content stringlengths 6 1.03M | input_ids listlengths 4 535k | ratio_char_token float64 0.68 8.61 | token_count int64 4 535k |
|---|---|---|---|
<gh_stars>1-10
module Yota
export grad, update!, @primitive, @grad
include("core.jl")
version() = v"0.1.0-2"
end
| [
27,
456,
62,
30783,
29,
16,
12,
940,
198,
21412,
575,
4265,
198,
198,
39344,
3915,
11,
4296,
28265,
2488,
19795,
1800,
11,
2488,
9744,
198,
198,
17256,
7203,
7295,
13,
20362,
4943,
198,
198,
9641,
3419,
796,
410,
1,
15,
13,
16,
13... | 2.25 | 52 |
<gh_stars>0
# TODO overload print/show/display function to not display model and data
struct jlModel
m::Ptr{mjModel}
qpos0::Array{mjtNum}
qpos_spring::Array{mjtNum}
body_parentid::Array{Cint}
body_rootid::Array{Cint}
body_weldid::Array{Cint}
body_mocapid::Array{Cint}
body_jntnum::Array{Cint}
... | [
27,
456,
62,
30783,
29,
15,
198,
198,
2,
16926,
46,
31754,
3601,
14,
12860,
14,
13812,
2163,
284,
407,
3359,
2746,
290,
1366,
198,
198,
7249,
474,
75,
17633,
198,
220,
220,
285,
3712,
46745,
90,
76,
73,
17633,
92,
628,
220,
220,
... | 1.634088 | 16,023 |
<filename>backend/anime_data/snapshots_25835.jl
{"score": 8.4, "timestamp": 1581707228.0, "score_count": 94509}
{"score": 8.4, "timestamp": 1579325618.0, "score_count": 93607}
{"score": 8.4, "timestamp": 1578234501.0, "score_count": 93336}
{"score": 8.4, "timestamp": 1574434062.0, "score_count": 91877}
{"score": 8.41, ... | [
27,
34345,
29,
1891,
437,
14,
272,
524,
62,
7890,
14,
45380,
20910,
62,
25600,
2327,
13,
20362,
198,
4895,
26675,
1298,
807,
13,
19,
11,
366,
16514,
27823,
1298,
24063,
1558,
2998,
23815,
13,
15,
11,
366,
26675,
62,
9127,
1298,
1004... | 2.330349 | 21,892 |
__precompile__()
module ReinforcementLearning
using POMDPs
using POMDPToolbox
using Discretizers
import POMDPs: isterminal, discount
import POMDPs: action, actions, action_index, n_actions
import POMDPs: states, state_index, n_states
export
isterminal,
actions,
step!,
reset!,
DiscretizedEnviron... | [
834,
3866,
5589,
576,
834,
3419,
198,
21412,
22299,
13442,
41730,
198,
198,
3500,
350,
2662,
6322,
82,
198,
3500,
350,
2662,
6322,
25391,
3524,
198,
3500,
8444,
1186,
11341,
198,
198,
11748,
350,
2662,
6322,
82,
25,
318,
23705,
282,
1... | 2.846715 | 137 |
function futuresdata(;
inputpath::String = PARAM[:inputpath],
futuresfile::String = PARAM[:capacity][:futuresfile],
futuresfileext::String = PARAM[:capacity][:futuresfileext],
)
futures = CSV.read("$inputpath\\$futuresfile.$futuresfileext") |> DataFrame
select!(futures, PARAM[:capacity][:futurescolumns])... | [
198,
198,
8818,
25650,
7890,
7,
26,
198,
220,
5128,
6978,
3712,
10100,
796,
29463,
2390,
58,
25,
15414,
6978,
4357,
198,
220,
25650,
7753,
3712,
10100,
796,
29463,
2390,
58,
25,
42404,
7131,
25,
69,
315,
942,
7753,
4357,
198,
220,
2... | 2.870149 | 670 |
<reponame>stevengj/NodesAndModes.jl<gh_stars>10-100
function basis(elem::Quad, N, r, s)
Np = convert(Int, (N + 1) * (N + 1))
sk = 1
V, Vr, Vs = ntuple(x->zeros(length(r), Np), 3)
for j = 0:N
P_j = jacobiP(s, 0, 0, j)
for i = 0:N
P_i = jacobiP(r, 0, 0, i)
V[:, sk]... | [
27,
7856,
261,
480,
29,
4169,
574,
70,
73,
14,
45,
4147,
1870,
44,
4147,
13,
20362,
27,
456,
62,
30783,
29,
940,
12,
3064,
198,
198,
8818,
4308,
7,
68,
10671,
3712,
4507,
324,
11,
399,
11,
374,
11,
264,
8,
198,
220,
220,
220,
... | 1.669659 | 557 |
#
# This file is a part of MolecularGraph.jl
# Licensed under the MIT License http://opensource.org/licenses/MIT
#
export
PlainHyperGraph,
plainhypergraph
struct PlainHyperGraph <: OrderedHyperGraph
incidences::Vector{Set{Int}}
edges::Vector{Set{Int}}
cache::Dict{Symbol,Any}
end
"""
plainhy... | [
2,
198,
2,
770,
2393,
318,
257,
636,
286,
38275,
37065,
13,
20362,
198,
2,
49962,
739,
262,
17168,
13789,
2638,
1378,
44813,
1668,
13,
2398,
14,
677,
4541,
14,
36393,
198,
2,
198,
198,
39344,
198,
220,
220,
220,
28847,
38197,
37065,... | 2.516245 | 831 |
function levendist1(s::AbstractString, t::AbstractString)
ls, lt = length(s), length(t)
if ls > lt
s, t = t, s
ls, lt = lt, ls
end
dist = collect(0:ls)
for (ind2, chr2) in enumerate(t)
newdist = Vector{Int}(ls+1)
newdist[1] = ind2
for (ind1, chr1) in enumerate... | [
8818,
23145,
437,
396,
16,
7,
82,
3712,
23839,
10100,
11,
256,
3712,
23839,
10100,
8,
198,
220,
220,
220,
43979,
11,
300,
83,
796,
4129,
7,
82,
828,
4129,
7,
83,
8,
198,
220,
220,
220,
611,
43979,
1875,
300,
83,
198,
220,
220,
... | 1.762918 | 329 |
<filename>test/runtests.jl
# Workaround for libz loading confusion.
@static if Sys.islinux()
using ImageMagick
end
using GtkObservables, Gtk.ShortNames, IntervalSets, Graphics, Colors,
TestImages, FileIO, FixedPointNumbers, RoundingIntegers, Dates, Cairo,
IdentityRanges
using Test
include("tools.jl")
... | [
27,
34345,
29,
9288,
14,
81,
2797,
3558,
13,
20362,
198,
2,
5521,
14145,
329,
9195,
89,
11046,
10802,
13,
198,
31,
12708,
611,
311,
893,
13,
271,
23289,
3419,
198,
220,
220,
220,
1262,
7412,
13436,
624,
198,
437,
198,
198,
3500,
4... | 2.096426 | 9,458 |
using Whirl
using Profile
using ProfileView
Profile.init(delay = 1e-2)
stepper = build(sod_shock_tube_builder(number_of_particles = 1000))
function profiling(n)
for _ = 1:n
step!(stepper, 1e-4)
end
end
@profview profiling(1)
@profview profiling(10000)
| [
3500,
854,
1901,
198,
3500,
13118,
198,
3500,
13118,
7680,
198,
198,
37046,
13,
15003,
7,
40850,
796,
352,
68,
12,
17,
8,
198,
198,
4169,
2848,
796,
1382,
7,
82,
375,
62,
39563,
62,
29302,
62,
38272,
7,
17618,
62,
1659,
62,
3911,
... | 2.552381 | 105 |
# Weighted row-replication
function replicate{T<:Real}(data::Matrix{T}, w::Vector{T})
num_samples, num_signals = size(data)
length(w) == num_samples ||
throw(DimensionMismatch("Inconsistent array lengths."))
replicated = zeros(int(sum(w)), num_signals)
j = 1
for i = 1:num_samples
for... | [
2,
14331,
276,
5752,
12,
35666,
3299,
198,
8818,
24340,
90,
51,
27,
25,
15633,
92,
7,
7890,
3712,
46912,
90,
51,
5512,
266,
3712,
38469,
90,
51,
30072,
198,
220,
220,
220,
997,
62,
82,
12629,
11,
997,
62,
12683,
874,
796,
2546,
... | 2.115727 | 337 |
<filename>test/distanceMetr/MeansMahalinobisTest.jl
using Revise, Parameters, Logging, Test
using CUDA
includet("C:\\GitHub\\GitHub\\NuclearMedEval\\src\\structs\\BasicStructs.jl")
includet("C:\\GitHub\\GitHub\\NuclearMedEval\\src\\utils\\CUDAGpuUtils.jl")
includet("C:\\GitHub\\GitHub\\NuclearMedEval\\src\\utils\\Itera... | [
27,
34345,
29,
9288,
14,
30246,
9171,
81,
14,
5308,
504,
40936,
14414,
672,
271,
14402,
13,
20362,
198,
3500,
5416,
786,
11,
40117,
11,
5972,
2667,
11,
6208,
198,
3500,
29369,
5631,
198,
259,
758,
316,
7203,
34,
25,
6852,
38,
270,
... | 2.26767 | 5,051 |
using ClickHouse: Column, chwrite, chread,
read_col, VarUInt, parse_typestring, result_type
using Dates
using CategoricalArrays
using UUIDs
import Sockets
using Sockets: IPv4, IPv6
using DecFP
@testset "Parse type" begin
r = parse_typestring("Int32")
@test r.name == :Int32
@test_throws ErrorExcept... | [
3500,
6914,
18102,
25,
29201,
11,
442,
13564,
11,
442,
961,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
1100,
62,
4033,
11,
12372,
52,
5317,
11,
21136,
62,
28004,
395,
1806,
11,
1255,
62,
4906,
198,
3500,
44712,
198,
3500,
32... | 2.29347 | 6,999 |
#function testNHHeldSuarezSphere
using CGDycore
OrdPoly = 4
OrdPolyZ=1
nz = 10
nPanel = 4
NF = 6 * nPanel * nPanel
# Cache
cache=CGDycore.Cache(OrdPoly, OrdPolyZ, nz, NF)
# Physical parameters
Param=CGDycore.PhysParameters(cache);
# Grid
Param.nPanel=nPanel;
Param.H=30000;
Param.Grid=CGDycore.CubedGrid(Param.nPane... | [
2,
8818,
1332,
33863,
1544,
335,
5606,
19655,
38882,
198,
3500,
29925,
35,
88,
7295,
628,
198,
35422,
34220,
796,
604,
198,
35422,
34220,
57,
28,
16,
198,
27305,
796,
838,
198,
77,
26639,
796,
604,
198,
21870,
796,
718,
1635,
299,
2... | 1.969889 | 2,524 |
#### Cauchy matrix
export Cauchy
"""
[`Cauchy` matrix](http://en.wikipedia.org/wiki/Cauchy_matrix)
```julia
Cauchy(x,y)[i,j]=1/(x[i]+y[j])
Cauchy(x)=Cauchy(x,x)
cauchy(k::Number)=Cauchy(collect(1:k))
julia> Cauchy([1,2,3],[3,4,5])
3x3 Cauchy{Int64}:
0.25 0.2 0.166667
0.2 0.166667 0.142857
0.16666... | [
4242,
327,
559,
29658,
17593,
198,
198,
39344,
327,
559,
29658,
198,
37811,
198,
58,
63,
34,
559,
29658,
63,
17593,
16151,
4023,
1378,
268,
13,
31266,
13,
2398,
14,
15466,
14,
34,
559,
29658,
62,
6759,
8609,
8,
198,
15506,
63,
73,
... | 1.858462 | 650 |
#Linear ARD Covariance Function
"""
LinArd <: Kernel
ARD linear kernel (covariance)
```math
k(x,x') = xᵀL⁻²x'
```
with length scale ``ℓ = (ℓ₁, ℓ₂, …)`` and ``L = diag(ℓ₁, ℓ₂, …)``.
"""
mutable struct LinArd{T<:Real} <: Kernel
"Length scale"
ℓ::Vector{T}
"Priors for kernel parameters"
priors::Array... | [
2,
14993,
451,
5923,
35,
39751,
2743,
590,
15553,
198,
198,
37811,
198,
220,
220,
220,
5164,
3163,
67,
1279,
25,
32169,
198,
198,
9795,
14174,
9720,
357,
66,
709,
2743,
590,
8,
198,
15506,
63,
11018,
198,
74,
7,
87,
11,
87,
11537,... | 1.994785 | 1,534 |
"Holds the tableau of an Specialised Partitioned Additive Runge-Kutta method for Variational systems."
struct TableauVSPARKsecondary{DT <: Number} <: AbstractTableau{DT}
name::Symbol
o::Int
s::Int
r::Int
ρ::Int
q::CoefficientsSPARK{DT}
p::CoefficientsSPARK{DT}
q̃::CoefficientsSPARK{DT}... | [
1,
39,
10119,
262,
3084,
559,
286,
281,
6093,
1417,
2142,
653,
276,
3060,
1800,
5660,
469,
12,
42,
315,
8326,
2446,
329,
15965,
864,
3341,
526,
198,
7249,
8655,
559,
53,
4303,
14175,
38238,
90,
24544,
1279,
25,
7913,
92,
1279,
25,
... | 1.770077 | 6,637 |
<reponame>MohHizzani/NumNN.jl<gh_stars>1-10
using ProgressMeter
using Random
using LinearAlgebra
include("layerForProp.jl")
"""
perform the chained forward propagation using recursive calls
input:
X := input of the forward propagation
cLayer := output layer
cnt := an internal counter used to cac... | [
27,
7856,
261,
480,
29,
38443,
39,
6457,
3216,
14,
33111,
6144,
13,
20362,
27,
456,
62,
30783,
29,
16,
12,
940,
198,
198,
3500,
18387,
44,
2357,
198,
3500,
14534,
198,
3500,
44800,
2348,
29230,
198,
198,
17256,
7203,
29289,
1890,
24... | 1.952754 | 7,027 |
<reponame>GrantHecht/JuSwarm<gh_stars>1-10
using JuSwarm, Test, SafeTestsets
@time begin
@time @safetestset "Sphere Function" begin include("sphere_function_test.jl") end
@time @safetestset "Rosenbrock Function" begin include("rosenbrock_function_test.jl") end
@time @safetestset "Ackley Function" begin include("ackle... | [
27,
7856,
261,
480,
29,
45431,
1544,
21474,
14,
33018,
10462,
1670,
27,
456,
62,
30783,
29,
16,
12,
940,
198,
198,
3500,
12585,
10462,
1670,
11,
6208,
11,
19978,
51,
3558,
1039,
198,
31,
2435,
2221,
198,
31,
2435,
2488,
49585,
316,
... | 2.875 | 152 |
<gh_stars>0
module SqState
const PROJECT_PATH = @__DIR__
include("read.jl")
include("utils.jl")
include("polynomial.jl")
include("wigner.jl")
end
| [
27,
456,
62,
30783,
29,
15,
198,
21412,
311,
80,
9012,
628,
220,
220,
220,
1500,
21965,
23680,
62,
34219,
796,
2488,
834,
34720,
834,
628,
220,
220,
220,
2291,
7203,
961,
13,
20362,
4943,
198,
220,
220,
220,
2291,
7203,
26791,
13,
... | 2.315068 | 73 |
<reponame>viniciuspiccoli/IL_simulations
export pack_input
# one il + protein + water
function pack_input(data, pdb_dir::String, nil, nwater, sides)
protein = data.protein
cation = data.cation
anion = data.anion
lx = round(Int64,sides[1] / 2)
ly = round(Int64,sides[2] / 2)
lz = round(Int64,sides[3... | [
27,
7856,
261,
480,
29,
7114,
291,
3754,
16564,
4033,
72,
14,
4146,
62,
14323,
5768,
198,
39344,
2353,
62,
15414,
198,
198,
2,
530,
4229,
1343,
7532,
1343,
1660,
220,
220,
198,
8818,
2353,
62,
15414,
7,
7890,
11,
279,
9945,
62,
15... | 2.16941 | 2,491 |
<reponame>appleparan/Mise.jl
"""
extract_col_feats(df, cols)
find mean, std, minimum, maximum in df[!, col]
default value of columns are all numeric columns except date
"""
function extract_col_statvals(df::DataFrame, cols::Array{Symbol, 1})
syms = []
types = []
vals = []
for col in cols
μ,... | [
27,
7856,
261,
480,
29,
18040,
1845,
272,
14,
44,
786,
13,
20362,
198,
37811,
198,
220,
220,
220,
7925,
62,
4033,
62,
5036,
1381,
7,
7568,
11,
951,
82,
8,
198,
198,
19796,
1612,
11,
14367,
11,
5288,
11,
5415,
287,
47764,
58,
282... | 2.147547 | 2,528 |
# This file is a part of project JuliaFEM.
# License is MIT: see https://github.com/JuliaFEM/NodeNumbering.jl/blob/master/LICENSE
using Documenter
using NodeNumbering
makedocs(
modules = [NodeNumbering],
sitename = "NodeNumbering.jl",
format = :html,
pages = [
"Introduction" => "index.md"... | [
2,
770,
2393,
318,
257,
636,
286,
1628,
22300,
37,
3620,
13,
198,
2,
13789,
318,
17168,
25,
766,
3740,
1378,
12567,
13,
785,
14,
16980,
544,
37,
3620,
14,
19667,
15057,
278,
13,
20362,
14,
2436,
672,
14,
9866,
14,
43,
2149,
24290,... | 2.206522 | 184 |
#module ResNetModule
export ResNet
import Knet
using Knet.Layers21: Conv, BatchNorm, Linear, Block, Add
using Knet.Ops21: relu, pool, mean
using Artifacts
"""
ResNet(; nblocks=(2,2,2,2), block=ResNetBottleneck, groups=1, bottleneck=1, classes=1000)
ResNet(name::String; pretrained=true)
Return a ResNet model... | [
2,
21412,
1874,
7934,
26796,
198,
39344,
1874,
7934,
198,
198,
11748,
509,
3262,
198,
3500,
509,
3262,
13,
43,
6962,
2481,
25,
34872,
11,
347,
963,
35393,
11,
44800,
11,
9726,
11,
3060,
198,
3500,
509,
3262,
13,
41472,
2481,
25,
823... | 2.452123 | 3,227 |
<reponame>crstnbr/LatPhysUnitcellLibrary.jl<filename>src/unitcells_3d/8_3_n.jl
################################################################################
#
# (8,3)n
#
################################################################################
# Implementation
# - implementation 1
# - labels <: Any
functi... | [
27,
7856,
261,
480,
29,
6098,
301,
77,
1671,
14,
24220,
43215,
26453,
3846,
23377,
13,
20362,
27,
34345,
29,
10677,
14,
20850,
46342,
62,
18,
67,
14,
23,
62,
18,
62,
77,
13,
20362,
198,
29113,
29113,
14468,
198,
2,
198,
2,
220,
... | 1.964948 | 4,479 |
# Gloriously inefficient ways of computing running & rolled quantities.
function _naive_inception_reduce(T, f::Function, block::Block, min_window::Int)
@assert min_window > 0
times = DateTime[]
values = T[]
buffer = value_type(block)[]
for (i, (time, value)) in enumerate(block)
# Push a new... | [
2,
2671,
273,
6819,
30904,
2842,
286,
14492,
2491,
1222,
11686,
17794,
13,
198,
198,
8818,
4808,
2616,
425,
62,
924,
1159,
62,
445,
7234,
7,
51,
11,
277,
3712,
22203,
11,
2512,
3712,
12235,
11,
949,
62,
17497,
3712,
5317,
8,
198,
... | 2.239076 | 5,630 |
<filename>experiments/2gaussianson1.jl
using FewShotAnomalyDetection
using Flux
using MLDataPattern
using FluxExtensions
using Adapt
using DataFrames
using CSV
import FewShotAnomalyDetection: loss, zparams
include("experimentalutils.jl")
include("vae.jl")
inputDim = 1
hiddenDim = 100
latentDim = 1
numLayers = 2
nonli... | [
27,
34345,
29,
23100,
6800,
14,
17,
4908,
1046,
1547,
261,
16,
13,
20362,
198,
3500,
20463,
28512,
2025,
24335,
11242,
3213,
198,
3500,
1610,
2821,
198,
3500,
10373,
6601,
47546,
198,
3500,
1610,
2821,
11627,
5736,
198,
3500,
30019,
198... | 2.725686 | 401 |
<gh_stars>0
module AdventOfCode
end
| [
27,
456,
62,
30783,
29,
15,
198,
21412,
33732,
5189,
10669,
198,
198,
437,
198
] | 2.466667 | 15 |
<filename>julia/perf/perf_ngsim_env.jl
using AutoEnvs
function perf_ngsim_env_step(n_steps=20000)
filepath = Pkg.dir("NGSIM", "data", "trajdata_i101_trajectories-0750am-0805am.txt")
params = Dict(
"trajectory_filepaths"=>[filepath],
)
env = NGSIMEnv(params)
action = [1.,0.]
reset(env)
... | [
27,
34345,
29,
73,
43640,
14,
525,
69,
14,
525,
69,
62,
782,
14323,
62,
24330,
13,
20362,
198,
3500,
11160,
4834,
14259,
198,
198,
8818,
23035,
62,
782,
14323,
62,
24330,
62,
9662,
7,
77,
62,
20214,
28,
2167,
405,
8,
198,
220,
2... | 2.044807 | 491 |
@testset "970.powerful-integers.jl" begin
@test powerful_integers(2, 3, 10) == Set([2, 3, 4, 5, 7, 9, 10])
@test powerful_integers(3, 5, 15) == Set([2, 4, 6, 8, 10, 14])
@test powerful_integers(1, 5, 15) == Set([2, 6])
@test powerful_integers(1, 1, 5) == Set([2])
end | [
31,
9288,
2617,
366,
43587,
13,
44548,
12,
18908,
364,
13,
20362,
1,
2221,
198,
220,
220,
220,
2488,
9288,
3665,
62,
18908,
364,
7,
17,
11,
513,
11,
838,
8,
6624,
5345,
26933,
17,
11,
513,
11,
604,
11,
642,
11,
767,
11,
860,
1... | 2.210938 | 128 |
<reponame>genkuroki/public<gh_stars>1-10
# ---
# jupyter:
# jupytext:
# formats: ipynb,jl:hydrogen
# text_representation:
# extension: .jl
# format_name: hydrogen
# format_version: '1.3'
# jupytext_version: 1.11.2
# kernelspec:
# display_name: Julia 1.8.0-DEV
# language: juli... | [
27,
7856,
261,
480,
29,
5235,
74,
1434,
4106,
14,
11377,
27,
456,
62,
30783,
29,
16,
12,
940,
198,
2,
11420,
198,
2,
474,
929,
88,
353,
25,
198,
2,
220,
220,
474,
929,
88,
5239,
25,
198,
2,
220,
220,
220,
220,
17519,
25,
209... | 2.688867 | 1,006 |
using Plasmons
using LinearAlgebra
using HDF5
using Test
using CUDA
using Adapt
CUDA.allowscalar(false)
@testset "Plasmons.jl" begin
@testset "fermidirac" begin
@test Plasmons.fermidirac(0.52193; mu = 0.4, kT = 0.1) ≈ 0.228059661030488549677
@test Plasmons.fermidirac(2.0; mu = -0.8, kT = 0.13) ≈ 4... | [
3500,
1345,
8597,
684,
198,
3500,
44800,
2348,
29230,
198,
3500,
5572,
37,
20,
198,
3500,
6208,
198,
3500,
29369,
5631,
198,
3500,
30019,
198,
198,
43633,
5631,
13,
12154,
1416,
282,
283,
7,
9562,
8,
198,
198,
31,
9288,
2617,
366,
3... | 1.604288 | 5,130 |
<gh_stars>1-10
"""
$(TYPEDEF)
`PointMass` is a simple environment useful for trying out and debugging new algorithms. The
task is simply to move a 2D point mass to a target position by applying x and y forces
to the mass.
# Spaces
* **State: (13, )**
* **Action: (2, )**
* **Observation: (6, )**
"""
struct PointM... | [
27,
456,
62,
30783,
29,
16,
12,
940,
198,
37811,
198,
220,
220,
220,
29568,
9936,
47,
1961,
25425,
8,
198,
198,
63,
12727,
20273,
63,
318,
257,
2829,
2858,
4465,
329,
2111,
503,
290,
28769,
649,
16113,
13,
383,
198,
35943,
318,
23... | 2.291498 | 988 |
@inline apply(o::Sum, diags::Vector{Diagram{W}}) where {W<:Number} = sum(d.weight for d in diags)
@inline apply(o::Prod, diags::Vector{Diagram{W}}) where {W<:Number} = prod(d.weight for d in diags)
@inline apply(o::Sum, diag::Diagram{W}) where {W<:Number} = diag.weight
@inline apply(o::Prod, diag::Diagram{W}) where {W<... | [
31,
45145,
4174,
7,
78,
3712,
13065,
11,
2566,
3775,
3712,
38469,
90,
18683,
6713,
90,
54,
11709,
8,
810,
1391,
54,
27,
25,
15057,
92,
796,
2160,
7,
67,
13,
6551,
329,
288,
287,
2566,
3775,
8,
198,
31,
45145,
4174,
7,
78,
3712,
... | 2.262418 | 1,067 |
<gh_stars>1-10
include("HyperRank.jl")
include("Hyper-Evec-Centrality-master\\centrality.jl")
include("Hyper-Evec-Centrality-master\\data_io.jl")
"""
`n_argmax`
==========
Returns the indices of the top `n` maximal elements in descending order of array value.
Arguments
---------
- `A::Vector{T}`: A v... | [
27,
456,
62,
30783,
29,
16,
12,
940,
198,
17256,
7203,
38197,
27520,
13,
20362,
4943,
201,
198,
17256,
7203,
38197,
12,
36,
35138,
12,
30645,
414,
12,
9866,
6852,
31463,
414,
13,
20362,
4943,
201,
198,
17256,
7203,
38197,
12,
36,
35... | 2.517549 | 1,567 |
<reponame>djsegal/FusionSystems.jl<gh_stars>0
@coeff function K_BS(reactor::Reactor)
K_b * K_n * K_T / (K_I^2)
end
| [
27,
7856,
261,
480,
29,
28241,
325,
13528,
14,
37,
4241,
11964,
82,
13,
20362,
27,
456,
62,
30783,
29,
15,
198,
31,
1073,
14822,
2163,
509,
62,
4462,
7,
260,
11218,
3712,
3041,
11218,
8,
198,
220,
509,
62,
65,
1635,
509,
62,
77,... | 1.95 | 60 |
<reponame>lrnv/Copulas.jl
"""
GumbelCopula{d,T}
Fields:
- θ::Real - parameter
Constructor
GumbelCopula(d, θ)
The [Gumbel](https://en.wikipedia.org/wiki/Copula_(probability_theory)#Most_important_Archimedean_copulas) copula in dimension ``d`` is parameterized by ``\\theta \\in [1,\\infty)``. It i... | [
27,
7856,
261,
480,
29,
14050,
48005,
14,
13379,
25283,
13,
20362,
198,
37811,
201,
198,
220,
220,
220,
402,
2178,
417,
13379,
4712,
90,
67,
11,
51,
92,
201,
198,
201,
198,
15878,
82,
25,
201,
198,
220,
532,
7377,
116,
3712,
15633... | 1.918228 | 587 |
### A Pluto.jl notebook ###
# v0.14.1
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
quote
loc... | [
21017,
317,
32217,
13,
20362,
20922,
44386,
198,
2,
410,
15,
13,
1415,
13,
16,
198,
198,
3500,
2940,
2902,
198,
3500,
21365,
18274,
4487,
198,
198,
2,
770,
32217,
20922,
3544,
2488,
21653,
329,
9427,
3458,
13,
1649,
2491,
428,
20922,
... | 1.810221 | 11,408 |
<reponame>Crghilardi/ForestBiometrics.jl
using ForestBiometrics
using DelimitedFiles
using Test
using Plots
datapath = joinpath(@__DIR__, "data")
data = readdlm(joinpath(datapath,"StandExam_data.csv"),',',header=true)
tl = Tree[]
for i in eachrow(data[1])
push!(tl,Tree(i[7], i[8], i[6], i[9]))
end
stand = Stand... | [
27,
7856,
261,
480,
29,
13916,
456,
346,
22490,
14,
34605,
23286,
908,
10466,
13,
20362,
198,
3500,
9115,
23286,
908,
10466,
198,
3500,
4216,
320,
863,
25876,
198,
3500,
6208,
198,
3500,
1345,
1747,
198,
198,
19608,
499,
776,
796,
465... | 2.278281 | 442 |
module Interfaces
export isinterfacetype, @interface, @implements, InterfaceImplementationError
struct InterfaceImplementationError <: Exception
msg::String
end
isinterfacetype(::Type{T}) where {T} = false
isinterfacetype(::Type{Type{T}}) where {T} = isinterfacetype(T)
function interface end
struct Interface
... | [
21412,
4225,
32186,
198,
198,
39344,
318,
3849,
38942,
2963,
431,
11,
2488,
39994,
11,
2488,
320,
1154,
902,
11,
26491,
3546,
32851,
12331,
198,
198,
7249,
26491,
3546,
32851,
12331,
1279,
25,
35528,
198,
220,
220,
220,
31456,
3712,
101... | 2.229612 | 5,383 |
using Waterfall
import Plots
# This file generates a histogram for each step in the cascade. It shows how a step HEIGHT
# depends on plotting order.
include(joinpath(WATERFALL_DIR,"src","includes.jl"))
include(joinpath(WATERFALL_DIR,"bin","io.jl"))
nperm = 1000
x = define_permute(df[[1:11;15],:], nperm; kwargs...)
d... | [
3500,
5638,
7207,
198,
11748,
1345,
1747,
198,
198,
2,
770,
2393,
18616,
257,
1554,
21857,
329,
1123,
2239,
287,
262,
44847,
13,
632,
2523,
703,
257,
2239,
11179,
9947,
198,
2,
8338,
319,
29353,
1502,
13,
198,
17256,
7,
22179,
6978,
... | 2.088933 | 506 |
using Pkg; Pkg.activate(@__DIR__)
using MuJoCo
mj_activate("/home/taylor/.mujoco/bin/mjkey.txt") # set location to MuJoCo key path
using LyceumMuJoCo, LyceumMuJoCoViz
using FiniteDiff
using IterativeLQR
using LinearAlgebra
using Random
# ## load MuJoCo model
path = joinpath(@__DIR__, "../../../env/box/deps/block.xm... | [
3500,
350,
10025,
26,
350,
10025,
13,
39022,
7,
31,
834,
34720,
834,
8,
198,
3500,
8252,
9908,
7222,
198,
76,
73,
62,
39022,
7203,
14,
11195,
14,
83,
7167,
11757,
76,
23577,
25634,
14,
8800,
14,
76,
73,
2539,
13,
14116,
4943,
1303... | 1.83484 | 1,659 |
<reponame>JuliaTagBot/AdventOfCode.jl
using Documenter, AdventOfCode
makedocs(sitename="AdventOfCode.jl")
deploydocs(
repo = "github.com/SebRollen/AdventOfCode.jl.git",
)
| [
27,
7856,
261,
480,
29,
16980,
544,
24835,
20630,
14,
2782,
1151,
5189,
10669,
13,
20362,
198,
3500,
16854,
263,
11,
33732,
5189,
10669,
198,
198,
76,
4335,
420,
82,
7,
48937,
12453,
2625,
2782,
1151,
5189,
10669,
13,
20362,
4943,
198... | 2.378378 | 74 |
using KernelDensity
import Statistics.std
function _defaultBandwidth(vs::Vector{Float64})
return std(vs)*1.06*length(vs)^(-1/5)
end
function _defaultBandwidth(xs::Vector{Float64}, ys::Vector{Float64})
@assert length(xs) == length(ys)
len = length(xs)
h1 = std(xs)*1.06*len^(-1/6)
h2 = std(ys)*1.06*len^(-1/6)... | [
3500,
32169,
35,
6377,
198,
11748,
14370,
13,
19282,
198,
198,
8818,
4808,
12286,
31407,
10394,
7,
14259,
3712,
38469,
90,
43879,
2414,
30072,
198,
220,
1441,
14367,
7,
14259,
27493,
16,
13,
3312,
9,
13664,
7,
14259,
8,
61,
32590,
16,... | 2.631868 | 728 |
<reponame>UnofficialJuliaMirrorSnapshots/TypeStability.jl-73ec333a-e3df-5994-9c7a-5ef2077ce03e
export check_function, check_method
export StabilityReport, is_stable
"""
check_function(func, signatures, acceptable_instability=Dict())
Check that the function is stable under each of the given signatures.
Return an... | [
27,
7856,
261,
480,
29,
3118,
16841,
16980,
544,
27453,
1472,
43826,
20910,
14,
6030,
1273,
1799,
13,
20362,
12,
4790,
721,
20370,
64,
12,
68,
18,
7568,
12,
20,
42691,
12,
24,
66,
22,
64,
12,
20,
891,
1238,
3324,
344,
3070,
68,
... | 2.270859 | 2,481 |
<reponame>lassepe/CUDA.jl
@testset "memory" begin
let
a,b = Mem.info()
# NOTE: actually testing this is pretty fragile on CI
#=@test a == =# CUDA.available_memory()
#=@test b == =# CUDA.total_memory()
end
# dummy data
T = UInt32
N = 5
data = rand(T, N)
nb = sizeof(data)
# buffers are untyped, so we u... | [
27,
7856,
261,
480,
29,
75,
21612,
431,
14,
43633,
5631,
13,
20362,
198,
31,
9288,
2617,
366,
31673,
1,
2221,
198,
198,
1616,
198,
220,
220,
220,
257,
11,
65,
796,
4942,
13,
10951,
3419,
198,
220,
220,
220,
1303,
24550,
25,
1682,
... | 2.160313 | 4,223 |
<gh_stars>1-10
# This file is a part of Julia. License is MIT: https://julialang.org/license
## linalg.jl: Some generic Linear Algebra definitions
# For better performance when input and output are the same array
# See https://github.com/JuliaLang/julia/issues/8415#issuecomment-56608729
function generic_rmul!(X::Abst... | [
27,
456,
62,
30783,
29,
16,
12,
940,
198,
2,
770,
2393,
318,
257,
636,
286,
22300,
13,
13789,
318,
17168,
25,
3740,
1378,
73,
377,
498,
648,
13,
2398,
14,
43085,
198,
198,
2235,
300,
1292,
70,
13,
20362,
25,
2773,
14276,
44800,
... | 2.157097 | 16,423 |
# This file is auto-generated by AWSMetadata.jl
using AWS
using AWS.AWSServices: device_farm
using AWS.Compat
using AWS.UUIDs
"""
create_device_pool(name, project_arn, rules)
create_device_pool(name, project_arn, rules, params::Dict{String,<:Any})
Creates a device pool.
# Arguments
- `name`: The device pool'... | [
2,
770,
2393,
318,
8295,
12,
27568,
416,
30865,
9171,
14706,
13,
20362,
198,
3500,
30865,
198,
3500,
30865,
13,
12298,
5432,
712,
1063,
25,
3335,
62,
43323,
198,
3500,
30865,
13,
40073,
198,
3500,
30865,
13,
52,
27586,
82,
198,
198,
... | 2.668352 | 36,189 |
<reponame>IntelLabs/Latte.jl
# Copyright (c) 2015, Intel Corporation
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice,
# this list o... | [
27,
7856,
261,
480,
29,
24123,
43,
8937,
14,
24220,
660,
13,
20362,
198,
2,
15069,
357,
66,
8,
1853,
11,
8180,
10501,
198,
2,
220,
198,
2,
2297,
396,
3890,
290,
779,
287,
2723,
290,
13934,
5107,
11,
351,
393,
1231,
198,
2,
17613... | 3.008216 | 852 |
<gh_stars>0
"""
$(TYPEDEF)
Simple in-memory data store with a specified data type and a specified key type.
"""
struct InMemoryDataStore{T, E <: AbstractBigDataEntry} <: AbstractDataStore{T}
data::Dict{Symbol, T}
entries::Dict{Symbol, E}
end
"""
$(SIGNATURES)
Create an in-memory store using a specific ... | [
27,
456,
62,
30783,
29,
15,
198,
37811,
198,
220,
220,
220,
29568,
9936,
47,
1961,
25425,
8,
198,
26437,
287,
12,
31673,
1366,
3650,
351,
257,
7368,
1366,
2099,
290,
257,
7368,
1994,
2099,
13,
198,
37811,
198,
7249,
554,
30871,
6601... | 2.785124 | 242 |
<gh_stars>0
"""
CosineKernel()
Cosine kernel.
# Definition
For inputs ``x, x' \\in \\mathbb{R}^d``, the cosine kernel is defined as
```math
k(x, x') = \\cos(\\pi \\|x-x'\\|_2).
```
"""
struct CosineKernel <: SimpleKernel end
kappa(::CosineKernel, d::Real) = cospi(d)
metric(::CosineKernel) = Euclidean()
Base.s... | [
27,
456,
62,
30783,
29,
15,
198,
37811,
198,
220,
220,
220,
10437,
500,
42,
7948,
3419,
198,
198,
36734,
500,
9720,
13,
198,
198,
2,
30396,
198,
198,
1890,
17311,
7559,
87,
11,
2124,
6,
26867,
259,
26867,
11018,
11848,
90,
49,
92,... | 2.284848 | 165 |
"""
Main module for `Helium.jl` -- a binary format writer and reader for matrix.
Three functions are exported from this module for public use:
- [`csv2he`](@ref). Convert a CSV file containing a matrix to the binary Helium format.
- [`writehe`](@ref). Write matrix in Helium format.
- [`readhe`](@ref). Read file in Heli... | [
37811,
198,
13383,
8265,
329,
4600,
12621,
1505,
13,
20362,
63,
1377,
257,
13934,
5794,
6260,
290,
9173,
329,
17593,
13,
198,
12510,
5499,
389,
29050,
422,
428,
8265,
329,
1171,
779,
25,
198,
12,
685,
63,
40664,
17,
258,
63,
16151,
... | 3.061135 | 229 |
# ------------------------------------------------------------------
# Licensed under the ISC License. See LICENSE in the project root.
# ------------------------------------------------------------------
"""
DomainView(domain, locations)
Return a view of `domain` at `locations`.
### Notes
This type implements ... | [
2,
16529,
438,
198,
2,
49962,
739,
262,
3180,
34,
13789,
13,
4091,
38559,
24290,
287,
262,
1628,
6808,
13,
198,
2,
16529,
438,
198,
198,
37811,
198,
220,
220,
220,
20021,
7680,
7,
27830,
11,
7064,
8,
198,
198,
13615,
257,
1570,
28... | 2.48 | 475 |
@init global hp = safe_pyimport("healpy")
function θϕ_to_xy((θ,ϕ))
r = 2cos(θ/2)
x = r*cos(ϕ)
y = -r*sin(ϕ)
x, y
end
function xy_to_θϕ((x,y))
r = sqrt(x^2+y^2)
θ = 2*acos(r/2)
ϕ = -atan(y,x)
θ, ϕ
end
function healpix_to_flat(healpix_map::Vector{T}, ::Type{P}; rot=(0,0,0)) where {Ns... | [
198,
31,
15003,
3298,
27673,
796,
3338,
62,
9078,
11748,
7203,
258,
282,
9078,
4943,
628,
198,
8818,
7377,
116,
139,
243,
62,
1462,
62,
5431,
19510,
138,
116,
11,
139,
243,
4008,
198,
220,
220,
220,
374,
796,
362,
6966,
7,
138,
11... | 1.863736 | 910 |
<reponame>ajozefiak/julia<filename>base/stat.jl
# This file is a part of Julia. License is MIT: https://julialang.org/license
# filesystem operations
export
ctime,
filemode,
filesize,
gperm,
isblockdev,
ischardev,
isdir,
isfifo,
isfile,
islink,
ismount,
ispath,
isse... | [
27,
7856,
261,
480,
29,
1228,
8590,
891,
32994,
14,
73,
43640,
27,
34345,
29,
8692,
14,
14269,
13,
20362,
198,
2,
770,
2393,
318,
257,
636,
286,
22300,
13,
13789,
318,
17168,
25,
3740,
1378,
73,
377,
498,
648,
13,
2398,
14,
43085,... | 2.259814 | 3,872 |
# most machines will be higher resolution than this, but we're playing it safe
const RESOLUTION = 1000 # 1 μs = 1000 ns
##############
# Parameters #
##############
type Parameters
seconds::Float64
samples::Int
evals::Int
overhead::Int
gctrial::Bool
gcsample::Bool
time_tolerance::Float64
... | [
2,
749,
8217,
481,
307,
2440,
6323,
621,
428,
11,
475,
356,
821,
2712,
340,
3338,
198,
9979,
15731,
3535,
35354,
796,
8576,
1303,
352,
18919,
82,
796,
8576,
36545,
198,
198,
7804,
4242,
2235,
198,
2,
40117,
1303,
198,
7804,
4242,
22... | 2.414244 | 1,376 |
# Various helper functions to calculate dimensions for operations
include("dim_helpers/ConvDims.jl")
include("dim_helpers/DenseConvDims.jl")
include("dim_helpers/DepthwiseConvDims.jl")
include("dim_helpers/PoolDims.jl")
"""
transpose_swapbatch(x::AbstractArray)
Given an AbstractArray, swap its batch and channel ... | [
2,
26386,
31904,
5499,
284,
15284,
15225,
329,
4560,
198,
17256,
7203,
27740,
62,
16794,
364,
14,
3103,
85,
35,
12078,
13,
20362,
4943,
198,
17256,
7203,
27740,
62,
16794,
364,
14,
35,
1072,
3103,
85,
35,
12078,
13,
20362,
4943,
198,
... | 2.578975 | 1,874 |
<gh_stars>100-1000
# Note that this script can accept some limited command-line arguments, run
# `julia build_tarballs.jl --help` to see a usage message.
using BinaryBuilder, Pkg
name = "SDL2_mixer"
version = v"2.0.4"
# Collection of sources required to complete build
sources = [
"http://www.libsdl.org/projects/S... | [
27,
456,
62,
30783,
29,
3064,
12,
12825,
198,
2,
5740,
326,
428,
4226,
460,
2453,
617,
3614,
3141,
12,
1370,
7159,
11,
1057,
198,
2,
4600,
73,
43640,
1382,
62,
18870,
21591,
13,
20362,
1377,
16794,
63,
284,
766,
257,
8748,
3275,
1... | 2.525298 | 672 |
<reponame>caodi/julia<filename>base/sparse/cholmod.jl
# This file is a part of Julia. License is MIT: https://julialang.org/license
module CHOLMOD
import Base: (*), convert, copy, eltype, get, getindex, show, size,
IndexStyle, IndexLinear, IndexCartesian, ctranspose
import Base.LinAlg: (\), A_mul_Bc, A_... | [
27,
7856,
261,
480,
29,
6888,
23130,
14,
73,
43640,
27,
34345,
29,
8692,
14,
82,
29572,
14,
354,
349,
4666,
13,
20362,
198,
2,
770,
2393,
318,
257,
636,
286,
22300,
13,
13789,
318,
17168,
25,
3740,
1378,
73,
377,
498,
648,
13,
2... | 2.148653 | 28,886 |
<reponame>JuliaBinaryWrappers/Trilinos_jll.jl
# Autogenerated wrapper script for Trilinos_jll for x86_64-w64-mingw32-libgfortran3-cxx03
export libamesos, libaztecoo, libbelos, libbelosepetra, libepetra, libepetraext, libifpack, libisorropia, libloca, liblocaepetra, liblocalapack, libnox, libnoxepetra, libnoxlapack, lib... | [
27,
7856,
261,
480,
29,
16980,
544,
33,
3219,
36918,
11799,
14,
2898,
346,
11996,
62,
73,
297,
13,
20362,
198,
2,
5231,
519,
877,
515,
29908,
4226,
329,
833,
346,
11996,
62,
73,
297,
329,
2124,
4521,
62,
2414,
12,
86,
2414,
12,
... | 2.046432 | 2,929 |
<reponame>oysteinsolheim/GraphNeuralNetworks.jl
# An example of graph classification
using Flux
using Flux:onecold, onehotbatch
using Flux.Losses: logitbinarycrossentropy
using Flux.Data: DataLoader
using GraphNeuralNetworks
using MLDatasets: TUDataset
using Statistics, Random
using CUDA
CUDA.allowscalar(false)
funct... | [
27,
7856,
261,
480,
29,
726,
4169,
1040,
349,
9096,
14,
37065,
8199,
1523,
7934,
5225,
13,
20362,
198,
2,
1052,
1672,
286,
4823,
17923,
198,
198,
3500,
1610,
2821,
198,
3500,
1610,
2821,
25,
505,
36673,
11,
530,
8940,
43501,
198,
35... | 2.089474 | 1,710 |
# This file is a part of Julia. License is MIT: https://julialang.org/license
## low-level pcre2 interface ##
module PCRE
include(string(length(Core.ARGS) >= 2 ? Core.ARGS[2] : "", "pcre_h.jl")) # include($BUILDROOT/base/pcre_h.jl)
const PCRE_LIB = "libpcre2-8"
const JIT_STACK = Ref{Ptr{Void}}(C_NULL)
const MATCH... | [
2,
770,
2393,
318,
257,
636,
286,
22300,
13,
13789,
318,
17168,
25,
3740,
1378,
73,
377,
498,
648,
13,
2398,
14,
43085,
198,
198,
2235,
1877,
12,
5715,
279,
7513,
17,
7071,
22492,
198,
198,
21412,
4217,
2200,
198,
198,
17256,
7,
8... | 1.981308 | 3,210 |
<reponame>ma-laforge/CMDimCircuits.jl
#demo_snp_rw.jl: sNp (Touchstone) file tests
#-------------------------------------------------------------------------------
using CMDimCircuits
CMDimCircuits.@using_CData()
#Get a demo display:
include(CMDimCircuits.demoplotcfgscript)
#Normally use something like:
#CMDimData.@i... | [
27,
7856,
261,
480,
29,
2611,
12,
5031,
30293,
14,
34,
12740,
320,
31560,
15379,
13,
20362,
198,
2,
9536,
78,
62,
16184,
79,
62,
31653,
13,
20362,
25,
264,
45,
79,
357,
35211,
6440,
8,
2393,
5254,
198,
2,
10097,
24305,
198,
198,
... | 2.459704 | 1,216 |
<reponame>TimoLarson/julia
# This file is a part of Julia. License is MIT: https://julialang.org/license
"""
MultiSelectMenu
A menu that allows a user to select a multiple options from a list.
# Sample Output
```julia
julia> request(MultiSelectMenu(options))
Select the fruits you like:
[press: d=done, a=all, n... | [
27,
7856,
261,
480,
29,
14967,
78,
43,
12613,
14,
73,
43640,
198,
2,
770,
2393,
318,
257,
636,
286,
22300,
13,
13789,
318,
17168,
25,
3740,
1378,
73,
377,
498,
648,
13,
2398,
14,
43085,
198,
198,
37811,
628,
220,
220,
220,
15237,
... | 2.819751 | 1,448 |
module STMOZOO
# execute your source file and export the module you made
include("example.jl")
export Example
end # module
| [
21412,
3563,
11770,
57,
6684,
628,
198,
2,
12260,
534,
2723,
2393,
290,
10784,
262,
8265,
345,
925,
198,
17256,
7203,
20688,
13,
20362,
4943,
198,
39344,
17934,
198,
198,
437,
1303,
8265,
198
] | 3.705882 | 34 |
export dimension
mutable struct ModAlgAss{S, T, V}
base_ring::S
action::Vector{T}
dimension::Int
M::AbstractAlgebra.FPModule{V}
isirreducible::Int
dimension_splitting_field::Int
algebra::AlgAss{V}
action_of_gens::Vector{T}
action_of_basis::Vector{T}
function ModAlgAss{S, T}(action::Vector{T}) wher... | [
39344,
15793,
198,
198,
76,
18187,
2878,
3401,
2348,
70,
8021,
90,
50,
11,
309,
11,
569,
92,
198,
220,
2779,
62,
1806,
3712,
50,
198,
220,
2223,
3712,
38469,
90,
51,
92,
198,
220,
15793,
3712,
5317,
198,
220,
337,
3712,
23839,
234... | 2.084882 | 1,991 |
<reponame>gdalle/PointProcesses.jl<filename>test/models.jl
@testset verbose = true "Models" begin
@testset "Poisson" begin
mark_dist = Dists.Categorical([0.1, 0.3, 0.6])
pp = PoissonProcess(5.0, mark_dist)
h = rand(pp, 0.0, 100.0)
pp_est = fit(PoissonProcess{Dists.Categorical}, h)
... | [
27,
7856,
261,
480,
29,
21287,
6765,
14,
12727,
18709,
274,
13,
20362,
27,
34345,
29,
9288,
14,
27530,
13,
20362,
198,
31,
9288,
2617,
15942,
577,
796,
2081,
366,
5841,
1424,
1,
2221,
198,
220,
220,
220,
2488,
9288,
2617,
366,
18833... | 2.14554 | 213 |
<reponame>dinarior/NearestNeighbors.jl
# A BallTree (also called Metric tree) is a tree that is created
# from successively splitting points into surrounding hyper spheres
# which radius are determined from the given metric.
# The tree uses the triangle inequality to prune the search space
# when finding the neighbors ... | [
27,
7856,
261,
480,
29,
67,
22050,
1504,
14,
8199,
12423,
46445,
32289,
13,
20362,
198,
2,
317,
6932,
27660,
357,
14508,
1444,
3395,
1173,
5509,
8,
318,
257,
5509,
326,
318,
2727,
198,
2,
422,
1943,
2280,
26021,
2173,
656,
7346,
871... | 2.197446 | 4,072 |
<gh_stars>1-10
using Weber
using Base.Test
rng() = MersenneTwister(1983)
@testset "Oddball Design" begin
pattern = oddball_paradigm(identity,20,150,rng=rng())
@test sum(pattern) == 20
@test length(pattern) == 170
@test !any(pattern[2:end] .& (pattern[2:end] .== pattern[1:(end-1)]))
@test any(pattern[3:end]... | [
27,
456,
62,
30783,
29,
16,
12,
940,
198,
3500,
28137,
198,
3500,
7308,
13,
14402,
198,
198,
81,
782,
3419,
796,
337,
364,
29727,
5080,
1694,
7,
29279,
8,
628,
198,
31,
9288,
2617,
366,
46,
1860,
1894,
8495,
1,
2221,
198,
220,
3... | 2.331671 | 401 |
using Test
using MathOptInterface
const MOI = MathOptInterface
const MOIT = MathOptInterface.Test
const MOIU = MathOptInterface.Utilities
const MOIB = MathOptInterface.Bridges
include("../utilities.jl")
mock = MOIU.MockOptimizer(MOIU.Model{Float64}())
config = MOIT.TestConfig()
bridged_mock = MOIB.Variable.RSOCtoSO... | [
3500,
6208,
198,
198,
3500,
16320,
27871,
39317,
198,
9979,
13070,
40,
796,
16320,
27871,
39317,
198,
9979,
13070,
2043,
796,
16320,
27871,
39317,
13,
14402,
198,
9979,
13070,
44958,
796,
16320,
27871,
39317,
13,
18274,
2410,
198,
9979,
1... | 2.030178 | 1,458 |
module SingleLayerQG
export
Problem,
set_q!,
updatevars!,
energy,
kinetic_energy,
potential_energy,
energy_dissipation,
energy_work,
energy_drag,
enstrophy,
enstrophy_dissipation,
enstrophy_work,
enstrophy_drag
using
CUDA,
Reexport,
DocStringExtensions
@reexport using FourierFlows
u... | [
21412,
14206,
49925,
48,
38,
198,
198,
39344,
198,
220,
20647,
11,
198,
220,
900,
62,
80,
28265,
198,
220,
4296,
85,
945,
28265,
628,
220,
2568,
11,
198,
220,
37892,
62,
22554,
11,
198,
220,
2785,
62,
22554,
11,
198,
220,
2568,
62... | 2.314881 | 8,689 |
# TODO: User supplied elemtype??
ValueInfoProto(name::String, inshape, elemtype=Float32) =
ValueInfoProto(
name=name,
_type=TypeProto(
tensor_type=TypeProto_Tensor(inshape, elemtype)
)
)
TypeProto_Tensor(inshape, elemtype) = TypeProto_Tensor(
elem_type=tp_tensor_elemtype(elemtype),
shape=T... | [
198,
2,
16926,
46,
25,
11787,
14275,
9766,
76,
4906,
3548,
198,
11395,
12360,
2964,
1462,
7,
3672,
3712,
10100,
11,
1035,
71,
1758,
11,
9766,
76,
4906,
28,
43879,
2624,
8,
796,
198,
11395,
12360,
2964,
1462,
7,
198,
220,
220,
220,
... | 2.383006 | 1,577 |
"""
$(TYPEDEF)
A system of difference equations.
# Fields
$(FIELDS)
# Example
```
using ModelingToolkit
@parameters σ ρ β
@variables t x(t) y(t) z(t) next_x(t) next_y(t) next_z(t)
eqs = [next_x ~ σ*(y-x),
next_y ~ x*(ρ-z)-y,
next_z ~ x*y - β*z]
de = DiscreteSystem(eqs,t,[x,y,z],[σ,ρ,β])
```
"""
str... | [
37811,
198,
3,
7,
9936,
47,
1961,
25425,
8,
198,
198,
32,
1080,
286,
3580,
27490,
13,
198,
198,
2,
23948,
198,
3,
7,
11674,
3698,
5258,
8,
198,
198,
2,
17934,
198,
198,
15506,
63,
198,
3500,
9104,
278,
25391,
15813,
198,
198,
31... | 2.178001 | 2,882 |
function WeightingOp(in,adj;w=1)
return in.*w
end
function WeightingOp(m::AbstractString,d::AbstractString,adj;w="NULL")
if (adj==true)
d1,h1,e1 = SeisRead(d)
d2,h2,e2 = SeisRead(w)
SeisWrite(m,d1[:,:].*d2[:,:],h1,e1)
else
d1,h1,e1 = SeisRead(m)
d2,h2,e2 = SeisRead(w)
SeisWrite(d,d1[:,:].*d2[:,:],h1,... | [
8818,
14331,
278,
18257,
7,
259,
11,
41255,
26,
86,
28,
16,
8,
628,
197,
7783,
287,
15885,
86,
198,
198,
437,
198,
198,
8818,
14331,
278,
18257,
7,
76,
3712,
23839,
10100,
11,
67,
3712,
23839,
10100,
11,
41255,
26,
86,
2625,
33991... | 1.853125 | 320 |
export OfflinePolicy, JuliaRLTransition, gen_JuliaRL_dataset
export calculate_CQL_loss, maximum_mean_discrepancy_loss
struct JuliaRLTransition
state
action
reward
terminal
next_state
end
Base.@kwdef struct OfflinePolicy{L,T} <: AbstractPolicy
learner::L
dataset::T
continuous::Bool
... | [
39344,
49749,
36727,
11,
22300,
7836,
8291,
653,
11,
2429,
62,
16980,
544,
7836,
62,
19608,
292,
316,
198,
198,
39344,
15284,
62,
34,
9711,
62,
22462,
11,
5415,
62,
32604,
62,
15410,
7856,
3883,
62,
22462,
198,
198,
7249,
22300,
7836,... | 2.268692 | 2,140 |
println("F1) Tests of the semi-empirical computation of ADK rates.")
@warn("\n\n !!! This example does not work properly at present !!! \n\n")
#
wa = 1
| [
198,
35235,
7203,
37,
16,
8,
30307,
286,
262,
10663,
12,
368,
4063,
605,
29964,
286,
5984,
42,
3965,
19570,
198,
198,
31,
40539,
7203,
59,
77,
59,
77,
220,
10185,
770,
1672,
857,
407,
670,
6105,
379,
1944,
220,
10185,
3467,
77,
59... | 2.924528 | 53 |
export build_wire_coordinates, get_minimum_intersection_distance
function build_wire_coordinates(wire_directions)
path = []
current_x = 0
current_y = 0
dx = Dict('L'=> -1, 'R'=> 1, 'U'=> 0, 'D'=> 0)
dy = Dict('L'=> 0, 'R'=> 0, 'U'=> 1, 'D'=> -1)
for direction_instruction in wire_directions
... | [
39344,
1382,
62,
21809,
62,
37652,
17540,
11,
651,
62,
39504,
62,
3849,
5458,
62,
30246,
628,
198,
8818,
1382,
62,
21809,
62,
37652,
17540,
7,
21809,
62,
12942,
507,
8,
198,
220,
220,
220,
3108,
796,
17635,
198,
220,
220,
220,
1459,... | 2.366957 | 575 |
@testset "execution" begin
############################################################################################
dummy() = return
@testset "@cuda" begin
@test_throws UndefVarError @cuda undefined()
@test_throws MethodError @cuda dummy(1)
@testset "low-level interface" begin
k = cufunction(dummy)
k(... | [
31,
9288,
2617,
366,
18558,
1009,
1,
2221,
198,
198,
29113,
29113,
14468,
7804,
4242,
198,
198,
67,
13513,
3419,
796,
1441,
198,
198,
31,
9288,
2617,
44212,
66,
15339,
1,
2221,
198,
198,
31,
9288,
62,
400,
8516,
13794,
891,
19852,
1... | 2.176948 | 11,461 |
"""
Provides functionality for working with HTTP headers in Genie.
"""
module Headers
import Revise, HTTP
import Genie
"""
set_headers!(req::HTTP.Request, res::HTTP.Response, app_response::HTTP.Response) :: HTTP.Response
Configures the response headers.
"""
function set_headers!(req::HTTP.Request, res::HTTP.Resp... | [
37811,
198,
15946,
1460,
11244,
329,
1762,
351,
14626,
24697,
287,
49405,
13,
198,
37811,
198,
21412,
7123,
364,
198,
198,
11748,
5416,
786,
11,
14626,
198,
11748,
49405,
198,
198,
37811,
198,
220,
220,
220,
900,
62,
50145,
0,
7,
4218... | 2.509855 | 761 |
export interest, rate
"""
interest(amount, rate)
Calculate interest from an ;amout; and interest rate of 'rate'.
"""
function interest(amount, rate)
return amount * (1 + rate)
end
"""
rate(amount, interest)
Calculate interest rate based on an 'amount' and 'interest'.
"""
function rate(amount, intere... | [
198,
198,
39344,
1393,
11,
2494,
198,
198,
37811,
198,
220,
220,
220,
1393,
7,
17287,
11,
2494,
8,
198,
198,
9771,
3129,
378,
1393,
422,
281,
2162,
321,
448,
26,
290,
1393,
2494,
286,
705,
4873,
4458,
198,
37811,
198,
8818,
1393,
... | 2.976471 | 170 |
using LinearAlgebra
struct ValueOne; end
ValueOne()
# Compute X <- a X + b I.
function matfun_axpby!(X,a,b,Y::UniformScaling)
m,n=size(X)
if ~(a isa ValueOne)
rmul!(X,a)
end
@inbounds for i=1:n
X[i,i]+=(b isa ValueOne) ? 1 : b
end
end
# Compute X <- a X + b Y.
function matfun_axp... | [
3500,
44800,
2348,
29230,
198,
198,
7249,
11052,
3198,
26,
886,
198,
11395,
3198,
3419,
198,
198,
2,
3082,
1133,
1395,
24293,
257,
1395,
1343,
275,
314,
13,
198,
8818,
2603,
12543,
62,
897,
79,
1525,
0,
7,
55,
11,
64,
11,
65,
11,
... | 1.937326 | 1,436 |
<reponame>000Justin000/IsingLite.jl
randspin() = [1,-1][rand(1:2)] # Generate a random spin
spingrid(n::Int) = [randspin() for i in 1:n, j in 1:n] # Generate a random spin array
magnetization(a::Array{Int, 2}) = mean(a) |> abs # Get magnetizations of the gr... | [
27,
7856,
261,
480,
29,
830,
33229,
830,
14,
3792,
278,
43,
578,
13,
20362,
198,
81,
1746,
11635,
3419,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,... | 2.082405 | 1,347 |
<filename>src/interpreter.jl
"""
runme(code::String = ",.", tapelen = 10000, a)
The interpreter of the HackMoji language.
# Arguments:
- `code`: Your HackMoji code given as a string
- `tapelen`: Your HackMoji memory given as number of bytes
"""
function runme(code::String=",.", tapelen=10000)
data = fill(UInt... | [
27,
34345,
29,
10677,
14,
3849,
3866,
353,
13,
20362,
198,
37811,
198,
220,
220,
220,
1057,
1326,
7,
8189,
3712,
10100,
796,
33172,
33283,
9814,
417,
268,
796,
33028,
11,
257,
8,
198,
198,
464,
28846,
286,
262,
18281,
16632,
7285,
3... | 2.024643 | 771 |
<gh_stars>1-10
import Observables
import AbstractPlotting
q1 = Biquaternion(Quaternion(rand(4)), ℝ³(rand(3)))
scene = AbstractPlotting.Scene()
radius = rand()
segments = rand(5:10)
color = AbstractPlotting.RGBAf0(rand(4)...)
transparency = false
sphere = Sphere(q1,
scene,
radius = radi... | [
27,
456,
62,
30783,
29,
16,
12,
940,
198,
11748,
19243,
2977,
198,
11748,
27741,
43328,
889,
628,
198,
80,
16,
796,
347,
1557,
9205,
295,
7,
4507,
9205,
295,
7,
25192,
7,
19,
36911,
2343,
226,
251,
126,
111,
7,
25192,
7,
18,
223... | 2.247525 | 303 |
<gh_stars>0
"""
elapsed_time(res, i)
Elapsed time in seconds for i-th picture
"""
elapsed_time(res::ExpImgsResults, i)::Float64 =
(Dates.Second(res.time[i] - res.exi.experiment_start)).value
"""
get_time_series(res::ExpImgsResults, tt)
get_time_series(res::ExpImgsResults)
tt in seconds since ex.exper... | [
27,
456,
62,
30783,
29,
15,
198,
37811,
198,
220,
220,
220,
42118,
62,
2435,
7,
411,
11,
1312,
8,
198,
198,
9527,
28361,
640,
287,
4201,
329,
1312,
12,
400,
4286,
198,
37811,
198,
417,
28361,
62,
2435,
7,
411,
3712,
16870,
3546,
... | 2.033193 | 5,212 |
<reponame>UnofficialJuliaMirror/DistributedFactorGraphs.jl-b5cc3c7e-6572-11e9-2517-99fb8daf2f04<gh_stars>10-100
# mutable struct FileDFG
# folderName::String
# FileDFG(folderName::String)::FileDFG = new(foldername)
# end
| [
27,
7856,
261,
480,
29,
3118,
16841,
16980,
544,
27453,
1472,
14,
20344,
6169,
41384,
37065,
82,
13,
20362,
12,
65,
20,
535,
18,
66,
22,
68,
12,
2996,
4761,
12,
1157,
68,
24,
12,
1495,
1558,
12,
2079,
21855,
23,
67,
1878,
17,
69... | 2.254902 | 102 |
<reponame>sethaxen/StatsMakie.jl<filename>src/StatsMakie.jl<gh_stars>0
module StatsMakie
using Observables
using AbstractPlotting
import AbstractPlotting: convert_arguments, used_attributes, plot!, combine, to_plotspec
using AbstractPlotting: plottype, Plot, PlotFunc, to_tuple
using AbstractPlotting: node_pairs, extre... | [
27,
7856,
261,
480,
29,
2617,
71,
897,
268,
14,
29668,
44,
461,
494,
13,
20362,
27,
34345,
29,
10677,
14,
29668,
44,
461,
494,
13,
20362,
27,
456,
62,
30783,
29,
15,
198,
21412,
20595,
44,
461,
494,
198,
198,
3500,
19243,
2977,
... | 3.071429 | 504 |
using Oceananigans.Solvers
using Oceananigans.Operators
using Oceananigans.ImmersedBoundaries: ImmersedBoundaryGrid, GridFittedBottom
using Oceananigans.Architectures
using Oceananigans.Grids: with_halo, isrectilinear
using Oceananigans.Fields: Field, ZReducedField
using Oceananigans.Architectures: device
import Ocean... | [
3500,
10692,
272,
34090,
13,
36949,
690,
198,
3500,
10692,
272,
34090,
13,
18843,
2024,
198,
3500,
10692,
272,
34090,
13,
24675,
20204,
49646,
3166,
25,
9543,
20204,
49646,
560,
41339,
11,
24846,
37,
2175,
34104,
198,
3500,
10692,
272,
... | 1.787421 | 6,614 |
module TestAlign
if VERSION >= v"0.5-"
using Base.Test
else
using BaseTestNext
const Test = BaseTestNext
end
using Bio
using Bio.Seq
using Bio.Align
using TestFunctions
# Generate a random valid alignment of a sequence of length n against a sequence
# of length m. If `glob` is true, generate a global al... | [
21412,
6208,
2348,
570,
198,
198,
361,
44156,
2849,
18189,
410,
1,
15,
13,
20,
21215,
198,
220,
220,
220,
1262,
7308,
13,
14402,
198,
17772,
198,
220,
220,
220,
1262,
7308,
14402,
10019,
198,
220,
220,
220,
1500,
6208,
796,
7308,
14... | 1.765897 | 15,066 |
<filename>test/runtests.jl
using Test
using Symbolics
using ForwardDiff
using LinearAlgebra
using SparseArrays
using DirectTrajectoryOptimization
const DTO = DirectTrajectoryOptimization
include("objective.jl")
include("dynamics.jl")
include("constraints.jl")
include("hessian_lagrangian.jl")
include("solve.jl") | [
27,
34345,
29,
9288,
14,
81,
2797,
3558,
13,
20362,
198,
3500,
6208,
198,
3500,
41327,
19615,
198,
3500,
19530,
28813,
198,
3500,
44800,
2348,
29230,
198,
3500,
1338,
17208,
3163,
20477,
198,
3500,
4128,
15721,
752,
652,
27871,
320,
163... | 3.13 | 100 |
<filename>src/Normal.jl
struct Normal{T}
μ::T
σ::T
function Normal(m,s)
μ,σ = promote(m,s)
T = typeof(μ)
new{T}(μ, σ)
end
end
Normal(;μ,σ) = Normal(μ,σ)
Base.show(io::IO, o::Normal) = print(io, "Normal(μ=$(o.μ), σ=$(o.σ))")
struct NormalRegressor end
function (o::NormalRegresso... | [
27,
34345,
29,
10677,
14,
26447,
13,
20362,
198,
7249,
14435,
90,
51,
92,
198,
220,
220,
220,
18919,
3712,
51,
198,
220,
220,
220,
18074,
225,
3712,
51,
198,
220,
220,
220,
2163,
14435,
7,
76,
11,
82,
8,
198,
220,
220,
220,
220,... | 2.020243 | 494 |
<filename>Journals/jl/NucleiCytoo_GradWaterTilesMerge.jl<gh_stars>10-100
InputFolder = './Images/NucleiCytoo/';
OutputFolder ='./Results/Images/NucleiCytoo/';
Fill = 1;
Lbl = 1;
@iA = '*C00*.tif';
@fxg_mGradWaterTiles [iA] > [L];
params.GaussianRadInt = 2;
params.ExtendedMinThr = 2;
/endf
@fxm_lTilesMerge [L, iA] > ... | [
27,
34345,
29,
41,
18408,
14,
20362,
14,
45,
14913,
72,
20418,
18820,
62,
42731,
19184,
51,
2915,
13102,
469,
13,
20362,
27,
456,
62,
30783,
29,
940,
12,
3064,
198,
20560,
41092,
796,
705,
19571,
29398,
14,
45,
14913,
72,
20418,
188... | 2.154185 | 227 |
<gh_stars>1-10
# This file is a part of Julia. License is MIT: https://julialang.org/license
struct BatchProcessingError <: Exception
data
ex
end
"""
pgenerate([::WorkerPool], f, c...) -> iterator
Apply `f` to each element of `c` in parallel using available workers and tasks.
For multiple collection arg... | [
27,
456,
62,
30783,
29,
16,
12,
940,
198,
2,
770,
2393,
318,
257,
636,
286,
22300,
13,
13789,
318,
17168,
25,
3740,
1378,
73,
377,
498,
648,
13,
2398,
14,
43085,
198,
198,
7249,
347,
963,
18709,
278,
12331,
1279,
25,
35528,
198,
... | 2.504171 | 4,076 |
export flattened_tup_chain
flattened_tup_chain(::Type{NamedTuple{(), Tuple{}}}; prefix = (Symbol(),)) = ()
flattened_tup_chain(::Type{T}; prefix = (Symbol(),)) where {T <: Real} =
(prefix,)
flattened_tup_chain(::Type{T}; prefix = (Symbol(),)) where {T <: SArray} =
(prefix,)
flattened_tup_chain(
::Type{T};
... | [
39344,
45096,
62,
83,
929,
62,
7983,
198,
198,
2704,
1078,
2945,
62,
83,
929,
62,
7983,
7,
3712,
6030,
90,
45,
2434,
51,
29291,
90,
22784,
309,
29291,
90,
11709,
19629,
21231,
796,
357,
13940,
23650,
22784,
4008,
796,
7499,
198,
270... | 2.300226 | 443 |
<gh_stars>0
function add_ref_el(el::XMLElement, param::MyXMLElement)
ref_el = new_element(name(param))
set_attribute(ref_el, bn.IDREF, get_id(param))
add_child(el, ref_el)
end
################################################################################
## Parameter
##################################... | [
27,
456,
62,
30783,
29,
15,
198,
8818,
751,
62,
5420,
62,
417,
7,
417,
3712,
37643,
2538,
1732,
11,
5772,
3712,
3666,
37643,
2538,
1732,
8,
198,
220,
220,
220,
1006,
62,
417,
796,
649,
62,
30854,
7,
3672,
7,
17143,
4008,
198,
22... | 2.72955 | 2,714 |
function resume_simulation_from_file(
input_file::String ,
output_file::String ;
t_end::Float64 = 0.0
)
if(t_end == 0)
println("ERROR: please specify a final time by setting 't_end' argument")
return
end
p0 = Param(
resume_simulation = :true,
... | [
8818,
15294,
62,
14323,
1741,
62,
6738,
62,
7753,
7,
198,
220,
220,
220,
5128,
62,
7753,
3712,
10100,
220,
837,
220,
198,
220,
220,
220,
5072,
62,
7753,
3712,
10100,
2162,
220,
198,
220,
220,
220,
256,
62,
437,
3712,
43879,
2414,
... | 1.964567 | 254 |
#import MAGMA
using MAGMA
#using MAGMA:gesvd!
using MAGMA: MagmaAllVec, gesvd!, libmagma
### some JuliaGPU packages, maybe useful (who knows)
using CUDAdrv
using CUDAapi
using CUDAnative
using CuArrays
#
using Test, LinearAlgebra
matrixToTest = rand(Float64, 2, 2)
right_answer = svd(matrixToTest).S
S = right_ans... | [
2,
11748,
28263,
5673,
198,
3500,
28263,
5673,
198,
198,
2,
3500,
28263,
5673,
25,
3212,
20306,
0,
198,
3500,
28263,
5673,
25,
2944,
2611,
3237,
53,
721,
11,
308,
274,
20306,
28265,
9195,
19726,
2611,
628,
198,
198,
21017,
617,
22300,... | 2.369565 | 230 |
<filename>test/FESpacesTests/SparseMatrixAssemblersTests.jl
module SparseMatrixAssemblers
using Test
using Gridap.Arrays
using Gridap.TensorValues
using Gridap.ReferenceFEs
using Gridap.Geometry
using Gridap.Fields
using Gridap.Algebra
using SparseArrays
using SparseMatricesCSR
using Gridap.FESpaces
using Gridap.CellD... | [
27,
34345,
29,
9288,
14,
37,
1546,
43076,
51,
3558,
14,
50,
29572,
46912,
1722,
4428,
8116,
51,
3558,
13,
20362,
198,
21412,
1338,
17208,
46912,
1722,
4428,
8116,
198,
198,
3500,
6208,
198,
3500,
24846,
499,
13,
3163,
20477,
198,
3500... | 2.276316 | 2,584 |
using PowerSystems
using NLsolve
const PSY = PowerSystems
############### Data Network ########################
include(joinpath(dirname(@__FILE__), "dynamic_test_data.jl"))
include(joinpath(dirname(@__FILE__), "data_utils.jl"))
############### Data Network ########################
threebus_file_dir = joinpath(dirname... | [
3500,
4333,
11964,
82,
198,
3500,
22879,
82,
6442,
198,
9979,
6599,
56,
796,
4333,
11964,
82,
198,
198,
7804,
4242,
21017,
6060,
7311,
1303,
14468,
4242,
21017,
198,
17256,
7,
22179,
6978,
7,
15908,
3672,
7,
31,
834,
25664,
834,
828,
... | 2.336815 | 766 |
<gh_stars>0
"""
Runs the atkinson94 algorithm and additionally plots the Stalactite text plot as described in the paper. Works for data with less than 100 points at the moment (since screen width of 80-120 is the standard).
# References
Atkinson, <NAME>. "Fast very robust methods for the detection of multiple outliers... | [
27,
456,
62,
30783,
29,
15,
198,
37811,
198,
10987,
82,
262,
379,
26030,
5824,
11862,
290,
36527,
21528,
262,
520,
282,
529,
578,
2420,
7110,
355,
3417,
287,
262,
3348,
13,
10933,
329,
1366,
351,
1342,
621,
1802,
2173,
379,
262,
258... | 2.214286 | 840 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.