content stringlengths 6 1.03M | input_ids listlengths 4 535k | ratio_char_token float64 0.68 8.61 | token_count int64 4 535k |
|---|---|---|---|
<reponame>pnavaro/AnalogDataAssimilation.jl
export AnEnKS
struct AnEnKS
np::Int64
end
"""
data_assimilation( yo, da)
Apply stochastic and sequential data assimilation technics using
model forecasting or analog forecasting.
"""
function forecast(da::DataAssimilation, yo::TimeSeries, mc::AnEnKS; progress ... | [
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2... | 1.757858 | 1,718 |
module MultivariateStochasticVolatility
# Import
using LinearAlgebra: diag, diagm, kron, I, cholesky
using Distributions: Normal, MvNormal, InverseWishart, MatrixNormal
# Constants
const REALMAT = Matrix{T} where T <:Real
const REALVEC = Vector{T} where T <:Real
# Include scripts
include("types.jl")
include("utils.j... | [
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264... | 3.165563 | 151 |
# This file is a part of BAT.jl, licensed under the MIT License (MIT).
# ToDo: Add literature references to AdaptiveMHTuning docstring.
"""
AdaptiveMHTuning(...) <: MHProposalDistTuning
Adaptive MCMC tuning strategy for Metropolis-Hastings samplers.
Adapts the proposal function based on the acceptance ratio an... | [
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220... | 2.583283 | 1,651 |
<reponame>aquatiko/ImageMorphology.jl
__precompile__()
module ImageMorphology
using ImageCore
include("dilation_and_erosion.jl")
include("thinning.jl")
export
dilate,
erode,
opening,
closing,
tophat,
bothat,
morphogradient,
morpholaplace,
thinning,
GuoAlgo
end # module
| [
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6... | 2.297101 | 138 |
"""
To deal with calcium issue:
Take tau_ca out of the calcium currents themselves, and put them in the soma, like a capacitance
What do compartments need?
Reversal potentials
Capacitance
Hooks
Time constants (Ca + V)
Dict(:V => -60., :Ca => 0.1),
Dict(:Cm => -60., :τCa => 10., :Cainf => 0.05, :ENa => -50.)
Dict(:Cₘ... | [
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198,... | 2.322785 | 316 |
using Kinetic, Plots, LinearAlgebra
using KitBase.JLD2
using Flux: onecold
cd(@__DIR__)
begin
set = Setup(case = "sod", space = "1d2f1v", maxTime = 0.15)
ps = PSpace1D(0.0, 1.0, 200, 1)
vs = VSpace1D(-5.0, 5.0, 100)
#gas = Gas(Kn = 1e-4, K = 2, γ = 5/3)
gas = Gas(Kn = 1e-3, K = 2, γ = 5/3)
#gas... | [
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3112... | 1.691545 | 2,117 |
<gh_stars>0
import JSON
struct Emitter
io::IO
cache::Cache
end
function make_emitter(io, verbose::Bool)
let cache = verbose ? NoopCache() : RollingCache()
Emitter(io, cache)
end
end
function emit_raw(e::Emitter, s::AbstractString)
print(e.io, s)
end
function emit_tag(e::Emitter, x::AbstractString)
e... | [
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220... | 2.305927 | 523 |
<reponame>JuliaNeuroscience/SpikeSynchrony.jl<gh_stars>1-10
using Test, SpikeSynchrony
@testset "SPIKE distance" begin include("SPIKEdistance_tests.jl") end
@testset "vanRossum" begin include("vanRossum_tests.jl") end
| [
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... | 2.7375 | 80 |
<filename>4-unit-3-demos/simple-matrix-view.jl
using Plots
using Random
Random.seed!(0)
A = randn(40,30)
heatmap(A)
| [
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1... | 2.230769 | 52 |
# Items are assembled by the rss_generator in a global feed and sub-feeds
# for each of the tag. So each item is a tuple with the string of the item
# and
struct RSSItem
item::String
date::Date
tags::Vector{String}
end
const RSS_ITEMS = Vector{RSSItem}()
"""
$SIGNATURES
If there's an RSS feed to generat... | [
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... | 2.290037 | 2,148 |
<reponame>hzgzh/NG
R=8.31451;p=101325;T=293.15
molemass=[
16.0430,30.0700,44.0970,58.1230,58.1230,72.1500,72.1500,72.1500,
86.1770,86.1770,86.1770,86.1770,86.1770,100.204,114.231,128.258,
142.285,28.0540,42.0810,56.1080,56.0180,56.1080,56.1080,70.1340,
40.0650,54.0920,54.0920,26.0380,70.1340,84.1610,98... | [
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1... | 1.326366 | 7,320 |
<filename>src/photosynthesis/FvCB.jl<gh_stars>0
"""
Farquhar–von Caemmerer–Berry (FvCB) model for C3 photosynthesis (Farquhar et al., 1980;
von Caemmerer and Farquhar, 1981).
The definition:
- `Tᵣ`: the reference temperature (°C) at which other parameters were measured
- `VcMaxRef`: maximum rate of Rubisco activity ... | [
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5... | 2.338474 | 6,160 |
<filename>src/PilotResponse/SimplePilotResponseImpl/SimplePilotResponseImpl.jl
# Author: <NAME>, <EMAIL>
# Date: 06/09/2014
module SimplePilotResponseImpl
export
initialize,
update,
updatePilotResponse,
SimplePilotResponse,
SimplePRResolutionAdvisory,
SimplePRCommand
usin... | [
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4... | 2.581121 | 678 |
<gh_stars>1-10
tu = (1, 2, "Hello")
# 1 based
println(tu[1])
println(tu[3])
named = (first = 100, second = 10)
println(named[1])
println(named.first)
map(x -> x * 10, named) |> println
(a, b, c) = 2:4
println(a, b, c)
println(length(tu))
println(lastindex(tu)) | [
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18... | 2.25641 | 117 |
<reponame>JuliaBinaryWrappers/Clingcon_jll.jl
# Use baremodule to shave off a few KB from the serialized `.ji` file
baremodule Clingcon_jll
using Base
using Base: UUID
import JLLWrappers
JLLWrappers.@generate_main_file_header("Clingcon")
JLLWrappers.@generate_main_file("Clingcon", UUID("f3fadb3f-5422-5a6c-be27-a20f6ff... | [
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262,
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13,
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63,
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198,
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21412... | 2.438356 | 146 |
# Interacting with the Petsc Options Database
export PetscOptionsSetValue, PetscOptionsClearValue, PetscOptionsView, PetscSetOptions, PetscClearOptions
"""
Typedef of PetscOptions
"""
const PetscOptions = Ptr{Void}
"""
PetscOptionsSetValue
**Inputs**
* arg1: the key (string)
* arg2: the value (string)
... | [
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198,... | 2.840678 | 590 |
# Tolerance
epsilon = 0.0001
"""
Function used to transform a column with numerical values into one or several binary columns.
Arguments:
- data: table which contains the column that will be binarized (1 row = 1 individual, 1 column = 1 feature);
- header: header of the column of data that will be binarized
- inte... | [
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490... | 2.179296 | 9,264 |
is_valid(x, ::Val{:one_hot}) = sum(x) == 1
function binarize(x, d::D, ::Val{:one_hot}) where {T <: Number, D <: DiscreteDomain{T}}
y = zeros(T, length(d))
is_in = false
for (i, v) in enumerate(get_domain(d))
if x == v
y[i] = 1
is_in = true
break
end
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51,
1279,... | 1.985537 | 484 |
<filename>src/qn/qnitensor.jl
function ITensor(::Type{ElT},
flux::QN,
inds::IndexSet) where {ElT<:Number}
blocks = nzblocks(flux,inds)
T = BlockSparseTensor(ElT,blocks,inds)
return itensor(T)
end
function ITensor(inds::QNIndex...)
T = BlockSparseTensor(IndexSet(inds))
retur... | [
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
28462,
3712,
... | 2.196119 | 2,422 |
function Center(P,h,n)
s=zeros(Float64,1,n-1)
Pc=zeros(Float64,1,n-1)
s2=0.0
for i in 1:n
if i != h
for j in 1:n-1
s[j]+=P[i][j]
end
end
end
for i in 1:n-1
Pc[i]=s[i]/n
end
return Pc
end
functi... | [
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22... | 1.415629 | 2,892 |
# Code in this file inspired by NetworkX.
"""
core_number(g)
Return the core number for each vertex in graph `g`.
A k-core is a maximal subgraph that contains vertices of degree `k` or more.
The core number of a vertex is the largest value `k` of a k-core containing
that vertex.
### Implementation Notes
Not imp... | [
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... | 2.536048 | 1,817 |
<gh_stars>0
module REPL
using Revise, SHA, Dates, Pkg
using Genie, Genie.Loggers, Genie.Configuration, Genie.Generator, Genie.Tester, Genie.Util, Genie.FileTemplates
const JULIA_PATH = joinpath(Sys.BINDIR, "julia")
"""
secret_token() :: String
Generates a random secret token to be used for configuring the SECR... | [
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1352,
11,
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13,
51,
7834,
... | 2.651024 | 1,172 |
using DifferentialEquations, Plots
function orego(du,u,p,t)
s,q,w = p
y1,y2,y3 = u
du[1] = s*(y2+y1*(1-q*y1-y2))
du[2] = (y3-(1+y1)*y2)/s
du[3] = w*(y1-y3)
end
p = [77.27,8.375e-6,0.161]
prob = ODEProblem(orego,[1.0,2.0,3.0],(0.0,360.0),p)
sol = solve(prob)
plot(sol)
plot(sol,vars=(1,2,3))
using Benchmar... | [
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220,
7043,
58,
... | 1.744108 | 1,188 |
<reponame>sthagen/facultyai-dash-bootstrap-components<gh_stars>10-100
using DashBootstrapComponents
popovers = html_div([
dbc_button("Hidden Arrow", id = "hide-arrow-target", className = "me-1", n_clicks = 0),
dbc_popover(
"I am a popover without an arrow!",
target = "hide-arrow-target",
... | [
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7146... | 2.272401 | 279 |
<reponame>UnofficialJuliaMirror/NKLandscapes.jl-89ab07c8-7ba9-54fb-a7de-e55844b2596b
# Test runner
include("unit.jl")
| [
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65,
198,... | 2.142857 | 56 |
unittests = [
"photoreceptor",
"analysis"]
println("===================")
println("Running unit tests:")
println("===================")
for t in unittests
tfile = t*".jl"
println(" * $(tfile) *")
include(string("unit/",tfile))
println()
println()
end
| [
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4326,
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7203,
4770,
855,
... | 2.814433 | 97 |
<reponame>Maximilian-Stefan-Ernst/julia_sem<filename>src/loss/ML/ML.jl
# Ordinary Maximum Likelihood Estimation
############################################################################
### Types
############################################################################
struct SemML{INV,M,M2,B,FT,GT,HT} <: SemLo... | [
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10062,
18991,
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198... | 1.841823 | 4,849 |
{"timestamp": 1580439731.0, "score": 7.17, "score_count": 224487}
{"timestamp": 1571343561.0, "score": 7.2, "score_count": 217726}
{"timestamp": 1567459194.0, "score": 7.2, "score_count": 216208}
{"timestamp": 1567156746.0, "score": 7.2, "score_count": 215890}
{"timestamp": 1565672257.0, "score": 7.2, "score_count": 21... | [
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27823,
1298,
23313,
19880,
2327,
5333,
13,
15,
11,
366,
26675,
1298,
... | 2.375812 | 2,770 |
module GeneralizedCRT
export crt
using Base.Threads
import Base.Threads.@spawn
const THRESHOLD1 = 7 # split longer input arrays to use binary instead of sequential algo
const THRESHOLD2 = 15 # split longer input arrays to different cpu threads
"""
crt(a, b, p, q)
Given `0 <= a < p` and `0 <= b < q` with `a == ... | [
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16,
796,
767,
220,
1303,
6626,
2392,
5128,
26515,
284,
7... | 1.994624 | 1,860 |
using SpecialFunctions
import NaNMath
import Calculus
# This implementation is essentially identical to the implementation in DualNumbers.jl
# force use of NaNMath functions in derivative calculations
function to_nanmath(x::Expr)
if x.head == :call
funsym = Expr(:.,:NaNMath,Base.Meta.quot(x.args[1]))
... | [
3500,
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198,
2,
2700,
779,
286,
11013,
32755,
776,
5499,
287,
27255,
... | 2.121113 | 1,222 |
struct CostFunctionUE <: CostFunction
tfc::TimeFunctionContainer
function CostFunctionUE(network::AbstractNetwork, fn::Function)
new(TimeFunctionContainer(network, fn))
end
end
function (f::CostFunctionUE)(x::Array{<:Real,1}, ids=nothing; returnitems=[:costs], tolls=nothing)
t, dt = nothing, ... | [
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2... | 2.154639 | 873 |
<reponame>ArjunNarayanan/MeshPlotter.jl
using MeshPlotter
using Test
@testset "MeshPlotter.jl" begin
# Write your tests here.
end
| [
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1,
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198,
220,
220,
220,
1303,
... | 2.7 | 50 |
@testset "ConditionalDistribution" begin
xlength = 3
zlength = 2
batchsize = 10
m = SplitLayer(zlength, [xlength,xlength], [identity,abs])
d = TuringMvNormal
p = ConditionalDistribution(d,m) |> gpu
# MvNormal
res = condition(p, rand(zlength) |> gpu)
μ = mean(res)
σ2 = var(res)
... | [
31,
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2617,
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838,
198,
220,
220,
220,
285,
796,
27758,
49925,
7,
... | 2.169492 | 531 |
<filename>examples_Julia/TEMP/arrays_dataframes.jl<gh_stars>0
using DataFrames
#Arrays and Dataframe example
dfGenPath = "C://Users//rapiduser//github//GRACE-ARPAE//examples_Julia//UC_DukeEnergy_Sample//inputs//data_generators.csv"
dfGenerator = CSV.read(dfGenPath, DataFrame)
myArr = [0.0, 0.0, 0.0, -0.0, -0.0, -0.... | [
27,
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29,
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2,
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290,
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1672,
628,
198,
7568,
13746,
15235... | 1.925094 | 801 |
<reponame>UnofficialJuliaMirror/StatisticalRethinking.jl-2d09df54-9d0f-5258-8220-54c2a3d4fbee
using StatsFuns,Distributions
import ForwardDiff, Zygote
f(x) = poislogpdf(x[1], x[2])
#new Poisson logpdf
poission_lpdf(x) = -x[1]+x[2]*log(x[1])-log(factorial(x[2]))
x = [.5,2.]
f(x) |> display
fd_grad1 =ForwardDiff.gradi... | [
27,
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29,
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12,
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1238,
12,
4051,
66,
17,
64,
18,
67,
19,
69,
... | 2.00303 | 330 |
using HTTP, Sockets
todos = """
ToDo 1: Getting groceries
ToDo 2: Visiting my therapist
ToDo 3: Getting a haircut
"""
const HOST = ip"127.0.0.1"
const PORT = 9999
const ROUTER = HTTP.Router() # 1
HTTP.@register(ROUTER, "GET", "/*", req -> HTTP.Response(200, "Hello")) # 2
HTTP.@register(ROUTER, "GET... | [
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5211,
513,
25,
18067,
257,
45548,
198,
37811,
198,
9979,
367,
10892,... | 2.25 | 188 |
using PSCOPF
using Test
using Dates
@testset verbose=true "test_init_firmness" begin
gen1 = PSCOPF.Networks.Generator("fuel_1_0", "bus_1", PSCOPF.Networks.PILOTABLE, 10., 100., 0., 10.,
Dates.Second(210*60), Dates.Second(210*60)) #dmo, dp
gen2 = PSCOPF.Networks.Generator( ... | [
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1,
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220,
220,
220,
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16,
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350,
6173,
3185,
37,
13,
7934... | 1.804585 | 6,325 |
<reponame>mzagorowska/InfiniteOpt.jl<gh_stars>0
## Define the new measure evaluation method
# Make alias for our new method
struct NewUniEvalMethod <: InfiniteOpt.MeasureToolbox.AbstractUnivariateMethod end
struct NewMultiEvalMethod <: InfiniteOpt.MeasureToolbox.AbstractMultivariateMethod end
# Extend generate_support... | [
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329,
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649,
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198,
7249,
968,
3118,
... | 2.69176 | 983 |
<reponame>ahalwright/CGP.jl
# Evolvability using a dictionary to keep track of the phenotypes that contribute to evolution evolvability.
# Additional objectives are to simplify the logical structure form geno_complexity() and to
# use neutral_evolution() and lambda_evolution() instead of mut_evolve()
export run_evo_d... | [
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13,
198,
2,
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15221,
389,
284... | 2.366084 | 3,603 |
<filename>src/lazyconcat.jl
# Lazy concatenation of AbstractVector's.
# Similar to Iterators.Flatten and some code has been reused from julia/base/iterators.jl
function _Vcat end
abstract type AbstractConcatArray{T,N} <: AbstractArray{T,N} end
struct Vcat{T,N,I} <: AbstractConcatArray{T,N}
arrays::I
global fu... | [
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587,
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422,
474,
43640,
14,
86... | 2.063495 | 5,276 |
<filename>Julia/dp/fibonacci.jl<gh_stars>100-1000
#= Finding the Nth number in the Fibonacci Sequence using Dynamic Programming
=#
## Function
function fibonacci(n)
f = Int64[]
push!(f, 0)
push!(f, 1)
for i = 3:n
temp = f[i-1] + f[i-2]
push!(f, temp)
end
return f[n]
end
## In... | [
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1271,
287,
262,
41566,
261,
44456,
45835,
1262,
26977,
30297,
198,
46249,
198,
198,... | 2.269962 | 263 |
<reponame>JuliaFinMetriX/Copulas.jl<gh_stars>1-10
module TestCTree
using Copulas
using Base.Test
##################
## constructors ##
##################
## CTreePaths
##------------
paths = Array{Int, 1}[[1, 4], [1, 2, 3], [5, 6], [5, 7, 8]]
tP = Copulas.CTreePaths(9, paths)
tP1 = Copulas.CTreePaths(9, [1, 4], [... | [
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198,
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6955,
25283,
198,
3500,
7308,
13,
14402,
628,
198,
14468,
2235,... | 2.258159 | 2,053 |
<reponame>baggepinnen/MatrixPencils.jl
"""
_sreduceB!(A::AbstractMatrix{T},E::AbstractMatrix{T},B::AbstractMatrix{T},Q::Union{AbstractMatrix{T},Nothing}, tol::Real;
fast = true, withQ = true) -> ρ
Reduce the `n x m` matrix `B` using an orthogonal or unitary similarity transformation `Q1` to ... | [
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33,
0,
7,
32,
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23839,
46912,
90,
51,
5512,
36,
3712,
23839,
46912,
... | 1.679307 | 22,810 |
<reponame>UnofficialJuliaMirrorSnapshots/AIBECS.jl-ace601d6-714c-11e9-04e5-89b7fad23838<gh_stars>0
using Documenter, AIBECS
ENV["DATADEPS_ALWAYS_ACCEPT"] = true
# Generate examples
include("generate.jl")
EXAMPLES_jl = [f for f in readdir(EXAMPLEDIR) if endswith(f, ".jl")]
EXAMPLES_md = [replace(f, ".jl" => ".md") for... | [
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68,
20,
12,
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65,
22,
69,
324,
23721,
2548,
27... | 2.251309 | 382 |
#!/usr/bin/env julia
variants = ARGS[1]
bed = ARGS[2]
function read(file)
if ismatch(r".gz$", file) # Gzipped .gz files
f = GZip.open(file)
lines = readlines(f)
close(f)
else
f = open(file)
lines = readlines(f)
close(f)
end
return lines
end
function expand(locci)
list = []
pos = sp... | [
2,
48443,
14629,
14,
8800,
14,
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474,
43640,
198,
198,
25641,
1187,
796,
220,
220,
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58,
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60,
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58,
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220,
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198,
8818,
1100,
7,
7753,
8,
198,
220,
220,
220,
611,
318,
15699,
... | 2.024447 | 859 |
"""
init_ls!(m::WSVarLmmModel; gniters::Integer = 5)
Initialize parameters of a `WSVarLmmModel` object from least squares estimate.
`m.β` is initialized to be `inv(sum(xi'xi)) * sum(xi'yi)`.
`m.Σγ` is initialized to be `inv(sum(zi'zi⊗zi'zi)) * sum(zi'ri⊗zi'ri)`.
`m.τ` is initialized to be `inv(sum(wi'wi)) * ... | [
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63,
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155... | 1.957048 | 4,214 |
# Make an instance of a ChronAgeData object for nSamples
nSamples = 4
smpl = NewChronAgeData(nSamples)
smpl.Name = ("Sample 1", "Sample 2", "Sample 3", "Sample 4") # Et cetera
smpl.Age .= [ 699.1, 708.8, 723.0, 754.0,] # Measured ages
smpl.Age_sigma .= [ 3.0, 7.0, 5.0, 5.0,] # Measure... | [
2,
6889,
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220,
220,
... | 2.392742 | 1,240 |
<gh_stars>0
module Arblib
include(joinpath(@__DIR__, "..", "deps", "deps.jl"))
function __init__()
check_deps()
end
export Arf, Arb, Acb
import Base: isfinite, isinf, isinteger, isnan, isone, isreal, iszero
include("macros.jl")
include("arb_types.jl")
include("types.jl")
include("rounding.jl")
include("precisi... | [
27,
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62,
30783,
29,
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82,
13,
20362,
48774,
198,
198,
8818,
11593,
15003,
834,
3419,
... | 2.538889 | 180 |
<filename>src/Lasem.jl
module Lasem
import Cairo: CairoContext # for rendering
const liblasem = "liblasem-0.6.5.dylib"
const libgobject = "libgobject-2.0.0.dylib"
################################################################################
# low-level wrapper for lasem functions
immutable L... | [
27,
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29,
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368,
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198,
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10123,
368,
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220,
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220,
220,
1500,
9195,
21921,
368,
796,
366,
8019,
21921,
368,
12,
15,
13,
2... | 2.241703 | 1,386 |
# ---
# title: 56. Merge Intervals
# id: problem56
# author: <NAME>
# date: 2020-10-31
# difficulty: Medium
# categories: Array, Sort
# link: <https://leetcode.com/problems/merge-intervals/description/>
# hidden: true
# ---
#
# Given an array of `intervals` where `intervals[i] = [starti, endi]`, merge all
# overlappin... | [
2,
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25,
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2,
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25,
13398,
198,
2,
9376,
25,
15690,
11,
33947,
... | 2.265905 | 613 |
# File copied from SMC.jl
"""
```
function scalar_reduce(args...)
```
Each individual iteration returns n scalars. The output is reduced to n vectors,
where the i-th vector contains all of the i-th scalars from each iteration.
The return type of reduce functions must be the same type as the tuple of
arguments passed i... | [
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1981,
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16578,
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13,
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5072,
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5322,
284,
299,
30104,
... | 2.44193 | 1,679 |
<reponame>mzgubic/CachedCalls.jl
module CachedCalls
using FilePathsBase
using FilePathsBase: /
using JLSO
export @cached_call, @hash_call
export cachedcalls_dir
const CACHEDCALLS_PATH = Ref{PosixPath}()
"""
@cached_call f(args; kwargs)
Caches the result of `f(args; kwargs)` to disk and returns the result. The ... | [
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... | 2.579461 | 1,743 |
raw_data = readlines("../inputs/day03.txt")
num_bits = length(raw_data[1])
function line2ints(line)
return map(x -> parse(Int, x), collect(line))
end
function get_power_consumption(data)
function get_gamma(average_vals)
output_string = ""
for val in average_vals
if val < 0.5
... | [
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220,... | 2.235688 | 1,345 |
## options.jl : stores estimator options
#
# A type that stores estimator options shared for all Estimators.
#
# This file is part of MultilevelEstimators.jl - A Julia toolbox for
# Multilevel Monte Carlo Methods (c) <NAME>, 2019
# valid options
default_options(::AbstractIndexSet, ::AbstractSampleMethod) = [:nb_of_war... | [
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203... | 1.596071 | 1,629 |
<gh_stars>1-10
f = open("jawiki-country.txt", "r")
for line in readlines(f)
if ismatch(r"\[\[Category:", line)
print(line)
end
end
| [
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2... | 2.153846 | 65 |
<gh_stars>1-10
module Maracas
include("test.jl")
export @test, @test_throws, @test_broken, @test_skip, @test_warn, @test_nowarn
export @testset
export @describe, @it, @unit, @skip, MARACAS_SETTING
export MaracasTestSet, DescribeTestSet, SpecTestSet, TestTestSet
export set_test_style, set_title_style, set_spec_style, se... | [
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... | 2.360528 | 1,667 |
<gh_stars>0
module LCMGL
depsjl = joinpath(dirname(@__FILE__), "..", "deps", "deps.jl")
isfile(depsjl) ? include(depsjl) : error("LCMGL not properly ",
"installed. Please run\nPkg.build(\"LCMGL\")")
import Base: unsafe_convert
using Libdl
export LCM, LCMGLClient,
switch_buffer,
begin_mode,
end_mode,
vertex,
... | [
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5... | 2.163593 | 2,494 |
using DynamicGrids, DynamicGridsInteract, Test, Colors, ColorSchemes, ImageMagick
# life glider sims
init = Bool[0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 1 1 1
0 0 0 0 0 1
0 0 0 0 1 0]
test3 = [0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 1 1
... | [
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... | 2.059856 | 1,253 |
<reponame>giannamars/DEBMicroTrait<filename>test/zhen_affinity_predictions.jl
using DEBmicroTrait
using CSV, DataFrames
using Roots
using Statistics
using HypothesisTests
df = CSV.read("/Users/glmarschmann/Data/Zhen/IsogenieGenomes.ecosysguilds.csv", DataFrame)
####################################################... | [
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3... | 2.108562 | 21,361 |
using Test
using SafeTestsets
@testset "shallow_water" begin
@safetestset "entropy_conservation_1d" begin include("entropy_conservation_1d.jl") end
end
| [
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... | 2.818182 | 55 |
export DIM1, DIM2, DIM3, Bounds
SVectorF{N} = SVector{N,Float}
struct Dim{N}
Dim{1}() = new()
Dim{2}() = new()
Dim{3}() = new()
end
const DIM1 = Dim{1}()
const DIM2 = Dim{2}()
const DIM3 = Dim{3}()
struct Bounds{N,T}
lower::SVector{N,T}
upper::SVector{N,T}
function Bounds(
lower::SV... | [
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module ExtendedWrapper
using JavaCall
const AbstractKalmanFilter = @jimport org.hipparchus.filtering.kalman.AbstractKalmanFilter
const Class = @jimport java.lang.Class
const ExtendedKalmanFilter = @jimport org.hipparchus.filtering.kalman.extended.ExtendedKalmanFilter
const JString = @jimport java.lang.String
const Ma... | [
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7... | 3.203991 | 451 |
<reponame>JuliaAstrodynamics/Orekit.jl<filename>gen/HipparchusWrapper/DistributionWrapper/DiscreteWrapper/hypergeometric_distribution.jl
function HypergeometricDistribution(arg0::jint, arg1::jint, arg2::jint)
return HypergeometricDistribution((jint, jint, jint), arg0, arg1, arg2)
end
function cumulative_probabilit... | [
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62,... | 2.903577 | 643 |
<reponame>wcwitt/JuLIP.jl
# included from Potentials.jl
# part of the module JuLIP.Potentials
using JuLIP: JVec, JMat, neighbourlist
using LinearAlgebra: I
using JuLIP.Chemistry: atomic_number
using NeighbourLists
export ZeroPairPotential, ZBLPotential,
LennardJones, lennardjones,
Morse, morse
# Fo... | [
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72... | 2.135961 | 5,303 |
<gh_stars>1-10
const TrackedComponentArray{V, D, N, DA, A, Ax} = ReverseDiff.TrackedArray{V,D,N,ComponentArray{V,N,A,Ax},DA}
maybe_tracked_array(val::AbstractArray, der, tape, inds, origin) = ReverseDiff.TrackedArray(val, der, tape)
function maybe_tracked_array(val::Real, der, tape, inds, origin::AbstractVector)
a... | [
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... | 2.24902 | 510 |
<filename>test/test_utils/ad_utils.jl<gh_stars>0
using Turing: gradient_logp_forward, gradient_logp_reverse
using Test
function test_ad(f, at = 0.5; rtol = 1e-8, atol = 1e-8)
isarr = isa(at, AbstractArray)
reverse = Tracker.data(Tracker.gradient(f, at)[1])
if isarr
forward = ForwardDiff.gradient(f,... | [
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... | 2.186189 | 1,144 |
<gh_stars>1-10
using Discretizers
import LPDM: default_action, next_actions, isterminal, bv_action_pool, adaptive_actions
import POMDPs: rand, actions
mutable struct LightDark2DLpdm <: AbstractLD2
# @with_kw mutable struct LightDark2DLpdm <: AbstractLD2
min_noise::Float64
min_noise_loc::Float64
Q::Matrix{F... | [
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26... | 1.961061 | 4,186 |
<reponame>JuliaGraphics/ColorSchemeTools.jl
using Test, ColorSchemes, ColorSchemeTools, FileIO, Colors
using ImageMagick, QuartzImageIO
function run_all_tests()
@testset "basic functions" begin
# load existing scheme from ColorSchemes.jl
hok = ColorSchemes.hokusai
@test length(hok) == 32
... | [
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... | 2.160629 | 2,926 |
function rm_count(deck)
r = 0
m = 0
for elem in deck
if elem.prints[1].rarity == 3
r += elem.amount
elseif elem.prints[1].rarity == 4
m += elem.amount
end
end
(r = r, m = m)
end
| [
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... | 1.789855 | 138 |
<reponame>astrorCoder/InterpHalo.jl<filename>src/example.jl
using InterpHalo, Plots
points = range(0, stop = 1, length = 201)
orbits = range(0, stop = 1, length = 100)
P = zeros(201,6,100)
for (valE,indE) in enumerate(orbits)
for (val,ind) in enumerate(points)
P[val,:,valE] = intH(ind,indE,2)
en... | [
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1... | 2.017167 | 233 |
# Plot the Objective over the course of the solve
using TrajOptSOCPs, Plots
function plotObjective(obj::objectiveFunc, trajList)
fList = [TrajOptSOCPs.fObjQP(obj, traj)[1] for traj in trajList]
pltObj = plot(fList, markershape = :square)
title!("Cost Function")
ylabel!("Cost Function")
xlabel!("... | [
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... | 2.568345 | 139 |
function unsafe_sizes(buff::Ptr{UInt8})
r = Lib.blosc_cbuffer_sizes(buff)
return (r.nbytes, r.cbytes)
end
"""
sizes(buff::Vector{UInt8}, offset = 1)
Get information about a compressed buffer at offset `offset` (1-indexed), as Tuple with values:
- the number of uncompressed bytes
- the number of compress... | [
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3... | 2.743929 | 453 |
<reponame>KristofferC/TOML.jl
Dict{String, Any}("1" => Dict{String, Any}("value" => "1", "type" => "integer")) | [
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4... | 2.340426 | 47 |
<filename>src/DBFs.jl<gh_stars>10-100
#!/usr/bin/env julia
module DBFs
const DBF = Vector{Float32}
export DBF
using Base.Cartesian
"""
use segmentation to get binary image to save memory usage
"""
function compute_DBF(seg::Array{T,3}, obj_id::T) where T
error("unimplemented")
end
"""
compute_DBF( point... | [
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... | 2.396295 | 3,293 |
<reponame>jacob-alt-del/Tennis-Refactoring-Kata
mutable struct TennisGame1
m_score1::Int
m_score2::Int
player1Name::String
player2Name::String
TennisGame1(player1Name, player2Name) = new(0, 0, player1Name, player2Name)
end
function Tenniskata.won_point(game::TennisGame1, playerName::String)
if playerNa... | [
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17,
3... | 1.983627 | 794 |
<filename>test/runtests.jl
using CFITSIO
using Test
using Aqua
function tempfitsfile(fn)
mktempdir() do dir
filename = joinpath(dir, "temp.fits")
fitsfile = fits_clobber_file(filename)
fn(fitsfile)
if fitsfile.ptr != C_NULL
# write some data to file to avoid errors on c... | [
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466,
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220... | 1.733401 | 14,850 |
<reponame>JuliaPackageMirrors/SliceSampler.jl
# Density type that is useful across a variety of the sampling methods.
# NB: The density computed by f need not be normalized.
type Density
f::Function
end
type DifferentiableDensity # maybe <: DifferentiableFunction ?
f::Function
gradient::Function
end
| [
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360,
6377,
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318,
4465,
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257,
4996,
286,
262,
19232,
5050,
13,
198,
2,
41354,
25,
383,
12109,
29231,
416,
277,
76... | 3.494382 | 89 |
function dissipator!(dρ_L::Array{ComplexF64,2},
ρ::Array{ComplexF64,2},
γs::Vector{Array{ComplexF64,2}},
γTs::Vector{Array{ComplexF64,2}},
γSqs::Vector{Array{ComplexF64,2}},
A::Array{ComplexF64, 2},
B::Array{ComplexF64, 2},
... | [
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0,
7,
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43,
3712,
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90,
5377,
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37,
2414,
11,
17,
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198,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
18074,
223,
3712,
19182,
90,
5377,
11141,
37,
2414,
11,
17,
... | 1.714204 | 7,026 |
#runs a regression on every firm
function regressbynonprofit!(df::DataFrame;
YField::Symbol = :lreturn,
bmfield::Symbol = Symbol(:pred_, YField),
XFields::Vector{Symbol} = [:lsp500ret],
groupbyfield::Symbol = :ein,
suppressintercept::Bool = false,
XNames::Vector{Symbol} = [:intercept; XFields],
YName:... | [
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76,
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13940,
23... | 2.400397 | 4,031 |
<reponame>UnofficialJuliaMirrorSnapshots/RegERMs.jl-d41b3cee-bfd5-59f4-ae46-2d539e075afd
immutable RidgeReg <: RegERM
X::Matrix # n x m matrix of n m-dimensional training examples
y::Vector # 1 x n vector with training classes
n::Int # number of training examples... | [
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19,
12,
3609,
3510,
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17,
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20,
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1... | 2.320624 | 577 |
using Fleetdm
using Test
@testset "Fleetdm.jl" begin
# Write your tests here.
end
| [
3500,
20001,
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220,
220,
220,
1303,
19430,
534,
5254,
994,
13,
198,
437,
198
] | 2.71875 | 32 |
<reponame>cossio/OneHot.jl
using OneHot
using Base: tail, front
X = OneHotArray(rand(1:4, 5), 4)
for i = 1:size(X,2)
@test OneHot.decode(X[:,i]) == X.c[i]
end
A = randn(3,4)
@test A[:, X.c] == @inferred A * X
@test A * X == A * Array(X)
B = randn(3,3)
@test_throws DimensionMismatch B * X
@test X[:,1:3] isa One... | [
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7,
16,
25,
19,
11,
642,
828,
604,
8,
198,
198,
1640,... | 2.038217 | 314 |
<filename>src/Storage/http.jl
using HTTP
"""
HTTPStore
A basic HTTP store without any credentials. The underlying data is supposed to be
consolidated and only read operations are supported. This store is compatible to
datasets being served through the [xpublish](https://xpublish.readthedocs.io/en/latest/)
python ... | [
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383,
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1366,
318,
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284,
307,
198,
5936,
10180,... | 2.65045 | 555 |
<filename>src/ViscousFlow.jl
module ViscousFlow
#using DocStringExtensions
using Reexport
using UnPack
@reexport using ImmersedLayers
@reexport using GridUtilities
export ViscousIncompressibleFlowProblem
export setup_grid, viscousflow_system, setup_problem, surface_point_spacing
#= Supporting functions =#
setup_pr... | [
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791,
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198,
31,
631,
87,
634,
1262,
9543,
20204,
43,
... | 2.292558 | 7,014 |
<filename>src/analytic_solution_lmax.jl
# Functionalities for analytical Solution for lmax assuming we have a linear cost
# function ℓ, a Ellispoidal (Convec) set Ω and linear Feedback gain K
using LazySets
"""
get_Ellipsoid(P, α)
Creates Ellipsoid `Ω := {x |x'Px ≤ α}` using LazySets"""
function get_Elli... | [
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2,
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226,
241,
11,
257,
7122,
8802,
47502,
357... | 1.788 | 1,500 |
<filename>test/ExternalDocstringsTests/src/ExternalDocstringsTests.jl<gh_stars>1-10
module ExternalDocstringsTests
using ExternalDocstrings
using ExternalDocstrings: transform_docstring
using Test
function f end
baremodule Sub
function f end
end
ExternalDocstrings.@define_docstrings
function test_f()
docstr = ... | [
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51,
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198,
198,
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34579,
23579,
37336,
198,... | 2.592857 | 420 |
<filename>src/SimpleSolvers.jl
module SimpleSolvers
using Distances
using ForwardDiff
using LinearAlgebra
using Printf
import Base.minimum
import Base.Callable
include("utils.jl")
export solve!
export config, result, state, status
export algorithm, objective
export soluti... | [
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220,
220,
220,
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198,
220,
220,
220,
126... | 2.385154 | 1,428 |
using FunctionalCollections
using JSON
import FunctionalCollections: append
export Node, node, instanceof, props
const WEBIO_NODE_MIME = MIME"application/vnd.webio.node+json"
Base.Multimedia.istextmime(::WEBIO_NODE_MIME) = true
const WEBIO_APPLICATION_MIME = MIME"application/vnd.webio.application+html"
Base.Multimed... | [
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62,
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1,
31... | 2.334988 | 2,821 |
<reponame>PetrKryslUCSD/FinEtoolsAcoustics.jl
using FinEtools
println("The interior sphere accelerates in the alternately in the positive
and negative x-direction, generating positive pressure ahead of it, negative
pressure behind. Time-dependent simulation.
")
rho = 1.21*phun("kg/m^3");# mass density
c = 343.0*phun(... | [
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262,
3983,
1286,
287,
262,
396... | 2.144876 | 2,264 |
<reponame>danielzhaotongliu/MALTrendsWeb
{"score_count": 42475, "timestamp": 1563108810.0, "score": 7.24}
{"score_count": 42271, "timestamp": 1562142557.0, "score": 7.24}
{"score_count": 41319, "timestamp": 1556466622.0, "score": 7.24}
{"score_count": 41047, "timestamp": 1555037517.0, "score": 7.24}
{"score_count": 409... | [
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1298,
1315,
5066,
940,
3459,
940,
13,
15,
11,
366,
26675,
1298,
... | 2.373451 | 565 |
module BallroomSkatingSystem
using Reexport
@reexport using DataFrames
using Statistics
include("helper_functions.jl")
include("skating_single_dance.jl")
include("skating_combined.jl")
export skating_single_dance, skating_combined
end
| [
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4943,
198,
17256,
7203,
8135,
803,
62,
29762,
... | 3.144737 | 76 |
<gh_stars>1-10
module ModelBasedCF
# package code goes here
using Persa
using ProgressMeter
using Statistics
using LinearAlgebra: norm
using Random: shuffle
abstract type MatrixFactorization{T} <: Persa.Model{T}
end
include("irsvd.jl")
include("rsvd.jl")
include("train.jl")
include("baseline.jl")
include("random.jl... | [
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14534,
2... | 3.008929 | 112 |
<filename>src/animation.jl
abstract type FiniteLengthAnimation{T} end
Base.Broadcast.broadcastable(f::FiniteLengthAnimation) = Ref(f)
"""
`Animation{T}`
An Animation that contains a `Vector` of `Keyframe`s and a `Vector` of `Easing`s
"""
struct Animation{T} <: FiniteLengthAnimation{T}
frames::Vector{Keyframe{T}}... | [
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7,
69,
8,
... | 2.300467 | 2,353 |
"""
mapをparalellで実行してidを各プロセスで表示する
"""
using Base.Iterators
using Distributed
@everywhere function miseru(x, y)
@show x y myid()
sleep(1)
return x^2 + y
end
function main()
N::Int64 = 8
# tuple -> list
vmiseru = pmap(x -> miseru(x...), product(1:N, 1:N))
@show vmiseru
... | [
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201,
198,
201,
198,
3500,
7308,
... | 1.896552 | 203 |
<reponame>rjdverbeek-tud/DDR2import.jl<gh_stars>0
@testset "Routes.jl" begin
filename = "data\\test.routes"
dc = DDR2import.Routes.read(filename)
@test dc["ABESI8TLIME"].type == "DP"
@test dc["ABDIL1ALFMD"].route[2].wp == "TUPOX"
@test dc["ABDIL1ALFTZ"].route[3].location_type == "SP"
@test dc["A... | [
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480,
29,
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274,
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1,
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198,
220,
220,
220,
29472,
796,
366,
... | 2 | 180 |
<filename>src/exec.jl
import BitIntegers: UInt256, @uint256_str
import SHA: sha256
Ast = Dict{String, Any}
abstract type AbstractTemplate end
Scope = Dict{String, AbstractTemplate}
struct Template <: AbstractTemplate
scopes::Vector{Scope}
end
struct Selector
id::Int64
name::String
end
struct Signal
end
st... | [
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198,
1... | 2.13118 | 16,870 |
<reponame>UnofficialJuliaMirror/GitLab.jl-ec55e9df-579d-5e55-a10d-b795213e2edd
##############
# Issue type #
##############
type Issue <: GitLabType
id::Nullable{Int}
iid::Nullable{Int}
project_id::Nullable{Int}
title::Nullable{GitLabString}
description::Nullable{GitLabString}
state::Nullable{G... | [
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67,
12,
65,
3720,
4309,
1485,
68,
17,
6048,
198,
7804,
4242,... | 2.540373 | 805 |
<filename>src/TreeStructure.jl
"""
TreeStructure
This defines the tree structure.
A tree has a name, a list of parents, a list of states, list of probabilities, and a list of children.
Name - a string representing the name of the tree.
List of parents - shows the parent of each node in the tree.
List of states - cont... | [
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29,
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11,
257,
1351,
286,
3397,
11,
257,
1351,
286,
2585,
11,
1351... | 2.031948 | 8,013 |
module FemtoCleaner
# For interactive development
using Revise
using Base.Distributed
using GitHub
using GitHub: GitHubAPI, GitHubWebAPI, Checks
using HTTP
using Deprecations
using CSTParser
using Deprecations: isexpr
using MbedTLS
using JSON
using AbstractTrees: children
using Base: LibGit2
include("workqueue.jl")
... | [
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11,
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198,
3500,
14626,
198... | 2.048462 | 10,338 |
<reponame>herysedra/covid19-mankaiza-clone2<gh_stars>0
push!(LOAD_PATH, joinpath(homedir(),"GitHub/KenyaCoV/src"))
using Plots,Parameters,Distributions,DifferentialEquations,JLD2,DataFrames,StatsPlots,FileIO,MAT,RecursiveArrayTools
import KenyaCoV
using LinearAlgebra:eigen
using Statistics: median, quantile
"""
Load a... | [
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343,
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553,
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270,
16... | 2.1 | 1,410 |
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