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/**
* This class models a cloud storage API.
*/
class Cloud {
int{L} cloud;
/**
* Put a value into the cloud.
*/
void put(int x) {
this.cloud = x;
}
/**
* Put the only value the cloud stores.
*/
int get() {
return this.cloud;
}
}
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] | 2.300813 | 123 |
# Tests for align.jl
@testset "Align" begin
coords_one = [
1.0 0.0 0.0;
0.0 1.0 0.0;
0.0 0.0 0.0;
]
coords_two = [
0.0 -1.0 0.0;
1.0 0.0 0.0;
1.0 1.0 1.0;
]
trans_one_test, trans_two_test, rotation_test = kabschalignment(coords_one, coords_two)
trans_one_real = [1/3, 1/3, 0]
trans_two_real = [-1/3, 1/3, 1]
rotation_real = [
0.0 -1.0 0.0;
1.0 0.0 0.0;
0.0 0.0 1.0;
]
@test isapprox(trans_one_test, trans_one_real)
@test isapprox(trans_two_test, trans_two_real)
@test isapprox(rotation_test, rotation_real)
coords_one = [
1.0 0.0 0.0;
0.0 1.0 0.0;
0.0 0.0 0.0;
]
coords_two = [
0.0 -1.0 0.0;
1.0 0.0 0.0;
1.0 1.0 1.0;
]
devs_test = displacements(coords_one, coords_two)
devs_real = [sqrt(3), sqrt(3), 1.0]
@test isapprox(devs_test, devs_real)
atom_one = Atom("CA", "ALA", 'A', 20, [0.0, 0.0, 0.0], "C")
atom_two = Atom("C", "ALA", 'A', 20, [0.0, 0.0, 0.0], "C")
atom_thr = Atom("CA", "ALA", 'A', 21, [0.0, 0.0, 0.0], "C")
atoms = [atom_one, atom_two, atom_thr]
coords_one = [
0.0 1.0 2.0;
0.0 1.0 0.0;
0.0 0.0 0.0;
]
coords_two = [
0.0 -1.0 0.0;
0.0 0.0 3.0;
3.0 3.0 3.0;
]
align!(coords_one, coords_two, atoms)
coords_one_real = [
0.0 0.5 3.0;
-1.0 1.5 3.0;
0.0 2.5 3.0;
]
coords_one_real = [
0.0 -1.0 0.0;
0.5 1.5 2.5;
3.0 3.0 3.0;
]
@test coords_one == coords_one_real
atom_one = Atom("CA", "ALA", 'A', 20, [0.0, 0.0, 0.0], "C")
atom_two = Atom("C", "ALA", 'A', 20, [0.0, 0.0, 0.0], "C")
atom_thr = Atom("CA", "ALA", 'A', 21, [0.0, 0.0, 0.0], "C")
atoms = [atom_one, atom_two, atom_thr]
coords_one = [
0.0 1.0 2.0;
0.0 1.0 0.0;
0.0 0.0 0.0;
]
coords_two = [
0.0 -1.0 0.0;
0.0 0.0 3.0;
3.0 3.0 3.0;
]
alignsimple!(coords_one, coords_two, atoms)
coords_one_real = [
0.0 -1.0 0.0;
0.5 1.5 2.5;
3.0 3.0 3.0;
]
@test coords_one == coords_one_real
atom_one = Atom("CA", "ALA", 'A', 20, [0.0, 0.0, 0.0], "C")
atom_two = Atom("C", "ALA", 'A', 20, [1.0, 1.0, 0.0], "C")
atom_thr = Atom("CA", "ALA", 'A', 21, [2.0, 0.0, 0.0], "C")
atoms = [atom_one, atom_two, atom_thr]
atoms_new = deepcopy(atoms)
coords_ref = [
0.0 -1.0 0.0;
0.0 0.0 3.0;
3.0 3.0 3.0;
]
alignatoms!(atoms, coords_ref)
coords_real = [
0.0 -1.0 0.0;
0.5 1.5 2.5;
3.0 3.0 3.0;
]
@test atomcoords(atoms) == coords_real
atoms_ref = atomcoords(atoms, coords_ref)
alignatoms!(atoms_new, atoms_ref)
@test atomcoords(atoms_new) == coords_real
atom_one = Atom("CA", "ALA", 'A', 20, [0.0, 0.0, 0.0], "C")
atom_two = Atom("CA", "ALA", 'A', 30, [0.0, 0.0, 0.0], "C")
struc_one = ModelledStructure(0.0, [0.0 5.0; 0.0 5.0; 0.0 5.0])
struc_two = ModelledStructure(0.0, [1.0 4.0; 1.0 4.0; 1.0 4.0])
ensemble = ModelledEnsemble([atom_one, atom_two], [struc_one, struc_two])
average_test = centroid(ensemble)
average_real = [0.5 4.5; 0.5 4.5; 0.5 4.5]
@test average_test == average_real
coords_one = [
0.0 3.0 13.0;
0.0 0.0 0.0;
0.0 0.0 0.0;
]
coords_two = [
0.0 5.0 10.0;
1.0 0.0 0.0;
0.0 0.0 0.0;
]
atom_one = Atom("CA", "ALA", 'A', 20, [0.0, 0.0, 0.0], "C")
atom_two = Atom("CA", "ALA", 'A', 30, [0.0, 0.0, 0.0], "C")
atom_thr = Atom("C", "ALA", 'A', 40, [0.0, 0.0, 0.0], "C")
atoms = [atom_one, atom_two, atom_thr]
@test isapprox(rmsd(coords_one, coords_two, atoms), sqrt(2.5))
coords_one = [
0.0 3.0;
0.0 0.0;
0.0 0.0;
]
coords_two = [
0.0 5.0;
1.0 0.0;
0.0 0.0;
]
@test isapprox(rmsd(coords_one, coords_two), sqrt(2.5))
end
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828,
19862,
17034,
7,
17,
13,
20,
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198,
437,
198
] | 1.629988 | 2,481 |
module Jags
using Compat, Pkg, Documenter, DelimitedFiles, Unicode, MCMCChains, StatsPlots
#### Includes ####
include("jagsmodel.jl")
include("jagscode.jl")
if !isdefined(Main, :Stanmodel)
include("utilities.jl")
end
"""The directory which contains the executable `bin/stanc`. Inferred
from `Main.JAGS_HOME` or `ENV["JAGS_HOME"]` when available. Use
`set_jags_home!` to modify."""
JAGS_HOME=""
function __init__()
global JAGS_HOME = if isdefined(Main, :JAGS_HOME)
eval(Main, :JAGS_HOME)
elseif haskey(ENV, "JAGS_HOME")
ENV["JAGS_HOME"]
else
println("Environment variable JAGS_HOME not found. Use set_jags_home!.")
""
end
end
"""Set the path for `Jags`.
Example: `set_jags_home!(homedir() * "/src/src/cmdstan-2.11.0/")`
"""
set_jags_home!(path) = global JAGS_HOME=path
#### Exports ####
export
# From this file
set_jags_home!,
# From Jags.jl
JAGS_HOME,
# From jagsmodel.jl
Jagsmodel,
# From jagscode.jl
jags
end # module
| [
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] | 2.348341 | 422 |
using Base.Test
reload("ForwardBackwardOptim")
m = ForwardBackwardOptim
tests = [
"optims"
]
for t in tests
tfile = string(t, ".jl")
println(" * $tfile ...")
include(tfile)
end
println("Finished testing.") | [
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] | 2.511111 | 90 |
#!/usr/bin/env julia
# https://github.com/JuliaEditorSupport/julia-emacs/blob/master/make-julia-latexsubs.jl
@assert VERSION >= v"1"
import REPL
"""
Create latex symbols formatted for elisp as either abbrev or hash table.
- ds : elisp output data structure - either "abbrev" or "hash"
- varname : name of hashtable if "hash" output
- dest : out stream
"""
function generate_unicode(ds::AbstractString, varname::AbstractString, dest::IO)
if dest != stdout
println(dest, ";; -*- coding: utf-8; mode: emacs-lisp; -*-")
end
if ds == "abbrev"
println(dest, "(define-abbrev-table 'unicode-latex-abbrev-table\n '(")
elseif ds == "hash"
println(dest, "(defvar $(varname) (make-hash-table :test 'equal))\n")
end
for (k, v) in sort!(collect(REPL.REPLCompletions.latex_symbols), by=last)
ks = escape_string(k)
vs = escape_string(v)
if ds == ""
println(dest, "\"$ks\" => \"$vs\"")
elseif ds == "hash" && occursin(r"^\\U[0-9A-Fa-f]+$", vs)
cp = vs[3:end]
println(dest,
"(let ((c (decode-char 'ucs #x$cp)))\n",
" (if c (puthash \"$ks\" (char-to-string c) $(varname))))")
else
if ds == "hash"
println(dest, "(puthash \"$ks\" \"$vs\" $(varname))")
elseif ds == "abbrev"
println(dest, " (\"$ks\" \"$vs\" nil :system t)")
else
println(dest, "\"$ks\" => \"$vs\"")
end
end
end
if ds == "abbrev"
println(dest,
" )\n",
" \"Unicode latex abbrevs (julia generated).\"\n",
" :regexp \"\\\\(\\\\\\\\[\\\\A-Za-z0-9^]+\\\\)\")")
end
end
# CLI
if length(ARGS) < 1
generate_unicode("", "", stdout)
else
dest = length(ARGS) > 2 ? open(ARGS[3], "w") : stdout
if ARGS[1] == "abbrev"
generate_unicode("abbrev", "", dest)
elseif ARGS[1] == "hash"
generate_unicode(ARGS[1], ARGS[2], dest)
else
println(dest, "Unknown type")
end
end
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] | 1.96286 | 1,077 |
# Energy calculation
export
total_energy,
kinetic_energy,
temperature,
potential_energy
"""
total_energy(s, neighbors=nothing)
Calculate the total energy of the system.
If the interactions use neighbor lists, the neighbors should be computed
first and passed to the function.
"""
total_energy(s, neighbors=nothing) = kinetic_energy(s) + potential_energy(s, neighbors)
kinetic_energy_noconvert(s) = sum(mass.(s.atoms) .* sum.(abs2, s.velocities)) / 2
"""
kinetic_energy(s)
Calculate the kinetic energy of the system.
"""
function kinetic_energy(s::System{D, G, T}) where {D, G, T}
ke = kinetic_energy_noconvert(s)
# Convert energy to per mol if required
if dimension(s.energy_units) == u"𝐋^2 * 𝐌 * 𝐍^-1 * 𝐓^-2"
return T(uconvert(s.energy_units, ke * Unitful.Na))
else
return T(uconvert(s.energy_units, ke))
end
end
const temp_conversion_factor = ustrip(u"nm^2 * u * K^-1 * ps^-2", Unitful.k)
"""
temperature(system)
Calculate the temperature of a system from the kinetic energy of the atoms.
"""
function temperature(s::System{D, G, T}) where {D, G, T}
ke = kinetic_energy_noconvert(s)
df = 3 * length(s) - 3
if unit(ke) == NoUnits
k = T(temp_conversion_factor)
else
k = T(uconvert(u"K^-1" * unit(ke), Unitful.k))
end
return 2 * ke / (df * k)
end
function check_energy_units(E, energy_units)
if unit(E) != energy_units
error("System energy units are ", energy_units, " but encountered energy units ",
unit(E))
end
end
@inline @inbounds function potential_energy_nounit(inters, coord_i, coord_j, atom_i, atom_j,
box_size, energy_units, weight_14::Bool=false)
dr = vector(coord_i, coord_j, box_size)
sum(inters) do inter
if weight_14
E = potential_energy(inter, dr, coord_i, coord_j, atom_i, atom_j,
box_size, true)
else
E = potential_energy(inter, dr, coord_i, coord_j, atom_i, atom_j,
box_size)
end
check_energy_units(E, energy_units)
return ustrip(E)
end
end
@views function potential_energy_inters(inters, coords, atoms, neighbors, box_size,
energy_units, weights_14)
coords_i, atoms_i = getindices_i(coords, neighbors), getindices_i(atoms, neighbors)
coords_j, atoms_j = getindices_j(coords, neighbors), getindices_j(atoms, neighbors)
@inbounds energies = potential_energy_nounit.((inters,), coords_i, coords_j,
atoms_i, atoms_j, (box_size,), energy_units, weights_14)
return sum(energies) * energy_units
end
"""
potential_energy(s, neighbors=nothing)
Calculate the potential energy of the system using the pairwise, specific and
general interactions.
If the interactions use neighbor lists, the neighbors should be computed
first and passed to the function.
potential_energy(inter::PairwiseInteraction, vec_ij, coord_i, coord_j,
atom_i, atom_j, box_size)
potential_energy(inter::SpecificInteraction, coords_i, coords_j,
box_size)
potential_energy(inter::SpecificInteraction, coords_i, coords_j,
coords_k, box_size)
potential_energy(inter::SpecificInteraction, coords_i, coords_j,
coords_k, coords_l, box_size)
potential_energy(inter, system, neighbors=nothing)
Calculate the potential energy due to a given interation type.
Custom interaction types should implement this function.
"""
function potential_energy(s::System{D, false, T}, neighbors=nothing) where {D, T}
n_atoms = length(s)
potential = zero(T) * s.energy_units
for inter in values(s.pairwise_inters)
if inter.nl_only
if isnothing(neighbors)
error("An interaction uses the neighbor list but neighbors is nothing")
end
@inbounds for ni in 1:neighbors.n
i, j, weight_14 = neighbors.list[ni]
dr = vector(s.coords[i], s.coords[j], s.box_size)
if weight_14
potential += potential_energy(inter, dr, s.coords[i], s.coords[j], s.atoms[i],
s.atoms[j], s.box_size, true)
else
potential += potential_energy(inter, dr, s.coords[i], s.coords[j], s.atoms[i],
s.atoms[j], s.box_size)
end
end
else
for i in 1:n_atoms
for j in (i + 1):n_atoms
dr = vector(s.coords[i], s.coords[j], s.box_size)
potential += potential_energy(inter, dr, s.coords[i], s.coords[j], s.atoms[i],
s.atoms[j], s.box_size)
end
end
end
end
for inter_list in values(s.specific_inter_lists)
potential += potential_energy(inter_list, s.coords, s.box_size)
end
for inter in values(s.general_inters)
potential += potential_energy(inter, s, neighbors)
end
return uconvert(s.energy_units, potential)
end
function potential_energy(s::System{D, true, T}, neighbors=nothing) where {D, T}
potential = zero(T) * s.energy_units
pairwise_inters_nonl = filter(inter -> !inter.nl_only, values(s.pairwise_inters))
if length(pairwise_inters_nonl) > 0
potential += potential_energy_inters(pairwise_inters_nonl, s.coords, s.atoms,
neighbors.all, s.box_size, s.energy_units, false)
end
pairwise_inters_nl = filter(inter -> inter.nl_only, values(s.pairwise_inters))
if length(pairwise_inters_nl) > 0 && length(neighbors.close.nbsi) > 0
potential += potential_energy_inters(pairwise_inters_nl, s.coords, s.atoms,
neighbors.close, s.box_size, s.energy_units, neighbors.close.weights_14)
end
for inter_list in values(s.specific_inter_lists)
potential += potential_energy(inter_list, s.coords, s.box_size)
end
for inter in values(s.general_inters)
potential += potential_energy(inter, s, neighbors)
end
return uconvert(s.energy_units, potential)
end
@views function potential_energy(inter_list::InteractionList2Atoms, coords, box_size)
return sum(potential_energy.(inter_list.inters, coords[inter_list.is], coords[inter_list.js],
(box_size,)))
end
@views function potential_energy(inter_list::InteractionList3Atoms, coords, box_size)
return sum(potential_energy.(inter_list.inters, coords[inter_list.is], coords[inter_list.js],
coords[inter_list.ks], (box_size,)))
end
@views function potential_energy(inter_list::InteractionList4Atoms, coords, box_size)
return sum(potential_energy.(inter_list.inters, coords[inter_list.is], coords[inter_list.js],
coords[inter_list.ks], coords[inter_list.ls], (box_size,)))
end
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] | 2.190887 | 3,248 |
#########
# setk! #
#########
function setk!(x, k_requested, v)
k = setk!_inner(x, k_requested, v)
if k > 0
error("Index ", k_requested, " is out of bounds.")
end
end
@generated function setk!_inner(x::T, k, v) where {T}
if T <: Array
if eltype(T) <: Real
quote
if k <= length(x)
x[k] = v
return 0
end
return k-length(x)
end
else
quote
for c = 1:length(x)
k = setk!_inner(x[c], k, v)
if k <= 0
return 0
end
end
end
end
elseif T <: Nothing
quote
# We shouldn't be trying to set a Nothing. Skip it.
# if k <= 1
# # x[k] = v
# return 0
# end
return k
end
elseif !isempty(fieldnames(T))
expr = Vector{Expr}()
for f in fieldnames(x)
push!(expr, quote
if typeof(x.$f) <: Real # For scalars, we must overwrite the field directly.
if k == 1
x.$f = v
return 0
end
k -= 1
elseif typeof(x.$f) <: Nothing # For nothings, we ignore the write.
# if k == 1
# x.$f = nothing
# return 0
# end
k # We didn't use any index for this.
else # For anything else, it's assumed that we can recur and set something inside the field.
k = setk!_inner(x.$f, k, v)
if k <= 0
return 0
end
end
end)
end
push!(expr, :( return k ))
Expr(:block, expr...)
else
error("I don't know how to set an index of a ", T, ".")
end
end
########
# getk #
########
# Get index k of x, which might be a struct of struct of vectors or something.
function getk(x, k_requested)
r, k = getk_inner(x, k_requested)
if k > 0
error("Index ", k_requested, " is out of bounds.")
end
return r
end
@generated function getk_inner(x::T, k) where {T}
if T <: Real
quote
if k == 1
return (x, 0)
end
return (0., k-1)
end
elseif T <: Nothing
quote
# if k == 1
# return (0., 0)
# end
return (0., k)
end
elseif T <: Array
if eltype(T) <: Real
quote
if k <= length(x)
return (x[k], 0)
end
return (0., k-length(x))
end
else
quote
for c = 1:length(x)
r, k = getk_inner(x[c], k)
if k <= 0
return (r, 0)
end
end
end
end
elseif !isempty(fieldnames(T))
expr = Vector{Expr}()
push!(expr, :( r = 0. ))
for f in fieldnames(x)
push!(expr, quote
r, k = getk_inner(x.$f, k)
if k <= 0
return (r, 0)
end
end)
end
push!(expr, :( return (0., k) ))
Expr(:block, expr...)
else
error("I don't know how to get an index of a ", T, ".")
end
end
###########
# stackem #
###########
# Stacks reals, arrays, and anything with fieldnames into a new vector, returning the vector.
stackem(y::T) where {T} = stackem!(Vector{Float64}(), y)
# Stacks reals, arrays, and anything with fieldnames into an existing vector. Super fast!
# We write this as an if on the type, because this allows us to do more than multiple dispatch.
# E.g., we can detect a struct by asking for its field names.
@generated function stackem!(x, y::T) where {T}
if T <: Real
:( push!(x, y) )
elseif T <: Enum
:( push!(x, Base.Enums.basetype(T)(y)) )
elseif T <: Array
if eltype(T) <: Real
:( append!(x, y) )
else
quote
for c = 1:length(y)
stackem!(x, y[c])
end
x
end
end
elseif !isempty(fieldnames(T)) # This is nice; we can't do this with multiple dispatch. Do this for + and * and remove the ModelStates thing?
Expr(:block, ( :( stackem!(x, y.$f) ) for f in fieldnames(T) )...)
elseif T <: Nothing # There's nothing to stack.
:(x) # :o
else
error("Sorry, I don't know how to stack a ", T, ".")
end
end
# Stack into a preallocated vector, starting at k+1. Returns k, the number elements written.
@generated function stackem!(x, y::T, k) where {T}
if T <: Real || T <: Enum
quote
x[k+1] = y
k+1
end
elseif T <: Array
if eltype(T) <: Real
quote
x[k+1:k+length(y)] = y[:]
k+length(y)
end
else
quote
for c = 1:length(y)
k = stackem!(x, y[c], k)
end
end
end
elseif !isempty(fieldnames(T)) # This is nice; we can't do this with multiple dispatch. Do this for + and * and remove the ModelStates thing?
Expr(:block, ( :( k = stackem!(x, y.$f, k) ) for f in fieldnames(T) )...)
elseif T <: Nothing
:(k) # Don't choke on that k, my friend.
else
error("Sorry, I don't know how to stack a ", T, ".")
end
end
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] | 1.763431 | 3,276 |
using GPX
using TimeZones
using LightXML: XMLDocument, save_file
author = GPXAuthor("S. Celles")
metadata = GPXMetadata(
name="07/11/2019 LFBI (09:32) LFBI (11:34)",
author=author,
time=ZonedDateTime("2019-01-01T00:00:0.000+00:00"), # ZonedDateTime("2019-01-01T00:00:00.000Z") when https://github.com/JuliaTime/TimeZones.jl/pull/227 will be merged
)
gpx = GPXDocument(metadata)
track = new_track(gpx)
track_segment = new_track_segment(track)
point = GPXPoint(0.0, 0.0, 0.0, ZonedDateTime("2019-11-09T09:14:17.473+00:00"), "")
push!(track_segment, point)
# println(gpx)
xdoc = XMLDocument(gpx)
println(xdoc)
fname = "generated.gpx"
save_file(xdoc, fname)
println("GPX file saved to \"$fname\"")
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] | 2.344262 | 305 |
using LinearAlgebraicRepresentation
Lar = LinearAlgebraicRepresentation
# Compute coboundary_1 in 2D via product FV * EV^t with fixing of redundancies
FV = [[1,2,3,4,5,17,16,12],
[1,2,3,4,6,7,8,9,10,11,12,13,14,15],
[4,5,9,11,12,13,14,15,16,17],
[2,3,6,7], [8,9,10,11]]
EV = [[1,2],[2,3],[3,4],[4,5],[1,12],[2,6],[3,7],[4,9],[5,17],[6,7],[8,9],
[8,10],[9,11],[10,11],[11,15],[12,13],[12,16],[13,14],[14,15],[16,17]]
V = Float64[ 0 2 5 7 10 2 5 3 7 3 7 0 3 3 7 0 10;
16 16 16 16 16 13 13 11 11 8 8 5 5 2 2 0 0]
# non-convex cells (convex=false), including outer cell (exterior=true)
copFE = Lar.coboundary_1( V::Lar.Points, FV::Lar.Cells, EV::Lar.Cells, false,true );
Matrix(copFE)
VV = [[k] for k in 1:size(V,2)];
using Plasm
Plasm.view( Plasm.numbering(3)((V,[VV, EV, FV])) )
# final solid visualization
copEV = Lar.coboundary_0(EV)
W = convert(Lar.Points, V')
triangulated_faces = Lar.triangulate2D(W, [copEV, copFE])
#triangulated_faces = Lar.triangulate2d(V, EV)
FVs = convert(Array{Lar.Cells}, triangulated_faces)
Plasm.viewcolor(V::Lar.Points, FVs::Array{Lar.Cells})
#TODO: debug ...
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] | 2.006981 | 573 |
# This dictionary maps easy to remember names to Youtube video IDs
# after adding an ID here, you can use the {{youtube <shortname>}}
# syntax in your markdown files to embed the video into the page!
videos = Dict(
"course-intro" => "vxjRWtWoD_w",
"installation" => "OOjKEgbt8AI",
"array-intro" => "CwDI-YOjWhc",
"cute-one" => "foN1_hAGfNg",
"images-intro" => "DGojI9xcCfg",
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"understanding-data" => "KrQV6mZ8hvI",
"introspection" => "IuOXXQR7dAo",
"covid-data" => "m1dmT-31Qwc",
"dataframes" => "Pt8Iz4Udg2E",
"functions-are-objects" => "_O-HBDZMLrM",
"covid-live" => "Yx055xdSkx0",
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"verbs-nouns" => "LvoLXE0U5og",
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] | 1.547813 | 1,349 |
using SciPy
using Test
@testset "SciPy.jl" begin
# Print configulations before start testings.
print_configulations()
@testset "cluster" begin
features = [[ 1.9 2.3];
[ 1.5 2.5];
[ 0.8 0.6];
[ 0.4 1.8];
[ 0.1 0.1];
[ 0.2 1.8];
[ 2.0 0.5];
[ 0.3 1.5];
[ 1.0 1.0]]
whitened = cluster.vq.whiten(features)
@test whitened[1, 1] == 2.739596711878806
end
@testset "constants" begin
@test constants.golden == 1.618033988749895
end
@testset "fft" begin
fft = SciPy.fft.fft(exp.(π/8 * collect(1:8)))
@test length(fft) == 8
end
@testset "integrate" begin
f(x) = x^2
result = SciPy.integrate.quad(f, 0, 4)
@test result[1] == 21.333333333333336
end
@testset "interpolate" begin
include("interpolateTest.jl")
end
@testset "stats" begin
include("statsTest.jl")
end
@testset "optimize" begin
include("optimizeTest.jl")
end
@testset "spatial" begin
include("spatialTest.jl")
end
end
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Optional{T} = Union{Nothing,T}
mutable struct Address
street::String
house_nr::String
zip_code::String
town::String
iid::DbId
end
function Address()
return Address("","","","",DbId())
end
mutable struct Employee
name::String
contact_person_first::String
contact_person_second::String
job_title::String
salutation::String
call_text::String
done::Bool
address_rel::Optional{Address}
callback_date::Optional{DateTime}
iid::DbId
end
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] | 2.652406 | 187 |
@testset "88.merge-sorted-array.jl" begin
nums1 = [1, 2, 3, 0, 0, 0]
m = 3
nums2 = [2, 5, 6]
n = 3
merge_sorted_array(nums1, m, nums2, n)
@test nums1 == [1, 2, 2, 3, 5, 6]
end
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] | 1.732759 | 116 |
using Surrogates
using LinearAlgebra
using Flux
using Flux: @epochs
using Zygote
using PolyChaos
using Test
#using Zygote: @nograd
#=
#FORWARD
###### 1D ######
lb = 0.0
ub = 10.0
n = 5
x = sample(n,lb,ub,SobolSample())
f = x -> x^2
y = f.(x)
#Radials
my_rad = RadialBasis(x,y,lb,ub,x->norm(x),2)
g = x -> ForwardDiff.derivative(my_rad,x)
g(5.0)
#Kriging
p = 1.5
my_krig = Kriging(x,y,p)
g = x -> ForwardDiff.derivative(my_krig,x)
g(5.0)
#Linear Surrogate
my_linear = LinearSurrogate(x,y,lb,ub)
g = x -> ForwardDiff.derivative(my_linear,x)
g(5.0)
#Inverse distance
p = 1.4
my_inverse = InverseDistanceSurrogate(x,y,p,lb,ub)
g = x -> ForwardDiff.derivative(my_inverse,x)
g(5.0)
#Lobachesky
n = 4
α = 2.4
my_loba = LobacheskySurrogate(x,y,α,n,lb,ub)
g = x -> ForwardDiff.derivative(my_loba,x)
g(5.0)
#Second order polynomial
my_second = SecondOrderPolynomialSurrogate(x,y,lb,ub)
g = x -> ForwardDiff.derivative(my_second,x)
g(5.0)
###### ND ######
lb = [0.0,0.0]
ub = [10.0,10.0]
n = 5
x = sample(n,lb,ub,SobolSample())
f = x -> x[1]*x[2]
y = f.(x)
#Radials
my_rad = RadialBasis(x,y,[lb,ub],z->norm(z),2)
g = x -> ForwardDiff.gradient(my_rad,x)
g([2.0,5.0])
#Kriging
theta = [2.0,2.0]
p = [1.9,1.9]
my_krig = Kriging(x,y,p,theta)
g = x -> ForwardDiff.gradient(my_krig,x)
g([2.0,5.0])
#Linear Surrogate
my_linear = LinearSurrogate(x,y,lb,ub)
g = x -> ForwardDiff.gradient(my_linear,x)
g([2.0,5.0])
#Inverse Distance
p = 1.4
my_inverse = InverseDistanceSurrogate(x,y,p,lb,ub)
g = x -> ForwardDiff.gradient(my_inverse,x)
g([2.0,5.0])
#Lobachesky
alpha = [1.4,1.4]
n = 4
my_loba_ND = LobacheskySurrogate(x,y,alpha,n,lb,ub)
g = x -> ForwardDiff.gradient(my_loba_ND,x)
g([2.0,5.0])
#Second order polynomial
my_second = SecondOrderPolynomialSurrogate(x,y,lb,ub)
g = x -> ForwardDiff.gradient(my_second,x)
g([2.0,5.0])
=#
##############
### ZYGOTE ###
##############
############
#### 1D ####
############
lb = 0.0
ub = 10.0
n = 5
x = sample(n,lb,ub,SobolSample())
f = x -> x^2
y = f.(x)
#Radials
my_rad = RadialBasis(x,y,lb,ub,rad = linearRadial)
g = x -> my_rad'(x)
g(5.0)
#Kriging
my_p = 1.5
my_krig = Kriging(x,y,lb,ub,p=my_p)
g = x -> my_krig'(x)
g(5.0)
#Linear Surrogate
my_linear = LinearSurrogate(x,y,lb,ub)
g = x -> my_linear'(x)
g(5.0)
#Inverse distance
my_p = 1.4
my_inverse = InverseDistanceSurrogate(x,y,lb,ub,p=my_p)
g = x -> my_inverse'(x)
g(5.0)
#Second order polynomial
my_second = SecondOrderPolynomialSurrogate(x,y,lb,ub)
g = x -> my_second'(x)
g(5.0)
#Lobachesky
n = 4
α = 2.4
my_loba = LobacheskySurrogate(x,y,lb,ub, alpha = α, n = 4)
g = x -> my_loba'(x)
g(0.0)
#NN
my_model = Chain(Dense(1,1), first)
my_loss(x, y) = Flux.mse(my_model(x), y)
my_opt = Descent(0.01)
n_echos = 1
my_neural = NeuralSurrogate(x,y,lb,ub,model=my_model,loss=my_loss,opt=my_opt,n_echos=1)
g = x->my_neural'(x)
g(3.4)
#Wendland
my_wend = Wendland(x,y,lb,ub)
g = x -> my_wend'(x)
g(3.0)
#MOE and VariableFidelity for free because they are Linear combinations
#of differentiable surrogates
#Polynomialchaos
n = 50
x = sample(n,lb,ub,SobolSample())
y = f.(x)
my_poli = PolynomialChaosSurrogate(x,y,lb,ub)
g = x -> my_poli'(x)
g(3.0)
#Gek
n = 10
lb = 0.0
ub = 5.0
x = sample(n,lb,ub,SobolSample())
f = x-> x^2
y1 = f.(x)
der = x->2*x
y2 = der.(x)
y = vcat(y1,y2)
my_gek = GEK(x,y,lb,ub)
g = x-> my_gek'(x)
g(3.0)
################
###### ND ######
################
lb = [0.0,0.0]
ub = [10.0,10.0]
n = 5
x = sample(n,lb,ub,SobolSample())
f = x -> x[1]*x[2]
y = f.(x)
#Radials
my_rad = RadialBasis(x,y,lb,ub,rad = linearRadial, scale_factor = 2.1)
g = x -> Zygote.gradient(my_rad,x)
g((2.0,5.0))
#Kriging
my_theta = [2.0,2.0]
my_p = [1.9,1.9]
my_krig = Kriging(x,y,lb,ub,p=my_p,theta=my_theta)
g = x -> Zygote.gradient(my_krig,x)
g((2.0,5.0))
#Linear Surrogate
my_linear = LinearSurrogate(x,y,lb,ub)
g = x -> Zygote.gradient(my_linear,x)
g((2.0,5.0))
#Inverse Distance
my_p = 1.4
my_inverse = InverseDistanceSurrogate(x,y,lb,ub,p=my_p)
g = x -> Zygote.gradient(my_inverse,x)
g((2.0,5.0))
#Lobachesky not working yet weird issue with Zygote @nograd
#=
Zygote.refresh()
alpha = [1.4,1.4]
n = 4
my_loba_ND = LobacheskySurrogate(x,y,alpha,n,lb,ub)
g = x -> Zygote.gradient(my_loba_ND,x)
g((2.0,5.0))
=#
#Second order polynomial mutating arrays
my_second = SecondOrderPolynomialSurrogate(x,y,lb,ub)
g = x -> Zygote.gradient(my_second,x)
g((2.0,5.0))
#NN
my_model = Chain(Dense(2,1), first)
my_loss(x, y) = Flux.mse(my_model(x), y)
my_opt = Descent(0.01)
n_echos = 1
my_neural = NeuralSurrogate(x,y,lb,ub,model=my_model,loss=my_loss,opt=my_opt,n_echos=1)
g = x -> Zygote.gradient(my_neural, x)
g((2.0,5.0))
#wendland
my_wend_ND = Wendland(x,y,lb,ub)
g = x -> Zygote.gradient(my_wend_ND,x)
g((2.0,5.0))
#MOE and VariableFidelity for free because they are Linear combinations
#of differentiable surrogates
#PolynomialChaos
n = 50
lb = [0.0,0.0]
ub = [10.0,10.0]
x = sample(n,lb,ub,SobolSample())
f = x -> x[1]*x[2]
y = f.(x)
my_poli_ND = PolynomialChaosSurrogate(x,y,lb,ub)
g = x -> Zygote.gradient(my_poli_ND,x)
@test_broken g((1.0,1.0)) #will work on Zygote0.5 when I will be able to update
n = 10
d = 2
lb = [0.0,0.0]
ub = [5.0,5.0]
x = sample(n,lb,ub,SobolSample())
f = x -> x[1]^2 + x[2]^2
y1 = f.(x)
grad1 = x -> 2*x[1]
grad2 = x -> 2*x[2]
function create_grads(n,d,grad1,grad2,y)
c = 0
y2 = zeros(eltype(y[1]),n*d)
for i = 1:n
y2[i+c] = grad1(x[i])
y2[i+c+1] = grad2(x[i])
c = c+1
end
return y2
end
y2 = create_grads(n,d,grad1,grad2,y)
y = vcat(y1,y2)
my_gek_ND = GEK(x,y,lb,ub)
g = x -> Zygote.gradient(my_gek_ND,x)
g((2.0,5.0))
###### ND -> ND ######
lb = [0.0, 0.0]
ub = [10.0, 2.0]
n = 5
x = sample(n,lb,ub,SobolSample())
f = x -> [x[1]^2, x[2]]
y = f.(x)
#NN
my_model = Chain(Dense(2,2))
my_loss(x, y) = Flux.mse(my_model(x), y)
my_opt = Descent(0.01)
n_echos = 1
my_neural = NeuralSurrogate(x,y,lb,ub,model=my_model,loss=my_loss,opt=my_opt,n_echos=1)
Zygote.gradient(x -> sum(my_neural(x)), (2.0, 5.0))
my_rad = RadialBasis(x,y,lb,ub,rad = linearRadial)
Zygote.gradient(x -> sum(my_rad(x)), (2.0, 5.0))
my_p = 1.4
my_inverse = InverseDistanceSurrogate(x,y,lb,ub,p=my_p)
my_inverse((2.0, 5.0))
Zygote.gradient(x -> sum(my_inverse(x)), (2.0, 5.0))
my_second = SecondOrderPolynomialSurrogate(x,y,lb,ub)
Zygote.gradient(x -> sum(my_second(x)), (2.0, 5.0))
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] | 1.915306 | 3,306 |
function _count_by_state(
events::EventObservations{T, M},
state::DiseaseState,
time::Float64) where {
T <: DiseaseStateSequence,
M <: ILM}
n_ids = 0
if state == State_I && State_R ∈ T
# E/I at or before time and I/R after time or never
for i = 1:individuals(events)
n_ids += events[state][i] <= time && ((events[State_R][i] > time) || (events[State_R][i] === NaN))
end
else
for i = 1:individuals(events)
n_ids += events[state][i] <= time
end
end
@debug "$n_ids individual(s) observered in state $state at t = $time"
return n_ids
end
function _obs_curve(
events::EventObservations{T, M},
state::DiseaseState,
min::Float64,
max::Float64) where {
T <: DiseaseStateSequence,
M <: ILM}
if min >= max
@error "Minimum time must be less than maximum time"
end
local times
if state == State_I && State_R ∈ T
times = events[[State_I; State_R]][:]
else
times = events[state]
end
times = times[Ref(min) .< times .< Ref(max)]
sort!(times)
insert!(times, 1, min)
push!(times, max)
counts = _count_by_state.(Ref(events), Ref(state), times)
return times, counts
end
@recipe function f(
events::EventObservations{T, M},
state::DiseaseState,
min::Float64,
max::Float64) where {
T <: DiseaseStateSequence,
M <: ILM}
xguide --> "Time"
yguide --> "N"
linewidth --> 2.0
linecolor --> _state_color(state)
label --> ""
seriestype --> :steppost
_obs_curve(events, state, min, max)
end
@recipe function f(
events::EventObservations{T, M},
state::DiseaseState) where {
T <: DiseaseStateSequence,
M <: ILM}
events, state, 0.0, maximum(events)
end
@recipe function f(
events::EventObservations{T, M},
min::Float64,
max::Float64) where {
T <: DiseaseStateSequence,
M <: ILM}
@series begin
linecolor --> _state_color(State_I)
label --> "I"
events, State_I, min, max
end
if State_R ∈ T
@series begin
linecolor --> _state_color(State_R)
label --> "R"
events, State_R, min, max
end
end
end
@recipe function f(
events::EventObservations{T, M}) where {
T <: DiseaseStateSequence,
M <: ILM}
events, minimum(events), maximum(events)
end
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7,
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828,
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7,
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437,
198
] | 2.462983 | 905 |
##### embarrassingly parallel computation is embarrassingly easy
# This computation is automatically distributed across
# all available compute nodes, and the result, reduced by summation (+),
# is returned at the calling node.
nheads = @parallel (+) for i=1:10000
rand(Bool)
end
#### multithreading
# at the command line,
# export JULIA_NUM_THREADS=4
Threads.nthreads()
# simple example
a = zeros(10)
Threads.@threads for i = 1:10
a[i] = Threads.threadid()
end
n=10
niters=4000000
a = zeros(n)
# if all works correctly, it will be faster than single threaded
# note: += is not atomic here
@time Threads.@threads for i = 1:niters
a[i%n+1] += 1
end
a = zeros(n)
@time for i = 1:niters
a[i%n+1] += 1
end
n=10
niters=4000000
a = zeros(n)
# if all works correctly, it will be faster than single threaded
# note: += is not atomic here
@time Threads.@threads for i = 1:niters
a[i%n+1] += 1
end
a = zeros(n)
@parallel (+) for i = 1:niters
a[i%n+1] += 1
end
| [
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] | 2.654054 | 370 |
using Test, Random, FillArrays
import LuxurySparse: IMatrix, PermMatrix
Random.seed!(2)
p1 = IMatrix{4}()
sp = sprand(ComplexF64, 4,4, 0.5)
ds = rand(ComplexF64, 4,4)
pm = PermMatrix([2,3,4,1], randn(4))
v = [0.5, 0.3im, 0.2, 1.0]
dv = Diagonal(v)
@testset "basic" begin
@test p1==copy(p1)
@test eltype(p1) == Bool
@test size(p1) == (4, 4)
@test size(p1, 1) == size(p1, 2) == 4
@test Matrix(p1) == [1 0 0 0; 0 1 0 0; 0 0 1 0; 0 0 0 1]
end
@testset "conversion" begin
for mat in [p1, pm, dv]
@test mat == SparseMatrixCSC(mat)
@test mat == Matrix(mat)
end
for mat in [p1, pm, dv]
@test mat == PermMatrix(mat)
end
@test Diagonal(p1) == p1
@test SparseMatrixCSC(Diagonal(Fill(2, 4))) ≈ Diagonal(Fill(2, 4))
end
@testset "sparse" begin
@test nnz(p1) == 4
@test nonzeros(p1) == ones(4)
end
@testset "linalg" begin
for op in [conj, real, transpose, copy, inv]
@test op(p1) == Matrix(I, 4, 4)
@test typeof(op(p1)) == typeof(p1)
end
@test imag(p1) == zeros(4, 4)
@test p1' == Matrix(I, 4, 4)
# This will be lazy evaluated in 0.7+
@static if VERSION < v"0.7-"
@test typeof(p1') == typeof(p1)
end
@test ishermitian(p1)
end
@testset "elementary" begin
@test all(isapprox.(conj(p1), conj(Matrix(p1))))
@test all(isapprox.(real(p1), real(Matrix(p1))))
@test all(isapprox.(imag(p1), imag(Matrix(p1))))
end
@testset "basicmath" begin
@test p1*2im == Matrix(p1)*2im
@test p1/2.0 == Matrix(p1)/2.0
end
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437,
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] | 2.001294 | 773 |
@testset "NoiseApproximation" begin
using DiffEqNoiseProcess, DiffEqBase, StochasticDiffEq
using Test
using DiffEqProblemLibrary.SDEProblemLibrary: importsdeproblems; importsdeproblems()
import DiffEqProblemLibrary.SDEProblemLibrary: prob_sde_linear, prob_sde_2Dlinear
prob = prob_sde_linear
integrator = init(prob,EM(),dt=0.01)
W = NoiseApproximation(integrator)
dt = 0.1
calculate_step!(W,dt,nothing,nothing)
dWold = W.dW
@test W.curW == W[1]
@test W.curt == 0.0
accept_step!(W,dt,nothing,nothing)
@test W.curW == 0.5 + dWold
@test W.curt == dt
@test W.curW + W.dW == W[end]
@test W.curW == W[11]
W = NoiseApproximation(integrator)
for i in 1:10
accept_step!(W,dt,nothing,nothing)
end
W.t[end] == 1.0
W = NoiseApproximation(integrator)
prob = NoiseProblem(W,(0.0,1.0))
sol = solve(prob;dt=0.1)
prob = prob_sde_2Dlinear
integrator = init(prob,EM(),dt=0.01)
W = NoiseApproximation(integrator)
prob = NoiseProblem(W,(0.0,1.0))
sol = solve(prob;dt=0.1)
end
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437,
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] | 2.315789 | 418 |
module SoftSquishyMatter
"""
Flush output so that jobs can be monitored on cluster.
"""
@inline println(args...) = println(stdout, args...)
@inline function println(io::IO, args...)
Base.println(io, args...)
flush(io)
end
using Random
using Serialization
using DataStructures
using Plots
include("definitions.jl")
include("runsimulation.jl")
include("activeforces.jl")
include("particle.jl")
include("integrators.jl")
include("celllist.jl")
include("interactions.jl")
include("externalforces.jl")
include("analysis/visualize.jl")
println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
println("SoftSquishyMatter Loaded!")
println("Michael Wang")
println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
end
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437,
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] | 3.294606 | 241 |
# TODO: Move SimpleLogger in here
| [
2,
16926,
46,
25,
10028,
17427,
11187,
1362,
287,
994,
198
] | 3.090909 | 11 |
module segment
import Term
import Term: remove_markup, remove_ansi
import ..style: apply_style, MarkupStyle
import ..measure: Measure
export Segment
# ---------------------------------------------------------------------------- #
# SEGMENT #
# ---------------------------------------------------------------------------- #
"""
Segment
stores one piece of text with all the styles applied to it.
"""
struct Segment
text::AbstractString # text with ANSI codes injected
plain::AbstractString # plain text with no style
measure::Measure # measure of plain text
end
# ------------------------------- constructors ------------------------------- #
"""
Segment(text::AbstractString)
Construct a Segment out of a string with markup.
"""
function Segment(text::Union{Segment,AbstractString})
if typeof(text) == Segment
return text
end
plain = remove_ansi(remove_markup(text))
# len(x) = (length ∘ remove_ansi ∘ remove_markup)(x)
# @info "Creating segment" len(text) len(apply_style(text)) len(remove_markup(apply_style(text)))
return Segment(remove_markup(apply_style(text)), plain, Measure(plain))
end
"""
Segment(text::Union{Segment, AbstractString}, markup::AbstractString)
Construct a Segment out of a plain string and a markup string with style info
"""
function Segment(text::Union{Segment,AbstractString}, markup::Union{Nothing,AbstractString})
return isnothing(markup) ? Segment(text) : Segment("[$markup]" * text)
end
"""
Segment(text::Union{Segment, AbstractString}, style::MarkupStyle)
Construct a Segment out of a plain string and a MarkupStyle object.
"""
function Segment(text::Union{Segment,AbstractString}, style::Union{Nothing,MarkupStyle})
if isnothing(style)
Segment(text, text, Measure(text))
else
Segment(apply_style(text, style), text, Measure(text))
end
end
Segment(text::Union{AbstractString,Segment}, null::Nothing) = Segment(text)
# --------------------------------- printing --------------------------------- #
"""print styled in stdout, info otherwise"""
function Base.show(io::IO, seg::Segment)
if io == stdout
print(io, seg.text)
else
print(io, "Segment \e[2m(size: $(seg.measure))\e[0m")
end
end
# ---------------------------------------------------------------------------- #
# LAYOUT #
# ---------------------------------------------------------------------------- #
"""
Term.fillin(segments::Vector{Segment})::Vector{Segment}
Ensure that for each segment the text has the same width
"""
function Term.fillin(segments::Vector{Segment})::Vector{Segment}
widths = [seg.measure.w for seg in segments]
w = max(widths...)
filled::Vector{Segment} = []
for seg in segments
push!(filled, Segment(seg.text * " "^(w-seg.measure.w)))
end
return filled
end
# -------------------------------- concatenate ------------------------------- #
"""
concatenate strings and segments
"""
Base.:*(seg::Segment, str::AbstractString) = Segment(seg.text * str)
Base.:*(str::AbstractString, seg::Segment) = Segment(str * seg.text)
Base.:*(seg1::Segment, seg2::Segment) = Segment(seg1.text * seg2.text)
end
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] | 2.915937 | 1,142 |
### A Pluto.jl notebook ###
# v0.12.10
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
quote
local el = $(esc(element))
global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : missing
el
end
end
# ╔═╡ 0a774706-1d0e-11eb-1d3a-6d641500d34d
using Pkg, DrWatson
# ╔═╡ 0a7776e0-1d0e-11eb-2a05-795e2470b260
begin
@quickactivate "StatisticsWithJuliaPlutoNotebooks"
using PlutoUI
using Plots
end
# ╔═╡ bc93b1e6-1d0d-11eb-3a1b-cf047fc8d621
md"## Listing 1.1"
# ╔═╡ 0a77d522-1d0e-11eb-1dfa-af3a546454ae
"There is more than one way to say hello:"
# ╔═╡ 0a83a6a4-1d0e-11eb-27cf-6b1ae5c75d6c
md"##### This is an array consisting of three strings."
# ╔═╡ 5cce7a9c-1d0e-11eb-0a59-43dceb80ee34
helloArray = ["Hello","G'day","Shalom"]
# ╔═╡ 0970cc02-1d35-11eb-0636-4d61aba864fe
md"##### Printing intermediate results in Pluto notebooks is a bit tedious currently, e.g.:"
# ╔═╡ ae5f130c-1d32-11eb-0322-4387a35fcae7
map(1:length(helloArray)) do i
gr = helloArray[i]
md" $gr world"
end
# ╔═╡ 33ffe1f2-2688-11eb-071d-ef4974b52b73
helloArray[1], "universe"
# ╔═╡ 5eee59a6-1d35-11eb-0e55-e53b9fef1b9c
md"##### Note the space in front of the first interpolation ( `$gr` )."
# ╔═╡ 5a9ab6da-1d33-11eb-27b2-a3ec1b2d8e02
map(1:3) do i
R = i+100
T = R^2
md"hello $R and $T"
end
# ╔═╡ 988eab90-1d33-11eb-1278-0f39acc4dcd0
map(1:3) do i
R = i+100
T = R^2
Text("hello $R and $T")
end
# ╔═╡ 0a8f6b9c-1d0e-11eb-0109-21ef005e63fe
md"##### Below we use a construct called a `comprehension' (or 'list comprehension')"
# ╔═╡ 0a8eef00-1d0e-11eb-11b7-d566161ac295
md"##### These squares are just perfect:"
# ╔═╡ 3d114318-1d10-11eb-2b93-ed04fda820ca
squares = [i^2 for i in 0:10]
# ╔═╡ 5eaa07b2-2689-11eb-23ce-55972c6a2861
md"##### Or, the above printing example:"
# ╔═╡ 8c1e52ee-2688-11eb-3fc7-ad83de5ec8c7
[("$(helloArray[i]) universe") for i in 1:length(helloArray)]
# ╔═╡ 0a9a2898-1d0e-11eb-1583-3bfdf887a967
md"##### You can loop on elements of arrays without having to use indexing. The last line of every code snippet is also evaluated as output (in addition to any figures and printing output shown previously)."
# ╔═╡ a0e7ab92-1d22-11eb-27fd-ef499034dd88
map(squares) do s
sqrt(s)
end
# ╔═╡ 8b5571fa-1d35-11eb-1fef-1749f6da2d5e
md"##### Another important concept in Julia is `broadcasting` over an Array:"
# ╔═╡ 464824a8-1d0e-11eb-08b0-39b95ed0c30c
sqrt.(squares)
# ╔═╡ aca76098-1d35-11eb-1cf3-1b68437e17f7
md"##### Finally, the most tricky difference between the Julia REPL and other notebook formats is the reactivity of Pluto. It's cool, but also prevents multiple definitions across cells. If this happens, Pluto will suggest to bracket the assigments in a single cell with `begin` and `end`"
# ╔═╡ 01420810-1d36-11eb-1846-b579d2238fcc
begin
x = 5
x = x^2
end
# ╔═╡ 9e47c86e-1d38-11eb-0dae-ed14a8c961ad
md"##### Example of Pluto reactivity."
# ╔═╡ 88c09530-1d37-11eb-1b90-1991d07df0c2
@bind y Slider(3:12; default=7, show_value=true)
# ╔═╡ eaf674e0-1d37-11eb-234e-cbd28ef92cda
y^2
# ╔═╡ 569dad26-1d38-11eb-3a0b-69598098bf7c
plot(rand(y^3), lab="y^3 samples", leg=:topleft)
# ╔═╡ 02efc864-1d0e-11eb-027c-2f992de0069e
md"## End of listing 1. 1"
# ╔═╡ Cell order:
# ╟─bc93b1e6-1d0d-11eb-3a1b-cf047fc8d621
# ╠═0a774706-1d0e-11eb-1d3a-6d641500d34d
# ╠═0a7776e0-1d0e-11eb-2a05-795e2470b260
# ╠═0a77d522-1d0e-11eb-1dfa-af3a546454ae
# ╟─0a83a6a4-1d0e-11eb-27cf-6b1ae5c75d6c
# ╠═5cce7a9c-1d0e-11eb-0a59-43dceb80ee34
# ╟─0970cc02-1d35-11eb-0636-4d61aba864fe
# ╠═ae5f130c-1d32-11eb-0322-4387a35fcae7
# ╠═33ffe1f2-2688-11eb-071d-ef4974b52b73
# ╟─5eee59a6-1d35-11eb-0e55-e53b9fef1b9c
# ╠═5a9ab6da-1d33-11eb-27b2-a3ec1b2d8e02
# ╠═988eab90-1d33-11eb-1278-0f39acc4dcd0
# ╟─0a8f6b9c-1d0e-11eb-0109-21ef005e63fe
# ╟─0a8eef00-1d0e-11eb-11b7-d566161ac295
# ╠═3d114318-1d10-11eb-2b93-ed04fda820ca
# ╟─5eaa07b2-2689-11eb-23ce-55972c6a2861
# ╠═8c1e52ee-2688-11eb-3fc7-ad83de5ec8c7
# ╟─0a9a2898-1d0e-11eb-1583-3bfdf887a967
# ╠═a0e7ab92-1d22-11eb-27fd-ef499034dd88
# ╟─8b5571fa-1d35-11eb-1fef-1749f6da2d5e
# ╠═464824a8-1d0e-11eb-08b0-39b95ed0c30c
# ╟─aca76098-1d35-11eb-1cf3-1b68437e17f7
# ╠═01420810-1d36-11eb-1846-b579d2238fcc
# ╟─9e47c86e-1d38-11eb-0dae-ed14a8c961ad
# ╠═88c09530-1d37-11eb-1b90-1991d07df0c2
# ╠═eaf674e0-1d37-11eb-234e-cbd28ef92cda
# ╠═569dad26-1d38-11eb-3a0b-69598098bf7c
# ╟─02efc864-1d0e-11eb-027c-2f992de0069e
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] | 1.808962 | 2,544 |
abstract type AbstractIterativeInversion end
struct ColumnarVortex{F,R,L,D,P}
ψ :: F
ϕ :: F
q :: F
x :: R
∂ :: R
b :: R
L :: L
domain :: D
params :: P
end
function ColumnarVortex(; domain, params)
ψ = new_field(domain)
ϕ = new_field(domain)
q = new_field(domain)
ρ = new_field(domain)
x = new_rhs(domain)
∂ = new_rhs(domain)
b = new_rhs(domain)
L = generate_∇²(domain)
return ColumnarVortex(ψ, ϕ, q, x, ∂, b, L, domain, params)
end
function initialize!(inv::ColumnarVortex, q′fun::Function, args...)
q′hfun = (x, y, z, args...) -> q′fun(x, y, NaN, args...)
q = inv.q
domain = inv.domain
params = inv.params
set_q!(q, domain, params, q′hfun, args...)
return inv
end
function solve!(inv::ColumnarVortex; verbose = false)
ψ = inv.ψ
ϕ = inv.ϕ
q = inv.q
x = inv.x
b = inv.b
∂ = inv.∂
L = inv.L
domain = inv.domain
params = inv.params
set_cv_∂ψ!(∂, domain, params)
set_cv_bψ!(b, q, domain)
@. b = b - ∂
idrs!(x, L, b; log = false, verbose = verbose)
field_from_rhs!(ψ, x, domain)
fill_ψ_halos!(ψ, domain, params)
set_cv_∂ϕ!(∂, domain, params)
set_cv_bϕ!(b, ψ, domain)
@. b = b - ∂
idrs!(x, L, b; log = false, verbose = verbose)
field_from_rhs!(ϕ, x, domain)
fill_ϕ_halos!(ϕ, domain, params)
return inv
end
function save_inversion_results(fname, inv::ColumnarVortex)
jldopen(fname, "w") do file
file["ψ"] = inv.ψ
file["ϕ"] = inv.ϕ
file["q"] = inv.q
file["domain"] = inv.domain
file["params"] = inv.params
end
end
struct LinearizedSparseInversion{F,R,S,LU,D,P}
ψ0 :: F
ϕ0 :: F
ψ′ :: F
ϕ′ :: F
q′ :: F
x :: R
b :: R
S :: S
LU :: LU
domain :: D
params :: P
end
function LinearizedSparseInversion(;
ψ0, ϕ0, domain, params, verbose = false
)
verbose && println("Allocating fields and RHS vectors")
ψ′, ϕ′, q′ = allocate_fields(domain)
x, b = allocate_linearized_rhs(domain)
fill_ψ_halos!(ψ0, domain, params)
fill_ϕ_halos!(ϕ0, domain, params)
verbose && println("Allocation complete")
verbose && println("Assembling sparse matrix operator")
S = generate_sparse_linearized_L(ψ0, ϕ0, domain)
verbose && println("Operator assembly complete")
verbose && print_sparse_statistics(S)
verbose && println("Computing LU decomposition")
LU = lu(S)
verbose && println("LU decomposition complete")
verbose && println("Lower triangular factor:")
verbose && print_sparse_statistics(LU.L)
verbose && println("Upper triangular factor:")
verbose && print_sparse_statistics(LU.U)
return LinearizedSparseInversion(
ψ0, ϕ0, ψ′, ϕ′, q′,
x, b, S, LU,
domain, params
)
end
function initialize!(inv::LinearizedSparseInversion, q′fun::Function, args...)
q′ = inv.q′
domain = inv.domain
params = inv.params
set_q′!(q′, domain, params, q′fun, args...)
return inv
end
function initialize!(inv::LinearizedSparseInversion, q′arr::AbstractArray)
@. inv.q′ = q′arr
return inv
end
function solve!(inv::LinearizedSparseInversion)
ϕ′ = inv.ϕ′
ψ′ = inv.ψ′
q′ = inv.q′
x = inv.x
b = inv.b
LU = inv.LU
domain = inv.domain
set_linearized_b!(b, q′, domain)
ldiv!(x, LU, b)
fields_from_linearized_rhs!(ϕ′, ψ′, x, domain)
fill_ψ′_halos!(ψ′, domain)
fill_ϕ′_halos!(ϕ′, domain)
return inv
end
function save_inversion_results(fname, inv::LinearizedSparseInversion)
jldopen(fname, "w") do file
file["ψ0"] = inv.ψ0
file["ϕ0"] = inv.ϕ0
file["ψ′"] = inv.ψ′
file["ϕ′"] = inv.ϕ′
file["q′"] = inv.q′
file["domain"] = inv.domain
file["params"] = inv.params
end
end
struct LinearizedInversion{F,R,L,D,P,T}
ψ0 :: F
ϕ0 :: F
ψ′ :: F
ϕ′ :: F
q′ :: F
x :: R
b :: R
L :: L
domain :: D
params :: P
atolϕ :: T
end
function LinearizedInversion(;
ψ0, ϕ0, domain, params, atolϕ = 1.0
)
ψ′, ϕ′, q′ = allocate_fields(domain)
x, b = allocate_linearized_rhs(domain)
L = generate_linearized_L(ψ0, ϕ0, domain; T = float_type(params))
fill_ψ_halos!(ψ0, domain, params)
fill_ϕ_halos!(ϕ0, domain, params)
atolϕ = atolϕ/(params.S*params.Π^2)
return LinearizedInversion(
ψ0, ϕ0, ψ′, ϕ′, q′,
x, b, L,
domain, params,
atolϕ
)
end
function initialize!(inv::LinearizedInversion, q′fun::Function, args...)
q′ = inv.q′
domain = inv.domain
params = inv.params
set_q′!(q′, domain, params, q′fun, args...)
return inv
end
function initialize!(inv::LinearizedInversion, q′arr::AbstractArray)
@. inv.q′ = q′arr
return inv
end
function solve!(inv::LinearizedInversion; Pl = IterativeSolvers.Identity(), verbose = false, use_atol = true)
ϕ′ = inv.ϕ′
ψ′ = inv.ψ′
q′ = inv.q′
x = inv.x
b = inv.b
L = inv.L
domain = inv.domain
set_linearized_b!(b, q′, domain)
atol = use_atol ? inv.atolϕ : zero(eltype(b))
idrs!(x, L, b; Pl = Pl, log = false, verbose = verbose, abstol = atol)
fields_from_linearized_rhs!(ϕ′, ψ′, x, domain)
fill_ψ′_halos!(ψ′, domain)
fill_ϕ′_halos!(ϕ′, domain)
return inv
end
function save_inversion_results(fname, inv::LinearizedInversion)
jldopen(fname, "w") do file
file["ψ0"] = inv.ψ0
file["ϕ0"] = inv.ϕ0
file["ψ′"] = inv.ψ′
file["ϕ′"] = inv.ϕ′
file["q′"] = inv.q′
file["domain"] = inv.domain
file["params"] = inv.params
end
end
struct NLInversion{F,R,LS,LG,D,P,S} <: AbstractIterativeInversion
ψ :: F
ϕ :: F
q :: F
xψ :: R
∂ψ :: R
bψ :: R
xϕ :: R
∂ϕ :: R
bϕ :: R
Lψ :: LS
Lϕ :: LG
domain :: D
params :: P
sψ :: S
sϕ :: S
end
function NLInversion(;
domain, params,
sψ = Solver(params; ω = 0.7),
sϕ = Solver(params; ω = 0.7)
)
ψ, ϕ, q = allocate_fields(domain)
xψ, ∂ψ, bψ, xϕ, ∂ϕ, bϕ = allocate_rhs(domain)
Lψ = generate_Lψ(ϕ, domain; T = float_type(params))
Lϕ = generate_Lϕ(ψ, domain; T = float_type(params))
return NLInversion(
ψ, ϕ, q,
xψ, ∂ψ, bψ,
xϕ, ∂ϕ, bϕ,
Lψ, Lϕ,
domain, params,
sψ, sϕ
)
end
function initialize!(inv::NLInversion, q′::Function, args...)
ψ = inv.ψ
ϕ = inv.ϕ
q = inv.q
domain = inv.domain
params = inv.params
set_background_ψ!(ψ, domain, params)
set_background_ϕ!(ϕ, domain, params)
set_q!(q, domain, params, q′, args...)
fill_ψ_halos!(ψ, domain, params)
fill_ϕ_halos!(ϕ, domain, params)
return inv
end
function initialize!(inv::NLInversion, ψi::AbstractArray, ϕi::AbstractArray, q′::Function, args...)
ψ = inv.ψ
ϕ = inv.ϕ
q = inv.q
domain = inv.domain
params = inv.params
@. ψ = ψi
@. ϕ = ϕi
set_q!(q, domain, params, q′, args...)
fill_ψ_halos!(ψ, domain, params)
fill_ϕ_halos!(ϕ, domain, params)
return inv
end
function iterate!(inv::NLInversion; verbose = false)
ψ = inv.ψ
ϕ = inv.ϕ
q = inv.q
xψ = inv.xψ
∂ψ = inv.∂ψ
bψ = inv.bψ
xϕ = inv.xϕ
∂ϕ = inv.∂ϕ
bϕ = inv.bϕ
Lψ = inv.Lψ
Lϕ = inv.Lϕ
domain = inv.domain
params = inv.params
sψ = inv.sψ
sϕ = inv.sϕ
set_∂ψ!(∂ψ, ϕ, domain, params)
set_bψ!(bψ, ψ, ϕ, q, domain)
@. bψ = bψ - ∂ψ
bicgstabl!(xψ, Lψ, bψ; log = false, verbose = false)
relax!(ψ, xψ, sψ, domain; verbose = verbose)
fill_ψ_halos!(ψ, domain, params)
set_∂ϕ!(∂ϕ, ψ, domain, params)
set_bϕ!(bϕ, ψ, ϕ, q, domain)
@. bϕ = bϕ - ∂ϕ
bicgstabl!(xϕ, Lϕ, bϕ; log = false, verbose = false)
relax!(ϕ, xϕ, sϕ, domain; verbose = verbose)
fill_ϕ_halos!(ϕ, domain, params)
return inv
end
function is_converged(inv::NLInversion)
return is_converged(inv.sψ) && is_converged(inv.sϕ)
end
function save_inversion_results(fname, inv::NLInversion)
is_converged(inv) || throw(ArgumentError("inv has not converged"))
jldopen(fname, "w") do file
file["ψ"] = inv.ψ
file["ϕ"] = inv.ϕ
file["q"] = inv.q
file["domain"] = inv.domain
file["params"] = inv.params
end
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] | 1.938194 | 4,320 |
println("\n\n\nStarting runtests.jl $(join(ARGS, " ")) ...")
using Tests
using KShiftsClustering
getdata(n) = 10*rand(1, n) .+ 0.5
centers = kshifts(getdata(1_000_000), 10)
@test all(round.(Int, sort(vec(centers))) .== collect(1:10))
centers = kshifts(getdata(1000), 10)
for i = 1:100
kshifts!(centers, getdata(1000))
end
@test all(round.(Int, sort(vec(centers))) .== collect(1:10))
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] | 2.302326 | 172 |
include("bike-attributes.jl")
mutable struct Bike
bike_attributes :: BikeAttributes
x_position :: Int
y_position :: Int
end | [
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] | 2.698113 | 53 |
@testset "correctname" begin
# not very complete. Need better way to test
@test LPWriter.correctname(repeat("x", 17)) == repeat("x", 16)
@test LPWriter.correctname(".x") == "x"
@test LPWriter.correctname("0x") == "x"
@test LPWriter.correctname("x^") == "x"
@test LPWriter.correctname("x*ds") == "xds"
@test LPWriter.correctname("x*ds[1]") == "xds1"
@test LPWriter.correctname("ex*ds[1]") == "xds1"
@test LPWriter.correctname("Ex*ds[1]") == "xds1"
end
@testset "verifyname" begin
# not very complete. Need better way to test
@test LPWriter.verifyname("x")
@test LPWriter.verifyname(repeat("x", 16))
@test LPWriter.verifyname(repeat("x", 17)) == false
@test LPWriter.verifyname(".x") == false
@test LPWriter.verifyname("0x") == false
@test LPWriter.verifyname("exe") == false
@test LPWriter.verifyname("ExE") == false
@test LPWriter.verifyname("x^") == false
@test LPWriter.verifyname("x*ds") == false
end
@testset "print_objective!" begin
io = IOBuffer()
LPWriter.print_objective!(io, [0, 1, -2.3, 4e3], ["A", "B", "C", "x"])
@test String(take!(io)) == "obj: 1 B - 2.3 C + 4e3 x\n"
close(io)
end
@testset "print_variable_coefficient!" begin
io = IOBuffer()
LPWriter.print_variable_coefficient!(io, -1.3, "x", true)
@test String(take!(io)) == "-1.3 x"
LPWriter.print_variable_coefficient!(io, 1.3, "x", true)
@test String(take!(io)) == "1.3 x"
LPWriter.print_variable_coefficient!(io, -1.3, "x", false)
@test String(take!(io)) == " - 1.3 x"
LPWriter.print_variable_coefficient!(io, 1.3, "x", false)
@test String(take!(io)) == " + 1.3 x"
close(io)
end
@testset "getrowsense" begin
# LE, GE, Eq, Ranged
row_sense, hasranged = LPWriter.getrowsense([-Inf, 0.], [0., Inf])
@test row_sense == [:(<=), :(>=)]
@test hasranged == false
row_sense, hasranged = LPWriter.getrowsense([1., -1.], [1., 1.])
@test row_sense == [:(==), :ranged]
@test hasranged == true
@test_throws Exception LPWriter.getrowsense([1.], [1., 1.])
@test_throws Exception LPWriter.getrowsense([-Inf], [Inf])
end
@testset "print_constraints!" begin
io = IOBuffer()
LPWriter.print_constraints!(io, [1 -1], [-Inf], [1.0], ["x", "y"], ["r1"])
@test String(take!(io)) == "Subject To\nr1: 1 x - 1 y <= 1.0\n"
LPWriter.print_constraints!(io, [1 0 -1], [-1.2], [Inf], ["x", "z", "y"], ["r1"])
@test String(take!(io)) == "Subject To\nr1: 1 x - 1 y >= -1.2\n"
@test_throws Exception LPWriter.print_constraints!(io, [1 -1], [-1.2], [1], ["x", "y"], ["r1"])
close(io)
end
@testset "print_bounds!" begin
io = IOBuffer()
LPWriter.print_bounds!(io, [-Inf, -Inf, -1, -1, 0, 1], [Inf, 2, Inf, 3, Inf, Inf], ["A", "B", "C", "D", "E", "F"])
@test String(take!(io)) == "Bounds\nA free\n-inf <= B <= 2\n-1 <= C <= +inf\n-1 <= D <= 3\n0 <= E <= +inf\n1 <= F <= +inf\n"
close(io)
end
@testset "print_category!" begin
io = IOBuffer()
@test_throws Exception LPWriter.print_category!(io, [:Cont, :SemiCont], ["A", "B"])
@test_throws Exception LPWriter.print_category!(io, [:Cont, :SemiInt], ["A", "B"])
LPWriter.print_category!(io, [:Cont, :Int], ["A", "B"])
@test String(take!(io)) == "General\nB\nBinary\n"
LPWriter.print_category!(io, [:Cont, :Bin], ["A", "B"])
@test String(take!(io)) == "General\nBinary\nB\n"
LPWriter.print_category!(io, [:Cont, :Bin, :Cont, :Int], ["A", "B", "C", "D"])
@test String(take!(io)) == "General\nD\nBinary\nB\n"
close(io)
end
@testset "print_sos!" begin
io = IOBuffer()
LPWriter.print_sos!(io, "csos1", (1, [1,2], [2.0, 4.0]), ["V1", "V2"])
@test String(take!(io)) == "csos1: S1:: V1:2 V2:4\n"
LPWriter.print_sos!(io, "anyname", (2, [2,3], [2.0, 4.0]), ["V1", "V2", "X"])
@test String(take!(io)) == "anyname: S2:: V2:2 X:4\n"
close(io)
end
@testset "write" begin
@testset "Quadratic Objectives" begin
io = IOBuffer()
@test_throws Exception LPWriter.write(io,
Array{Float64}(0,0), [], [], [], [], [], :Max, Symbol[],
LPWriter.SOS[], [1 0; 0 1])
close(io)
end
@testset "Bad sense" begin
io = IOBuffer()
@test_throws Exception LPWriter.write(io,
Array{Float64}(0,0), [], [], [], [], [], :maximum, Symbol[],
LPWriter.SOS[], Array{Float64}(0,0))
close(io)
end
# @testset "Special Ordered Sets" begin
# io = IOBuffer()
# @test_throws Exception LPWriter.write(io,
# Array{Float64}(0,0), [], [], [], [], [], :Max, Symbol[],
# LPWriter.SOS[LPWriter.SOS(2, [5,6,7], [1,2,3])], Array{Float64}(0,0))
# close(io)
# end
@testset "write" begin
io = IOBuffer()
LPWriter.write(io,
[
1 0 0 0 0 0 0 0;
0 1 0 0 0 0 0 0;
0 0 1 0 0 0 0 0;
0 0 0 0 1 1 1 0
],
[-Inf, -Inf, -Inf, 5.5, 0, 0, 0, 0],
[3, 3, 3, Inf, 1, 1, 1, 1],
[0,0,0,-1,1,0,0,0],
[0, 2, -Inf, -Inf],
[Inf, Inf, 2.5, 1],
:Max,
[:Cont, :Cont, :Cont, :Int, :Cont, :Cont, :Cont, :Bin],
LPWriter.SOS[
(1, [1,3,5], [1.0, 2.0, 3.0]),
(2, [2,4,5], [2.0, 1.0, 2.5])
],
Array{Float64}(0,0),
"TestModel",
["V$i" for i in 1:8],
["CON$i" for i in 1:4]
)
MODEL1 = replace(readstring(joinpath(@__DIR__, "model1.lp")), "\r\n", "\n")
@test String(take!(io)) == MODEL1
close(io)
end
@testset "write2" begin
io = IOBuffer()
LPWriter.write(io,
[
1 0 0 0 0 0 0 0;
0 1 0 0 0 0 0 0;
0 0 1 0 0 0 0 0;
0 0 0 0 1 1 1 0
],
[-Inf, -Inf, -Inf, 5.5, 0, 0, 0, 0],
[3, 3, 3, Inf, 1, 1, 1, 1],
[0,0,0,-1,1,0,0,0],
[0, 2, -Inf, -Inf],
[Inf, Inf, 2.5, 1],
:Min,
[:Cont, :Cont, :Cont, :Int, :Cont, :Cont, :Cont, :Bin],
LPWriter.SOS[],
Array{Float64}(0,0),
"TestModel",
["V[$(i)]" for i in 1:8],
["$(i)CON$i" for i in 1:4]
)
MODEL2 = replace(readstring(joinpath(@__DIR__, "model2.lp")), "\r\n", "\n")
@test String(take!(io)) == MODEL2
close(io)
end
end
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# Sample script for plotting fieldlines with handpicked seeds, multi-processing version.
#
# To run on a single node,
# julia -p $ncores demo_fieldline_mp_pyplot.jl
#
# Hongyang Zhou, hyzhou@umich.edu
using Distributed, ParallelDataTransfer, Glob
@everywhere using Vlasiator, PyPlot, PyCall, Printf, LaTeXStrings, FieldTracer
@everywhere using Vlasiator: RE
function generate_seeds(coordmin, coordmax, dim_, nseeds)
seeds = Matrix{Float64}(undef, 2, nseeds)
for i in 1:nseeds
seeds[1,i] = coordmin[dim_[1]] +
(coordmax[dim_[1]] - coordmin[dim_[1]]) / nseeds * (i - 1)
seeds[2,i] = -20RE
end
seeds
end
@everywhere function init_figure(pArgs, norm, ticks, seeds, extent)
fig, ax = plt.subplots(1, 1; num=myid(),
figsize=(6, 8), constrained_layout=true)
fontsize = "x-large"
ax.set_aspect("equal")
# Set border line widths
for loc in ("left", "bottom", "right", "top")
edge = get(ax.spines, loc, nothing)
edge.set_linewidth(2.0)
end
ax.xaxis.set_tick_params(width=2.0, length=3)
ax.yaxis.set_tick_params(width=2.0, length=3)
ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
ax.set_xlabel(pArgs.strx; fontsize)
ax.set_ylabel(pArgs.stry; fontsize)
ax.set_title("Density"; fontsize)
x1, x2 = Vlasiator.get_axis(pArgs)
range1 = searchsortedfirst(x1, extent[1]):searchsortedlast(x1, extent[2])
range2 = searchsortedfirst(x2, extent[3]):searchsortedlast(x2, extent[4])
fakedata = zeros(Float32, length(range2), length(range1))
c = ax.pcolormesh(x1[range1], x2[range2], fakedata; norm, cmap=matplotlib.cm.turbo)
format = matplotlib.ticker.FormatStrFormatter("%.1f")
cb1 = colorbar(c; ax, ticks, format)
cb1.ax.set_ylabel("[amu/cc]"; fontsize)
cb1.outline.set_linewidth(1.0)
fakeline = [0.0, 1.0]
ls = [ax.plot(fakeline, fakeline, color="w") for _ in 1:size(seeds,2)]
return fig, ax, c, ls, range1, range2
end
@everywhere function update_plot!(ax, c, ls, range1, range2, dim_, seeds, grid1, grid2,
outdir, file)
isfile(outdir*file[end-8:end-5]*".png") && return
println("file = $file")
meta = load(file)
data = Vlasiator.prep2d(meta, "proton/vg_rho", :mag)'
c.set_array(data[range2,range1] ./ 1f6)
str_title = @sprintf "Density pulse run, t= %4.1fs" meta.time
ax.set_title(str_title; fontsize="x-large")
b = meta["vg_b_vol"]
b1 = reshape(b[dim_[1],:], meta.ncells[dim_[1]], meta.ncells[dim_[2]])
b2 = reshape(b[dim_[2],:], meta.ncells[dim_[1]], meta.ncells[dim_[2]])
# Find existing arrow annotations
annotations = [child for child in ax.get_children() if
pybuiltin(:isinstance)(child, matplotlib.text.Annotation)]
# Remove existing arrows
for a in annotations
a.remove()
end
# Add new arrows along field lines
for i in axes(seeds,2)
startx, starty = seeds[:,i]
x1, y1 = trace(b1, b2, startx, starty, grid1, grid2;
ds=0.5, maxstep=4000, gridtype="ndgrid")
x1 ./= RE
y1 ./= RE
if length(x1) < 5; continue; end
ls[i][1].set_xdata(x1)
ls[i][1].set_ydata(y1)
add_arrow(ls[i][1])
end
savefig(outdir*file[end-8:end-5]*".png", bbox_inches="tight")
end
function make_jobs(files)
for f in files
put!(jobs, f)
end
end
@everywhere function do_work(jobs, status,
outdir, pArgs, norm, ticks, grid1, grid2, dim_, seeds, extent)
fig, ax, c, ls, range1, range2 = init_figure(pArgs, norm, ticks, seeds, extent)
while true
file = take!(jobs)
update_plot!(ax, c, ls, range1, range2, dim_, seeds, grid1, grid2, outdir, file)
put!(status, true)
end
close(fig)
end
############################################################################################
files = glob("bulk*.vlsv", ".")
nfile = length(files)
# Set output directory
outdir = "out/"
const jobs = RemoteChannel(()->Channel{String}(nfile))
const status = RemoteChannel(()->Channel{Bool}(nworkers()))
axisunit = EARTH # contour plot axes unit
extent = [0., 20., -20., 20.] # [RE], default full domain: [-Inf32, Inf32, -Inf32, Inf32]
# Upper/lower limits for each variable
ρmin, ρmax = 0.0, 11.0 # [amu/cc]
meta = load(files[1])
# Construct pieces for plotting
pArgs = Vlasiator.set_args(meta, "proton/vg_rho", axisunit; normal=:none)
norm, ticks = Vlasiator.set_colorbar(Linear, ρmin, ρmax)
# Mark spatial dimensions
dim_ = pArgs.stry[1] == 'Z' ? (1,3) : (1,2)
(;coordmin, coordmax, ncells) = meta
# Generate regular Cartesian range
grid1 = range(coordmin[dim_[1]], coordmax[dim_[1]], length=ncells[dim_[1]])
grid2 = range(coordmin[dim_[2]], coordmax[dim_[2]], length=ncells[dim_[2]])
# Generate seeds for in-plane field line tracing
nseeds = 10
seeds = generate_seeds(coordmin, coordmax, dim_, nseeds)
println("Total number of files: $nfile")
println("Running with $(nworkers()) workers...")
@async make_jobs(files) # Feed the jobs channel with all files to process.
@sync for p in workers()
@async remote_do(do_work, p, jobs, status,
outdir, pArgs, norm, ticks, grid1, grid2, dim_, seeds, extent)
end
let n = nfile
t = @elapsed while n > 0 # wait for all jobs to complete
take!(status)
n -= 1
end
println("Finished in $(round(t, digits=2))s.")
end | [
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329,
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284,
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220,
220,
220,
220,
220,
1011,
0,
7,
13376,
8,
198,
220,
220,
220,
220,
220,
299,
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352,
198,
220,
220,
886,
198,
220,
220,
44872,
7203,
18467,
1348,
287,
29568,
744,
7,
83,
11,
19561,
28,
17,
4008,
82,
19570,
198,
437
] | 2.397396 | 2,227 |
function molecule(::Molecule"H₂O")
return """
O 1.2091536548 1.7664118189 -0.0171613972
H 2.1984800075 1.7977100627 0.0121161719
H 0.9197881882 2.4580185570 0.6297938830
"""
end
molecule(m::Molecule"water") = molecule(alias(m))
alias(::Molecule"water") = Molecule"H₂O"()
| [
8818,
27756,
7,
3712,
44,
2305,
23172,
1,
39,
158,
224,
224,
46,
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220,
1441,
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220,
440,
352,
13,
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7050,
4943,
796,
25726,
23172,
1,
39,
158,
224,
224,
46,
1,
3419,
198
] | 2.143939 | 132 |
using Test
@testset "App" begin
include("HealthHandler.jl")
end | [
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] | 2.833333 | 24 |
using Catlab.CategoricalAlgebra
using Catlab.Present
using Catlab.Theories
using Catlab.Graphs.BasicGraphs: TheoryGraph
using Catlab.Graphs
using DataStructures: OrderedDict
"""
Reference: CT for computing science:
https://www.math.mcgill.ca/triples/Barr-Wells-ctcs.pdf
We are concerned with "Regular" sketches, where no node is the vertex of more
than one cone.
Here are also interesting examples + explanation of connection to Essentially
Algebraic Theories: https://www.math.mcgill.ca/barr/papers/sketch.pdf
Section 7.7 describes how this is essentially the same as the
syntactic version. Maybe useful to help convert between.
FD theories add cocones and model sum types. This goes beyond what we want to
model with a GAT, though may be interesting to consider for the model
enumeration project.
Also check Fiore: categorical semantics of dependent type theory
"""
"""
Section 7.3+10.1.3 Shorthand Caveats:
2. nodes a×b×c IMPLICITLY have a cone with projection legs.
3. likewise nodes labeled 1 are implicitly terminal (empty base limit)
4. arrows labeled ⟨f₁,f₂,...⟩:a->b₁×b₂×... , where edges fₙ all share a codomain, are implicitly assumed to have diagrams:
fᵢ
a ⟶ bᵢ
⟨f₁,f₂,...⟩ ↓ ↗ pᵢ
b₁×b₂×...
5. arrows labeled f₁×f₂×...: a₁×a₂×...->b₁×b₂×...
are implicitly assumed to have diagrams:
pᵢ
a₁×a₂×... --> aᵢ
f₁×f₂×...↓ ↓ fᵢ
b₁×b₂×... --> bᵢ
pᵢ
6. diagrams of the form of two paths with a common start and end can be specified as s₁;s₂;... = t₁;t₂;...
7. If one leg of the above paths is empty, we require an identity arrow and set the other path to be equal to it.
8. Nodes labeled a×ᵧb implies the existence of a cone
a×ᵧb
↙ ↘
a→ γ ←b
9. an arrow s: a↣b (i.e. s is monic) implies the existence of a cone
a
id ╱ | ╲ id
↙ ↓s ↘
a ⟶ b ⟵ a
s s
"""
"""
Combinatorial representation of data of a finite limit sketch
It is not yet clear whether this captures everything we want to capture, e.g.
we expect that the diagrams are trees without cycles so that there are only a
finite number of equations to check, but there could be alternative styles of
diagrams that would work better in a CSet.
"""
@present TheoryFLSketch(FreeSchema) begin
# Main Graph
(V, E)::Ob
(src, tgt)::Hom(E,V)
# Diagrams
(Dv, De)::Ob
root::Hom(V, Dv)
(dSrc, dTgt)::Hom(De,Dv) # graph data of all diagrams (disjoint union)
dV::Hom(Dv, V) # Partition union of graphs
dE::Hom(De, V) # via map into V
# Cones
(Cone, Leg, Cv, Ce)::Ob
(cSrc, cTgt)::Hom(Ce,Cv) # graph data of all cone bases (disjoint union)
apex::Hom(Cone, V) # Which object is the cone vertex?
legCone::Hom(Leg, Cone) # Which Cone does this leg belong to?
legTgt::Hom(Leg, Cv)
legEdge::Hom(Leg, E)
cV::Hom(Cv, Cone) # Partition cone graph
cE::Hom(Ce, Cone) # via map into Cone
# Homorphisms data
cvMap::Hom(Cv, V)
ceMap::Hom(Ce, E)
dvMap::Hom(Dv, V)
deMap::Hom(De, E)
# Diagrams/cones don't touch each other
compose(cSrc, cV) == cE # EQUATION ON Ce
compose(cTgt, cV) == cE # EQUATION ON Ce
compose(dSrc, dV) == dE # EQUATION ON De
compose(dTgt, dV) == dE # EQUATION ON De
# Root of each diagram is the right vertex
compose(root, dV) == id(V) # EQUATION ON V
compose(root, dvMap) == id(V) # EQUATION ON V
# Homomorphism properties
compose(legEdge, src) == compose(legCone, apex) # EQUATION ON CONE LEGS
compose(legEdge, tgt) == compose(legTgt, cvMap) # EQUATION ON CONE LEGS
compose(deMap, src) == compose(dSrc, dvMap) # EQUATION ON De
compose(deMap, tgt) == compose(dTgt, dvMap) # EQUATION ON De
compose(ceMap, src) == compose(cSrc, cvMap) # EQUATION ON Ce
compose(ceMap, tgt) == compose(cTgt, cvMap) # EQUATION ON Ce
end;
"""Append either _src or _tgt to a symbol"""
function add_srctgt(fk::Symbol, src::Bool)::Symbol
return Symbol(string(fk) * "_" * (src ? "src" : "tgt"))
end
"""Append both _src and _tgt to a symbol"""
function add_srctgt(fk::Symbol)::Pair{Symbol, Symbol}
return add_srctgt(fk, true) => add_srctgt(fk, false)
end
if !isdefined(Main, :Fls) # because we are currently running this as a script
const Fls = CSetType(TheoryFLSketch, index=[
:src, :tgt, :dSrc, :dV, :dE, :cV, :cE, :apex, :legCone]);
end
"""Edges and vertices labeled by symbols"""
@present TheoryLabeledGraph <: TheoryGraph begin
Label::Data
vlabel::AttrType(V,Label)
elabel::AttrType(E,Label)
end;
# we don't want unique_index=[:vlabel, :elabel] because we want to use
# labels to specify homomorphisms which may not be injective
@acset_type LabeledGraph(TheoryLabeledGraph, index=[:src,:tgt]){Symbol};
"""Annotate a FLS CSet with labels"""
struct LabeledFLS
fls::Fls
labels::Pair{Vector{Symbol}, Vector{Symbol}} # label nodes and edges
function LabeledFLS(f::Fls, l::Pair{Vector{Symbol}, Vector{Symbol}})
for labs in l
length(labs)==length(Set(labs)) || error("labels must be unique: $labs")
end
length(l[1]) == nparts(f, :V) || error("incorrect # of vlabels $(l[1])")
length(l[2]) == nparts(f, :E) || error("incorrect # of elabels $(l[2])")
return new(f, l)
end
end
"""Access vertex labels of graph underlying FLS"""
function vi(fls::LabeledFLS, v::Symbol)::Int
return findfirst(==(v), fls.labels[1])
end
"""Access edge labels of graph underlying FLS"""
function ei(fls::LabeledFLS, v::Symbol)::Int
return findfirst(==(v), fls.labels[2])
end
"""No cones, trivial diagrams"""
function FLSinit(g::LabeledGraph)::LabeledFLS
fls = Fls()
add_parts!(fls, :V, nv(g))
add_parts!(fls, :Dv, nv(g), dV=1:nv(g), dvMap=1:nv(g))
add_parts!(fls, :E, ne(g))
set_subpart!(fls, :src, g[:src])
set_subpart!(fls, :tgt, g[:tgt])
set_subpart!(fls, :root, 1:nv(g))
return LabeledFLS(fls, g[:vlabel] => g[:elabel])
end
"""Get the commutivity diagram of a FLS"""
function get_diagram(fls::LabeledFLS, root::Symbol)::LabeledGraph
res = LabeledGraph()
f = fls.fls
rooti = vi(fls, root)
nodes = f.indices[:dV][rooti]
edges = f.indices[:dE][rooti]
nodedict = Dict([i=>e for (e, i) in enumerate(nodes)])
vlab = [fls.labels[1][f[:dvMap][i]] for i in nodes]
add_parts!(res, :V, length(nodes), vlabel=vlab)
edata = [(e, f[:dSrc][e], f[:dTgt][e]) for e in edges]
for (e, s, t) in edata
elab = fls.labels[2][f[:deMap][e]]
add_part!(res, :E, src=nodedict[s], tgt=nodedict[t], elabel=elab)
end
return res
end
"""Get whole cone (apex last index), or just the base of of the cone"""
function get_cone(fls::LabeledFLS, apex::Symbol, base::Bool=false
)::Union{Nothing, LabeledGraph}
f = fls.fls
apexes = fls.labels[1][f[:apex]]
cone_ind = findfirst(==(apex), apexes)
if cone_ind === nothing
return nothing
end
res = LabeledGraph()
cv, ce = [f.indices[x][cone_ind] for x in [:cV, :cE]]
reind = [findfirst(==(i), cv) for i in 1:(isempty(cv) ? 0 : maximum(cv))]
vlab = fls.labels[1][f[:cvMap][cv]]
add_parts!(res, :V, length(cv), vlabel=vlab)
elab = fls.labels[2][f[:ceMap][ce]]
es = reind[f[:cSrc][ce]]
et = reind[f[:cTgt][ce]]
add_parts!(res, :E, length(ce), elabel=elab, src=es, tgt=et)
if !base
pex = add_part!(res, :V, vlabel=fls.labels[1][f[:apex][cone_ind]])
legs = f.indices[:legCone][cone_ind]
el = fls.labels[2][f[:legEdge][legs]]
tg = reind[f[:legTgt][legs]]
add_parts!(res, :E, length(legs), elabel=el, src=pex, tgt=tg)
end
return res
end
"""Extract the underlying graph (no diagrams/cones)"""
function get_schema(fls::LabeledFLS)::LabeledGraph
res = LabeledGraph()
add_parts!(res, :V, length(fls.labels[1]), vlabel=fls.labels[1])
add_parts!(res, :E, length(fls.labels[2]), src=fls.fls[:src], tgt=fls.fls[:tgt], elabel=fls.labels[2])
return res
end
"""Get all commuting paths starting at a particular node"""
function all_paths(fls, root::Symbol)::OrderedDict{Vector{Symbol}, Int}
res = OrderedDict{Vector{Symbol}, Int}()
rooti = vi(fls, root)
f = fls.fls
rootind = f[:root][rooti]
stack = Tuple{Int,Vector{Symbol}}[(rootind, [])]
seen = Set()
while !isempty(stack)
currnode, currpath = pop!(stack)
res[currpath] = currnode
if !(currnode in seen)
push!(seen, currnode)
for e in f.indices[:dSrc][currnode]
orig_e = f[:deMap][e]
e_symbol = fls.labels[2][orig_e]
nextnode = f[:dTgt][e]
push!(stack, (nextnode, vcat(currpath, [e_symbol])))
end
end
end
return res
end
"""
Add a diagram where the first node is the root. Labels indicate homomorphism.
"""
function add_diagram!(fls::LabeledFLS, d::LabeledGraph)::Nothing
rootless = LabeledGraph()
add_parts!(rootless, :V, nv(d), vlabel=d[:vlabel])
for (e, (ss, tt)) in enumerate(zip(d[:src], d[:tgt]))
if tt != 1
add_part!(rootless, :E, elabel=d[:elabel][e], src=ss, tgt=tt)
end
end
topological_sort(rootless) # must be acyclic, after removing loops to root
root = d[:vlabel][1]
rooti = vi(fls, root)
# paths that already exist in the FLS
location = all_paths(fls, root)
f = fls.fls
seen = Dict{Int, Int}() # index in d => index in FLS diagram
# (index in d, index in FLS diagram, path in d)
stack = Tuple{Int,Int,Vector{Symbol}}[(1, f[:root][rooti], Symbol[])]
while !isempty(stack)
dnode, currnode, currpath = pop!(stack)
seen[dnode] = currnode
vlabel = d[:vlabel][dnode]
nextedges = d.indices[:src][dnode]
for e in nextedges
elab = d[:elabel][e]
orig_edge = ei(fls, elab)
nextpath = vcat(currpath, [elab])
seenflag = false
if haskey(location, nextpath)
nextnode = location[nextpath]
else
next_d = d[:tgt][e]
next_orig = vi(fls, d[:vlabel][next_d])
if haskey(seen, next_d)
nextnode = seen[next_d]
seenflag=true
else
nextnode = add_part!(f, :Dv, dV=rooti, dvMap=next_orig)
end
add_part!(f, :De, dSrc=currnode, dTgt=nextnode,
dE=rooti, deMap=orig_edge)
end
if !seenflag
push!(stack, (d[:tgt][e], nextnode, nextpath))
end
end
end
end
"""
Add a cone, assuming the last vertex of the graph is the apex
"""
function add_cone!(fls::LabeledFLS, c::LabeledGraph)::Nothing
f = fls.fls
conetgt = vi(fls, c[:vlabel][end])
prevcones = vcat(f.indices[:apex]...)
@assert !(conetgt in prevcones) "No more than one cone on a given vertex"
cone_id = add_part!(f, :Cone)
vert_ids = add_parts!(f, :Cv, nv(c)-1, cV=cone_id,
cvMap=[vi(fls, l) for l in c[:vlabel][1:end-1]])
nonlegs = [i for (i, t) in enumerate(c[:src]) if t!=nv(c)]
legs = [i for (i, t) in enumerate(c[:src]) if t==nv(c)]
emap, lemap = [[ei(fls, l) for l in c[:elabel][x]]
for x in [nonlegs, legs]]
esrc = vert_ids[c[:src][nonlegs]]
etgt, letgt = [vert_ids[c[:tgt][x]] for x in [nonlegs, legs]]
add_parts!(f, :Ce, length(nonlegs), cE=cone_id, ceMap=emap, cSrc=esrc, cTgt=etgt)
set_subpart!(f, cone_id, :apex, vi(fls, c[:vlabel][nv(c)]))
add_parts!(f, :Leg, length(legs), legCone=cone_id,
legEdge=lemap, legTgt=letgt)
return nothing
end
"""
Create a linear sketch (no cones) based on a CSet presentation
"""
function catpres_to_linear(pres::Presentation)::LabeledFLS
obs = pres.generators[:Ob]
homs = pres.generators[:Hom]
vlab = [x.args[1] for x in obs]
odict = Dict([o => i for (i, o) in enumerate(obs)])
idsymbs = [Symbol("_id_"*string(v)) for v in vlab]
elab = vcat([x.args[1] for x in homs], idsymbs)
# Create initial graph
g = LabeledGraph()
n, nh = length(vlab), length(homs)
add_parts!(g, :V, n, vlabel=vlab)
srcs = vcat([odict[h.type_args[1]] for h in homs], 1:n)
tgts = vcat([odict[h.type_args[2]] for h in homs], 1:n)
add_parts!(g, :E, nh+n, src=srcs, tgt=tgts, elabel=elab)
fls = FLSinit(g)
# Add diagrams
for (p1, p2) in pres.equations
d = paths_to_diagram(p1, p2)
add_diagram!(fls, d)
end
return fls
end
"""Create a diagram from a path equality"""
function paths_to_diagram(p1, p2)::LabeledGraph
d = LabeledGraph()
root = p1.type_args[1].args[1]
add_part!(d, :V, vlabel=root)
function add_path!(start::Bool, p)::Nothing
typ = typeof(p).parameters[1]
if typ == :id
if start
tgt = add_part!(d, :V, vlabel=root)
else
tgt = nparts(d, :V)
end
elab = Symbol("_id_"*string(root))
add_part!(d, :E, src=1, tgt=tgt, elabel=elab)
elseif typ == :compose
homs = p.args
curr = 1
if start
last = length(homs) + 1
else
last = nparts(d, :V)
end
for (i, hom) in enumerate(homs)
add_part!(d, :V, vlabel=hom.type_args[2].args[1])
if i == length(homs)
tgt = last
else
tgt = curr+1
end
add_part!(d, :E, src=curr, tgt=tgt, elabel=hom.args[1])
curr += 1
end
elseif typ == :generator
if start
last = add_part!(d, :V, vlabel=p.type_args[2].args[1])
else
last = nparts(d, :V)
end
add_part!(d, :E, src=1, tgt = last, elabel=p.args[1])
else
@assert false "Not prepared for type $typ"
end
return nothing
end
add_path!(true, p1)
add_path!(false, p2)
return d
end
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] | 2.217151 | 5,982 |
# Test specific data for one network:
println("- number/case9 check")
mpc = loadcase("case9")
gencost = [
2.0 1500.0 0.0 3.0 0.11 5.0 150.0
2.0 2000.0 0.0 3.0 0.085 1.2 600.0
2.0 3000.0 0.0 3.0 0.1225 1.0 335.0
]
@test mpc["gencost"] == gencost
# Ensure these networks exist in the `data`:
println("- file check")
@test issubset(["case96","case1354pegase"], casenames())
# Ensure every network's mpc Dict contains these keys:
println("- key check")
# key_list = ["gen";"branch" ;"docstring";"bus"]
key_list = ["gen";"branch";"bus"]
key_test = Bool[]
for name in casenames()
mpc = loadcase(name,describe=false)
push!(key_test,key_list ⊆ [key for key in keys(mpc)])
end
@test all(key_test)
println("- graceful failure test")
@test_throws ErrorException loadcase("invalidcasename")
@test_throws ErrorException loadcase("doesnotexist.mat")
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"""
grdvolume(cmd0::String="", arg1=[], kwargs...)
Reads one 2-D grid and returns xyz-triplets.
Full option list at [`grdvolume`](http://gmt.soest.hawaii.edu/doc/latest/grdvolume.html)
Parameters
----------
- **C** : **contour** : -- Str or List -- Flags = cval or low/high/delta or rlow/high or rcval
Find area, volume and mean height (volume/area) inside the cval contour.
[`-C`](http://gmt.soest.hawaii.edu/doc/latest/grdvolume.html#c)
- **L** : **base_level** : -- Number -- Flags = base
Also add in the volume from the level of the contour down to base [Default base is contour].
[`-L`](http://gmt.soest.hawaii.edu/doc/latest/grdvolume.html#l)
- $(GMT.opt_R)
- **S** : **unit** : -- Str -- Flags = e|f|k|M|n|u
For geographical grids, append a unit from e|f|k|M|n|u [Default is meter (e)].
[`-S`](http://gmt.soest.hawaii.edu/doc/latest/grdvolume.html#s)
- **T** : -- Str -- Flags = [c|h]
Determine the single contour that maximized the average height (= volume/area).
[`-T`](http://gmt.soest.hawaii.edu/doc/latest/grdvolume.html#t)
- $(GMT.opt_V)
- **Z** : **scale** : -- Str or List -- Flags = fact[/shift]
Optionally subtract shift before scaling data by fact. [Default is no scaling].
[`-Z`](http://gmt.soest.hawaii.edu/doc/latest/grdvolume.html#z)
- $(GMT.opt_f)
- $(GMT.opt_o)
"""
function grdvolume(cmd0::String="", arg1=[]; kwargs...)
length(kwargs) == 0 && occursin(" -", cmd0) && return monolitic("grdvolume", cmd0, arg1) # Speedy mode
d = KW(kwargs)
cmd, = parse_R("", d)
cmd = parse_V_params(cmd, d)
cmd, = parse_f(cmd, d)
cmd, = parse_o(cmd, d)
cmd = add_opt(cmd, 'C', d, [:C :contour])
cmd = add_opt(cmd, 'L', d, [:L :base_level])
cmd = add_opt(cmd, 'S', d, [:S :unit])
cmd = add_opt(cmd, 'T', d, [:T])
cmd = add_opt(cmd, 'Z', d, [:Z :scale])
cmd, got_fname, arg1 = find_data(d, cmd0, cmd, 1, arg1)
return common_grd(d, cmd, got_fname, 1, "grdvolume", arg1) # Finish build cmd and run it
end
# ---------------------------------------------------------------------------------------------------
grdvolume(arg1=[], cmd0::String=""; kw...) = grdvolume(cmd0, arg1; kw...) | [
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module LayerDicts
export LayerDict
struct LayerDict{K, V} <: AbstractDict{K, V}
dicts::Vector{<:AbstractDict}
end
function LayerDict(dicts::Tuple{Vararg{AbstractDict{K, V}}}) where {K, V}
return LayerDict{K, V}(collect(dicts))
end
function LayerDict(dicts::AbstractVector{<:AbstractDict{K, V}}) where {K, V}
return LayerDict{K, V}(dicts)
end
function LayerDict(dicts::AbstractVector{<:AbstractDict})
K, V = _kv_types(dicts)
return LayerDict{K, V}(dicts)
end
function LayerDict(dicts::Tuple{Vararg{AbstractDict}})
K, V = _kv_types(dicts)
return LayerDict{K, V}(collect(dicts))
end
LayerDict(::Tuple{}) = LayerDict{Any, Any}(AbstractDict[])
LayerDict(dicts::AbstractDict...) = LayerDict(dicts)
function _kv_types(dicts)
if isempty(dicts)
return (Any, Any)
end
first_dict = first(dicts)
K = keytype(first_dict)
V = valtype(first_dict)
for dict in dicts[2:end]
if K === Any && V === Any
break
end
K = typejoin(K, keytype(dict))
V = typejoin(V, valtype(dict))
end
return (K, V)
end
function Base.keys(ld::LayerDict{K}) where K
key_set = Set{K}()
for dict in ld.dicts
union!(key_set, keys(dict))
end
return key_set
end
Base.length(ld::LayerDict) = length(keys(ld))
function Base.iterate(ld::LayerDict, state=(keys(ld),))
ld_keys = first(state)
key_state = Base.tail(state)
@assert length(key_state) == 0 || length(key_state) == 1
iter = iterate(ld_keys, key_state...)
iter === nothing && return nothing
key, new_key_state = iter
return (key => ld[key]), (ld_keys, new_key_state)
end
function Base.getindex(ld::LayerDict{K, V}, key) where {K, V}
for dict in ld.dicts
if haskey(dict, key)
return dict[key]::V
end
end
throw(KeyError(key))
end
function Base.haskey(ld::LayerDict, key)
for dict in ld.dicts
if haskey(dict, key)
return true
end
end
return false
end
function Base.get(ld::LayerDict{K, V}, key, default::D) where {K, V, D}
ReturnType = typejoin(V, D)
for dict in ld.dicts
if haskey(dict, key)
return dict[key]::ReturnType
end
end
return default
end
function Base.get(f::Base.Callable, ld::LayerDict, key)
for dict in ld.dicts
if haskey(dict, key)
return dict[key]
end
end
return f()
end
end
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] | 2.18984 | 1,122 |
@testset "1038.binary-search-tree-to-greater-sum-tree.jl" begin
@test bst_to_gst(
TreeNode{Int}([
4, 1, 6, 0, 2, 5, 7, nothing, nothing, nothing, 3, nothing, nothing, nothing, 8
]),
) == TreeNode{Int}([
30,
36,
21,
36,
35,
26,
15,
nothing,
nothing,
nothing,
33,
nothing,
nothing,
nothing,
8,
])
@test bst_to_gst(TreeNode{Int}([0, nothing, 1])) == TreeNode{Int}([1, nothing, 1])
@test bst_to_gst(TreeNode{Int}([1, 0, 2])) == TreeNode{Int}([3, 3, 2])
@test bst_to_gst(TreeNode{Int}([3, 2, 4, 1])) == TreeNode{Int}([7, 9, 4, 10])
@test bst_to_gst(nothing) == nothing
end
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] | 1.788462 | 416 |
using Indexing
if VERSION < v"0.7-"
using Base.Test
else
using Test
end
@testset "getindices" begin
d = Dict(:a => "Alice", :b => "Bob", :c => "Charlie")
@test getindices(d, [:a, :c]) == ["Alice", "Charlie"]
@test getindices(d, (:a, :c)) == ("Alice", "Charlie")
@test getindices(d, Dict(:aa => :a, :cc => :c)) == Dict(:aa => "Alice", :cc => "Charlie")
@static if VERSION > v"0.7-"
@test getindices(d, (aa = :a, cc = :c)) == (aa = "Alice", cc = "Charlie")
end
@test getindices(d, :) == d
v = [11, 12, 13]
@test (getindices(v, 2)::Array{Int, 0})[] == 12
@test getindices(v, [1, 3]) == [11, 13]
@test getindices(v, Dict(:a => 1, :c => 3)) == Dict(:a => 11, :c => 13)
@test getindices(v, (1, 3)) === (11, 13)
@static if VERSION > v"0.7-"
@test getindices(v, (a = 1, c = 3)) === (a = 11, c = 13)
end
@test getindices(v, :) == v
t = (11, 12, 13)
@test getindices(t, [1, 3]) == [11, 13]
@test getindices(t, Dict(:a => 1, :c => 3)) == Dict(:a => 11, :c => 13)
@test getindices(t, (1, 3)) == (11, 13)
@static if VERSION > v"0.7-"
@test getindices(t, (a = 1, c = 3)) === (a = 11, c = 13)
end
@test getindices(t, :) == t
@static if VERSION > v"0.7-"
nt = (a = 1, b = 2.0, c = "three")
@test getindices(nt, [:a, :c]) == [1, "three"]
@test getindices(nt, Dict(:aa => :a, :cc => :c)) == Dict(:aa => 1, :cc => "three")
@test getindices(nt, (:a, :c)) == (1, "three")
@test getindices(nt, (aa = :a, cc = :c)) == (aa = 1, cc = "three")
@test getindices(nt, :) == nt
end
end
@testset "setindices!" begin
d = Dict(:a => "Alice", :b => "Bob", :c => "Charlie")
d2 = copy(d)
setindices!(d2, "Someone", [:a, :c])
@test d2 == Dict(:a => "Someone", :b => "Bob", :c => "Someone")
d3 = copy(d)
setindices!(d3, "Someone", (:a, :c))
@test d3 == Dict(:a => "Someone", :b => "Bob", :c => "Someone")
d4 = copy(d)
setindices!(d4, "Someone", Dict(:aa => :a, :cc => :c))
@test d4 == Dict(:a => "Someone", :b => "Bob", :c => "Someone")
@static if VERSION > v"0.7-"
d5 = copy(d)
setindices!(d5, "Someone", (aa = :a, cc = :c))
@test d5 == Dict(:a => "Someone", :b => "Bob", :c => "Someone")
end
d6 = copy(d)
setindices!(d6, "Someone", :)
@test d6 == Dict(:a => "Someone", :b => "Someone", :c => "Someone")
v = [11, 12, 13]
v2 = copy(v)
setindices!(v2, 20, [1, 3])
@test v2 == [20, 12, 20]
v3 = copy(v)
setindices!(v3, 20, (1, 3))
@test v3 == [20, 12, 20]
v4 = copy(v)
setindices!(v4, 20, Dict(:a => 1, :c => 3))
@test v4 == [20, 12, 20]
@static if VERSION > v"0.7-"
v5 = copy(v)
setindices!(v5, 20, (a = 1, c = 3))
@test v5 == [20, 12, 20]
end
v6 = copy(v)
setindices!(v6, 20, :)
@test v6 == [20, 20, 20]
end
@testset "view" begin
d = Dict(:a => "Alice", :b => "Bob", :c => "Charlie")
d2 = copy(d)
@test view(d, [:a, :c])::ViewArray == ["Alice", "Charlie"]
@test view(d, Dict(:aa => :a, :cc => :c))::ViewDict == Dict(:aa => "Alice", :cc => "Charlie")
av = view(d, [:a, :c])
@test parent(av) === d
@test av[1] == "Alice"
@test Indexing.axes(av) === (Base.OneTo(2),)
av[1] = "Someone"
@test d == Dict(:a => "Someone", :b => "Bob", :c => "Charlie")
dv = view(d2, Dict(:aa => :a, :cc => :c))
@test parent(dv) === d2
@test dv[:aa] == "Alice"
@test keys(dv) ⊆ [:aa, :cc] # Probably will want to change this
@test length(keys(dv)) == 2
@test haskey(dv, :aa)
dv[:aa] = "No-one"
@test d2 == Dict(:a => "No-one", :b => "Bob", :c => "Charlie")
v = [11, 12, 13]
@test view(v, Dict(:a =>1 , :c => 3))::ViewDict == Dict(:a => 11, :c => 13)
dv2 = view(v, Dict(:a =>1 , :c => 3))
@test parent(dv2) === v
@test dv2[:a] == 11
@test keys(dv2) ⊆ [:a, :c]
@test length(keys(dv2)) == 2
@test haskey(dv2, :a)
@test first(dv2) === (:a => 11) || first(dv2) === (:c => 13)
dv2[:a] = 21
@test v == [21, 12, 13]
@test ViewArray(d, [:b, :c])::ViewArray == ["Bob", "Charlie"]
@test ViewVector(d, [:b, :c])::ViewVector == ["Bob", "Charlie"]
@test ViewMatrix(d, [:b :c; :c :b])::ViewMatrix == ["Bob" "Charlie"; "Charlie" "Bob"]
end
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2599,
25,
7680,
46912,
6624,
14631,
18861,
1,
366,
37136,
8172,
366,
37136,
1,
366,
18861,
8973,
198,
437,
198
] | 1.963996 | 2,222 |
# Define the AbstractGP type and its API.
"""
abstract type AbstractGP end
Supertype for various Gaussian process (GP) types. A common interface is provided for
interacting with each of these objects. See [1] for an overview of GPs.
[1] - C. E. Rasmussen and C. Williams. "Gaussian processes for machine learning".
MIT Press. 2006.
"""
abstract type AbstractGP end
"""
mean(f::AbstractGP, x::AbstractVector)
Computes the mean vector of the multivariate Normal `f(x)`.
"""
Statistics.mean(::AbstractGP, ::AbstractVector)
"""
cov(f::AbstractGP, x::AbstractVector)
Compute the `length(x)` by `length(x)` covariance matrix of the multivariate Normal `f(x)`.
"""
Statistics.cov(::AbstractGP, x::AbstractVector)
"""
var(f::AbstractGP, x::AbstractVector)
Compute only the diagonal elements of `cov(f(x))`.
"""
Statistics.var(::AbstractGP, ::AbstractVector)
"""
cov(f::AbstractGP, x::AbstractVector, y::AbstractVector)
Compute the `length(x)` by `length(y)` cross-covariance matrix between `f(x)` and `f(y)`.
"""
Statistics.cov(::AbstractGP, x::AbstractVector, y::AbstractVector)
"""
mean_and_cov(f::AbstractGP, x::AbstractVector)
Compute both `mean(f(x))` and `cov(f(x))`. Sometimes more efficient than separately
computation, particularly for posteriors.
"""
StatsBase.mean_and_cov(f::AbstractGP, x::AbstractVector) = (mean(f, x), cov(f, x))
"""
mean_and_var(f::AbstractGP, x::AbstractVector)
Compute both `mean(f(x))` and the diagonal elements of `cov(f(x))`. Sometimes more efficient
than separately computation, particularly for posteriors.
"""
StatsBase.mean_and_var(f::AbstractGP, x::AbstractVector) = (mean(f, x), var(f, x))
for (m, f) in [
(:Statistics, :mean),
(:Statistics, :var),
(:Statistics, :cov),
(:StatsBase, :mean_and_cov),
(:StatsBase, :mean_and_var),
]
@eval function $m.$f(::AbstractGP)
return error(
"`",
$f,
"(f::AbstractGP)` is not defined (on purpose!).\n",
"Please provide an `AbstractVector` of locations `x` at which you wish to compute your ",
$f,
$((f === :mean_and_cov || f === :mean_and_var) ? " vectors" : " vector"),
", and call `",
$f,
"(f(x))`\n",
"For more details please have a look at the AbstractGPs docs.",
)
end
end
| [
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] | 2.59516 | 909 |
module Fluxes
using Adapt
using DocStringExtensions
export AbstractFlux,
FluxLW, FluxSW, init_flux_sw, set_flux_to_zero!, add_to_flux!
abstract type AbstractFlux{FT<:AbstractFloat,FTA2D<:AbstractArray{FT,2}} end
"""
FluxLW{FT,FTA2D}
Upward, downward and net longwave fluxes at each level.
# Fields
$(DocStringExtensions.FIELDS)
"""
struct FluxLW{FT<:AbstractFloat,FTA2D<:AbstractArray{FT,2}} <:
AbstractFlux{FT,FTA2D}
"upward flux `[W/m²]` `(nlev,ncol)`"
flux_up::FTA2D
"downward flux `[W/m²]` `(nlev,ncol)`"
flux_dn::FTA2D
"net flux `[W/m²]` `(nlev,ncol)`"
flux_net::FTA2D
end
FluxLW(flux_up, flux_dn, flux_net) =
FluxLW{eltype(flux_up),typeof(flux_up)}(flux_up, flux_dn, flux_net)
Adapt.@adapt_structure FluxLW
function FluxLW(
ncol::Int,
nlay::Int,
::Type{FT},
::Type{DA},
) where {FT<:AbstractFloat,DA}
flux_up = DA{FT}(undef, nlay + 1, ncol)
flux_dn = DA{FT}(undef, nlay + 1, ncol)
flux_net = DA{FT}(undef, nlay + 1, ncol)
return FluxLW{FT,typeof(flux_net)}(flux_up, flux_dn, flux_net)
end
"""
FluxSW{FT,FTA2D}
Upward, downward and net shortwave fluxes at each level.
# Fields
$(DocStringExtensions.FIELDS)
"""
struct FluxSW{FT<:AbstractFloat,FTA2D<:AbstractArray{FT,2}} <:
AbstractFlux{FT,FTA2D}
"upward flux `[W/m²]` `(nlev,ncol)`"
flux_up::FTA2D
"downward flux `[W/m²]` `(nlev,ncol)`"
flux_dn::FTA2D
"net flux `[W/m²]` `(nlev,ncol)`"
flux_net::FTA2D
"direct downward flux `[W/m²]` `(nlev,ncol)`"
flux_dn_dir::FTA2D
end
FluxSW(flux_up, flux_dn, flux_net, flux_dn_dir) =
FluxSW{eltype(flux_up),typeof(flux_up)}(
flux_up,
flux_dn,
flux_net,
flux_dn_dir,
)
Adapt.@adapt_structure FluxSW
function FluxSW(
ncol::Int,
nlay::Int,
::Type{FT},
::Type{DA},
) where {FT<:AbstractFloat,DA}
flux_up = DA{FT}(undef, nlay + 1, ncol)
flux_dn = DA{FT}(undef, nlay + 1, ncol)
flux_net = DA{FT}(undef, nlay + 1, ncol)
flux_dn_dir = DA{FT}(undef, nlay + 1, ncol)
return FluxSW{FT,typeof(flux_net)}(flux_up, flux_dn, flux_net, flux_dn_dir)
end
"""
set_flux_to_zero!(flux::FluxLW{FT}) where {FT<:AbstractFloat}
Set longwave flux to zero
"""
function set_flux_to_zero!(flux::FluxLW{FT}) where {FT<:AbstractFloat}
flux.flux_up .= FT(0)
flux.flux_dn .= FT(0)
flux.flux_net .= FT(0)
return nothing
end
"""
set_flux_to_zero!(flux::FluxSW{FT}) where {FT<:AbstractFloat}
Set shortwave flux to zero
"""
function set_flux_to_zero!(flux::FluxSW{FT}) where {FT<:AbstractFloat}
flux.flux_up .= FT(0)
flux.flux_dn .= FT(0)
flux.flux_net .= FT(0)
flux.flux_dn_dir .= FT(0)
return nothing
end
"""
add_to_flux!(flux1::FluxLW, flux2::FluxLW)
add longwave flux2 to longwave flux1
flux1 .+= flux2
"""
function add_to_flux!(flux1::FluxLW, flux2::FluxLW)
flux1.flux_up .+= flux2.flux_up
flux1.flux_dn .+= flux2.flux_dn
flux1.flux_net .+= flux2.flux_net
return nothing
end
"""
add_to_flux!(flux1::FluxSW, flux2::FluxSW)
add shortwave flux2 to shortwave flux1
flux1 .+= flux2
"""
function add_to_flux!(flux1::FluxSW, flux2::FluxSW)
flux1.flux_up .+= flux2.flux_up
flux1.flux_dn .+= flux2.flux_dn
flux1.flux_net .+= flux2.flux_net
flux1.flux_dn_dir .+= flux2.flux_dn_dir
return nothing
end
end
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for ( julia_fun, ippf_prefix ) in [ ( :"insert julia function name", "ippsFunctionBaseName" ) ]
for ( "TypeSignatures" ) in "AnArrayOfTuples"
julia_fun! = symbol(string(julia_fun, '!')) # in-place version if it makes sense
@eval begin
function $(julia_fun!)( buffer, "julia function arguments" )
sigLen = length( signal )
outLen = length( buffer )
@ippscall( $ippfsr, ( "the c function argument types" ),
"julia varables to pass to the c function")
buffer
end # function
$(julia_fun)( "julia function arguments" ) = $(julia_fun!)( "create a buffer", "julia function arguments" )
end # eval
end # type loop
end # function loop | [
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# Simple Text File
immutable Simple <: FileFormat end
const _simpleparser_start = 2
const _simpleparser_first_final = 2
const _simpleparser_error = 0
const _simpleparser_en_main = 2
const __simpleparser_nfa_targs = Int8[ 0, 0 , ]
const __simpleparser_nfa_offsets = Int8[ 0, 0, 0, 0 , ]
const __simpleparser_nfa_push_actions = Int8[ 0, 0 , ]
const __simpleparser_nfa_pop_trans = Int8[ 0, 0 , ]
type SimpleParser <: AbstractParser
state::Ragel.State
seqbuf::BufferedOutputStream{ BufferedStreams.EmptyStreamSource}
function SimpleParser(input::BufferedInputStream)
begin
cs = convert( Int , _simpleparser_start );
end
return new(Ragel.State(cs, input), BufferedOutputStream())
end
end
Base.eltype(::Type{SimpleParser}) = Line
type Line
data::ASCIIString
Line() = new()
Line(line) = new(line)
end
Base.convert(::Type{ASCIIString}, line::Line) = line.data
function Base.open(input::BufferedInputStream, ::Type{Simple})
return SimpleParser(input)
end
Base.copy(line::Line) = Line(copy(line.data))
Ragel.@generate_read_fuction(
"_simpleparser",
SimpleParser,
Line,
begin
begin
if ( p == pe )
@goto _test_eof
end
if ( cs == 2 )
@goto st_case_2
elseif ( cs == 1 )
@goto st_case_1
elseif ( cs == 0 )
@goto st_case_0
end
@goto st_out
@label ctr1
begin
output.data = Ragel.@ascii_from_anchor!
yield = true
begin
p+= 1;
cs = 2;
@goto _out
end
end
begin
state.linenum += 1
end
@goto st2
@label st2
p+= 1;
if ( p == pe )
@goto _test_eof2
end
@label st_case_2
if ( (data[1+(p )])== 10 )
begin
@goto st0
end
end
begin
@goto ctr3
end
@label ctr2
begin
output.data = Ragel.@ascii_from_anchor!
yield = true
begin
p+= 1;
cs = 1;
@goto _out
end
end
@goto st1
@label ctr3
begin
Ragel.@anchor!
end
@goto st1
@label st1
p+= 1;
if ( p == pe )
@goto _test_eof1
end
@label st_case_1
if ( (data[1+(p )]) == 10 )
begin
@goto ctr1
end
elseif ( (data[1+(p )]) == 13 )
begin
@goto ctr2
end
end
begin
@goto st1
end
@label st_case_0
@label st0
cs = 0;
@goto _out
@label st_out
@label _test_eof2
cs = 2;
@goto _test_eof
@label _test_eof1
cs = 1;
@goto _test_eof
@label _test_eof
begin
end
@label _out
begin
end
end
end
)
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] | 2.317047 | 921 |
const POSITIVES = 1:∞
struct Skip{Synthesizer}
synthesizer::Synthesizer
time::TIME
end
function make_series(skip::Skip, sample_rate)
make_series(skip.synthesizer, sample_rate)[
(round(Int, skip.time * sample_rate)+1):end
]
end
"""
Map(a_function, synthesizers...)
Map `a_function` over `synthesizers`. Supports [`make_series`](@ref).
```jldoctest
julia> using AudioSchedules
julia> using Unitful: Hz
julia> first(make_series(Map(sin, Cycles(440Hz)), 44100Hz))
0.06264832417874369
```
"""
struct Map{AFunction,Synthesizers}
a_function::AFunction
synthesizers::Synthesizers
function Map(a_function::AFunction, synthesizers...) where {AFunction}
new{AFunction,typeof(synthesizers)}(a_function, synthesizers)
end
end
export Map
"""
make_series(synthesizer, sample_rate)
Return an iterator that will the play the `synthesizer` at `sample_rate` (with frequency units, like `Hz`).
The iterator should yield `Float64`s between -1 and 1.
Assumes that iterators will never end while they are scheduled.
"""
function make_series(a_map::Map, sample_rate)
broadcast(
a_map.a_function,
map(let sample_rate = sample_rate
function (synthesizer)
make_series(synthesizer, sample_rate)
end
end, a_map.synthesizers)...,
)
end
export make_series
"""
Line(start_level, slope)
A line from `start_level` (unitless) with `slope` (with units per time like `1/s`). Supports
[`make_series`](@ref) and [`segments`](@ref).
```jldoctest
julia> using AudioSchedules
julia> using Unitful: Hz, s
julia> first(make_series(Line(0, 1 / s), 44100Hz))
2.2675736961451248e-5
```
"""
struct Line
start_level::Float64
slope::RATE
end
export Line
function make_series(line::Line, sample_rate)
line.start_level .+ (line.slope / sample_rate) .* POSITIVES
end
"""
segments(shape, start_level, duration, end_level)
Called for each envelope segment passed to [`push!`](@ref). Return a tuple of pairs in the form `(segment, duration)`,
where duration has units of time (like `s`), for a segment of shape `shape`.
```jldoctest
julia> using AudioSchedules
julia> using Unitful: s
julia> segments(Grow, 1, 1s, ℯ)
((Grow(1.0, 1.0 s⁻¹), 1 s),)
```
"""
function segments(::Type{Line}, start_level, duration, end_level)
((Line(start_level, (end_level - start_level) / duration), duration),)
end
export segments
const τ = 2 * π
# TODO: No need to cycle here; sin is periodic?
"""
Cycles(frequency)
Cycles from 0 to 2π to repeat at a `frequency` (with frequency units, like `Hz`).
Supports [`make_series`](@ref).
```jldoctest
julia> using AudioSchedules
julia> using Unitful: Hz
julia> first(make_series(Cycles(440Hz), 44100Hz))
0.06268937721449021
```
"""
struct Cycles
frequency::FREQUENCY
end
export Cycles
function make_series(cycles::Cycles, sample_rate)
((cycles.frequency / sample_rate * τ) .* POSITIVES) .% τ
end
"""
Grow(start_level, rate)
Exponentially grow or decay from `start_level` (unitless), at a continuous `rate` (with units per time like `1/s`).
Supports [`make_series`](@ref) and [`segments`](@ref).
```jldoctest
julia> using AudioSchedules
julia> using Unitful: Hz, s
julia> first(make_series(Grow(1, 1 / s), 44100Hz))
1.0000226759940578
```
"""
struct Grow
start_level::Float64
rate::RATE
end
export Grow
function make_series(grow::Grow, sample_rate)
grow.start_level .* exp(grow.rate / sample_rate) .^ POSITIVES
end
function segments(::Type{Grow}, start_level, duration, end_level)
((Grow(start_level, log(end_level / start_level) / duration), duration),)
end
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] | 2.588944 | 1,411 |
using Bokeh; autoopen(true)
m = BCFL22C()
@time lzbar, lg = simulate_exog(m);
# κ = [0.05, 0.95, -0.1 0.5]
κ = [0.0, 1.0, 0.0]
κ0, κ1, κ2 = κ
ξ = 0.05
deg = 3
sim_data = X[1:capT-1, 2:end]
l♠, κ = main()
fstv = FullState(1.0, 2.0, 3.0, 5.0)
fst = FullState(κ, κ, κ, κ)
asarray(fst)
st = TimeTState([1,2], [3,4])
for (i, x) in enumerate(fst)
@show i,x
end
Matrix(fst)
plot(grid[:, 1], grid[:, 2], "b*")
plot(LHS)
plot(fsts.l♠)
pf = PolicyFunction{1}(ones(5))
evaluate(pf, fst)
Matrix(fst)
Matrix{Float64}(fst)
plot(l♠)
linear_coefs(m, lzbar, lg, κ, maxiter=5)
capT = length(lzbar)
♠ = ones(capT+1)
# simulate ♠ forward and solve for sa along the way
@inbounds for t=1:capT
# extract time t state
♠t = ♠[t]
lzt = lzbar[t]
# update ♠_{t+1}
♠p = b0+ b1*♠t + b2*lzt
♠[t+1] = ♠p
end
t
showplot(plot(1:capT, ♠[1:capT]))
showplot(plot(1:capT, lzbar))
plot(l♠)
a1 = 1-1e-15
t = 48
showplot(plot(1:length(c1)', [c1 c2], legends=["c1", "c2"]))
using StatsBase
@doc quantile
foobar(m, 0.4, 1.0)
qnwmonomial(2, eye(2), :second)
qnwgh(4, 2)[2]
extrema(exp(lzbar))
1/0.95
♠sim = ♠
zbarsim = exp(lzbar)
showplot(plot(♠sim, zbarsim, "."))
showplot(plot(eds[:,1], eds[:,2], "."))
x = linspace(0, 2pi)
y1 = sin(x)
y2 = cos(x)
y3 = tan(x)
plot(x, [y1 y2 y3], "rs|bo|g*")
showplot()
tol=1e-8
deg = 3 # 5.13 seconds
deg = 5 # 6.48 seconds
κ = κ_old
maxit = 5000
it
x = rand(2, 10)
showplot(plot(l♠))
showplot(plot(LHS))
showplot(plot([c1[1:t-1] c2[1:t-1]], legends=["c1", "c2"]))
#=
omega = 1-0.972; sigma =-2/3;
xi = 1.004;
ratio_phi = 1.9375;
b = @(a) xi/(1+((1-omega)^2/omega^2)^(1/(sigma-1))*(a/(1-a)));
c1 = @(a) ((1-omega)*a^sigma + omega*(xi-b(a))^sigma)^(1/sigma);
c2 = @(a) ((1-omega)*b(a)^sigma + omega*(1-a)^sigmaIs )^(1/sigma);
ratio_implied = @(a) c2(a)^(1-sigma)*omega*(1-a)^(sigma-1)/(c1(a)^(1-sigma)*(1-omega)*a^(sigma-1));
tol=1; alb = eps; aub = 1-eps;
while tol>1e-5
a = (alb+aub)/2;
foc = ratio_implied(a) - ratio_phi;
if foc<=0
alb = a;
else
aub = a;
end
tol = abs(foc);
tol
pause
end
=#
n_complete(2, 3)
pf = PolicyFunction{3}(rand(10))
@code_warntype evaluate(pf, [1.0, 2.0])
state = [1.0, 2.0]
complete_polynomial([1 3], 3)
complete_polynomial(rand(5, 2), 3)
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] | 1.727891 | 1,323 |
using LightGraphs
using SimpleWeightedGraphs
using Test
testdir = dirname(@__FILE__)
testgraphs(g) = [g, SimpleWeightedGraph{UInt8,Float64}(g), SimpleWeightedGraph{Int16,Float32}(g)]
testdigraphs(g) = [g, SimpleWeightedDiGraph{UInt8,Float64}(g), SimpleWeightedDiGraph{Int16,Float32}(g)]
testsimplegraphs(g) = [g, LightGraphs.SimpleGraph{UInt8}(g), LightGraphs.SimpleGraph{Int16}(g)]
testsimpledigraphs(g) = [g, LightGraphs.SimpleDiGraph{UInt8}(g), LightGraphs.SimpleDiGraph{Int16}(g)]
tests = [
"simpleweightededge",
"simpleweightedgraph",
"overrides",
"persistence",
"connectivity"
]
@testset "SimpleWeightedGraphs" begin
for t in tests
tp = joinpath(testdir, "$(t).jl")
include(tp)
end
end
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] | 2.390323 | 310 |
module μodule
export @μ
macro μ(words::Symbol...)
token = :μ
for word in words
token = μagic(word, token)
end
return esc(token)
end
function μagic(word::Symbol, token)
glyphs = [Symbol(glyph) for glyph in string(word)]
while length(glyphs) > 0
glyph = eval(pop!(glyphs))
token = glyph(glyphs, token)
end
return token
end
Table = Dict([
:a => :+
:e => :^
:i => :*
:o => :/
:q => :\
:u => :-
])
a(glyphs, token) = token
b(glyphs, token) = :(x = $token; (x, x))
d(glyphs, token) = (length(glyphs) > 0 ? (f = Table[pop!(glyphs)]; :(reduce($f, $token))) :
Expr(token.head, :reduce, token.args[2:end]...))
e(glyphs, token) = (:(x = $token; ($(eval(Expr(:call, pop!(glyphs), glyphs, :(:x)))), $(eval(Expr(:call, pop!(glyphs), glyphs, :(:x)))))))
f(glyphs, token) = (fname = Symbol(glyphs...); empty!(glyphs); :($fname(μ) = $token))
g(glyphs, token) = (fname = Symbol(glyphs...); empty!(glyphs); :($fname(μ...) = $token))
h(glyphs, token) = (:(first($token)))
k(glyphs, token) = :(collect($token))
l(glyphs, token) = :(last($token))
m(glyphs, token) = (f = Table[pop!(glyphs)]; :(map($f, $token)))
n(glyphs, token) = :(x = $token; (one(x), x))
p(glyphs, token) = :(prod($token))
q(glyphs, token) = Expr(token.head, reverse(token.args))
r(glyphs, token) = (reverse!(glyphs); token)
s(glyphs, token) = :(sum($token))
t(glyphs, token) = (:($token[2:end]))
u(glyphs, token) = :(x = $token; UnitRange(x...))
w(glyphs, token) = (n = Int(first("$(pop!(glyphs))")); :($token, $n))
!(glyphs::Vector{Symbol}, token) = (:())
end
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] | 2.113577 | 766 |
export getAnalyticalConstGrad2D,getAnalyticalConstGrad3D,getAnalyticalConstGradInv2D,getAnalyticalConstGradInv3D,getSmoothGaussianMedium,getSmoothFactoredModel,getSmoothFactoredModel3D
function getAnalyticalConstGrad2D(n::Array{Int64,1},h::Array{Float64,1})
src = [1,div(n[2],2)];
source1 = (src[1]-1)*h[1];
source2 = (src[2]-1)*h[2];
(X1,X2) = ndgrid((0:(n[1]-1))*h[1],(0:(n[2]-1))*h[2]);
a = -0.4;
s0 = 2.0;
kappaSquared = s0^2 .+ 2.0*a*(X1.-source1);
# matshow(kappaSquared); colorbar();
SBarSquared = s0^2 .+ a*(X1.-source1);
rSquared = ((X1.-source1).^2 .+ (X2.-source2).^2);
sigmaSquarred = 2*rSquared./(SBarSquared + sqrt.(SBarSquared.^2 .- (a^2).*rSquared));
sigma = sqrt.(sigmaSquarred);
T_exact = SBarSquared.*sigma .- (a^2).*(sigma.^3)./6;
return kappaSquared,src,T_exact;
end
function getAnalyticalConstGrad3D(n::Array{Int64,1},h::Array{Float64,1})
src = [div(n[1],2),div(n[2],2),1];
source1 = (src[1]-1)*h[1];
source2 = (src[2]-1)*h[2];
source3 = (src[3]-1)*h[3];
(X1,X2,X3) = ndgrid((0:(n[1]-1))*h[1],(0:(n[2]-1))*h[2],(0:(n[3]-1))*h[3]);
# a = -1.75;
a = -1.65;
s0 = 2.0;
kappaSquared = s0^2 .+ 2*a*(X3.-source3);
SBarSquared = s0^2 .+ a*(X3.-source3);
rSquared = (X1.-source1).^2 .+ (X2.-source2).^2 .+ (X3.-source3).^2;
sigmaSquarred = 2*rSquared./(SBarSquared .+ sqrt.(SBarSquared.^2 .- (a^2).*rSquared));
sigma = sqrt.(sigmaSquarred);
T_exact = SBarSquared.*sigma .- (a^2).*(sigma.^3)./6;
return kappaSquared,src,T_exact;
end
function getAnalyticalConstGradInv2D(n::Array{Int64,1},h::Array{Float64,1})
src = [1,div(n[2],2)];
source1 = (src[1]-1)*h[1];
source2 = (src[2]-1)*h[2];
(X1,X2) = ndgrid((0:(n[1]-1))*h[1],(0:(n[2]-1))*h[2]);
a = 1.0;
ainv = (1.0./a);
s0 = 2.0;
Kappa = 1.0./(1.0./s0 .+ a*(X1.-source1));
T_exact = ainv*acosh.(1.0.+(0.5*s0*a*a).*Kappa.*((X1.-source1).^2 .+ (X2.-source2).^2));
G2_exact = ainv*(1.0./sqrt.((1.0.+(0.5*s0*a*a).*Kappa.*((X1.-source1).^2 .+ (X2 .- source2).^2)).^2 .- 1)).*(0.5*s0*a*a).*Kappa.*2.0.*(X2.-source2);
G1_exact = ainv*(1.0./sqrt.((1.0.+(0.5*s0*a*a).*Kappa.*((X1.-source1).^2 .+ (X2 .- source2).^2)).^2 .- 1)).*((0.5*s0*a*a).*Kappa.*2.0.*(X1.-source1)
+ (0.5*s0*a*a).*((X1.-source1).^2 + (X2.-source2).^2).*(-(Kappa.^2)).*a);
kappaSquared = Kappa.^2;
return kappaSquared,src,T_exact;
end
function getAnalyticalConstGradInv3D(n::Array{Int64,1},h::Array{Float64,1})
src = [div(n[1],2),div(n[1],2),1];
source1 = (src[1]-1)*h[1];
source2 = (src[2]-1)*h[2];
source3 = (src[3]-1)*h[3];
(X1,X2,X3) = ndgrid((0:(n[1]-1))*h[1],(0:(n[2]-1))*h[2],(0:(n[3]-1))*h[3]);
a = 1.0;
ainv = (1.0./a);
s0 = 2.0;
Kappa = 1.0./(1.0./s0 .+ a*(X3.-source3));
Rsquared = (X1.-source1).^2 .+ (X2.-source2).^2 .+ (X3.-source3).^2;
T_exact = ainv*acosh.(1.0.+(0.5*s0*a*a).*Kappa.*(Rsquared));
G1_exact = ainv*(1.0./sqrt.((1.0.+(0.5*s0*a*a).*Kappa.*Rsquared).^2 .- 1.0)).*(0.5*s0*a*a).*Kappa.*2.0.*(X1.-source1);
G2_exact = ainv*(1.0./sqrt.((1.0.+(0.5*s0*a*a).*Kappa.*Rsquared).^2 .- 1.0)).*(0.5*s0*a*a).*Kappa.*2.0.*(X2.-source2);
G3_exact = ainv*(1.0./sqrt.((1.0.+(0.5*s0*a*a).*Kappa.*Rsquared).^2 .- 1.0)).*((0.5*s0*a*a).*Kappa.*2.0.*(X3.-source3)
.+ (0.5*s0*a*a).*(Rsquared).*(-(Kappa.^2)).*a);
kappaSquared = Kappa.^2;
return kappaSquared,src,T_exact;
end
function getSmoothGaussianMedium(n::Array{Int64,1},h::Array{Float64,1})
src = div.(n,4);
kappaSquared = [];
T_exact = [];
if length(n)==2
(T1_exact,G11_exact,G12_exact) = getSmoothFactoredModel(n,h);
(T0,G01,G02,L0) = getAnalytic2DeikonalSolutionAll(n,h,src);
G1_exact = T0.*G11_exact + G01.*T1_exact;
G2_exact = T0.*G12_exact + G02.*T1_exact;
kappaSquared = G1_exact.*G1_exact + G2_exact.*G2_exact;
# matshow(sqrt(kappaSquared)); colorbar();
# xlabel("y");
# ylabel("x");
# xticks(0:div(n[2],8):n[2],0:1:8);
# yticks(0:div(n[1],4):n[1],0:1:4);
#figure()
#CS = contour(kappaSquared,50);colorbar();title("kappaSquared");
#clabel(CS, inline=1, fontsize=10)
#figure()
#contour(G1_exact);colorbar();title("G1_exact");
T_exact = T0.*T1_exact;
#figure()
#contour(T_exact,100);colorbar();title("T exact");
else
(T1_exact,G11_exact,G12_exact,G13_exact) = getSmoothFactoredModel3D(n,h);
(T0,G01,G02,G03) = getAnalytic3DeikonalSolutionAll(n,h,src);
G1_exact = T0.*G11_exact + G01.*T1_exact;
G2_exact = T0.*G12_exact + G02.*T1_exact;
G3_exact = T0.*G13_exact + G03.*T1_exact;
kappaSquared = G1_exact.*G1_exact + G2_exact.*G2_exact + G3_exact.*G3_exact;
# Er = kappaSquared;
# for k=1:n[3]
# matshow(reshape(Er[:,:,k],n[1],n[2]));colorbar();title("kappaSQ");
# end
T_exact = T0.*T1_exact;
end
return kappaSquared,src,T_exact;
end
function getSmoothFactoredModel(n::Array{Int64,1},h::Array{Float64,1})
xsrc = zeros(2);
src_kappa = zeros(Int64,2);
src_kappa[1] = div(n[1],3);
src_kappa[2] = div(n[2],4);
xsrc[1] = (src_kappa[1]-1)*h[1];
xsrc[2] = (src_kappa[2]-1)*h[2];
X1,X2 = ndgrid((0:(n[1]-1))*h[1],(0:(n[2]-1))*h[2]);
sigma = 0.1;
T1_exact = (exp.( - (sigma*((X1 .- xsrc[1]).^2) + 4*sigma*((X2.-xsrc[2]).^2))) .+ 1)/2;
G11_exact = -2*sigma*(X1 .- xsrc[1]).*exp.( - (sigma*((X1 .- xsrc[1]).^2) + 4*sigma*((X2.-xsrc[2]).^2)))/2;
G12_exact = -8*sigma*(X2 .- xsrc[2]).*exp.( - (sigma*((X1 .- xsrc[1]).^2) + 4*sigma*((X2.-xsrc[2]).^2)))/2;
return T1_exact,G11_exact,G12_exact;
end
function getSmoothFactoredModel3D(n::Array{Int64,1},h::Array{Float64,1})
xsrc = zeros(3);
src_kappa = zeros(Int64,3);
src_kappa[1] = div(n[1],3);
src_kappa[2] = div(n[2],4);
src_kappa[3] = div(n[3],2);
xsrc[1] = (src_kappa[1]-1)*h[1];
xsrc[2] = (src_kappa[2]-1)*h[2];
xsrc[3] = (src_kappa[3]-1)*h[3];
X1,X2,X3 = ndgrid((0:(n[1]-1))*h[1],(0:(n[2]-1))*h[2],(0:(n[3]-1))*h[3]);
Sigma = [0.1,0.4,0.2];
EXPRSkewedSquared = exp.(-Sigma[1]*((X1 .- xsrc[1]).^2) .- Sigma[2]*((X2.-xsrc[2]).^2) .- Sigma[3]*((X3.-xsrc[3]).^2));
T1_exact = EXPRSkewedSquared/2.0 .+ 0.5;
G11_exact = -Sigma[1]*(X1 .- xsrc[1]).*EXPRSkewedSquared;
G12_exact = -Sigma[2]*(X2 .- xsrc[2]).*EXPRSkewedSquared;
G13_exact = -Sigma[3]*(X3 .- xsrc[3]).*EXPRSkewedSquared;
return T1_exact,G11_exact,G12_exact,G13_exact;
end
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] | 1.786009 | 3,388 |
# Implement experiments to measure the success of subgoal evolution as a function of:
# 1. Funcs
# 2. numinteriors
# 3. numinputs # 4. numoutputs
# 5. Length of goallist
# 5. max_steps
# Uses randomly generated goallist
# Keep track of number of "worse" and "same" updates of Chromosome since its last improvement
export run_mut_evolution, run_result, run_result_to_tuple
using DataFrames
using CSV
using Statistics
using Distributions
using Distributed
indiv_result_type = Main.CGP.indiv_result_type
#=
iterations = 4
numinputs = 2:2
numoutputs = 2:2
nodearity = 2
numinteriors = 18:18
numlevelsback = 6:6
ngoals = 4:4
goallistlength=8:8
levelsback=6:6
maxsteps = 400:400
run_mut_evolution( iterations, numinputs, numoutputs, numinteriors, goallistlength, maxsteps, levelsback, "testdata.csv" )
=#
#context = construct_contexts(numinputs)[numinputs]
#p = Parameters(numinputs, numoutputs, nodearity, numinteriors, numlevelsback)
#iterations = 20
#print_parameters(p)
function run_mut_evolution( numiterations::Int64, numinputs::IntRange, numoutputs::IntRange,
numinteriors::IntRange, goallistlength::IntRange, maxsteps::IntRange,
levelsback::IntRange, hamming_rng::IntRange, csvfile::String;
base::Float64=2.0, active_only::Bool=false, avgfit_rng::IntRange=false )
maxints_for_degen=20
max_numinteriors = collect(numinteriors)[end]
test_MyInt(max_numinteriors)
ftf_param = 0.95
nodearity = 2
run_result_list = indiv_result_type[]
df = DataFrame()
df.numinputs=Int64[]
df.numoutputs=Int64[]
df.numints=Int64[]
df.levelsback=Int64[]
df.ngoals=Int64[]
#df.hamming_sel=Bool[]
#df.robust_sel=Bool[]
#df.avgfitness=Bool[]
#df.fault_tol=Bool[]
#df.active_only=Bool[]
df.maxsteps=Int64[]
#df.gl_reps=Int64[]
#df.fit_limit=Float64[]
df.hdist=Float64[]
df.steps=Int64[]
df.same=Int64[]
df.worse=Int64[]
df.better=Int64[]
df.nactive=Int64[]
df.redundancy=Float64[]
df.complexity=Float64[]
df.degeneracy=Float64[]
df.sdegeneracy=Float64[]
println("size(df): ",size(df))
for num_inputs = numinputs
for num_outputs = numoutputs
fit_limit = Float64(num_outputs)
funcs = default_funcs(num_inputs)
for num_interiors = numinteriors
for num_goals = goallistlength
println("numinputs: ",num_inputs," numoutputs: ",num_outputs," numints: ",num_interiors," numgoals: ",num_goals)
for levsback = levelsback
for hamming_sel = hamming_rng
for avgfitness = avgfit_rng
for active_only = [false]
#println("hamming_sel: ",hamming_sel," active_only: ",active_only)
for max_steps = maxsteps
for _ = 1:numiterations
p = Parameters( num_inputs, num_outputs, nodearity, num_interiors, levsback )
rr = run_result( p, num_goals, hamming_sel, active_only, max_steps )
push!(run_result_list,rr)
#Base.push!( df, new_row )
end
end
end
end
end
end
end
end
end
end
new_run_result_list = pmap(r->run_mut_evolve!(r,maxints_for_degen=maxints_for_degen,base=base),run_result_list)
#new_run_result_list = map(r->run_mut_evolve!(r,maxints_for_degen=maxints_for_degen,base=base),run_result_list)
for r = new_run_result_list
new_row = run_result_to_tuple(r)
println("len row: ",length(new_row))
Base.push!( df, new_row )
end
println(default_funcs(2))
open( csvfile, "w" ) do f
println(f,"# funcs: ", Main.CGP.default_funcs(numinputs[end]))
println(f,"# nodearity: ",nodearity)
#println(f,"# active_only: ",active_only)
println(f,"# max_steps",maxsteps)
CSV.write( f, df, append=true, writeheader=true )
end
#println(df)
df
end
function run_mut_evolve!( rr::indiv_result_type; maxints_for_degen::Int64, gl_repetitions::Int64=1,
ftf_param::Float64=0.95, base::Float64=2.0 )
#println("run_mut_evolve! fault_tol: ",rr.fault_tol)
nodearity = 2 # built-in default
p = Parameters( numinputs=rr.numinputs, numoutputs=rr.numoutputs, numinteriors=rr.numints, numlevelsback=rr.levelsback )
#print_parameters( p )
gl = randgoallist(rr.ngoals,rr.numinputs,rr.numoutputs,repetitions=gl_repetitions)
#println("gl: ",gl)
funcs = default_funcs(rr.numinputs)
c = random_chromosome( p, funcs )
sav_c = deepcopy(c)
(new_c,rr.steps,rr.worse,rr.same,rr.better,output,matched_goals,matched_goals_list) =
mut_evolve(c,gl,funcs,rr.maxsteps,hamming_sel=rr.hamming_sel )
rr.hdist = hamming_distance( output_values(c), output_values(new_c), rr.numinputs )
rr.nactive = number_active( new_c )
rr.redundancy = redundancy( new_c, base=base )
rr.complexity = rr.numints <= maxints_for_degen ? complexity5( new_c, base=base ) : 0.0
rr.degeneracy = rr.numints <= maxints_for_degen ? degeneracy( new_c, base=base ) : 0.0
rr.sdegeneracy = rr.numints <= maxints_for_degen ? degeneracy( new_c, base=base, mutinf=mutinf2 ) : 0.0
rr
end
function run_result( p::Parameters, num_goals::Int64, hamming_sel::Bool, active_only::Bool, max_steps::Int64 )
indiv_result_type(
p.numinputs,
p.numoutputs,
p.numinteriors,
p.numlevelsback,
num_goals,
hamming_sel,
#avgfitness,
active_only,
max_steps,
#gl_reps,
#fault_tol,
#fit_limit,
0.0, # hdist
0, # steps
0, # same
0, # worse
0, # better
0, # nactive
0.0, # redundancy
0.0, # complexity
0.0, # degeneracy
0.0 # sdegeneracy
)
end
function run_result_to_tuple( rr::indiv_result_type )
(
rr.numinputs,
rr.numoutputs,
rr.numints,
rr.levelsback,
rr.ngoals,
#rr.hamming_sel,
#rr.avgfitness,
#rr.fault_tol,
#rr.active_only,
rr.maxsteps,
#rr.gl_reps,
#rr.fit_limit,
rr.hdist,
rr.steps,
rr.same,
rr.worse,
rr.better,
rr.nactive,
rr.redundancy,
rr.complexity,
rr.degeneracy,
rr.sdegeneracy
)
end
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796,
4277,
62,
12543,
6359,
7,
22510,
62,
15414,
82,
8,
220,
198,
220,
220,
220,
220,
220,
329,
997,
62,
3849,
12706,
796,
997,
3849,
12706,
198,
220,
220,
220,
220,
220,
220,
220,
329,
997,
62,
2188,
874,
796,
467,
439,
396,
13664,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
44872,
7203,
77,
7230,
1996,
82,
25,
33172,
22510,
62,
15414,
82,
553,
220,
997,
22915,
82,
25,
33172,
22510,
62,
22915,
82,
553,
220,
997,
29503,
25,
33172,
22510,
62,
3849,
12706,
553,
220,
997,
2188,
874,
25,
33172,
22510,
62,
2188,
874,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
443,
14259,
1891,
796,
2974,
1891,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
8891,
2229,
62,
741,
796,
8891,
2229,
62,
81,
782,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
42781,
69,
3659,
796,
42781,
11147,
62,
81,
782,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
4075,
62,
8807,
796,
685,
9562,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
35235,
7203,
2763,
2229,
62,
741,
25,
33172,
2763,
2229,
62,
741,
553,
220,
4075,
62,
8807,
25,
33172,
5275,
62,
8807,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
3509,
62,
20214,
796,
3509,
20214,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
4808,
796,
352,
25,
22510,
2676,
602,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
796,
40117,
7,
997,
62,
15414,
82,
11,
997,
62,
22915,
82,
11,
18666,
451,
414,
11,
997,
62,
3849,
12706,
11,
443,
14259,
1891,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
374,
81,
796,
1057,
62,
20274,
7,
279,
11,
997,
62,
2188,
874,
11,
8891,
2229,
62,
741,
11,
4075,
62,
8807,
11,
3509,
62,
20214,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
5143,
62,
20274,
62,
4868,
11,
21062,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
14881,
13,
14689,
0,
7,
47764,
11,
649,
62,
808,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
198,
220,
886,
198,
220,
649,
62,
5143,
62,
20274,
62,
4868,
796,
279,
8899,
7,
81,
3784,
5143,
62,
21973,
62,
1990,
6442,
0,
7,
81,
11,
9806,
29503,
62,
1640,
62,
13500,
268,
28,
9806,
29503,
62,
1640,
62,
13500,
268,
11,
8692,
28,
8692,
828,
5143,
62,
20274,
62,
4868,
8,
198,
220,
1303,
3605,
62,
5143,
62,
20274,
62,
4868,
796,
3975,
7,
81,
3784,
5143,
62,
21973,
62,
1990,
6442,
0,
7,
81,
11,
9806,
29503,
62,
1640,
62,
13500,
268,
28,
9806,
29503,
62,
1640,
62,
13500,
268,
11,
8692,
28,
8692,
828,
5143,
62,
20274,
62,
4868,
8,
198,
220,
329,
374,
796,
649,
62,
5143,
62,
20274,
62,
4868,
198,
220,
220,
220,
649,
62,
808,
796,
1057,
62,
20274,
62,
1462,
62,
83,
29291,
7,
81,
8,
198,
220,
220,
220,
44872,
7203,
11925,
5752,
25,
33172,
13664,
7,
3605,
62,
808,
4008,
198,
220,
220,
220,
7308,
13,
14689,
0,
7,
47764,
11,
649,
62,
808,
1267,
198,
220,
886,
198,
220,
44872,
7,
12286,
62,
12543,
6359,
7,
17,
4008,
198,
220,
1280,
7,
269,
21370,
7753,
11,
366,
86,
1,
1267,
466,
277,
198,
220,
220,
220,
44872,
7,
69,
553,
2,
1257,
6359,
25,
33172,
8774,
13,
34,
16960,
13,
12286,
62,
12543,
6359,
7,
77,
7230,
1996,
82,
58,
437,
60,
4008,
198,
220,
220,
220,
44872,
7,
69,
553,
2,
18666,
451,
414,
25,
33172,
77,
375,
451,
414,
8,
198,
220,
220,
220,
1303,
35235,
7,
69,
553,
2,
4075,
62,
8807,
25,
33172,
5275,
62,
8807,
8,
198,
220,
220,
220,
44872,
7,
69,
553,
2,
3509,
62,
20214,
1600,
9806,
20214,
8,
198,
220,
220,
220,
44189,
13,
13564,
7,
277,
11,
47764,
11,
24443,
28,
7942,
11,
3551,
25677,
28,
7942,
1267,
198,
220,
886,
198,
220,
1303,
35235,
7,
7568,
8,
198,
220,
47764,
198,
437,
198,
198,
8818,
1057,
62,
21973,
62,
1990,
6442,
0,
7,
374,
81,
3712,
521,
452,
62,
20274,
62,
4906,
26,
3509,
29503,
62,
1640,
62,
13500,
268,
3712,
5317,
2414,
11,
1278,
62,
260,
6449,
1756,
3712,
5317,
2414,
28,
16,
11,
220,
198,
220,
220,
220,
220,
220,
10117,
69,
62,
17143,
3712,
43879,
2414,
28,
15,
13,
3865,
11,
2779,
3712,
43879,
2414,
28,
17,
13,
15,
1267,
198,
220,
1303,
35235,
7203,
5143,
62,
21973,
62,
1990,
6442,
0,
8046,
62,
83,
349,
25,
33172,
21062,
13,
69,
1721,
62,
83,
349,
8,
198,
220,
18666,
451,
414,
796,
362,
220,
220,
1303,
3170,
12,
259,
4277,
198,
220,
279,
796,
40117,
7,
997,
15414,
82,
28,
21062,
13,
77,
7230,
1996,
82,
11,
997,
22915,
82,
28,
21062,
13,
22510,
22915,
82,
11,
997,
3849,
12706,
28,
21062,
13,
22510,
29503,
11,
997,
46170,
1891,
28,
21062,
13,
46170,
1891,
1267,
198,
220,
1303,
4798,
62,
17143,
7307,
7,
279,
1267,
198,
220,
1278,
796,
43720,
2188,
439,
396,
7,
21062,
13,
782,
78,
874,
11,
21062,
13,
77,
7230,
1996,
82,
11,
21062,
13,
22510,
22915,
82,
11,
260,
6449,
1756,
28,
4743,
62,
260,
6449,
1756,
8,
198,
220,
1303,
35235,
7203,
4743,
25,
33172,
4743,
8,
198,
220,
1257,
6359,
796,
4277,
62,
12543,
6359,
7,
21062,
13,
77,
7230,
1996,
82,
8,
220,
198,
220,
269,
796,
4738,
62,
28663,
418,
462,
7,
279,
11,
1257,
6359,
1267,
198,
220,
6799,
62,
66,
796,
2769,
30073,
7,
66,
8,
198,
220,
357,
3605,
62,
66,
11,
21062,
13,
20214,
11,
21062,
13,
86,
7615,
11,
21062,
13,
31642,
11,
21062,
13,
27903,
11,
22915,
11,
31409,
62,
2188,
874,
11,
31409,
62,
2188,
874,
62,
4868,
8,
796,
220,
198,
220,
220,
220,
220,
220,
4517,
62,
1990,
6442,
7,
66,
11,
4743,
11,
12543,
6359,
11,
21062,
13,
9806,
20214,
11,
2763,
2229,
62,
741,
28,
21062,
13,
2763,
2229,
62,
741,
1267,
198,
220,
374,
81,
13,
71,
17080,
796,
8891,
2229,
62,
30246,
7,
5072,
62,
27160,
7,
66,
828,
5072,
62,
27160,
7,
3605,
62,
66,
828,
374,
81,
13,
77,
7230,
1996,
82,
1267,
198,
220,
374,
81,
13,
77,
5275,
796,
1271,
62,
5275,
7,
649,
62,
66,
1267,
198,
220,
374,
81,
13,
445,
917,
3883,
796,
49052,
7,
649,
62,
66,
11,
2779,
28,
8692,
1267,
198,
220,
374,
81,
13,
41887,
414,
796,
374,
81,
13,
22510,
29503,
19841,
3509,
29503,
62,
1640,
62,
13500,
268,
5633,
13357,
20,
7,
649,
62,
66,
11,
2779,
28,
8692,
1267,
1058,
657,
13,
15,
198,
220,
374,
81,
13,
13500,
877,
1590,
796,
374,
81,
13,
22510,
29503,
19841,
3509,
29503,
62,
1640,
62,
13500,
268,
5633,
25419,
1590,
7,
649,
62,
66,
11,
2779,
28,
8692,
1267,
1058,
657,
13,
15,
198,
220,
374,
81,
13,
82,
13500,
877,
1590,
796,
374,
81,
13,
22510,
29503,
19841,
3509,
29503,
62,
1640,
62,
13500,
268,
5633,
25419,
1590,
7,
649,
62,
66,
11,
2779,
28,
8692,
11,
4517,
10745,
28,
21973,
10745,
17,
1267,
1058,
657,
13,
15,
198,
220,
374,
81,
198,
437,
198,
198,
8818,
1057,
62,
20274,
7,
279,
3712,
48944,
11,
997,
62,
2188,
874,
3712,
5317,
2414,
11,
8891,
2229,
62,
741,
3712,
33,
970,
11,
4075,
62,
8807,
3712,
33,
970,
11,
3509,
62,
20214,
3712,
5317,
2414,
1267,
198,
220,
773,
452,
62,
20274,
62,
4906,
7,
198,
220,
220,
220,
279,
13,
77,
7230,
1996,
82,
11,
198,
220,
220,
220,
279,
13,
22510,
22915,
82,
11,
198,
220,
220,
220,
279,
13,
22510,
3849,
12706,
11,
198,
220,
220,
220,
279,
13,
22510,
46170,
1891,
11,
198,
220,
220,
220,
997,
62,
2188,
874,
11,
198,
220,
220,
220,
8891,
2229,
62,
741,
11,
198,
220,
220,
220,
1303,
615,
70,
69,
3659,
11,
198,
220,
220,
220,
4075,
62,
8807,
11,
198,
220,
220,
220,
3509,
62,
20214,
11,
198,
220,
220,
220,
1303,
4743,
62,
260,
862,
11,
198,
220,
220,
220,
1303,
69,
1721,
62,
83,
349,
11,
198,
220,
220,
220,
1303,
11147,
62,
32374,
11,
198,
220,
220,
220,
657,
13,
15,
11,
220,
220,
220,
1303,
289,
17080,
198,
220,
220,
220,
657,
11,
220,
220,
220,
220,
220,
1303,
4831,
198,
220,
220,
220,
657,
11,
220,
220,
220,
220,
220,
1303,
976,
198,
220,
220,
220,
657,
11,
220,
220,
220,
220,
220,
1303,
4785,
198,
220,
220,
220,
657,
11,
220,
220,
220,
220,
220,
1303,
1365,
198,
220,
220,
220,
657,
11,
220,
220,
220,
220,
220,
1303,
299,
5275,
220,
198,
220,
220,
220,
657,
13,
15,
11,
220,
220,
220,
1303,
49052,
198,
220,
220,
220,
657,
13,
15,
11,
220,
220,
220,
1303,
13357,
198,
220,
220,
220,
657,
13,
15,
11,
220,
220,
220,
1303,
25419,
1590,
198,
220,
220,
220,
657,
13,
15,
220,
220,
220,
220,
1303,
264,
13500,
877,
1590,
198,
220,
1267,
198,
437,
198,
198,
8818,
1057,
62,
20274,
62,
1462,
62,
83,
29291,
7,
374,
81,
3712,
521,
452,
62,
20274,
62,
4906,
1267,
198,
357,
198,
220,
374,
81,
13,
77,
7230,
1996,
82,
11,
198,
220,
374,
81,
13,
22510,
22915,
82,
11,
198,
220,
374,
81,
13,
22510,
29503,
11,
198,
220,
374,
81,
13,
46170,
1891,
11,
198,
220,
374,
81,
13,
782,
78,
874,
11,
198,
220,
1303,
21062,
13,
2763,
2229,
62,
741,
11,
198,
220,
1303,
21062,
13,
615,
70,
69,
3659,
11,
198,
220,
1303,
21062,
13,
69,
1721,
62,
83,
349,
11,
198,
220,
1303,
21062,
13,
5275,
62,
8807,
11,
198,
220,
374,
81,
13,
9806,
20214,
11,
198,
220,
1303,
21062,
13,
4743,
62,
260,
862,
11,
198,
220,
1303,
21062,
13,
11147,
62,
32374,
11,
198,
220,
374,
81,
13,
71,
17080,
11,
198,
220,
374,
81,
13,
20214,
11,
198,
220,
374,
81,
13,
31642,
11,
198,
220,
374,
81,
13,
86,
7615,
11,
198,
220,
374,
81,
13,
27903,
11,
198,
220,
374,
81,
13,
77,
5275,
11,
198,
220,
374,
81,
13,
445,
917,
3883,
11,
198,
220,
374,
81,
13,
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] | 2.170662 | 2,795 |
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#
# Description
# ==============================================================================
#
# Functions to manage the SPI.
#
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
export init_spi, spi_transfer, spi_transfer!
################################################################################
# Initialization
################################################################################
"""
init_spi(devices; mode = 0, max_speed_hz = 4_000_000, bits_per_word = 8)
Initialize the SPI devices. `devices` can be a string with the path to `spidev`
or a vector of strings with a set of SPI devices that will be initialized.
# Keywords
* `mode`: Set the mode of the SPI. (**Default** = 0)
* `max_speed_hz`: Maximum allowed speed in SPI communication [Hz].
(**Default** = 4_000_000)
* `bits_per_word`: Number of bits per word in SPI communication.
(**Default** = 8)
Notice that all keywords can be a `Integer`, when the configuration will be
applied to all SPI devices, or a `Vector` of `Integers`, when different
configurations can be applied to the initialized devices.
"""
@inline init_spi(devices::String; kwargs...) = init_spi([devices]; kwargs...)
function init_spi(devices::AbstractVector{String};
mode::Union{Integer,Vector{Integer}} = 0,
max_speed_hz::Union{Integer,Vector{Integer}} = 4_000_000,
bits_per_word::Union{Integer,Vector{Integer}} = 8)
# Number of devices that the user wants to initialize.
num_init_devices = length(devices)
# If the arguments are not vectors, then transform them to vectors
# considering the size of `devices`.
if !(typeof(mode) <: AbstractVector)
mode = [mode for _ = 1:num_init_devices]
end
if !(typeof(max_speed_hz) <: AbstractVector)
max_speed_hz = [max_speed_hz for _ = 1:num_init_devices]
end
if !(typeof(bits_per_word) <: AbstractVector)
bits_per_word = [bits_per_word for _ = 1:num_init_devices]
end
# If we have already initialized, then close all the devices first.
objects.spi_init && close_spi()
# Initialize each device.
spidev = Vector{SPIDEV}(undef, 0)
@inbounds for i = 1:num_init_devices
@assert (mode[i] ≥ 0) error("Invalid SPI mode.")
@assert (max_speed_hz[i] > 0) error("Invalid maximum speed [Hz].")
@assert (bits_per_word[i] > 0) error("Invalid number of bits per word.")
try
# Open the device
# ==================================================================
io = open(devices[i], "w+")
# Configure SPI interface
# ==================================================================
_ioctl(io, SPI_IOC_WR_MODE, Ref(mode[i]))
_ioctl(io, SPI_IOC_RD_MODE, Ref(mode[i]))
_ioctl(io, SPI_IOC_WR_BITS_PER_WORD, Ref(bits_per_word[i]))
_ioctl(io, SPI_IOC_RD_BITS_PER_WORD, Ref(bits_per_word[i]))
_ioctl(io, SPI_IOC_WR_MAX_SPEED_HZ, Ref(max_speed_hz[i]))
_ioctl(io, SPI_IOC_RD_MAX_SPEED_HZ, Ref(max_speed_hz[i]))
# Pack values
# ==================================================================
spidev_i = SPIDEV(io, max_speed_hz[i], bits_per_word[i])
push!(spidev, spidev_i)
catch
error("Could not open `$(devices[i])`. Make sure you have the required permissions.")
end
end
# Get the size of SPI buffer.
buffer_size = parse(Int, read("/sys/module/spidev/parameters/bufsiz", String))
objects.spidev = spidev
objects.spi_buffer_size = buffer_size
objects.spi_init = true
return nothing
end
"""
close_spi()
Close all SPI connections.
"""
function close_spi()
if objects.spi_init
@inbounds for d in objects.spidev
close(d.io)
end
objects.spidev = Vector{SPIDEV}(undef, 0)
objects.spi_init = false
end
return nothing
end
################################################################################
# Transfer
################################################################################
"""
spi_transfer(devid, tx_buf; kwargs...)
Execute a full duplex transfer to SPI device `devid`. `devid` is the ID of the
SPI device considering the initialization order when the function `init_spi` was
called.
`tx_buf` can be a vector of `Integer`, in which only one message will be sent,
or a vector of vectors of `Integer`, in which multiple messages will be sent.
The result is returned in an object with the same type of `tx_buf` together with
the number of words received.
!!! note "Allocations"
This function will perform allocations because they create the vector that
will be returned. If this is not desired, then use the in-place version
`spi_transfer!`.
# Keywords
The same keywords of `spi_transfer!` can be used.
"""
function spi_transfer(devid::Integer, tx_buf::AbstractVector{T}; kwargs...) where
T<:Integer
# Allocate the vector that will hold the returned words.
rx_buf = zeros(T, length(tx_buf))
# Perform the transfer.
ret = spi_transfer!(devid, tx_buf, rx_buf; kwargs...)
return rx_buf, ret
end
function spi_transfer(devid::Integer, tx_buf::AbstractVector{T}; kwargs...) where
T<:Vector{U} where U<:Integer
# Allocate the vectors that will hold the returned words.
rx_buf = zeros.(U, length.(tx_buf))
# Perform the transfer.
ret = spi_transfer!(devid, tx_buf, rx_buf; kwargs...)
return rx_buf, ret
end
"""
spi_transfer!(devid, tx_buf, rx_buf; kwargs...)
Execute a full duplex transfer to SPI device `devid`. `devid` is the ID of the
SPI device considering the initialization order when the function `init_spi` was
called.
`tx_buf` can be a vector of `Integer`, in which only one message will be sent,
or a vector of vectors of `Integer`, in which multiple messages will be sent.
The received data will be stored in `rx_buf` that must have the same type of
`tx_buf` and enough size.
This function returns the number of bytes received.
!!! note "Allocations"
This function will not allocate only if the number of messages sent is lower
than the constant `BaremetalPi._SPI_BUFFER_SIZE`. Otherwise, it will perform
an allocation because it must allocate a vector of
`struct_spi_ioc_transfer`.
# Keywords
* `max_speed_hz`: If > 0, then override the default maximum transfer speed with
this value [Hz]. (**Default** = 0)
* `delay_usecs`: If ≥ 0, then override the default delay with this value.
(**Default** = -1)
* `bits_per_word`: If > 0, then override the number of bits per word with this
value. (**Default** = 0)
* `cs_change`: If `false`, the deselect the device at the end of the transfer.
"""
function spi_transfer!(devid::Integer,
tx_buf::AbstractVector{T},
rx_buf::AbstractVector{T};
max_speed_hz::Integer = 0,
delay_usecs::Integer = -1,
bits_per_word::Integer = 8,
cs_change::Bool = false) where T<:Vector{U} where U <:Integer
@assert objects.spi_init "SPI not initialized. Run init_spi()."
@assert (0 < devid ≤ length(objects.spidev)) "SPI device ID is out of bounds."
spidev = objects.spidev[devid]
# Number of messages to be transmitted.
num_msgs = length(tx_buf)
@assert (length(rx_buf) ≥ num_msgs) "The number of buffers in `rx_buf` must be equal or bigger than the number of buffers in `tx_buf`."
# Check default parameters.
max_speed_hz ≤ 0 && (max_speed_hz = spidev.max_speed_hz)
delay_usecs < 0 && (delay_usecs = 0)
bits_per_word ≤ 0 && (bits_per_word = spidev.bits_per_word)
# Check if we can use the allocated buffer to transfer the message.
if num_msgs > _SPI_BUFFER_SIZE
descs = Vector{struct_spi_ioc_transfer}(undef, num_msgs)
else
descs = spidev.bdescs
end
@inbounds for i = 1:num_msgs
msg_size = length(tx_buf[i])
@assert (msg_size*bits_per_word ≤ objects.spi_buffer_size*8) "The message to be transmitted is larger than the SPI buffer."
@assert (length(rx_buf[i]) ≥ msg_size) "The length of `rx_buf[i]` must be equal or bigger than that of `tx_buf[i]`."
# Create the structure that contains the information of the SPI transfer.
descs[i] = struct_spi_ioc_transfer(pointer(tx_buf[i]),
pointer(rx_buf[i]),
# In SPI, the number of transmitted
# and received words are always the
# same.
msg_size*sizeof(U),
max_speed_hz,
delay_usecs,
bits_per_word,
cs_change)
end
# Execute the transfer.
return _ioctl(fd(spidev.io), SPI_IOC_MESSAGE(num_msgs), descs)
end
function spi_transfer!(devid::Integer,
tx_buf::AbstractVector{T},
rx_buf::AbstractVector{T};
max_speed_hz::Integer = 0,
delay_usecs::Integer = -1,
bits_per_word::Integer = 8,
cs_change::Bool = false) where T<:Integer
@assert objects.spi_init "SPI not initialized. Run init_spi()."
@assert (0 < devid ≤ length(objects.spidev)) "SPI device ID is out of bounds."
spidev = objects.spidev[devid]
# Check default parameters.
max_speed_hz ≤ 0 && (max_speed_hz = spidev.max_speed_hz)
delay_usecs < 0 && (delay_usecs = 0)
bits_per_word ≤ 0 && (bits_per_word = spidev.bits_per_word)
msg_size = length(tx_buf)
@assert (msg_size*bits_per_word ≤ objects.spi_buffer_size*8) "The message to be transmitted is larger than the SPI buffer."
@assert (length(rx_buf) ≥ msg_size) "The length of `rx_buf` must be equal or bigger than that of `tx_buf`."
# Create the structure that contains the information of the SPI transfer.
desc = struct_spi_ioc_transfer(pointer(tx_buf),
pointer(rx_buf),
# In SPI, the number of transmitted and
# received words are always the same.
msg_size*sizeof(T),
max_speed_hz,
delay_usecs,
bits_per_word,
cs_change)
# Execute the transfer.
return _ioctl(fd(spidev.io), SPI_IOC_MESSAGE(1), Ref(desc))
end
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4808,
952,
34168,
7,
952,
11,
49091,
62,
40,
4503,
62,
18564,
62,
26094,
50,
62,
18973,
62,
54,
12532,
11,
6524,
7,
9895,
62,
525,
62,
4775,
58,
72,
60,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4808,
952,
34168,
7,
952,
11,
49091,
62,
40,
4503,
62,
35257,
62,
26094,
50,
62,
18973,
62,
54,
12532,
11,
6524,
7,
9895,
62,
525,
62,
4775,
58,
72,
60,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4808,
952,
34168,
7,
952,
11,
49091,
62,
40,
4503,
62,
18564,
62,
22921,
62,
4303,
41841,
62,
39,
57,
11,
6524,
7,
9806,
62,
12287,
62,
32179,
58,
72,
60,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4808,
952,
34168,
7,
952,
11,
49091,
62,
40,
4503,
62,
35257,
62,
22921,
62,
4303,
41841,
62,
39,
57,
11,
6524,
7,
9806,
62,
12287,
62,
32179,
58,
72,
60,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
6400,
3815,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
38093,
28,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
599,
485,
85,
62,
72,
796,
6226,
14114,
53,
7,
952,
11,
3509,
62,
12287,
62,
32179,
58,
72,
4357,
10340,
62,
525,
62,
4775,
58,
72,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
2777,
485,
85,
11,
599,
485,
85,
62,
72,
8,
198,
220,
220,
220,
220,
220,
220,
220,
4929,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4049,
7203,
23722,
407,
1280,
4600,
3,
7,
42034,
58,
72,
12962,
44646,
6889,
1654,
345,
423,
262,
2672,
21627,
19570,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
628,
220,
220,
220,
1303,
3497,
262,
2546,
286,
49091,
11876,
13,
198,
220,
220,
220,
11876,
62,
7857,
796,
21136,
7,
5317,
11,
1100,
7203,
14,
17597,
14,
21412,
14,
2777,
485,
85,
14,
17143,
7307,
14,
29325,
82,
528,
1600,
10903,
4008,
628,
220,
220,
220,
5563,
13,
2777,
485,
85,
220,
220,
220,
220,
220,
220,
220,
220,
220,
796,
599,
485,
85,
198,
220,
220,
220,
5563,
13,
2777,
72,
62,
22252,
62,
7857,
796,
11876,
62,
7857,
198,
220,
220,
220,
5563,
13,
2777,
72,
62,
15003,
220,
220,
220,
220,
220,
220,
220,
796,
2081,
628,
220,
220,
220,
1441,
2147,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
1969,
62,
2777,
72,
3419,
198,
198,
26125,
477,
49091,
8787,
13,
198,
198,
37811,
198,
8818,
1969,
62,
2777,
72,
3419,
198,
220,
220,
220,
611,
5563,
13,
2777,
72,
62,
15003,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
259,
65,
3733,
329,
288,
287,
5563,
13,
2777,
485,
85,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1969,
7,
67,
13,
952,
8,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
5563,
13,
2777,
485,
85,
796,
20650,
90,
4303,
14114,
53,
92,
7,
917,
891,
11,
657,
8,
198,
220,
220,
220,
220,
220,
220,
220,
5563,
13,
2777,
72,
62,
15003,
796,
3991,
198,
220,
220,
220,
886,
628,
220,
220,
220,
1441,
2147,
198,
437,
198,
198,
29113,
29113,
14468,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20558,
198,
29113,
29113,
14468,
198,
198,
37811,
198,
220,
220,
220,
599,
72,
62,
39437,
7,
7959,
312,
11,
27765,
62,
29325,
26,
479,
86,
22046,
23029,
198,
198,
23002,
1133,
257,
1336,
32597,
2588,
4351,
284,
49091,
3335,
4600,
7959,
312,
44646,
4600,
7959,
312,
63,
318,
262,
4522,
286,
262,
198,
4303,
40,
3335,
6402,
262,
37588,
1502,
618,
262,
2163,
4600,
15003,
62,
2777,
72,
63,
373,
198,
7174,
13,
198,
198,
63,
17602,
62,
29325,
63,
460,
307,
257,
15879,
286,
4600,
46541,
47671,
287,
543,
691,
530,
3275,
481,
307,
1908,
11,
198,
273,
257,
15879,
286,
30104,
286,
4600,
46541,
47671,
287,
543,
3294,
6218,
481,
307,
1908,
13,
198,
198,
464,
1255,
318,
4504,
287,
281,
2134,
351,
262,
976,
2099,
286,
4600,
17602,
62,
29325,
63,
1978,
351,
198,
1169,
1271,
286,
2456,
2722,
13,
198,
198,
10185,
3465,
366,
3237,
20968,
1,
628,
220,
220,
220,
770,
2163,
481,
1620,
49157,
780,
484,
2251,
262,
15879,
326,
198,
220,
220,
220,
481,
307,
4504,
13,
1002,
428,
318,
407,
10348,
11,
788,
779,
262,
287,
12,
5372,
2196,
198,
220,
220,
220,
4600,
2777,
72,
62,
39437,
0,
44646,
198,
198,
2,
7383,
10879,
198,
198,
464,
976,
26286,
286,
4600,
2777,
72,
62,
39437,
0,
63,
460,
307,
973,
13,
198,
198,
37811,
198,
8818,
599,
72,
62,
39437,
7,
7959,
312,
3712,
46541,
11,
27765,
62,
29325,
3712,
23839,
38469,
90,
51,
19629,
479,
86,
22046,
23029,
810,
198,
220,
220,
220,
309,
27,
25,
46541,
628,
220,
220,
220,
1303,
1439,
13369,
262,
15879,
326,
481,
1745,
262,
4504,
2456,
13,
198,
220,
220,
220,
374,
87,
62,
29325,
796,
1976,
27498,
7,
51,
11,
4129,
7,
17602,
62,
29325,
4008,
628,
220,
220,
220,
1303,
35006,
262,
4351,
13,
198,
220,
220,
220,
1005,
796,
599,
72,
62,
39437,
0,
7,
7959,
312,
11,
27765,
62,
29325,
11,
374,
87,
62,
29325,
26,
479,
86,
22046,
23029,
628,
220,
220,
220,
1441,
374,
87,
62,
29325,
11,
1005,
198,
437,
198,
198,
8818,
599,
72,
62,
39437,
7,
7959,
312,
3712,
46541,
11,
27765,
62,
29325,
3712,
23839,
38469,
90,
51,
19629,
479,
86,
22046,
23029,
810,
198,
220,
220,
220,
309,
27,
25,
38469,
90,
52,
92,
810,
471,
27,
25,
46541,
628,
220,
220,
220,
1303,
1439,
13369,
262,
30104,
326,
481,
1745,
262,
4504,
2456,
13,
198,
220,
220,
220,
374,
87,
62,
29325,
796,
1976,
27498,
12195,
52,
11,
4129,
12195,
17602,
62,
29325,
4008,
628,
220,
220,
220,
1303,
35006,
262,
4351,
13,
198,
220,
220,
220,
1005,
796,
599,
72,
62,
39437,
0,
7,
7959,
312,
11,
27765,
62,
29325,
11,
374,
87,
62,
29325,
26,
479,
86,
22046,
23029,
628,
220,
220,
220,
1441,
374,
87,
62,
29325,
11,
1005,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
599,
72,
62,
39437,
0,
7,
7959,
312,
11,
27765,
62,
29325,
11,
374,
87,
62,
29325,
26,
479,
86,
22046,
23029,
198,
198,
23002,
1133,
257,
1336,
32597,
2588,
4351,
284,
49091,
3335,
4600,
7959,
312,
44646,
4600,
7959,
312,
63,
318,
262,
4522,
286,
262,
198,
4303,
40,
3335,
6402,
262,
37588,
1502,
618,
262,
2163,
4600,
15003,
62,
2777,
72,
63,
373,
198,
7174,
13,
198,
198,
63,
17602,
62,
29325,
63,
460,
307,
257,
15879,
286,
4600,
46541,
47671,
287,
543,
691,
530,
3275,
481,
307,
1908,
11,
198,
273,
257,
15879,
286,
30104,
286,
4600,
46541,
47671,
287,
543,
3294,
6218,
481,
307,
1908,
13,
198,
198,
464,
2722,
1366,
481,
307,
8574,
287,
4600,
40914,
62,
29325,
63,
326,
1276,
423,
262,
976,
2099,
286,
198,
63,
17602,
62,
29325,
63,
290,
1576,
2546,
13,
198,
198,
1212,
2163,
5860,
262,
1271,
286,
9881,
2722,
13,
198,
198,
10185,
3465,
366,
3237,
20968,
1,
628,
220,
220,
220,
770,
2163,
481,
407,
31935,
691,
611,
262,
1271,
286,
6218,
1908,
318,
2793,
198,
220,
220,
220,
621,
262,
6937,
4600,
33,
533,
28469,
38729,
13557,
4303,
40,
62,
19499,
45746,
62,
33489,
44646,
15323,
11,
340,
481,
1620,
198,
220,
220,
220,
281,
20157,
780,
340,
1276,
31935,
257,
15879,
286,
198,
220,
220,
220,
4600,
7249,
62,
2777,
72,
62,
72,
420,
62,
39437,
44646,
198,
198,
2,
7383,
10879,
198,
198,
9,
4600,
9806,
62,
12287,
62,
32179,
63,
25,
1002,
1875,
657,
11,
788,
20957,
262,
4277,
5415,
4351,
2866,
351,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
428,
1988,
685,
7399,
4083,
357,
1174,
19463,
1174,
796,
657,
8,
198,
9,
4600,
40850,
62,
1904,
6359,
63,
25,
1002,
26870,
657,
11,
788,
20957,
262,
4277,
5711,
351,
428,
1988,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
1174,
19463,
1174,
796,
532,
16,
8,
198,
9,
4600,
9895,
62,
525,
62,
4775,
63,
25,
1002,
1875,
657,
11,
788,
20957,
262,
1271,
286,
10340,
583,
1573,
351,
428,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
13,
357,
1174,
19463,
1174,
796,
657,
8,
198,
9,
4600,
6359,
62,
3803,
63,
25,
1002,
4600,
9562,
47671,
262,
748,
9509,
262,
3335,
379,
262,
886,
286,
262,
4351,
13,
198,
198,
37811,
198,
8818,
599,
72,
62,
39437,
0,
7,
7959,
312,
3712,
46541,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
27765,
62,
29325,
3712,
23839,
38469,
90,
51,
5512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
374,
87,
62,
29325,
3712,
23839,
38469,
90,
51,
19629,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3509,
62,
12287,
62,
32179,
3712,
46541,
796,
657,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5711,
62,
1904,
6359,
3712,
46541,
796,
532,
16,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10340,
62,
525,
62,
4775,
3712,
46541,
796,
807,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
50115,
62,
3803,
3712,
33,
970,
796,
3991,
8,
810,
309,
27,
25,
38469,
90,
52,
92,
810,
471,
1279,
25,
46541,
628,
220,
220,
220,
2488,
30493,
5563,
13,
2777,
72,
62,
15003,
366,
4303,
40,
407,
23224,
13,
5660,
2315,
62,
2777,
72,
3419,
526,
198,
220,
220,
220,
2488,
30493,
357,
15,
1279,
1614,
312,
41305,
4129,
7,
48205,
13,
2777,
485,
85,
4008,
366,
4303,
40,
3335,
4522,
318,
503,
286,
22303,
526,
628,
220,
220,
220,
599,
485,
85,
796,
5563,
13,
2777,
485,
85,
58,
7959,
312,
60,
628,
220,
220,
220,
1303,
7913,
286,
6218,
284,
307,
18307,
13,
198,
220,
220,
220,
997,
62,
907,
14542,
796,
4129,
7,
17602,
62,
29325,
8,
628,
220,
220,
220,
2488,
30493,
357,
13664,
7,
40914,
62,
29325,
8,
26870,
997,
62,
907,
14542,
8,
366,
464,
1271,
286,
39334,
287,
4600,
40914,
62,
29325,
63,
1276,
307,
4961,
393,
5749,
621,
262,
1271,
286,
39334,
287,
4600,
17602,
62,
29325,
63,
526,
628,
220,
220,
220,
1303,
6822,
4277,
10007,
13,
198,
220,
220,
220,
3509,
62,
12287,
62,
32179,
220,
41305,
657,
11405,
357,
9806,
62,
12287,
62,
32179,
220,
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198,
437,
198
] | 2.334103 | 4,762 |
export rule
@rule Gamma(:out, Marginalisation) (m_α::PointMass, m_θ::PointMass) = Gamma(mean(m_α), mean(m_θ))
@rule Gamma(:out, Marginalisation) (q_α::PointMass, q_θ::PointMass) = Gamma(mean(q_α), mean(q_θ)) | [
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] | 2.271739 | 92 |
export richardson_lucy_iterative
"""
richardson_lucy_iterative(measured, psf; <keyword arguments>)
Classical iterative Richardson-Lucy iteration scheme for deconvolution.
`measured` is the measured array and `psf` the point spread function.
Converges slower than the optimization approach of `deconvolution`
# Keyword Arguments
- `regularizer=GR()`: A regularizer function. Can be exchanged
- `λ=0.05`: A float indicating the total weighting of the regularizer with
respect to the global loss function
- `iterations=100`: Specifies number of iterations.
# Example
```julia-repl
julia> using DeconvOptim, TestImages, Colors, Noise;
julia> img = Float32.(testimage("resolution_test_512"));
julia> psf = Float32.(generate_psf(size(img), 30));
julia> img_b = conv(img, psf);
julia> img_n = poisson(img_b, 300);
julia> @time res = richardson_lucy_iterative(img_n, psf);
```
"""
function richardson_lucy_iterative(measured, psf;
regularizer=GR(),
λ=0.05,
iterations=100,
conv_dims=1:ndims(psf))
otf, conv_temp = plan_conv(measured, psf, conv_dims)
otf_conj = conj.(otf)
# initializer
rec = abs.(conv_temp(measured, otf))#ones(eltype(measured), size(measured))
# buffer for gradient
buffer_grad = let
if !isnothing(regularizer)
gradient(regularizer, rec)[1]
else
nothing
end
end
∇reg(x) = buffer_grad .= gradient(regularizer, x)[1]
buffer = copy(measured)
iter_without_reg(rec) = begin
buffer .= measured ./ (conv_temp(rec, otf))
conv_temp(buffer, otf_conj)
end
iter_with_reg(rec) = buffer .= (iter_without_reg(rec) .- λ .* Base.invokelatest(∇reg, rec))
iter = isnothing(regularizer) ? iter_without_reg : iter_with_reg
for i in 1:iterations
rec .*= iter(rec)
end
return rec
end
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] | 2.274914 | 873 |
module InterfaceTests
using Contour, Test
function setup()
nx, ny = 10, 10
xs = sort!(rand(nx))
ys = sort!(rand(ny))
zs = rand(nx, ny)
xs, ys, zs
end
xs, ys, zs = setup()
cs = @inferred contours(xs, ys, zs)
for c in levels(cs)
for l in lines(c)
x, y = coordinates(l)
@assert typeof(x) == typeof(y) == Vector{Float64}
end
end
end # InterfaceTests
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] | 2.046392 | 194 |
include("euler/euler.jl")
using .Calculus: fibonacci_index, fibonacci_numbers
using BenchmarkTools
BenchmarkTools.DEFAULT_PARAMETERS.samples = 100
function compute(n::Int)::Int
index = fibonacci_index(n)
fibonacci = fibonacci_numbers(index + 1, Int)
last_sum, new_sum = 0, 0
result = sum(fibonacci[1:4]) - 1
for i ∈ 5:index
last_sum, new_sum = new_sum, new_sum + last_sum + fibonacci[i - 3]
result += new_sum + fibonacci[i]
end
return result
end
compute(13)
compute(23_416_728_348_467_685)
@benchmark compute(23_416_728_348_467_685) | [
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] | 2.29249 | 253 |
@testset "Constructors and basic properties" begin
let F = FullBinner()
@test typeof(F) <: AbstractVector{Float64}
@test eltype(F) == Float64
@test ndims(F) == 1
@test length(F) == 0
@test size(F) == (0,)
@test lastindex(F) == 0
@test axes(F) == (Base.OneTo(0),)
@test isempty(F)
end
let F = FullBinner(ComplexF64)
@test typeof(F) <: AbstractVector{ComplexF64}
@test eltype(F) == ComplexF64
end
let x = [0.84, 0.381169, 0.34174, 0.888868, 0.0973183, 0.722725, 0.0957878, 0.432443, 0.755033, 0.864252]
F = FullBinner(x)
@test length(F) == 10
@test lastindex(F) == 10
@test axes(F) == (Base.OneTo(10),)
@test !isempty(F)
end
let x = [rand(2,3) for _ in 1:100]
F = FullBinner(x)
@test length(F) == 100
@test eltype(F) == Array{Float64,2}
end
end
@testset "Scalars statistics" begin
# Real
let F = FullBinner(1:10_000)
@test isapprox(std_error(F), 361.4079699881821)
bs, stds, cum_stds = all_binning_errors(F)
@test bs == 1:312
@test isapprox(sum(stds), 106377.96306621947) # take sum as approx. hash
@test isapprox(sum(cum_stds), 75541.44622415205)
@test isapprox(tau(F), 77.86159630295694)
# beta: convergence
@test !BinningAnalysis.isconverged(F)
end
# Test 0/0 bug in R_value
@test std_error(FullBinner(fill(1.0, 100))) == 0.0
# Complex
let F = FullBinner((1:10_000) .+ ((10_000:-1:1) .* im))
@test isapprox(std_error(F), 511.10805270701564)
bs, stds, cum_stds = all_binning_errors(F)
@test bs == 1:312
@test isapprox(sum(stds), 150441.1581058718) # take sum as approx. hash
@test isapprox(sum(cum_stds), 106831.73777147368)
@test isapprox(tau(F), 77.86159630295694)
# beta: convergence
@test !BinningAnalysis.isconverged(F)
end
# R -> tau conversion
@test BinningAnalysis._tau(2.4) == 0.7
end
@testset "Arrays statistics" begin
# Real
Random.seed!(123)
let F = FullBinner([rand(2,3) for _ in 1:100])
@test length(F) == 100
@test isapprox(std_error(F), [0.029184472105069394 0.029581605926346424 0.027793717502753976; 0.029105387394205307 0.02741415651581391 0.029933054433434834])
@test isapprox(tau(F), [0.030183772076860294 0.027610002544459222 -0.047279410457739646; -0.003567404598109447 0.03282685243862249 0.06928989962602228])
end
# Complex
Random.seed!(123)
let F = FullBinner([rand(ComplexF64, 2,3) for _ in 1:100])
@test length(F) == 100
@test isapprox(std_error(F), [0.04445492633322362 0.04004496543964919 0.039737207226072296; 0.04099334255252945 0.039004215520294906 0.0409503504806149])
@test isapprox(tau(F), [0.062160208644724047 -0.005039096051545122 0.037487751473977315; 0.03850424600452085 -0.032173894672710424 0.0320190059417359])
end
end
@testset "Cosmetics (show, print, etc.)" begin
F = FullBinner();
# empty binner
oldstdout = stdout
(read_pipe, write_pipe) = redirect_stdout()
println(F) # compact
show(write_pipe, MIME"text/plain"(), F) # full
redirect_stdout(oldstdout);
close(write_pipe);
# compact
@test readline(read_pipe) == "FullBinner{Float64,Array{Float64,1}}()"
# full
@test readline(read_pipe) == "FullBinner{Float64,Array{Float64,1}}"
@test readline(read_pipe) == "| Count: 0"
@test length(readlines(read_pipe)) == 0
close(read_pipe);
# filled binner
Random.seed!(1234)
append!(F, rand(1000))
(read_pipe, write_pipe) = redirect_stdout()
show(write_pipe, MIME"text/plain"(), F)
redirect_stdout(oldstdout);
close(write_pipe);
@test readline(read_pipe) == "FullBinner{Float64,Array{Float64,1}}"
@test readline(read_pipe) == "| Count: 1000"
@test readline(read_pipe) == "| Mean: 0.49685"
@test length(readlines(read_pipe)) == 0
close(read_pipe);
end | [
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220,
220,
220,
220,
220,
220,
2488,
9288,
1288,
4906,
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8,
6624,
48436,
2414,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
299,
67,
12078,
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37,
8,
6624,
352,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
4129,
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37,
8,
6624,
657,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
2546,
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37,
8,
6624,
357,
15,
35751,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
938,
9630,
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37,
8,
6624,
657,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
34197,
7,
37,
8,
6624,
357,
14881,
13,
3198,
2514,
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15,
828,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
318,
28920,
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37,
8,
198,
220,
220,
220,
886,
628,
220,
220,
220,
1309,
376,
796,
6462,
33,
5083,
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5377,
11141,
37,
2414,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
2099,
1659,
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37,
8,
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38469,
90,
5377,
11141,
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2414,
92,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
1288,
4906,
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37,
8,
6624,
19157,
37,
2414,
198,
220,
220,
220,
886,
628,
220,
220,
220,
1309,
2124,
796,
685,
15,
13,
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11,
657,
13,
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11,
657,
13,
2682,
22985,
11,
657,
13,
3459,
3459,
3104,
11,
657,
13,
2931,
4790,
24839,
11,
657,
13,
22,
24403,
1495,
11,
657,
13,
2931,
38907,
3695,
11,
657,
13,
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1731,
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11,
657,
13,
2425,
1120,
2091,
11,
657,
13,
39570,
22800,
60,
198,
220,
220,
220,
220,
220,
220,
220,
376,
796,
6462,
33,
5083,
7,
87,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
4129,
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37,
8,
6624,
838,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
938,
9630,
7,
37,
8,
6624,
838,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
34197,
7,
37,
8,
6624,
357,
14881,
13,
3198,
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7,
940,
828,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
5145,
271,
28920,
7,
37,
8,
198,
220,
220,
220,
886,
628,
198,
220,
220,
220,
1309,
2124,
796,
685,
25192,
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17,
11,
18,
8,
329,
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287,
352,
25,
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60,
198,
220,
220,
220,
220,
220,
220,
220,
376,
796,
6462,
33,
5083,
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87,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
4129,
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37,
8,
6624,
1802,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
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4906,
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37,
8,
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15690,
90,
43879,
2414,
11,
17,
92,
198,
220,
220,
220,
886,
198,
437,
628,
198,
198,
31,
9288,
2617,
366,
3351,
282,
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1,
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198,
220,
220,
220,
1303,
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198,
220,
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1309,
376,
796,
6462,
33,
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16,
25,
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62,
830,
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198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
318,
1324,
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62,
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37,
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13,
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220,
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11,
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11,
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301,
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477,
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768,
62,
48277,
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37,
8,
198,
220,
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220,
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275,
82,
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25,
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198,
220,
220,
220,
220,
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2488,
9288,
318,
1324,
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7,
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7,
301,
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828,
15696,
26514,
13,
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355,
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13,
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198,
220,
220,
220,
220,
220,
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9288,
318,
1324,
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301,
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828,
767,
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13,
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198,
220,
220,
220,
220,
220,
220,
220,
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9288,
318,
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7,
83,
559,
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37,
828,
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13,
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4846,
1270,
25710,
45214,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
12159,
25,
40826,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
5145,
33,
23062,
32750,
13,
271,
1102,
332,
2004,
7,
37,
8,
198,
220,
220,
220,
886,
628,
220,
220,
220,
1303,
6208,
657,
14,
15,
5434,
287,
371,
62,
8367,
198,
220,
220,
220,
2488,
9288,
14367,
62,
18224,
7,
13295,
33,
5083,
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16,
13,
15,
11,
1802,
22305,
6624,
657,
13,
15,
628,
220,
220,
220,
1303,
19157,
198,
220,
220,
220,
1309,
376,
796,
6462,
33,
5083,
19510,
16,
25,
940,
62,
830,
8,
764,
10,
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940,
62,
830,
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16,
25,
16,
8,
764,
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545,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
318,
1324,
13907,
7,
19282,
62,
18224,
7,
37,
828,
642,
1157,
13,
24045,
20,
1983,
2998,
25150,
2414,
8,
198,
220,
220,
220,
220,
220,
220,
220,
275,
82,
11,
336,
9310,
11,
10973,
62,
301,
9310,
796,
477,
62,
8800,
768,
62,
48277,
7,
37,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
275,
82,
6624,
352,
25,
27970,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
318,
1324,
13907,
7,
16345,
7,
301,
9310,
828,
6640,
39710,
13,
21273,
940,
44617,
1507,
8,
1303,
1011,
2160,
355,
5561,
13,
12234,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
318,
1324,
13907,
7,
16345,
7,
36340,
62,
301,
9310,
828,
838,
3104,
3132,
13,
22,
2718,
3324,
20198,
27412,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
318,
1324,
13907,
7,
83,
559,
7,
37,
828,
8541,
13,
4521,
1314,
4846,
1270,
25710,
45214,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
12159,
25,
40826,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
5145,
33,
23062,
32750,
13,
271,
1102,
332,
2004,
7,
37,
8,
198,
220,
220,
220,
886,
628,
220,
220,
220,
1303,
371,
4613,
256,
559,
11315,
198,
220,
220,
220,
2488,
9288,
20828,
768,
32750,
13557,
83,
559,
7,
17,
13,
19,
8,
6624,
657,
13,
22,
198,
437,
628,
628,
198,
31,
9288,
2617,
366,
3163,
20477,
7869,
1,
2221,
198,
220,
220,
220,
1303,
6416,
198,
220,
220,
220,
14534,
13,
28826,
0,
7,
10163,
8,
198,
220,
220,
220,
1309,
376,
796,
6462,
33,
5083,
26933,
25192,
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17,
11,
18,
8,
329,
4808,
287,
352,
25,
3064,
12962,
198,
220,
220,
220,
220,
220,
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220,
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9288,
4129,
7,
37,
8,
6624,
1802,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
318,
1324,
13907,
7,
19282,
62,
18224,
7,
37,
828,
685,
15,
13,
48891,
1507,
2598,
4761,
940,
1120,
3388,
34626,
657,
13,
48891,
3365,
1433,
46712,
2075,
2682,
2414,
1731,
657,
13,
44698,
3720,
2718,
1558,
1120,
23195,
2670,
4304,
26,
657,
13,
48891,
13348,
32220,
34626,
21261,
22996,
657,
13,
15,
28857,
1415,
1314,
2996,
21273,
1485,
6420,
657,
13,
48891,
6052,
22515,
2598,
31380,
28978,
2682,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
318,
1324,
13907,
7,
83,
559,
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37,
828,
685,
15,
13,
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26514,
22745,
3104,
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27696,
657,
13,
44698,
5333,
830,
1495,
2598,
33459,
23148,
532,
15,
13,
48000,
1983,
5824,
940,
2231,
3324,
2670,
27720,
26,
532,
15,
13,
405,
2327,
3134,
1821,
2231,
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34825,
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13,
3070,
2078,
2075,
5332,
1731,
2548,
5237,
21626,
657,
13,
3312,
24,
2078,
4089,
2079,
5237,
1899,
1828,
2078,
12962,
198,
220,
220,
220,
886,
628,
220,
220,
220,
1303,
19157,
198,
220,
220,
220,
14534,
13,
28826,
0,
7,
10163,
8,
198,
220,
220,
220,
1309,
376,
796,
6462,
33,
5083,
26933,
25192,
7,
5377,
11141,
37,
2414,
11,
362,
11,
18,
8,
329,
4808,
287,
352,
25,
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198,
220,
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37,
8,
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198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
318,
1324,
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7,
19282,
62,
18224,
7,
37,
828,
685,
15,
13,
43977,
2231,
2920,
2075,
20370,
1828,
35667,
657,
13,
3023,
405,
31911,
39111,
2670,
33300,
1129,
657,
13,
15,
33372,
2718,
22745,
1828,
31980,
1828,
4846,
26,
657,
13,
36676,
2079,
31380,
13381,
1495,
1959,
2231,
657,
13,
15,
2670,
405,
3682,
18742,
1238,
1959,
2920,
3312,
657,
13,
15,
29416,
1120,
14877,
22148,
21,
19442,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
318,
1324,
13907,
7,
83,
559,
7,
37,
828,
685,
15,
13,
3312,
17,
14198,
21315,
2414,
2857,
16102,
2857,
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15,
13,
405,
1120,
2670,
2931,
32417,
1314,
2231,
18376,
657,
13,
15,
2718,
2780,
34483,
20198,
2670,
3324,
27936,
26,
657,
13,
15,
2548,
33580,
1731,
8054,
2231,
1238,
5332,
532,
15,
13,
49959,
1558,
2548,
5824,
3134,
1983,
13464,
1731,
657,
13,
3070,
1264,
12865,
3270,
38547,
30743,
12962,
198,
220,
220,
220,
886,
198,
437,
628,
628,
198,
198,
31,
9288,
2617,
366,
36734,
27757,
357,
12860,
11,
3601,
11,
3503,
2014,
1,
2221,
198,
220,
220,
220,
376,
796,
6462,
33,
5083,
9783,
198,
220,
220,
220,
1303,
6565,
275,
5083,
198,
220,
220,
220,
1468,
19282,
448,
796,
14367,
448,
198,
220,
220,
220,
357,
961,
62,
34360,
11,
3551,
62,
34360,
8,
796,
18941,
62,
19282,
448,
3419,
198,
220,
220,
220,
44872,
7,
37,
8,
1303,
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198,
220,
220,
220,
905,
7,
13564,
62,
34360,
11,
337,
12789,
1,
5239,
14,
25638,
18109,
828,
376,
8,
1303,
1336,
198,
220,
220,
220,
18941,
62,
19282,
448,
7,
727,
19282,
448,
1776,
198,
220,
220,
220,
1969,
7,
13564,
62,
34360,
1776,
628,
220,
220,
220,
1303,
16001,
198,
220,
220,
220,
2488,
9288,
1100,
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366,
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90,
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11,
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11,
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220,
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1100,
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366,
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11,
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90,
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11,
16,
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1,
198,
220,
220,
220,
2488,
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1100,
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7,
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62,
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366,
91,
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25,
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1,
198,
220,
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7,
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198,
220,
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1969,
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220,
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220,
1303,
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275,
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198,
220,
220,
220,
14534,
13,
28826,
0,
7,
1065,
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8,
198,
220,
220,
220,
24443,
0,
7,
37,
11,
43720,
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4008,
198,
220,
220,
220,
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961,
62,
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11,
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18941,
62,
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220,
220,
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11,
337,
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198,
220,
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19282,
448,
7,
727,
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448,
1776,
198,
220,
220,
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1969,
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13564,
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1776,
198,
220,
220,
220,
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1100,
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7,
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366,
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33,
5083,
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2414,
11,
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90,
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11,
16,
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1,
198,
220,
220,
220,
2488,
9288,
1100,
1370,
7,
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62,
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6624,
366,
91,
2764,
25,
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1,
198,
220,
220,
220,
2488,
9288,
1100,
1370,
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62,
34360,
8,
6624,
366,
91,
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] | 2.074949 | 1,948 |
# Unit examples of the FourierAnalysis Package for julia language
# v 0.0.1 - last update 24th of September 2019
#
# MIT License
# Copyright (c) 2019, Marco Congedo, CNRS, Grenobe, France:
# https://sites.google.com/site/marcocongedo/home
# ? CONTENTS :
# This example shows how to compute cross-spectra
# and how to extract inforation from them.
# ~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~ #
# #
# ~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~~¤~ #
using FourierAnalysis, FFTW, LinearAlgebra, Statistics, Plots, Plots.Measures
# add module for reading the two EEG text files to be used ater
push!(LOAD_PATH, @__DIR__)
using IOtxt
# Get EEG file names with complete path (they have extension .txt)
S=getFilesInDir(@__DIR__; ext=(".txt",))
# read the two EEG data files and put them in a Matrix object
X1=readEEG(S[1])
X2=readEEG(S[2])
# Cross-Spectra of EEG data
##########################################
t, sr, slide, tapering = 512, 128, 64, harris4
# gather some attributes to obtain nice spectra plots
spectraArgs=(left_margin = 2mm,
bottom_margin = 2mm,
xtickfont = font(10, "Times"),
ytickfont = font(10, "Times"))
# spectra
S=spectra(X1, sr, t; tapering=tapering, func=√)
plot(S; ytitle="Amplitude (\\muV)", spectraArgs...)
# smoothed spectra
S2=spectra(X1, sr, t; tapering=tapering, smoothing=blackmanSmoother, func=√)
plot(S2; ytitle="Amplitude (\\muV)", spectraArgs...)
#cross-spectra
𝙎=crossSpectra(X1, sr, t; tapering=tapering, tril=true)
# smooth a-posteriori the cross-spectra
𝙎2=smooth(blackmanSmoother, 𝙎)
# mean cross-spectra in 8Hz-12Hz range
alpha=mean(𝙎, (8, 12))
# extract all cross-spectra in 8Hz-12Hz range
E=extract(𝙎, (8, 12))
# mean smoothed cross-spectra in 8Hz-12Hz range
alpha=mean(𝙎2, (8, 12))
# extract smoothed cross-spectra in 8Hz-12Hz range
E=extract(𝙎2, (8, 12))
# get amplitude spectra from cross-spectra and compare with S (the long way)
S__=sqrt.(Real.([𝙎.y[i][j, j] for i=1:length(𝙎.y), j=1:size(X1, 2)]))
# the short way using FourierAnalysis
S_=Spectra(𝙎; func=√)
# check they are the same
norm(S.y-S_.y)
# cross-spectra of several data matrix at once
t, sr, slide, tapering = 1024, 128, 512, harris4
𝐗=[X1, X2]
# Compute the cross-spectra altogether
𝓢=crossSpectra(𝐗, sr, t; tapering=slepians(sr, t))
# mean cross-spectrum in 8Hz-12Hz range for each cross-spectra in 𝓢
alpha=mean(𝓢, (8, 12))
# mean of the above cross-spectra in one pass
alpha=mean(mean(𝓢, (8, 12)))
# the above computation without checking for homogeneity of elements in 𝓢 (faster)
alpha=mean(mean(𝓢, (8, 12), check=false))
# extract cross-spectra in 8Hz-12Hz range for all cross-spectra in 𝓢
E=extract(𝓢, (8, 12))
# average cross-spectra in 8Hz-12Hz range for 𝓢[1] only
Y=mean(𝓢[1], (8, 12))
# This (8 is the frequency in Hz)
Y=mean(𝓢[1], 8)
# is equivalent to this (64 is the bin where discrete Fourier Frequency 8Hz is)
𝓢[1].y[64]
# and equivalent to this
Y=extract(𝓢[1], 8)
# and equivalent to this
Y=extract(𝓢[1], (8, 8))
# Compute the lower-trianguler part of the cross-spectra
𝓢=crossSpectra(𝐗, sr, t; tapering=slepians(sr, t), tril=true)
# average cross-spectra in 8Hz-12Hz range for all 𝕃Vector in 𝓢
Y=mean(𝓢, (8, 12))
# extract cross-spectra in 8Hz-12Hz range for all 𝕃Vector in 𝓢
E=extract(𝓢, (8, 12))
# cross-spectra 𝓢[1] averaged in 1Hz band-pass regions
Z=bands(𝓢[1], 1)
# all cross-spectra in 𝓢 averaged in 1Hz band-pass regions
Z=bands(𝓢, 1)
# get amplitude spectra from cross-spectra (the long way)
S_=sqrt.(Real.([𝓢[1].y[i][j, j] for i=1:length(𝓢[1].y), j=1:size(X1, 2)]))*1.5687746
plot(S_)
# the short way using FourierAnalysis
S_sl=Spectra(𝓢[1]; func=x->sqrt(x)*1.5687746)
norm(S_sl.y-S_) # must be zero
# Compute non-linear cross-spectra
𝓢=crossSpectra(𝐗, sr, t; tapering=slepians(sr, t), tril=true, nonlinear=true)
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# Much of this code is lifted from LineSearches.jl
# I modified it to accept StaticArrays and not allocate
# Some of the optimization code is adapted from Optim.jl
@with_kw struct BackTracking{TF, TI}
c_1::TF = 1e-4
ρ_hi::TF = 0.5
ρ_lo::TF = 0.1
iterations::TI = 1_000
maxstep::TF = Inf
end
abstract type BackTrackingOrder end
struct Order2 <: BackTrackingOrder end
struct Order3 <: BackTrackingOrder end
struct Order0 <: BackTrackingOrder end
ordernum(::Order2) = 2
ordernum(::Order3) = 3
struct StaticOptimizationResults{Tx, Th, Tf}
initial_x::Tx
minimizer::Tx
minimum::Tf
iterations::Int
g_converged::Bool
g_tol::Tf
f_calls::Int
g_calls::Int
g::Tx
h::Th
end
struct StaticBFGS end
struct StaticNewton end
setresult(x::AbstractVector) = DiffResults.HessianResult(x)
setresult(x::Number) = DiffResults.DiffResult(x, x, x)
initialh(x::StaticVector{P,T}) where {P,T} = SMatrix{P,P,T}(I)
initialh(x::AbstractVector) = [i == j ? 1. : 0. for i in 1:size(x, 1), j in 1:size(x, 1)]
initialh(x::Number) = one(x)
setgradient!(res, f, x::AbstractVector) = ForwardDiff.gradient!(res, f, x)
setgradient!(res, f, x::Number) = ForwardDiff.derivative!(res, f, x)
sethessian!(res, f, x::AbstractVector) = ForwardDiff.hessian!(res, f, x)
function sethessian!(res, f, x::Number)
dx = ForwardDiff.Dual(ForwardDiff.Dual(x, one(typeof(x))), one(typeof(x)))
out = f(dx)
val = out.value.value
grad = out.partials.values[1].value
hess = out.partials.values[1].partials[1]
DiffResults.DiffResult(val, grad, hess)
end
function getgradient(res::DiffResults.ImmutableDiffResult)
DiffResults.gradient(res)
end
function getgradient(res::DiffResults.MutableDiffResult)
copy(DiffResults.gradient(res))
end
function getgradient(res::DiffResults.ImmutableDiffResult{1,T,Tuple{T}}) where T <: Number
DiffResults.derivative(res)
end
function getgradient(res::DiffResults.ImmutableDiffResult{1,T,Tuple{T, T}}) where T <: Number
DiffResults.derivative(res)
end
function gethessian(res::DiffResults.MutableDiffResult)
copy(DiffResults.hessian(res))
end
function gethessian(res::DiffResults.ImmutableDiffResult)
DiffResults.hessian(res)
end
function soptimize(f, x::Union{StaticVector{P,T}, TN, AbstractVector}; bto::BackTrackingOrder = Order2(), tol = 1e-8,
updating = true, maxiter = 200) where {P,T, TN <: Number}
res = setresult(x)
ls = BackTracking()
order = ordernum(bto)
xinit = copy(x)
x_new = copy(x)
hx = gethessian(res)
hold = initialh(x)::typeof(hx)
jold = copy(x); s = copy(x)
jx = jold
@unpack c_1, ρ_hi, ρ_lo, iterations = ls
iterfinitemax = -log2(eps(eltype(x)))
sqrttol = sqrt(eps(Float64))
α_0 = 1.
f_calls = 0
g_calls = 0
for n = 1:maxiter
## Compute
res = sethessian!(res, f, x); f_calls +=1; g_calls +=1; # Obtain gradient
ϕ_0 = DiffResults.value(res)
## Check convergence
isfinite(ϕ_0) || return StaticOptimizationResults(xinit, NaN*x,
NaN, n, false, tol, f_calls, g_calls, jx, hx)
jx = getgradient(res)
norm(jx, Inf) < tol && return StaticOptimizationResults(xinit, x,
ϕ_0, n, true, tol, f_calls, g_calls, jx, hx)
hx = gethessian(res)
## Compute search directions
s = -hx\jx
dϕ_0 = dot(jx, s)
if dϕ_0 >= 0. # If bad, reset search direction
hx = hold
s = -jx
dϕ_0 = dot(jx, s)
end
## Perform line search
# Count the total number of iterations
iteration = 0
ϕx_0, ϕx_1 = ϕ_0, ϕ_0
α_1, α_2 = α_0, α_0
ϕx_1 = f(x + α_1*s); f_calls +=1;
# Hard-coded backtrack until we find a finite function value
iterfinite = 0
while !isfinite(ϕx_1) && iterfinite < iterfinitemax
iterfinite += 1
α_1 = α_2
α_2 = α_1/2
ϕx_1 = f(x + α_2*s); f_calls += 1;
end
# Backtrack until we satisfy sufficient decrease condition
while ϕx_1 > ϕ_0 + c_1 * α_2 * dϕ_0
# If this part is reached we did not accept the initial linesearch
# guess, so we will need to update on the next iterations
# Increment the number of steps we've had to perform
iteration += 1
# Ensure termination
if iteration > iterations
error("Linesearch failed to converge, reached maximum iterations $(iterations).",
α_2)
end
# Shrink proposed step-size:
if order == 2 || iteration == 1
# backtracking via quadratic interpolation:
# This interpolates the available data
# f(0), f'(0), f(α)
# with a quadractic which is then minimised; this comes with a
# guaranteed backtracking factor 0.5 * (1-c_1)^{-1} which is < 1
# provided that c_1 < 1/2; the backtrack_condition at the beginning
# of the function guarantees at least a backtracking factor ρ.
α_tmp = - (dϕ_0 * α_2^2) / ( 2 * (ϕx_1 - ϕ_0 - dϕ_0*α_2) )
else
div = 1. / (α_1^2 * α_2^2 * (α_2 - α_1))
a = (α_1^2*(ϕx_1 - ϕ_0 - dϕ_0*α_2) - α_2^2*(ϕx_0 - ϕ_0 - dϕ_0*α_1))*div
b = (-α_1^3*(ϕx_1 - ϕ_0 - dϕ_0*α_2) + α_2^3*(ϕx_0 - ϕ_0 - dϕ_0*α_1))*div
if isapprox(a, zero(a), atol = eps(Float64))
α_tmp = dϕ_0 / (2*b)
else
# discriminant
d = max(b^2 - 3*a*dϕ_0, 0.)
# quadratic equation root
α_tmp = (-b + sqrt(d)) / (3*a)
end
end
α_1 = α_2
α_tmp = NaNMath.min(α_tmp, α_2*ρ_hi) # avoid too small reductions
α_2 = NaNMath.max(α_tmp, α_2*ρ_lo) # avoid too big reductions
# Evaluate f(x) at proposed position
ϕx_0, ϕx_1 = ϕx_1, f(x + α_2*s); f_calls += 1;
end
alpha, fpropose = α_2, ϕx_1
s = alpha*s
x = x + s # Update x
jold = copy(jx)
end
return StaticOptimizationResults(xinit, NaN*x,
NaN, maxiter, false, tol, f_calls, g_calls, jx, hx)
end
function Base.show(io::IO, r::StaticOptimizationResults)
@printf io "Results of Static Optimization Algorithm\n"
@printf io " * Initial guess: [%s]\n" join(r.initial_x, ",")
@printf io " * Minimizer: [%s]\n" join(r.minimizer, ",")
@printf io " * Minimum: [%s]\n" join(r.minimum, ",")
@printf io " * ∇f(x): [%s]\n" join(r.g, ",")
@printf io " * Hf(x) is size [%s]\n" join(size(r.h), ",")
@printf io " * Number of iterations: [%s]\n" join(r.iterations, ",")
@printf io " * Number of function calls: [%s]\n" join(r.f_calls, ",")
@printf io " * Number of gradient calls: [%s]\n" join(r.g_calls, ",")
@printf io " * Converged: [%s]\n" join(r.g_converged, ",")
return
end
### Modified Newton algorithm for root-finding
# I made this up, I do not know if it has nice convergence properties
# It first computes the value and derivative and performs a Newton step
# Then it computes the value and the potentially unnecessary derivative at the Newton point
# If this an improvement (closer to 0), the Newton step is accepted and it performs another Newton step
# If this is worse, it backtracks if the candidate is not finite
# Then it performs a Halley step using the function value at the candidate point
# If this is an improvement, the Halley step is accepted
# If it's still not an improvement, simple backtracking is done until it is
function snewton(f, x::Number; maxiter = 200, tol = 1e-8)
res = DiffResults.DiffResult(x, (x,))
iterfinitemax = -log2(eps(eltype(x)))
α_0 = 1.
res = ForwardDiff.derivative!(res, f, x) # Obtain gradient
ϕ_0 = DiffResults.value(res)
abs(ϕ_0) < tol && return (x = x, fx = ϕ_0, isroot = true, iter = 0)
isfinite(ϕ_0) || return (x = NaN*x, fx = NaN, isroot = false, iter = 0)
needsupdate = false
for n = 1:maxiter
if needsupdate
res = ForwardDiff.derivative!(res, f, x) # Obtain gradient
needsupdate = false
end
ϕ_0 = DiffResults.value(res)
abs(ϕ_0) < tol && return (x = x, fx = ϕ_0, isroot = true, iter = n)
isfinite(ϕ_0) || return (x = NaN*x, fx = NaN, isroot = false, iter = n)
jx = DiffResults.derivative(res)
x2 = x - ϕ_0/jx
# Count the total number of iterations
iteration = 0
ϕx_0, ϕx_1 = ϕ_0, ϕ_0
α_1, α_2 = α_0, α_0
res = ForwardDiff.derivative!(res, f, x2) # Obtain gradient
ϕx_1 = DiffResults.value(res)
abs(ϕx_1) < tol && return (x = x2, fx = ϕx_1, isroot = true, iter = n)
# Hard-coded backtrack until we find a finite function value
iterfinite = 0
x2old = x2
while !isfinite(ϕx_1) && iterfinite < iterfinitemax
needsupdate = true
iterfinite += 1
α_1 = α_2
α_2 = α_1/2
x2 = (1 - α_2)*x + α_2*x2old # convex combination
ϕx_1 = f(x2)
end
α_h = 1.
# Backtrack until we satisfy sufficient decrease condition
if abs(ϕx_1) > abs(ϕx_0) # Closer to 0?
needsupdate = true
# Increment the number of steps we've had to perform
# Interpolate available data using quadratic
hx = 2(ϕx_1 - ϕx_0 - jx*(x2 - x) ) / (x2 - x)^2 # Taylor approximation
x2 = x - 2ϕx_0*jx / (2jx^2 - ϕx_0*hx) # Halley step
ϕx_1 = f(x2)
iteration = 0
# If still not closer to 0, do simple backtracking until we are
while abs(ϕx_1) > abs(ϕx_0)
iteration += 1
α_1 = α_h
α_2 = α_1/2
x2 = (1 - α_2)*x + α_2*x2 # convex combination
ϕx_1 = f(x2)
iteration > 30 && error("Failed to converge")
end
end
x = x2
end
return (x = NaN*x, fx = NaN, isroot = false, iter = maxiter)
end
### Copied from Tamas Papp on Discourse
"""
bisection(f, a, b; fa = f(a), fb = f(b), ftol, wtol)
Bisection algorithm for finding the root ``f(x) ≈ 0`` within the initial bracket
`[a,b]`.
Returns a named tuple
`(x = x, fx = f(x), isroot = ::Bool, iter = ::Int, ismaxiter = ::Bool)`.
Terminates when either
1. `abs(f(x)) < ftol` (`isroot = true`),
2. the width of the bracket is `≤wtol` (`isroot = false`),
3. `maxiter` number of iterations is reached. (`isroot = false, maxiter = true`).
which are tested for in the above order. Therefore, care should be taken not to make `wtol` too large.
"""
function bisection(f, a::Real, b::Real;
ftol = √eps(), wtol = 0., maxiter = 100)
a, b = float(a), float(b)
fa, fb = f(a), f(b)
fa * fb ≤ 0 || error("Not a bracket")
(isfinite(a) && isfinite(b)) || error("Not finite")
_bisection(f, a, b, fa, fb, ftol, wtol, maxiter)
end
function _bisection(f, a, b, fa, fb, ftol, wtol, maxiter)
iter = 0
abs(fa) < ftol && return (x = a, fx = fa, isroot = true, iter = iter, ismaxiter = false)
abs(fb) < ftol && return (x = b, fx = fb, isroot = true, iter = iter, ismaxiter = false)
while true
iter += 1
m = middle(a, b)
fm = f(m)
abs(fm) < ftol && return (x = m, fx = fm, isroot = true, iter = iter, ismaxiter = false)
abs(b-a) ≤ wtol && return (x = m, fx = fm, isroot = false, iter = iter, ismaxiter = false)
if fa * fm > 0
a, fa = m, fm
else
b, fb = m, fm
end
iter == maxiter && return (x = m, fx = fm, isroot = false, iter = iter, ismaxiter = true)
end
end
sroot(f, x::Number) = snewton(f, x)
sroot(f, x::Tuple{T, T}) where T <: Number = @inbounds bisection(f, x[1], x[2])
function sroot(f, x::AbstractVector;
updating = true, tol = 1e-8, maxiter = 200)
f2(s) = sum(x -> x^2, f(s))
soptimize(f2, x, updating = updating, tol = tol, maxiter = maxiter )
end
#=
Algorithm:
Keep the actual hessian around
Restrict it to the active variables
Only then do the inversion (linear system)
Inverting the restricted hessian will be identical to just solving a lower dimensional problem
To keep dimensions constant, zero out the inactive part of the hessian, but fill the
diagonal with 1. This will be block diagonal and the inverse of the active region
will be the inverse of the hessian of the lower dimensional problem. The inverse
of the inactive region will just be 1 on the diagonal.
=#
function constrained_soptimize(f, x::Union{StaticVector{P,T}, TN, AbstractVector};
bto::BackTrackingOrder = Order2(), tol = 1e-7, lower = -Inf*x,
upper = Inf*x, maxiter = 200) where {P,T, TN <: Number}
res = setresult(x)
ls = BackTracking()
order = ordernum(bto)
xinit = copy(x)
x_new = copy(x)
hx = gethessian(res)
hold = initialh(x)::typeof(hx)
jold = copy(x); s = copy(x)
xold = copy(x)
jx = copy(jold)
@unpack c_1, ρ_hi, ρ_lo, iterations = ls
iterfinitemax = -log2(eps(eltype(x)))
sqrttol = sqrt(eps(Float64))
α_0 = 1.
f_calls = 0
g_calls = 0
clamp.(x, lower, upper) == x || error("Initial guess not in the feasible region")
for n = 1:maxiter
## Compute the gradient if needed
res = sethessian!(res, f, x); f_calls +=1; g_calls +=1; # Obtain hessian
ϕ_0 = DiffResults.value(res)
## Check convergence
isfinite(ϕ_0) || return StaticOptimizationResults(xinit, NaN*x,
NaN, n, false, tol, f_calls, g_calls, jx, hx)
jx = getgradient(res)
# Binding set 1
# true if free
s1l = .!( ((x .<= lower) .& (jx .>= 0)) .| ((x .>= upper) .& (jx .<= 0)) )
hx = gethessian(res)
function cdiag(x, c, i)
if c
return x
else
if i == 1.
return 1.
else
return 0.
end
end
end
isbinding(i, j) = i & j
# Binding set 2
binding = isbinding.(s1l, s1l')
#sizex = size(x)[1]
Ix = initialh(x) # An identity matrix of correct type
Hbar = cdiag.(hx, binding, Ix)
Sbargrad = (Hbar\jx)
s2l = .!( ((x .<= lower) .& (Sbargrad .> 0)) .| ((x .>= upper) .& (Sbargrad .< 0)) )
sl = s1l .& s2l
binding = isbinding.(sl, sl')
Hhat = cdiag.(hx, binding, Ix)
# set jxc to 0 if var in binding set
jxc = jx .* sl
norm(jxc, Inf) < tol && return StaticOptimizationResults(xinit, x,
ϕ_0, n, true, tol, f_calls, g_calls, jx, hx)
s = (-Hhat\jx) .* sl
dϕ_0 = dot(jx, s)
if dϕ_0 >= 0. # If bad, reset search direction
hx = hold
binding = isbinding.(s1l, s1l')
Shat = hx .* binding
s = -Shat*jx
dϕ_0 = dot(jx, s)
end
## Perform line search
# Count the total number of iterations
iteration = 0
ϕx_0, ϕx_1 = ϕ_0, ϕ_0
α_1, α_2 = α_0, α_0
ϕx_1 = f(clamp.(x + α_1*s, lower, upper)); f_calls +=1;
# Hard-coded backtrack until we find a finite function value
iterfinite = 0
while !isfinite(ϕx_1) && iterfinite < iterfinitemax
iterfinite += 1
α_1 = α_2
α_2 = α_1/2
ϕx_1 = f(clamp.(x + α_2*s, lower, upper)); f_calls += 1;
end
# Backtrack until we satisfy sufficient decrease condition
while ϕx_1 > ϕ_0 + c_1 * α_2 * dϕ_0
# If this part is reached we did not accept the initial linesearch
# guess, so we will need to update on the next iterations
# Increment the number of steps we've had to perform
iteration += 1
# Ensure termination
if iteration > iterations
error("Linesearch failed to converge, reached maximum iterations $(iterations).",
α_2)
end
# Shrink proposed step-size:
if order == 2 || iteration == 1
# backtracking via quadratic interpolation:
# This interpolates the available data
# f(0), f'(0), f(α)
# with a quadractic which is then minimised; this comes with a
# guaranteed backtracking factor 0.5 * (1-c_1)^{-1} which is < 1
# provided that c_1 < 1/2; the backtrack_condition at the beginning
# of the function guarantees at least a backtracking factor ρ.
α_tmp = - (dϕ_0 * α_2^2) / ( 2 * (ϕx_1 - ϕ_0 - dϕ_0*α_2) )
else
div = 1. / (α_1^2 * α_2^2 * (α_2 - α_1))
a = (α_1^2*(ϕx_1 - ϕ_0 - dϕ_0*α_2) - α_2^2*(ϕx_0 - ϕ_0 - dϕ_0*α_1))*div
b = (-α_1^3*(ϕx_1 - ϕ_0 - dϕ_0*α_2) + α_2^3*(ϕx_0 - ϕ_0 - dϕ_0*α_1))*div
#if norm(a) <= eps(Float64) + sqrttol*norm(a)
if isapprox(a, zero(a))
α_tmp = dϕ_0 / (2*b)
else
# discriminant
d = max(b^2 - 3*a*dϕ_0, 0.)
# quadratic equation root
α_tmp = (-b + sqrt(d)) / (3*a)
end
end
α_1 = α_2
α_tmp = NaNMath.min(α_tmp, α_2*ρ_hi) # avoid too small reductions
α_2 = NaNMath.max(α_tmp, α_2*ρ_lo) # avoid too big reductions
# Evaluate f(x) at proposed position
ϕx_0, ϕx_1 = ϕx_1, f(clamp.(x + α_2*s, lower, upper)); f_calls += 1;
end
alpha, fpropose = α_2, ϕx_1
s = alpha*s
xnew = clamp.(x + s, lower, upper) # Update x
s = xnew - x
xold = x
x = xnew
jold = copy(jx)
end
return StaticOptimizationResults(xinit, x,
NaN, maxiter, false, tol, f_calls, g_calls, jx, hx)
end
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] | 2.007435 | 9,012 |
using LazySequences
# Cons
c = cons(1, [42])
# Test first(s::Cons)
@assert first(c) == 1
# Test rest(s::Cons)
@assert first(rest(c)) == 42
ct = cat([1], [42])
# Test first(s::Cat)
@assert first(ct) == 1
# Test rest(s::Cat)
# Test getindex implementation
fibs = cat([0, 1], @lazyseq map(+, rest(fibs), fibs))
@assert fibs[1] == 0
@assert fibs[2] == 1
@assert fibs[3] == 1
@assert fibs[4] == 2
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] | 2.329412 | 170 |
# 定義の仕方がpython (:) と違って => を つかう
fruits = Dict("apple"=> 1, "banana"=> 2, "orange"=>3)
println(fruits)
# アクセスはキー
println(fruits["apple"])
fruits["mango"] = 4
println(fruits)
# 削除
pop!(fruits,"banana")
println(fruits)
# 削除part2
delete!(fruits,"apple")
println(fruits)
# 順序という概念がないから数値インデックスでアクセスはできない
# println(fruits[1])
# 辞書型の操作
println(haskey(fruits, "apple"))
# get(dict, key, default)
# ないときはdefault
println(get(fruits,"apple",42))
println(get(fruits,"mango",42))
# キーたちを取得したい時
println(keys(fruits))
# valueたちを取得したいとき
println(values(fruits))
# 複数の辞書もマージできるよ
a = Dict("apple"=>1,"banana"=>2)
b = Dict("banana"=>1,"peach"=>2)
# 後から来た方は上書きされる
println(merge(a,b))
println(merge(b,a))
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] | 1.63615 | 426 |
abstract type AbstractStorageFormulation <: AbstractDeviceFormulation end
struct BookKeeping <: AbstractStorageFormulation end
struct BookKeepingwReservation <: AbstractStorageFormulation end
#################################################Storage Variables#################################
function AddVariableSpec(
::Type{T},
::Type{U},
::PSIContainer,
) where {T <: ActivePowerInVariable, U <: PSY.Storage}
return AddVariableSpec(;
variable_name = make_variable_name(T, U),
binary = false,
expression_name = :nodal_balance_active,
sign = -1.0,
lb_value_func = x -> 0.0,
)
end
function AddVariableSpec(
::Type{T},
::Type{U},
::PSIContainer,
) where {T <: ActivePowerOutVariable, U <: PSY.Storage}
return AddVariableSpec(;
variable_name = make_variable_name(T, U),
binary = false,
expression_name = :nodal_balance_active,
lb_value_func = x -> 0.0,
)
end
function AddVariableSpec(
::Type{T},
::Type{U},
::PSIContainer,
) where {T <: ReactivePowerVariable, U <: PSY.Storage}
return AddVariableSpec(;
variable_name = make_variable_name(T, U),
binary = false,
expression_name = :nodal_balance_reactive,
)
end
function AddVariableSpec(
::Type{T},
::Type{U},
::PSIContainer,
) where {T <: EnergyVariable, U <: PSY.Storage}
return AddVariableSpec(;
variable_name = make_variable_name(T, U),
binary = false,
lb_value_func = x -> 0.0,
)
end
function AddVariableSpec(
::Type{T},
::Type{U},
::PSIContainer,
) where {T <: ReserveVariable, U <: PSY.Storage}
return AddVariableSpec(; variable_name = make_variable_name(T, U), binary = true)
end
################################## output power constraints#################################
function DeviceRangeConstraintSpec(
::Type{<:RangeConstraint},
::Type{ActivePowerOutVariable},
::Type{T},
::Type{<:BookKeeping},
::Type{<:PM.AbstractPowerModel},
feedforward::Union{Nothing, AbstractAffectFeedForward},
use_parameters::Bool,
use_forecasts::Bool,
) where {T <: PSY.Storage}
return DeviceRangeConstraintSpec(;
range_constraint_spec = RangeConstraintSpec(;
constraint_name = make_constraint_name(
RangeConstraint,
ActivePowerOutVariable,
T,
),
variable_name = make_variable_name(ActivePowerOutVariable, T),
limits_func = x -> PSY.get_output_active_power_limits(x),
constraint_func = device_range!,
constraint_struct = DeviceRangeConstraintInfo,
),
)
end
function DeviceRangeConstraintSpec(
::Type{<:RangeConstraint},
::Type{ActivePowerInVariable},
::Type{T},
::Type{<:BookKeeping},
::Type{<:PM.AbstractPowerModel},
feedforward::Union{Nothing, AbstractAffectFeedForward},
use_parameters::Bool,
use_forecasts::Bool,
) where {T <: PSY.Storage}
return DeviceRangeConstraintSpec(;
range_constraint_spec = RangeConstraintSpec(;
constraint_name = make_constraint_name(
RangeConstraint,
ActivePowerInVariable,
T,
),
variable_name = make_variable_name(ActivePowerInVariable, T),
limits_func = x -> PSY.get_input_active_power_limits(x),
constraint_func = device_range!,
constraint_struct = DeviceRangeConstraintInfo,
),
)
end
function DeviceRangeConstraintSpec(
::Type{<:RangeConstraint},
::Type{ActivePowerOutVariable},
::Type{T},
::Type{<:BookKeepingwReservation},
::Type{<:PM.AbstractPowerModel},
feedforward::Union{Nothing, AbstractAffectFeedForward},
use_parameters::Bool,
use_forecasts::Bool,
) where {T <: PSY.Storage}
return DeviceRangeConstraintSpec(;
range_constraint_spec = RangeConstraintSpec(;
constraint_name = make_constraint_name(
RangeConstraint,
ActivePowerOutVariable,
T,
),
variable_name = make_variable_name(ActivePowerOutVariable, T),
bin_variable_names = [make_variable_name(ReserveVariable, T)],
limits_func = x -> PSY.get_output_active_power_limits(x),
constraint_func = reserve_device_semicontinuousrange!,
constraint_struct = DeviceRangeConstraintInfo,
),
)
end
function DeviceRangeConstraintSpec(
::Type{<:RangeConstraint},
::Type{ActivePowerInVariable},
::Type{T},
::Type{<:BookKeepingwReservation},
::Type{<:PM.AbstractPowerModel},
feedforward::Union{Nothing, AbstractAffectFeedForward},
use_parameters::Bool,
use_forecasts::Bool,
) where {T <: PSY.Storage}
return DeviceRangeConstraintSpec(;
range_constraint_spec = RangeConstraintSpec(;
constraint_name = make_constraint_name(
RangeConstraint,
ActivePowerInVariable,
T,
),
variable_name = make_variable_name(ActivePowerInVariable, T),
bin_variable_names = [make_variable_name(ReserveVariable, T)],
limits_func = x -> PSY.get_input_active_power_limits(x),
constraint_func = reserve_device_semicontinuousrange!,
constraint_struct = DeviceRangeConstraintInfo,
),
)
end
"""
This function adds the reactive power limits of generators when there are CommitmentVariables
"""
function add_constraints!(
psi_container::PSIContainer,
::Type{<:RangeConstraint},
::Type{ReactivePowerVariable},
devices::IS.FlattenIteratorWrapper{St},
model::DeviceModel{St, D},
::Type{S},
feedforward::Union{Nothing, AbstractAffectFeedForward},
) where {St <: PSY.Storage, D <: AbstractStorageFormulation, S <: PM.AbstractPowerModel}
constraint_infos = Vector{DeviceRangeConstraintInfo}(undef, length(devices))
for (ix, d) in enumerate(devices)
name = PSY.get_name(d)
limits = PSY.get_reactive_power_limits(d)
constraint_infos[ix] = DeviceRangeConstraintInfo(name, limits)
end
device_range!(
psi_container,
RangeConstraintSpecInternal(
constraint_infos,
make_constraint_name(RangeConstraint, ReactivePowerVariable, St),
make_variable_name(ReactivePowerVariable, St),
),
)
return
end
########################## Make initial Conditions for a Model #############################
function initial_conditions!(
psi_container::PSIContainer,
devices::IS.FlattenIteratorWrapper{St},
::Type{D},
) where {St <: PSY.Storage, D <: AbstractStorageFormulation}
storage_energy_init(psi_container, devices)
return
end
############################ Energy Capacity Constraints####################################
function energy_capacity_constraints!(
psi_container::PSIContainer,
devices::IS.FlattenIteratorWrapper{St},
model::DeviceModel{St, D},
::Type{S},
feedforward::Union{Nothing, AbstractAffectFeedForward},
) where {St <: PSY.Storage, D <: AbstractStorageFormulation, S <: PM.AbstractPowerModel}
constraint_infos = Vector{DeviceRangeConstraintInfo}(undef, length(devices))
for (ix, d) in enumerate(devices)
name = PSY.get_name(d)
limits = PSY.get_state_of_charge_limits(d)
constraint_info = DeviceRangeConstraintInfo(name, limits)
add_device_services!(constraint_info, d, model)
constraint_infos[ix] = constraint_info
end
device_range!(
psi_container,
RangeConstraintSpecInternal(
constraint_infos,
make_constraint_name(ENERGY_CAPACITY, St),
make_variable_name(ENERGY, St),
),
)
return
end
############################ book keeping constraints ######################################
function make_efficiency_data(
devices::IS.FlattenIteratorWrapper{St},
) where {St <: PSY.Storage}
names = Vector{String}(undef, length(devices))
in_out = Vector{InOut}(undef, length(devices))
for (ix, d) in enumerate(devices)
names[ix] = PSY.get_name(d)
in_out[ix] = PSY.get_efficiency(d)
end
return names, in_out
end
function energy_balance_constraint!(
psi_container::PSIContainer,
devices::IS.FlattenIteratorWrapper{St},
::Type{D},
::Type{S},
feedforward::Union{Nothing, AbstractAffectFeedForward},
) where {St <: PSY.Storage, D <: AbstractStorageFormulation, S <: PM.AbstractPowerModel}
efficiency_data = make_efficiency_data(devices)
energy_balance(
psi_container,
get_initial_conditions(psi_container, ICKey(EnergyLevel, St)),
efficiency_data,
make_constraint_name(ENERGY_LIMIT, St),
(
make_variable_name(ACTIVE_POWER_IN, St),
make_variable_name(ACTIVE_POWER_OUT, St),
make_variable_name(ENERGY, St),
),
)
return
end
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] | 2.432856 | 3,701 |
# This file is a part of Julia. License is MIT: https://julialang.org/license
# BEGIN 0.7 deprecations
# PR #22062
function set_remote_url(repo::LibGit2.GitRepo, url::AbstractString; remote::AbstractString="origin")
Base.depwarn(string(
"`LibGit2.set_remote_url(repo, url; remote=remote)` is deprecated, use ",
"`LibGit2.set_remote_url(repo, remote, url)` instead."), :set_remote_url)
set_remote_url(repo, remote, url)
end
function set_remote_url(path::AbstractString, url::AbstractString; remote::AbstractString="origin")
Base.depwarn(string(
"`LibGit2.set_remote_url(path, url; remote=remote)` is deprecated, use ",
"`LibGit2.set_remote_url(path, remote, url)` instead."), :set_remote_url)
set_remote_url(path, remote, url)
end
function prompt(msg::AbstractString; default::AbstractString="", password::Bool=false)
Base.depwarn(string(
"`LibGit2.prompt(msg::AbstractString; default::AbstractString=\"\", password::Bool=false)` is deprecated, use ",
"`result = Base.prompt(msg, default=default, password=password); result === nothing ? \"\" : result` instead."), :prompt)
coalesce(Base.prompt(msg, default=default, password=password), "")
end
# PR #26437
# when this deprecation is deleted, remove all calls to it, and remove the keyword of:
# `payload` from "src/LibGit2.jl"
function deprecate_payload_keyword(f, sig, payload)
if payload !== nothing
Base.depwarn(string(
"`LibGit2.$f($sig; payload=cred)` is deprecated, use ",
"`LibGit2.$f($sig; credentials=cred)` instead."), f)
end
end
@deprecate get_creds!(cache::CachedCredentials, credid, default) get!(cache, credid, default)
@eval Base @deprecate merge!(repo::$(GitRepo), args...; kwargs...) $(LibGit2.merge!)(repo, args...; kwargs...)
@eval Base @deprecate push!(w::$(GitRevWalker), arg) $(LibGit2.push!)(w, arg)
@eval Base @deprecate count(diff::$(GitDiff)) $(LibGit2.count)(diff)
@eval Base @deprecate count(idx::$(GitIndex)) $(LibGit2.count)(idx)
@eval Base @deprecate count(rb::$(GitRebase)) $(LibGit2.count)(rb)
@eval Base @deprecate count(tree::$(GitTree)) $(LibGit2.count)(tree)
@eval Base @deprecate count(f::Function, walker::$(GitRevWalker); kwargs...) $(LibGit2.count)(f, walker; kwargs...)
@eval Base @deprecate map(f::Function, walker::$(GitRevWalker); kwargs...) $(LibGit2.map)(f, walker; kwargs...)
# PR #24594
@deprecate AbstractCredentials AbstractCredential false
@deprecate UserPasswordCredentials UserPasswordCredential false
@deprecate SSHCredentials SSHCredential false
@deprecate hex(id::LibGit2.GitHash) string(id)
@deprecate hex(id::LibGit2.GitShortHash) string(id)
| [
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] | 2.561722 | 1,045 |
using DrWatson
@quickactivate :TimeProbeSeismic
close("all")
ee = (0, .8*25, .206*25, 0)
n, d, m, m0 = h5read(datadir("models", "overthrust_model.h5"), "n", "d", "m", "m0")
m0[:, 20:end] = imfilter(m0[:, 20:end] ,Kernel.gaussian(5));
n = Tuple(n)
d = Tuple(d)
vp_t = m'.^(-.5);
vp_0 = m0'.^(-.5);
inds = Dict(j=>i for (i,j)=enumerate([2^k for k=1:8]))
plt_dict = (cmap=:gist_ncar, vmin=1.5, vmax=6.0, extent=ee, aspect=:auto)
files = readdir(datadir("fwi_overthrust"))
dfts = [f for f=files if occursin("dft", f)]
probed = [f for f=files if occursin("ps", f)]
trinds = [200, 480, 680]
# True and initial model
figure(figsize=(12, 9))
subplot(2,1,1)
imshow(vp_t; plt_dict...)
# title("True")
xlabel("X (km)")
ylabel("Depth (km)")
vlines(x=[t*d[1]/1000 for t=trinds], colors=:k, ymin=0, ymax=ee[3])
subplot(2,1,2)
imshow(vp_0; plt_dict...)
# title("Initial")
xlabel("X (km)")
ylabel("Depth (km)")
tight_layout()
wsave(plotsdir("fwi_overthrust", "Init.png"), gcf())
# Load true
res = wload(datadir("fwi_overthrust", "fwi_std.bson"))
vp_std = reshape(res[:x], n)'.^(-.5)
ϕ_std = res[:ϕ_trace]
# DFT
ϕ_dft = Array{Any}(undef, 8)
vp_dft = Array{Any}(undef, 8)
figure(figsize=(12, 9))
subplot(3,3,1)
imshow(vp_std; plt_dict...)
title("FWI")
xlabel("X (km)")
ylabel("Depth (km)")
for f in dfts
res = wload(datadir("fwi_overthrust", f))
num = parse(Int, split(split(f, "_")[2], ".")[1][4:end])
vp_dft[inds[num]] = reshape(res[:x], n)'.^(-.5)
subplot(3, 3, inds[num]+1)
imshow(vp_dft[inds[num]]; plt_dict...)
title("OTDFT $(num)")
xlabel("X (km)")
ylabel("Depth (km)")
ϕ_dft[inds[num]] = res[:ϕ_trace]
end
tight_layout()
wsave(plotsdir("fwi_overthrust", "DFT_fwi.png"), gcf())
# probed
ϕ_ps = Array{Any}(undef, 8)
vp_ps = Array{Any}(undef, 8)
figure(figsize=(12, 9))
subplot(3,3,1)
imshow(vp_std; plt_dict...)
title("Standard")
xlabel("X (km)")
ylabel("Depth (km)")
for f in probed
res = wload(datadir("fwi_overthrust", f))
num = parse(Int, split(split(f, "_")[2], ".")[1][3:end])
vp_ps[inds[num]] = reshape(res[:x], n)'.^(-.5)
subplot(3, 3, inds[num]+1)
imshow(vp_ps[inds[num]]; plt_dict...)
title("PFWI $(num)")
xlabel("X (km)")
ylabel("Depth (km)")
ϕ_ps[inds[num]] = res[:ϕ_trace]
end
tight_layout()
wsave(plotsdir("fwi_overthrust", "probed_fwi.png"), gcf())
# Traces
depth = range(0, ee[3], length=n[2])
for t in trinds
fig, axs = subplots(nrows=2, ncols=4, figsize=(9, 5), sharex=true, sharey=true)
fig.subplots_adjust(wspace=0, hspace=0)
for i=1:8
axs[i].plot(vp_t[:, t], depth, label="True")
axs[i].plot(vp_std[:, t], depth, label="FWI")
axs[i].plot(vp_ps[i][:, t], depth, label="PFWI $(2^i)")
axs[i].plot(vp_dft[i][:, t], depth, label="OTDFT $(2^i)")
axs[i].set_ylim(depth[end], depth[1])
axs[i].legend(loc="lower left")
i%2 == 0 && axs[i].set_xlabel(L"$V_p$")
i < 3 && axs[i].set_ylabel("Depth (km)")
end
title("X=$((t-1)*25/1000) km")
# tight_layout()
wsave(plotsdir("fwi_overthrust", "vertical_trace_$(t).png"), gcf())
end
for t in trinds
fig, axs = subplots(nrows=1, ncols=3, figsize=(12, 9), sharey=true)
fig.subplots_adjust(wspace=0)
for (n,i)=enumerate([1, 4, 5])
axs[n].plot(vp_t[:, t], depth, label="True")
axs[n].plot(vp_std[:, t], depth, label="FWI")
axs[n].plot(vp_ps[i][:, t], depth, label="PFWI $(2^i)")
axs[n].plot(vp_dft[i][:, t], depth, label="OTDFT $(2^i)")
axs[n].set_ylim(depth[end], depth[1])
axs[n].legend(loc="lower left")
n==1 && axs[n].set_ylabel("Depth (km)")
n==2 && axs[n].set_xlabel(L"$V_p$")
n==2 && axs[n].set_title("X=$((t-1)*25/1000) km")
end
# tight_layout()
wsave(plotsdir("fwi_overthrust", "vertical_trace_$(t)_select.png"), gcf())
end
# Convergence
cols = ["b", "r", "y", "c", "g", "m", :lime, :indigo]
figure(figsize=(12, 9))
for i=1:8
plot(ϕ_ps[i]/ϕ_ps[i][1], color=cols[i], linestyle=:dashed, marker="o", label="PFWI $(2^i)")
end
plot(ϕ_std/ϕ_std[1], label="STD")
for i=1:8
plot(ϕ_dft[i]/ϕ_dft[i][1], color=cols[i], linestyle=:dashed, marker="^", label="OTDFT $(2^i)")
end
legend(loc="upper right", ncol=2)
xlabel("Iteration")
ylabel("Normalized objective")
wsave(plotsdir("fwi_overthrust", "convergence.png"), gcf())
# FWI plot for SEG abstract
figure(figsize=(12, 9))
subplot(3,3,1)
imshow(vp_t; plt_dict...)
title("True")
xlabel("X (km)")
ylabel("Depth (km)")
vlines(x=[t*d[1]/1000 for t=trinds], colors=:k, ymin=0, ymax=ee[3])
subplot(3,3,2)
imshow(vp_0; plt_dict...)
title("Initial")
xlabel("X (km)")
ylabel("Depth (km)")
subplot(3,3,3)
imshow(vp_std; plt_dict...)
title("FWI")
xlabel("X (km)")
ylabel("Depth (km)")
for (j, ps)=zip([4,5,6], [4, 16, 64])
i = inds[ps]
subplot(3, 3, j)
imshow(vp_ps[i]; plt_dict...)
title("PFWI $(ps)")
xlabel("X (km)")
ylabel("Depth (km)")
subplot(3, 3, j+3)
imshow(vp_dft[i]; plt_dict...)
title("OTDFT $(ps)")
xlabel("X (km)")
ylabel("Depth (km)")
end
tight_layout()
wsave(plotsdir("fwi_overthrust", "probed_vs_dft.png"), gcf())
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function standard_normal_gausshermite(n::Int)
ϵᵢ, wᵢ = gausshermite(n) # approximates exp(-x²)
ϵᵢ .*= sqrt(2.) # Normalize ϵᵢ and wᵢ nodes to approximate standard normal
wᵢ ./= sqrt(π)
return ϵᵢ, wᵢ
end
"""
```
gausshermite_expectation(f, μ, σ, n = 10)
gausshermite_expectation(f, μ, Σ, n = 10)
gausshermite_expectation(f, μ, Σ, ns)
```
calculates the expectation of a function of a Gaussian random variable/vector.
The first method evalulates ``\\mathbb{E}[f(X)]`` where ``X \\sim N(\\mu, \\sigma)``,
while the other two methods evaluate ``\\mathbb{E}[f(X)]`` where
``X \\sim \\mathcal{N}(\\mu, \\Sigma)`` and ``\\Sigma`` is diagonal.
The latter two methods differ in that the first assumes the same number of
quadrature points in every dimension while the second does not.
### Inputs
- `f::Function`: some function of a random variable. If `f(x) = x`, then
`gausshermite_expectation(f, μ, σ)` calculates the mean of ``N(\\mu, \\sigma)``
using 10-point Gauss-Hermite quadrature.
- `μ::Number` or `μ::AbstractVector`: mean of the Gaussian random variable/vector.
- `σ::Number`: standard deviation of the Gaussian random variable.
- `Σ::AbstractVector`: diagonal of the variance-covariance matrix of
the Gaussian random vector.
- `n::Int`: number of quadrature points to use
- `ns::AbstractVector{Int}` or `ns::NTuple{N, Int} where N`: number of quadrature points to use
in each dimension of the Gaussian random vector.
"""
function gausshermite_expectation(f::Function, μ::Number, σ::Number, n::Int = 10)
ϵᵢ, wᵢ = gausshermite(n)
ϵᵢ .*= sqrt(2.) # Normalize ϵᵢ and wᵢ nodes to approximate standard normal
# wᵢ ./= sqrt(π) # This step done later to reduce number of computations
if μ ≈ 0.
return sum([wᵢ[i] * f(ϵᵢ[i] * σ) for i in 1:n]) / sqrt(π)
else
return sum([wᵢ[i] * f(ϵᵢ[i] * σ + μ) for i in 1:n]) / sqrt(π)
end
end
function gausshermite_expectation(f::Function, μ::AbstractVector{S},
Σ::AbstractVector{<: Number}, n::Int = 10) where {S <: Number}
d = length(μ)
@assert length(Σ) == d "The length of μ and Σ must be the same."
ϵ, w = gausshermite(n)
ϵ .*= sqrt(2.) # Normalize ϵ and w nodes to approximate standard normal
# w ./= sqrt(π) # This step done later to reduce number of computations
# Evaluate over the tensor grid
feval = Array{S}(undef, (n for i in 1:d)...)
allCI = CartesianIndices(feval)
if all(μ .≈ 0.)
@simd for CI in allCI
feval[CI] = f([ϵ[i] for i in Tuple(CI)] .* Σ)
end
else
@simd for CI in allCI
feval[CI] = f([ϵ[i] for i in Tuple(CI)] .* Σ + μ)
end
end
for n_dim in 1:(d - 1)
# Iteratively integrate out each dimension, i.e. law of iterated expectations
iter = CartesianIndices(tuple(Tuple(1:n for i in 1:(d - n_dim))...,
Tuple(1:1 for i in 1:n_dim)...)) # Create CartesianIndices for all remaining dimensions
# ((1:n for i in 1:(d - n_dim + 1))..., (1 for i in 1:(n_dim - 1))...) creates a Tuple of 1:n for the dimensions
# that are not to be integrated out and uses 1s for the remaining dimensions. We want to use each dimension of feval
# from 1 to (d - n_dim) (inclusive). So on the first iteration, the tuple should be (1:n, 1:n).
# We then assign it to the dimensions of feval from 1 to (d - n_dim - 1) (inclusive) to avoid allocations
feval[iter] .= dropdims(sum(mapslices(fᵢ -> fᵢ .* w, (@view feval[((1:n for i in 1:(d - n_dim + 1))...,
(1 for i in 1:(n_dim - 1))...)...]),
dims = (d - n_dim) + 1), dims = (d - n_dim) + 1), dims = (d - n_dim) + 1)
end
# Handle final integration on its own
return sum(w .* (@view feval[:, (1 for i in 1:(d - 1))...])) / π^(d / 2)
end
function gausshermite_expectation(f::Function, μ::AbstractVector{S},
Σ::AbstractVector{<: Number}, ns::AbstractVector{Int}) where {S <: Number}
d = length(μ)
@assert length(Σ) == d "The length of μ and Σ must be the same."
ϵ = Dict{Int, Vector{S}}()
w = Dict{Int, Vector{S}}()
for i in 1:d
ϵ[i], w[i] = gausshermite(ns[i])
ϵ[i] .*= sqrt(2.) # Normalize ϵ and w nodes to approximate standard normal
# w[i] ./= sqrt(π) # This step done later to reduce number of computations
end
# Evaluate over the tensor grid
feval = Array{S}(undef, (n for n in ns)...)
allCI = CartesianIndices(feval)
if all(μ .≈ 0.)
@simd for CI in allCI
feval[CI] = f([ϵ[n_dim][gridᵢ] for (n_dim, gridᵢ) in enumerate(Tuple(CI))] .* Σ)
end
else
@simd for CI in allCI
feval[CI] = f([ϵ[n_dim][gridᵢ] for (n_dim, gridᵢ) in enumerate(Tuple(CI))] .* Σ + μ)
end
end
# Iteratively integrate out each dimension, i.e. law of iterated expectations
for n_dim in 1:(d - 1)
iter = CartesianIndices(tuple(Tuple(1:ns[i] for i in 1:(d - n_dim))...,
Tuple(1:1 for i in 1:n_dim)...))
feval[iter, 1] .= dropdims(sum(mapslices(fᵢ -> fᵢ .* w[d - n_dim + 1], (@view feval[((1:ns[i] for i in 1:(d - n_dim + 1))...,
(1 for i in 1:(n_dim - 1))...)...]),
dims = (d - n_dim) + 1), dims = (d - n_dim) + 1), dims = (d - n_dim) + 1)
end
# Handle final integration on its own
return sum(w[1] .* (@view feval[:, (1 for i in 1:(d - 1))...])) / π^(d / 2)
end
function gausshermite_expectation(f::Function, μ::AbstractVector{S},
Σ::AbstractVector{<: Number}, ns::NTuple{N, Int}) where {S<: Number, N}
d = length(μ)
@assert length(Σ) == d "The length of μ and Σ must be the same."
ϵ = Dict{Int, Vector{S}}()
w = Dict{Int, Vector{S}}()
for i in 1:d
ϵ[i], w[i] = gausshermite(ns[i])
ϵ[i] .*= sqrt(2.) # Normalize ϵ and w nodes to approximate standard normal
# w[i] ./= sqrt(π) # This step done later to reduce number of computations
end
# Evaluate over the tensor grid
feval = Array{S}(undef, (n for n in ns)...)
allCI = CartesianIndices(feval)
if all(μ .≈ 0.)
@simd for CI in allCI
feval[CI] = f([ϵ[n_dim][gridᵢ] for (n_dim, gridᵢ) in enumerate(Tuple(CI))] .* Σ)
end
else
@simd for CI in allCI
feval[CI] = f([ϵ[n_dim][gridᵢ] for (n_dim, gridᵢ) in enumerate(Tuple(CI))] .* Σ + μ)
end
end
# Iteratively integrate out each dimension, i.e. law of iterated expectations
for n_dim in 1:(d - 1)
iter = CartesianIndices(tuple(Tuple(1:ns[i] for i in 1:(d - n_dim))...,
Tuple(1:1 for i in 1:n_dim)...))
feval[iter, 1] .= dropdims(sum(mapslices(fᵢ -> fᵢ .* w[d - n_dim + 1], (@view feval[((1:ns[i] for i in 1:(d - n_dim + 1))...,
(1 for i in 1:(n_dim - 1))...)...]),
dims = (d - n_dim) + 1), dims = (d - n_dim) + 1), dims = (d - n_dim) + 1)
end
# Handle final integration on its own
return sum(w[1] .* (@view feval[:, (1 for i in 1:(d - 1))...])) / π^(d / 2)
end
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] | 2.048098 | 3,680 |
using SpecialFunctions, RecursiveArrayTools, DifferentialEquations, Plots
using ConservationLawsParticles
# model
V1(t, x) = 1 + sin(x)/2
V2(t, x) = -1 - cos(x)/2
Wₐ′(t,x) = sign(x) / (abs(x) + 1) + x^3/20
Wᵣ(t, x) = 1 / (abs(x) + 1)
mob1(ρ, σ) = max(1 - ρ - σ/2, 0)
mob2(ρ, σ) = max(1 - ρ/2 - σ, 0)
attr = SampledInteraction(Wₐ′)
rep = IntegratedInteraction(Wᵣ)
model = ParabolicModel(
(V1, V2),
((attr, rep), (rep, attr)),
(mob1, mob2),
(SimpleDiffusion(1/16), SimpleDiffusion(1/8)))
mmodel = ParabolicModel(
(V1, V2),
((attr, rep), (rep, attr)),
(mob1, mob2),
(MinDiffusion(1/16), MinDiffusion(1/8)))
# initial condition
n = 80
x0 = ArrayPartition(
vcat(range(-5, -3, length=n÷2), range(-2, -1, length=n÷2)),
vcat(range(1, 2, length=n÷2), range(2.5, 3, length=n÷4), range(3.5, 4, length=n÷4)))
# time span
tspan = (0., 12.0)
# ODE system for the particles
prob = ODEProblem(abstract_velocities_gen!, x0, tspan, model)
mprob = ODEProblem(abstract_velocities_gen!, x0, tspan, mmodel)
# solve it
abstol = reltol = 1e-7
@time sol = solve(prob, BS5(); abstol=abstol, reltol=reltol);
@time msol = solve(mprob, BS5(); abstol=abstol, reltol=reltol);
# plot the particle trajectories
plot(legend=false, title="Trajectories", xlabel="time", ylabel="position")
plot!(sol, vars=1:1:n; color=1)
plot!(sol, vars=n+1:1:2n; color=2)
plot!(msol, vars=1:1:n; color=1, ls=:dot)
plot!(msol, vars=n+1:1:2n; color=2, ls=:dot)
savefig("multi-spec-2.png")
# plot an animation of the density
anim = @animate for t in range(tspan...; length=100)
plot(title="Density", xlabel="position", ylabel="density",
xlims=(-6,5), ylims=(0,1), legend=false)
densityplot!(sol(t).x[1]; color=1)
densityplot!(sol(t).x[2]; color=2)
densityplot!(msol(t).x[1]; color=1, ls=:dot)
densityplot!(msol(t).x[2]; color=2, ls=:dot)
end
gif(anim, "multi-spec-2.gif")
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] | 2.109131 | 898 |
module ICNBenchmarks
# usings
using BenchmarkTools
using CompositionalNetworks
using ConstraintDomains
using Constraints
using CSV
using DataFrames
using DataVoyager
using Dictionaries
using Distributed
using DrWatson
using JSON
using Statistics
using StatsBase
using Tables
#using SharedArrays
# imports
import Constraints: make_error
# constants
export ALL_PARAMETERS
export BENCHED_CONSTRAINTS
# others
export analyse_composition
export analyze_icn
export compositions_benchmark
export icn_benchmark
export search_space
export visualize_compositions
export visualize_icn
# includes
include("constants.jl")
include("search_space.jl")
include("extra_constraints.jl")
include("icn.jl")
include("composition.jl")
include("analyze.jl")
end
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] | 3.351351 | 222 |
## ExoplanetsSysSim/src/star.jl
## (c) 2015 Eric B. Ford
#using Distributions
@compat abstract type StarAbstract end # Check does using StarAbstract cause a significant performance hit
immutable Star <: StarAbstract
radius::Float64
mass::Float64
flux::Float64 # relevant once have multiple stars in one target
ld::LimbDarkeningParamAbstract # TODO SCI DETAIL: add limb darkening param?
id::Int64 # id for looking up properties in stellar catalog
end
#typealias SingleStar Star
SingleStar = Star
immutable BinaryStar <: StarAbstract
primary::Star
secondary::Star
orbit::Orbit
end
immutable MultipleStar <: StarAbstract # Will we want to handle triple, quad systems?
component::Vector{StarAbstract}
orbit::Vector{Orbit}
end
flux(s::Star) = s.flux # Demo of how to specify function behavior that depends on the derived type
flux(s::BinaryStar) = s.primary.flux + s.secondary.flux
flux(s::MultipleStar) = sum( flux, s.component)
mass(s::Star) = s.mass
mass(s::BinaryStar) = s.primary.mass + s.secondary.mass
mass(s::MultipleStar) = sum( mass, s.component)::Float64
function generate_stars(sim_param::SimParam)
generate_star = get_function(sim_param,"generate_star")
num_target_stars = get_int(sim_param,"num_targets_sim_pass_one")
star_list = Array{StarAbstract}(num_target_stars)
for i in 1:num_target_stars
s = generate_star(sim_param)
star_list[i] = s
#star_list[i] = generate_star(sim_param)
end
return star_list
end
function generate_star_dumb(sim_param::SimParam)
r = rand(Uniform(0.8,1.3))::Float64
m = rand(Normal(r,0.1))::Float64
while m<0.0
m = rand(Normal(r,0.1))::Float64
end
f = rand(Normal(1.0,0.1))::Float64
while f<0.0
f = 1.0+0.1*randn()
end
ld = LimbDarkeningParamQuadratic(0.4603,0.2291) # TODO: Once we implement limb darkening
return SingleStar(r,m,f,ld,0)
#return SingleStar(r,m,f,0)
end
function test_star_constructors(sim_param::SimParam)
star_tmp = generate_star_dumb(sim_param)
f1 = flux(star_tmp)
f2 = flux(BinaryStar(star_tmp,star_tmp,Orbit(10.0,0.0,0.0,0.0,0.0,0.0)))
f4 = flux(MultipleStar([star_tmp for i in 1:4], [Orbit(10.0,0.0,0.0,0.0,0.0,0.0) for i in 1:4]) )
# println("# Fluxes: ", f1, " ", f2, " ", f4)
star_list = generate_stars(sim_param)
return true
end
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] | 2.362849 | 1,039 |
using Test, LinearMaps, LinearAlgebra
@testset "function maps" begin
N = 100
function myft(v::AbstractVector)
# not so fast fourier transform
N = length(v)
w = zeros(complex(eltype(v)), N)
for k = 1:N
kappa = (2*(k-1)/N)*pi
for n = 1:N
w[k] += v[n]*exp(kappa*(n-1)*im)
end
end
return w
end
MyFT = @inferred LinearMap{ComplexF64}(myft, N) / sqrt(N)
U = Matrix(MyFT) # will be a unitary matrix
@test @inferred U'U ≈ Matrix{eltype(U)}(I, N, N)
CS = @inferred LinearMap(cumsum, 2)
@test size(CS) == (2, 2)
@test @inferred !issymmetric(CS)
@test @inferred !ishermitian(CS)
@test @inferred !isposdef(CS)
@test @inferred !(LinearMaps.ismutating(CS))
@test @inferred Matrix(CS) == [1. 0.; 1. 1.]
@test @inferred Array(CS) == [1. 0.; 1. 1.]
CS = @inferred LinearMap(cumsum, 10; ismutating=false)
v = rand(10)
cv = cumsum(v)
@test CS * v == cv
@test *(CS, v) == cv
@test_throws ErrorException CS' * v
CS = @inferred LinearMap(cumsum, x -> reverse(cumsum(reverse(x))), 10; ismutating=false)
cv = cumsum(v)
@test @inferred CS * v == cv
@test @inferred *(CS, v) == cv
@test @inferred CS' * v == reverse!(cumsum(reverse(v)))
@test @inferred mul!(similar(v), transpose(CS), v) == reverse!(cumsum(reverse(v)))
CS! = @inferred LinearMap(cumsum!, 10; ismutating=true)
@test @inferred LinearMaps.ismutating(CS!)
@test @inferred CS! * v == cv
@test @inferred *(CS!, v) == cv
@test @inferred mul!(similar(v), CS!, v) == cv
@test_throws ErrorException CS!'v
@test_throws ErrorException transpose(CS!) * v
CS! = @inferred LinearMap{ComplexF64}(cumsum!, 10; ismutating=true)
v = rand(ComplexF64, 10)
cv = cumsum(v)
@test @inferred LinearMaps.ismutating(CS!)
@test @inferred CS! * v == cv
@test @inferred *(CS!, v) == cv
@test @inferred mul!(similar(v), CS!, v) == cv
@test_throws ErrorException CS!'v
@test_throws ErrorException adjoint(CS!) * v
CS! = LinearMap{ComplexF64}(cumsum!, (y, x) -> (copyto!(y, x); reverse!(y); cumsum!(y, y)), 10; ismutating=true)
@test @inferred LinearMaps.ismutating(CS!)
@test @inferred CS! * v == cv
@test @inferred *(CS!, v) == cv
@test @inferred mul!(similar(v), CS!, v) == cv
@test @inferred CS' * v == reverse!(cumsum(reverse(v)))
@test @inferred mul!(similar(v), transpose(CS), v) == reverse!(cumsum(reverse(v)))
@test @inferred mul!(similar(v), adjoint(CS), v) == reverse!(cumsum(reverse(v)))
# Test fallback methods:
L = @inferred LinearMap(x -> x, x -> x, 10)
v = randn(10)
@test @inferred (2 * L)' * v ≈ 2 * v
@test @inferred transpose(2 * L) * v ≈ 2 * v
L = @inferred LinearMap{ComplexF64}(x -> x, x -> x, 10)
v = rand(ComplexF64, 10)
@test @inferred (2 * L)' * v ≈ 2 * v
@test @inferred transpose(2 * L) * v ≈ 2 * v
end
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] | 2.156295 | 1,382 |
using jInv.Mesh;
using FactoredEikonalFastMarching;
using Printf
#using PyPlot
#close("all")
include("runAccuracyExperiment.jl");
include("getWorkUnit.jl");
"""
A function for running the experiments in the paper:
Eran Treister and Eldad Haber, A fast marching algorithm for the factored eikonal equation, Journal of Computational Physics, 324, 210-225, 2016.
To acctually run the experiments in the paper: increase numOfRefinements
"""
function runPaperExperiments(numOfRefinements)
println("******************** 2D experiments ***************************");
I = [4,8];
n = zeros(Int64,2);
h0 = [0.05,0.05];
WU = zeros(numOfRefinements)
for k = 1:numOfRefinements
h = (0.5^k)*h0;
n[1] = round(Int64,(I[1]/h[1])+1);
n[2] = round(Int64,(I[2]/h[2])+1);
WU[k] = GetWorkunitForLoop(n,h);
end
println("******************** Gradient Slowness ***************************");
println("h\t\tn\t\terror(1st)[max,l2]\ttime(1st)\terror(2nd)[max,l2]\ttime(2nd)")
h0 = [0.05,0.05];
for k=1:numOfRefinements
h = (0.5^k)*h0;
n[1] = round(Int64,(I[1]/h[1])+1);
n[2] = round(Int64,(I[2]/h[2])+1);
(kappaSquared,src,T_exact) = getAnalyticalConstGrad2D(n,h);
runExperimentAndWriteResults(kappaSquared,h,src,n,T_exact,WU[k]);
end
println("******************** Gradient Velocity ***************************");
println("h\t\tn\t\terror(1st)[max,l2]\ttime(1st)\terror(2nd)[max,l2]\ttime(2nd)")
h0 = [0.05,0.05];
for k=1:numOfRefinements
h = (0.5^k)*h0;
n[1] = round(Int64,(I[1]/h[1])+1);
n[2] = round(Int64,(I[2]/h[2])+1);
(kappaSquared,src,T_exact) = getAnalyticalConstGradInv2D(n,h);
runExperimentAndWriteResults(kappaSquared,h,src,n,T_exact,WU[k]);
end
println("******************** Gaussian factor ***************************");
println("h\t\tn\t\terror(1st)[max,l2]\ttime(1st)\terror(2nd)[max,l2]\ttime(2nd)")
h0 = [0.05,0.05];
for k=1:numOfRefinements
h = (0.5^k)*h0;
n[1] = round(Int64,(I[1]/h[1])+1);
n[2] = round(Int64,(I[2]/h[2])+1);
(kappaSquared,src,T_exact) = getSmoothGaussianMedium(n,h);
runExperimentAndWriteResults(kappaSquared,h,src,n,T_exact,WU[k]);
end
println("*******************************************************************************")
println("*******************************************************************************")
println("************************* 3-D EXPERIMENTS *************************************")
println("*******************************************************************************")
println("*******************************************************************************")
##########################################################################################
##########################################################################################
# 3D experiments:
##########################################################################################
I = [1.6,1.6,0.8];
n = zeros(Int64,3);
h0 = [0.1,0.1,0.1];
WU = zeros(numOfRefinements)
for k = 1:numOfRefinements
h = (0.5^k)*h0;
n[1] = round(Int64,(I[1]/h[1])+1);
n[2] = round(Int64,(I[2]/h[2])+1);
n[3] = round(Int64,(I[3]/h[3])+1);
WU[k] = GetWorkunitForLoop(n,h);
end
println("******************** Gradient Slowness ***************************");
println("h\t\tn\t\terror(1st)[max,l2]\ttime(1st)\terror(2nd)[max,l2]\ttime(2nd)")
for k=1:numOfRefinements
h = (0.5^k)*h0;
n[1] = round(Int64,(I[1]/h[1])+1);
n[2] = round(Int64,(I[2]/h[2])+1);
n[3] = round(Int64,(I[3]/h[3])+1);
(kappaSquared,src,T_exact) = getAnalyticalConstGrad3D(n,h);
runExperimentAndWriteResults(kappaSquared,h,src,n,T_exact,WU[k]);
end
println("******************** Gradient Velocity ***************************");
println("h\t\tn\t\terror(1st)[max,l2]\ttime(1st)\terror(2nd)[max,l2]\ttime(2nd)")
for k=1:numOfRefinements
h = (0.5^k)*h0;
n[1] = round(Int64,(I[1]/h[1])+1);
n[2] = round(Int64,(I[2]/h[2])+1);
n[3] = round(Int64,(I[3]/h[3])+1);
(kappaSquared,src,T_exact) = getAnalyticalConstGradInv3D(n,h);
runExperimentAndWriteResults(kappaSquared,h,src,n,T_exact,WU[k]);
end
println("******************** Gaussian factor ***************************");
println("h\t\tn\t\terror(1st)[max,l2]\ttime(1st)\terror(2nd)[max,l2]\ttime(2nd)")
for k=1:numOfRefinements
h = (0.5^k)*h0;
n[1] = round(Int64,(I[1]/h[1])+1);
n[2] = round(Int64,(I[2]/h[2])+1);
n[3] = round(Int64,(I[3]/h[3])+1);
(kappaSquared,src,T_exact) = getSmoothGaussianMedium(n,h);
runExperimentAndWriteResults(kappaSquared,h,src,n,T_exact,WU[k]);
end
return;
end
############################################################################################
runPaperExperiments(2);
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module MaxHelpingHandHeatWaveNoColorGrade
using ..Ahorn, Maple
@mapdef Effect "MaxHelpingHand/HeatWaveNoColorGrade" HeatWaveNoColorGrade(only::String="*", exclude::String="", controlColorGradeWhenActive::Bool=false)
placements = HeatWaveNoColorGrade
function Ahorn.canFgBg(effect::HeatWaveNoColorGrade)
return true, true
end
end
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] | 3.054054 | 111 |
import LsqFit
# #############################################################################
#Moving average filtering
#Apply filter
function movavgfilt{T}(y::Array{T,1},N::Int64)
#Usage:
# ys = movavgfilt(y,N)
# y Signal to be smoothed
# N Size of window
# ys Smoothed output
#Initiate
ys::Array{Float64,1} = Array(Float64,length(y))
#Compute mean
for i=1:length(y)
s::Int64 = minimum([floor((N-1)/2),i-1,length(y)-i])
ys[i] = sum(y[i-s:i+s])/(2*s+1)
end
return ys
end
# #############################################################################
#Savitzsky-Golay filtering
#Get coefficients
function sgolaycoeff{T<:Real}(z::Array{T,1},deg::Int64,diff::Int64=0)
#Usage:
# C = sgolaycoeff(z,deg)
# C = sgolaycoeff(z,deg,diff)
# z Fitting window (eg: [-5:5])
# deg Degree of polynomial for fit
# diff Number of differentiations (must be <= deg, default 0 for
# smoothing)
# C Matrix for transforming window values to polynomial coefficients,
# where (i,j)th entry is the multiplier to the jth value for the
# (i-1) order term in the fitting polynomial.
#Check input
assert(diff <= deg)
#Solve normal equations
J::Array{T,2} = broadcast(^,z,transpose(0:deg))
Jt::Array{T,2} = transpose(J)
C::Array{Float64,2} = inv(Jt*J)*Jt
#Get coefficients for derivatives
for i = 1:diff
C = broadcast(*,[1:size(C,1)-1],C[2:end,:])
end
return C
end
#Apply filter
function sgolayfilt{T<:Real}(y::Array{T,1},N::Int64;deg::Int64=2,diff::Int64=0,multi::Int64=1)
#Usage:
# ys = sgolayfilt(y,N)
# ys = sgolayfilt(x,y,N)
# ys = sgolayfilt(..., deg=deg)
# ys = sgolayfilt(..., diff=diff)
# ys = sgolayfilt(..., multi=multi)
# y Signal to be smoothed
# N Size of window
# x Distribution of signal events (default [1:length(y)] for uniform)
# deg Degree of polynomial for fit
# diff Number of differentiations (must be <= deg, default 0 for
# smoothing)
# multi Number of times to filter
# ys Smoothed signal
#Check input
if diff > deg
error("Differential order cannot exceed polynomial fit degree.")
end
m::Int64 = div(N-1,2)
if 2*m+1 <= deg
error("Fitting window N = $N is too small for polynomial fit of degree $deg.")
end
if multi < 1
error("Number of times to filter must be at least 1.")
end
#Initiate
C::Array{Float64,2} = sgolaycoeff([-m:m],deg,diff)
ys::Array{Float64,1} = Array(Float64,length(y))
#Starting
a::Array{Float64,1} = C*y[1:2m+1]
for i=1:m
ys[i] = sum(a.*(i-m-1).^[0:length(a)-1])
end
#Middle
C1::Array{Float64,1} = C[1,:][:]
for i=m+1:length(y)-m
ys[i] = dot(C1,y[i-m:i+m])
end
#Ending
a = C*y[end-2m:end]
for i=length(y)-m+1:length(y)
ys[i] = sum(a.*(i-length(y)+m).^[0:length(a)-1])
end
if multi == 1
return ys
else
return sgolayfilt(ys,N,deg=deg,diff=0,multi=multi-1)
end
end
function sgolayfilt{T<:Real}(x::Array{T,1},y::Array{T,1},N::Int64;deg::Int64=2,diff::Int64=0)
#Check input
if diff > deg
error("Differential order cannot exceed polynomial fit degree.")
end
m::Int64 = div(N-1,2)
if 2*m+1 <= deg
error("Fitting window N = $N is too small for polynomial fit of degree $deg.")
end
if multi < 1
error("Number of times to filter must be at least 1.")
end
#Initiate
ys::Array{Float64,1} = Array(Float64,length(y))
#Starting
C::Array{Float64,2} = sgolaycoeff(x[1:2m+1],deg,diff)
a::Array{Float64,1} = C*y[1:2m+1]
for i=1:m
ys[i] = sum(a.*x[i].^[0:length(a)-1])
end
#Middle
for i=m+1:length(y)-m
C = sgolaycoeff(x[i-m:i+m],deg,diff)
a = C*y[i-m:i+m]
ys[i] = sum(a.*x[i].^[0:length(a)-1])
end
#Ending
C = sgolaycoeff(x[end-2*m:end],deg,diff)
a = C*y[end-2*m:end]
for i=length(y)-m+1:length(y)
ys[i] = sum(a.*x[i].^[0:length(a)-1])
end
if multi == 1
return ys
else
return sgolayfilt(ys,N,deg=deg,diff=0,multi=multi-1)
end
end
# #############################################################################
#Robust Local Weighted Scatter-plot Smooth
function robust_lowess{T}(y::Array{T,1},N::Int64;deg::Int64=1,robust::Bool=true)
#Initiate
ys::Array{Float64,1} = Array(Float64,length(y))
w::Array{Float64,1} = Array(Float64,N)
s::Int64 = floor((N-1)/2)
#Modelling function
f(x::Array,p::Array{Float64,1}) = sum(broadcast(^,x,transpose([0:length(p)-1]))*p,2)
f(x::Int64,p::Array{Float64,1}) = sum(p.*x.^[0:length(p)-1])
#Conduct regression
for i=1:length(y)
#Get regression region
if s > i-1
range::Array{Int64,1} = [1:N]
elseif s > length(y)-i
range = [(length(y)-N+1):length(y)]
else
range = [(i-s):(i+s)]
end
#Compute local weights
w = (1-(abs(range-i)/maximum(abs(range-i))).^3).^3
#Conduct fit
if length(range) == 1
#Fit is always exact
ys[i] = y[i]
else
p0::Array{Float64,1} = zeros(Float64,minimum([deg+1,length(range)-1]))
fit = LsqFit.curve_fit(f,range-i,y[range],w,p0)
ys[i] = fit.param[1]
end
end
if !robust; return ys; end
for iter=1:5
#Find outliers
resid::Array{Float64,1} = ys-y
madev6::Float64 = 6*median(abs(resid))
inliers::Array{Bool,1} = (abs(resid) .< madev6)
#Compute Robust weights
rw::Array{Float64,1} = zeros(Float64,length(y))
rw[inliers] = (1-(resid/madev6).^2).^2
#Conduct regression
for i=1:length(y)
#Get regression region
if s > i-1
range::Array{Int64,1} = [1:N]
elseif s > length(y)-i
range = [(length(y)-N+1):length(y)]
else
range = [(i-s):(i+s)]
end
#Compute local weights
w = (1-(abs(range-i)/maximum(abs(range-i))).^3).^3
#Conduct fit
if length(range) == 1
#Fit is always exact
ys[i] = y[i]
else
p0::Array{Float64,1} = zeros(Float64,minimum([deg+1,length(range)-1]))
fit = LsqFit.curve_fit(f,range-i,y[range],w.*rw[range],p0)
ys[i] = fit.param[1]
end
end
end
return ys
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] | 1.867383 | 3,725 |
##############################
# Mapping functions on Dicts #
##############################
function mapvals!(f, d)
for (k,v) in d
d[k] = f(v)
end
return d
end
mapvals(f, d) = Dict(zip(collect(keys(d)), map(f, collect(values(d)))))
mapkeys(f, d) = Dict(zip(map(f, collect(keys(d))), collect(values(d))))
mapkv(f, d) = Dict(map(f, collect(d)))
#############
# Filtering #
#############
Base.filter!(f::Function, kt::Ket) = (filter!(f, dict(kt)); return kt)
Base.filter!(f::Function, br::Bra) = (filter!((k,v)->f(k,v'), dict(br)); return br)
Base.filter!(f::Function, op::OpSum) = (filter!(f, dict(op)); return op)
Base.filter!(f::Function, opc::DualOpSum) = (filter!((k,v)->f(k',v'), dict(opc)); return opc)
Base.filter(f::Function, kt::Ket) = similar(kt, filter(f, dict(kt)))
Base.filter(f::Function, br::Bra) = similar(br, filter((k,v)->f(k,v'), dict(br)))
Base.filter(f::Function, op::OpSum) = similar(op, filter(f, dict(op)))
Base.filter(f::Function, opc::DualOpSum) = similar(opc, filter((k,v)->f(k',v'), dict(opc)))
Base.filter(f::Function, op::OuterProduct) = filter(f, convert(OpSum, op))
########################
# mapcoeffs!/mapcoeffs #
########################
mapcoeffs!(f::Union(Function,DataType), k::Ket) = (mapvals!(f, dict(k)); return k)
mapcoeffs!(f::Union(Function,DataType), b::Bra) = (mapvals!(v->f(v')', dict(b)); return b)
mapcoeffs!(f::Union(Function,DataType), op::OpSum) = (mapvals!(f, dict(op)); return op)
mapcoeffs!(f::Union(Function,DataType), opc::DualOpSum) = (mapvals!(v->f(v')', dict(opc)); return opc)
mapcoeffs(f::Union(Function,DataType), kt::Ket) = similar(kt, mapvals(f, dict(kt)))
mapcoeffs(f::Union(Function,DataType), br::Bra) = similar(br, mapvals(v->f(v')', dict(br)))
mapcoeffs(f::Union(Function,DataType), op::OpSum) = similar(op, mapvals(f, dict(op)))
mapcoeffs(f::Union(Function,DataType), opc::DualOpSum) = similar(opc, mapvals(v->f(v')', dict(opc)))
mapcoeffs(f::Union(Function,DataType), op::OuterProduct) = mapcoeffs(f, convert(OpSum, op))
########################
# maplabels!/maplabels #
########################
maplabels(f::Union(Function,DataType), s::DiracState) = similar(s, mapkeys(f, dict(s)))
maplabels(f::Union(Function,DataType), op::OpSum) = similar(op, mapkeys(f, dict(op)))
maplabels(f::Union(Function,DataType), opc::DualOpSum) = OpSum(ptype(opc), mapkeys(label->f(label'), dict(op)))
maplabels(f::Union(Function,DataType), op::OuterProduct) = maplabels(f, convert(OpSum, op))
#######
# map #
#######
Base.map(f::Union(Function,DataType), obj::AbstractDirac) = similar(obj, mapkv(kv->f(kv[1], kv[2]), dict(obj)))
Base.map(f::Union(Function,DataType), br::Bra) = similar(br, mapkv(kv->br_tup(f(kv[1], kv[2]')), dict(d)))
Base.map(f::Union(Function,DataType), opc::DualOpSum) = OpSum(ptype(opc), mapkv(kv->f(kv[1]', kv[2]'), dict(d)))
Base.map(f::Union(Function,DataType), op::OuterProduct) = map(f, convert(OpSum, op))
br_tup(tup) = (tup[1], tup[2]')
export maplabels!,
mapcoeffs!,
maplabels,
mapcoeffs | [
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] | 2.400319 | 1,254 |
abstract type AngularQuadrature end
abstract type UnstructuredMesh_2D end
abstract type LinearUnstructuredMesh_2D <: UnstructuredMesh_2D end
abstract type QuadraticUnstructuredMesh_2D <: UnstructuredMesh_2D end
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] | 3.149254 | 67 |
module Sparse
using SparseArrays
"""
Allocates a block sparse matrix that stores nnz non-zero entries.
The matrix contains only zeros after allocation.
Input:
rows: A vector describing the number of elements in each block row the
block matrix has.
columns: A vector describing the number of elements in each block column the
block matrix has.
Output:
Z: Sparse matrix (all zeros)
Example:
>> Z = block_matrix([2, 3],[2, 3])
>> size(Z)
ans =
5 5
"""
function block_matrix(rows::AbstractArray, columns::AbstractArray)
m = sum(rows);
n = sum(columns);
Z = spzeros(m,n);
return Z
end
"""
Z = block_matrix_insert(B,rows,columns,block)
Inserts a submatrix into a block matrix at block position (i,j)
Input:
B: Block matrix (see block_matrix.m for a description).
rows: Number of elements in each block row of the block matrix.
columns: Number of elements in each block row of the block matrix.
i,j: Block indices denoting where the submatrix should be placed.
block: The submatrix to insert into the block matrix.
The size of the submatrix must match the number given in rows(i)
and columns(j).
Example:
>> Z = block_matrix([2, 3],[2, 3])
>> B = block_matrix_insert(Z,[2, 3],[2, 3],1,2,ones(2,3))
>> B
B =
(1,3) 1
(2,3) 1
(1,4) 1
(2,4) 1
(1,5) 1
(2,5) 1
>> full(B)
ans =
0 0 1 1 1
0 0 1 1 1
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
"""
function block_matrix_insert(B::AbstractArray,
rows::AbstractArray,
columns::AbstractArray,
i::Int64,
j::Int64,
block::AbstractArray)
@assert(i <= length(rows), "Index out of bounds.")
@assert(j <= length(columns), "Index out of bounds.")
@assert(size(block,1) == rows[i], "Dimension mismatch")
@assert(size(block,2) == columns[j],"Dimension mismatch")
r = vcat([1],rows)
c = vcat([1],columns)
row_offset = sum(r[1:i])
col_offset = sum(c[1:j])
B[row_offset:row_offset+r[i+1]-1,col_offset:col_offset+c[j+1]-1] = block
return B
end
function block_matrix_2x2(a11::AbstractArray,
a12::AbstractArray,
a21::AbstractArray,
a22::AbstractArray)
m1 = size(a11, 1)
n1 = size(a11, 2)
m2 = size(a22, 1)
n2 = size(a22, 2)
@assert size(a12, 1) == size(a11, 1)
@assert size(a12, 2) == size(a22, 2)
@assert size(a21, 1) == size(a22, 1)
@assert size(a21, 2) == size(a11, 2)
rows = [m1, m2]
cols = [n1, n2]
A = block_matrix(rows, cols)
A = block_matrix_insert(A, rows, cols, 1, 1, a11)
A = block_matrix_insert(A, rows, cols, 1, 2, a12)
A = block_matrix_insert(A, rows, cols, 2, 1, a21)
A = block_matrix_insert(A, rows, cols, 2, 2, a22)
return A
end
function diag_block_matrix_2x2(a11::AbstractArray,
a22::AbstractArray)
m1 = size(a11, 1)
n1 = size(a11, 2)
m2 = size(a22, 1)
n2 = size(a22, 2)
rows = [m1, m2]
cols = [n1, n2]
A = block_matrix(rows, cols)
A = block_matrix_insert(A, rows, cols, 1, 1, a11)
A = block_matrix_insert(A, rows, cols, 2, 2, a22)
return A
end
end
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] | 1.924379 | 1,891 |
const CPC = Ptr{Cvoid}
const CPCType = Cstring
abstract type AbstractPC{T} end
mutable struct PC{T} <: AbstractPC{T}
ptr::Ptr{Cvoid}
end
scalartype(::AbstractPC{T}) where {T} = T
@for_libpetsc begin
function PC{$PetscScalar}(comm::MPI.Comm)
pc = PC{$PetscScalar}(C_NULL)
@chk ccall((:PCCreate, $libpetsc), PetscErrorCode, (MPI.MPI_Comm, Ptr{CPC}), comm, pc)
finalizer(destroy, pc)
return pc
end
function PC(ksp::KSP{$PetscScalar})
pc = PC{$PetscScalar}(C_NULL)
@chk ccall((:KSPGetPC, $libpetsc), PetscErrorCode, (CKSP, Ptr{CPC}), ksp, pc)
incref(pc) # need to manually increment the reference counter
finalizer(destroy, pc)
return pc
end
function destroy(pc::AbstractPC{$PetscScalar})
finalized($petsclib) ||
@chk ccall((:PCDestroy, $libpetsc), PetscErrorCode, (Ptr{CPC},), pc)
return nothing
end
function settype!(pc::AbstractPC{$PetscScalar}, pctype::String)
@chk ccall((:PCSetType, $libpetsc), PetscErrorCode, (CPC, Cstring), pc, pctype)
return nothing
end
function setpc!(ksp::KSP{$PetscScalar}, pc::AbstractPC{$PetscScalar})
@chk ccall((:KSPSetPC, $libpetsc), PetscErrorCode, (CKSP, CPC), ksp, pc)
return nothing
end
function gettype(pc::AbstractPC{$PetscScalar})
t_r = Ref{CPCType}()
@chk ccall((:PCGetType, $libpetsc), PetscErrorCode, (CPC, Ptr{CPCType}), pc, t_r)
return unsafe_string(t_r[])
end
function view(pc::AbstractPC{$PetscScalar}, viewer::AbstractViewer{$PetscLib}=ViewerStdout($petsclib, getcomm(pc)))
@chk ccall((:PCView, $libpetsc), PetscErrorCode,
(CPC, CPetscViewer),
pc, viewer);
return nothing
end
end
Base.show(io::IO, pc::AbstractPC) = _show(io, pc)
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5662,
8,
796,
4808,
12860,
7,
952,
11,
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8,
198
] | 2.035088 | 912 |
using Test
using SafeTestsets
@safetestset "Reference shapes tests" begin include("referenceshapes_test.jl") end
@safetestset "Domain tests" begin include("domain_test.jl") end
| [
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using Mimi
_default_years = 2000:2100
_default_regions = [:A, :B]
function run_getindex(; years = collect(_default_years), regions = _default_regions)
# Test with one scalar parameter, one 1-D timestep array, and one 2-D timestep array
types = [Mimi.ScalarModelParameter{Float64}, _get_timesteparray_type(years, 1), _get_timesteparray_type(years, 2)]
names = [:d1, :d2, :d3]
values = [
types[1](4.),
types[2](Array{Union{Missing, Float64}, 1}(rand(length(years)))),
types[3](Array{Union{Missing, Float64}, 2}(rand(length(years), length(regions))))
]
datum = NamedTuple{(names...,), Tuple{types...,}}(values)
clock = _get_clock(years)
while ! Mimi.finished(clock)
ts = Mimi.timestep(clock)
_run_timestep(datum, ts)
Mimi.advance(clock)
end
end
function _run_timestep(datum::NamedTuple, ts)
datum.d1
datum.d2[ts]
datum.d3[ts, :]
nothing
end
function _get_timesteparray_type(years, num_dims, dtype=Float64)
if Mimi.isuniform(years)
first, stepsize = Mimi.first_and_step(years)
last = years[last]
T = Mimi.TimestepArray{Mimi.FixedTimestep{first, stepsize, last}, Union{dtype, Missing}, num_dims}
else
T = Mimi.TimestepArray{Mimi.VariableTimestep{(years...,)}, Union{dtype, Missing}, num_dims}
end
return T
end
function _get_clock(years)
if Mimi.isuniform(years)
last = years[end]
first, stepsize = Mimi.first_and_step(years)
return Mimi.Clock{Mimi.FixedTimestep}(first, stepsize, last)
else
last_index = findfirst(isequal(last), years)
times = (years[1:last_index]...,)
return Mimi.Clock{Mimi.VariableTimestep}(times)
end
end | [
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14967,
395,
538,
92,
7,
22355,
8,
198,
220,
220,
220,
886,
198,
437
] | 2.223634 | 787 |
using ASTInterface
using Test
@testset "ASTInterface.jl" begin
# Write your tests here.
end
| [
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39317,
201,
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1,
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220,
220,
220,
1303,
19430,
534,
5254,
994,
13,
201,
198,
437,
201,
198
] | 2.783784 | 37 |
Random.seed!(43)
X = randn(100, 20)
Xup = rand(25, 20)
l = size(Xup, 1) + 1
Xprim = vcat(X, Xup)[l:end,:]
@testset "data updat" begin
@test dataupdat(X, Xup) ≈ Xprim
end
@testset "moment updates" begin
x = ones(6, 2)
y = 2*ones(2,2)
M3 = moment(x, 3)
M4 = moment(x, 4)
M3up = momentupdat(M3, x, y)
@testset "simple test" begin
Mup = moment(dataupdat(x,y),3)
@test Array(Mup) ≈ Array(M3up)
x0 = Array{Float64}(undef, (0,0))
y0 = Array{Float64}(undef, (0,0))
@test M3 == momentupdat(M3, x0, y0)
end
@testset "moment array" begin
Ma = momentarray(x, 4, 2)
@test Array(Ma[3]) ≈ Array(M3)
@test Array(Ma[4]) ≈ Array(M4)
MM = momentupdat(Ma, x, y)
@test Array(M3up) ≈Array(MM[3])
end
end
@testset "moment exceptions" begin
x = ones(10,4);
y = 2*ones(5,3);
m = moment(x, 3);
@test_throws DimensionMismatch momentupdat(m, x, y)
y = 2*ones(5,4)
@test_throws DimensionMismatch momentupdat(m, x[:, 1:3], y)
y = 2*ones(15,4)
@test_throws BoundsError momentupdat(m, x, y)
end
@testset "moments to cumulants" begin
m1 = moment(X, 1)
m2 = moment(X, 2)
m3 = moment(X, 3)
m4 = moment(X, 4)
m5 = moment(X, 5)
c = cumulants(X, 5)
m2c = [m1, m2, m3, m4, m5]
@testset "moms2cums!" begin
moms2cums!(m2c)
@test Array(c[1]) ≈ Array(m2c[1])
@test Array(c[2]) ≈ Array(m2c[2])
@test Array(c[3]) ≈ Array(m2c[3])
@test Array(c[4]) ≈ Array(m2c[4])
@test Array(c[5]) ≈ Array(m2c[5])
end
@testset "cums2moms" begin
mm = cums2moms(c);
@test Array(mm[1]) ≈ Array(m1)
@test Array(mm[2]) ≈ Array(m2)
@test Array(mm[3]) ≈ Array(m3)
@test Array(mm[4]) ≈ Array(m4)
@test Array(mm[5]) ≈ Array(m5)
end
end
@testset "simple cumulants update" begin
x = ones(10, 3)
y = 2*ones(2,3)
s = DataMoments(x, 6, 2)
c2 = cumulantsupdate!(s, y)
xx = dataupdat(x,y)
c3 = cumulants(xx, 6)
@test Array(c2[1]) ≈ Array(c3[1])
@test Array(c2[2]) ≈ Array(c3[2])
@test Array(c2[3]) ≈ Array(c3[3])
@test Array(c2[4]) ≈ Array(c3[4])
@test Array(c2[5]) ≈ Array(c3[5])
@test Array(c2[6]) ≈ Array(c3[6])
end
@testset "cumulants updates larger data" begin
c = cumulants(X)
Xp = dataupdat(X, Xup)
s = DataMoments(X, 4, 4)
C = cumulantsupdate!(s, Xup)
@test s.X ≈ Xp
@test s.b == 4
@test s.d == 4
@test Array(s.M[4]) ≈ Array(moment(Xp, 4))
CC = cumulants(Xp, 4)
@test Array(C[3]) ≈ Array(CC[3])
@test Array(C[4]) ≈ Array(CC[4])
end
@testset "multiprocessing cumulants update" begin
addprocs(2)
eval(Expr(:toplevel, :(@everywhere using CumulantsUpdates)))
Xp = dataupdat(X, Xup)
s = DataMoments(X, 4, 4)
C = cumulantsupdate!(s, Xup)
@test s.X ≈ Xp
@test s.b == 4
@test s.d == 4
@test Array(s.M[4]) ≈ Array(moment(Xp, 4))
CC = cumulants(Xp, 4)
@test Array(C[3]) ≈ Array(CC[3])
@test Array(C[4]) ≈ Array(CC[4])
end
@testset "cumulants update exceptions" begin
x = ones(10,4);
y = 2*ones(5,3);
s = DataMoments(x, 4, 2)
@test_throws DimensionMismatch cumulantsupdate!(s, y)
y = 2*ones(15,4)
@test_throws BoundsError cumulantsupdate!(s, y)
s1 = DataMoments(x[:, 1:3], 4, 2)
y = 2*ones(5,4)
@test_throws UndefVarError cumulantsupdat(s1, y)
end
println(VERSION)
if VERSION <= v"1.6.1"
@testset "save and load" begin
x = ones(10,4);
s = DataMoments(x, 4, 2)
@test savedm(s, "/tmp/cumdata.jld2") == nothing
#println(load("/tmp/cumdata.jld2"))
s1 = loaddm("/tmp/cumdata.jld2")
@test s1.X == s.X
@test s1.d == s.d
@test s1.b == s.b
@test Array(s1.M[4]) == Array(s.M[4])
end
end
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198,
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198,
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] | 1.950484 | 1,858 |
# Copyright (c) 2019 Guido Kraemer
# Copyright (c) 2019 Simon Castano
# Distributed under the MIT software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
"""
TxOut
Each output spends a certain number of satoshis, placing them under control of
anyone who can satisfy the provided pubkey script.
A `TxOut` is composed of
- `value::UInt64`, number of satoshis to spend. May be zero; the sum of all
outputs may not exceed the sum of satoshis previously spent to the outpoints
provided in the input section. (Exception: coinbase transactions spend the
block subsidy and collected transaction fees.)
- `scriptpubkey::Script` which defines the conditions which must be
satisfied to spend this output.
"""
struct TxOut
value :: UInt64
scriptpubkey :: Script
end
"""
TxOut(io::IOBuffer)
Parse an `IOBuffer` to a `TxOut`
"""
function TxOut(io::IOBuffer)
value = read(io, UInt64)
scriptpubkey = Script(io)
TxOut(value, scriptpubkey)
end
function Base.show(io::IO, output::TxOut)
println(io, "Transaction output: ₿" * string(output.value/100000000))
println(io, "ScriptPubKey:")
println(io, output.scriptpubkey)
end
"""
serialize(tx::TxOut) -> Vector{UInt8}
Returns the byte serialization of the transaction output
"""
function serialize(tx::TxOut)
result = bytes(tx.value, len=8, little_endian=true)
append!(result, serialize(tx.scriptpubkey))
return result
end
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] | 3.01833 | 491 |
∑ = sum
using StatsFuns:logistic
sigmoid = logistic
using CSV:File
using DataFrames
using Printf:@sprintf
# DataTypes
using LinearAlgebra:Transpose
Numeric = Union{Int64,Float64}
NumericV = Union{Array{Int64,1},Array{Float64,1}}
NumericM = Union{Array{Int64,2},Array{Float64,2},Transpose{Float64,Array{Float64,2}},Transpose{Int64,Array{Int64,2}}}
arrayNumericV = Union{Array{Array{Int64,1},1},Array{Array{Float64,1},1}}
TupleNumericM = Tuple{NumericM, NumericM};
function maskBiasUnits(array::NumericV)::NumericV
mask = ones(size(array));
mask[1,1] = 0;
maskedArray = array.*mask;
end
function maskBiasUnits(array::NumericM)::NumericM
mask = ones(size(array));
mask[:,1] .= 0;
maskedArray = array.*mask;
end
function powerElements(array::Union{NumericV,NumericM}, power::Int)::NumericM
# function defined to be broadcasted to elements of a tuple
array = array.^power
end
function getLabels(y::NumericV)::NumericV
labels = collect(BitSet(y));
sort!(labels)
end
function sortArrayByCol(array::Array{Any,2}, col::Int)
array = DataFrame(array);
sort!(array, col)
array = Matrix(array)
end
function getLayersQ(all_θ)::Int
θ_matricesQ = length(collect(all_θ));
layersQ = θ_matricesQ+1;
end
function nnClasificationFeedForward(all_θ, X, y, labels, layersQ::Int=getLayersQ(all_θ))
labelsQ = length(labels);
hx_and_indices = reshape([],0,labelsQ+1);
for lb in labels
ly = 1;
lb_idx = findall(==(lb), y)
a_ly = X[lb_idx,:];
ly += 1;
while ly ≤ layersQ
# add bias units to a_ly_lb
a_ly = [ones(size(a_ly)[1],1) a_ly];
a_previous_ly = a_ly;
θ_previous_ly = all_θ[ly-1];
z_ly = a_previous_ly*θ_previous_ly';
a_ly = sigmoid.(z_ly);
ly += 1;
end
ly = 1;
hx_lb = a_ly;
hx_and_indices_lb = hcat(hx_lb, lb_idx);
hx_and_indices = vcat(hx_and_indices, hx_and_indices_lb);
end
hx_and_indices = sortArrayByCol(hx_and_indices, labelsQ+1);
hx = hx_and_indices[:,1:labelsQ];
end
function nnCostClasification(all_θ::TupleNumericM, hx, y::NumericV, labels::NumericV, λ::Numeric)::Numeric
J = 0;
for lb in labels
lb_idx = findall(==(lb), y)
hx_lb = hx[lb_idx,:];
y_lb = vec(zeros(size(labels))); y_lb[lb] = 1;
J += ∑(-((log.(hx_lb))*y_lb)-(log.(1 .- hx_lb)*(1 .- y_lb)));
end
casesQ = size(hx)[1];
J = (1/casesQ)*J;
# add regularization cost
J += (λ/(2*casesQ))* ∑(∑.(powerElements.(maskBiasUnits.(all_θ),2)));
end
function calcSigmoidDerivate(z::Numeric)::Numeric
sigmoid(z)*(1-sigmoid(z));
end
function calcSigmoidDerivate(z::NumericM)::NumericM
sigmoid.(z).*(1 .- sigmoid.(z));
end
calcSigmoidGradient = calcSigmoidDerivate;
function calcNiceInitϵ(layerInputsQ::Int, layerOutputsQ::Int)::Numeric
initϵ = √6/√(layerInputsQ+layerOutputsQ)
end
function genRandomInitialθ(layerInputsQ::Int, layerOutputsQ::Int, initϵ::Numeric=calcNiceInitϵ(layerInputsQ,layerOutputsQ))::NumericM
initϵInt = floor(initϵ*100);
randInitialθ = rand(-initϵInt:initϵInt,layerOutputsQ,layerInputsQ+1);
randInitialθ = randInitialθ./100;
end
function substractElements(sub_array::Array{Any,1},vector::NumericV)
sub_array .- vectors
end
function gen_initial_z(all_θ, labelsQ::Int)
initial_z_singleLy = [];
for θ in all_θ
initial_z_singleLy = [initial_z_singleLy, ones(size(θ))];
end
initial_z_singleLy[1]=initial_z_singleLy[1][2];
initial_z = [];
for lb in labelsQ
initial_z = [initial_z, initial_z_singleLy];
end
initial_z = initial_z[2]
end
function nnClasificationBackPropagation(hx, all_θ, layersQ::Int, y, labels)
# calculate the gradients -i.e. partial derivates with respect to θ- of the cost (J)
nodesQ = labelsQ = length(labels);
casesQ = size(hx)[1];
ly = layersQ;
idx_v = collect(1:labelsQ);
outputLayer = map(idx -> (vec(hx[:,idx])), idx_v); #Matrix of subarrays where each of them correspond to each row of hx'
δ_ly = map(idx -> allCasesOutputLayer[idx] .- y, idx_v);
ly -= 1;
while ly ≥ 2
δ_previous_ly = δ_ly;
θ_ly = all_θ[ly];
z_ly =
end
#parameters
# csv files -> DataFrames -> arrays
X = Matrix{Float64}(File("./X.csv", header=false)
|> DataFrame);
y = vec(Matrix(File("./y.csv", header=false) |> DataFrame));
θ1 = Matrix{Float64}(File("./theta1.csv", header=false)
|> DataFrame);
θ2 = Matrix{Float64}(File("./theta2.csv", header=false) |> DataFrame);
λ = 1;
#implementation
all_θ = (θ1, θ2);
labels = getLabels(y);
layersQ = getLayersQ(all_θ);
hx = nnClasificationFeedForward(all_θ, X, y, labels, layersQ);
J = nnCostClasification(all_θ, hx, y, labels, λ);
allCasesδ_ly = nnClasificationBackPropagation(hx, layersQ, y, labels); | [
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] | 2.142043 | 2,281 |
xlocations(ex) = Expr(:call, :($YaoLocations.Locations), ex)
xctrl_locations(ex) = Expr(:call, :($YaoLocations.CtrlLocations), ex)
"""
@gate <locs> => <gate>
Syntax sugar for `apply(gate, locs)`, must be used inside `@device`.
See also [`@device`](@ref).
!!! tips
You don't have to write `@gate` in most cases inside `@device`.
But in case there is ambuigity, you can annotate the expression
with `@gate` explicitly.
"""
macro gate(ex::Expr)
@match ex begin
:($locs => $gate) => esc(xcall(GlobalRef(Intrinsics, :apply), gate, xlocations(locs)))
_ => error("syntax: invalid syntax, expect @gate <locs> => <gate>")
end
end
"""
@ctrl <ctrl_locs> <locs> => <gate>
Syntax sugar for `apply(gate, locs, ctrl_locs)`, must be used inside `@device`.
See also [`@device`](@ref).
"""
macro ctrl(ctrl_locs, ex::Expr)
@match ex begin
:($locs => $gate) => esc(
xcall(GlobalRef(Intrinsics, :apply), gate, xlocations(locs), xctrl_locations(ctrl_locs)),
)
_ => error("syntax: invalid syntax, expect @ctrl <ctrl_locs> <locs> => <gate>")
end
end
"""
@measure locs
Syntax sugar for `measure(locs)`.
"""
macro measure(locs)
esc(xcall(GlobalRef(Intrinsics, :measure), xlocations(locs)))
end
"""
@barrier locs
Syntax sugar for `barrier(locs)`.
"""
macro barrier(locs)
esc(xcall(GlobalRef(Intrinsics, :barrier), xlocations(locs)))
end
"""
@device <function def>
Annotate a Julia function as YaoLang device kernel.
"""
macro device(ex)
esc(device_m(__module__, ex))
end
"""
routine_stub(routine, args...)
A function to store the raw `CodeInfo` of defined routines
to support multiple dispatch. We don't directly define `main`
`gate`, `ctrl` etc. because we can't support multiple dispatch
on them directly.
"""
function routine_stub end
function device_m(mod::Module, ex)
is_function(ex) || error("expect a function definition")
jlfn = JLFunction(ex)
isnothing(jlfn.kwargs) || error("kwargs is not supported")
return codegen_routine(jlfn)
end
function codegen_routine(jlfn::JLFunction)
typename =
isnothing(jlfn.name) ? gensym(:routine) :
Meta.isexpr(jlfn.name, :(::)) ? jlfn.name.args[end] : Symbol("#", jlfn.name, "#")
return quote
$(codegen_routine_type(jlfn, typename))
$(codegen_device(jlfn, typename))
$(codegen_inference_limit_heuristics(jlfn, typename))
$(codegen_routine_stub(jlfn, typename))
$(codegen_binding(jlfn, typename))
end
end
function codegen_routine_type(def::JLFunction, typename)
def.name isa Symbol || isnothing(def.name) || return
name = isnothing(def.name) ? typename : def.name
jlstruct =
JLStruct(; name = typename, supertype = :($YaoCompiler.GenericRoutine{$(QuoteNode(name))}))
return codegen_ast(jlstruct)
end
function codegen_binding(def::JLFunction, typename)
Meta.isexpr(def.name, :(::)) && return
if isnothing(def.name)
return :(Core.@__doc__ $typename())
else
return :(Core.@__doc__ const $(def.name) = $typename())
end
end
function codegen_device(def::JLFunction, typename)
self = gensym(:self)
args = name_only.(def.args)
jlfn = JLFunction(;
name = :($self::$typename),
args = def.args,
whereparams = def.whereparams,
rettype = def.rettype,
line = def.line,
body = quote
$YaoCompiler.Operation($self, $(xtuple(args...)))
end,
)
return codegen_ast(jlfn)
end
function inference_limit_heuristics end
function codegen_inference_limit_heuristics(def::JLFunction, typename)
self = gensym(:self)
args = name_only.(def.args)
jlfn = JLFunction(;
name = :($YaoCompiler.inference_limit_heuristics),
args = [:($self::$typename), def.args...],
whereparams = def.whereparams,
rettype = def.rettype,
line = def.line,
body = quote
$YaoCompiler.Operation($self, $(xtuple(args...)))
end,
)
return codegen_ast(jlfn)
end
function codegen_routine_stub(def::JLFunction, typename)
self = @match def.name begin
:($name::$type) => name
:(::$type) => gensym(:self)
_ => gensym(:self)
end
jlfn = JLFunction(;
name = :($YaoCompiler.routine_stub),
args = [:($self::$typename), def.args...],
whereparams = def.whereparams,
rettype = def.rettype,
line = def.line,
body = transpile_gate_syntax(transpile_intrinsic(def.body)),
)
return codegen_ast(jlfn)
end
function is_syntax_macro(ex)
@match ex begin
Symbol("@gate") => true
Symbol("@ctrl") => true
Symbol("@measure") => true
Symbol("@barrier") => true
Expr(:., :YaoCompiler, QuoteNode(name)) => is_syntax_macro(name)
Expr(:., YaoCompiler, QuoteNode(name)) => is_syntax_macro(name)
GlobalRef(YaoCompiler, name) => is_syntax_macro(name)
_ => false
end
end
# NOTE: locs => gate is only a syntax sugar for gate(gate, locs)
function transpile_intrinsic(ex)
@match ex begin
Expr(:call, :apply, gate, locs) =>
xcall(GlobalRef(Intrinsics, :apply), gate, xlocations(locs))
Expr(:call, :apply, gate, locs, ctrl) =>
xcall(GlobalRef(Intrinsics, :apply), gate, xlocations(locs), xctrl_locations(ctrl))
Expr(:call, :measure, locs) => xcall(GlobalRef(Intrinsics, :measure), xlocations(locs))
Expr(:call, :barrier, locs) => xcall(GlobalRef(Intrinsics, :barrier), xlocations(locs))
Expr(:call, :expect, locs) => xcall(GlobalRef(Intrinsics, :expect), xlocations(locs))
# check misused keyword error
Expr(:call, :apply, _...) => error("syntax: apply is a preserved intrinsic function")
Expr(:call, :measure, _...) => error("syntax: apply is a preserved intrinsic function")
Expr(:call, :barrier, _...) => error("syntax: apply is a preserved intrinsic function")
Expr(:call, :expect, _...) => error("syntax: apply is a preserved intrinsic function")
Expr(head, args...) => Expr(head, map(transpile_intrinsic, args)...)
_ => ex
end
end
function transpile_gate_syntax(ex)
@match ex begin
# this only treat => syntax in block/let/if/for etc. as gate stmt
:($locs => $gate) => xcall(GlobalRef(Intrinsics, :apply), gate, xlocations(locs))
# this will appear in anonymous function definition
# TODO: disambuigity this and function contains only single line
# @device function circuit(theta, phi)
# 1 => X
# end
# Expr(:block, stmt1, line::LineNumberNode, stmt2) => ex
Expr(:macrocall, Symbol("@device"), _...) => error("syntax: cannot have nested @device")
Expr(:macrocall, name, args...) => begin
if is_syntax_macro(name)
return ex
else
# we force top scope locs=>gate to be treated as gate stmt inside
# all @device region including non-YaoCompiler macros to make things
# like @inbounds etc. work
return Expr(:macrocall, name, map(transpile_gate_syntax, args)...)
end
end
# we only white list other syntax here to be safe
Expr(:block, args...) ||
Expr(:if, args...) ||
Expr(:elseif, args...) ||
Expr(:let, args...) ||
Expr(:for, args...) ||
Expr(:try, args...) => Expr(ex.head, map(transpile_gate_syntax, args)...)
Expr(:function, call, body) => Expr(:function, call, transpile_gate_syntax(body))
_ => ex
end
end
@generated function Intrinsics.apply(::AbstractRegister, op::Operation{P,Args}) where {P,Args}
ci, nargs = obtain_codeinfo(op)
new = NewCodeInfo(ci)
register = insert!(new.slots, 2, Symbol("#register#"))
device = insert!(new.slots, 3, Symbol("#op#"))
unpack_device!(new, device, nargs)
for (v, stmt) in new
@switch stmt begin
@case Expr(:call, GlobalRef(&Intrinsics, name), args...)
new[v] = xcall(GlobalRef(Intrinsics, name), register, args...)
@case Expr(:(=), slot, Expr(:call, GlobalRef(&Intrinsics, name), args...))
new[v] = Expr(:(=), slot, Expr(:call, GlobalRef(Intrinsics, name), register, args...))
@case _
nothing
end
end
code_info = finish(new)
if code_info.method_for_inference_limit_heuristics === nothing
method = first(methods(inference_limit_heuristics, Tuple{P,Args.parameters...}))
code_info.method_for_inference_limit_heuristics = method
end
return code_info
end
function _update_slot_stmt(f, new, v, stmt)
@switch stmt begin
@case Expr(:call, _...)
new[v] = f(stmt)
@case Expr(:(=), slot, rhs)
new[v] = Expr(:(=), slot, f(rhs))
@case _
nothing
end
end
@generated function Intrinsics.apply(
::AbstractRegister,
op::Operation{P,Args},
::Locations,
) where {P,Args}
ci, nargs = obtain_codeinfo(op)
new = NewCodeInfo(ci)
register = insert!(new.slots, 2, Symbol("#register#"))
device = insert!(new.slots, 3, Symbol("#op#"))
glob_locs = insert!(new.slots, 4, Symbol("#locs#"))
unpack_device!(new, device, nargs)
for (v, stmt) in new
_update_slot_stmt(new, v, stmt) do stmt
@switch stmt begin
@case Expr(:call, GlobalRef(&Intrinsics, :apply), gate, locs)
new_locs = insert!(new, v, xcall(GlobalRef(Base, :getindex), glob_locs, locs))
return xcall(GlobalRef(Intrinsics, :apply), register, gate, new_locs)
@case Expr(:call, GlobalRef(&Intrinsics, :apply), gate, locs, ctrl)
new_locs = insert!(new, v, xcall(GlobalRef(Base, :getindex), glob_locs, locs))
new_ctrl = insert!(new, v, xcall(GlobalRef(Base, :getindex), glob_locs, ctrl))
return xcall(GlobalRef(Intrinsics, :apply), register, gate, new_locs, new_ctrl)
@case Expr(:call, GlobalRef(&Intrinsics, :measure), locs)
new_locs = insert!(new, v, xcall(GlobalRef(Base, :getindex), glob_locs, locs))
return xcall(GlobalRef(Intrinsics, :measure), register, new_locs)
@case Expr(:call, GlobalRef(&Intrinsics, :barrier), locs)
new_locs = insert!(new, v, xcall(GlobalRef(Base, :getindex), glob_locs, locs))
return xcall(GlobalRef(Intrinsics, :barrier), register, new_locs)
@case Expr(:call, GlobalRef(&Intrinsics, :expect), locs)
new_locs = insert!(new, v, xcall(GlobalRef(Base, :getindex), glob_locs, locs))
return xcall(GlobalRef(Intrinsics, :expect), register, new_locs)
@case _
return stmt
end
end
end
code_info = finish(new)
if code_info.method_for_inference_limit_heuristics === nothing
method = first(methods(inference_limit_heuristics, Tuple{P,Args.parameters...}))
code_info.method_for_inference_limit_heuristics = method
end
return code_info
end
@generated function Intrinsics.apply(
::AbstractRegister,
op::Operation{P,Args},
::Locations,
::CtrlLocations,
) where {P,Args}
ci, nargs = obtain_codeinfo(op)
new = NewCodeInfo(ci)
register = insert!(new.slots, 2, Symbol("#register#"))
device = insert!(new.slots, 3, Symbol("#op#"))
glob_locs = insert!(new.slots, 4, Symbol("#locs#"))
glob_ctrl = insert!(new.slots, 5, Symbol("#ctrl#"))
unpack_device!(new, device, nargs)
for (v, stmt) in new
_update_slot_stmt(new, v, stmt) do stmt
@switch stmt begin
@case Expr(:call, GlobalRef(&Intrinsics, :apply), gate, locs)
new_locs = insert!(new, v, xcall(GlobalRef(Base, :getindex), glob_locs, locs))
return xcall(GlobalRef(Intrinsics, :apply), register, gate, new_locs, glob_ctrl)
@case Expr(:call, GlobalRef(&Intrinsics, :apply), gate, locs, ctrl)
new_locs = push!(new, xcall(GlobalRef(Base, :getindex), glob_locs, locs))
new_ctrl = push!(new, xcall(GlobalRef(Base, :getindex), glob_locs, ctrl))
new_ctrl =
push!(new, xcall(GlobalRef(YaoLocations, :merge_locations), new_ctrl, glob_ctrl))
return xcall(GlobalRef(Intrinsics, :apply), register, gate, new_locs, new_ctrl)
@case Expr(:call, GlobalRef(&Intrinsics, :measure), locs)
return :(error("cannot apply quantum control on measurement"))
@case Expr(:call, GlobalRef(&Intrinsics, :barrier), locs)
new_locs = insert!(new, v, xcall(GlobalRef(Base, :getindex), glob_locs, locs))
return xcall(GlobalRef(Intrinsics, :barrier), register, new_locs)
@case Expr(:call, GlobalRef(&Intrinsics, :expect), locs)
return :(error("cannot apply quantum control on measurement (expectation)"))
@case _
return stmt
end
end
end
code_info = finish(new)
if code_info.method_for_inference_limit_heuristics === nothing
method = first(methods(inference_limit_heuristics, Tuple{P,Args.parameters...}))
code_info.method_for_inference_limit_heuristics = method
end
return code_info
end
function obtain_codeinfo(::Type{Operation{P,Args}}) where {P,Args}
nargs = length(Args.parameters)
tt = Tuple{P,Args.parameters...}
ms = methods(routine_stub, tt)
@assert length(ms) == 1
method = first(ms)
method_args = Tuple{typeof(routine_stub),tt.parameters...}
mi = Core.Compiler.specialize_method(method, method_args, Core.svec())
ci = Core.Compiler.retrieve_code_info(mi)
name = routine_name(P)
linetable = Any[]
for lineinfo in ci.linetable
push!(
linetable,
Core.LineInfoNode(
lineinfo.module,
name,
lineinfo.file,
lineinfo.line,
lineinfo.inlined_at,
),
)
end
ci.linetable = linetable
ci.edges = Core.MethodInstance[mi]
return ci, nargs
end
function unpack_device!(new::NewCodeInfo, op::NewSlotNumber, nargs::Int)
# %parent = op.parent
parent = push!(new, Expr(:call, GlobalRef(Base, :getfield), op, QuoteNode(:parent)))
if nargs > 0
# %args = op.args
args = push!(new, Expr(:call, GlobalRef(Base, :getfield), op, QuoteNode(:args)))
end
# %self = %parent
push!(new, Expr(:(=), SlotNumber(2), parent))
for k in 1:nargs
push!(new, Expr(:(=), SlotNumber(k + 2), xcall(GlobalRef(Base, :getindex), args, k)))
end
return new
end
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] | 2.192574 | 6,787 |
module EquivalentCircuits
export circuitevolution
export parameteroptimisation
export loadpopulation
export Circuit, EquivalentCircuit
using Random, Combinatorics, GeneralizedGenerated, DelimitedFiles, Distributions, Optim
import Base: isless, length
include("Circuits.jl")
include("CircuitFunction.jl")
include("EvolutionOperators.jl")
include("ObjectiveFunction.jl")
include("OptimizeParameters.jl")
include("SimulateImpedance.jl")
include("CircuitSimplification.jl")
include("CircuitLibrary.jl")
include("RedundancyTesting.jl")
include("CircuitEvolution.jl")
end
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] | 3.033493 | 209 |
# Ported to Julia by Andy Ferris, 2016, and re-released under an MIT license.
#/**
# * Copyright (c) Charles Karney (2008-2015) <charles@karney.com> and licensed
# * under the MIT/X11 License. For more information, see
# * http://geographiclib.sourceforge.net/
# **********************************************************************/
"""
(x, y, gamma, k) = polarst_fwd(northpole::Bool, k0::Float64, tm::TransverseMercator, lat, lon)
Perform polar-stereographic projection of `lat` and `lon` with respect to north
or south pole `northpole` and horizontal scaling `k0` (`= 0.994` for UPS).
`γ` and `k` are the local convergence and scaling factors, respectively.
"""
function polarst_fwd(northp::Bool, k0::Float64, tm::TransverseMercator, lat, lon) # k0 is scale factor...
lat = LatFix(lat) * (northp ? 1 : -1)
tau = tand(lat)
secphi = hypot(1.0, tau)
taup = taupf(tau, tm.e2) # TODO revert to C++ es here?
rho = hypot(1.0, taup) + abs(taup)
rho = (taup >= 0 ? (lat != 90 ? 1/rho : 0.0) : rho)
rho *= 2 * k0 * tm.a / tm.c
k = (lat != 90 ? (rho / tm.a) * secphi * sqrt(tm.e2m + tm.e2 / (secphi*secphi)) : k0)
x = sind(lon)
y = cosd(lon)
x *= rho
y *= (northp ? -rho : rho)
gamma = AngNormalize(northp ? lon : -lon)
return (x, y, gamma, k)
end
"""
(lat, lon, gamma, k) = polarst_inv(northp::Bool, k0::Float64, tm::TransverseMercator, x, y)
Invert polar-stereographic projection of `x` and `y` with respect to north
or south pole `northpole` and horizontal scaling `k0` (`= 0.994` for UPS).
`γ` and `k` are the local convergence and scaling factors, respectively.
"""
function polarst_inv(northp::Bool, k0::Float64, tm::TransverseMercator, x, y) # k0 is scale factor...
rho = hypot(x, y)
t = (rho > 0 ? rho / (2 * k0 * tm.a / tm.c) : eps(Float64)*eps(Float64))
taup = (1 / t - t) / 2
tau = tauf(taup, tm.e2) # TODO revert to C++ es here?
secphi = hypot(1.0, tau)
k = (rho > 0 ? (rho / tm.a) * secphi * sqrt(tm.e2m + tm.e2 / (secphi*secphi)) : k0)
lat = (northp ? 1 : -1) * atand(tau)
lon = atan2(x, northp ? -y : y ) * 180/pi
gamma = AngNormalize(northp ? lon : -lon)
return (lat, lon, gamma, k)
end
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] | 2.341463 | 943 |
module MarkerFile
using ..LIKWID: LibLikwid
"""
Reads in the result file of an application run instrumented by the LIKWID Marker API.
*Note:* julia must have been started under `likwid-perfctr ... -m`.
"""
function read(fp::AbstractString)
ret = LibLikwid.perfmon_readMarkerFile(fp)
return ret ≥ 0
end
"""
Return the number of regions in an application run.
"""
numregions() = Int(LibLikwid.perfmon_getNumberOfRegions())
"""
Return the region tag for the region identified by `ridx`.
"""
regiontag(ridx) = unsafe_string(LibLikwid.perfmon_getTagOfRegion(ridx))
"""
Return the group id for the region identified by `ridx`.
"""
regiongroup(ridx) = Int(LibLikwid.perfmon_getGroupOfRegion(ridx))
"""
Return the number of events of the region identified by `ridx`.
"""
regionevents(ridx) = Int(LibLikwid.perfmon_getEventsOfRegion(ridx))
"""
Return the number of metrics of the region identified by `ridx`.
"""
regionmetrics(ridx) = Int(LibLikwid.perfmon_getMetricsOfRegion(ridx))
"""
Return the number of threads of the region identified by `ridx`.
"""
regionthreads(ridx) = Int(LibLikwid.perfmon_getThreadsOfRegion(ridx))
"""
Return the accumulated measurement time for the region identified by `rid` and the thread index `tidx`.
"""
regiontime(ridx, tidx) = LibLikwid.perfmon_getTimeOfRegion(ridx, tidx)
"""
Return the call count for the region identified by `rid` and the thread index `tidx`.
"""
regioncount(ridx, tidx) = LibLikwid.perfmon_getCountOfRegion(ridx, tidx)
"""
Return the call count for the region identified by `ridx`, the event index `eidx` and the thread index `tidx`.
"""
regionresult(ridx, eidx, tidx) = LibLikwid.perfmon_getResultOfRegionThread(ridx, eidx, tidx)
"""
Return the call count for the region identified by `ridx`, the metric index `midx` and the thread index `tidx`.
"""
regionmetric(ridx, midx, tidx) = LibLikwid.perfmon_getMetricOfRegionThread(ridx, midx, tidx)
end # module
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3095,
87,
11,
29770,
87,
8,
198,
198,
437,
1303,
8265,
198
] | 2.910876 | 662 |
using Test
using MaxEntropyGraphs
include("./models.jl")
| [
3500,
6208,
198,
3500,
5436,
14539,
28338,
37065,
82,
198,
198,
17256,
7,
1911,
14,
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13,
20362,
4943,
628
] | 2.95 | 20 |
using PrettyTables
function get_fermi()
rex = r"@@@ Average Fock Time:\s([0-9]*\.?[0-9]*)"
fpath = joinpath(@__DIR__, "fermi/output.dat")
timings = zeros(22)
i = 1
for l = eachline(fpath)
m = match(rex, l)
if m !== nothing
timings[i] = m.captures[1] |> String |> x->parse(Float64,x)
i += 1
end
end
@assert i == 23 # meaning that 22 entries were found
return timings
end
function get_psi4()
Frex = r"HF:\sForm\sF\s+:.+?([0-9]*\.?[0-9]*)w\s+([0-9]+)\scalls"
Grex = r"HF:\sForm\sG\s+:.+?([0-9]*\.?[0-9]*)w\s+([0-9]+)\scalls"
cd(joinpath(@__DIR__, "psi4"))
timings = zeros(22)
# For each molecule
for i = eachindex(timings)
Ncalls = []
Ft = []
Gt = []
for l = eachline("S$i/timer.dat")
Fm = match(Frex, l)
Gm = match(Grex, l)
if Fm !== nothing
t = Fm.captures[1] |> String |> x->parse(Float64,x)
N = Fm.captures[2] |> String |> x->parse(Int,x)
push!(Ncalls, N)
push!(Ft, t)
end
if Gm !== nothing
t = Gm.captures[1] |> String |> x->parse(Float64,x)
N = Gm.captures[2] |> String |> x->parse(Int,x)
push!(Ncalls, N)
push!(Gt, t)
end
end
# Make sure all data is consistent
@assert all(Ncalls .== Ncalls[1])
@assert all(Ft .== Ft[1])
@assert all(Gt .== Gt[1])
# Compute timing
timings[i] = (Ft[1] + Gt[1]) / Ncalls[1]
end
cd("..")
return timings
end
if abspath(PROGRAM_FILE) == @__FILE__
fermi = get_fermi()
psi4 = get_psi4()
@pt :header = ["Fermi", "Psi4"] [fermi psi4]
end
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] | 1.739845 | 1,034 |
# Add dependencies
using Pkg
Pkg.add(Pkg.PackageSpec(;name="Git", version="1.2.1"))
Pkg.add(Pkg.PackageSpec(;name="TOML", version="1.0.0"))
using TOML
using Git
# Extract the version number to be updated
VERSION = ""
if length(ARGS) > 0
VERSION = ARGS[1]
end
GITHUB_REPOSITORY = ENV["GITHUB_REPOSITORY"]
TOKEN = ""
URL = ""
if haskey(ENV, "GITHUB_TOKEN")
TOKEN = ENV["GITHUB_TOKEN"]
URL = "https://x-access-token:$(TOKEN)@github.com/$(GITHUB_REPOSITORY).git"
end
# Read the Project.toml file in the package
fname = "Project.toml";
dict_project = TOML.parsefile(fname)
# If no version is given, use the version number in the Project.toml
if isempty(VERSION)
VERSION = dict_project["version"]
else
# Santity
VERSION = split(VERSION, "-")[1]
VERSION = replace(VERSION, "v" => "")
VERSION = replace(VERSION, "V" => "")
# Update the version number in the Project.toml
if dict_project["version"] != VERSION
dict_project["version"] = VERSION
open(fname, "w") do io
TOML.print(io, dict_project)
end
# Commit the new Project.toml
run(`$(git()) add Project.toml`)
run(`$(git()) commit -m "Update version to v$(VERSION)"`)
run(`$(git()) restore .`)
if isempty(URL)
run(`$(git()) push`)
else
run(`$(git()) push $(URL)`)
end
end
end
# Set output
run(`echo "::set-output name=version::$(VERSION)"`) | [
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] | 2.281447 | 636 |
module TestPreservingFuncs
using Base.Test
using DataArrays
using DataFrames
using Dates
using TimeData
println("Running type preserving function tests")
allTypes = (:Timedata, :Timenum, :Timematr)
################
## hcat tests ##
################
tm = Timematr(rand(2, 3))
hcat(tm[:, 1], tm[:, 2], tm[:, 3])
td = Timedata(rand(2, 3))
hcat(td[:, 1], td[:, 2])
hcat(td[:, 1], td[:, 2], td[:, 1])
hcat(tm[:, 1], tm[:, 2], tm[:, 1])
@test_throws ErrorException hcat(tm[:, 1], td[:, 2])
@test_throws ErrorException hcat(tm[:, 1], tm[:, 2], td[:, 2])
################
## vcat tests ##
################
## test information retrieval functions
for t in allTypes
eval(quote
## unequal column names
td = $(t)(rand(10, 2))
td2 = $(t)(rand(10,2), [:a, :b])
@test_throws ErrorException vcat(td, td2)
## unequal indices
td = $(t)(rand(10,2), [:a, :b])
td2 = $(t)(rand(10,2), [:a, :b],
[Date(2010,1,1):Date(2010,1,10)])
@test_throws ErrorException vcat(td, td2)
## vcat
df = DataFrame()
df[:a] = @data([0.4, 0.3])
df[:b] = @data([0.3, 0.8])
dats1 = [Date(2014,1,1):Date(2014,1,2)]
dats2 = [Date(2014,1,3):Date(2014,1,4)]
td = $(t)(df, dats1)
td2 = $(t)(df, dats2)
td3 = $(t)(vcat(df, df), [dats1, dats2])
td4 = $(t)(vcat(df, df, df), [dats1, dats2, dats1])
## multiple arguments
@test isequal(vcat(td, td2), td3)
@test isequal(vcat(td, td2, td), td4)
## throw error for unequal types
td = Timedata(rand(4, 3))
tm = Timematr(rand(4, 3))
@test_throws ErrorException vcat(tm, td)
@test_throws ErrorException vcat(tm, td, td)
end)
end
##################
## flipud tests ##
##################
for t in allTypes
eval(quote
nams = [:a, :b]
vals = [0.2 0.4; 0.6 0.8]
df = composeDataFrame(vals, nams)
df2 = composeDataFrame(flipud(vals), nams)
td = $(t)(df)
expFlipped = $(t)(df2, flipud(idx(td)))
@test flipud(td) == expFlipped
@test isequal(expFlipped, flipud(td))
@test flipud(flipud(td)) == td
@test isequal(flipud(flipud(td)), td)
end)
end
##########
## narm ##
##########
df = DataFrame()
df[:a] = @data([3, NA])
df[:b] = @data([4, NA])
td = Timedata(df)
tn = Timenum(df)
tm = Timematr(rand(2, 3))
td = Timedata(df)
dfShort = DataFrame()
dfShort[:a] = @data([3])
dfShort[:b] = @data([4])
tdShort = Timedata(dfShort)
tnShort = Timenum(dfShort)
tm = Timematr(rand(2, 3))
@test narm(td) == tdShort
@test narm(tn) == tnShort
@test narm(tm) == tm
########################
## asTd / asTm / asTn ##
########################
## test isaRowVector
@test isaRowVector([1 2 3])
@test !isaRowVector([1, 2, 3])
kk = Array(Float64, 1, 1)
@test isaRowVector(kk)
@test !isaRowVector([1])
@test_throws ErrorException isaRowVector(1)
## test asArrayOfEqualDimensions
td = Timedata(rand(2, 2))
@test asArrayOfEqualDimensions([1 2], td) == [1 2; 1 2]
@test asArrayOfEqualDimensions([1, 2], td) == [1 1; 2 2]
kk = Array(Int, 1, 1)
kk[1] = 1
@test asArrayOfEqualDimensions([1], td[1, 1]) == kk
@test asArrayOfEqualDimensions(1, td[1, 1]) == kk
@test asArrayOfEqualDimensions(1, td) == [1 1; 1 1]
@test_throws ErrorException asArrayOfEqualDimensions([3 4; 5 6], td[1, 1])
df = DataFrame(x1 = [1, 2], x2 = [1, 2])
@test TimeData.asTd([1, 2], td) == TimeData.Timedata(df)
df = DataFrame(x1 = [1, 1], x2 = [2, 2])
@test TimeData.asTd([1 2], td) == TimeData.Timedata(df)
end
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] | 2.050251 | 1,791 |
using Statistics
import Base.Meta: isexpr
"""
ret = @freshexec [setup_ex] ex
Runs `ex` in an external process and gets back the final result (, which is supposed to be
such a simple Julia object that we can restore it from its string representation).
Running in external process can be useful for testing JET analysis, because:
- the first time analysis is not affected by native code cache and JET's global report cache
- we can do something that might break a Julia process without wondering it breaks the
original test process later on
The optional positional argument `setup_ex` runs before each execution of `ex` and defaults
to `JET_LOAD_EX`, which just loads JET into the process.
"""
macro freshexec(args...)
args = map(a->Expr(:quote,a), args)
return Expr(:escape, Expr(:call, GlobalRef(@__MODULE__, :freshexec), __module__, args...))
end
freshexec(mod, ex) = freshexec(mod, JET_LOAD_EX, ex)
freshexec(args...) = _freshexec(args..., collect_last_result)
function collect_last_result(exs)
lines = string.(exs)
lines[end] = "ret = $(lines[end])"
return join(lines, '\n')
end
"""
stats = @benchmark_freshexec [setup_ex] ex
Runs `ex` in an external process and collects execution statistics from [`@timed`](@ref).
This is particularly useful for testing the performance of first-time analysis, where
the native code cache and JET's global report cache have no effect for its performance.
The optional positional argument `setup_ex` runs before each execution of `ex` and its
execution statistis are not included in the benchmark result; it defaults to
`JET_WARMUP_EX`, which loads JET and runs a warm up analysis `@profile identity(nothing)`.
"""
macro benchmark_freshexec(args...)
args = map(a->Expr(:quote,a), args)
return Expr(:escape, Expr(:call, GlobalRef(@__MODULE__, :benchmark_freshexec), __module__, args...))
end
benchmark_freshexec(mod, ex) = benchmark_freshexec(mod, JET_WARMUP_EX, ex)
benchmark_freshexec(args...) = _freshexec(args..., collect_statistics)
function collect_statistics(exs)
return """
ret = @timed begin
$(join(exs, '\n'))
nothing # ensure `stats` can be parsed
end
"""
end
"""
stats = @nbenchmark_freshexec [ntimes = 5] [setup_ex] ex
Runs `ex` in an external process multiple times (which can be configured by the optional
keyword argument `ntimes`), and collects execution statistics from [`@timed`](@ref).
The statistics are generated by taking the `mean` of all the trials.
This is particularly useful for benchmarking the performance of first-time analysis, where
the native code cache and JET's global report cache have no effect for its performance.
The optional positional argument `setup_ex` runs before each execution of `ex` and its
execution statistis are not included in the benchmark result; it defaults to
`JET_WARMUP_EX`, which loads JET and runs a warm up analysis `@profile identity(nothing)`.
"""
macro nbenchmark_freshexec(args...)
isn(x) = isexpr(x, :(=)) && first(x.args) === :ntimes
i = findfirst(isn, args)
ntimes = i === nothing ? 5 : Expr(:quote, last(args[i].args))
args = map(a->Expr(:quote,a), filter(!isn, args))
return Expr(:escape, Expr(:call, GlobalRef(@__MODULE__, :nbenchmark_freshexec), ntimes, __module__, args...))
end
nbenchmark_freshexec(ntimes, mod, ex) = nbenchmark_freshexec(ntimes, mod, JET_WARMUP_EX, ex)
function nbenchmark_freshexec(ntimes, args...)
stats = [_freshexec(args..., collect_statistics) for _ in 1:ntimes]
return (; time = mean(getproperty.(stats, :time)),
bytes = mean(getproperty.(stats, :bytes)),
gctime = mean(getproperty.(stats, :gctime)),
)
end
function _freshexec(mod, setup_ex, ex, exs2script)
# we need to flatten block expression into a toplevel expression to correctly handle
# e.g. macro expansions
setup_exs = isexpr(setup_ex, :block) ? setup_ex.args : [setup_ex]
setup_script = join(string.(setup_exs), '\n')
exs = isexpr(ex, :block) ? ex.args : [ex]
script = exs2script(exs)
prog = """
old = stdout
rw, wr = redirect_stdout()
$(setup_script)
$(script)
redirect_stdout(old)
close(rw); close(wr)
println(stdout, repr(ret))
"""
cmd = Cmd([JULIA_BIN, "-e", prog])
io = IOBuffer()
run(pipeline(cmd; stdout = io))
return Core.eval(mod, Meta.parse(String(take!(io))))
end
const JET_LOAD_EX = :(using JET)
const JET_WARMUP_EX = quote
using JET
@profile_call identity(nothing) # warm-up for JET analysis
end
const JULIA_BIN = normpath(Sys.BINDIR, "julia")
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] | 2.833742 | 1,630 |
@test_throws ArgumentError OrbitalIndex(-1, -2)
@test_throws ArgumentError OrbitalIndex(3, -1)
@test_throws ArgumentError OrbitalIndex(3, 5)
@testset ">> Operators" begin
@test OrbitalIndex(0, 0) == OrbitalIndex(0, 0)
@test OrbitalIndex(2, 1) == OrbitalIndex(2, 1)
@test OrbitalIndex(2, 1) != OrbitalIndex(2, 0)
@test OrbitalIndex(2, 1) ≠ OrbitalIndex(2, 0)
@test OrbitalIndex(2, 1) > OrbitalIndex(2, 0)
@test OrbitalIndex(2, 1) > OrbitalIndex(1, 0)
@test OrbitalIndex(2, 1) < OrbitalIndex(3, 0)
end
@testset ">> Linear indexing for first numbers" begin
index = 1
for n in 0:100
for l in 0:n
orb = OrbitalIndex(n, l)
@test convert(Integer, orb) == index
@test convert(OrbitalIndex, convert(Integer, orb)) == orb
index += 1
end
end
end
@testset ">> conversions for n=$n" for n in rand(0:1000000, 5)
@testset ">> conversions for l=$l" for l in rand(0:n, 5)
orb = OrbitalIndex(n, l)
back = convert(OrbitalIndex, convert(Integer, orb))
@test typeof(back) === typeof(orb)
@test back == orb
end
end
@testset ">> String indexing" begin
@test nl"2, 2" == OrbitalIndex(2, 2)
@test nl"3, 2" == OrbitalIndex(3, 2)
end
@testset ">> Printing" begin
buffer = IOBuffer()
print(buffer, nl"12, 4")
seekstart(buffer)
@test readstring(buffer) == "n=12, l=4"
end
@testset ">> looping by 1" begin
index = 3
for u in nl"1,1":nl"5, 2"
@test convert(Integer, u) == index
index += 1
end
@test convert(Integer, nl"5, 3") == index
end
@testset ">> looping by (n, 0)" begin
n = 1
index = 0
for u in nl"1,1":nl"2, 0":nl"5, 2"
@test u.l == 1
@test u.n == n
index = convert(Integer, u)
n += 2
end
@test index == convert(Integer, nl"5, 1")
end
@testset ">> looping by (n, l)" begin
expected = [OrbitalIndex(1, 1), OrbitalIndex(4, 3), OrbitalIndex(7, 5)]
actual = [u for u in nl"1,1":nl"3, 2":nl"9, 2"]
@test expected == actual
end
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1,
18,
11,
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1,
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] | 2.227273 | 924 |
module TestCompositeSimple
using Test
using Mimi
import Mimi:
ComponentId, ComponentPath, ComponentDef, AbstractComponentDef,
CompositeComponentDef, ModelDef, build, time_labels, compdef, find_comp,
import_params!
@defcomp Comp1 begin
par_1_1 = Parameter(index=[time]) # external input
var_1_1 = Variable(index=[time]) # computed
foo = Parameter()
function run_timestep(p, v, d, t)
v.var_1_1[t] = p.par_1_1[t]
end
end
@defcomp Comp2 begin
par_2_1 = Parameter(index=[time]) # connected to Comp1.var_1_1
var_2_1 = Variable(index=[time]) # computed
foo = Parameter()
function run_timestep(p, v, d, t)
v.var_2_1[t] = p.par_2_1[t] + p.foo
end
end
@defcomposite A begin
Component(Comp1)
Component(Comp2)
foo1 = Parameter(Comp1.foo)
foo2 = Parameter(Comp2.foo, default=100)
connect(Comp2.par_2_1, Comp1.var_1_1)
end
@defcomposite Top begin
Component(A)
fooA1 = Parameter(A.foo1)
fooA2 = Parameter(A.foo2)
end
m = Model()
set_dimension!(m, :time, 2005:2020)
add_comp!(m, Top)
update_param!(m, :Top, :fooA1, 10)
update_param!(m, :Top, :par_1_1, 1:16) # unshared
run(m)
m = Model()
set_dimension!(m, :time, 2005:2020)
add_comp!(m, Top)
update_param!(m, :Top, :fooA1, 10)
@test_throws ErrorException add_shared_param!(m, :par_1_1, 1:16) # need to give indices
add_shared_param!(m, :par_1_1, 1:16, dims = [:time]) # shared
connect_param!(m, :Top, :par_1_1, :par_1_1)
run(m)
end
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include("Tanh.jl")
include("jacobian.jl")
using Base.Test
X = map(Float32, randn(5, 3))
l = Tanh{Float32}()
# Test grad. wrt. input X
Dfwd = jacobian_fwd(l, X)
Dbwd = jacobian_bwd(l, X)
@test_approx_eq Dfwd Dbwd
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import POMDPs.initialstate
const IVec8 = SVector{8, Int}
@with_kw struct AODiscreteVDPTagPOMDP <: POMDP{TagState, TagAction, IVec8}
cpomdp::VDPTagPOMDP = VDPTagPOMDP()
angles::Array{Float64, 1} = range(0, stop=2*pi, length=11)[1:end-1]
binsize::Float64 = 0.5
end
AODiscreteVDPTagPOMDP(p::VDPTagPOMDP, n_angles::Int, binsize::Float64) = AODiscreteVDPTagPOMDP(p, range(0, stop=2*pi, length=n_angles+1)[1:end-1], binsize)
@with_kw struct ADiscreteVDPTagPOMDP <: POMDP{TagState, TagAction, Vec8}
cpomdp::VDPTagPOMDP = VDPTagPOMDP()
angles::Array{Float64, 1} = range(0, stop=2*pi, length=11)[1:end-1]
end
ADiscreteVDPTagPOMDP(p::VDPTagPOMDP, n_angles::Int) = ADiscreteVDPTagPOMDP(p, range(0, stop=2*pi, length=n_angles+1)[1:end-1])
const DiscreteVDPTagProblem = Union{AODiscreteVDPTagPOMDP, ADiscreteVDPTagPOMDP}
cproblem(p::DiscreteVDPTagProblem) = p.cpomdp::VDPTagPOMDP
mdp(p::DiscreteVDPTagProblem) = mdp(cproblem(p))::VDPTagMDP
convert_s(::Type{T}, x::T, p) where T = x
convert_a(::Type{T}, x::T, p) where T = x
convert_o(::Type{T}, x::T, p) where T = x
# observation
function convert_o(::Type{IVec8}, o::Vec8, p::AODiscreteVDPTagPOMDP)
return floor.(Int, (o./p.binsize)::Vec8)::IVec8
end
# convert_o(::Type{Vec8}, o::Int, p::DiscreteVDPTagProblem) = (o-0.5)*2*pi/p.n_obs_angles
n_states(p::DiscreteVDPTagProblem) = Inf
n_actions(p::DiscreteVDPTagProblem) = 2*length(p.angles)
POMDPs.discount(p::DiscreteVDPTagProblem) = discount(cproblem(p))
POMDPs.isterminal(p::DiscreteVDPTagProblem, s::TagState) = mdp(p).tag_terminate && norm(s.agent-s.target) < mdp(p).tag_radius
POMDPs.actions(p::DiscreteVDPTagProblem) = [TagAction(look, angle) for look in [false, true] for angle in p.angles]
POMDPs.actionindex(p::DiscreteVDPTagProblem, a::TagAction) = a.look * length(p.angles) + findfirst(x->x==a.angle, p.angles)
POMDPs.transition(p::DiscreteVDPTagProblem, s::TagState, a::TagAction) = transition(cproblem(p), s, a)
POMDPs.initialstate(p::DiscreteVDPTagProblem) = VDPInitDist()
POMDPs.reward(p::DiscreteVDPTagProblem, s::TagState, a::TagAction, sp::TagState) = reward(cproblem(p), s, a, sp)
POMDPs.observation(p::ADiscreteVDPTagPOMDP, a::TagAction, sp::TagState) = observation(cproblem(p), a, sp)
struct DiscreteBeamDist
beam_dist::BeamDist
pomdp::POMDP
end
POMDPs.observation(p::AODiscreteVDPTagPOMDP, a::TagAction, sp::TagState) = DiscreteBeamDist(observation(cproblem(p), a, sp), p)
rand(rng::AbstractRNG, d::DiscreteBeamDist) = convert_o(IVec8, rand(rng, d.beam_dist), d.pomdp)
function POMDPs.pdf(d::DiscreteBeamDist, o::IVec8)
p = 1.0
lower = o .* d.pomdp.binsize
upper = (o .+ 1) .* d.pomdp.binsize
d = d.beam_dist
for i in 1:length(o)
if i == d.abeam
p *= cdf(d.an, upper[i]) - cdf(d.an, lower[i])
else
p *= cdf(d.n, upper[i]) - cdf(d.n, lower[i])
end
end
return p
end | [
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] | 2.124188 | 1,385 |
module REPLMode
import Pkg3
using Pkg3.Types
using Pkg3.Display
using Pkg3.Operations
import Base: LineEdit, REPL, REPLCompletions
import Base.Random: UUID
using Base.Markdown
const cmds = Dict(
"help" => :help,
"?" => :help,
"status" => :status,
"st" => :status,
"." => :status,
"search" => :search,
"find" => :search,
"/" => :search,
"add" => :add,
"install" => :add,
"+" => :add,
"rm" => :rm,
"remove" => :rm,
"uninstall" => :rm,
"-" => :rm,
"up" => :up,
"update" => :up,
"upgrade" => :up,
"test" => :test,
"gc" => :gc,
"fsck" => :fsck,
"preview" => :preview,
)
const opts = Dict(
"env" => :env,
"project" => :project,
"p" => :project,
"manifest" => :manifest,
"m" => :manifest,
"major" => :major,
"minor" => :minor,
"patch" => :patch,
"fixed" => :fixed,
"coverage" => :coverage,
)
function parse_option(word::AbstractString)
m = match(r"^(?: -([a-z]) | --([a-z]{2,})(?:\s*=\s*(\S*))? )$"ix, word)
m == nothing && cmderror("invalid option: ", repr(word))
k = m.captures[1] != nothing ? m.captures[1] : m.captures[2]
haskey(opts, k) || cmderror("invalid option: ", repr(word))
m.captures[3] == nothing ?
(:opt, opts[k]) : (:opt, opts[k], String(m.captures[3]))
end
let uuid = raw"(?i)[0-9a-z]{8}-[0-9a-z]{4}-[0-9a-z]{4}-[0-9a-z]{4}-[0-9a-z]{12}(?-i)",
name = raw"(\w+)(?:\.jl)?"
global name_re = Regex("^$name\$")
global uuid_re = Regex("^$uuid\$")
global name_uuid_re = Regex("^$name\\s*=\\s*($uuid)\$")
end
const lex_re = r"^[\?\./\+\-] | [^@\s]+\s*=\s*[^@\s]+ | @\s*[^@\s]* | [^@\s]+"x
function tokenize(cmd::String)::Vector{Tuple{Symbol,Vararg{Any}}}
tokens = Tuple{Symbol,Vararg{Any}}[]
# TODO: handle string-quoted values, e.g. path names
words = map(m->m.match, eachmatch(lex_re, cmd))
help_mode = false
while !isempty(words)
word = shift!(words)
if word[1] == '-' && length(word) > 1
push!(tokens, parse_option(word))
else
word in keys(cmds) || cmderror("invalid command: ", repr(word))
push!(tokens, (:cmd, cmds[word]))
help_mode || cmds[word] != :help && break
help_mode = true
end
end
if isempty(tokens) || tokens[end][1] != :cmd
cmderror("no package command given")
end
while !isempty(words)
word = shift!(words)
if word[1] == '-'
push!(tokens, parse_option(word))
elseif word[1] == '@'
push!(tokens, (:ver, VersionRange(strip(word[2:end]))))
elseif ismatch(uuid_re, word)
push!(tokens, (:pkg, UUID(word)))
elseif ismatch(name_re, word)
push!(tokens, (:pkg, String(match(name_re, word).captures[1])))
elseif ismatch(name_uuid_re, word)
m = match(name_uuid_re, word)
push!(tokens, (:pkg, String(m.captures[1]), UUID(m.captures[2])))
else
cmderror("invalid argument: ", repr(word))
end
end
return tokens
end
function do_cmd(repl::Base.REPL.AbstractREPL, input::String)
try
tokens = tokenize(input)
local env_opt::Union{String,Void} = get(ENV, "JULIA_ENV", nothing)
env = EnvCache(env_opt)
do_cmd!(env, tokens, repl)
catch err
if err isa CommandError
Base.display_error(repl.t.err_stream, ErrorException(err.msg), Ptr{Void}[])
else
Base.display_error(repl.t.err_stream, err, Base.catch_backtrace())
end
end
end
function do_cmd!(env, tokens, repl)
local cmd::Symbol
while !isempty(tokens)
token = shift!(tokens)
if token[1] == :cmd
cmd = token[2]
break
elseif token[1] == :opt
if token[2] == :env
length(token) == 3 ||
cmderror("the `--env` option requires a value")
env_opt = token[3]
else
cmderror("unrecognized option: `--$(token[2])`")
end
else
cmderror("misplaced token: ", token)
end
end
cmd == :help ? do_help!(env, tokens, repl) :
cmd == :preview ? do_preview!(env, tokens, repl) :
cmd == :rm ? do_rm!(env, tokens) :
cmd == :add ? do_add!(env, tokens) :
cmd == :up ? do_up!(env, tokens) :
cmd == :status ? do_status!(env, tokens) :
cmd == :test ? do_test!(env, tokens) :
cmderror("`$cmd` command not yet implemented")
end
function do_preview!(env, tokens, repl)
isempty(tokens) && cmderror("`preview` needs a command")
env.preview[] = true
word = tokens[1][2]
word in keys(cmds) || cmderror("invalid command: ", repr(word))
tokens[1] = (:cmd, cmds[word])
do_cmd!(env, tokens, repl)
end
const help = Base.Markdown.parse("""
**Synopsis**
pkg> [--env=...] cmd [opts] [args]
**Environment**
The `--env` meta option determines which project environment to manipulate. By
default, this looks for a git repo in the parents directories of the current
working directory, and if it finds one, it uses that as an environment. Otherwise,
it uses a named environment (typically found in `~/.julia/environments`) looking
for environments named `v$(VERSION.major).$(VERSION.minor).$(VERSION.patch)`,
`v$(VERSION.major).$(VERSION.minor)`, `v$(VERSION.major)` or `default` in order.
**Commands**
What action you want the package manager to take:
`help`: show this message
`status`: summarize contents of and changes to environment
`add`: add packages to project
`rm`: remove packages from project or manifest
`up`: update packages in manifest
`preview`: previews a subsequent command without affecting the current state
`test`: run tests for packages
""")
const helps = Dict(
:help => md"""
help
Display this message.
help cmd ...
Display usage information for commands listed.
Available commands: `help`, `status`, `add`, `rm`, `up`
""", :status => md"""
status
status [-p|--project]
status [-m|--manifest]
Show the status of the current environment. By default, the full contents of
the project file is summarized, showing what version each package is on and
how it has changed since the last git commit (if in a git repo), as well as
any changes to manifest packages not already listed. In `--project` mode, the
status of the project file is summarized. In `--project` mode, the status of
the project file is summarized.
""", :add => md"""
add pkg[=uuid] [@version] ...
Add package `pkg` to the current project file. If `pkg` could refer to
multiple different packages, specifying `uuid` allows you to disambiguate.
`@version` optionally allows specifying which versions of packages. Versions
may be specified by `@1`, `@1.2`, `@1.2.3`, allowing any version with a prefix
that matches, or ranges thereof, such as `@1.2-3.4.5`.
""", :rm => md"""
rm [-p|--project] pkg[=uuid] ...
Remove package `pkg` from the project file. Since the name `pkg` can only
refer to one package in a project this is unambiguous, but you can specify
a `uuid` anyway, and the command is ignored, with a warning if package name
and UUID do not mactch. When a package is removed from the project file, it
may still remain in the manifest if it is required by some other package in
the project. Project mode operation is the default, so passing `-p` or
`--project` is optional unless it is preceded by the `-m` or `--manifest`
options at some earlier point.
rm [-m|--manifest] pkg[=uuid] ...
Remove package `pkg` from the manifest file. If the name `pkg` refers to
multiple packages in the manifest, `uuid` disambiguates it. Removing a package
from the manifest forces the removal of all packages that depend on it, as well
as any no-longer-necessary manifest packages due to project package removals.
""", :up => md"""
up [-p|project] [opts] pkg[=uuid] [@version] ...
up [-m|manifest] [opts] pkg[=uuid] [@version] ...
opts: --major | --minor | --patch | --fixed
Update the indicated package within the constraints of the indicated version
specifications. Versions may be specified by `@1`, `@1.2`, `@1.2.3`, allowing
any version with a prefix that matches, or ranges thereof, such as `@1.2-3.4.5`.
In `--project` mode, package specifications only match project packages, while
in `manifest` mode they match any manifest package. Bound level options force
the following packages to be upgraded only within the current major, minor,
patch version; if the `--fixed` upgrade level is given, then the following
packages will not be upgraded at all.
""", :preview => md"""
preview cmd
Runs the command `cmd` in preview mode. This is defined such that no side effects
will take place i.e. no packages are downloaded and neither the project nor manifest
is modified.
""", :test => md"""
test [opts] pkg[=uuid] ...
opts: --coverage
Run the tests for package `pkg`. This is done by running the file `test/runtests.jl`
in the package directory. The option `--coverage` can be used to run the tests with
coverage enabled.
""",
)
function do_help!(
env::EnvCache,
tokens::Vector{Tuple{Symbol,Vararg{Any}}},
repl::Base.REPL.AbstractREPL,
)
disp = Base.REPL.REPLDisplay(repl)
if isempty(tokens)
Base.display(disp, help)
return
end
help_md = md""
for token in tokens
if token[1] == :cmd
if haskey(helps, token[2])
isempty(help_md.content) ||
push!(help_md.content, md"---")
push!(help_md.content, helps[token[2]].content)
else
cmderror("Sorry, I don't have any help for the `$(token[2])` command.")
end
else
error("This should not happen")
end
end
Base.display(disp, help_md)
end
function do_rm!(env::EnvCache, tokens::Vector{Tuple{Symbol,Vararg{Any}}})
# tokens: package names and/or uuids
mode = :project
pkgs = PackageSpec[]
while !isempty(tokens)
token = shift!(tokens)
if token[1] == :pkg
push!(pkgs, PackageSpec(token[2:end]...))
pkgs[end].mode = mode
elseif token[1] == :ver
cmderror("`rm` does not take version specs")
elseif token[1] == :opt
if token[2] in (:project, :manifest)
length(token) == 2 ||
cmderror("the --$(token[2]) option does not take an argument")
mode = token[2]
else
cmderror("invalid option for `rm`: --$(token[2])")
end
end
end
isempty(pkgs) &&
cmderror("`rm` – list packages to remove")
Pkg3.API.rm(env, pkgs)
end
function do_add!(env::EnvCache, tokens::Vector{Tuple{Symbol,Vararg{Any}}})
# tokens: package names and/or uuids, optionally followed by version specs
isempty(tokens) &&
cmderror("`add` – list packages to add")
tokens[1][1] == :ver &&
cmderror("package name/uuid must precede version spec `@$(tokens[1][2])`")
pkgs = PackageSpec[]
while !isempty(tokens)
token = shift!(tokens)
if token[1] == :pkg
push!(pkgs, PackageSpec(token[2:end]...))
elseif token[1] == :ver
pkgs[end].version = VersionSpec(token[2])
isempty(tokens) || tokens[1][1] == :pkg ||
cmderror("package name/uuid must precede version spec `@$(tokens[1][2])`")
elseif token[1] == :opt
cmderror("`add` doesn't take options: --$(join(token[2:end], '='))")
end
end
Pkg3.API.add(env, pkgs)
end
function do_up!(env::EnvCache, tokens::Vector{Tuple{Symbol,Vararg{Any}}})
# tokens:
# - upgrade levels as options: --[fixed|patch|minor|major]
# - package names and/or uuids, optionally followed by version specs
mode = :project
pkgs = PackageSpec[]
level = UpgradeLevel(:major)
last_token_type = :cmd
while !isempty(tokens)
token = shift!(tokens)
if token[1] == :pkg
push!(pkgs, PackageSpec(token[2:end]..., level))
pkgs[end].mode = mode
elseif token[1] == :ver
pkgs[end].version = VersionSpec(token[2])
last_token_type == :pkg ||
cmderror("package name/uuid must precede version spec `@$(token[2])`")
elseif token[1] == :opt
if token[2] in (:project, :manifest)
length(token) == 2 ||
cmderror("the --$(token[2]) option does not take an argument")
mode = token[2]
elseif token[2] in (:major, :minor, :patch, :fixed)
length(token) == 2 ||
cmderror("the --$(token[2]) option does not take an argument")
level = UpgradeLevel(token[2])
else
cmderror("invalid option for `up`: --$(token[2])")
end
end
last_token_type = token[1]
end
Pkg3.API.up(env, pkgs; level=level, mode=mode)
end
function do_status!(env::EnvCache, tokens::Vector{Tuple{Symbol,Vararg{Any}}})
mode = :combined
while !isempty(tokens)
token = shift!(tokens)
if token[1] == :opt
if token[2] in (:project, :manifest)
length(token) == 2 ||
cmderror("the --$(token[2]) option does not take an argument")
mode = token[2]
else
cmderror("invalid option for `status`: --$(token[2])")
end
else
cmderror("`status` does not take arguments")
end
end
Pkg3.Display.status(env, mode)
end
# TODO , test recursive dependencies as on option.
function do_test!(env::EnvCache, tokens::Vector{Tuple{Symbol,Vararg{Any}}})
pkgs = PackageSpec[]
coverage = false
while !isempty(tokens)
token = shift!(tokens)
if token[1] == :pkg
if length(token) == 2
pkg = PackageSpec(token[2])
pkg.mode = :manifest
push!(pkgs, pkg)
else
cmderror("`test` only takes a set of packages to test")
end
elseif token[1] == :opt
if token[2] == :coverage
coverage = true
else
cmderror("invalid option for `test`: --$(token[2])")
end
else
# TODO: Better error message
cmderror("invalid usage for `test`")
end
end
isempty(pkgs) && cmderror("`test` takes a set of packages")
Pkg3.API.test(env, pkgs; coverage = coverage)
end
function create_mode(repl, main)
pkg_mode = LineEdit.Prompt("pkg> ";
prompt_prefix = Base.text_colors[:blue],
prompt_suffix = "",
sticky = true)
if VERSION >= v"0.7.0-DEV.1747"
pkg_mode.repl = repl
end
hp = main.hist
hp.mode_mapping[:pkg] = pkg_mode
pkg_mode.hist = hp
search_prompt, skeymap = LineEdit.setup_search_keymap(hp)
prefix_prompt, prefix_keymap = LineEdit.setup_prefix_keymap(hp, pkg_mode)
pkg_mode.on_done = (s, buf, ok) -> begin
ok || return REPL.transition(s, :abort)
input = String(take!(buf))
REPL.reset(repl)
do_cmd(repl, input)
REPL.prepare_next(repl)
REPL.reset_state(s)
s.current_mode.sticky || REPL.transition(s, main)
end
mk = REPL.mode_keymap(main)
# ^C should not exit prompt
delete!(mk, "^C")
b = Dict{Any,Any}[
skeymap, mk, prefix_keymap, LineEdit.history_keymap,
LineEdit.default_keymap, LineEdit.escape_defaults
]
pkg_mode.keymap_dict = LineEdit.keymap(b)
return pkg_mode
end
function repl_init(repl)
main_mode = repl.interface.modes[1]
pkg_mode = create_mode(repl, main_mode)
push!(repl.interface.modes, pkg_mode)
keymap = Dict{Any,Any}(
']' => function (s,args...)
if isempty(s) || position(LineEdit.buffer(s)) == 0
buf = copy(LineEdit.buffer(s))
LineEdit.transition(s, pkg_mode) do
LineEdit.state(s, pkg_mode).input_buffer = buf
end
else
LineEdit.edit_insert(s, ']')
end
end
)
main_mode.keymap_dict = LineEdit.keymap_merge(main_mode.keymap_dict, keymap)
return
end
end
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] | 2.186438 | 7,713 |
using ThreeBodyDecay
using Parameters
using Test
@testset "Wigner angle permutations" begin
mp = 0.938; mK = 0.49367; mpi = 0.13957; mXic = 2.46867
tbs = ThreeBodySystem(mp,mK,mpi; m0=mXic)
σs = randomPoint(tbs.ms)
@unpack m1,m2,m3,m0 = tbs.ms
# (23) cosζ31_for1 = cosζ23_for1
@test cosζk1_for1(2,[σs.σ1,σs.σ2,σs.σ3],[m1^2,m2^2,m3^2,m0^2]) ==
cosζk1_for1(3,[σs.σ1,σs.σ3,σs.σ2],[m1^2,m3^2,m2^2,m0^2])
# (31) cosζ21_for2 = cosζ32_for2
@test cosζk2_for2(3,[σs.σ1,σs.σ2,σs.σ3],[m1^2,m2^2,m3^2,m0^2]) ==
cosζk2_for2(1,[σs.σ3,σs.σ2,σs.σ1],[m3^2,m2^2,m1^2,m0^2])
# (12) cosζ32_for3 = cosζ13_for3
@test cosζk3_for3(1,[σs.σ1,σs.σ2,σs.σ3],[m1^2,m2^2,m3^2,m0^2]) ==
cosζk3_for3(2,[σs.σ2,σs.σ1,σs.σ3],[m2^2,m1^2,m3^2,m0^2])
# (123) cosζ31_for1 = cosζ21_for2
@test cosζk2_for2(1,[σs.σ1,σs.σ2,σs.σ3],[m1^2,m2^2,m3^2,m0^2]) ==
cosζk1_for1(3,[σs.σ2,σs.σ3,σs.σ1],[m2^2,m3^2,m1^2,m0^2])
# (123)^2 cosζ31_for1 = cosζ23_for3
@test cosζk3_for3(2,[σs.σ1,σs.σ2,σs.σ3],[m1^2,m2^2,m3^2,m0^2]) ==
cosζk1_for1(3,[σs.σ3,σs[1],σs[2]],[m3^2,m1^2,m2^2,m0^2])
#
end
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] | 1.392727 | 825 |
#TODO: Fill
"""
```WIP``
this 'File Container' has the main instrutions
"""
# include("src/includes.jl") reads the below correctly
include("includes.jl")
function main()
drAccount(dr::Enum,cr::Enum,drAccount ,crAccount,amount)
if (dr == 1 && cr == 1) #dr drAccount (+), cr crAccount (+) #inflow type
# break
end
#=
else if (dr == 2 && cr == 2) # dr crAccount , cr drAccount
(dr,crAccount,amount), (cr,drAccount,amount)
#
break
end
#elseif ()
=#
end
main() | [
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] | 2.101449 | 276 |
using Luxor
using OffsetArrays
function make_drawing(width, height, img_path, bkg_color, origin_p)
d = Drawing(width, height, img_path)
background(bkg_color)
origin(origin_p)
return d
end
width = 500
height = 500
path = "voronoi.png"
color = "black"
my_draw = make_drawing(width, height, path, color, O)
slope(x₁, y₁, x₂, y₂) = (y₂ - y₁) / (x₂ - x₁)
slope(P1, P2) = (P2.y - P1.y) / (P2.x - P1.x)
midpoint(x₁, y₁, x₂, y₂) = ((x₁ + x₂) / 2, (y₁ + y₂) / 2)
midpoint(P1, P2) = ((P1.x + P2.x) / 2, (P2.y + P2.y) / 2)
point_slope(x, x₁, y₁, m) = (x, m * (x - x₁) + y₁)
mat = zeros(width, height)
p1 = (x = 125, y = 125)
p2 = (x = 375, y = 375)
sp = slope(p1, p2)
mp = midpoint(p1, p2)
d(x1, y1, x2, y2, x, y) = (x - x1) * (y2 - y1) - (y - y1) * (x2 - x1)
h1_plane = []
h2_plane = []
h3_plane = []
for x in 1:500, y in 1:500
if d(500, 0, mp[1], mp[2], x, y) < 0
push!(h1_plane, Point(x, y))
elseif d(500, 0, mp[1], mp[2], x, y) > 0
push!(h2_plane, Point(x, y))
else
push!(h3_plane, Point(x, y))
end
end
# for p in vals
# mat[Int(p.x), Int(p.y)] = 1
# end
# function half_plane(p1, p2, width, height)
# mp = midpoint(p1.x, p1.y, p2.x, p2.y) |> Point
# sp = slope(p1.x, p1.y, p2.x, p2.y)
# first_point = point_slope(-250, mp.x, mp.y, sp == 0 ? 0 : -1 / sp) |> Point
# second_point = point_slope(250, mp.x, mp.y, sp == 0 ? 0 : -1 / sp) |> Point
# half_1 = [first_point, second_point, Point(width / 2, height / 2)]
# half_2 = [first_point, second_point, Point(-width / 2, height / 2)]
# return half_1, half_2
# end
setcolor(1, 0, 0, 1)
for c in h1_plane
circle(c, 1; action = :stroke)
end
setcolor(0, 0, 1, 1)
for c in h2_plane
circle(c, 1; action = :stroke)
end
setcolor(1, 1, 0, 1)
for c in h3_plane
circle(c, 1; action = :stroke)
end
setcolor(1, 0, 0, 1)
circle(p1.x, p1.y, 15, action = :fill)
setcolor(0, 0, 1, 1)
circle(p2.x, p2.y, 15, action = :fill)
finish()
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11,
2223,
796,
1058,
20797,
8,
198,
198,
15643,
680,
3419,
198
] | 1.873308 | 1,034 |
using Test
using ThreadGantt
@testset "stuff" begin
@test 1 == 1
end
| [
3500,
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3500,
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1,
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] | 2.551724 | 29 |
function __new__(T, args...)
# @show T
# @show args
# note: we also add __new__() to the list of primitives so it's not overdubbed recursively
if T <: NamedTuple
return T(args)
else
return T(args...)
end
end
__tuple__(args...) = tuple(args...)
__getfield__(args...) = getfield(args...)
function module_functions(modl)
res = Vector{Function}()
for s in Base.names(modl; all=true)
isdefined(modl, s) || continue
fn = getfield(modl, s)
if fn isa Function # && match(r"^[a-z#]+$", string(s)) != nothing
push!(res, fn)
end
end
return res
end
const PRIMITIVES = Set{Any}(vcat(
module_functions(Base),
module_functions(Core),
module_functions(Core.Intrinsics),
[Broadcast.materialize, Broadcast.broadcasted, Colon(), (:),
Base.not_int,
# our own special functions
__new__, __tuple__, __getfield__, namedtuple, guess_device]));
include("cassette.jl")
# include("interp.jl")
include("irtools.jl")
trace = irtrace
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] | 2.361991 | 442 |
using Crux
using POMDPModels
using Test
using CUDA
using Flux
using Random
## mdp_data
d1 = mdp_data(ContinuousSpace(3), ContinuousSpace(4), 100)
d2 = mdp_data(ContinuousSpace(3), ContinuousSpace(4), 100, [:weight, :t, :advantage, :return, :logprob])
# @test_throws ErrorException mdp_data(ContinuousSpace(3), ContinuousSpace(4), 100, [:bad_key]) # Now this just throws a warning
d3 = mdp_data(ContinuousSpace(3), ContinuousSpace(4), 100, ArrayType = USE_CUDA ? CuArray : Array)
@test d1[:s] == zeros(Float32, 3, 100) && d2[:s] == zeros(Float32, 3, 100) && size(d3[:s]) == (3, 100)
@test d1[:sp] == zeros(Float32, 3, 100) && d2[:sp] == zeros(Float32, 3, 100) && size(d3[:sp]) == (3, 100)
@test d1[:a] == zeros(Bool, 4, 100) && size(d2[:a]) == (4, 100) && size(d3[:a]) == (4, 100)
@test d1[:r] == zeros(Float32, 1, 100) && size(d2[:r]) == (1, 100) && size(d3[:r]) == (1, 100)
@test d1[:done] == zeros(Bool, 1, 100) && size(d2[:done]) == (1, 100) && size(d3[:done]) == (1, 100)
@test d2[:return] == zeros(1, 100) && d2[:advantage] == zeros(1, 100) && d2[:logprob] == zeros(1, 100) && d2[:t] == zeros(1, 100) && d2[:weight] == ones(1, 100)
@test !haskey(d1, :return) && !haskey(d1, :advantage) && !haskey(d1, :logprob) && !haskey(d1, :t) && !haskey(d1, :weight)
USE_CUDA && @test d3[:s] isa CuArray
## circular_indices
circ_inds(start, Nsteps, l) = mod1.(start:start + Nsteps - 1, l)
@test circ_inds(4, 60, 100) == [4:63 ...]
@test circ_inds(1, 100, 100) == [1:100 ...]
@test circ_inds(1, 101, 100) == [1:100 ..., 1]
@test circ_inds(1, 120, 100) == [1:100 ..., 1:20 ...]
@test circ_inds(90, 20, 100) == [90:100 ..., 1:9 ...]
## Get last N indices test
# A not full buffer
b = ExperienceBuffer(ContinuousSpace(2), ContinuousSpace(1), 100)
d = Dict(:s => 2*ones(2,50), :a => ones(Bool, 1,50), :sp => ones(2,50), :r => ones(1,50), :done => zeros(1,50), :weight=>zeros(1,50))
push!(b, d)
@test get_last_N_indices(b, 10) == collect(41:50)
@test get_last_N_indices(b, 1) == [50]
@test get_last_N_indices(b, 50) == collect(1:50)
@test get_last_N_indices(b, 51) == collect(1:50)
@test get_last_N_indices(b, 1000) == collect(1:50)
# full buffer
push!(b, d)
push!(b, d)
@test get_last_N_indices(b, 10) == collect(41:50)
@test get_last_N_indices(b, 1) == [50]
@test get_last_N_indices(b, 50) == collect(1:50)
@test get_last_N_indices(b, 51) == [100, collect(1:50)...]
@test get_last_N_indices(b, 100) == [collect(51:100)..., collect(1:50)...]
@test get_last_N_indices(b, 1000) == [collect(51:100)..., collect(1:50)...]
## Priority Param Construction
p = PriorityParams(100, α=0.7)
@test length(p.priorities) == 100
@test p.α == 0.7f0
@test p.max_priority == 1.0f0
p2 = PriorityParams(1000, p)
@test length(p2.priorities)==1000
@test p2.α == p.α
@test p2.max_priority == p.max_priority
## Construction
b = ExperienceBuffer(ContinuousSpace(2), DiscreteSpace(4), 100,)
bpriority = ExperienceBuffer(ContinuousSpace(2), DiscreteSpace(4), 50, prioritized=true)
USE_CUDA && (b_gpu = b |> gpu)
bcopy = ExperienceBuffer(deepcopy(b.data), elements=0)
@test b.data == bcopy.data
@test b.priority_params == bcopy.priority_params
bcopy = ExperienceBuffer(deepcopy(b.data), prioritized=true)
@test isprioritized(bcopy)
@test b isa ExperienceBuffer{Array}
if USE_CUDA
@test b_gpu isa ExperienceBuffer{CuArray}
@test length(keys(b.data)) == length(keys(b_gpu.data))
end
@test :s in keys(b.data)
@test :a in keys(b.data)
@test :sp in keys(b.data)
@test :r in keys(b.data)
@test :done in keys(b.data)
@test !haskey(b, :weight)
@test haskey(bpriority, :weight)
@test bpriority.data[:weight] == ones(Float32, 1, 50)
@test size(b[:a]) == (4,0)
# Buffer_like function
bsmall = buffer_like(b, capacity=3, device=cpu)
@test Crux.device(bsmall) == cpu
@test keys(bsmall) == keys(b)
@test capacity(bsmall) == 3
@test length(bsmall) == 0
## Base functions
@test keys(b) == keys(b.data)
@test size(b[:s]) == (2,0)
@test length(b) == 0
@test capacity(b) == 100
USE_CUDA && @test size(b_gpu[:s]) == (2,0)
USE_CUDA && @test length(b_gpu) == 0
USE_CUDA && @test capacity(b_gpu) == 100
@test isprioritized(bpriority)
@test Crux.device(b) == cpu
@test Crux.device(bpriority) == cpu
USE_CUDA && @test Crux.device(b_gpu) == gpu
# Convert to and from GPU
USE_CUDA && @test Crux.device(gpu(b)) == gpu
USE_CUDA && @test Crux.device(cpu(b_gpu)) == cpu
## push!
#push dictionary with one element
d = Dict(:s => 2*ones(2,1), :a => ones(Bool, 4,1), :sp => ones(2,1), :r => ones(1,1), :done => zeros(1,1), :weight=>zeros(1,1))
push!(b, d)
@test length(b) == 1
@test b[:s] == 2*ones(2,1)
@test b[:a] == ones(Int, 4,1)
@test b[:sp] == ones(2,1)
@test b[:r] == ones(1,1)
@test b[:done] == zeros(1,1)
# push dictionary with more than one element
d = Dict(:s => 3*ones(2,3), :a => rand(4,3) .< 0.5, :sp => 5*ones(2,3), :r => 6*ones(1,3), :done => ones(1,3), :weight=>zeros(1,3))
push!(b, d)
@test length(b) == 4
@test b[:s][:,2:end] == 3*ones(2,3)
@test b[:a][:,2:end] == d[:a]
@test b[:sp][:,2:end] == 5*ones(2,3)
@test b[:r][:,2:end] == 6*ones(1,3)
@test b[:done][:,2:end] == ones(1,3)
# push a buffer
push!(b, b)
@test length(b) == 8
for k in keys(b)
@test b[k][:, 1:4] == b[k][:, 5:8]
end
## Reservoir storage
b = ExperienceBuffer(ContinuousSpace(2), DiscreteSpace(4), 10,)
d3 = Dict(:s => 3*ones(2,3), :a => rand(4,3) .< 0.5, :sp => 5*ones(2,3), :r => 6*ones(1,3), :done => ones(1,3), :weight=>zeros(1,3), :episode_end=> ones(1,3))
d1 = Dict(:s => 2*ones(2,1), :a => ones(Bool, 4,1), :sp => ones(2,1), :r => ones(1,1), :done => zeros(1,1), :weight=>zeros(1,1), :episode_end => zeros(1,1))
for i=1:10
push_reservoir!(b, d1)
@test length(b) == i
end
push_reservoir!(b, d3)
## clear!
bcopy = deepcopy(b)
clear!(bcopy)
@test length(bcopy) == 0
@test bcopy.next_ind == 1
## minibatch
I = [1,2,4]
d2 = minibatch(b, I)
for k in keys(d2)
@test all(d2[k] .== b[k][:, I])
end
## Split
@test Crux.split_batches(100, [0.5, 0.5]) == [50,50]
@test Crux.split_batches(100, [1.0]) == [100]
@test Crux.split_batches(100, [1/3, 1/3, 1/3]) == [34,33,33]
@test_throws AssertionError Crux.split_batches(100, 0.4)
v = rand(3,100)
bsplit = ExperienceBuffer(Dict(:a => v))
b1, b2 = split(bsplit, [0.5, 0.5])
@test length(b1) == 50
@test b1[:a] == v[:,1:50]
@test length(b2) == 50
@test b2[:a] == v[:,51:100]
## update_priorities!
update_priorities!(bpriority, [1,2,3], [1., 2., 3.])
@test bpriority.priority_params.max_priority == 3.0
@test bpriority.priority_params.priorities[1] ≈ 1f0^bpriority.priority_params.α
@test bpriority.priority_params.priorities[2] ≈ 2f0^bpriority.priority_params.α
@test bpriority.priority_params.priorities[3] ≈ 3f0^bpriority.priority_params.α
push!(bpriority, d)
push!(bpriority, d)
@test bpriority.priority_params.max_priority == 3.0
for i=1:6
@test bpriority.priority_params.priorities[i] ≈ 3f0^bpriority.priority_params.α
end
## sampling
# uniform sample
t = ExperienceBuffer(ContinuousSpace(2), DiscreteSpace(4), 10)
rand!(t, b)
t = ExperienceBuffer(ContinuousSpace(2), DiscreteSpace(4), 3)
Random.seed!(0)
ids = rand(1:length(b), 3)
Random.seed!(0)
rand!(t, b)
for k in keys(t)
@test t[k] == b[k][:,ids]
end
# Test the multi-buffer sampling
t1 = ExperienceBuffer(ContinuousSpace(2), DiscreteSpace(4), 10)
d = Dict(:s => ones(2,1), :a => ones(Bool, 4,1), :sp => ones(2,1), :r => ones(1,1), :done => zeros(1,1))
push!(t1, d)
t2 = ExperienceBuffer(ContinuousSpace(2), DiscreteSpace(4), 10)
d = Dict(:s => 2*ones(2,1), :a => ones(Bool, 4,1), :sp => ones(2,1), :r => ones(1,1), :done => zeros(1,1))
push!(t2, d)
t3 = ExperienceBuffer(ContinuousSpace(2), DiscreteSpace(4), 10)
d = Dict(:s => 3*ones(2,1), :a => ones(4,1), :sp => ones(2,1), :r => ones(1,1), :done => zeros(1,1))
push!(t3, d)
t = ExperienceBuffer(ContinuousSpace(2), DiscreteSpace(4), 10)
rand!(t, t1, t2, t3)
@test all(t[:s][:, 1:4] .== 1.0)
@test all(t[:s][:, 5:7] .== 2.0)
@test all(t[:s][:, 8:10] .== 3.0)
# Priority samples
bpriority[:s] .= rand(Float32, 2, 6)
update_priorities!(bpriority, [1:6...], [1.:6. ...])
t = ExperienceBuffer(ContinuousSpace(2), DiscreteSpace(4), 1000, [:weight])
priorities = [bpriority.priority_params.priorities[i] for i=1:length(bpriority)]
rand!(t, bpriority)
# Test the the frequency of samples is proportional to their priority
freqs = [sum(t.indices .== i) for i=1:6] ./ length(t)
probs = priorities ./ sum(priorities)
relerr = abs.(freqs .- probs) ./ probs
@test all(relerr .< 0.01)
@test all(t[:s] .== bpriority[:s][:,t.indices])
@test all(t[:weight] .<= 1.)
## merge
# bmerge = merge(b, b, capacity = 300)
# @test length(bmerge) == 200
# @test capacity(bmerge) == 300
# @test bmerge[:s][:, 1:100] == bmerge[:s][:, 101:200]
## Multi-D states
b2d = ExperienceBuffer(ContinuousSpace((2,2), UInt8), DiscreteSpace(4), 100;)
@test ndims(b2d[:s]) == 3
b2d[:s]
d = Dict(:s => 3*ones(2,2,1), :a => ones(4,1), :sp => ones(2,2,1), :r => ones(1,1), :done => zeros(1,1))
push!(b2d, d)
@test all(b2d[:s] .== 3)
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] | 2.214586 | 4,045 |
# Unit testing of (bounded) univariate discrete distributions
#
# Here, bounded means the sample values are bounded.
#
# Distributions covered by this suite:
#
# - Bernoulli
# - Categorical
# - DiscreteUniform
#
using Distributions
using Base.Test
import StatsBase: entropy
distlist = [
Bernoulli(0.1),
Bernoulli(0.5),
Bernoulli(0.9),
Categorical([0.1, 0.9]),
Categorical([0.5, 0.5]),
Categorical([0.9, 0.1]),
Categorical([0.2, 0.5, 0.3]),
DiscreteUniform(0, 3),
DiscreteUniform(2.0, 5.0),
Binomial(1, 0.5),
Binomial(100, 0.1),
Binomial(100, 0.9)]
if length(ARGS) > 0
newdistlist = {}
for arg in ARGS
a = eval(parse(arg))
if isa(a, DataType)
append!(newdistlist, filter(x -> isa(x,a),distlist))
elseif isa(a,Distribution)
push!(newdistlist, a)
end
end
distlist = newdistlist
end
for d in distlist
length(ARGS) > 0 && println(d)
xmin = minimum(d)
xmax = maximum(d)
@assert isa(xmin, Int)
@assert isa(xmax, Int)
@assert xmin <= xmax
####
#
# Part 1: testing the capability of sampling
#
####
n = 10000
# check that we can generate a single random draw
draw = rand(d)
@test isa(draw, Int)
@test xmin <= draw <= xmax
# check that we can generate many random draws at once
x = rand(d, n)
@test isa(x, Vector{Int})
@test xmin <= minimum(x) <= maximum(x) <= xmax
# check that we can generate many random draws in-place
rand!(d, x)
@test xmin <= minimum(x) <= maximum(x) <= xmax
####
#
# Part 2: testing insupport
#
####
x = [xmin:xmax]
n = length(x)
@test !insupport(d, xmin-1)
@test !insupport(d, xmax+1)
for i = 1:n
@test insupport(d, x[i])
end
@test all(insupport(d, x))
####
#
# Part 3: testing evaluation
#
####
p = Array(Float64, n)
c = Array(Float64, n)
cc = Array(Float64, n)
lp = Array(Float64, n)
lc = Array(Float64, n)
lcc = Array(Float64, n)
ci = 0.
for i in 1 : n
p[i] = pdf(d, x[i])
ci += p[i]
c[i] = cdf(d, x[i])
cc[i] = ccdf(d, x[i])
@test_approx_eq ci c[i]
@test_approx_eq c[i] + cc[i] 1.0
lp[i] = logpdf(d, x[i])
lc[i] = logcdf(d, x[i])
lcc[i] = logccdf(d, x[i])
@test_approx_eq_eps lp[i] log(p[i]) 1.0e-12
@test_approx_eq_eps lc[i] log(c[i]) 1.0e-12
@test_approx_eq_eps lcc[i] log(cc[i]) 1.0e-12
if !isa(d, Binomial)
@test quantile(d, c[i] - 1.0e-8) == x[i]
@test cquantile(d, cc[i] + 1.0e-8) == x[i]
@test invlogcdf(d, lc[i] - 1.0e-8) == x[i]
if 0.0 < c[i] < 1.0
@test invlogccdf(d, lcc[i] + 1.0e-8) == x[i]
end
end
end
# check consistency of scalar-based and vectorized evaluation
@test_approx_eq pdf(d, x) p
@test_approx_eq cdf(d, x) c
@test_approx_eq ccdf(d, x) cc
@test_approx_eq logpdf(d, x) lp
@test_approx_eq logcdf(d, x) lc
@test_approx_eq logccdf(d, x) lcc
####
#
# Part 4: testing statistics
#
####
xf = float64(x)
xmean = dot(p, xf)
xvar = dot(p, abs2(xf .- xmean))
xstd = sqrt(xvar)
xentropy = entropy(p)
xskew = dot(p, (xf .- xmean).^3) / (xstd.^3)
xkurt = dot(p, (xf .- xmean).^4) / (xvar.^2) - 3.0
@test_approx_eq mean(d) xmean
@test_approx_eq var(d) xvar
@test_approx_eq std(d) xstd
@test_approx_eq skewness(d) xskew
@test_approx_eq kurtosis(d) xkurt
@test_approx_eq entropy(d) xentropy
end
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] | 1.882145 | 1,977 |
# Nominal Models
@testset "Nominal Models" begin
data = CSV.read(joinpath(pkgdir(Econometrics), "data", "insure.csv"), DataFrame, select = [:insure, :age, :male, :nonwhite, :site]) |>
dropmissing |>
(data -> transform!(data, [:insure, :site] .=> categorical, renamecols = false))
model = fit(
EconometricModel,
@formula(insure ~ age + male + nonwhite + site),
data,
contrasts = Dict(:insure => DummyCoding(base = "Uninsure")),
)
@test sprint(show, model) ==
"Probability Model for Nominal Response\nCategories: Uninsure, Indemnity, Prepaid\nNumber of observations: 615\nNull Loglikelihood: -555.85\nLoglikelihood: -534.36\nR-squared: 0.0387\nLR Test: 42.99 ∼ χ²(10) ⟹ Pr > χ² = 0.0000\nFormula: insure ~ 1 + age + male + nonwhite + site\n───────────────────────────────────────────────────────────────────────────────────────────────────\n PE SE t-value Pr > |t| 2.50% 97.50%\n───────────────────────────────────────────────────────────────────────────────────────────────────\ninsure: Indemnity ~ (Intercept) 1.28694 0.59232 2.17271 0.0302 0.123689 2.4502\ninsure: Indemnity ~ age 0.00779612 0.0114418 0.681372 0.4959 -0.0146743 0.0302666\ninsure: Indemnity ~ male -0.451848 0.367486 -1.22957 0.2193 -1.17355 0.269855\ninsure: Indemnity ~ nonwhite -0.217059 0.425636 -0.509965 0.6103 -1.05296 0.618843\ninsure: Indemnity ~ site: 2 1.21152 0.470506 2.57493 0.0103 0.287497 2.13554\ninsure: Indemnity ~ site: 3 0.207813 0.366293 0.56734 0.5707 -0.511547 0.927172\ninsure: Prepaid ~ (Intercept) 1.55666 0.596327 2.61041 0.0093 0.385533 2.72778\ninsure: Prepaid ~ age -0.00394887 0.0115993 -0.340439 0.7336 -0.0267287 0.018831\ninsure: Prepaid ~ male 0.109846 0.365187 0.300793 0.7637 -0.607343 0.827035\ninsure: Prepaid ~ nonwhite 0.757718 0.419575 1.80592 0.0714 -0.0662835 1.58172\ninsure: Prepaid ~ site: 2 1.32456 0.469789 2.81947 0.0050 0.401941 2.24717\ninsure: Prepaid ~ site: 3 -0.380175 0.372819 -1.01973 0.3083 -1.11235 0.352001\n───────────────────────────────────────────────────────────────────────────────────────────────────"
β, V, σ = coef(model), vcov(model), stderror(model)
@test β ≈ [
1.286943,
0.0077961,
-0.4518496,
-0.2170589,
1.211563,
0.2078123,
1.556656,
-0.0039489,
0.1098438,
0.7577178,
1.324599,
-0.3801756,
] rtol = 1e-3
@test V ≈
[
0.35084518 -0.00590456 -0.02519209 -0.01837512 -0.08995794 -0.08264268 0.29928937 -0.00510107 -0.02257270 -0.01585492 -0.07392512 -0.06808314
-0.00590456 0.00013092 -0.00046225 -0.00028066 0.00041004 0.00040470 -0.00509454 0.00011354 -0.00039387 -0.00024122 0.00033847 0.00035236
-0.02519209 -0.00046225 0.13504644 0.01565749 0.01009559 0.00933967 -0.02288847 -0.00039497 0.11365141 0.01369872 0.00945927 0.00802593
-0.01837512 -0.00590456 -0.02519209 0.18116609 0.01886597 -0.02821348 -0.01638629 -0.00023179 0.01314666 0.15068111 0.01664999 -0.02345786
-0.08995794 -0.00590456 -0.02519209 -0.01837512 0.22138224 0.064050660 -0.07438947 0.00035751 0.00845363 0.01594691 0.19895500 0.05189742
-0.08264268 0.00040470 0.00933967 -0.02821348 0.06405066 0.134170290 -0.06796097 0.00034828 0.00826890 -0.02353016 0.05182727 0.11060487
0.29928937 -0.00509454 -0.02288847 -0.01638629 -0.07438947 -0.06796097 0.35560782 -0.00601966 -0.02683161 -0.02105510 -0.08991231 -0.08165509
-0.00510107 0.00011354 -0.00039497 -0.00023179 0.00035751 0.00034828 -0.00601966 0.00013455 -0.00047223 -0.00028680 0.00039990 0.0004179
-0.02257270 -0.00039387 0.11365141 0.01314666 0.00845363 0.00826890 -0.02683161 -0.00047223 0.13336253 0.01625685 0.01101870 0.00922333
-0.01585492 -0.00024122 0.01369872 0.15068111 0.01594691 -0.02353016 -0.02105510 -0.00028680 0.01625685 0.17604391 0.02099458 -0.03008391
-0.07392512 0.00033847 0.00945927 0.01664999 0.19895500 0.05182727 -0.08991231 0.00039990 0.01101870 0.02099458 0.22070774 0.06273376
-0.06808314 0.00035236 0.00802593 -0.02345786 0.05189742 0.11060487 -0.08165509 0.00041790 0.00922333 -0.03008391 0.06273376 0.13899387
] |>
Hermitian rtol = 1e-3
@test σ ≈ [
0.5923219,
0.0114418,
0.3674867,
0.4256361,
0.4705127,
0.3662926,
0.5963286,
0.0115994,
0.3651883,
0.4195759,
0.4697954,
0.3728188,
] rtol = 1e-4
end
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] | 1.873786 | 2,575 |
module SplitApplyCombine
using Base: @propagate_inbounds, @pure, promote_op
using Indexing
# Syntax
export @_
# collections -> scalar
export only
# collections -> collections
import Base: merge, merge!
export mapmany
# collections -> collections of collections
export group, groupinds, Groups, groupview, groupreduce, splitdims, splitdimsview, SplitDimsArray
# colletions of collections -> collections
export flatten #, flattenview
# collections of collections -> collections of collections
export innerjoin, ⨝, leftgroupjoin
# `pivot` or similar - like transpose, but nested.
include("only.jl")
include("merge.jl")
include("map.jl")
include("group.jl")
include("innerjoin.jl")
include("leftgroupjoin.jl")
include("splitdims.jl")
# Syntax
include("underscore.jl")
# Silly definitions missing from Base
# ===================================
# this should always work
Base.haskey(a, i) = i ∈ keys(a)
# mini-compat (more for my knowledge than anything)
if VERSION < v"0.7-"
Base.keys(v::AbstractVector) = indices(v)[1]
Base.keys(a::AbstractArray) = CartesianRange(indices(a)...)
Base.keys(::NTuple{N,Any}) where {N} = Base.OneTo(N)
Base.keys(::Number) = Base.OneTo(1)
# A Nullable is a container with 0 or 1 values... so...
Base.start(::Nullable) = false
Base.done(n::Nullable, i::Bool) = isnull(n) | i
Base.next(n::Nullable, i::Bool) = (n.valie, true)
Base.first(n::Nullable) = get(n)
Base.last(n::Nullable) = get(n)
@propagate_inbounds function Base.getindex(n::Nullable)
@boundscheck if !n.hasvalue
return NullException()
end
return n.value
end
end
end # module
# Random thoughts:
# merge! many other mutating ops returns collection, so:
# why does setindex! return the set value(s), not the collection???
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# This file is auto-generated by AWSMetadata.jl
using AWS
using AWS.AWSServices: iot_events_data
using AWS.Compat
using AWS.UUIDs
"""
batch_put_message(messages)
batch_put_message(messages, params::Dict{String,<:Any})
Sends a set of messages to the AWS IoT Events system. Each message payload is transformed
into the input you specify (\"inputName\") and ingested into any detectors that monitor
that input. If multiple messages are sent, the order in which the messages are processed
isn't guaranteed. To guarantee ordering, you must send messages one at a time and wait for
a successful response.
# Arguments
- `messages`: The list of messages to send. Each message has the following format: '{
\"messageId\": \"string\", \"inputName\": \"string\", \"payload\": \"string\"}'
"""
batch_put_message(messages; aws_config::AbstractAWSConfig=global_aws_config()) = iot_events_data("POST", "/inputs/messages", Dict{String, Any}("messages"=>messages); aws_config=aws_config)
batch_put_message(messages, params::AbstractDict{String, <:Any}; aws_config::AbstractAWSConfig=global_aws_config()) = iot_events_data("POST", "/inputs/messages", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("messages"=>messages), params)); aws_config=aws_config)
"""
batch_update_detector(detectors)
batch_update_detector(detectors, params::Dict{String,<:Any})
Updates the state, variable values, and timer settings of one or more detectors (instances)
of a specified detector model.
# Arguments
- `detectors`: The list of detectors (instances) to update, along with the values to update.
"""
batch_update_detector(detectors; aws_config::AbstractAWSConfig=global_aws_config()) = iot_events_data("POST", "/detectors", Dict{String, Any}("detectors"=>detectors); aws_config=aws_config)
batch_update_detector(detectors, params::AbstractDict{String, <:Any}; aws_config::AbstractAWSConfig=global_aws_config()) = iot_events_data("POST", "/detectors", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("detectors"=>detectors), params)); aws_config=aws_config)
"""
describe_detector(detector_model_name)
describe_detector(detector_model_name, params::Dict{String,<:Any})
Returns information about the specified detector (instance).
# Arguments
- `detector_model_name`: The name of the detector model whose detectors (instances) you
want information about.
# Optional Parameters
Optional parameters can be passed as a `params::Dict{String,<:Any}`. Valid keys are:
- `"keyValue"`: A filter used to limit results to detectors (instances) created because of
the given key ID.
"""
describe_detector(detectorModelName; aws_config::AbstractAWSConfig=global_aws_config()) = iot_events_data("GET", "/detectors/$(detectorModelName)/keyValues/"; aws_config=aws_config)
describe_detector(detectorModelName, params::AbstractDict{String, <:Any}; aws_config::AbstractAWSConfig=global_aws_config()) = iot_events_data("GET", "/detectors/$(detectorModelName)/keyValues/", params; aws_config=aws_config)
"""
list_detectors(detector_model_name)
list_detectors(detector_model_name, params::Dict{String,<:Any})
Lists detectors (the instances of a detector model).
# Arguments
- `detector_model_name`: The name of the detector model whose detectors (instances) are
listed.
# Optional Parameters
Optional parameters can be passed as a `params::Dict{String,<:Any}`. Valid keys are:
- `"maxResults"`: The maximum number of results to return at one time.
- `"nextToken"`: The token for the next set of results.
- `"stateName"`: A filter that limits results to those detectors (instances) in the given
state.
"""
list_detectors(detectorModelName; aws_config::AbstractAWSConfig=global_aws_config()) = iot_events_data("GET", "/detectors/$(detectorModelName)"; aws_config=aws_config)
list_detectors(detectorModelName, params::AbstractDict{String, <:Any}; aws_config::AbstractAWSConfig=global_aws_config()) = iot_events_data("GET", "/detectors/$(detectorModelName)", params; aws_config=aws_config)
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