content stringlengths 5 1.03M | input_ids listlengths 4 823k | ratio_char_token float64 0.4 12.5 | token_count int64 4 823k |
|---|---|---|---|
struct InterfaceOperators
operators::Any
celltooperator::Any
ncells::Any
numoperators::Any
function InterfaceOperators(operators, celltooperator)
ncells = length(celltooperator)
numcouples, numoperators = size(operators)
@assert numcouples == 4
@assert all(celltooper... | [
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2163,... | 2.233685 | 5,302 |
# Script
using Distributions, PyPlot, BayesianNonparametricStatistics
β=0.5
θ = sumoffunctions(vcat([faberschauderone],[faberschauder(j,k) for j in 0:4 for k in 1:2^j]),vcat([1.0],[(-1)^(j*k)*2^(-β*j) for j in 0:4 for k in 1:2^j]))
x = 0.0:0.001:1.0
y = θ.(x)
# Uncomment the following lines to plot θ.
# clf()
# pl... | [
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36434,... | 2.063253 | 332 |
"""
Berlage(; <keyword arguments>)
Create a Berlage wavelet.
# Arguments
**Keyword arguments**
* `dt::Real=0.002`: sampling interval in secs.
* `f0::Real=20.0`: central frequency in Hz.
* `m::Real=2`: exponential parameter of Berlage wavelet.
* `alpha::Real=180.0`: alpha parameter of Berlage wavelet in rad/secs... | [
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... | 2.161111 | 360 |
struct Lennnon2000Air <: ThermoState.ThermoModel end
const TAU_MAX_EXP_87 = 0.4207493606569795
const lemmon2000_air_R = 8.314510
const lemmon2000_air_T_reducing = 132.6312
const lemmon2000_air_P_reducing = 3.78502E6
const lemmon2000_air_rho_reducing = 10447.7
const lemmon2000_air_rho_reducing_inv = 1.0/lemmon2000_air_... | [
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... | 1.861965 | 2,311 |
export SingleLayer
"""
singleLayer
σ(K*s+b)
where K,b are trainable weights
"""
struct SingleLayer
end
mσ(x::AbstractArray{R}) where R<:Real = abs.(x)+log.(R(1) .+ exp.(-R(2)*abs.(x)))
mdσ(x::AbstractArray{R}) where R<:Real = tanh.(x)
md2σ(x::AbstractArray{R}) where R<:Real = one(eltype(x)) .- tanh.(x).^2
"""
eva... | [
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87,... | 1.906939 | 4,309 |
"""
parseFunctionNode(nodeDict::Dict)
Parses a [`FunctionNode`](@ref) from a node set configuration file.
"""
function parseFunctionNode(nodeDict::Dict)
func = get(nodeDict, "function", false)
if func == false
error("function field is not set in FunctionNode")
else
aux = 0
try
... | [
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2... | 2.331475 | 3,587 |
export DepthMap
import ImageView
type DepthMap
camera :: M34
depth :: Array{Float32, 2}
nxcorr :: Array{Float32, 2}
end
function DepthMap(view, nbrs, voi, w = 3)
cam = view.camera
im = view.image
(nc, nx, ny) = size(im)
mn = LibAminda.mean_and_inverse_deviation(im, w)
# determine depth range, resolu... | [
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11... | 2.091429 | 525 |
# This file is a part of Julia. License is MIT: https://julialang.org/license
module REPLCompletions
export completions, shell_completions, bslash_completions, completion_text
using Base.Meta
using Base: propertynames, something
abstract type Completion end
struct KeywordCompletion <: Completion
keyword::Strin... | [
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... | 2.280064 | 13,047 |
# This file is a part of JuliaFEM.
# License is MIT: see https://github.com/JuliaFEM/JuliaFEM.jl/blob/master/LICENSE.md
using JuliaFEM
using JuliaFEM.Preprocess
using JuliaFEM.Testing
function JuliaFEM.get_mesh(::Type{Val{Symbol("two elements 1.0x0.5 with 0.1 gap in y direction")}})
mesh = Mesh()
add_node!(me... | [
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... | 2.258367 | 1,494 |
using StatsBase
# Support some of the weighted statistics function in StatsBase
# NOTES:
# - Ambiguity errors are still possible for weights with overly specific methods (e.g., UnitWeights)
# - Ideally, when the weighted statistics is moved to Statistics.jl we can remove this entire file.
# https://github.com/Julia... | [
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1539... | 2.387769 | 1,439 |
# Compute tracks from entity edge data.
using JLD, PyPlot
include("findtracks.jl")
include("findtrackgraph.jl")
# Load edge incidence matrix.
# Load the data file
file_dir = joinpath(Base.source_dir(),"../1EntityAnalysis/Entity.jld")
E = load(file_dir)["E"]
#E = loadassoc(file_dir)
E = logical(E)
# Set prefixes
p = ... | [
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struct SVRG_basic_iterable{R<:Real,C<:RealOrComplex{R},Tx<:AbstractArray{C},Tf,Tg}
F::Array{Tf} # smooth term
g::Tg # nonsmooth term
x0::Tx # initial point
N::Int # of data points in the finite sum problem
L::Maybe{Union{Array{R},R}}... | [
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1... | 1.914427 | 1,823 |
# CuArray{ComplexF32}
function gpu_downconvert!(
downconverted_signal::CuVector{ComplexF32},
carrier::CuVector{ComplexF32},
signal::CuVector{ComplexF32},
start_sample::Integer,
num_samples_left::Integer
)
@. @views downconverted_signal[start_sample:num_samples_left + start_sample - 1] =
... | [
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11920,... | 2.414361 | 1,337 |
module ComradeDynesty
using Comrade
using AbstractMCMC
using TupleVectors
using Reexport
@reexport using Dynesty
Comrade.samplertype(::Type{<:NestedSampler}) = Comrade.IsCube()
Comrade.samplertype(::Type{<:DynamicNestedSampler}) = Comrade.IsCube()
function AbstractMCMC.sample(post::Comrade.TransformedPosterior,
... | [
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... | 2.137778 | 450 |
using Test
using POMDPs
using Random
let
struct M <: POMDP{Int, Int, Char} end
@test_throws MethodError generate_s(M(), 1, 1, MersenneTwister(4))
POMDPs.transition(::M, ::Int, ::Int) = [1]
@test generate_s(M(), 1, 1, MersenneTwister(4)) == 1
@test_throws MethodError generate_sor(M(), 1, 1, Mersenne... | [
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1... | 2.190476 | 336 |
let
doc = open("$testdir/example.html") do example
example |> readstring |> parsehtml
end
io = IOBuffer()
print(io, doc)
seek(io, 0)
newdoc = io |> readstring |> parsehtml
@test newdoc == doc
end
| [
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#=##############################################################################
# DESCRIPTION
Utilities.
# AUTHORSHIP
* Author : Eduardo J. Alvarez
* Email : Edo.AlvarezR@gmail.com
* Created : Sep 2018
* License : MIT License
=#####################################################################... | [
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105... | 1.925057 | 2,642 |
if Base.libllvm_version >= v"7.0"
include(joinpath("gcn_intrinsics", "math.jl"))
end
include(joinpath("gcn_intrinsics", "indexing.jl"))
include(joinpath("gcn_intrinsics", "assertion.jl"))
include(joinpath("gcn_intrinsics", "synchronization.jl"))
include(joinpath("gcn_intrinsics", "extras.jl"))
| [
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... | 2.471074 | 121 |
using LinearAlgebra
export transform,
backtransform
"""
backtransform(Rsets::ReachSolution, options::Options)
Undo a coordinate transformation.
### Input
- `Rsets` -- flowpipe
- `option` -- problem options containing an `:transformation_matrix` entry
### Output
A new flowpipe where each reach set has... | [
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78... | 2.955574 | 1,238 |
function renderloop(screen::Screen; framerate = 1/30, prerender = () -> nothing)
try
while isopen(screen)
t = time()
GLFW.PollEvents() # GLFW poll
prerender()
make_context_current(screen)
render_frame(screen)
GLFW.SwapBuffers(to_native(... | [
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... | 2.090209 | 1,818 |
"""
module that holds functions needed to react to scrolling
Generally first we need to pass the GLFW callback to the Rocket obeservable
code adapted from https://discourse.julialang.org/t/custom-subject-in-rocket-jl-for-mouse-events-from-glfw/65133/3
"""
module ReactToScroll
using ModernGL, ..DisplayWords,Rocket, G... | [
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7... | 2.610805 | 1,999 |
module SMRTypes
import Base: show
export cSMRWMrkChannel, SMRWMrkChannel, cSMRContChannel, SMRContChannel,
cSMREventChannel, SMREventChannel, cSMRMarkerChannel, SMRMarkerChannel,
cSMRChannelInfo, cSMRChannelInfoArray, SMRChannelInfo, show,
channel_string
const MARKER_SIZE = UInt8(4)
abstract ty... | [
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... | 2.379638 | 2,210 |
# This file is a part of Julia. License is MIT: https://julialang.org/license
using Random
using LinearAlgebra
function isnan_type(::Type{T}, x) where T
isa(x, T) && isnan(x)
end
@testset "clamp" begin
@test clamp(0, 1, 3) == 1
@test clamp(1, 1, 3) == 1
@test clamp(2, 1, 3) == 2
@test clamp(3, 1,... | [
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7,
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6030,
90,
... | 1.758228 | 22,240 |
@testset "zygote_adjoints" begin
rng = MersenneTwister(123456)
x = rand(rng, 5)
y = rand(rng, 5)
r = rand(rng, 5)
Q = Matrix(Cholesky(rand(rng, 5, 5), 'U', 0))
@assert isposdef(Q)
gzeucl = gradient(:Zygote, [x, y]) do xy
evaluate(Euclidean(), xy[1], xy[2])
end
gzsqeucl = gra... | [
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366,
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2124,
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43720,
7,
81,
782,
11,
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8,
198,
220,
220,
... | 1.894336 | 918 |
module TestDay05
import AOC2021.Day05.part1, AOC2021.Day05.part2
using Test
test_input_raw = """
0,9 -> 5,9
8,0 -> 0,8
9,4 -> 3,4
2,2 -> 2,1
7,0 -> 7,4
6,4 -> 2,0
0,9 -> 2,9
3,4 -> 1,4
0,0 -> 8,8
5,5 -> 8,2"""
test_input = [string(x) for x in split(test_input_raw, "\n")]
@testset "Day 05" begin
@testset "part 1... | [
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11... | 1.962025 | 237 |
@doc raw"""
BrownianMotionTorus(n::Int)
Returns a hidden state model corresponding to a Brownian motion on an `n`-dimensional torus, with initial condition drawn uniformly at random.
"""
struct BrownianMotionTorus <: HiddenStateModel{Vector{Float64}, ContinuousTime}
n::Int
end
... | [
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8246,
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4373,
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51,
15125,
7,
77,
3712,
5317,
8,
198,
198,
35561,
257,
7104,
1181,
2746,
11188,
284,
257,
4373,
666,
6268,
319,
281,
4600,
77,
63,
12,
19577,
7332,
385,
11,
351,
4238,
... | 1.791492 | 1,904 |
# Helper functions
sameSense(pra::Int, ra::Int) = mod(pra,2)==mod(ra,2)
downSense(ra::Int) = (ra>0) .& (mod(ra,2)==1)
upSense(ra::Int) = (ra>0) .& (mod(ra,2)==0)
# Reward function for VerticalCAS MDP
function POMDPs.reward(mdp::VerticalCAS_MDP, s::stateType, ra::actType)
h = s[1]; vown = s[2]; vint = s[3]; pra = s... | [
2,
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... | 1.688213 | 1,315 |
name = "LLVM"
llvm_full_version = v"11.0.1+3"
libllvm_version = v"11.0.1+3"
# Include common LLVM stuff
include("../common.jl")
build_tarballs(ARGS, configure_extraction(ARGS, llvm_full_version, name, libllvm_version; experimental_platforms=true, assert=true)...; skip_audit=true, julia_compat="1.6")
| [
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10,
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1,
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2,
40348,
2219,
271... | 2.525 | 120 |
include("MultiFidelityABC.jl")
mkpath("figures")
using StatsPlots, Random
println("#### Repressilator")
println("# Loading data")
bm = MakeBenchmarkCloud("repressilator/output")
epsilons = (50.0,50.0)
sample_size = 10^4
println("# Fig 1")
fig1a = view_distances(bm[1:sample_size], epsilons)
fig1b = view_distances(bm[... | [
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601,
346,
1352,
4943,
198,
35235,
7203,
2,
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1366,
494... | 2.325982 | 1,451 |
struct Lattice{D,T,M,U}
site::Vector{Site{D,T}} # sorted by ID
coord_order::Vector{Site{D,T}} # sort by coord
neighbors::Vector{Vector{Int}}
types::Vector{U}
end
function Lattice(coord, types::Vector;
nbhood = VonNeumann(),
type_list = unique(types)
)
dimension = coord isa Matrix ? size(coord, 1)... | [
7249,
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1078,
501,
90,
35,
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11,
44,
11,
52,
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220,
6349,
62,
2875,
3712,
38469,
90,
29123,
90,
35,
11,
51,
11709,
1303,... | 2.444514 | 3,208 |
using LinearAlgebra, Jets, JetPack, Test
n1,n2 = 33,44
@testset "JopLog, correctness T=$(T)" for T in (Float64,Float32,Complex{Float64},Complex{Float32})
F = JopLog(JetSpace(T,n1,n2))
x1 = rand(domain(F)) .+ T(0.0001)
@test F*x1 ≈ log.(x1)
end
@testset "JopLog, linearity test, T=$(T)" for T in (Float64,F... | [
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29230,
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309,
43641,
7,
51,
16725,
329,
309,
287,
357,
43879,
241... | 2.111111 | 792 |
using FileIO, BedgraphFiles
using Bedgraph
using IteratorInterfaceExtensions
using TableTraits
using DataFrames
using Query
using Test
using Logging
# old_logger = global_logger(ConsoleLogger(stdout, Logging.Debug))
module Bag
using Bedgraph
const chroms = ["chr19", "chr19", "chr19", "chr19", "chr19", "chr19", "... | [
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198,
3500,
43301,
198,
198,
3500,
6208,
198,
3500,
5972,
2667,
... | 2.7 | 1,080 |
using Test
using LinearAlgebraicRepresentation
Lar = LinearAlgebraicRepresentation
using ViewerGL
GL = ViewerGL
@testset "GLUtils.jl" begin
# function lar4mesh(verts,cells) # cells are triangles
@testset "lar4mesh" begin
@test
@test
@test
@test
end
# function two2three(points)
... | [
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8763,
198,
198,
31,
9288,
2617,
366,
8763,
18274,
4487,
13,
203... | 2.131545 | 783 |
# This is Subset Tournament CMSA-ES as proposed by Robert Feldt in the paper:
# R. Feldt, "Covariate Subset Tournaments for High-Dimensional Blackbox Optimization with Covariance Matrix Adapting Evolutionary Strategies", 2014
function normalize_utilities(utilities)
utilities / sum(utilities)
end
function linear_ut... | [
2,
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318,
3834,
2617,
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4090,
12,
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5150,
416,
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34,
709,
2743,
378,
3834,
2617,
309,
16950,
329,
3334,
12,
35,
16198,
2619,
3524,... | 2.604946 | 5,055 |
using BinaryBuilder
name = "Chafa"
version = v"1.4.1"
sources = [
ArchiveSource("https://hpjansson.org/chafa/releases/chafa-$(version).tar.xz",
"46d34034f4c96d120e0639f87a26590427cc29e95fe5489e903a48ec96402ba3"),
]
script = raw"""
cd ${WORKSPACE}/srcdir/chafa-*/
if [[ "${target}" == *darwin* ]... | [
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198,
198,
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1,
16,
13,
19,
13,
16,
1,
198,
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82,
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685,
198,
220,
220,
220,
20816,
7416,
7203,
5450,
1378,
24831,
73,
44038,
13,
2398,
14,
354,
2... | 2.351085 | 507 |
"""
ULMFiT - Text Classifier
This is wrapper around the LanguageMode struct. It has three fields:
vocab : contains the same vocabulary from the LanguageModel
rnn_layers : contains same DroppedEmebeddings, LSTM (AWD_LSTM) and VarDrop layers of LanguageModel except for last softmax layer
linear_layers ... | [
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397,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1058,
4909,
262... | 2.38677 | 3,855 |
## 2D Panels
#==========================================================================================#
abstract type AbstractPanel2D <: AbstractPanel end
struct Panel2D{T <: Real} <: AbstractPanel2D
p1 :: SVector{2,T}
p2 :: SVector{2,T}
end
struct WakePanel2D{T <: Real} <: AbstractPanel2D
p1 :: SVect... | [
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25,
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26639,
17... | 2.426029 | 1,798 |
##################################################################################
# This file is part of ModelBaseEcon.jl
# BSD 3-Clause License
# Copyright (c) 2020, Bank of Canada
# All rights reserved.
##################################################################################
export Transformation, NoTrans... | [
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8,
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11,
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286,
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198,
2,
1439,
2489,
10395,
13,
19... | 3.718704 | 679 |
mutable struct Model{T, TV<:AbstractVector{T}, TC<:AbstractVector{<:Function}}
dim::Int
objective::Function
ineq_constraints::TC
box_max::TV
box_min::TV
end
GPUUtils.whichdevice(m::Model) = whichdevice(m.box_max)
dim(m::Model) = m.dim
min(m::Model, i::Integer) = m.box_min[i]
max(m::Model, i::Intege... | [
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220,
220,
220,
9432,
3712,
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198,
220,
220,
220,
2... | 2.231539 | 1,598 |
#-------- Inverses of DPR1
function inv{T}(A::SymDPR1{T},i::Integer,tols::Vector{Float64})
# COMPUTES: inverse of a shifted SymDPR1 matrix A=diagm(A.D)+A.r*A.u*A.u',
# inv(A-A.D[i]*I) which is a SymArrow.
# Uses higher precision to compute top of the arrow element accurately, if
# needed.
# tols=... | [
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90,
43879,
2414,
30072,
628,
220,
220,
220,
1303,
24301,
3843,
... | 1.659746 | 8,188 |
# Tests for Mamlmquist DEA Model
@testset "MalmquistDEAModel" begin
## Test Mamlmquist DEA Model with 1 input and 1 output
X = Array{Float64,3}(undef, 5, 1, 2)
X[:, :, 1] = [2; 3; 5; 4; 4];
X[:, :, 2] = [1; 2; 4; 3; 4];
Y = Array{Float64,3}(undef, 5, 1, 2)
Y[:, :, 1] = [1; 4; 6; 3; 5];
Y[:... | [
2,
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329,
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31,
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75,
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28647,
9104,
351,
352,
5128,
290,
352,
5072,
198,
22... | 1.82359 | 1,950 |
# Implements node data types and associated helper functions
export
SumNode,
ProductNode,
CategoricalDistribution,
IndicatorFunction,
GaussianDistribution
"""
Node Data Structures
Implement a labeled sparse matrix.
"""
abstract type Node end
" Sum node data type "
struct SumNode <: Node
chi... | [
2,
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11,
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220,
220,
220,
327,
2397,
12409,
20344,
3890,
11,
198,
220,
220,
220... | 2.827586 | 1,334 |
function petk06()
#
# M. Petkovic testing polynomials, page 146
#
y = [-1.0*[1;1;1;1];3*[1;1;1];-im;-im];
p1 = reverse(poly(y).a)
p2 = [1.0;-2;5];
p2 = conv(p2,p2);
p = conv(p1,p2);
z = [-1.0 4; 3 3; -im 2; 1+2*im 2; 1-2*im 2];
p, PolyZeros(z)
end
| [
8818,
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74,
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3419,
198,
2,
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2,
337,
13,
4767,
74,
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296,
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198,
2,
198,
220,
220,
220,
331,
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16,
13,
15,
9,
58,
16,
26,
16,
26,
16,
26,
16,
11208,
18,
9,
58,... | 1.630058 | 173 |
module TPT
export TPTSystem,
# Basic information
ncomp,
composition,
numberdensity,
totalnumberdensity,
temperature,
# Structural properties
structurefactor,
paircorrelation,
cavityfunction,
nndistance,
# Interatomi... | [
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309,
11571,
201,
198,
201,
198,
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24525,
4694,
6781,
11,
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201,
198,
220,
220,
220,
220,
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220,
220,
299,
5589,
11,
201,
198,
220,
220,
220,
220,
220,
22... | 2.128543 | 988 |
import SMC
import Distributions
using DataFrames
include("hmm_serialization.jl")
include("schema.jl")
@everywhere begin
using SMC
using Distributions
include("smc_samplers.jl")
include("../aide.jl")
end
function generate_aide_estimates(hmm::HiddenMarkovModel,
observati... | [
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507,
198,
3500,
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62,
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13,
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4943,
198,
198,
31,
16833,
3003,
2221,
198,
220,
220,
220,
1262,
... | 2.177498 | 2,062 |
## utility functions
import OffsetArrays
import ImageFiltering
import ShiftedArrays
export cov_avg!
export boxsmooth!
export outest_bounds #
"""
outest_bounds(cx,sx) -> px0
Helper function to find maximum padding in pixels required to accomodate all query points `cx` outside of the image size 1:`sx`.
# Argument... | [
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39849,
62,
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70,
0,
198,
39344,
3091,
5796,
5226,
0,
198,
39344,
503,
395,
62,
65,
3733,
... | 2.226147 | 1,897 |
# insert functions
"""
insert_boundary!(tier, time; split_at = 0)
insert_boundary!(tg, num, time; split_at = 0)
Insert a boundary at `time` in an interval `tier`, which can also be specified
by its number in a `TextGrid`. This action splits an existing interval and
increases the size of the tier by 1.
The ke... | [
2,
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198,
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198,
220,
220,
220,
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62,
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0,
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8,
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220,
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560,
0,
7,
25297,
11,
997,
11,
640,
26,
6626,
62,
265,
796,
6... | 2.859753 | 5,583 |
######################
# 1: The Julia type for ToricVarieties
######################
abstract type AbstractNormalToricVariety end
struct NormalToricVariety <: AbstractNormalToricVariety
polymakeNTV::Polymake.BigObject
end
export NormalToricVariety
struct AffineNormalToricVariety <: AbstractNormalToricVarie... | [
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... | 2.788588 | 2,436 |
# Download links in `assets.csv`
dir = joinpath(@__DIR__, "Pluto", "frontend", "offline_assets")
rm(dir, force=true, recursive=true)
mkpath(dir)
for url in readlines(joinpath(@__DIR__, "assets.csv") )
@info "Downloading: $url"
file = touch(joinpath(dir, basename(url)))
download(url, file)
end | [
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62,
19668,
4943,
198,
26224,
7,
15908,
11,
2700,
28,
7942,
... | 2.579832 | 119 |
import sbp
err = sbp.MMS
using Test
using LinearAlgebra
using DataStructures
@testset "extract_vars" begin
n = [4, 8, 12]
data = Array(1:sum(n))
vars = OrderedDict("u" => n[1], "v" => n[2], "w" => n[3])
ans_u = Array(1:n[1])
ans_v = Array(n[1]+1:n[1] + n[2])
ans_w = Arra... | [
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31,
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366,
2302,
974,
62,
85,
945,
1,
2221,
198,
220,
220,
220,
220,
220,
... | 1.797781 | 811 |
{"timestamp": 1580591738.0, "score_count": 39747, "score": 7.45}
{"timestamp": 1580205822.0, "score_count": 39008, "score": 7.45}
{"timestamp": 1579597688.0, "score_count": 37448, "score": 7.46}
{"timestamp": 1578990311.0, "score_count": 35377, "score": 7.46}
{"timestamp": 1578377542.0, "score_count": 31908, "score": 7... | [
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16514,
27823,
1298,
1315,
1795,
1238,
3365,
1828,
13,
15,
11,
366,
26675,... | 2.358187 | 684 |
#================================
All kinds of functions related to pruning
=================================#
"""
get_next_prune_constraint(com::CS.CoM, constraint_idxs_vec)
Check which function will be called for pruning next. This is based on `constraint_idxs_vec`. The constraint with the lowest
value is... | [
2,
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7,
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7902,
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3... | 2.185407 | 4,002 |
using SuiteSparseMatrixCollection
using MatrixMarket
using SuiteSparseGraphBLAS
SuiteSparseGraphBLAS.gbset(SuiteSparseGraphBLAS.FORMAT, SuiteSparseGraphBLAS.BYROW)
using BenchmarkTools
using SparseArrays
using LinearAlgebra
include("tc.jl")
include("pr.jl")
graphs = [
#"karate",
#"com-Youtube",
#"as-Skitter... | [
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50,
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1404,... | 2.270506 | 573 |
### A Pluto.jl notebook ###
# v0.17.4
using Markdown
using InteractiveUtils
# ╔═╡ 3668f786-9597-11eb-01a1-87d34b49eef9
begin
#packages for I/O, interpolation, etc
using MITgcmTools, MeshArrays, Plots, PlutoUI
PICKUP_hs94_download()
🏁 = "🏁"
"Downloads and packages : complete."
end
# ╔═╡ 19095067-33f5-495... | [
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24,
43239,
12,
1157,... | 1.859883 | 24,815 |
# MIT License
# Copyright (c) Microsoft Corporation.
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, mer... | [
2,
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2,
286,
428,
3788,
290,
3917,
10314,
3696,
357,
1169,
366... | 2.19683 | 4,984 |
using Distributed
using Images
using FileIO
using Mmap
# calculate a distributed fractal
width = 40000
height = 20000
rmin = -2.5
rmax = 1.5
imin = -1.25
imax = 1.25
iter = 500
epsilon = 0.25
mapfile = "zset-$(width)-$(height)"
s = open(mapfile)
n = read(s, Int)
println("size of zset is $n")
if n != (width * heig... | [
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81,
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532,
17,
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20,
198,
81,
9... | 2.458774 | 473 |
const POWER_SYSTEM_DESCRIPTOR_FILE =
joinpath(dirname(pathof(PowerSystems)), "descriptors", "power_system_inputs.json")
const INPUT_CATEGORY_NAMES = [
("branch", InputCategory.BRANCH),
("bus", InputCategory.BUS),
("dc_branch", InputCategory.DC_BRANCH),
("gen", InputCategory.GENERATOR),
("load"... | [
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20147,
1968,
669,
1600,
366,
6477,
62,
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62,
15414,
82,
... | 2.182766 | 25,333 |
using Inequality
using StatsBase
using Test
using DataFrames
df_1 = DataFrame(v = [8,5,1,3,5,6,7,6,3],
w = collect(0.1:0.1:0.9))
df_2 = DataFrame(v = repeat([8,5,1,3,5,6,7,6,3],2),
w = repeat(collect(0.1:0.1:0.9),2),
group = [1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2])
@t... | [
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11,
20,
11,
21,
11,
22,
11,
21,
11,
18,
4357,
198,
220,... | 1.7407 | 13,656 |
module StructuredQueries
using Compat
export Cursor,
Grouped,
@with,
source,
graph
include("utils.jl")
# grouped
include("grouped/grouped.jl")
include("grouped/show.jl")
#verbs
include("verbs/verbs.jl")
include("verbs/primitives.jl")
include("verbs/expr/assignment_expr_ops.jl")
in... | [
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220,
220,
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2... | 2.62963 | 351 |
# This file is a part of Julia. License is MIT: http://julialang.org/license
struct Rational{T<:Integer} <: Real
num::T
den::T
function Rational{T}(num::Integer, den::Integer) where T<:Integer
num == den == zero(T) && throw(ArgumentError("invalid rational: zero($T)//zero($T)"))
g = den < 0... | [
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25,
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198,
220,
220,
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51,
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22... | 1.999233 | 6,521 |
# Use baremodule to shave off a few KB from the serialized `.ji` file
baremodule CGAL2_jll
using Base
using Base: UUID
import JLLWrappers
JLLWrappers.@generate_main_file_header("CGAL2")
JLLWrappers.@generate_main_file("CGAL2", UUID("a133c068-ba04-5466-9207-ec1c2ac43820"))
end # module CGAL2_jll
| [
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3500,
7308,
25,
471,
27586,
198,
11748,
449,
3069,
36918,... | 2.483333 | 120 |
function warning_callback(message::String)
global TEST_CALLBACK = true
error("checking that error are supported")
end
@testset "Errors" begin
err = ChemfilesError("oops")
iobuf = IOBuffer()
show(iobuf, err)
@test String(iobuf.data[1:(19 + length(err.message))]) == "\"Chemfiles error: oops\""
... | [
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7,
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389,
4855,
4943,
198,
437,
198,
198,
31,
9288,
2617,
366,
9139,
5965... | 2.460177 | 904 |
using PySerial
using Base.Test
@test typeof(list_ports()) == Array{Any,1}
| [
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7308,
13,
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62,
3742,
28955,
6624,
15690,
90,
7149,
11,
16,
92,
198
] | 2.777778 | 27 |
using KiteConnect
using Test
@test_throws ArgumentError KiteConnect.ltp("INFY")
| [
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3500,
6208,
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31,
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12331,
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13313,
13,
2528,
79,
7203,
1268,
43833,
4943,
628
] | 2.964286 | 28 |
# ----------------------
# -- read --
# ----------------------
function read_nodenum(skipnum)
"""
xmax : 仮想セルも含めたnodeのxの数
ymax : 仮想セルも含めたnodeのyの数
"""
fff=[]
open("grid/nodesnum", "r") do f
fff=read(f,String)
end
fff=split(fff,"\n",keepempty=false)
num_nodes=leng... | [
2,
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438,
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2,
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1100,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1377,
198,
2,
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438,
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1100,
62,
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375,
44709,
7,
48267,
22510,
8,
198,
220,
220,
220,
37227,
220,
198,
220,
220,
220... | 1.837061 | 1,878 |
# This file describes how we decide which logger (e.g. LogText vs LogValue vs LogHistograms)
# to use for what data, and any preprocessing
"""
preprocess(name, val, data)
This method takes a tag `name` and the value `val::T` pair. If type `T` can be
serialized to TensorBoard then the pair is pushed to `data`, oth... | [
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3691,
5972,
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8,
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2,
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11,
290,
597,
662,
36948,
198,
198,
37811,
198,
220,
220,
220,
662,... | 2.941485 | 1,333 |
function solve()
a = readline()
a == uppercase(a) ? "A" : "a"
end
println(solve())
| [
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220,
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1,
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7,
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198
] | 2.190476 | 42 |
using BinaryBuilder, Pkg
name = "GLPK"
version = v"5.0"
# Collection of sources required to build GLPK
sources = [
ArchiveSource("http://ftpmirror.gnu.org/gnu/glpk/glpk-$(version.major).$(version.minor).tar.gz",
"4a1013eebb50f728fc601bdd833b0b2870333c3b3e5a816eeba921d95bec6f15"),
]
# Bash recip... | [
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3672,
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366,
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410,
1,
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15,
1,
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82,
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796,
685,
198,
220,
220,
220,
20816,... | 2.638393 | 448 |
module UI
import GLFW
using ModernGL
include("gltools.jl")
GLFW.Init()
# OS X-specific GLFW hints to initialize the correct version of OpenGL
@osx_only begin
GLFW.WindowHint(GLFW.CONTEXT_VERSION_MAJOR, 3)
GLFW.WindowHint(GLFW.CONTEXT_VERSION_MINOR, 2)
GLFW.WindowHint(GLFW.OPENGL_PROFILE, GLFW.OPENGL_C... | [
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70,
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3419,
198,
220,
198,
2,
7294,
1395,
12,
11423,
10188,
24160,
20269,
284... | 2.425971 | 824 |
abstract type AbstractBC{T} <: AbstractDiffEqAffineOperator{T} end
abstract type AtomicBC{T} <: AbstractBC{T} end
"""
Robin, General, and in general Neumann, Dirichlet and Bridge BCs
are not necessarily linear operators. Instead, they are affine
operators, with a constant term Q*x = Qa*x + Qb.
"""
abstract type Aff... | [
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2749,
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51,
92,
886,
198,
198,
37811,
198,
... | 2.220544 | 3,972 |
const HubbardConf = Array{Int8, 2} # conf === hsfield === discrete Hubbard Stratonovich field (Hirsch field)
const HubbardDistribution = Int8[-1,1]
"""
Famous attractive (negative U) Hubbard model on a cubic lattice.
Discrete Hubbard Stratonovich transformation (Hirsch transformation) in the density/charge channel,
su... | [
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16,
11,
16,
6... | 2.653783 | 1,837 |
export expandModelNearest, getSimilarLinearModel, addAbsorbingLayer
export addAbsorbingLayer, smoothModel, smooth3
export velocityToSlowSquared,slowSquaredToVelocity,velocityToSlow,slowToSlowSquared,slowSquaredToSlow
export slowToLeveledSlowSquared,getModelInvNewton
using Statistics
using jInv.Mesh
function slowToLev... | [
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11,
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451,
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11,
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11,
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18,
198,
39344,
15432,
2514,
36423,
22266,
1144,
11,
38246,
22266,
... | 1.895738 | 3,050 |
using Test
using MarketData
using TimeSeries
using MarketTechnicals
@testset "Levels" begin
@testset "floor pivots" begin
# values verified by various website calculators
@test isapprox(values(floorpivots(ohlc)[:r3])[1] , 123.310, atol=.01)
@test isapprox(values(floorpivots(ohlc)[:r2])[1] , 119.52... | [
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198,
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1,
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198,
31,
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2617,
366,
28300,
16767,
1747,
1,
2221,
198,
220,
220,
220,
13... | 2.117955 | 763 |
"""
# Description
Rewrite an expression to remove all use of backticks.
# Arguments
1. `e::Any`: An expression.
# Return Values
1. `e::Any`: An expression in which backticks have been removed.
# Examples
```
julia> remove_backticks(:(`mean(a)`))
:(mean(a))
```
"""
function remove_backticks(@nospecialize(e::Any)... | [
198,
37811,
198,
2,
12489,
198,
198,
30003,
6525,
281,
5408,
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83,
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63,
25,
1052,
5408,
13,
198,
198,
2,
8229,
27068,
198,
1... | 2.131944 | 288 |
using Measures
const CategoricalAesthetic =
Union{Nothing, IndirectArray}
const NumericalAesthetic =
Union{Nothing, AbstractMatrix, AbstractVector}
const NumericalOrCategoricalAesthetic =
Union{CategoricalAesthetic, NumericalAesthetic}
@varset Aesthetics begin
x, Union{NumericalOrCategori... | [
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11... | 2.07668 | 6,964 |
export RobotBasedMagneticFieldStaticProtocolParams, RobotBasedMagneticFieldStaticProtocol, measurement, filename
Base.@kwdef struct RobotBasedMagneticFieldStaticProtocolParams <: RobotBasedProtocolParams
positions::Union{Positions, Missing} = missing
postMoveWaitTime::typeof(1.0u"s") = 0.5u"s"
numCooldowns::Inte... | [
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1... | 3.375874 | 1,144 |
doc"""
tanh(x::Var)
Hyperbolic tangent function.
"""
Base.tanh(x::Var) = Var(tanh(x.data), ∇tanh!, (x,))
Base.tanh(x::Array) = tanh.(x)
Base.tanh(x::CuArray) = CUDNN.tanh(x)
Base.tanh(x::Node) = Node(tanh, (x,))
function ∇tanh!(y::Var, x::Var)
isnothing(x.grad) && return
∇tanh!(y.data, y.grad, x.data, x.g... | [
15390,
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7,
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1... | 1.878594 | 313 |
"""
static_analysis(assembly; kwargs...)
Perform a static analysis of the system of nonlinear beams contained in
`assembly`. Return the resulting system and a flag indicating whether the
iteration procedure converged.
# Keyword Arguments
- `prescribed_conditions = Dict{Int,PrescribedConditions{Float64}}()`:
... | [
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287,
198,
63,
41873,
44646,
8229,
262,
7186,
1080,
290,
257,
6056,
... | 2.498696 | 16,489 |
# Multidimensional arrays
z = zeros(Float64, 2, 3)
println(typeof(z))
# Declare array of dimension n x m
n, m = 2, 4
arr = Array{Int}(undef, 2, 4)
println(arr)
println(size(arr))
arr2 = Array{Int}(undef, 3, 2, 2)
println(arr2)
println(size(arr2))
s = ones(String, 1, 3)
println(s)
# Note that s is considered a "row ma... | [
2,
7854,
312,
16198,
26515,
198,
89,
796,
1976,
27498,
7,
43879,
2414,
11,
362,
11,
513,
8,
198,
35235,
7,
4906,
1659,
7,
89,
4008,
198,
198,
2,
16691,
533,
7177,
286,
15793,
299,
2124,
285,
198,
77,
11,
285,
796,
362,
11,
604,
... | 2.369099 | 233 |
function randuint() :: UInt32
Base.llvmcall((
"""
define i32 @randuint() #0 {
%1 = tail call { i32, i32 } @llvm.x86.rdrand.32() #1
%2 = extractvalue { i32, i32 } %1, 0
ret i32 %2
}
; Function Attrs: nounwind
declare { i32, i32 } @llvm.x86.rdrand.32() #1
attributes #0 = { nounwind ssp uwtable "less-precise-... | [
198,
8818,
43720,
28611,
3419,
7904,
471,
5317,
2624,
198,
220,
220,
220,
7308,
13,
297,
14761,
13345,
19510,
198,
37811,
198,
13086,
1312,
2624,
2488,
25192,
28611,
3419,
1303,
15,
1391,
198,
220,
4064,
16,
796,
7894,
869,
1391,
1312,
... | 2.127907 | 344 |
using LazyTaylorSeries
using Test
@testset "Basic usage" begin
t = Taylor1((t, i) -> (i == 1) ? 1.0 : 0.0, Float64[]) # define variable
t2 = Taylor1((t, i) -> (i == 1) ? 1.0 : 0.0, Dict{Int,Float64}()) # define variable
@test t[0] == 0 == t2[0]
@test t[1] == 1 == t2[1]
@test t[2] == 0 == t2[2]... | [
3500,
406,
12582,
29907,
27996,
198,
3500,
6208,
628,
198,
31,
9288,
2617,
366,
26416,
8748,
1,
2221,
198,
220,
220,
220,
256,
220,
796,
8121,
16,
19510,
83,
11,
1312,
8,
4613,
357,
72,
6624,
352,
8,
5633,
352,
13,
15,
1058,
657,
... | 1.78553 | 387 |
using Documenter, MyPkg1
makedocs(;
modules=[MyPkg1],
format=Documenter.HTML(),
pages=[
"Home" => "index.md",
],
repo="https://github.com/XiaodongMa-MRI/MyPkg1.jl/blob/{commit}{path}#L{line}",
sitename="MyPkg1.jl",
authors="Xiaodong Ma",
assets=String[],
)
deploydocs(;
repo... | [
3500,
16854,
263,
11,
2011,
47,
10025,
16,
198,
198,
76,
4335,
420,
82,
7,
26,
198,
220,
220,
220,
13103,
41888,
3666,
47,
10025,
16,
4357,
198,
220,
220,
220,
5794,
28,
24941,
263,
13,
28656,
22784,
198,
220,
220,
220,
5468,
4188... | 2.022346 | 179 |
import Base
include("production.jl")
"""
A grammar, represented as a tuple ``G=(N,T,P,S)``
"""
struct Grammar
"The nonterminal symbols Set"
N::Set
"The terminal symbols Set"
T::Set
"The productions Array"
P::Array
"The starting symbol"
S::AbstractString
iscontextfree::Bool
end
###... | [
11748,
7308,
198,
198,
17256,
7203,
25493,
13,
20362,
4943,
198,
198,
37811,
198,
32,
23491,
11,
7997,
355,
257,
46545,
7559,
38,
16193,
45,
11,
51,
11,
47,
11,
50,
8,
15506,
198,
37811,
198,
7249,
20159,
3876,
198,
220,
220,
220,
... | 2.366938 | 1,349 |
const ALWB_URI = URI(scheme="http", host="www.bom.gov.au", path="/jsp/awra/thredds/fileServer/AWRACMS")
abstract type DataMode end
"""
Values <: DataMode
Get the dataset as regular measured values.
"""
struct Values <: DataMode end
"""
Deciles <: DataMode
Get the dataset in relative deciles.
"""
struct De... | [
198,
9979,
8355,
45607,
62,
47269,
796,
43975,
7,
15952,
1326,
2625,
4023,
1600,
2583,
2625,
2503,
13,
65,
296,
13,
9567,
13,
559,
1600,
3108,
35922,
73,
2777,
14,
707,
430,
14,
400,
445,
9310,
14,
7753,
10697,
14,
12298,
49,
2246,
... | 2.378389 | 2,471 |
"""
scattering_field(args)
Returns a function which gives the average scattering coefficients for any vector `x` inside the material. This field is defined by Equation (3.13) in [AL Gower and G Kristensson, "Effective waves for random three-dimensional particulate materials", (2021)](https://arxiv.org/pdf/2010.00... | [
198,
37811,
198,
220,
220,
220,
45765,
62,
3245,
7,
22046,
8,
198,
198,
35561,
257,
2163,
543,
3607,
262,
2811,
45765,
44036,
329,
597,
15879,
4600,
87,
63,
2641,
262,
2587,
13,
770,
2214,
318,
5447,
416,
7889,
341,
357,
18,
13,
1... | 2.881301 | 2,460 |
##############
# Owner Type #
##############
@ghdef mutable struct Owner
typ::Union{String, Nothing}
email::Union{String, Nothing}
name::Union{String, Nothing}
login::Union{String, Nothing}
bio::Union{String, Nothing}
company::Union{String, Nothing}
location::Union{String, Nothing}
avat... | [
7804,
4242,
2235,
198,
2,
23853,
5994,
1303,
198,
7804,
4242,
2235,
198,
198,
31,
456,
4299,
4517,
540,
2878,
23853,
198,
220,
220,
220,
2170,
3712,
38176,
90,
10100,
11,
10528,
92,
198,
220,
220,
220,
3053,
3712,
38176,
90,
10100,
... | 2.7671 | 1,769 |
################################
## Generic DataFile interface ##
################################
# This provides common methods that could be applicable to any
# interface for reading variables out of a file, e.g. HDF5,
# JLD, or MAT files. This is the super class of HDF5File, HDF5Group,
# JldFile, JldGroup, Matlabv5... | [
29113,
198,
2235,
42044,
6060,
8979,
7071,
22492,
198,
29113,
198,
2,
770,
3769,
2219,
5050,
326,
714,
307,
9723,
284,
597,
198,
2,
7071,
329,
3555,
9633,
503,
286,
257,
2393,
11,
304,
13,
70,
13,
5572,
37,
20,
11,
198,
2,
449,
... | 2.536337 | 688 |
mutable struct LUFactorization{Tv,Ti} <: AbstractLUFactorization{Tv,Ti}
A::Union{Nothing,ExtendableSparseMatrix{Tv,Ti}}
fact::Union{Nothing,SuiteSparse.UMFPACK.UmfpackLU{Tv,Ti}}
phash::UInt64
end
"""
```
LUFactorization(;valuetype=Float64, indextype=Int64)
LUFactorization(matrix)
```
Default Juli... | [
76,
18187,
2878,
406,
36820,
11218,
1634,
90,
51,
85,
11,
40533,
92,
1279,
25,
27741,
43,
36820,
11218,
1634,
90,
51,
85,
11,
40533,
92,
198,
220,
220,
220,
317,
3712,
38176,
90,
18465,
11,
11627,
437,
540,
50,
29572,
46912,
90,
5... | 2.205882 | 340 |
# CEP
"""
setup_cep_opt_sets(ts_data::ClustData,opt_data::CEPData,opt_config::Dict{String,Any})
fetching sets from the time series (ts_data) and capacity expansion model data (opt_data) and returning Dictionary with Sets as Symbols
"""
function setup_opt_cep_set(ts_data::ClustData,
opt_d... | [
2,
327,
8905,
198,
37811,
198,
220,
220,
220,
9058,
62,
344,
79,
62,
8738,
62,
28709,
7,
912,
62,
7890,
3712,
2601,
436,
6601,
11,
8738,
62,
7890,
3712,
5222,
5760,
1045,
11,
8738,
62,
11250,
3712,
35,
713,
90,
10100,
11,
7149,
... | 2.313312 | 15,467 |
# requires LinearAlgebra, Images, ImageView
include("GrahamScan.jl")
using ImageView, Images
using SparseArrays
using Random
using LinearAlgebra
using Statistics
function FindRoughEdge(A)
# Find the rough coordinates of the non-horizontal edges of shapes in A
B = Float64.(A);
abs.(B[:, 2:end] - B[:, 1:en... | [
2,
4433,
44800,
2348,
29230,
11,
5382,
11,
7412,
7680,
198,
198,
17256,
7203,
45821,
33351,
13,
20362,
4943,
198,
198,
3500,
7412,
7680,
11,
5382,
198,
3500,
1338,
17208,
3163,
20477,
198,
3500,
14534,
198,
3500,
44800,
2348,
29230,
198... | 2.299646 | 1,979 |
module NamedIndicesMeta
using NamedDims, ImageCore, ImageMetadata, ImageAxes, FieldProperties,
AxisIndices, LightGraphs, SimpleWeightedGraphs, Reexport, MappedArrays
using Base: tail
import ImageAxes: timeaxis, timedim, colordim, checknames, isstreamedaxis
export
NamedDimsArray,
AxisIndicesArray,
N... | [
21412,
34441,
5497,
1063,
48526,
198,
198,
3500,
34441,
35,
12078,
11,
7412,
14055,
11,
7412,
9171,
14706,
11,
7412,
31554,
274,
11,
7663,
2964,
18200,
11,
198,
220,
220,
220,
220,
220,
38349,
5497,
1063,
11,
4401,
37065,
82,
11,
1742... | 2.782609 | 368 |
### A Pluto.jl notebook ###
# v0.14.4
using Markdown
using InteractiveUtils
# ╔═╡ d01a9b4f-8b55-4607-abb6-717d227fcd48
begin
using PlutoUI, LinearAlgebra
PlutoUI.TableOfContents(aside=true)
end
# ╔═╡ 479a40d9-b81e-442b-9962-f972b110a4dd
# Pkg.checkout("SpecialMatrices")
using SpecialMatrices
# ╔═╡ 574e86bb-159c-4... | [
21017,
317,
32217,
13,
20362,
20922,
44386,
198,
2,
410,
15,
13,
1415,
13,
19,
198,
198,
3500,
2940,
2902,
198,
3500,
21365,
18274,
4487,
198,
198,
2,
2343,
243,
242,
28670,
22880,
94,
288,
486,
64,
24,
65,
19,
69,
12,
23,
65,
2... | 1.828712 | 15,798 |
# This file is a part of Julia. License is MIT: https://julialang.org/license
using Test
# code_native / code_llvm (issue #8239)
# It's hard to really test these, but just running them should be
# sufficient to catch segfault bugs.
module ReflectionTest
using Test, Random
function test_ir_reflection(freflect, f, ty... | [
2,
770,
2393,
318,
257,
636,
286,
22300,
13,
13789,
318,
17168,
25,
3740,
1378,
73,
377,
498,
648,
13,
2398,
14,
43085,
198,
198,
3500,
6208,
198,
198,
2,
2438,
62,
30191,
1220,
2438,
62,
297,
14761,
357,
21949,
1303,
23,
23516,
8... | 2.341793 | 13,751 |
using Plots
include("src/pwl_approx.jl")
## Define a submodular cardinality based function via Combined gadget
k = 2
w = random_scb_function(k::Int64)
w = 10*w/maximum(w)
pl = scatter(0:k,w,legend = false, xticks = 0:k)
epsi = 10.0
z0, zk, a, b, cgf = SubCardFun_to_CGF_weights(w,epsi,false)
Jtrue = length(a) + 1
J =... | [
3500,
1345,
1747,
198,
198,
17256,
7203,
10677,
14,
79,
40989,
62,
1324,
13907,
13,
20362,
4943,
198,
198,
2235,
2896,
500,
257,
850,
4666,
934,
38691,
414,
1912,
2163,
2884,
32028,
42892,
198,
74,
796,
362,
198,
86,
796,
4738,
62,
... | 2.097276 | 257 |
@enum Shape CIRCLE RECTANGLE ROUNDED_RECTANGLE DISTANCEFIELD TRIANGLE
@enum CubeSides TOP BOTTOM FRONT BACK RIGHT LEFT
struct Grid{N, T <: AbstractRange}
dims::NTuple{N, T}
end
Base.ndims(::Grid{N,T}) where {N,T} = N
Grid(ranges::AbstractRange...) = Grid(ranges)
function Grid(a::Array{T, N}) where {N, T}
s = ... | [
31,
44709,
25959,
327,
4663,
29931,
371,
9782,
15567,
2538,
371,
15919,
1961,
62,
23988,
15567,
2538,
360,
8808,
19240,
44603,
37679,
15567,
2538,
198,
31,
44709,
23315,
50,
1460,
28662,
347,
29089,
2662,
8782,
35830,
28767,
33621,
12509,
... | 2.286923 | 1,537 |
# Variable reference.
mutable struct VariableNode <: AbstractSQLNode
name::Symbol
VariableNode(; name::Union{Symbol, AbstractString}) =
new(Symbol(name))
end
VariableNode(name) =
VariableNode(name = name)
"""
Var(; name)
Var(name)
Var.name Var."name" Var[name] Var["... | [
2,
35748,
4941,
13,
198,
198,
76,
18187,
2878,
35748,
19667,
1279,
25,
27741,
17861,
19667,
198,
220,
220,
220,
1438,
3712,
13940,
23650,
628,
220,
220,
220,
35748,
19667,
7,
26,
1438,
3712,
38176,
90,
13940,
23650,
11,
27741,
10100,
... | 2.420949 | 506 |
# packages -
using Dates
using Optim
using JSON
using DataFrames
using Statistics
using LsqFit
using Reexport
@reexport using PooksoftBase
# include my code -
include("./base/Types.jl")
include("./base/Checks.jl")
include("./base/Intrinsic.jl")
include("./base/Binary.jl")
include("./base/Ternary.jl")
include("./base/G... | [
2,
10392,
532,
198,
3500,
44712,
198,
3500,
30011,
198,
3500,
19449,
198,
3500,
6060,
35439,
198,
3500,
14370,
198,
3500,
406,
31166,
31805,
198,
3500,
797,
39344,
198,
31,
631,
87,
634,
1262,
350,
566,
4215,
14881,
198,
198,
2,
2291,... | 2.661458 | 192 |
module test_Variables
import ModiaMath
# Desired:
# using Test
#
# In order that Test needs not to be defined in the user environment, it is included via ModiaMath:
@static if VERSION < v"0.7.0-DEV.2005"
using Base.Test
else
using ModiaMath.Test
end
mutable struct Revolute <: ModiaMath.AbstractComponentWi... | [
21412,
1332,
62,
23907,
2977,
198,
198,
11748,
3401,
544,
37372,
198,
198,
2,
2935,
1202,
25,
198,
2,
220,
220,
1262,
6208,
198,
2,
198,
2,
554,
1502,
326,
6208,
2476,
407,
284,
307,
5447,
287,
262,
2836,
2858,
11,
340,
318,
3017,... | 1.942417 | 5,783 |
# Note that this script can accept some limited command-line arguments, run
# `julia build_tarballs.jl --help` to see a usage message.
using BinaryBuilder
name = "iso_codes"
version = v"4.3"
# Collection of sources required to build iso-codes
sources = [
"https://salsa.debian.org/iso-codes-team/iso-codes/-/archiv... | [
2,
5740,
326,
428,
4226,
460,
2453,
617,
3614,
3141,
12,
1370,
7159,
11,
1057,
198,
2,
4600,
73,
43640,
1382,
62,
18870,
21591,
13,
20362,
1377,
16794,
63,
284,
766,
257,
8748,
3275,
13,
198,
3500,
45755,
32875,
198,
198,
3672,
796,... | 3.065491 | 397 |
# This file is a part of TypeDBClient. License is MIT: https://github.com/Humans-of-Julia/TypeDBClient.jl/blob/main/LICENSE
# ---------------------------------------------------------------------------------
module DatabaseManagerRequestBuilder
using ..TypeDBClient: Proto
create_req(name::String) = Proto.CoreDataba... | [
2,
770,
2393,
318,
257,
636,
286,
5994,
11012,
11792,
13,
220,
13789,
318,
17168,
25,
3740,
1378,
12567,
13,
785,
14,
32661,
504,
12,
1659,
12,
16980,
544,
14,
6030,
11012,
11792,
13,
20362,
14,
2436,
672,
14,
12417,
14,
43,
2149,
... | 2.589072 | 6,680 |
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