math functions to CUDA.
"""
function cudaAdapt_(code::Expr)
#Subs all math
l = [i for i in names(Base.Math) if isdefined(CUDA,i) && i != :(^)]
for i in l
code = subs_(code,i,Meta.parse(string("CUDA.","$i")))
end
#Subs power
code = postwalk(x->@capture(x, a_^b_) && typeof(b) != Int ? :(Float32($a)^$b) : x, code)
#Subs vectors
for i in [:zeros,:ones]
code = subs_(code,i,Meta.parse(string("CUDA.","$i")))
end
code = subs_(code,:Array,Meta.parse(string("CUDA.CuArray")))
code = subs_(code,INT,INTCUDA)
code = subs_(code,FLOAT,FLOATCUDA)
return code
end | [
37811,
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1,
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3697,
46,
1404,
43633,
5631,
8,
628,
220,
220,
220,
1441,
2438,
198,
437
] | 2.294118 | 306 |
module LiquidsStructureGrids
### Imports
using Reexport
@reexport using ApproximationGrids
@reexport using LiquidsStructure
import ApproximationGrids: ApproximationGrid
### Exports
export StructureFactorGrid
### Implemetation
struct StructureFactorGrid{S <: StructureFactor, G <: ApproximationGrid}
f::S
grid::G
end
function Base.show(io::IO, ::MIME"text/plain", s::StructureFactorGrid)
print(io, "$(s.f),$(s.grid)")
end
end # module
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437,
628,
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437,
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198
] | 2.886076 | 158 |
""" :( """
function DryBubble_θ_liq_ice(::Type{FT}) where {FT}
z_in = FT[
25., 75., 125., 175., 225., 275., 325., 375., 425.,
475., 525., 575., 625., 675., 725., 775., 825., 875.,
925., 975., 1025., 1075., 1125., 1175., 1225., 1275., 1325.,
1375., 1425., 1475., 1525., 1575., 1625., 1675., 1725., 1775.,
1825., 1875., 1925., 1975., 2025., 2075., 2125., 2175., 2225.,
2275., 2325., 2375., 2425., 2475., 2525., 2575., 2625., 2675.,
2725., 2775., 2825., 2875., 2925., 2975., 3025., 3075., 3125.,
3175., 3225., 3275., 3325., 3375., 3425., 3475., 3525., 3575.,
3625., 3675., 3725., 3775., 3825., 3875., 3925., 3975., 4025.,
4075., 4125., 4175., 4225., 4275., 4325., 4375., 4425., 4475.,
4525., 4575., 4625., 4675., 4725., 4775., 4825., 4875., 4925.,
4975., 5025., 5075., 5125., 5175., 5225., 5275., 5325., 5375.,
5425., 5475., 5525., 5575., 5625., 5675., 5725., 5775., 5825.,
5875., 5925., 5975., 6025., 6075., 6125., 6175., 6225., 6275.,
6325., 6375., 6425., 6475., 6525., 6575., 6625., 6675., 6725.,
6775., 6825., 6875., 6925., 6975., 7025., 7075., 7125., 7175.,
7225., 7275., 7325., 7375., 7425., 7475., 7525., 7575., 7625.,
7675., 7725., 7775., 7825., 7875., 7925., 7975., 8025., 8075.,
8125., 8175., 8225., 8275., 8325., 8375., 8425., 8475., 8525.,
8575., 8625., 8675., 8725., 8775., 8825., 8875., 8925., 8975.,
9025., 9075., 9125., 9175., 9225., 9275., 9325., 9375., 9425.,
9475., 9525., 9575., 9625., 9675., 9725., 9775., 9825., 9875.,
9925., 9975.
]
θ_liq_ice_in = FT[
299.9834, 299.9836, 299.9841, 299.985 , 299.9864, 299.9883,
299.9907, 299.9936, 299.9972, 300.0012, 300.0058, 300.011 ,
300.0166, 300.0228, 300.0293, 300.0363, 300.0436, 300.0512,
300.0591, 300.0672, 300.0755, 300.0838, 300.0921, 300.1004,
300.1086, 300.1167, 300.1245, 300.132 , 300.1393, 300.1461,
300.1525, 300.1583, 300.1637, 300.1685, 300.1726, 300.1762,
300.179 , 300.1812, 300.1826, 300.1833, 300.1833, 300.1826,
300.1812, 300.179 , 300.1762, 300.1727, 300.1685, 300.1637,
300.1584, 300.1525, 300.1461, 300.1393, 300.1321, 300.1245,
300.1167, 300.1087, 300.1005, 300.0922, 300.0838, 300.0755,
300.0673, 300.0592, 300.0513, 300.0437, 300.0364, 300.0294,
300.0228, 300.0167, 300.0111, 300.0059, 300.0013, 299.9972,
299.9937, 299.9908, 299.9884, 299.9865, 299.9851, 299.9842,
299.9837, 299.9835, 299.9835, 299.9835, 299.9835, 299.9835,
299.9835, 299.9835, 299.9835, 299.9835, 299.9835, 299.9835,
299.9835, 299.9835, 299.9835, 299.9835, 299.9835, 299.9835,
299.9835, 299.9835, 299.9835, 299.9835, 299.9835, 299.9835,
299.9835, 299.9835, 299.9835, 299.9835, 299.9835, 299.9835,
299.9835, 299.9835, 299.9835, 299.9835, 299.9835, 299.9835,
299.9836, 299.9836, 299.9836, 299.9836, 299.9836, 299.9836,
299.9836, 299.9836, 299.9836, 299.9836, 299.9836, 299.9836,
299.9836, 299.9836, 299.9836, 299.9836, 299.9836, 299.9836,
299.9836, 299.9836, 299.9836, 299.9836, 299.9836, 299.9836,
299.9836, 299.9836, 299.9836, 299.9836, 299.9836, 299.9836,
299.9836, 299.9836, 299.9836, 299.9836, 299.9836, 299.9836,
299.9836, 299.9836, 299.9836, 299.9836, 299.9836, 299.9836,
299.9836, 299.9836, 299.9836, 299.9836, 299.9836, 299.9836,
299.9836, 299.9836, 299.9836, 299.9836, 299.9836, 299.9836,
299.9836, 299.9836, 299.9836, 299.9836, 299.9836, 299.9836,
299.9836, 299.9836, 299.9836, 299.9836, 299.9837, 299.9837,
299.9837, 299.9837, 299.9837, 299.9837, 299.9837, 299.9837,
299.9837, 299.9837, 299.9837, 299.9837, 299.9837, 299.9837,
299.9837, 299.9837, 299.9837, 299.9837, 299.9837, 299.9837,
299.9837, 299.9837
]
profile = Dierckx.Spline1D(z_in, θ_liq_ice_in; k = 1)
return profile
end
""" :( """
function DryBubble_updrafts_z(::Type{FT}) where {FT}
z_in = FT[
75., 125., 175., 225., 275., 325., 375., 425., 475.,
525., 575., 625., 675., 725., 775., 825., 875., 925.,
975., 1025., 1075., 1125., 1175., 1225., 1275., 1325., 1375.,
1425., 1475., 1525., 1575., 1625., 1675., 1725., 1775., 1825.,
1875., 1925., 1975., 2025., 2075., 2125., 2175., 2225., 2275.,
2325., 2375., 2425., 2475., 2525., 2575., 2625., 2675., 2725.,
2775., 2825., 2875., 2925., 2975., 3025., 3075., 3125., 3175.,
3225., 3275., 3325., 3375., 3425., 3475., 3525., 3575., 3625.,
3675., 3725., 3775., 3825., 3875., 3925.]
return z_in
end
""" :( """
function DryBubble_updrafts_θ_liq_ice(::Type{FT}) where {FT}
z_in = DryBubble_updrafts_z(FT)
θ_liq_in = FT[
299.9882, 299.996 , 300.0063, 300.0205, 300.04 , 300.0594,
300.0848, 300.1131, 300.1438, 300.1766, 300.2198, 300.2567,
300.2946, 300.3452, 300.3849, 300.4245, 300.4791, 300.5182,
300.574 , 300.6305, 300.6668, 300.7222, 300.7771, 300.8074,
300.8591, 300.9092, 300.9574, 300.9758, 301.0182, 301.0579,
301.0944, 301.1276, 301.1572, 301.1515, 301.1729, 301.1902,
301.2033, 301.2122, 301.2167, 301.2169, 301.2127, 301.2041,
301.1913, 301.1743, 301.1533, 301.1593, 301.1299, 301.097 ,
301.0606, 301.0212, 300.9788, 300.9607, 300.9125, 300.8625,
300.8108, 300.7806, 300.7256, 300.6701, 300.6338, 300.5772,
300.5212, 300.482 , 300.4272, 300.3875, 300.3354, 300.2968,
300.2587, 300.2216, 300.1782, 300.1452, 300.1143, 300.0859,
300.0603, 300.0408, 300.0211, 300.0067, 299.9963, 299.9884]
profile = Dierckx.Spline1D(z_in, θ_liq_in; k = 1)
return profile
end
""" :( """
function DryBubble_updrafts_area(::Type{FT}) where {FT}
z_in = DryBubble_updrafts_z(FT)
Area_in = FT[
0.04 , 0.055, 0.07 , 0.08 , 0.085, 0.095, 0.1 , 0.105, 0.11 ,
0.115, 0.115, 0.12 , 0.125, 0.125, 0.13 , 0.135, 0.135, 0.14 ,
0.14 , 0.14 , 0.145, 0.145, 0.145, 0.15 , 0.15 , 0.15 , 0.15 ,
0.155, 0.155, 0.155, 0.155, 0.155, 0.155, 0.16 , 0.16 , 0.16 ,
0.16 , 0.16 , 0.16 , 0.16 , 0.16 , 0.16 , 0.16 , 0.16 , 0.16 ,
0.155, 0.155, 0.155, 0.155, 0.155, 0.155, 0.15 , 0.15 , 0.15 ,
0.15 , 0.145, 0.145, 0.145, 0.14 , 0.14 , 0.14 , 0.135, 0.135,
0.13 , 0.13 , 0.125, 0.12 , 0.115, 0.115, 0.11 , 0.105, 0.1 ,
0.095, 0.085, 0.08 , 0.07 , 0.055, 0.04]
profile = Dierckx.Spline1D(z_in, Area_in; k = 1)
return profile
end
""" :( """
function DryBubble_updrafts_w(::Type{FT}) where {FT}
z_in = DryBubble_updrafts_z(FT)
W_in = FT[
0.017 , 0.0266, 0.0344, 0.0417, 0.0495, 0.0546, 0.061 , 0.0668,
0.0721, 0.0768, 0.0849, 0.0887, 0.092 , 0.0996, 0.1019, 0.1037,
0.1106, 0.1114, 0.1179, 0.1243, 0.1238, 0.1297, 0.1355, 0.1335,
0.1387, 0.1437, 0.1485, 0.1448, 0.1489, 0.1527, 0.1564, 0.1597,
0.1628, 0.1565, 0.1588, 0.1609, 0.1626, 0.1641, 0.1652, 0.166 ,
0.1665, 0.1667, 0.1666, 0.1662, 0.1655, 0.1736, 0.1722, 0.1706,
0.1686, 0.1664, 0.1639, 0.1698, 0.1667, 0.1634, 0.1599, 0.1641,
0.1601, 0.1559, 0.1589, 0.1543, 0.1496, 0.1514, 0.1464, 0.1475,
0.1422, 0.1425, 0.1424, 0.1419, 0.1361, 0.135 , 0.1335, 0.1316,
0.1294, 0.1302, 0.1271, 0.1264, 0.1269, 0.1256]
profile = Dierckx.Spline1D(z_in, W_in; k = 1)
return profile
end
""" :( """
function DryBubble_updrafts_T(::Type{FT}) where {FT}
z_in = DryBubble_updrafts_z(FT)
T_in = FT[
299.2557, 298.775 , 298.2969, 297.8227, 297.3536, 296.8843,
296.421 , 295.9603, 295.502 , 295.0456, 294.5994, 294.1468,
293.6951, 293.2556, 292.8054, 292.3549, 291.9188, 291.4677,
291.0325, 290.5978, 290.1434, 289.7073, 289.2706, 288.81 ,
288.3698, 287.928 , 287.4842, 287.0118, 286.5622, 286.1099,
285.6544, 285.1957, 284.7335, 284.2379, 283.7677, 283.2937,
282.8157, 282.3337, 281.8476, 281.3574, 280.8631, 280.3649,
279.8626, 279.3565, 278.8467, 278.362 , 277.8447, 277.3241,
276.8006, 276.2742, 275.7454, 275.2388, 274.705 , 274.1694,
273.6327, 273.1155, 272.576 , 272.0363, 271.514 , 270.9736,
270.4339, 269.9094, 269.3711, 268.8465, 268.311 , 267.7877,
267.2649, 266.7432, 266.2159, 265.698 , 265.1821, 264.6685,
264.1574, 263.6518, 263.1461, 262.6451, 262.1476, 261.6524]
profile = Dierckx.Spline1D(z_in, T_in; k = 1)
return profile
end
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] | 1.820021 | 4,745 |
const ElectronSite = TagType"Electron"
function siteinds(::ElectronSite,
N::Int; kwargs...)
conserve_qns = get(kwargs,:conserve_qns,false)
conserve_sz = get(kwargs,:conserve_sz,conserve_qns)
conserve_nf = get(kwargs,:conserve_nf,conserve_qns)
conserve_parity = get(kwargs,:conserve_parity,conserve_qns)
if conserve_sz && conserve_nf
em = QN(("Nf",0,-1),("Sz", 0)) => 1
up = QN(("Nf",1,-1),("Sz",+1)) => 1
dn = QN(("Nf",1,-1),("Sz",-1)) => 1
ud = QN(("Nf",2,-1),("Sz", 0)) => 1
return [Index(em,up,dn,ud;tags="Site,Electron,n=$n") for n=1:N]
elseif conserve_nf
zer = QN("Nf",0,-1) => 1
one = QN("Nf",1,-1) => 2
two = QN("Nf",2,-1) => 1
return [Index(zer,one,two;tags="Site,Electron,n=$n") for n=1:N]
elseif conserve_sz
em = QN(("Sz", 0),("Pf",0,-2)) => 1
up = QN(("Sz",+1),("Pf",1,-2)) => 1
dn = QN(("Sz",-1),("Pf",1,-2)) => 1
ud = QN(("Sz", 0),("Pf",0,-2)) => 1
return [Index(em,up,dn,ud;tags="Site,Electron,n=$n") for n=1:N]
elseif conserve_parity
zer = QN("Pf",0,-2) => 1
one = QN("Pf",1,-2) => 2
two = QN("Pf",0,-2) => 1
return [Index(zer,one,two;tags="Site,Electron,n=$n") for n=1:N]
end
return [Index(4,"Site,Electron,n=$n") for n=1:N]
end
function state(::ElectronSite,
st::AbstractString)
if st == "Emp" || st == "0"
return 1
elseif st == "Up" || st == "↑"
return 2
elseif st == "Dn" || st == "↓"
return 3
elseif st == "UpDn" || st == "↑↓"
return 4
end
throw(ArgumentError("State string \"$st\" not recognized for Electron site"))
return 0
end
function op(::ElectronSite,
s::Index,
opname::AbstractString)::ITensor
Emp = s(1)
EmpP = s'(1)
Up = s(2)
UpP = s'(2)
Dn = s(3)
DnP = s'(3)
UpDn = s(4)
UpDnP = s'(4)
Op = emptyITensor(s',dag(s))
if opname == "Nup"
Op[UpP, Up] = 1.
Op[UpDnP, UpDn] = 1.
elseif opname == "Ndn"
Op[DnP, Dn] = 1.
Op[UpDnP, UpDn] = 1.
elseif opname == "Nupdn"
Op[UpDnP, UpDn] = 1.
elseif opname == "Ntot"
Op[UpP, Up] = 1.
Op[DnP, Dn] = 1.
Op[UpDnP, UpDn] = 2.
elseif opname == "Cup"
Op[EmpP, Up] = +1.
Op[DnP, UpDn] = +1.
elseif opname == "Cdagup"
Op[UpP, Emp] = +1.
Op[UpDnP, Dn] = +1.
elseif opname == "Cdn"
Op[EmpP, Dn] = +1.
Op[UpP, UpDn] = -1.
elseif opname == "Cdagdn"
Op[DnP, Emp] = +1.
Op[UpDnP, Up] = -1.
# Aup,Adagup,Adn,Adagdn below
# are "bosonic" versions of
# the creation/annihilation
# C operators defined above
elseif opname == "Aup"
Op[EmpP, Up] = 1.
Op[DnP, UpDn] = 1.
elseif opname == "Adagup"
Op[UpP, Emp] = 1.
Op[UpDnP, Dn] = 1.
elseif opname == "Adn"
Op[EmpP, Dn] = 1.
Op[UpP, UpDn] = 1.
elseif opname == "Adagdn"
Op[DnP, Emp] = 1.
Op[UpDnP, Up] = 1.
elseif opname=="F" || opname=="FermiPhase" || opname=="FP"
Op[UpP, Up] = -1.
Op[EmpP, Emp] = 1.
Op[DnP, Dn] = -1.
Op[UpDnP, UpDn] = 1.
elseif opname == "Fup"
Op[EmpP, Emp] = 1.
Op[UpP, Up] = -1.
Op[DnP, Dn] = 1.
Op[UpDnP, UpDn] = -1.
elseif opname == "Fdn"
Op[EmpP, Emp] = 1.
Op[UpP, Up] = 1.
Op[DnP, Dn] = -1.
Op[UpDnP, UpDn] = -1.
elseif opname == "Sᶻ" || opname == "Sz"
Op[UpP, Up] = 0.5
Op[DnP, Dn] = -0.5
elseif opname == "Sˣ" || opname == "Sx"
Op[DnP, Up] = 0.5
Op[UpP, Dn] = 0.5
elseif opname=="S+" || opname=="Sp" || opname == "S⁺" || opname == "Splus"
Op[UpP, Dn] = 1.0
elseif opname=="S-" || opname=="Sm" || opname == "S⁻" || opname == "Sminus"
Op[DnP, Up] = 1.0
elseif opname == "Emp" || opname == "0"
pEmp = emptyITensor(s)
pEmp[Emp] = 1.0
return pEmp
elseif opname == "Up" || opname == "↑"
pU = emptyITensor(s)
pU[Up] = 1.0
return pU
elseif opname == "Dn" || opname == "↓"
pD = emptyITensor(s)
pD[Dn] = 1.0
return pD
elseif opname == "UpDn" || opname == "↑↓"
pUD = emptyITensor(s)
pUD[UpDn] = 1.0
return pUD
else
throw(ArgumentError("Operator name $opname not recognized for ElectronSite"))
end
return Op
end
function has_fermion_string(::ElectronSite,
s::Index,
opname::AbstractString)::Bool
if opname=="Cup" || opname=="Cdagup" || opname=="Cdn" || opname=="Cdagdn"
return true
end
return false
end
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] | 1.843063 | 2,364 |
import java.io.BufferedReader;
import java.io.ByteArrayInputStream;
import java.io.ByteArrayOutputStream;
import java.io.InputStreamReader;
import java.io.PrintStream;
import java.nio.file.Files;
import java.nio.file.OpenOption;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.nio.file.StandardOpenOption;
import java.security.KeyPair;
import java.security.KeyPairGenerator;
import java.security.PrivateKey;
import java.security.PublicKey;
import java.util.ArrayList;
import accrue.cryptoerase.runtime.CryptoLibrary;
public class Log {
private final SimpleIRCClient parent;
private static final OpenOption[] defaultOpenOptions = { StandardOpenOption.TRUNCATE_EXISTING, StandardOpenOption.CREATE };
// ANNOTATION: key declaration
private PrivateKey PRIVKEY(L /parent.clearHistory T){L} privkey = null;
// ANNOTATION: key declaration
private PublicKey PUBKEY(L /parent.clearHistory T){L} pubkey = null;
// ANNOTATION: field erasure type
private ArrayList{L /parent.clearHistory T} buffer;
private int historyLength;
public Log(SimpleIRCClient parent) {
this.parent = parent;
KeyPair kp = null;
try {
KeyPairGenerator kpg = KeyPairGenerator.getInstance("RSA");
kpg.initialize(2048); // 2048 is the keysize
kp = kpg.generateKeyPair();
} catch (Exception e) {}
privkey = kp.getPrivate();
pubkey = kp.getPublic();
// privkey = (PRIVKEY(L /parent.clearHistory T){L}) kp.getPrivate();
// pubkey = (PUBKEY(L /parent.clearHistory T){L}) kp.getPublic();
buffer = new ArrayList();
historyLength = 0;
}
public int historyLength() {
return (this.historyLength < 1) ? 0 : this.historyLength - 1;
}
public ArrayList getScreen(int num) {
try {
if (num < 0 || num >= historyLength - 1) {
return null;
}
Path file = Paths.get("irclog." + num, new String[0]);
byte[] bytes = Files.readAllBytes(file);
ArrayList compressed = CryptoLibrary.decryptStrings(privkey, bytes);
return CryptoLibrary.decompressStrings(compressed);
} catch (Throwable e) {}
return null;
}
public void writeLine(String line) {
try {
int bufferSize = 0;
try {
buffer.add(line);
bufferSize = buffer.size();
} catch (Throwable e) {}
// Declassify whether we've received more than 20 messages since the last write
// ANNOTATION: declassification
boolean writeOut = ({L}) bufferSize >= 20;
if (writeOut) {
ArrayList compressed = null;
try { compressed = CryptoLibrary.compressStrings(buffer); } catch (Throwable e) {}
byte[] bytes = CryptoLibrary.encryptStrings(pubkey, compressed);
/*
// INJECTABLE FAULT: attempt to write high data to disk
byte[] bytes = new byte[100];
bytes[0] = ({H}) ((byte)42);
*/
Path file = Paths.get("irclog." + historyLength, new String[0]);
Files.write(file, bytes, defaultOpenOptions);
buffer.clear();
historyLength++;
}
} catch (Throwable e) {}
}
}
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] | 2.711849 | 1,114 |
# The following is taken from StatsBase
# https://github.com/JuliaStats/StatsBase.jl/blob/master/src/sampling.jl
# which is available under the following MIT license:
# Copyright (c) 2012-2016: Dahua Lin, Simon Byrne, Andreas Noack, Douglas Bates, John Myles White, Simon Kornblith, and other contributors.
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
function fisher_yates_sample!(rng::AbstractRNG, a::AbstractArray, x::AbstractArray)
n = length(a)
k = length(x)
k <= n || error("length(x) should not exceed length(a)")
inds = Vector{Int}(undef, n)
for i = 1:n
@inbounds inds[i] = i
end
@inbounds for i = 1:k
j = rand(rng, i:n)
t = inds[j]
inds[j] = inds[i]
inds[i] = t
x[i] = a[t]
end
return x
end
fisher_yates_sample!(a::AbstractArray, x::AbstractArray) =
fisher_yates_sample!(Random.GLOBAL_RNG, a, x)
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# single line
#=
Multi-
line
comment
=#
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module Atmos
export AtmosModel,
AtmosAcousticLinearModel, AtmosAcousticGravityLinearModel,
RemainderModel,
AtmosLESConfiguration,
AtmosGCMConfiguration
using LinearAlgebra, StaticArrays
using ..VariableTemplates
using ..MoistThermodynamics
using ..PlanetParameters
import ..MoistThermodynamics: internal_energy
using ..SubgridScaleParameters
using GPUifyLoops
using ..MPIStateArrays: MPIStateArray
using ..Mesh.Grids: VerticalDirection, HorizontalDirection, min_node_distance
import CLIMA.DGmethods: BalanceLaw, vars_aux, vars_state, vars_gradient,
vars_diffusive, flux_nondiffusive!,
flux_diffusive!, source!, wavespeed, boundary_state!,
gradvariables!, diffusive!, init_aux!, init_state!,
update_aux!, LocalGeometry, lengthscale,
resolutionmetric, DGModel, calculate_dt,
nodal_update_aux!, num_state,
num_integrals,
vars_integrals, vars_reverse_integrals,
indefinite_stack_integral!,
reverse_indefinite_stack_integral!,
integral_load_aux!, integral_set_aux!,
reverse_integral_load_aux!,
reverse_integral_set_aux!
import ..DGmethods.NumericalFluxes: boundary_state!,
boundary_flux_diffusive!,
NumericalFluxNonDiffusive,
NumericalFluxGradient,
NumericalFluxDiffusive
"""
AtmosModel <: BalanceLaw
A `BalanceLaw` for atmosphere modeling.
# Usage
AtmosModel(orientation, ref_state, turbulence, moisture, radiation, source,
boundarycondition, init_state)
"""
struct AtmosModel{FT,O,RS,T,M,P,R,S,BC,IS} <: BalanceLaw
orientation::O
ref_state::RS
turbulence::T
moisture::M
precipitation::P
radiation::R
source::S
# TODO: Probably want to have different bc for state and diffusion...
boundarycondition::BC
init_state::IS
end
function calculate_dt(grid, model::AtmosModel, Courant_number)
T = 290.0
return Courant_number * min_node_distance(grid, VerticalDirection()) / soundspeed_air(T)
end
abstract type AtmosConfiguration end
struct AtmosLESConfiguration <: AtmosConfiguration end
struct AtmosGCMConfiguration <: AtmosConfiguration end
function AtmosModel{FT}(::Type{AtmosLESConfiguration};
orientation::O=FlatOrientation(),
ref_state::RS=HydrostaticState(LinearTemperatureProfile(FT(200),
FT(280),
FT(grav) / FT(cp_d)),
FT(0)),
turbulence::T=SmagorinskyLilly{FT}(0.21),
moisture::M=EquilMoist(),
precipitation::P=NoPrecipitation(),
radiation::R=NoRadiation(),
source::S=( Gravity(),
Coriolis(),
GeostrophicForcing{FT}(7.62e-5, 0, 0)),
# TODO: Probably want to have different bc for state and diffusion...
boundarycondition::BC=NoFluxBC(),
init_state::IS=nothing) where {FT<:AbstractFloat,O,RS,T,M,P,R,S,BC,IS}
@assert init_state ≠ nothing
atmos = (
orientation,
ref_state,
turbulence,
moisture,
precipitation,
radiation,
source,
boundarycondition,
init_state,
)
return AtmosModel{FT,typeof.(atmos)...}(atmos...)
end
function AtmosModel{FT}(::Type{AtmosGCMConfiguration};
orientation::O = SphericalOrientation(),
ref_state::RS = HydrostaticState(
LinearTemperatureProfile(
FT(200),
FT(280),
FT(grav) / FT(cp_d)),
FT(0)),
turbulence::T = SmagorinskyLilly{FT}(0.21),
moisture::M = EquilMoist(),
precipitation::P = NoPrecipitation(),
radiation::R = NoRadiation(),
source::S = (Gravity(), Coriolis()),
boundarycondition::BC = NoFluxBC(),
init_state::IS=nothing) where {FT<:AbstractFloat,O,RS,T,M,P,R,SU,S,BC,IS}
@assert init_state ≠ nothing
atmos = (
orientation,
ref_state,
turbulence,
moisture,
precipitation,
radiation,
source,
boundarycondition,
init_state,
)
return AtmosModel{FT,typeof.(atmos)...}(atmos...)
end
function vars_state(m::AtmosModel, FT)
@vars begin
ρ::FT
ρu::SVector{3,FT}
ρe::FT
turbulence::vars_state(m.turbulence, FT)
moisture::vars_state(m.moisture, FT)
radiation::vars_state(m.radiation, FT)
end
end
function vars_gradient(m::AtmosModel, FT)
@vars begin
u::SVector{3,FT}
h_tot::FT
turbulence::vars_gradient(m.turbulence,FT)
moisture::vars_gradient(m.moisture,FT)
end
end
function vars_diffusive(m::AtmosModel, FT)
@vars begin
∇h_tot::SVector{3,FT}
turbulence::vars_diffusive(m.turbulence,FT)
moisture::vars_diffusive(m.moisture,FT)
end
end
function vars_aux(m::AtmosModel, FT)
@vars begin
∫dz::vars_integrals(m, FT)
∫dnz::vars_reverse_integrals(m, FT)
coord::SVector{3,FT}
orientation::vars_aux(m.orientation, FT)
ref_state::vars_aux(m.ref_state,FT)
turbulence::vars_aux(m.turbulence,FT)
moisture::vars_aux(m.moisture,FT)
radiation::vars_aux(m.radiation,FT)
end
end
function vars_integrals(m::AtmosModel,FT)
@vars begin
radiation::vars_integrals(m.radiation,FT)
end
end
function vars_reverse_integrals(m::AtmosModel,FT)
@vars begin
radiation::vars_reverse_integrals(m.radiation,FT)
end
end
include("orientation.jl")
include("ref_state.jl")
include("turbulence.jl")
include("moisture.jl")
include("precipitation.jl")
include("radiation.jl")
include("source.jl")
include("boundaryconditions.jl")
include("linear.jl")
include("remainder.jl")
"""
flux_nondiffusive!(m::AtmosModel, flux::Grad, state::Vars, aux::Vars,
t::Real)
Computes flux non-diffusive flux portion of `F` in:
```
∂Y
-- = - ∇ • (F_{adv} + F_{press} + F_{nondiff} + F_{diff}) + S(Y)
∂t
```
Where
- `F_{adv}` Advective flux ; see [`flux_advective!`]@ref()
- `F_{press}` Pressure flux ; see [`flux_pressure!`]@ref()
- `F_{diff}` Fluxes that state gradients; see [`flux_diffusive!`]@ref()
"""
@inline function flux_nondiffusive!(m::AtmosModel, flux::Grad, state::Vars,
aux::Vars, t::Real)
ρ = state.ρ
ρinv = 1/ρ
ρu = state.ρu
u = ρinv * ρu
# advective terms
flux.ρ = ρ * u
flux.ρu = ρ * u .* u'
flux.ρe = u * state.ρe
# pressure terms
p = pressure(m.moisture, m.orientation, state, aux)
if m.ref_state isa HydrostaticState
flux.ρu += (p-aux.ref_state.p)*I
else
flux.ρu += p*I
end
flux.ρe += u*p
flux_radiation!(m.radiation, m, flux, state, aux, t)
flux_moisture!(m.moisture, m, flux, state, aux, t)
end
function gradvariables!(atmos::AtmosModel, transform::Vars, state::Vars, aux::Vars, t::Real)
ρinv = 1/state.ρ
transform.u = ρinv * state.ρu
transform.h_tot = total_specific_enthalpy(atmos.moisture, atmos.orientation, state, aux)
gradvariables!(atmos.moisture, transform, state, aux, t)
gradvariables!(atmos.turbulence, transform, state, aux, t)
end
function diffusive!(atmos::AtmosModel, diffusive::Vars, ∇transform::Grad, state::Vars, aux::Vars, t::Real)
diffusive.∇h_tot = ∇transform.h_tot
# diffusion terms required for SGS turbulence computations
diffusive!(atmos.turbulence, atmos.orientation, diffusive, ∇transform, state, aux, t)
# diffusivity of moisture components
diffusive!(atmos.moisture, diffusive, ∇transform, state, aux, t)
end
@inline function flux_diffusive!(atmos::AtmosModel, flux::Grad, state::Vars,
diffusive::Vars, aux::Vars, t::Real)
ν, τ = turbulence_tensors(atmos.turbulence, state, diffusive, aux, t)
D_t = (ν isa Real ? ν : diag(ν)) * inv_Pr_turb
d_h_tot = -D_t .* diffusive.∇h_tot
flux_diffusive!(atmos, flux, state, τ, d_h_tot)
flux_diffusive!(atmos.moisture, flux, state, diffusive, aux, t, D_t)
end
#TODO: Consider whether to not pass ρ and ρu (not state), foc BCs reasons
@inline function flux_diffusive!(atmos::AtmosModel, flux::Grad, state::Vars,
τ, d_h_tot)
flux.ρu += τ * state.ρ
flux.ρe += τ * state.ρu
flux.ρe += d_h_tot * state.ρ
end
@inline function wavespeed(m::AtmosModel, nM, state::Vars, aux::Vars, t::Real)
ρinv = 1/state.ρ
u = ρinv * state.ρu
return abs(dot(nM, u)) + soundspeed(m.moisture, m.orientation, state, aux)
end
function update_aux!(dg::DGModel, m::AtmosModel, Q::MPIStateArray, t::Real)
FT = eltype(Q)
auxstate = dg.auxstate
if num_integrals(m, FT) > 0
indefinite_stack_integral!(dg, m, Q, auxstate, t)
reverse_indefinite_stack_integral!(dg, m, Q, auxstate, t)
end
nodal_update_aux!(atmos_nodal_update_aux!, dg, m, Q, t)
return true
end
function atmos_nodal_update_aux!(m::AtmosModel, state::Vars, aux::Vars,
t::Real)
atmos_nodal_update_aux!(m.moisture, m, state, aux, t)
atmos_nodal_update_aux!(m.radiation, m, state, aux, t)
atmos_nodal_update_aux!(m.turbulence, m, state, aux, t)
end
function integral_load_aux!(m::AtmosModel, integ::Vars, state::Vars, aux::Vars)
integral_load_aux!(m.radiation, integ, state, aux)
end
function integral_set_aux!(m::AtmosModel, aux::Vars, integ::Vars)
integral_set_aux!(m.radiation, aux, integ)
end
function reverse_integral_load_aux!(m::AtmosModel, integ::Vars, state::Vars, aux::Vars)
reverse_integral_load_aux!(m.radiation, integ, state, aux)
end
function reverse_integral_set_aux!(m::AtmosModel, aux::Vars, integ::Vars)
reverse_integral_set_aux!(m.radiation, aux, integ)
end
# TODO: figure out a nice way to handle this
function init_aux!(m::AtmosModel, aux::Vars, geom::LocalGeometry)
aux.coord = geom.coord
atmos_init_aux!(m.orientation, m, aux, geom)
atmos_init_aux!(m.ref_state, m, aux, geom)
atmos_init_aux!(m.turbulence, m, aux, geom)
end
"""
source!(m::AtmosModel, source::Vars, state::Vars, diffusive::Vars, aux::Vars, t::Real)
Computes flux `S(Y)` in:
```
∂Y
-- = - ∇ • F + S(Y)
∂t
```
"""
function source!(m::AtmosModel, source::Vars, state::Vars, diffusive::Vars, aux::Vars, t::Real)
atmos_source!(m.source, m, source, state, diffusive, aux, t)
end
boundary_state!(nf, m::AtmosModel, x...) =
atmos_boundary_state!(nf, m.boundarycondition, m, x...)
function init_state!(m::AtmosModel, state::Vars, aux::Vars, coords, t, args...)
m.init_state(m, state, aux, coords, t, args...)
end
boundary_flux_diffusive!(nf::NumericalFluxDiffusive,
atmos::AtmosModel,
F,
state⁺, diff⁺, aux⁺, n⁻,
state⁻, diff⁻, aux⁻,
bctype, t,
state1⁻, diff1⁻, aux1⁻) =
atmos_boundary_flux_diffusive!(nf, atmos.boundarycondition, atmos,
F,
state⁺, diff⁺, aux⁺, n⁻,
state⁻, diff⁻, aux⁻,
bctype, t,
state1⁻, diff1⁻, aux1⁻)
end # module
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] | 1.97395 | 6,142 |
using ApproxFun, LinearAlgebra, Test
import ApproxFun: Infinity, ∞
@testset "Helper" begin
@testset "interlace" begin
@test ApproxFun.interlace!([-1.0],0) == [-1.0]
@test ApproxFun.interlace!([1.0,2.0],0) == [2.0,1.0]
@test ApproxFun.interlace!([1,2,3],0) == [2,1,3]
@test ApproxFun.interlace!([1,2,3,4],0) == [3,1,4,2]
@test ApproxFun.interlace!([-1.0],1) == [-1.0]
@test ApproxFun.interlace!([1.0,2.0],1) == [1.0,2.0]
@test ApproxFun.interlace!([1,2,3],1) == [1,3,2]
@test ApproxFun.interlace!([1,2,3,4],1) == [1,3,2,4]
@test ApproxFun.interlace(collect(6:10),collect(1:5)) == ApproxFun.interlace!(collect(1:10),0)
@test ApproxFun.interlace(collect(1:5),collect(6:10)) == ApproxFun.interlace!(collect(1:10),1)
end
@testset "Iterators" begin
@test cache(ApproxFun.BlockInterlacer((1:∞,[2],[2])))[1:6] ==
[(1,1),(2,1),(2,2),(3,1),(3,2),(1,2)]
@test collect(ApproxFun.BlockInterlacer(([2],[2],[2]))) ==
[(1,1),(1,2),(2,1),(2,2),(3,1),(3,2)]
end
# TODO: Tensorizer tests
end
@testset "Domain" begin
@test 0.45-0.65im ∉ Segment(-1,1)
@test reverseorientation(Arc(1,2,(0.1,0.2))) == Arc(1,2,(0.2,0.1))
@test 0.1 ∈ PeriodicSegment(2π,0)
@test 100.0 ∈ PeriodicSegment(0,2π)
@test -100.0 ∈ PeriodicSegment(0,2π)
@test ApproxFun.AnySegment() == ApproxFun.AnySegment()
@test 10.0 ∈ PeriodicLine()
@test -10.0 ∈ PeriodicLine()
@test -10.0+im ∉ PeriodicLine()
@test ApproxFun.Vec(0,0.5) ∈ PeriodicSegment(ApproxFun.Vec(0.0,0), ApproxFun.Vec(0,1))
@test ApproxFun.dimension(Domain{Float64}) == 1
@test ApproxFun.dimension(Segment{Float64}) == 1
@test ApproxFun.dimension(ChebyshevInterval()) == 1
@test ApproxFun.dimension(ChebyshevInterval()^2) == 2
@test ApproxFun.dimension(ChebyshevInterval()^3) == 3
@test ApproxFun.Vec(1,0) ∈ Circle((0.,0.),1.)
@test isambiguous(convert(ApproxFun.Point,ApproxFun.AnyDomain()))
@test isambiguous(ApproxFun.Point(ApproxFun.AnyDomain()))
@test_skip ApproxFun.Point(NaN) == ApproxFun.Point(NaN)
end
@time include("MatrixTest.jl")
@time include("ClenshawTest.jl")
@time include("ChebyshevTest.jl")
@time include("FourierTest.jl")
@time include("ComplexTest.jl")
@time include("NumberTypeTest.jl")
@time include("broadcastingtest.jl")
@time include("OperatorTest.jl")
@time include("ODETest.jl")
@time include("VectorTest.jl")
@time include("SingularitiesTest.jl")
@time include("IntegralEquationsTest.jl")
@time include("SpacesTest.jl")
@time include("JacobiTest.jl")
@time include("LaguerreTest.jl")
@time include("HermiteTest.jl")
@time include("ETDRK4Test.jl")
@time include("MultivariateTest.jl")
@time include("PDETest.jl")
@time include("ExtrasTest.jl")
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] | 2.15103 | 1,311 |
"""
CartInd(i1, i2, is...)
Represents a "scalar" index of a multidimensional associative (`Assoc`/`Arr`).
"""
struct CartInd{N, T <: NTuple{N,Any}}
inds::T
end
CartInd(inds::T) where {N, T <: NTuple{N, Any}} = CartInd{N, T}(inds)
CartInd{N}(inds::T) where {N, T <: NTuple{N, Any}} = CartInd{N, T}(inds)
CartInd(inds...) = CartInd(inds)
"""
CartProd(indices1, indices2, ...)
Represents the Cartesian product of the index sets `indices1`, `indices2`, ect. The elements
are `CartInd(first(indices1), first(indices2), ...)` and so-on.
"""
struct CartProd{N, I <: CartInd{N}, T <: CartInd{N}, Inds <: NTuple{N,Any}} <: Associative{I, T}
indices::Inds
end
# TODO constructors that `collect` on `KeyIterator`s and so-on?
# The indices of a Cartesian product are another Cartesian Product.
# e.g. if c = CartProd(([:a,:b,:c], 2:4)), then indices(c) = CartProd(OneTo(3), OneTo(3))
indices(c::CartProd) = CartProd(map(indices, c.indices))
IndexStyle(c::CartProd) = IndexDirect()
@propagate_inbounds function getindex(c::CartProd{N}, i::CartInd{N})
CartInd(map(getindex, c.indices, i.inds))
end
# Eventually this falls back to a Cartesian range
start(c::CartProd{N, NTuple{N, OneTo}}) = map(start, c.indices)
function next(c::CartProd{N}, inds::NTuple{N, Any})
end
done(c::CartProd{N}, inds::NTuple{N, Any}) = all(map(done, c.indices, i))
# TODO CartSlice - may be a mixture of ranges and scalars, dimensions following APL rules | [
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3173
] | 2.450847 | 590 |
type Subplots <: PlotWidget
widget::PyObject
plots::Vector{Plot}
n::Int
nrowsOverride::Int
ncolsOverride::Int
nrows::Int
ncols::Int
Subplots() = new(PLOT.SubplotWidget(), [], 0, 0, 0)
end
function updateGrid(sp::Subplots)
if sp.nrowsOverride == 0
if sp.ncolsOverride == 0
sp.nrows = round(Int, sqrt(sp.n))
sp.ncols = ceil(Int, sp.n / sp.nrows)
else
sp.ncols = sp.ncolsOverride
sp.nrows = ceil(Int, sp.n / sp.ncols)
end
else
sp.nrows = sp.nrowsOverride
sp.ncols = ceil(Int, sp.n / sp.nrows)
end
end
function getplot(sp::Subplots, c::Int)
while c > length(sp.plots)
push!(sp.plots, Plot())
sp.n += 1
updateGrid(sp)
end
sp.plots[c]
end
function refresh(sp::Subplots)
for plt in sp.plots
refresh(plt)
end
widgets = reshape([[plt.widget for plt in sp.plots] ; fill(nothing, sp.nrows*sp.ncols - sp.n)], sp.ncols, sp.nrows)'
sp.widget[:addFigures](widgets)
nothing
end
subplot(y; kvs...) = subplot(; y = y, kvs...)
subplot(x, y; kvs...) = subplot(; x = x, y = y, kvs...)
function subplot(; kvs...)
sp = Subplots()
resizewidget(sp, 800, 600)
moveToLastScreen(sp)
d = Dict(kvs)
sp.nrowsOverride = get(d, :nrows, 0)
sp.ncolsOverride = get(d, :ncols, 0)
if !((:show, false) in kvs)
push!(kvs, (:show, true))
end
oplot(sp; kvs...)
sp
end
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] | 2.149842 | 634 |
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
mutable struct VirtualMachineScaleSetVMNetworkProfileConfiguration <: SwaggerModel
networkInterfaceConfigurations::Any # spec type: Union{ Nothing, Vector{VirtualMachineScaleSetNetworkConfiguration} } # spec name: networkInterfaceConfigurations
function VirtualMachineScaleSetVMNetworkProfileConfiguration(;networkInterfaceConfigurations=nothing)
o = new()
validate_property(VirtualMachineScaleSetVMNetworkProfileConfiguration, Symbol("networkInterfaceConfigurations"), networkInterfaceConfigurations)
setfield!(o, Symbol("networkInterfaceConfigurations"), networkInterfaceConfigurations)
o
end
end # type VirtualMachineScaleSetVMNetworkProfileConfiguration
const _property_map_VirtualMachineScaleSetVMNetworkProfileConfiguration = Dict{Symbol,Symbol}(Symbol("networkInterfaceConfigurations")=>Symbol("networkInterfaceConfigurations"))
const _property_types_VirtualMachineScaleSetVMNetworkProfileConfiguration = Dict{Symbol,String}(Symbol("networkInterfaceConfigurations")=>"Vector{VirtualMachineScaleSetNetworkConfiguration}")
Base.propertynames(::Type{ VirtualMachineScaleSetVMNetworkProfileConfiguration }) = collect(keys(_property_map_VirtualMachineScaleSetVMNetworkProfileConfiguration))
Swagger.property_type(::Type{ VirtualMachineScaleSetVMNetworkProfileConfiguration }, name::Symbol) = Union{Nothing,eval(Base.Meta.parse(_property_types_VirtualMachineScaleSetVMNetworkProfileConfiguration[name]))}
Swagger.field_name(::Type{ VirtualMachineScaleSetVMNetworkProfileConfiguration }, property_name::Symbol) = _property_map_VirtualMachineScaleSetVMNetworkProfileConfiguration[property_name]
function check_required(o::VirtualMachineScaleSetVMNetworkProfileConfiguration)
true
end
function validate_property(::Type{ VirtualMachineScaleSetVMNetworkProfileConfiguration }, name::Symbol, val)
end
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437,
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] | 4.122153 | 483 |
import Base.+, Base.-, Base./, Base.*, Base.^
import SchumakerSpline.evaluate
function evaluate(f::Undefined_Function, point::Union{Real,Date,DateTime,DatePeriod})
missing
end
function (s::Undefined_Function)(x::Union{Real,Date,DateTime,DatePeriod})
return missing
end
"""
derivative(f::UnivariateFunction)
This calculates the derivative of the function and returns it as a `UnivariateFunction`.
### Inputs
* `f` - A `UnivariateFunction`.
### Returns
* A `UnivariateFunction`.
"""
function derivative(f::Undefined_Function)
return f
end
"""
indefinite_integral(f::UnivariateFunction)
This calculates the indefinite integral of a `UnivariateFunction`.
### Inputs
* `f` - A `UnivariateFunction`.
### Returns
* A `UnivariateFunction`.
"""
function indefinite_integral(f::Undefined_Function)
return f
end
function +(f::Undefined_Function, number::Real)
return f
end
function -(f::Undefined_Function, number::Real)
return f
end
function *(f::Undefined_Function, number::Real)
return f
end
function /(f::Undefined_Function, number::Real)
return f
end
function ^(f::Undefined_Function, number::Integer)
return f
end
function +(f1::Undefined_Function, f2::Undefined_Function)
return f1
end
function +(f1::Undefined_Function, f2::PE_Function)
return f1
end
function +(f1::Undefined_Function, f2::Sum_Of_Functions)
return f1
end
function +(f1::Undefined_Function, f2::Piecewise_Function)
return f1
end
function +(f1::PE_Function, f2::Undefined_Function)
return +(f2,f1)
end
function +(f1::Sum_Of_Functions, f2::Undefined_Function)
return +(f2,f1)
end
function +(f1::Piecewise_Function, f2::Undefined_Function)
return +(f2,f1)
end
function -(f1::Undefined_Function, f2::Undefined_Function)
return f1
end
function -(f1::Undefined_Function, f2::PE_Function)
return f1
end
function -(f1::Undefined_Function, f2::Sum_Of_Functions)
return f1
end
function -(f1::Undefined_Function, f2::Piecewise_Function)
return f1
end
function -(f1::PE_Function, f2::Undefined_Function)
return -(f2,f1)
end
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function /(f1::Sum_Of_Functions, f2::Undefined_Function)
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function /(f1::Piecewise_Function, f2::Undefined_Function)
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] | 2.593286 | 1,281 |
__precompile__(true)
module cNODE
# libraries
using Distributed
using Flux
using Flux.Optimise: update!, apply!
using DiffEqFlux
using Zygote: gradient
using MLDataPattern: eachbatch, splitobs, shuffleobs
using DifferentialEquations: ODEProblem, solve, Tsit5
using Distributions: Normal, Uniform
using Statistics, LinearAlgebra
using SparseArrays, SharedArrays
using DelimitedFiles
# functions
include("./module/generator.jl")
export getParameters, getIC, getGLV
export getSteadyState, getRewiredNetwork
export generate_data
include("./module/trainer.jl")
export FitnessLayer
export getModel
export predict
export loss
export train_reptile
include("./module/loader.jl")
export import_data
export split_data
# Parameters for module
const synthetic_data = ["Strength","Connectivity","Universality","Noise","Rewiring"]
const real_data = ["Drosophila_Gut","Soil_Vitro","Soil_Vivo","Human_Gut","Human_Oral","Ocean"]
const S = [.1,.15,.2]
const C = [0.5,0.75,1]
const U = [0.1,1,2]
const E = [.001,.01,.025]
const R = [.01,.1,.25]
export synthetic_data, real_data
export S, C, U, E, R
end
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20,
11,
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] | 2.617834 | 471 |
function survive(blood_type, donated_blood)
# Your code goes here!
return true
end
| [
8818,
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] | 3.033333 | 30 |
function metapop()
# make metapopulation
mp = ZTMetaPopulation{LanguageDynamics.MomentumSelector}(7, 2.5)
# add speakers
for i = 1:200
inject!(LanguageDynamics.MomentumSelector(0.01, 0.02, 1.0, 4, 0.1), mp)
end
# Travel + rendezvous
while mp.nactions < 0.5*200*100#_000
travel!(mp)
rendezvous!(mp)
end
end
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] | 2.333333 | 144 |
using MAVs, NLOptControl, BenchmarkTools, JLD, FileIO, DataFrames
#'''
#This function creates the benchmark tests
#'''
function bench(;baseBench::Bool=false)
if baseBench
name="master.jld"
else
name="pr.jld"
master=load("master.jld", "results")
m1=median(master["psMethods"])
end
# create a DataFrame
fname = joinpath(dirname(@__FILE__), "data.jld")
df = DataFrames.DataFrame()
# Define a parent BenchmarkGroup to contain our suite
const suite = BenchmarkGroup()
# Add some child groups to our benchmark suite.
suite["psMethods"] = BenchmarkGroup(["integrationScheme","Nck"])
for scheme in (:lgrExplicit,:lgrImplicit)
for Nck in ([10,8,6],[12,10,8,6])
c=defineCase(;(:mode=>:autoBench));
setMisc!(c;integrationScheme=scheme,Nck=Nck,max_cpu_time=20.0)
n=initializeAutonomousControl(c);
suite["psMethods"][scheme,Nck] = @benchmarkable optimize!($n) samples=10
end
end
paramspath = joinpath(dirname(@__FILE__), "params.jld")
if isfile(paramspath)
loadparams!(suite, BenchmarkTools.load(paramspath, "suite"), :evals);
else
tune!(suite)
BenchmarkTools.save(paramspath, "suite", params(suite));
end
results=run(suite,verbose=true); # run all benchmarks
BenchmarkTools.save(name, "results", results) # save results to JLD file
m2=median(results["psMethods"]);
# save the data
file = jldopen(fname, "w")
write(file, "df", df)
close(file)
if !baseBench
return m2, judge(m2,m1)
else
return m2
end
end
paramspath = joinpath(Pkg.dir("MAVs/examples/Benchmarks/"), "params.jld")
loadparams!(suite, BenchmarkTools.load(paramspath, "suite"), :evals);
master=load("master.jld", "results")
###########
file = joinpath(Pkg.dir("MAVs/examples/Benchmarks/"), "temp.jld")
res=master;
writeresults(file,res)
#https://github.com/JuliaCI/PkgBenchmark.jl/blob/master/src/runbenchmark.jl
function writeresults(file, res)
save(File(format"JLD", file), "time", time(), "trials", res)
end
#https://github.com/JuliaCI/PkgBenchmark.jl/blob/master/src/runbenchmark.jl
function readresults(file)
JLD.jldopen(file,"r") do f
read(f, "trials")
end
end
#=
if saveresults
tosave = if promptsave
print("File results of this run? (commit=$(sha[1:6]), resultsdir=$resultsdir) (Y/n) ")
response = readline() |> strip
response == "" || lowercase(response) == "y"
else true end
if tosave
!isdir(resultsdir) && mkpath(resultsdir)
resfile = joinpath(resultsdir, sha*".jld")
writeresults(resfile, res)
info("Results of the benchmark were written to $resfile")
end
=#
#=
m=median(run(suite,verbose=true,seconds=10));
save("m2.jld","m",params(m));
# compare
tempDict=load("m1.jld")
m_temp=get(tempDict,"m1",0)
judge(m_temp,m)
##################OLD
#'''
# schemes=[:lgrExplicit,:lgrImplicit]
# m1=benchMark(schemes[1])
# m2=benchMark(schemes[2])
# judge(m1,m2)
#'''
function benchMark(scheme)
c=defineCase(;(:mode=>:autoBench));
setMisc!(c;integrationScheme=scheme)
n=initializeAutonomousControl(c);
b=@benchmark optimize!($n) samples=10
return median(b)
end
=#
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437,
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198
] | 2.325053 | 1,409 |
using DelayedKalmanFilter
using Documenter
makedocs(;
modules=[DelayedKalmanFilter],
authors="Joshua Burton",
repo="https://github.com/burtonjosh/DelayedKalmanFilter.jl/blob/{commit}{path}#L{line}",
sitename="DelayedKalmanFilter.jl",
format=Documenter.HTML(;
prettyurls=get(ENV, "CI", "false") == "true",
canonical="https://burtonjosh.github.io/DelayedKalmanFilter.jl",
assets=String[],
),
pages=[
"Home" => "index.md",
],
)
deploydocs(;
repo="github.com/burtonjosh/DelayedKalmanFilter.jl",
)
| [
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] | 2.326446 | 242 |
# tests for the policy and coord tables types and related functions.
# using Base.Test
# push!(LOAD_PATH, ".")
# include("includes.jl")
function get_test_policy()
q_table = zeros(4^7, 36)
range = collect(1:4)
q_grid = RectangleGrid(range, range, range, range, range, range, range)
policy = PolicyTable(q_table, q_grid, 4^7)
end
function get_test_coordination_table()
c_table = zeros(4^7)
c_table[1] = 1
c_table[2] = -1
range = collect(1:4)
c_grid = RectangleGrid(range, range, range, range, range, range, range)
coordination_table = CoordinationTable(c_table, c_grid, 4^7)
end
# policy table tests
function test_policy_table_constructor()
get_test_policy()
end
function test_get_utilities()
policy = get_test_policy()
policy.q_table[1, 1] = 10
policy.q_table[2, 2] = 9
belief = spzeros(policy.num_states, 1)
belief[1] = .5
belief[2] = .5
utilities = get_utilities(policy, belief)
expected_utilities = zeros(size(policy.q_table, 2))
expected_utilities[1] = .5 * 10
expected_utilities[2] = .5 * 9
@test utilities == expected_utilities
end
function test_make_coordination_table()
q_table = zeros(2, 36)
q_table[1, 1] = 1
q_table[2, 36] = 2
q_table[2, 4] = 1
joint_actions = get_joint_actions(ACTIONS)
c_table = make_coordination_table(q_table, joint_actions)
@test c_table[1] == 1
end
function test_get_sense_action_parameters()
a1 = -10.
a2 = +10.
sense = get_action_sense(a1, a2)
@test sense == :different_sense
a1 = 0.
sense = get_action_sense(a1, a2)
@test sense == :neither_sense
a1 = +10.
sense = get_action_sense(a1, a2)
@test sense == :same_sense
end
function test_get_sense_coord_table_parameters()
c_table = get_test_coordination_table()
belief = spzeros(c_table.num_states, 1)
belief[1] = .6
belief[2] = .4
sense = get_coordination_sense(c_table, belief)
@test sense == :same_sense
belief[1] = .4
belief[2] = .6
sense = get_coordination_sense(c_table, belief)
@test sense == :different_sense
belief[1] = .3
belief[2] = .3
belief[3] = .4
sense = get_coordination_sense(c_table, belief)
@test sense == :neither_sense
end
tic()
# policy tests
test_policy_table_constructor()
test_get_utilities()
# coord tests
test_make_coordination_table()
test_get_sense_action_parameters()
test_get_sense_coord_table_parameters()
toc() | [
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] | 2.416339 | 1,016 |
# v0.6.0
function printlyrics()
const gifts = split("""
A partridge in a pear tree.
Two turtle doves
Three french hens
Four calling birds
Five golden rings
Six geese a-laying
Seven swans a-swimming
Eight maids a-milking
Nine ladies dancing
Ten lords a-leaping
Eleven pipers piping
Twelve drummers drumming
""", '\n')
const days = split("""
first second third fourth fifth
sixth seventh eighth ninth tenth
eleventh twelfth""")
for (n, day) in enumerate(days)
g = gifts[n:-1:1]
print("\nOn the $day day of Christmas\nMy true love gave to me:\n")
if n == 1
print(join(g[1:end], '\n'), '\n')
else
print(join(g[1:end-1], '\n'), " and\n", g[end], '\n')
end
end
end
printlyrics()
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] | 2.275 | 360 |
using DiffEqBase: remake, solve, ODEProblem, EnsembleProblem, EnsembleThreads
####################################################################################################
# this function takes into accound a parameter passed to the vector field
# Putting the options `save_start = false` seems to give bugs with Sundials
function flowTimeSol(x, p, tm, pb::ODEProblem; alg = Euler(), kwargs...)
_prob = remake(pb; u0 = x, tspan = (zero(eltype(tm)), tm), p = p)
# the use of concrete_solve makes it compatible with Zygote
sol = solve(_prob, alg; save_everystep = false, kwargs...)
return (t = sol.t[end], u = sol[end])
end
# this function is a bit different from the previous one as it is geared towards parallel computing of the flow.
function flowTimeSol(x::AbstractMatrix, p, tm, epb::EnsembleProblem; alg = Euler(), kwargs...)
# modify the function which asigns new initial conditions
# see docs at https://docs.sciml.ai/dev/features/ensemble/#Performing-an-Ensemble-Simulation-1
_prob_func = (prob, ii, repeat) -> prob = remake(prob, u0 = x[:, ii], tspan = (zero(eltype(tm[ii])), tm[ii]), p = p)
_epb = setproperties(epb, output_func = (sol, i) -> ((t = sol.t[end], u = sol[end]), false), prob_func = _prob_func)
sol = solve(_epb, alg, EnsembleThreads(); trajectories = size(x, 2), save_everystep = false, kwargs...)
# sol.u contains a vector of tuples (sol_i.t[end], sol_i[end])
return sol.u
end
flow(x, p, tm, pb::ODEProblem; alg = Euler(), kwargs...) = flowTimeSol(x, p, tm, pb; alg = alg, kwargs...).u
flow(x, p, tm, pb::EnsembleProblem; alg = Euler(), kwargs...) = flowTimeSol(x, p, tm, pb; alg = alg, kwargs...)
flow(x, tm, pb::Union{ODEProblem, EnsembleProblem}; alg = Euler(), kwargs...) = flow(x, nothing, tm, pb; alg = alg, kwargs...)
####################################################################################################
# function used to compute the derivative of the flow, so pb encodes the variational equation
function dflow(x::AbstractVector, p, dx, tm, pb::ODEProblem; alg = Euler(), kwargs...)
n = length(x)
_prob = remake(pb; u0 = vcat(x, dx), tspan = (zero(eltype(tm)), tm), p = p)
# the use of concrete_solve makes it compatible with Zygote
sol = solve(_prob, alg, save_everystep = false; kwargs...)[end]
return (t = tm, u = sol[1:n], du = sol[n+1:end])
end
# same for Parallel computing
function dflow(x::AbstractMatrix, p, dx, tm, epb::EnsembleProblem; alg = Euler(), kwargs...)
N = size(x,1)
_prob_func = (prob, ii, repeat) -> prob = remake(prob, u0 = vcat(x[:, ii], dx[:, ii]), tspan = (zero(eltype(tm[ii])), tm[ii]), p = p)
_epb = setproperties(epb, output_func = (sol,i) -> ((t = sol.t[end], u = sol[end][1:N], du = sol[end][N+1:end]), false), prob_func = _prob_func)
sol = solve(_epb, alg, EnsembleThreads(); trajectories = size(x, 2), save_everystep = false, kwargs...)
return sol.u
end
dflow(x, dx, tspan, pb::Union{ODEProblem, EnsembleProblem}; alg = Euler(), kwargs...) = dflow(x, nothing, dx, tspan, pb; alg = alg, kwargs...)
####################################################################################################
# this function takes into accound a parameter passed to the vector field
function dflow_fd(x, p, dx, tm, pb::ODEProblem; alg = Euler(), δ = 1e-9, kwargs...)
sol1 = flow(x .+ δ .* dx, p, tm, pb; alg = alg, kwargs...)
sol2 = flow(x , p, tm, pb; alg = alg, kwargs...)
return (t = tm, u = sol2, du = (sol1 .- sol2) ./ δ)
end
function dflow_fd(x, p, dx, tm, pb::EnsembleProblem; alg = Euler(), δ = 1e-9, kwargs...)
sol1 = flow(x .+ δ .* dx, p, tm, pb; alg = alg, kwargs...)
sol2 = flow(x , p, tm, pb; alg = alg, kwargs...)
return [(t = sol1[ii][1], u = sol2[ii][2], du = (sol1[ii][2] .- sol2[ii][2]) ./ δ) for ii = 1:size(x,2) ]
end
dflow_fd(x, dx, tm, pb::Union{ODEProblem, EnsembleProblem}; alg = Euler(), δ = 1e-9, kwargs...) = dflow_fd(x, nothing, dx, tm, pb; alg = alg, δ = δ, kwargs...)
####################################################################################################
# this gives access to the full solution, convenient for Poincaré shooting
# this function takes into accound a parameter passed to the vector field and returns the full solution from the ODE solver. This is useful in Poincare Shooting to extract the period.
function flowFull(x, p, tm, pb::ODEProblem; alg = Euler(), kwargs...)
_prob = remake(pb; u0 = x, tspan = (zero(tm), tm), p = p)
sol = solve(_prob, alg; kwargs...)
end
function flowFull(x, p, tm, epb::EnsembleProblem; alg = Euler(), kwargs...)
_prob_func = (prob, ii, repeat) -> prob = remake(prob, u0 = x[:, ii], tspan = (zero(eltype(tm[ii])), tm[ii]), p = p)
_epb = setproperties(epb, prob_func = _prob_func)
sol = solve(_epb, alg, EnsembleThreads(); trajectories = size(x, 2), kwargs...)
end
flowFull(x, tm, pb::Union{ODEProblem, EnsembleProblem}; alg = Euler(), kwargs...) = flowFull(x, nothing, tm, pb; alg = alg, kwargs...)
####################################################################################################
# Structures related to computing ODE/PDE Flows
"""
$(TYPEDEF)
$(TYPEDFIELDS)
# Simplified constructor(s)
We provide a simple constructor where you only pass the vector field `F`, the flow `ϕ` and its differential `dϕ`:
fl = Flow(F, ϕ, dϕ)
# Simplified constructors for DifferentialEquations.jl
There are some simple constructors for which you only have to pass a `prob::ODEProblem` or `prob::EnsembleProblem` (for parallel computation) from `DifferentialEquations.jl` and an ODE time stepper like `Tsit5()`. Hence, you can do for example
fl = Flow(F, prob, Tsit5(); kwargs...)
where `kwargs` is passed to `DiffEqBase::solve`. If your vector field depends on parameters `p`, you can define a `Flow` using
fl = Flow(F, p, prob, Tsit5(); kwargs...)
Finally, you can pass two `ODEProblem` where the second one is used to compute the variational equation:
fl = Flow(F, p, prob1::ODEProblem, alg1, prob2::ODEProblem, alg2; kwargs...)
"""
@with_kw struct Flow{TF, Tf, Tts, Tff, Td, Tse, Tprob, TprobMono, Tfs}
"The vector field `(x, p) -> F(x, p)` associated to a Cauchy problem,"
F::TF = nothing
"The flow (or semigroup) associated to the Cauchy problem `(x, p, t) -> flow(x, p, t)`. Only the last time point must be returned."
flow::Tf = nothing
"Flow which returns the tuple (t, u(t)). Optional, mainly used for plotting on the user side. Please use `nothing` as default."
flowTimeSol::Tts = nothing
"The flow (or semigroup) associated to the Cauchy problem `(x, p, t) -> flow(x, p, t)`. The whole solution on the time interval [0,t] must be returned. It is not strictly necessary to provide this, mainly used for plotting on the user side. Please use `nothing` as default."
flowFull::Tff = nothing
"The differential `dflow` of the flow w.r.t. `x`, `(x, p, dx, t) -> dflow(x, p, dx, t)`. One important thing is that we require `dflow(x, dx, t)` to return a Named Tuple: `(t = t, u = flow(x, p, t), du = dflow(x, p, dx, t))`, the last component being the value of the derivative of the flow."
dflow::Td = nothing
"Serial version of dflow. Used internally when using parallel multiple shooting. Please use `nothing` as default."
dfSerial::Tse = nothing
"[Internal] store the ODEProblem associated to the flow of the Cauchy problem"
prob::Tprob = nothing
"[Internal] store the ODEProblem associated to the flow of the variational problem"
probMono::TprobMono = nothing
"[Internal] Serial version of the flow"
flowSerial::Tfs = nothing
end
# constructors
Flow(F, fl, df = nothing) = Flow(F = F, flow = fl, dflow = df, dfSerial = df)
# callable struct
(fl::Flow)(x, p, t; k...) = fl.flow(x, p, t; k...)
(fl::Flow)(x, p, dx, t; k...) = fl.dflow(x, p, dx, t; k...)
(fl::Flow)(::Val{:Full}, x, p, t; k...) = fl.flowFull(x, p, t; k...)
(fl::Flow)(::Val{:TimeSol}, x, p, t; k...) = fl.flowTimeSol(x, p, t; k...)
(fl::Flow)(::Val{:SerialTimeSol}, x, p, t; k...) = fl.flowSerial(x, p, t; k...)
(fl::Flow)(::Val{:SerialdFlow}, x, p, dx, t; k...) = fl.dfSerial(x, p, dx, t; k...)
"""
Creates a Flow variable based on a `prob::ODEProblem` and ODE solver `alg`. The vector field `F` has to be passed, this will be resolved in the future as it can be recovered from `prob`. Also, the derivative of the flow is estimated with finite differences.
"""
# this constructor takes into accound a parameter passed to the vector field
function Flow(F, p, prob::Union{ODEProblem, EnsembleProblem}, alg; kwargs...)
probserial = prob isa EnsembleProblem ? prob.prob : prob
return Flow(F = F,
flow = (x, p, t; kw2...) -> flow(x, p, t, prob; alg = alg, kwargs..., kw2...),
flowTimeSol = (x, p, t; kw2...) -> flowTimeSol(x, p, t, prob; alg = alg, kwargs..., kw2...),
flowFull = (x, p, t; kw2...) -> flowFull(x, p, t, prob; alg = alg, kwargs..., kw2...),
dflow = (x, p, dx, t; kw2...) -> dflow_fd(x, p, dx, t, prob; alg = alg, kwargs..., kw2...),
# serial version of dflow. Used for the computation of Floquet coefficients
dfSerial = (x, p, dx, t; kw2...) -> dflow_fd(x, p, dx, t, probserial; alg = alg, kwargs..., kw2...),
flowSerial = (x, p, t; kw2...) -> flowTimeSol(x, p, t, probserial; alg = alg, kwargs..., kw2...),
prob = prob, probMono = nothing,
)
end
function Flow(F, p, prob1::Union{ODEProblem, EnsembleProblem}, alg1, prob2::Union{ODEProblem, EnsembleProblem}, alg2; kwargs...)
probserial1 = prob1 isa EnsembleProblem ? prob1.prob : prob1
probserial2 = prob2 isa EnsembleProblem ? prob2.prob : prob2
return Flow(F = F,
flow = (x, p, t; kw2...) -> flow(x, p, t, prob1, alg = alg1; kwargs..., kw2...),
flowTimeSol = (x, p, t; kw2...) -> flowTimeSol(x, p, t, prob1; alg = alg1, kwargs..., kw2...),
flowFull = (x, p, t; kw2...) -> flowFull(x, p, t, prob1, alg = alg1; kwargs..., kw2...),
dflow = (x, p, dx, t; kw2...) -> dflow(x, p, dx, t, prob2; alg = alg2, kwargs..., kw2...),
# serial version of dflow. Used for the computation of Floquet coefficients
dfSerial = (x, p, dx, t; kw2...) -> dflow(x, p, dx, t, probserial2; alg = alg2, kwargs..., kw2...),
flowSerial = (x, p, t; kw2...) -> flowTimeSol(x, p, t, probserial1; alg = alg1, kwargs..., kw2...),
prob = prob1, probMono = prob2,
)
end
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] | 2.678478 | 3,810 |
# This shows how to plot a 3D seismic tomography model
# The paper that describes it is:
#
# Zhao, L., Paul, A., Malusà, M.G., Xu, X., Zheng, T., Solarino, S., Guillot, S., Schwartz, S., Dumont, T., Salimbeni, S., Aubert, C., Pondrelli, S., Wang, Q., Zhu, R., 2016. Continuity of the Alpine slab unraveled by high-resolution P wave tomography. Journal of Geophysical Research: Solid Earth 121, 8720–8737. doi:10.1002/2016JB013310
#
# You will need to download the file `Zhao_etal_JGR_2016_Pwave_Alps_3D_k60.txt` from:
# https://seafile.rlp.net/d/a50881f45aa34cdeb3c0/
#
# And make sure that you are in the same directory as the data file
using DelimitedFiles, GeophysicalModelGenerator
# Load data:
data=readdlm("Zhao_etal_JGR_2016_Pwave_Alps_3D_k60.txt",' ',Float64,'\n', skipstart=0,header=false)
lon = data[:,1];
lat = data[:,2];
depth = data[:,3];
dVp_perc = data[:,4];
# Create 3D regular grid:
resolution = (length(unique(lon)), length(unique(lat)), length(unique(depth)))
Lon = reshape(lon, resolution);
Lat = reshape(lat, resolution);
Depth = reshape(depth, resolution);
dVp_perc_3D = reshape(dVp_perc, resolution);
# save paraview file
Data_set = GeoData(Lon,Lat,Depth,(dVp_Percentage=dVp_perc_3D,))
Write_Paraview(Data_set, "Zhao_etal_2016_dVp_percentage")
# extract cross-sections
Data_cross = CrossSection(Data_set, Depth_level=-100km)
Write_Paraview(Data_cross, "Zhao_CrossSection_100km")
Data_cross = CrossSection(Data_set, Lon_level=10)
Write_Paraview(Data_cross, "Zhao_CrossSection_Lon10")
Data_cross = CrossSection(Data_set, Lon_level=10, Interpolate=true)
Write_Paraview(Data_cross, "Zhao_CrossSection_Lon10_interpolated");
Data_cross = CrossSection(Data_set, Start=(1.0,39), End=(18,50))
Write_Paraview(Data_cross, "Zhao_CrossSection_diagonal")
# Extract a 3D subset of the data
Data_subset = ExtractSubvolume(Data_set,Lon_level=(5,12), Lat_level=(40,45))
Write_Paraview(Data_subset, "Zhao_Subset")
Data_subset_interp = ExtractSubvolume(Data_set,Lon_level=(5,12), Lat_level=(40,45), Interpolate=true)
Write_Paraview(Data_subset, "Zhao_Subset_interp")
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] | 2.428571 | 896 |
module SSLTestTiling
using SimpleSDMLayers
using Test
# hcat / vcat
l1 = SimpleSDMPredictor(WorldClim, BioClim, 1; left=0.0, right=10.0, bottom=0.0, top=10.0)
l2 = SimpleSDMPredictor(WorldClim, BioClim, 1; left=0.0, right=10.0, bottom=10.0, top=20.0)
l3 = SimpleSDMPredictor(WorldClim, BioClim, 1; left=10.0, right=20.0, bottom=0.0, top=10.0)
l4 = SimpleSDMPredictor(WorldClim, BioClim, 1; left=10.0, right=20.0, bottom=10.0, top=20.0)
l5 = SimpleSDMPredictor(WorldClim, BioClim, 1; left=0.0, right=20.0, bottom=0.0, top=20.0)
ml1 = hcat(l1, l3)
vl1 = vcat(l1, l2)
ml2 = hcat(l2, l4)
vl2 = vcat(l3, l4)
vml = vcat(ml1, ml2)
mvl = hcat(vl1, vl2)
@test all(vml.grid == mvl.grid)
for l in (vml, mvl)
@test all(l.grid == l5.grid)
@test size(l) == size(l5)
@test stride(l) == stride(l5)
@test longitudes(l) == longitudes(l5)
@test latitudes(l) == latitudes(l5)
@test l.left == l5.left
@test l.right == l5.right
@test l.bottom == l5.bottom
@test l.top == l5.top
end
end | [
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"String for unit multiplier in option line"
const unitstrings = Dict{ Float64, String }(
1e0 => "Hz",
1e3 => "kHz",
1e6 => "MHz",
1e9 => "GHz"
)
"Default unit value to use in option line."
const default_unit_value = 1e9
"Strings for parameter format symbols in option line."
const parameterstrings = Dict{ Symbol, String }(
:ScatteringParameters => "S",
:AdmittanceParameters => "Y",
:ImpedanceParameters => "Z",
:HybridHParameters => "H",
:HybridGParameters => "G",
)
"Defaoult parameter format symbo to use in option line."
const default_parameter_symbol = :ScatteringParameters
"String for data format symbols in option line."
const formatstrings = Dict{ Symbol, String }(
:DecibelAngle => "dB",
:MagnitudeAngle => "MA",
:RealImaginary => "RI"
)
"Default data format symbol to use in option line."
const default_format_symbol = :MagnitudeAngle
"Default characteristic iimpedance to use in option line."
const default_resistance_string = "50"
"""
write_comment_line( comment )
Returns a comment line string for a comment.
"""
write_comment_line( comment ) = "!" * comment
"""
write_frequency_unit_option( [ value ] )
Returns an option line string for a valid frequency unit multiplier.
"""
write_frequency_unit_option( value = default_unit_value ) = unitstrings[ value ]
"""
write_parameter_option( [ symbol ] )
Returns an option line string for a valid parameter option symbol.
"""
write_parameter_option( symbol = default_parameter_symbol ) = parameterstrings[ symbol ]
"""
write_format_option( [ symbol ] )
Returns an option line string for a valid data format symbol.
"""
write_format_option( symbol = default_format_symbol ) = formatstrings[ symbol ]
"""
write_resistance_option( [ resistance ] )
Returns an option line string for the characteristic impedance.
"""
write_resistance_option( resistance = 50 ) = string( resistance )
"""
write_option_line( [ options ] )
Returns the option line string for an option structure.
"""
function write_option_line( options::Options = Options() )
"# " *
write_frequency_unit_option( options.unit ) * " " *
write_parameter_option( options.parameter ) * " " *
write_format_option( options.format ) * " " *
"R " * write_resistance_option( options.resistance )
end
"""
compstring( z )
Returns a pair with the real and imaginary components of a complex number.
"""
compstring( z ) = string( real( z ) ) * " " * string( imag( z ) )
"""
comp2ma( z )
Returns a pair with the linear magnitude and angle in degrees of a complex number.
"""
comp2ma( z ) = ( abs( z ), rad2deg( angle( z ) ) )
"""
comp2da( z )
Returns a pair with the magnitude in dB and angle in degrees of a complex number.
"""
function comp2da( z )
m, a = comp2ma( z )
20log10( m ), a
end
"""
pairstring( pair )
Returns a string combining the components of a pair.
"""
pairstring( pair ) = string( pair[ 1 ] ) * " " * string( pair[ 2 ] )
"""
mastring( z )
Returns a string in magnitude / angle format of a complex number.
"""
mastring( z ) = pairstring( comp2ma( z ) )
"""
dastring( z )
Returns a string in dB / angle format of a complex number.
"""
dastring( z ) = pairstring( comp2da( z ) )
"Holds conversion functions for parameter format symbols when writing."
const WriteConversions = Dict{ Symbol, Function }(
:RealImaginary => compstring,
:MagnitudeAngle => mastring,
:DecibelAngle => dastring
)
"""
write_data( data, N [ options ] )
Returns a string for N-port Touchstone formated lines for a data point.
"""
function write_data( data::DataPoint, N::Integer, options::Options = Options() )
freqstring = string( data.frequency / options.unit )
if N == 2
paras = reshape( data.parameter, 1, 4 )
lines = 1
else
paras = data.parameter
lines = N
end
if options.parameter == :ImpedanceParameters
paras = map( x -> x / options.resistance, paras )
end
if options.parameter == :AdmittanceParameters
paras = map( x -> x * options.resistance, paras )
end
if N == 2
if options.parameter == :HybridGParameters
paras[ 1 ] *= options.resistance
paras[ 4 ] /= options.resistance
end
if options.parameter == :HybridHParameters
paras[ 1 ] /= options.resistance
paras[ 4 ] *= options.resistance
end
end
strings = map( snn -> WriteConversions[ options.format ]( snn ), paras )
datastrings = ones( String, lines )
for row in 1:lines
# V 1.0 not more than 4 pairs ( + freq ) in a line
if N > 4
for col in 1:N
if col > 0 && col % 4 == 0
strings[ row, col ] *= "\n"
end
end
end
datastrings[ row ] = replace( join( strings[ row, : ], " " ), "\n " => "\n" )
end
datastrings[ 1 ] = freqstring * " " * datastrings[ 1 ]
datastring = join( datastrings, "\n" )
if datastring[ end ] == "\n"
return datastring
else
return datastring * "\n"
end
end
function writeNoiseData( nd::NoiseDataPoint, options::Options = Options(), version = 1 )
freqstring = string( nd.frequency / options.unit )
noisefigstring = 20log10( nd.minNoiseFigure )
reflcoeffstring = mastring( nd.reflCoeff )
effnoiseresstring = version == 1 ? string( nd.effNoiseRes / options.resistance ) : string( nd.effNoiseRes )
res = "$freqstring $noisefigstring $reflcoeffstring $effnoiseresstring\n"
return res
end
"""
write_touchstone_stream( stream, ts )
Writes formated Touchstone data for a TouchstoneData structure to a stream.
"""
function write_touchstone_stream( stream::IO, ts::TouchstoneData )
for comment in ts.comments
write( stream, write_comment_line( comment ) )
write( stream, "\n" )
end
write( stream, write_option_line( ts.options ) )
write( stream, "\n" )
#
# ports = -1
# if size( ts.data, 1 ) > 0
# ports = size( ts.data[ 1 ].parameter, 1 )
# end
#
for datapoint in ts.data
write( stream, write_data( datapoint, ports( ts ), ts.options ) )
end
for ndp in ts.noiseData
write( stream, writeNoiseData( ndp, ts.options ) )
end
end
"""
write_touchstone_string( ts )
Returns a string with formated Touchstone data for a TouchstoneData structure.
"""
function write_touchstone_string( ts::TouchstoneData )
buffer = IOBuffer()
write_touchstone_stream( buffer, ts )
String( take!( copy( buffer ) ) )
end
"""
write_touchstone_file( filename, ts )
Writes formated Touchstone data for a TouchstoneData structure to a file.
"""
function write_touchstone_file( filename::String, ts::TouchstoneData )
open( filename, "w" ) do io
write_touchstone_stream( io, ts )
end
end
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] | 2.926269 | 2,265 |
using Decimals
using Compat.Test
@testset "Decimal constructor" begin
@test isa(d, Array{Decimal,1})
end
| [
3500,
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] | 2.634146 | 41 |
"""
gcmarray{T, N}
gcmarray data structure. Available constructors:
```
gcmarray{T,N}(grid::gcmgrid,f::Array{Array{T,2},N},
fSize::Array{NTuple{N, Int}},fIndex::Array{Int,1})
gcmarray(grid::gcmgrid,f::Array{Array{T,2},N}) where {T,N}
gcmarray(grid::gcmgrid,f::Array{Array{T,N},1}) where {T,N}
gcmarray(grid::gcmgrid,fSize::Array{NTuple{N, Int}},fIndex::Array{Int,1})
gcmarray(<same as above>,n3::Int)
gcmarray(<same as above>,n3::Int,n4::Int)
gcmarray(grid::gcmgrid)
gcmarray(grid::gcmgrid,::Type{T})
gcmarray(grid::gcmgrid,::Type{T},n3::Int)
gcmarray(grid::gcmgrid,::Type{T},n3::Int,n4::Int)
```
"""
struct gcmarray{T, N} <: AbstractMeshArray{T, N}
grid::gcmgrid
f::Array{Array{T,2},N}
fSize::Array{NTuple{2, Int}}
fIndex::Array{Int,1}
end
function gcmarray(grid::gcmgrid,f::Array{Array{T,2},N}) where {T, N}
gcmarray{T,N}(grid,f,grid.fSize,collect(1:grid.nFaces))
end
function gcmarray(grid::gcmgrid,f::Array{Array{T,N},1}) where {T, N}
nFaces=grid.nFaces
if N>2
n3=size(f[1],3); n4=size(f[1],4);
g=Array{Array{T,2},3}(undef,nFaces,n3,n4)
for I in eachindex(view(g,1:nFaces,1:n3,1:n4))
g[I]=view(f[I[1]],:,:,I[2],I[3])
end
n4==1 ? g=dropdims(g,dims=3) : nothing
gcmarray{T,ndims(g)}(grid,g,grid.fSize,collect(1:nFaces))
else
gcmarray{T,1}(grid,f,grid.fSize,collect(1:nFaces))
end
end
function gcmarray(grid::gcmgrid,::Type{T},
fSize::Union{Array{NTuple{2, Int},1},NTuple{2, Int}},
fIndex::Union{Array{Int,1},Int}) where {T}
nFaces=length(fIndex)
f=Array{Array{T,2},1}(undef,nFaces)
isa(fSize,NTuple) ? fSize=[fSize] : nothing
isa(fIndex,Int) ? fIndex=[fIndex] : nothing
for a=1:nFaces
f[a]=Array{T}(undef,fSize[a])
end
gcmarray{T,1}(grid,f,fSize,fIndex)
end
function gcmarray(grid::gcmgrid,::Type{T},
fSize::Union{Array{NTuple{2, Int},1},NTuple{2, Int}},
fIndex::Union{Array{Int,1},Int},n3::Int) where {T}
nFaces=length(fIndex)
f=Array{Array{T,2},2}(undef,nFaces,n3)
isa(fSize,NTuple) ? fSize=[fSize] : nothing
isa(fIndex,Int) ? fIndex=[fIndex] : nothing
for a=1:nFaces; for i3=1:n3;
f[a,i3]=Array{T}(undef,fSize[a]...)
end; end;
gcmarray{T,2}(grid,f,fSize,fIndex)
end
function gcmarray(grid::gcmgrid,::Type{T},
fSize::Union{Array{NTuple{2, Int},1},NTuple{2, Int}},
fIndex::Union{Array{Int,1},Int},n3::Int,n4::Int) where {T}
nFaces=length(fIndex)
f=Array{Array{T,2},3}(undef,nFaces,n3,n4)
isa(fSize,NTuple) ? fSize=[fSize] : nothing
isa(fIndex,Int) ? fIndex=[fIndex] : nothing
for a=1:nFaces; for i4=1:n4; for i3=1:n3;
f[a,i3,i4]=Array{T}(undef,fSize[a]...)
end; end; end;
gcmarray{T,3}(grid,f,fSize,fIndex)
end
# +
function gcmarray(grid::gcmgrid)
nFaces=grid.nFaces
fSize=grid.fSize
fIndex=collect(1:grid.nFaces)
T=grid.ioPrec
gcmarray(grid,T,fSize,fIndex)
end
function gcmarray(grid::gcmgrid,::Type{T}) where {T}
nFaces=grid.nFaces
fSize=grid.fSize
fIndex=collect(1:grid.nFaces)
gcmarray(grid,T,fSize,fIndex)
end
function gcmarray(grid::gcmgrid,::Type{T},n3::Int) where {T}
nFaces=grid.nFaces
fSize=grid.fSize
fIndex=collect(1:grid.nFaces)
gcmarray(grid,T,fSize,fIndex,n3)
end
function gcmarray(grid::gcmgrid,::Type{T},n3::Int,n4::Int) where {T}
nFaces=grid.nFaces
fSize=grid.fSize
fIndex=collect(1:grid.nFaces)
gcmarray(grid,T,fSize,fIndex,n3,n4)
end
# -
# # Interface Methods
# +
Base.size(A::gcmarray) = size(A.f)
Base.size(A::gcmarray, dim::Integer) = size(A)[dim]
# +
function Base.getindex(A::gcmarray{T, N}, I::Vararg{Union{Int,Array{Int},AbstractUnitRange,Colon}, N}) where {T,N}
J=1:length(A.fIndex)
!isa(I[1],Colon) ? J=J[I[1]] : nothing
nFaces=length(J)
tmpf=A.f[I...]
if isa(tmpf,Array{eltype(A),2})
tmp=tmpf
else
n3=Int(length(tmpf)/nFaces)
K=(A.grid,eltype(A),A.fSize[J],A.fIndex[J])
n3>1 ? tmp=gcmarray(K...,n3) : tmp=gcmarray(K...)
for I in eachindex(tmpf); tmp.f[I] = view(tmpf[I],:,:); end
end
return tmp
end
"""
getindexetc(A::gcmarray, I::Vararg{_}) where {T,N}
Same as getindex but also returns the face size and index
"""
function getindexetc(A::gcmarray{T, N}, I::Vararg{Union{Int,Array{Int},AbstractUnitRange,Colon}, N}) where {T,N}
f=A[I...]
fSize=A.fSize[I[1]]
fIndex=A.fIndex[I[1]]
return f,fSize,fIndex
end
# -
function Base.setindex!(A::gcmarray{T, N}, v, I::Vararg{Int, N}) where {T,N}
return (A.f[I...] = v)
end
function Base.view(A::gcmarray{T, N}, I::Vararg{Union{Int,AbstractUnitRange,Colon}, N}) where {T,N}
J=1:length(A.fIndex)
!isa(I[1],Colon) ? J=J[I[1]] : nothing
nFaces=length(J)
tmpf=view(A.f,I...)
n3=Int(length(tmpf)/nFaces) #length(tmpf)>nFaces ? n3=Int(length(tmpf)/nFaces) : n3=1
K=(A.grid,eltype(A),A.fSize[J],A.fIndex[J])
n3>1 ? tmp=gcmarray(K...,n3) : tmp=gcmarray(K...)
for I in eachindex(tmpf); tmp.f[I] = view(tmpf[I],:,:); end
return tmp
end
# ### Custom pretty-printing, similar, and broadcast
function Base.show(io::IO, z::gcmarray{T, N}) where {T,N}
printstyled(io, " gcmarray \n",color=:normal)
printstyled(io, " grid type = ",color=:normal)
printstyled(io, "$(z.grid.class)\n",color=:blue)
printstyled(io, " data type = ",color=:normal)
printstyled(io, "$(eltype(z))\n",color=:blue)
printstyled(io, " tile array = ",color=:normal)
printstyled(io, "$(size(z))\n",color=:blue)
printstyled(io, " tile sizes = ",color=:normal)
printstyled(io, "$(size(z[1]))\n",color=:blue)
for iFace=2:length(z.fIndex)
printstyled(io, " ",color=:normal)
printstyled(io, "$(size(z[iFace]))\n",color=:blue)
end
return
end
function Base.similar(A::gcmarray)
if ndims(A)==1
B=gcmarray(A.grid,eltype(A),A.fSize,A.fIndex)
else
B=gcmarray(A.grid,eltype(A),A.fSize,A.fIndex,size(A,2))
end
return B
end
# ### Customize broadcasting
Base.BroadcastStyle(::Type{<:gcmarray}) = Broadcast.ArrayStyle{gcmarray}()
function Base.similar(bc::Broadcast.Broadcasted{Broadcast.ArrayStyle{gcmarray}}, ::Type{ElType}) where ElType
# Scan the inputs for the gcmarray:
A = find_gcmarray(bc)
# Create the gcmarray output:
if ndims(A)==1
B=gcmarray(A.grid,ElType,A.fSize,A.fIndex)
else
B=gcmarray(A.grid,ElType,A.fSize,A.fIndex,size(A,2))
end
return B
end
find_gcmarray(bc::Base.Broadcast.Broadcasted) = find_gcmarray(bc.args)
find_gcmarray(args::Tuple) = find_gcmarray(find_gcmarray(args[1]), Base.tail(args))
find_gcmarray(x) = x
find_gcmarray(a::gcmarray, rest) = a
find_gcmarray(::Any, rest) = find_gcmarray(rest)
####
import Base: copyto!
# Specialize this method if all you want to do is specialize on typeof(dest)
@inline function copyto!(dest::MeshArrays.gcmarray, bc::Broadcast.Broadcasted{Nothing})
axes(dest) == axes(bc) || throwdm(axes(dest), axes(bc))
# Performance optimization: broadcast!(identity, dest, A) is equivalent to copyto!(dest, A) if indices match
if bc.f === identity && bc.args isa Tuple{AbstractArray} # only a single input argument to broadcast!
A = bc.args[1]
if axes(dest) == axes(A)
return copyto!(dest, A)
end
end
bc′ = Broadcast.preprocess(dest, bc)
@simd for I in eachindex(bc′)
#@inbounds dest[I] = bc′[I]
@inbounds dest[I] = gcmarray_getindex_evalf(bc′,I)
end
return dest
end
function gcmarray_getindex_evalf(bc,I)
@boundscheck checkbounds(bc, I)
args = Broadcast._getindex(bc.args, I)
return bc.f.(args...)
end
###
"""
nFacesEtc(a::gcmarray)
Return nFaces, n3 (1 in 2D case; >1 otherwise)
"""
function nFacesEtc(a::gcmarray)
nFaces=length(a.fIndex)
ndims(a.f)>1 ? n3=size(a.f,2) : n3=1
ndims(a.f)>2 ? n4=size(a.f,3) : n4=1
return nFaces, n3, n4
end
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] | 2.064807 | 3,765 |
module Variance
using ValidatedNumerics;
using Distributions;
using Dierckx;
abstract BaseEstimator
function estimate(values, method::BaseEstimator)
error("Unsupported method $method")
end
immutable byConfidenceProbability <: BaseEstimator
confidence_probability :: Real
end
function estimate(values, method :: byConfidenceProbability)
average = mean(values)
count = length(values)
gamma_down, gamma_up = getGammas(method.confidence_probability)
quantile_down = getChiSquareQuantile(gamma_down, count)
quantile_up = getChiSquareQuantile(gamma_up, count)
reduced = mapreduce((x) -> (x - average)^2, +, values)
@interval(reduced/quantile_down, reduced/quantile_up)
end
immutable byMeanAbsoluteDeviation <: BaseEstimator
confidence_probability :: Real
end
function estimate(values, method :: byMeanAbsoluteDeviation)
average = mean(values)
count = length(values)
quantile_down, quantile_up = getMeanAbsDeviationQuantiles(method.confidence_probability, count)
mean_abs_deviation = mapreduce((x) -> abs(x - average), +, values)/count
@interval(mean_abs_deviation/quantile_down, mean_abs_deviation/quantile_up)^2
end
immutable byPointVariance <: BaseEstimator
confidence_probability :: Real
variance :: Real
end
function estimate(values, method::byPointVariance)
count = length(values)
gamma_down, gamma_up = getGammas(method.confidence_probability)
quantile_down = getChiSquareQuantile(gamma_down, count)
quantile_up = getChiSquareQuantile(gamma_up, count)
fixed_variance = method.variance * (1 + 0.254 / (count - 1))^2
term = (count - 1) * fixed_variance
@interval(term / quantile_down, term / quantile_up)
end
function getGammas(confidence_probability)
(1 + confidence_probability) * 0.5, (1 - confidence_probability) * 0.5
end
function getChiSquareQuantile(value, count)
d = Chisq(count - 1)
quantile(d, value)
end
function getMeanAbsDeviationQuantiles(confidence_probability, count)
if confidence_probability == 0.90
const knots = [
2 1.386 0.044;
3 1.276 0.166;
4 1.224 0.254;
5 1.187 0.305;
6 1.158 0.360;
7 1.135 0.394;
8 1.116 0.422;
9 1.100 0.445;
10 1.086 0.464;
]
elseif confidence_probability == 0.95
const knots = [
2 1.585 0.022;
3 1.417 0.116;
4 1.344 0.199;
5 1.292 0.260;
6 1.253 0.306;
7 1.222 0.342;
8 1.196 0.372;
9 1.175 0.396;
10 1.156 0.417;
]
elseif confidence_probability == 0.99
const knots = [
2 1.985 0.004;
3 1.703 0.073;
4 1.590 0.145;
5 1.507 0.203;
6 1.445 0.250;
7 1.397 0.287;
8 1.358 0.318;
9 1.326 0.344;
10 1.299 0.366;
]
else
error("Value=$confidence_probability is not allowed")
end
x_knots = knots[:, 1]
if count > last(x_knots)
error("Error $count too high")
end
y_down_knots = knots[:, 2]
down_itp = Spline1D(x_knots, y_down_knots, k=1, bc="extrapolate")
y_up_knots = knots[:, 3]
up_itp = Spline1D(x_knots, y_up_knots, k=1, bc="extrapolate")
evaluate(down_itp, count), evaluate(up_itp, count)
end
end
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