content stringlengths 6 1.03M | input_ids listlengths 4 535k | ratio_char_token float64 0.68 8.61 | token_count int64 4 535k |
|---|---|---|---|
# Autogenerated wrapper script for Emoslib_jll for i686-linux-gnu-libgfortran3
export emos
using eccodes_jll
using FFTW_jll
using CompilerSupportLibraries_jll
JLLWrappers.@generate_wrapper_header("Emoslib")
JLLWrappers.@declare_file_product(emos)
function __init__()
JLLWrappers.@generate_init_header(eccodes_jll, F... | [
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54... | 2.331776 | 214 |
<reponame>emilemathieu/NTL.jl
# Utilities for Gibbs sampling with geometric inter-arrivals
function update_geometric_interarrival_param!(p::Vector{Float64},PP::Vector{Int},T::Vector{Int},n::Int,params::Vector{Float64})
"""
- `p`: current geometric parameter for the interarrival time distribution (will be updat... | [
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371... | 2.855556 | 360 |
<gh_stars>1-10
# Wrapper for level-1 BLIS matrix routines.
#
# Level1v API of common forms are put together.
#
macro blis_group_level1m_form1(funcname)
return quote
@blis_ccall_group($funcname,
Cvoid,
diagoffa, BliDoff,
diaga, ... | [
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8... | 1.43038 | 1,422 |
<reponame>YingboMa/julia<gh_stars>1-10
# This file is a part of Julia. License is MIT: https://julialang.org/license
"""
Some{T}
A wrapper type used in `Union{Some{T}, Nothing}` to distinguish between the absence
of a value ([`nothing`](@ref)) and the presence of a `nothing` value (i.e. `Some(nothing)`).
Use [`c... | [
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198,
... | 2.493812 | 808 |
# ------------------------------------------------------------------
# Licensed under the MIT License. See LICENSE in the project root.
# ------------------------------------------------------------------
# helper function to extract raw data
# from uniform scaling objects
raw(a::UniformScaling) = a.λ
raw(a) = a
"""
... | [
2,
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3... | 2.067551 | 1,658 |
<gh_stars>0
test = """
..#.#..#####.#.#.#.###.##.....###.##.#..###.####..#####..#....#..#..##..###..######.###...####..#..#####..##..#.#####...##.#.#..#.##..#.#......#.###.######.###.####...#.##.##..#..#..#####.....#.#....###..#.##......#.....#..#..#..##..#...##.######.####.####.#.#...#.......#..#.#.#...####.##.#..... | [
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13,
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492,
4242,
2,
492,
2,
1106,
2,
49... | 2.288688 | 1,105 |
<reponame>jd-lara/PowerSimulationsDynamics.jl
struct SimulationInputs
dynamic_injectors::Vector{DynamicWrapper{<:PSY.DynamicInjection}}
static_injectors::Vector
static_loads::Vector
dynamic_branches::Vector{BranchWrapper}
injection_n_states::Int
branches_n_states::Int
variable_count::Int
... | [
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220,
220,
220,
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752,
669,
3712,
38469,
90,
44090,
36918,
2848,
90,
27,
25,
3705,
56,
... | 2.173613 | 6,382 |
using AutoHOOT
using Test
const ad = AutoHOOT.autodiff
const gops = AutoHOOT.graphops
@testset "test rewrite expression" begin
a1 = ad.Variable(name = "a1", shape = [3, 2])
a2 = ad.Variable(name = "a2", shape = [2, 3])
x = ad.einsum("ik,kj->ij", a1, a2)
y = ad.einsum("sm,ml->sl", a1, a2)
gops.rewr... | [
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9288,
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1,
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19... | 2.195965 | 347 |
function polyMap(Θ,z)
g = 0.
for i in 1:1:length(Θ)
g += Θ[i]⋅z^(i-1)
end
return g
end
function cfy(yi, yp, yn, theta)
return ( (yi - polyMap(theta, yp)) ^ 2 + (yn - polyMap(theta, yi)) ^ 2)
end
function noiseMix3(p1::Float64, lam1::Float64, lam2::Float64)
if rand() < p1
z = rand(Normal(0, sqrt(1 ... | [
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7,
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58,
72,
60,
158,
233,
227,
89,
61,
7,
72,
... | 1.90521 | 1,593 |
<gh_stars>10-100
using Coverage
coverage_src = process_folder("../src")
coverage_test = process_folder("../test")
coverage = merge_coverage_counts(coverage_src, coverage_test)
LCOV.writefile("htmlcov/coverage.info", coverage)
| [
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... | 2.910256 | 78 |
<gh_stars>1-10
# This file is a part of RadiationDetectorDSP.jl, licensed under the MIT License (MIT).
"""
rc_filter(RC::Real)
Return a DSP.jl-compatible RC-filter.
"""
function rc_filter(RC::Real)
T = float(typeof(RC))
α = 1 / (1 + RC)
Biquad(T(α), T(0), T(0), T(α - 1), T(0))
end
export rc_filter
... | [
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357,
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737,
628,
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198,
220,
220,
220,
48321,
62,
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7,
739... | 2.378837 | 1,238 |
<reponame>JuliaTagBot/VegaStreams.jl<gh_stars>1-10
module VegaStreams
# Use README as the docstring of the module:
@doc let path = joinpath(dirname(@__DIR__), "README.md")
include_dependency(path)
replace(read(path, String), r"^```julia"m => "```jldoctest README")
end VegaStreams
export vegastream
using Elec... | [
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30310,
12124,
82,
198,
198,
2,
5765,
20832,
11682,
355,
262,
2205,
8841,
286,
262,
8265,
25,
1... | 2.323077 | 2,080 |
export defaultRecoParams
function defaultRecoParams()
params = Dict{Symbol,Any}()
params[:reco] = "direct"
params[:reconSize] = (32,32)
params[:sparseTrafoName] = "Wavelet"
params[:regularization] = "L1"
params[:λ] = 0.0
params[:normalizeReg] = false
params[:solver] = "admm"
params[:ρ] = 5.e-2
para... | [
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60,
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366,
12942,
1,
198,
220,
42287... | 2.637885 | 1,853 |
<reponame>MichaelSven/ITensors.jl
export ProjMPO,
LProj,
RProj,
product
mutable struct ProjMPO
lpos::Int
rpos::Int
nsite::Int
H::MPO
LR::Vector{ITensor}
ProjMPO(H::MPO) = new(0,length(H)+1,2,H,fill(ITensor(),length(H)))
end
nsite(pm::ProjMPO) = pm.nsite
length(pm::ProjMPO) = length(pm... | [
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2... | 1.738705 | 1,328 |
include("MetaDict.jl")
export Image, ImageHeader, ImageFlags
@MRD.enm ImageType::UInt16 magnitude = 1 phase = 2 real = 3 imag = 4 complex = 5
@MRD.enm TypeIndex::UInt16 ushort = 1 short = 2 uint = 3 int = 4 float = 5 double = 6 cxfloat =
7 cxdouble = 8
datatype_to_type = Base.Dict{TypeIndex.Enm,Type}([
(Typ... | [
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359... | 2.245358 | 2,531 |
<filename>src/io.jl<gh_stars>0
"""
write_wat(filename, m::Module)
Write the WebAssembly module `m` to WebAssembly text format in `filename`.
"""
function write_wat(filename, m::Module)
open(filename, "w") do f
show(f, m)
end
end
"""
write_wat(filename, m::Module)
Write the WebAssembly module `m` to W... | [
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198,
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262,
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4600,
76,
63,
284,
5313,
49670,
242... | 2.570815 | 233 |
# Masks
const SF_FORMAT_ENDMASK = 0x30000000
const SF_FORMAT_TYPEMASK = 0x0FFF0000
const SF_FORMAT_SUBMASK = 0x0000FFFF
# Endian-ness options
const SF_ENDIAN_FILE = 0x00000000 # Default file endian-ness.
const SF_ENDIAN_LITTLE = 0x10000000 # Force little endian-ness.
const SF_ENDIAN_BIG = 0x200000... | [
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1436... | 2.365711 | 1,534 |
<reponame>mipals/SymEGRSSMatrices
# Removing t = 0, such that Σ is invertible
t = Vector(0.1:0.1:100); p = 2;
# Creating generators U,V that result in a positive-definite matrix Σ
Ut, Vt = spline_kernel(t', p)
K = SymEGRQSMatrix(Ut,Vt,ones(size(Ut,2)))
x = randn(size(K,1))
Kfull = Matrix(K)
# Testing multiplication
... | [
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7,
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25,
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16,
25,
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1776,... | 2.030864 | 324 |
<gh_stars>0
value(x) = x
cuify(x) = error("To use LinSolveGPUFactorize, you must do `using CuArrays`")
promote_u0(u0,p,t0) = u0
promote_tspan(u0,p,tspan,prob,kwargs) = tspan
get_tmp(x) = nothing
isdistribution(u0) = false
function SciMLBase.tmap(args...)
error("Zygote must be added to differentiate Zygote? If you se... | [
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1258... | 2.134884 | 7,310 |
<reponame>UnofficialJuliaMirrorSnapshots/JFVM.jl-d32f81f0-000d-5c7c-8375-24efa40f8589
# ===============================
# Written by AAE
# <NAME>, Winter 2014
# simulkade.com
# ===============================
# =============================== SOLVERS ===================================
function solveLinearPDE(m::MeshS... | [
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... | 1.883 | 7,000 |
<reponame>cortner/PoSH.jl
abstract type AbstractSChain{TT} end
struct SChain{TT} <: AbstractSChain{TT}
F::TT
end
struct TypedChain{TT, IN, OUT} <: AbstractSChain{TT}
F::TT
end
# construct a chain recursively
chain(F1, F2, args...) = chain( chain(F1, F2), args... )
# for most arguments, just form a tuple
c... | [
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37... | 2.179387 | 1,795 |
<gh_stars>0
#using NearestNeighbors: Metric
#import Distances: evaluate
using StatsBase: mode
#evaluate(dist::dwt, a, b) = dtw_distance(a, b, dist.w)
"""
`dtw_distance(a, b, w)` is the basic dynamic time wraping function.
where `a` & `b` are the time series matrices and `w` is the percentage
of window for warpin... | [
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220,
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198,
2,
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4307,
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25,
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198,
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25,
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198,
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2,
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7,
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3712,
67,
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11,
257,
11,
275,
8,... | 1.754992 | 1,302 |
module HeroIcons
export HeroIcon
function __init__()
if !isdir(joinpath(@__DIR__, "..", "deps", "heroicons-0.4.2"))
error("HeroIcons should be rebuilt.")
end
end
struct HeroIcon
name::String
style::Symbol
data::String
css::String
function HeroIcon(name::String; style::Symbol = :o... | [
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366,
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82,
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34... | 2.297767 | 403 |
<reponame>Wynand/TimeZones.jl
module TZData
using Printf
using ...TimeZones: DEPS_DIR
using ...TimeZones: @artifact_str
import Pkg
using Pkg.Artifacts: artifact_hash
# Note: The tz database is made up of two parts: code and data. TimeZones.jl only requires
# the "tzdata" archive or more specifically the "tz source"... | [
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433... | 2.594132 | 409 |
plt = Observable{Any}(plot_signal(raw_ismrmrd; darkmode))
ui = dom"div"(plt)
map!(p->plot_signal(p; darkmode), plt, sig_obs)
content!(w, "div#content", ui)
| [
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79,
... | 2.197183 | 71 |
using Revise
using ADCME
using NNFEM
using JLD2
using PyCall
using LinearAlgebra
reset_default_graph()
stress_scale = 1e5
strain_scale = 1.0
force_scale = 1.0
fiber_size = 2
porder = 2
nntype = "stiffmat"
include("nnutil.jl")
H0 = [1.04167e6 2.08333e5 0.0
2.08333e5 1.04167e6 0.0
0.0 ... | [
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786,
198,
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49169,
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3500,
399,
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3620,
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449,
11163,
17,
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3500,
9485,
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42503,
62,
12286,
62,
34960,
3419,
198,
198,
41494,
62,
9888,
796,
352,
68,
... | 2.01232 | 487 |
# STFT/ISTFT
function blackman(n::Integer)
const a0, a1, a2 = 0.42, 0.5, 0.08
t = 2*pi/(n-1)
[a0 - a1*cos(t*k) + a2*cos(t*k*2) for k=0:n-1]
end
function hanning(n::Integer)
[0.5*(1-cos(2*pi*k/(n-1))) for k=0:n-1]
end
# countframes returns the number of frames that will be processed.
function countfra... | [
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8818,
2042,
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7,
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220,
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11,
657,
13,
20,
11,
657,
13,
2919,
198,
220,
220,
220,
256,
7... | 2.072889 | 1,125 |
<filename>src/plans/nonmutating_manifolds_plans.jl
#
# For the manifolds that are nonmutating only, we have to introduce a few special cases
#
function get_gradient!(p::GradientProblem{AllocatingEvaluation}, ::AbstractFloat, x)
X = p.gradient!!(p.M, x)
return X
end
function get_hessian!(p::HessianProblem{Alloca... | [
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19... | 2.224919 | 618 |
<reponame>UnofficialJuliaMirrorSnapshots/Azure.jl-34b51195-c7f2-5807-8107-6ca017e2682c<gh_stars>0
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
mutable struct RouteTablePropertiesFormat <: SwaggerModel
routes::Any # spe... | [
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12,
23,
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12,
21,
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29326,
68,
2075,
6469,
66,
27,
456,
... | 3.116176 | 680 |
<filename>scratch/aug_22_gap_study_sherlock.jl<gh_stars>10-100
using Multilane
using StatsBase
using MCTS
using JLD
behaviors=Dict{String,Any}()
for p in linspace(0., 3/4, 4)
wv = (1-p)/8*ones(9)
wv[1] = p
behaviors[@sprintf("agents_%03d", 100*p)] =
DiscreteBehaviorSet(Multilane.NINE_BEHAVIORS,... | [
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29,
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14881,
198,
3500,
337,
4177,
50,
198,
3500,
4... | 2.033557 | 894 |
# All the types native to ogl, wgl and vulkan shaders
const number_types = (Float32, Cint, Cuint, Cdouble)
const small_vecs = (((StaticVector{N, T} for T in number_types, N in (2, 3, 4)))...,)
const small_mats = (((StaticArray{Tuple{i, j}, T, 2} for T in ShaderAbstractions.number_types, i in (2, 3, 4), j in (2, 3, 4)... | [
198,
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600,
11,
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600,
11,
327,
23352,
8,
198,
9979,
1402,
62,
303,
6359,
796... | 2.809524 | 1,995 |
<reponame>jishnub/Kronecker.jl
@testset "Kronecker powers" begin
A = [0.1 0.4; 0.6 2.1]
B = [1 2 3 4; 5 6 7 8]
K1 = kronecker(A, 3)
K2 = kronecker(B, 3)
K1dense = kron(A, A, A)
K2dense = kron(B, B, B)
@testset "Types and basic properties" begin
@test K1 isa AbstractKroneckerProdu... | [
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21,
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16,
... | 1.806349 | 945 |
# ---
# title: 873. Length of Longest Fibonacci Subsequence
# id: problem873
# author: <NAME>
# date: 2020-10-31
# difficulty: Medium
# categories: Array, Dynamic Programming
# link: <https://leetcode.com/problems/length-of-longest-fibonacci-subsequence/description/>
# hidden: true
# ---
#
# A sequence `X_1, X_2, ...,... | [
2,
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25,
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2,
8722,
25,... | 2.292285 | 674 |
<filename>src/loss/n-misc.jl
export timeslotmat
export adjustLossWeights
"""
timeslotmat(matrix::AbstractMatrix, timestamp::AbstractVector; dim=2, slotvalue=1.0)
mark `matrix` with `timestamp`, all elements of `timestamp` is ∈ (0,1), standing for time ratio
# Example
julia> timeslotmat(reshape(1:24,2,12), [0... | [
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29,
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14,
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12,
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220,
220,
220,
1661,
26487,
6759,
7,
6759,
8609,
3712,
23839,
46912,
11,
41033,... | 2.045977 | 696 |
#
# PauliString struct
#
struct PauliString
N::Int # length
sites::Vector{Int} # To do: change this to bit number for "used sites" 000101 = operator at site 4 and 6
# vector which contains site indicator where the string has non-trivial operators
baseIdx::Vector{Int} # indicates the operator (σx, σy, σz)
coef:... | [
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25,
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428,
284,
1643,
1271,
329,
366,
1484... | 2.08688 | 3,994 |
<filename>test/nonlinearleastsquares.jl
using LeastSquaresOptim, Printf, SparseArrays, Test
# simple factor model
# only problem with "real" optimization
# nice example because J'J is not invertible
# but cholfact in sparse handles this case
function factor()
name = "factor"
function f!(fvec, x)
fvec[... | [
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29,
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14,
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11,
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20477,
11,
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198,
198,
2,
2829,
5766,
2746,
198,
2,
691,
1917,
351,
366... | 1.573415 | 1,798 |
<reponame>Kolaru/ChainRules.jl
#=
These implementations were ported from the wonderful DiffLinearAlgebra
package (https://github.com/invenia/DiffLinearAlgebra.jl).
=#
using LinearAlgebra: BlasFloat
_zeros(x) = fill!(similar(x), zero(eltype(x)))
#####
##### `BLAS.dot`
#####
frule((Δself, Δx, Δy), ::typeof(BLAS.dot),... | [
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261,
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29,
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198,
26495,
357,
5450,
1378,
12567,
13,
785,
14,
259,
574,
544,
14,
28813,
... | 1.594223 | 5,678 |
<filename>src/Ragel.jl
# Ragel
# =====
#
# Utilities for the Ragel state machine compiler.
#
# This file is a part of BioJulia.
# License is MIT: https://github.com/BioJulia/Bio.jl/blob/master/LICENSE.md
module Ragel
export tryread!
using BufferedStreams
import Bio.IO: FileFormat, AbstractReader
# A type keeping tr... | [
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29,
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2,
198,
2,
770,
2393,
318,
257,
636,
286,
16024,
16980,
544,
13,
198,... | 2.194665 | 3,149 |
function play_trajectory(vis, problem, robot, x)
ee_positions = get_ee_path(problem, robot, x)
show_ee_path(vis, ee_positions)
nₓ = robot.n_q + robot.n_v + robot.n_τ # dimension of each mesh point
ind_q = hcat([range(1 + (i * nₓ), length=robot.n_q) for i = (1:problem.num_knots) .- 1]...)
q_mat = x... | [
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652,
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220,
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220,
220,
220,
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62,
1453,
62,
6978,
... | 1.976318 | 1,309 |
<reponame>mbrueckner/AED.jl<gh_stars>0
## A discrete distribution representing a point mass (DiracPM) or several point masses (MultvariateDiracPM)
## Like Distributions.Categorical but remembers the posiition of the point mass
## This is needed to specify the priors under the null and alternative hypotheses
struct Dir... | [
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480,
29,
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694,
1008,
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330,
5868,
8,
393,
1811,
966,
14568,
357,
15205,
25641,
378,
35... | 2.667513 | 394 |
using Conda
using Compat
using PyCall
backend = "theano"
try
keras = pyimport("keras")
if VersionNumber(keras[:__version__]) >= v"2.0.2"
Compat.@info("Using Keras $(keras[:__version__]) -> $(keras[:__path__])")
global backend = keras[:backend][:backend]()
else
Compat.@error("Invali... | [
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1,
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198,
220,
220,
220,
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292,
796,
12972,
11748,
7203,
6122,
292,
4943,
198,
220,
220,
220,
611,
10628,
1... | 2.290378 | 582 |
##########################################################################
# Copyright 2017 <NAME>.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENS... | [
29113,
29113,
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2,
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198,
2,
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2,
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11,
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362,
13,
15,
357,
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366,
34156,
15341,
198,
2,
345,
743,
407,
779,
428,
2393,
2845,
287,
11846,
351,
26... | 3.004286 | 2,100 |
import .CuArrays: CuArray
import .CuArrays.CUDAdrv: CuPtr, synchronize
import .CuArrays.CUDAdrv.Mem: DeviceBuffer
function Base.cconvert(::Type{MPIPtr}, buf::CuArray{T}) where T
Base.cconvert(CuPtr{T}, buf) # returns DeviceBuffer
end
# CuArrays <= v1.3
function Base.unsafe_convert(::Type{MPIPtr}, buf::DeviceBuffe... | [
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764,
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25,
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81,
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764,
46141,
3163,
20477,
13,
34,
8322,
2782,
81,
85,
13,
13579,
25,
1623... | 2.448101 | 395 |
# Run code for previous steps with plotting turned off.
make_plots_orig_4 = isdefined(Main,:make_plots) ? make_plots : true
make_plots = false
include("neid_solar_4_extract_chunks.jl")
make_plots = make_plots_orig_4
if make_plots
using Plots
end
# Set parameters for this analysis
oversample_fac_chunks = 2
ove... | [
2,
5660,
2438,
329,
2180,
4831,
351,
29353,
2900,
572,
13,
198,
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62,
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62,
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62,
19,
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318,
23211,
7,
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11,
25,
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62,
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8,
5633,
787,
62,
489,
1747,
1058,
2081,
198,
787,
62,
489,
1747,
... | 2.14159 | 3,270 |
<reponame>UnofficialJuliaMirrorSnapshots/Setfield.jl-efcf1570-3423-57d1-acb7-fd33fddbac46
export @set, @lens, @set!
using MacroTools
"""
@set assignment
Return a modified copy of deeply nested objects.
# Example
```jldoctest
julia> using Setfield
julia> struct T;a;b end
julia> t = T(1,2)
T(1, 2)
julia> @set t... | [
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65,
330,
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198,
3... | 2.216358 | 3,619 |
<reponame>Jetafull/AlgorithmsInJulia
# Insertion sort
function insertionsort!(arr, low, high)
@inbounds for i = (low+1):high
j = i
x = arr[i]
while j > low && arr[j] < arr[j-1]
exch!(arr, j, j-1)
j -= 1
end
end
end
insertsort!(arr) = insertionsort!(arr,... | [
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220,
220,
220,
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259,
65,
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329,
1312,
796,
357... | 2.134933 | 2,001 |
<filename>src/LyapunovFunctions/control_lyapunov_functions.jl
"Abstract Lyapunov function type"
abstract type LyapunovFunction <: CertificateFunction end
"""
ControlLyapunovFunction
Control Lyapunov function V for a control affine system
# Fields
- `V`: function V(x) that represents the CLF
- `∇V`: function ∇V(x)... | [
27,
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29,
10677,
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1,
198,
397,
8709,
2099,
9334,
499,
403,
709,
22203,
1279,
... | 2.546875 | 320 |
<reponame>UnofficialJuliaMirrorSnapshots/MultidimensionalTables.jl-d8164373-46bb-572c-9bed-61caa11c4d4e<gh_stars>1-10
# an ordering to be used in sorting DictArrays or Labeledarrays.
immutable AbstractArrayLT{N,O,F} <: Base.Ordering
ords::O #NTuple{N,Base.Ordering}
fields::F #NTuple{N,Vector}
end
AbstractArrayLT(a... | [
27,
7856,
261,
480,
29,
3118,
16841,
16980,
544,
27453,
1472,
43826,
20910,
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312,
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51,
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12,
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66,
12,
24,
3077,
12,
5333,
6888,
64,
1157,
66,
19,
67,
... | 2.376866 | 1,876 |
<gh_stars>1-10
using SummationByParts
function nodecalc(sbp::TetSBP, isDG::Bool)
vtx = [0.0 0 0; 1 0 0; 0 1 0; 0 0 1]
r1 = vtx[1, :]
r2 = vtx[2, :]
r3 = vtx[3, :]
r4 = vtx[4, :]
T = zeros(3,3)
T[:, 1] = r2 - r1
T[:, 2] = r3 - r1
T[:, 3] = r4 - r1
# create operator
if isDG
coords = Summatio... | [
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62,
30783,
29,
16,
12,
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198,
3500,
5060,
76,
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42670,
628,
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66,
7,
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35,
38,
3712,
33,
970,
8,
198,
220,
410,
17602,
796,
685,
15,
13... | 1.940678 | 708 |
<gh_stars>1-10
function Distributions.fit(::Type{BayesNet}, data::DataFrame, dag::DAG, cpd_types::Vector{DataType})
length(cpd_types) == nv(dag) || throw(DimensionMismatch("dag and cpd_types must have the same length"))
cpds = Array{CPD}(length(cpd_types))
tablenames = names(data)
for (i, target) in e... | [
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29,
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12,
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19778,
11,
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3712,
35,
4760,
11,
269,
30094,
62,
19199,
3712,
38469,
90,
6601,
6030,
30072,
628... | 2.5173 | 3,526 |
<filename>src/PwDynamicDefinition.jl
module PwDynamicDefinition
using ValidatedNumerics
using ..DynamicDefinition
using ..Contractors
using TaylorSeries: Taylor1
using ..DynamicDefinition: derivative, orientation
export PwMap, preim, nbranches, plottable
"""
Dynamic based on a piecewise monotonic map.
The map is de... | [
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16,
1... | 2.672783 | 1,308 |
<gh_stars>1000+
using HDF5
function test_hdf5_data_layer(backend::Backend, async, T, eps)
println("-- Testing $(async ? "(Async)" : "") HDF5 Data Layer on $(typeof(backend)){$T}...")
############################################################
# Prepare Data for Testing
#######################################... | [
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2956... | 2.561695 | 1,864 |
<reponame>KristofferC/NamedArrays.jl
#73
na = NamedArray([1, 2], ([1, missing],), ("A",))
let one = na[Name(1)], two = na[Name(missing)]
@test one == 1
@test two == 2
end
#39
include("init-namedarrays.jl")
v = n[1, :]
@test sin.(v).array == sin.(v.array)
@test namesanddim(sin.(v)) == namesanddim(v)
| [
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... | 2.296296 | 135 |
## this is based on GLFW
struct KeyEvent
key::Cint
action::Cint
mod::UInt16
end
# Key and button actions
@enum(Action::Cint,
RELEASE = 0,
PRESS = 1,
REPEAT = 2,
)
@enum(Key::Cint,
KEY_UNKNOWN = (UInt32)(0),
KEY_RETURN = (UInt32)(13),
KEY_ESCAP... | [
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5317,
1433,
198,
437,
198,
198,
2,
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290... | 1.896312 | 5,044 |
<gh_stars>1-10
using MLJBase
using TextAnalysis
@testset "basic use" begin
# add some test docs
docs = ["Hi my name is Sam.", "How are you today?"]
# convert to ngrams
ngram_vec = ngrams.(documents(Corpus(NGramDocument.(docs))))
# train tfidf transformer
tfidf_transformer = MLJText.TfidfTrans... | [
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220,
220,
34165,
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14631,
... | 2.401515 | 2,376 |
<gh_stars>1-10
addprocs(20);
@everywhere include("loadMod.jl");
@everywhere const GUROBI_ENV = Gurobi.Env();
caseList = [13,33,123];
NN = 1000;
Δt = 0.25;
N = 5;
iterMax = 20;
pathListDRaw = load("pathHist_1000.jld");
TList = [24,36,48,72,96];
for ci in 1:length(caseList)
pathDictA = pathListDRaw["pathDict"][ci]... | [
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62,
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53,
796,
402,
1434,
8482,
13,
48... | 1.853774 | 848 |
"""
```
Model1002{T} <: AbstractRepModel{T}
```
The `Model1002` type defines the structure of Model1002 (same as
Model997 but uses longrate without adjusting for term premia.)
### Fields
#### Parameters and Steady-States
* `parameters::Vector{AbstractParameter}`: Vector of all time-invariant model
parameters.
* `... | [
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... | 2.039611 | 35,748 |
## Implements bitonic merge networks that can merge two sorted SIMD vectors.
## The generated function supports any possible type and size.
using SIMD
"""
bitonic_merge(input_a::Vec{N,T}, input_b::Vec{N,T}) where {N,T}
Merges two `SIMD.Vec` objects of the same type and size using a bitonic sort network. The inp... | [
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220,
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1643,... | 1.855749 | 2,870 |
<gh_stars>10-100
function f(x::AbstractIntVar,alpha::Float64)
return 1/(assignedValue(x)+1)^alpha
end
"""
struct CPReward2 <: AbstractReward end
CPReward2 is a variant of CPReward with better theoretical properties concerning the variations of the function..
"""
mutable struct CPReward2 <: AbstractReward
v... | [
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11395,
7,
87,
47762,
16,
8,
61,
26591,
198,
437,
198,
198,
... | 2.90404 | 792 |
using Documenter, GenomicFeatures
makedocs(
format = :html,
sitename = "GenomicFeatures.jl",
pages = [
"Home" => "index.md",
"Intervals" => "intervals.md",
"I/O" => [
"BED" => "io/bed.md",
"GFF3" => "io/gff3.md",
"BigWig" => "io/bigwig.md",
... | [
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11,
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220,
220,
220,
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796,
366,
13746,
10179,
23595,
13,
20362,
1600,
198,
220,
220,
220,
5468,
796,... | 2.015873 | 315 |
<reponame>roSievers/jtac
# -------- MCTS Nodes -------------------------------------------------------- #
mutable struct Node
action :: ActionIndex # How did we get here?
current_player :: Int # Who is allowed an action in this situation?
parent :: Union{Node, Nothing} # Wher... | [
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198,
220,
220,
220,
2223,
7904,
7561,
15732,
220,
220,
220,
220,
220,
220... | 2.756422 | 2,102 |
<filename>src/parameters.jl
using ExcelReaders
function getdice2013excelparameters(filename)
p = Dict{Symbol,Any}()
T = 60
#Open RICE_2010 Excel File to Read Parameters
f = openxl(filename)
p[:a1] = getparams(f, "B25:B25", :single, "Base", 1) #Damage coefficient on temperature
... | [
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11,
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... | 2.170303 | 4,721 |
<reponame>yakir12/PyDrive.jl
using PyCall
using Conda
if PyCall.conda
Conda.add_channel("conda-forge")
Conda.add("pydrive")
else
try
pyimport("pydrive")
# See if it works already
catch ee
typeof(ee) <: PyCall.PyError || rethrow(ee)
error("""
Python pydrive not installed
... | [
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220,
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64,
13,
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62,
17620,
7203,
66,
13533,
1... | 2.553265 | 291 |
<reponame>nishaChandramoorthy/linearResponse
using PyPlot
using JLD
function plot_stable_sens()
X = load("../data/obj_erg_avg/cos4y_s4.jld")
s4_arr = X["s4"]
J_arr = X["J"]
fig, ax = subplots(1,1)
ax.plot(s4_arr, J_arr, ".", ms=10.0)
ax.xaxis.set_tick_params(labelsize=32)
ax.yaxis.set_tick_params(labelsize... | [
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62,
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641,
3419,
198,
197,
55,
796,
3440,
7203,
40720,
7890,
14,
26801,
62,... | 1.764706 | 510 |
<gh_stars>0
const Rₑ_m = 6371008.7714
| [
27,
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29,
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158,
224,
239,
62,
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2718,
3064,
23,
13,
3324,
1415,
198
] | 1.652174 | 23 |
module Lagrangian
using Compat, JuMP
export
# Solve for the Lagrangian duals
lagrangian_method!,
# Relax, solve, then store
lagrangiansolve!,
# Structs to implement methods
LevelMethod,
SubgradientMethod,
KelleyMethod,
# Structs to solve the primal
LinearProgram,
# Each method needs to know about the problem being sol... | [
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666,
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82,
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666,
62,
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28265,
198,
2,
46883,
11,
8494,
11,
788,
3650,
198,
30909,... | 2.39307 | 1,847 |
using HomotopyContinuation2.ModelKit
@testset "ModelKit" begin
@testset "SymEngine" begin
@test Expression(MathConstants.catalan) isa Expression
@test Expression(MathConstants.e) isa Expression
@test Expression(MathConstants.pi) isa Expression
@test Expression(MathConstants.γ) isa E... | [
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198,
220,
220,
220,
220,
220,
220,
220,
2488,
9288,
4... | 1.644815 | 10,068 |
using Distributions
using SMC
using KernelDensity
using PyPlot
using LaTeXStrings
include("../math.jl")
include("../aide.jl")
# where to write plots to
const PLOT_DIR = "plots"
if !Base.Filesystem.isdir(PLOT_DIR)
Base.Filesystem.mkdir(PLOT_DIR)
end
"""
An unnormalized target density defined by a Gaussian prior a... | [
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3500,
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198,
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13,
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198,
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7203,
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64,
485,
13,
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4943,
198,... | 2.44878 | 5,203 |
<gh_stars>1-10
using DFTK: spglib_spacegroup_number, spglib_standardize_cell
using LinearAlgebra
using Test
@testset "spglib" begin
a = 10.3
Si = ElementPsp(:Si, psp=load_psp("hgh/lda/Si-q4"))
Ge = ElementPsp(:Ge, psp=load_psp("hgh/lda/Ge-q4"))
# silicon
lattice = a / 2 * [[0 1 1.]; [1 0 1.]; [1 1... | [
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31,
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2617... | 2.177314 | 1,534 |
<filename>src/vtk_calculator.jl
# AUTO GENERATED FILE - DO NOT EDIT
export vtk_calculator
"""
vtk_calculator(;kwargs...)
vtk_calculator(children::Any;kwargs...)
vtk_calculator(children_maker::Function;kwargs...)
A Calculator component.
Calculator is exposing a source or filter to a downstream filter
It ... | [
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220,
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... | 3.008032 | 498 |
<reponame>krystophny/GeometricIntegrators.jl
abstract type IntegratorRK{dType, tType} <: DeterministicIntegrator{dType, tType} end
abstract type IntegratorPRK{dType, tType} <: IntegratorRK{dType, tType} end
@inline equation(integrator::IntegratorRK) = integrator.params.equ
@inline timestep(integrator::IntegratorRK) =... | [
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... | 2.816327 | 196 |
<gh_stars>1-10
using Test
import Pluto: Notebook, ServerSession, ClientSession, Cell, updated_topology, is_just_text
@testset "Analysis" begin
notebook = Notebook([
Cell(""),
Cell("md\"a\""),
Cell("html\"a\""),
Cell("md\"a \$b\$\""),
Cell("md\"a ``b``\""),
Cell("""
... | [
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31,
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366,
32750,
1,... | 2.043946 | 1,115 |
<filename>src/julia/experiments/0_gen_arrays.jl
using Revise
using FastGroupBy, BenchmarkTools
#const N = Int64(2e9/8)
#const N = 100_000_000
#const N = UInt32(2^31)
const N = UInt32(2^30)
#const N = Int(2^31-1)
const K = UInt32(100)
#const id4 = rand(1:K, N)
const id6 = rand(Int32(1):Int32(round(N/K)), N)
const v1 ... | [
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7,
17,
68... | 1.974425 | 391 |
#=
There exists a staircase with N steps, and you can climb up either 1 or 2 steps at a time. Given N, write a function that returns the number of unique ways you can climb the staircase. The order of the steps matters.
For example, if N is 4, then there are 5 unique ways:
1, 1, 1, 1
2, 1, 1
1, 2, 1
1... | [
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262,
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286,
3748,
2842,
345,
460,
12080,
262,
27656,
... | 2.592992 | 371 |
<gh_stars>10-100
using LaplacianOpt
using Test
import Memento
import JuMP
import LinearAlgebra
import GLPK
import MathOptInterface
const LOpt = LaplacianOpt
const LA = LinearAlgebra
const MOI = MathOptInterface
# Suppress warnings during testing
LOpt.logger_config!("error")
glpk_optimizer = JuMP.optimizer_with_a... | [
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... | 2.666667 | 201 |
<reponame>gdkrmr/Changepoints.jl
#=
Calculate recursions from paper http://eprints.lancs.ac.uk/745/1/online_chpt4.pdf
We are going to calculate an approximation to the posterior - approximate because particle filtering
is involved to increase the speed of the algorithm but reduce the computtional cost, this is an onl... | [
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16,
14,
251... | 2.753378 | 296 |
<reponame>chaoskey/FenicsPy.jl
module FenicsPy
using PyCall
export @pydef, @py_str
# must be explicitly imported to be extended
import Base: split, inv, transpose, div, diff, abs, sign, sqrt, exp, cos, sin, tan, acos, asin, atan, cosh, sinh, tanh, log, replace, adjoint, *, +, -, /, ^, ==, <<
import PyPlot: plot
... | [
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37,
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873,
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21412,
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873,
20519,
198,
198,
3500,
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198,
198,
39344,
2488,
9078,
4299,
11,
2488,
9078,
62,
2536,
198,
198,
2,
1276,
307,
11777,
173... | 2.231817 | 3,671 |
"""
$(TYPEDEF)
"""
struct LinearProblem{uType,isinplace,F,bType,P,K} <: AbstractLinearProblem{bType,isinplace}
A::F
b::bType
u0::uType
p::P
kwargs::K
@add_kwonly function LinearProblem{iip}(A,b,p=NullParameters();u0=nothing,
kwargs...) where iip
... | [
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14993,
451,
40781,
90,
65,
6030,
11,
45763,
5372,
92,
198... | 2.130081 | 2,214 |
using LoopVectorization, Test
function reference_mul4!(target_arr, src, range_a, range_b, padded_axis_a, padded_axis_b)
@inbounds @fastmath for a1i ∈ eachindex(range_a),
a2i ∈ eachindex(range_a),
b1i ∈ eachindex(range_b),
b2i ∈ eachindex(range_b)
a1 = range_a[a1i]
a2 = range_a[a2i]
b1 = ran... | [
198,
3500,
26304,
38469,
1634,
11,
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198,
198,
8818,
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62,
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19,
0,
7,
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11,
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62,
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62,
64,
11,
44582,
62,
22704,
62,
65,
8,
198,
220,
248... | 1.954424 | 1,865 |
import FiniteDifferences
function ngradient(f, args...)
fdm = FiniteDifferences.central_fdm(5, 1)
FiniteDifferences.grad(fdm, f, args...)
end
function gradcheck(f, args...; atol=1e-5, rtol=1e-5)
y_grads = grad(f, args...)[2]
# don't check gradient w.r.t. function since ngradient can't do it
y_gra... | [
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7,
20,
11,
352,
8,
198,
220,
220,
220,
4463,
578,
28813,
49... | 2.110368 | 299 |
@everywhere begin
using DrWatson
import JSON
using Logging
using HDF5
using PWS
end
f = ARGS[1]
dict = JSON.parsefile(projectdir("_research", "tmp", f))["params"]
@info "Read file" file = projectdir("_research", "tmp", f)
duration = dict["duration"]
num_responses = dict["num_responses"]
run_name ... | [
31,
16833,
3003,
2221,
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220,
220,
220,
1262,
1583,
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220,
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37,
20,
198,
220,
220,
220,
1262,
350,
19416,
198,
437,
198,
... | 2.522277 | 808 |
Inclusivity(args...) = @test_deprecated Intervals.Inclusivity(args...)
@testset "Inclusivity" begin
@testset "constructor" begin
for (s, f) in [(false, false), (false, true), (true, false), (true, true)]
inc = Inclusivity(s, f)
@test (first(inc), last(inc)) == (s, f)
end
... | [
818,
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3458,
7,
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9288,
62,
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198,
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31,
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1,
2221,
198,
220,
220,
220,
2488,
9288,
2617,
366,
41571,
273,
... | 2.15956 | 1,454 |
<filename>src/query.jl
const prolog = "https://pubchem.ncbi.nlm.nih.gov/rest/pug/"
"""
cid = get_cid(name="glucose")
cid = get_cid(smiles="C([C@@H]1[C@H]([C@@H]([C@H](C(O1)O)O)O)O)O")
Return the PubChem **c**ompound **id**entification number for the specified compound.
"""
function get_cid(; name=nothing, smi... | [
27,
34345,
29,
10677,
14,
22766,
13,
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198,
9979,
386,
6404,
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366,
5450,
1378,
12984,
15245,
13,
10782,
8482,
13,
21283,
76,
13,
37373,
13,
9567,
14,
2118,
14,
79,
1018,
30487,
198,
198,
37811,
198,
220,
220,
220,
269,
312,... | 2.280899 | 3,204 |
using OptimizationAlgorithms
const Optimization = OptimizationAlgorithms
# optimization of kernel hyperparameters of Gaussian Process
# could add optimization w.r.t. leave-one-out loss
# TODO: optimization of noise variance
# IDEA: pass inference method (Cholesky, Iterative, Stochastic, ...)
optimize(k, θ, x, y, σ²::R... | [
3500,
30011,
1634,
2348,
7727,
907,
198,
9979,
30011,
1634,
796,
30011,
1634,
2348,
7727,
907,
198,
198,
2,
23989,
286,
9720,
8718,
17143,
7307,
286,
12822,
31562,
10854,
198,
2,
714,
751,
23989,
266,
13,
81,
13,
83,
13,
2666,
12,
5... | 2.387551 | 1,703 |
center(w) = flex_row(w)
center(w::Widget) = w
center(w::Widget{:toggle}) = flex_row(w)
manipulatelayout(::WidgetTheme) = t -> node(:div, map(center, values(components(t)))..., map(center, t.output))
function widget(::WidgetTheme, x::Observable; label = nothing)
if label === nothing
x
else
Widg... | [
16159,
7,
86,
8,
796,
7059,
62,
808,
7,
86,
8,
198,
16159,
7,
86,
3712,
38300,
8,
796,
266,
198,
16159,
7,
86,
3712,
38300,
90,
25,
44256,
30072,
796,
7059,
62,
808,
7,
86,
8,
198,
198,
805,
541,
5039,
39786,
7,
3712,
38300,
... | 2.52071 | 169 |
##################################################
## For the complex-real rotator case we need a diagonal matrix to store the phase
## This is a means to make this generic with respect to rotator type
## The factorization can have an identity diagonal or a real one.
##
## XXX: Should this just use LinearAlgebra.D... | [
29113,
14468,
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198,
198,
2235,
1114,
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12,
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761,
257,
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262,
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2235,
770,
318,
257,
220,
1724,
284,
787,
428,
14276,
351,
2461,
284,
5724,
1352,
2099,
198,
2235... | 1.838655 | 8,863 |
<gh_stars>0
using Test, Random, Distributed, Statistics
Random.seed!(0)
using Hyperopt, Plots
f(a,b=true;c=10) = sum(@. 100 + (a-3)^2 + (b ? 10 : 20) + (c-100)^2) # This function must be defined outside testsets to avoid scoping issues
@testset "Hyperopt" begin
@testset "Random sampler" begin
@info "Test... | [
27,
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62,
30783,
29,
15,
198,
3500,
6208,
11,
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198,
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0,
7,
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69,
7,
64,
11,
65,
28,
7942,
26,
66,
28,
940,
8,
796,
2160,
... | 1.94314 | 3,025 |
abstract type AbstractVirtualVolume{T} end
in(p::AbstractCoordinatePoint{T, 3}, avv::AbstractVirtualVolume{T}) where {T <: SSDFloat} = in(p, avv.geometry)
| [
397,
8709,
2099,
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90,
51,
92,
886,
198,
198,
259,
7,
79,
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12727,
90,
51,
11,
513,
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85,
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37725,
31715,
90,
51,
30072,
810,
1391,
51,
1279,
25,
6723,
8068,
5439,
265,... | 2.785714 | 56 |
<reponame>chemicalfiend/Oceananigans.jl
using Oceananigans.Utils: prettytime
using Oceananigans: short_show
"""Show the innards of a `Model` in the REPL."""
Base.show(io::IO, model::ShallowWaterModel{G, A, T}) where {G, A, T} =
print(io, "ShallowWaterModel{$(Base.typename(A)), $T}",
"(time = $(prettytime(m... | [
27,
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261,
480,
29,
31379,
12463,
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272,
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25,
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62,
12860,
198,
198,
37811,
15307,
262,
3527,
1371,
28... | 2.370044 | 227 |
abstract type AbstractProjector end
struct L2Projector <: AbstractProjector
func_ip::Interpolation
geom_ip::Interpolation
M_cholesky #::SuiteSparse.CHOLMOD.Factor{Float64}
dh::MixedDofHandler
set::Vector{Int}
node2dof_map::Dict{Int64, Array{Int64,N} where N}
fe_values::Union{CellValues,Not... | [
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220,
220,
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62,
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3712,
9492,
16104,
3... | 2.369929 | 4,363 |
########################################################################
# REINDEXING & OP BINARIZATION #
########################################################################
function reindex(op::Call, st::Dict)
new_args = [get(st, x, x) for x in op.args]
new_id = g... | [
29113,
29113,
7804,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
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220,
220,
220,
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347,
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14887,
6234,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
... | 2.23328 | 4,411 |
<gh_stars>100-1000
using OrdinaryDiffEq
using Trixi
###############################################################################
# semidiscretization of the compressible ideal GLM-MHD equations
gamma = 5/3
equations = IdealGlmMhdEquations2D(gamma)
initial_condition = initial_condition_convergence_test
# Get the... | [
27,
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62,
30783,
29,
3064,
12,
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198,
198,
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2304,
1186,
1634,
286,
262,
27413,
856,
7306,
10188,
44,
12,
... | 2.283724 | 1,364 |
<reponame>bhalonen/ZipFile.jl
# This file was copied from https://github.com/dcjones/Zlib.jl
#
# Zlib is licensed under the MIT License:
#
# > Copyright (c) 2013: <NAME>
# >
# > Permission is hereby granted, free of charge, to any person obtaining
# > a copy of this software and associated documentation files (the
# > ... | [
27,
7856,
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29,
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422,
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198,
2,
198,
2,
1168,
8019,
318,
11971,
739,
262,
... | 2.13745 | 5,231 |
<reponame>SamRodkey/Comonicon.jl
"fake registry"
module Registry
using Test
using Comonicon
@cast add(path) = @test path === "abc"
@cast rm(path) = @test path === "abc"
end
@cast Registry
| [
27,
7856,
261,
480,
29,
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2539,
14,
5377,
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4749,
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261,
4749,
198,
198,
31,
2701,
751,
7,
6978,
8,
796,
2488,
9288,
3108,
2... | 2.704225 | 71 |
<filename>test/Atmos/Dycore/tracers_test.jl
using MPI
using CLIMA.Topologies
using CLIMA.Grids
using CLIMA.AtmosDycore.VanillaAtmosDiscretizations
using CLIMA.MPIStateArrays
using CLIMA.ODESolvers
using CLIMA.LowStorageRungeKuttaMethod
using CLIMA.GenericCallbacks
using CLIMA.AtmosDycore
using CLIMA.MoistThermodynamics... | [
27,
34345,
29,
9288,
14,
2953,
16785,
14,
35,
88,
7295,
14,
2213,
49908,
62,
9288,
13,
20362,
198,
3500,
4904,
40,
198,
3500,
7852,
3955,
32,
13,
9126,
5823,
198,
3500,
7852,
3955,
32,
13,
8642,
2340,
198,
3500,
7852,
3955,
32,
13... | 1.957183 | 3,550 |
# abstract supertype of specific semidiscretizations such as
# - SemidiscretizationHyperbolic for hyperbolic conservation laws
# - SemidiscretizationEulerGravity for Euler with self-gravity
abstract type AbstractSemidiscretization end
"""
AbstractEquations{NDIMS, NVARS}
An abstract supertype of specific equatio... | [
198,
2,
12531,
2208,
4906,
286,
2176,
5026,
312,
2304,
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4582,
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355,
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2,
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65,
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3657,
198,
2,
532,
12449,
312,
2304,
1186,
1634,
36,
18173,
38,
169... | 3.565436 | 596 |
<filename>src/inverseparticipationratio.jl
module IPR
import ..ED
using ..SimLib
using ..SimLib: FArray
using SpinSymmetry: basissize
using SharedArrays: sdata
import Statistics
export ipr, inverse_participation_ratio, IPRData, IPRDataDescriptor, load_ipr, InverseParticipationRatio
## Data structure
"""
struct... | [
27,
34345,
29,
10677,
14,
259,
4399,
48013,
341,
10366,
952,
13,
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198,
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314,
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198,
198,
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198,
3500,
11485,
8890,
25835,
25,
376,
19182,
198,
3500,
28002,
13940,
3020,
11... | 2.523551 | 1,104 |
<reponame>Nongchao/PowerSystems.jl
"""Accepts rating as a Float64 and then creates a TwoPartCost."""
function TwoPartCost(variable_cost::T, args...) where {T <: VarCostArgs}
return TwoPartCost(VariableCost(variable_cost), args...)
end
"""Accepts rating as a Float64 and then creates a ThreePartCost."""
function Thr... | [
27,
7856,
261,
480,
29,
45,
506,
354,
5488,
14,
13434,
11964,
82,
13,
20362,
198,
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38855,
82,
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257,
48436,
2414,
290,
788,
8075,
257,
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13729,
526,
15931,
198,
8818,
4930,
7841,
13729,
7,
45286,
62,
15805,
... | 2.38153 | 1,072 |
<reponame>JuliaPOMDP/POMDPLinter.jl
const TupleType = Type # should be Tuple{T1,T2,...}
const Req = Tuple{Function, TupleType}
abstract type AbstractRequirementSet end
mutable struct Unspecified <: AbstractRequirementSet
requirer
parent::Union{Nothing, Any}
end
Unspecified(requirer) = Unspecified(requirer, n... | [
27,
7856,
261,
480,
29,
16980,
544,
47,
2662,
6322,
14,
47,
2662,
6322,
43,
3849,
13,
20362,
198,
9979,
309,
29291,
6030,
796,
5994,
1303,
815,
307,
309,
29291,
90,
51,
16,
11,
51,
17,
42303,
92,
198,
9979,
797,
80,
796,
309,
29... | 2.160957 | 4,554 |
# test 3: number of trial paths tests
using Distributed;
addprocs(20);
@everywhere include("loadMod.jl");
@everywhere const GUROBI_ENV = Gurobi.Env();
#pmap(i -> importIpopt(),1:30);
NList = [1,5,10,15,20];
dataList = Dict();
iterMax = 20;
NN = 5;
caseList = [13,33,123];
T = 96;
τ = Int64(1/6*T);
Δt = 0.25;
pathTrain... | [
2,
1332,
513,
25,
1271,
286,
4473,
13532,
5254,
198,
3500,
4307,
6169,
26,
198,
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1676,
6359,
7,
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198,
31,
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15341,
198,
31,
16833,
3003,
1500,
402,
4261,
46,
3483,
62,
16... | 2.099851 | 671 |
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