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// Copyright (C) 2016-2026, by Arseny Kapoulkine (arseny.kapoulkine@gmail.com)
var MeshoptSimplifier = (function () {
// Built with clang version 22.1.0-wasi-sdk
// Built from meshoptimizer 1.3
var wasm =
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const GraphQLJSON = require('graphql-type-json');
const axios = require('axios');
const { get, keys } = require('lodash');
const Issue = require('github-api/dist/components/Issue');
const Repository = require('github-api/dist/components/Repository');
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717579f10532282db80b6dc7bba7da52f7e6a195683d9ca77eab234ca48d05fe | JavaScript | 72,302 | 1,910 | /**************************
* Dnn_noise_naming Experiment *
JS code created with PsychoPy Builder and edited manually
Copyright (c) Laurent Caplette 2021
**************************/
import { PsychoJS } from './lib_bugfix/core-2020.1.js';
import * as core from './lib_bugfix/core-2020.1.js';
import { TrialHandler }... |
1f9d37c2c285c85ce0795c79d481fb09eddd511aa101b16d615bb0db0e727354 | JavaScript | 72,539 | 1,911 | /**************************
* Dnn_noise_naming Experiment *
JS code created with PsychoPy Builder and edited manually
Copyright (c) Laurent Caplette 2021
**************************/
import { PsychoJS } from './lib_bugfix/core-2020.1.js';
import * as core from './lib_bugfix/core-2020.1.js';
import { TrialHandler }... |
562ea631747f1402ac8af5de88968e7656d740b5e9e27008328576d1c2f45974 | JavaScript | 78,129 | 2,627 | /**
* jspsych.js
* Josh de Leeuw
*
* documentation: docs.jspsych.org
*
**/
window.jsPsych = (function() {
var core = {};
//
// private variables
//
// options
var opts = {};
// experiment timeline
var timeline;
// flow control
var global_trial_index = 0;
var current_trial = {};
var curr... |
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760c8a0141521e835cf21361d19c1b0d44749d344d2891bc494376ca26d42ac7 | JavaScript | 87,695 | 1 | Search.setIndex({docnames:["Tutorials-reference","Tutorials/Tutorial-1-Overview-of-Workflow","Tutorials/Tutorial-2-Organization-of-data-in-package-objects","Tutorials/Tutorial-3-plotting-module","Tutorials/Tutorial-4-nwb-conversion","Tutorials/Tutorial-5-suite2p-module","Tutorials/Tutorial-6-anndata-module","Tutorials/... |
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* Jssor 19.0
* http://www.jssor.com/
*
* Licensed under the MIT license:
* http://www.opensource.org/licenses/MIT
*
* TERMS OF USE - Jssor
*
* Copyright 2014 Jssor
*
* Permission is hereby granted, free of charge, to any person obtaining
* a copy of this software and associated documentation files (the... |
24fedbee71a3af9067ee92144c8e607e1940477146436fa2b0047fdd100d923f | JavaScript | 107,057 | 2 | /*! For license information please see docsearch.311603f71f0cc9f1b649.js.LICENSE.txt */
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const { ObjectID } = require('mongodb');
const database = require('../src/database');
const helperFunctions = require('../src/auth-helpers');
const brainageFNC = require('./data/coinstac-brainage-fnc.json');
const combatDC = require('./data/coinstac-combat-dc.json');
const dlmeFS = requir... |
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b5aea0892716d03db1c5fdd059e4d5a9660eb8ebc54d8f83765f1d3335a21c6a | JavaScript | 117,487 | 4,426 | export const EQTL_SIGNIFICANT_SNPS = {
"PTM-glia": [
{
fdr: 1.3882915685078614,
trait: "glia-caQTLs",
score: -0.15065241,
TF_impact: "none",
snp: "rs17444771",
coordinates: { chromosome: "chr1", position: 222156232 },
},
{
fdr: 0.2625991190832032,
trait: "ne... |
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* Combined by jsDelivr.
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* - /npm/markmap-view@0.14.4/dist/index.min.js
*
* Do NOT use SRI with dynamically generated files! More information: https://www.jsdelivr.com/using-sri-with-dynamic-files
*/
/*! markmap-lib v0.14.4 | MIT License ... |
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* Select2 4.0.3
* https://select2.github.io
*
* Released under the MIT license
* https://github.com/select2/select2/blob/master/LICENSE.md
*/
(function (factory) {
if (typeof define === 'function' && define.amd) {
// AMD. Register as an anonymous module.
define(['jquery'], factory);
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6cc9f77ac57bbb0146a5518217df2dd8aae6088d67098285c2edcceca142c8c6 | JavaScript | 170,767 | 4,107 | /// <reference path="Jssor.js" />
/*
* Jssor.Slider 19.0
* http://www.jssor.com/
*
* Licensed under the MIT license:
* http://www.opensource.org/licenses/MIT
*
* TERMS OF USE - Jssor.Slider
*
* Copyright 2014 Jssor
*
* Permission is hereby granted, free of charge, to any person obtaining
* a copy of th... |
0ec5982a8577814528606e3ffa5f090b45a709c89792a51d4f75d76089b2c340 | JavaScript | 200,000 | 1 | jsme.onScriptDownloaded(['var $wnd = $wnd || window.parent;var __gwtModuleFunction = $wnd.jsme;var $sendStats = __gwtModuleFunction.__sendStats;$sendStats(\'moduleStartup\', \'moduleEvalStart\');var $gwt_version = "2.5.1";var $strongName = \'97E315A2BEBFD9BADE705633631BC068\';var $doc = $wnd.document;function __gwtStar... |
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27c26d52b5b3486862d0991797a7aa897bd1bec2a31f02b4030b026c9449c194 | JavaScript | 200,000 | 7,594 | /*!
* jQuery JavaScript Library v2.2.3
* http://jquery.com/
*
* Includes Sizzle.js
* http://sizzlejs.com/
*
* Copyright jQuery Foundation and other contributors
* Released under the MIT license
* http://jquery.org/license
*
* Date: 2016-04-05T19:26Z
*/
(function( global, factory ) {
if ( typeof module ==... |
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3ba5c5c836739d9c8f73236b260121076b016a1754626466d2b5ab73ae7df6b4 | JavaScript | 200,000 | 7,641 | export const GWAS_SIGNIFICANT_SNPS = {
"PTM-glia": [
{
fdr: 1.0210143590303093,
trait: "ADHD-meta-filtered",
score: -0.0018630028,
TF_impact: "none",
snp: "rs76708321",
coordinates: { chromosome: "chr2", position: 10810992 },
},
{
fdr: 2.312070131102486,
tra... |
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"minRunLength": 3,
"maxRunLength": null,
"applyMinForLastRunLength": false,
"sessionIndex": [
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faa9a6e746cfa6da498bc8f1c82d1f8ab3ec427b5ac21cd7282a62b6af421baa | JavaScript | 200,004 | 2 | /*! For license information please see swagger-ui-es-bundle.js.LICENSE.txt */
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30ca51002e4cae82dedd6d1154560755618bb7b1322f0de1d31b7eb83750f5dc | Julia | 434 | 15 | module BayesianNetworkRegression
using Random, DataFrames, LinearAlgebra, StatsBase, InvertedIndices, ProgressMeter, Distributions
using StaticArrays, TypedTables, GaussianDistributions, Distributed, Statistics
using Base.Threads,KahanSummation
using Dates
using MCMCDiagnosticTools
include("gig... |
90db287401a2b64775321dee0cdcc1610c9842874e5d0a420f6681553c4d3ea7 | Julia | 648 | 21 | using DataFrames,CSV
include("../../old-src/utils.jl")
include("../../old-src/plot_output.jl")
function analyze_γ(simnum,casenum)
γ = upper_triangle(Matrix(DataFrame(CSV.File("juliacon/results/simulation$(simnum)_case$(casenum)_gammas.csv"))))
γ₀= DataFrame(CSV.File("juliacon/data/simulation$(simnum)_case$(c... |
53e31f2d6cad6aa1cf87d761b066162f74b66244d00fa05af81c08030ffc75aa | Julia | 649 | 29 | import InteractiveUtils: versioninfo
using BayesianNetworkRegression,Test,LinearAlgebra,Distributions
using CSV,DataFrames,StaticArrays,TypedTables,Random,Distributed,JLD2
println(versioninfo())
@static if VERSION ≥ v"1.7.0-DEV.620"
println(BLAS.get_config())
else
@show BLAS.vendor()
if startswith(string(B... |
99c34d7b095022dd6f35be6eda1906aed5dec3ac512594b186571d7cd56757e4 | Julia | 763 | 24 | using Documenter, BayesianNetworkRegression
makedocs(
sitename="BayesianNetworkRegression.jl",
authors="Samuel Ozminkowski, Claudia Solís-Lemus, and contributors",
modules=[BayesianNetworkRegression],
format = Documenter.HTML(prettyurls = get(ENV, "CI", nothing) == "true"),
pages = [
"Home"... |
b38a09f14ff10bb09b02a23cbefddf66e8ece1a2f767e3fd0c7fffc19833f94b | Julia | 826 | 25 | ##
using MRIgeneralizedBloch
using SpecialFunctions
using Test
## rectangular pulse
T2s = 10e-6
TRF = 5e-3
α = π
ω0 = 10e3 * 2π # rad/s
ω1 = α/TRF
Rrf_sf = graham_saturation_rate_single_frequency(ω0 -> lineshape_superlorentzian(ω0, T2s), ω1, TRF, ω0)
Rrf_sp = graham_saturation_rate_spectral(ω0 -> lineshape_superloren... |
64819b144c4bf3ea145cf8cb44aa33f3fe060138f78f1d65c85b6bf515bf41c4 | Julia | 830 | 25 | ##
using MRIgeneralizedBloch
using SpecialFunctions
using Test
## rectangular pulse
T2s = 10e-6
TRF = 5e-3
α = π
ω0 = 10e3 * 2π # rad/s
ω1 = α/TRF
Rrf_sf = graham_saturation_rate_single_frequency(ω0 -> lineshape_superlorentzian(ω0, T2s), ω1, TRF, ω0)
Rrf_sp = graham_saturation_rate_spectral(ω0 -> lineshape_superloren... |
80f152d3001873ebe59bf8b74c2a85d8d54e266ee797ca358ef0729bfe4aac9d | Julia | 1,183 | 43 | ##
using MRIgeneralizedBloch
using DifferentialEquations
using StaticArrays
using Test
## choose random parameters
# this test should only be passed for small α and long TRF
α = rand() * π/2
TRF = 500e-6 + 1000e-6 * rand()
ω1 = α / TRF
B1 = 0.7 + 0.6 * rand()
ω0 = 0
m0s = 0.2 * rand()
m0f = 1 - m0s
R1f = 0.5 + 0.2 * r... |
668b3522fb639bf24490cdd67b2e3fe66c3dc2817712cb038fe761885889e0e3 | Julia | 1,202 | 51 | using HTTP
using BufferedStreams
function load_first_datapoint(filename; set_phase=:real)
data = load_Data(filename)
M = data[1,:]
if set_phase == :real
phase = angle(M[end])
M = M .* exp(-1im * phase)
M = real.(M)
elseif set_phase == :abs
M = abs.(M)
end
return... |
90e7b6bce3d42cbd2167e0cc26b8d86341b96696416bb4e7d6cd9e4b3eda4d0a | Julia | 1,236 | 44 | ##
using MRIgeneralizedBloch
using DelayDiffEq
using DifferentialEquations
using StaticArrays
using Test
## choose random parameters
# this test should only be passed for small α and long TRF
α = rand() * π/2
TRF = 500e-6 + 1000e-6 * rand()
ω1 = α / TRF
B1 = 0.7 + 0.6 * rand()
ω0 = 0
m0s = 0.2 * rand()
m0f = 1 - m0s
R... |
2ff1c3414b837077f083b1ecbbf8ba727343f003c81db91d82a88bc44a5a6998 | Julia | 1,349 | 47 | ##
using MRIgeneralizedBloch
using DelayDiffEq
using DifferentialEquations
using StaticArrays
using Test
## choose random parameters
# this test should only be passed for small α and long TRF
α = rand() * π/2
TRF = 500e-6 + 1000e-6 * rand()
ω1 = α / TRF
B1 = 0.7 + 0.6 * rand()
ω0 = 0 # Graham's spectral model is only ... |
2702a1450a2cfb77fa560300d890a8615ed6f8467f928510f193326a4343272e | Julia | 1,360 | 54 | ##
using MRIgeneralizedBloch
using DifferentialEquations
using StaticArrays
using Test
## choose random parameters
# this test should only be passed for small α and long TRF
α = rand() * π/2
TRF = 500e-6 + 1000e-6 * rand()
ω1 = α / TRF
B1 = 0.7 + 0.6 * rand()
ω0 = 0 # Graham's spectral model is only implemented for on... |
f89cdd21906647e3fd514df4461188688327e2331af3efe22ec64291131479ab | Julia | 1,382 | 50 | using BenchmarkTools
using MRIgeneralizedBloch
using Test
##
τmin = 0
τmax = 100
##
Gfull = greens_superlorentzian
print("Time to pre-calculate the interpolation:")
Ginte = @btime interpolate_greens_function(greens_superlorentzian, τmin, τmax)
##
for i = 1:100
τ = τmin + (τmax - τmin) * rand()
@test Gfull(τ... |
7f89755342426b5ec4cf52352e7691558c5e3f5b0863a62b85a51af2531cdcb6 | Julia | 1,530 | 53 | using BenchmarkTools
using MRIgeneralizedBloch
using Test
using FiniteDifferences
##
τmin = 0
τmax = 100
##
Gfull = greens_superlorentzian
print("Time to pre-calculate the interpolation:")
Ginte = @btime interpolate_greens_function(greens_superlorentzian, τmin, τmax)
##
for i = 1:100
τ = τmin + (τmax - τmin) * ... |
73bbc5d6becad856be42c922b9318c7142e004f89db5d369b6e24934e110efa7 | Julia | 1,558 | 58 | ##
using MRIgeneralizedBloch
using DifferentialEquations
using StaticArrays
using Test
## choose random parameters
α = 0
TFP = 100e-6 + 900e-6 * rand()
ω1 = α
B1 = 1
ω0 = 1000 * rand() - 500
m0s = 0.2 * rand()
m0f = 1 - m0s
R1f = 0.5 + 0.2 * rand()
R1s = 2 + rand()
R2f = 1 / (40e-3 + 100e-3 * rand())
T2s = 5e-6 + 10e-... |
5efdc8cfd001d7d82024bfd38ac7d9d63b3c6361ec122f42366295df69a7f696 | Julia | 1,574 | 58 | ##
using MRIgeneralizedBloch
using DifferentialEquations
using StaticArrays
using Test
## choose random parameters
α = 0
TFP = 100e-6 + 900e-6 * rand()
ω1 = α
B1 = 1
ω0 = 1000 * rand() - 500
m0s = 0.2 * rand()
m0f = 1 - m0s
R1f = 0.5 + 0.2 * rand()
R1s = 2 + rand()
R2f = 1 / (40e-3 + 100e-3 * rand())
T2s = 5e-6 + 10e-... |
7867715407350f5953224363faa025167befaf4cd846f453e678531e05e749ef | Julia | 1,644 | 48 | using Random,Statistics,Plots,Distributions
function plot_dists()
d = Distributions.GeneralizedInverseGaussian(1,2,1.1)
d2 = Distributions.GeneralizedInverseGaussian(0.9,0.9,0.5)
d3 = Distributions.GeneralizedInverseGaussian(0.2,0.4,0.4)
d4 = Distributions.GeneralizedInverseGaussian(2,3,2)
draws =... |
053a100041231df3ccffd2eb5ba1b019b75020892b14c8c36cfd92660e4bd0b7 | Julia | 1,656 | 45 | ##
using MRIgeneralizedBloch
using DifferentialEquations
using StaticArrays
using Test
R2slT = precompute_R2sl()
## choose random parameters
Npulse = 500
α = π/3 * sin.(4π * (1:Npulse) / Npulse)
α[1] = π
# TRF = similar(α)
# TRF .= 1e-3 # long TRF to make Graham and gBloch match better
TRF = 100e-6 .+ 400e-6 * rand(Np... |
a8dbf87c10fe12b4ae054b1464359950c1cc1ccda9a85b48e087510ebea9e040 | Julia | 1,684 | 60 | ##
using MRIgeneralizedBloch
using DifferentialEquations
using StaticArrays
using Test
## choose random parameters
α = rand() * π
TRF = 100e-6 + 900e-6 * rand()
ω1 = α / TRF
B1 = 0.7 + 0.6 * rand()
ω0 = 1000 * rand() - 500
m0s = 0.2 * rand()
m0f = 1 - m0s
R1f = 0.5 + 0.2 * rand()
R1s = 2 + rand()
R2f = 1 / (40e-3 + 10... |
1be9fec7e652c2e3e5bfebd2d5012a20fcdc061c50032258f17a8681c1c8a392 | Julia | 1,703 | 57 | ##
using MRIgeneralizedBloch
using DelayDiffEq
using DifferentialEquations
using StaticArrays
using Test
## choose random parameters
α = rand() * π
TRF = 100e-6 + 900e-6 * rand()
ω1 = α / TRF
B1 = 0.7 + 0.6 * rand()
ω0 = 1000 * rand() - 500
m0s = 0.2 * rand()
m0f = 1 - m0s
R1f = 0.5 + 0.2 * rand()
R1s = 2 + rand()
R2f... |
7f2361b9fa1178814ab34ddb6d72a60f15141bf7a9aeac8af99ae85d6f643d11 | Julia | 1,781 | 46 | include("ImpedanceAnalyzer_Functions.jl")
Sweep1 = UnpackSweepRead("FreqSweep\\2022_09_02_11_40_09_Sweep_100-100000Hz_Proximity_Inductor.tdms")
Sweep1Low = UnpackSweepRead("FreqSweep\\2022_09_04_13_56_24_Sweep_ 5-100Hz_Proximity_Inductor_LowFreq.tdms")
f = Sweep1.FreqVec
Z = Sweep1.ZMagVec.*exp.(1im*Sweep1.ZPhaseV... |
a94449219694d245fd1d2362b2794a60a38531c5328fea2ebfa3281b7c3683ea | Julia | 1,781 | 53 | using MRIgeneralizedBloch
using Test
using LinearAlgebra
using StaticArrays
R2slT = precompute_R2sl()
##
Npulse = 100
α = abs.(π/2 * sin.(π/2 * ((0:Npulse-1) / Npulse .+ 0.5)))
TRF = 300e-6 .+ 200e-6 * cos.(π * (1:Npulse) / Npulse)
α[1] = π
TRF[1] = 500e-6
grad_moment = [:crusher; fill(:balanced, length(α)-1)]
ω1 = α ... |
4653d56a6783f9d1a8192ca4671cb5c394bf5a792b53d21ff79684ae5e33baee | Julia | 1,801 | 46 | ##
using MRIgeneralizedBloch
using DifferentialEquations
using StaticArrays
using Test
R2slT = precompute_R2sl()
## choose random parameters
Npulse = 500
α = π/3 * sin.(4π * (1:Npulse) / Npulse)
α[1] = π
# TRF = similar(α)
# TRF .= 1e-3 # long TRF to make Graham and gBloch match better
TRF = 100e-6 .+ 400e-6 * rand(Np... |
ad16e3486af70fd2e3b10ee674320a97d0f9b1cef4df61156fa5eed81641d1a5 | Julia | 1,829 | 78 | module MRIgeneralizedBloch
using QuadGK
using DifferentialEquations
using Interpolations
using ApproxFun
import Cubature
using SpecialFunctions
using StaticArrays
using LinearAlgebra
using NLsolve
using ExponentialUtilities
using Polyester
using LsqFit
using LinearAlgebra
export apply_hamiltonian_gbloch!
export apply... |
603c4c058d2b4c488454186299d31c6ea68e0a1bc22f1003092308d3e69ce83e | Julia | 1,956 | 89 | using CSV,Random,Distributions,LinearAlgebra,Printf
include("../old-src/BayesNet.jl")
function parse_CL_args()
args = ArgParseSettings()
@add_arg_table! args begin
"--case", "-c"
help="Case number, 1 or 2 controls sparsity parameter"
arg_type = Int
default = 1
e... |
66de0ef529e1ce6402255d3a83b3b2ded8785fbae0c116d0bafa33104b3d9f87 | Julia | 1,977 | 55 | using MRIgeneralizedBloch
using Test
using LinearAlgebra
using StaticArrays
##
Npulse = 100
α = abs.(π/2 * sin.(π/2 * ((0:Npulse-1) / Npulse .+ 0.5)))
TRF = 300e-6 .+ 200e-6 * cos.(π * (1:Npulse) / Npulse)
α[1] = π
TRF[1] = 500e-6
isInversionPulse = [true; falses(length(α)-1)]
ω1 = α ./ TRF
R2slT = precompute_R2sl(ω1... |
cca25f215b96fc6c751721841f597ed9551d33dda50d4cd6d2513d90fb5a1b04 | Julia | 2,032 | 73 | using Test
using MRIgeneralizedBloch
using LinearAlgebra
R2slT = precompute_R2sl()
## define control
# Npulse = 100
# α = abs.(π/2 * sin.(π/2 * ((0:Npulse-1) / Npulse .+ 0.5)))
# TRF = 300e-6 .+ 200e-6 * cos.(π * (1:Npulse) / Npulse)
# α[1] = π
# TRF[1] = 500e-6
using MAT
control = matread(normpath(joinpath(pathof(MRI... |
11d6910f8f658dbb97d3087168e531767df9935a0028d4903af0ada7715fa28e | Julia | 2,109 | 65 | ## taken from https://github.com/TuringLang/MCMCDiagnosticTools.jl
## note that if the Rhat can be computed even with only 1 chain
## as the function will split all chains.
function rhat(
chains::AbstractArray{<:Union{Missing,Real},3}
)
# compute size of matrices (each chain is split!)
niter = size(chains, ... |
3e6fe7a4bcdfed026aabc67c13b3bb1cdf9e910aa243565fef92a7de523bc255 | Julia | 2,132 | 87 | module MRIgeneralizedBloch
using QuadGK
using DelayDiffEq
using DifferentialEquations
using OrdinaryDiffEqHighOrderRK
using Interpolations
using ApproxFun
import Cubature
using SpecialFunctions
using StaticArrays
using LinearAlgebra
using NLsolve
using ExponentialUtilities
using LsqFit
export apply_hamiltonian_gbloch... |
92593d5067f9ecf605b10ffd1fbd86b839ed1fca61e3741ec1403ec32e257d09 | Julia | 2,160 | 75 | using Test
using MRIgeneralizedBloch
using LinearAlgebra
R2slT = precompute_R2sl()
## define control
# Npulse = 100
# α = abs.(π/2 * sin.(π/2 * ((0:Npulse-1) / Npulse .+ 0.5)))
# TRF = 300e-6 .+ 200e-6 * cos.(π * (1:Npulse) / Npulse)
# α[1] = π
# TRF[1] = 500e-6
using MAT
control = matread(normpath(joinpath(pathof(MRI... |
b0b4663e288b56869859a0dcd3a678a50bad2591d228f5b5d68b741bfaa33cd4 | Julia | 2,189 | 77 | include("realistic_sim.jl")
include("unrealistic_sim.jl")
g_rng = MersenneTwister(734)
@quickactivate
for πₑ=[0.3,0.8]
for μₑ=[0.8,1.6]
for n=[500,1000]
for k=[8,22]
for type=["additive_phylo","additive_random","interaction_phylo","interaction_random","redundant_phylo","redund... |
e008a10f6d7fb9a520dd674839cb40f8883a6beeead5f4cdec6d1444e90cb339 | Julia | 2,225 | 45 | #md # [](@__BINDER_ROOT_URL__/build_literate/Greens_functions.ipynb)
# # Green's Functions
# The Green's functions are given by the Fourier transform of the corresponding lineshapes. For a Lorentzian lineshape, the Green's function is
# ```math
# G(t,\tau) = \exp (-R_2^s (t-\ta... |
76da8ecd40e1fed359fca344546c94cd2879eeaf8647dc243dcf9f9d1f7e75c9 | Julia | 2,272 | 94 | using MRIgeneralizedBloch
using Test
@testset "Greens_Interpolation" begin
include("Greens_Interpolation.jl")
end
@testset "apply_hamiltonian_gbloch!" begin
for i = 1:10 # test different random initializations
include("gBloch_Hamiltonian.jl")
end
end
@testset "apply_hamiltonian_gbloch! shaped RF ... |
a48b156b03bcd7003a0c0c792930ea27a6f5757c597a5730cd7c060fd00a9514 | Julia | 2,272 | 94 | using MRIgeneralizedBloch
using Test
@testset "Greens_Interpolation" begin
include("Greens_Interpolation.jl")
end
@testset "apply_hamiltonian_gbloch!" begin
for _ = 1:10 # test different random initializations
include("gBloch_Hamiltonian.jl")
end
end
@testset "apply_hamiltonian_gbloch! shaped RF ... |
6925b4178bb3016e5fdb676539626ada52428220e527dd41db2e8b0cfb90bc60 | Julia | 2,519 | 79 | #!/usr/bin/env julia
"""
Script for generating default.prm from params.json
"""
using JSON
"""
val2str(val::Any)
Convert a Julia value to a string containing a Matlab eval-able value.
"""
function val2str(val::Any)
if isa(val, Array{Any})
emptystrings = isa.(val, String) .& isempty.(val)
val[emptys... |
81873cd342bd85cc7fe9d33ec86b3230c6a9a41fc1d3082cd6066a144a2835fe | Julia | 2,523 | 46 | # Test with test1.csv data
data_in = DataFrame(CSV.File(joinpath(@__DIR__, "data", "test1.csv")))
X = Matrix(data_in[:,1:190])
y = data_in[:,191]
R = 5
truth = load("data/gen_test_results.jld2")
res = BayesianNetworkRegression.generate_samples!(X, y, R, η=1.01,ζ=1.0,ι=1.0,aΔ=1.0,bΔ=1.0,
ν=10, nburn=200, nsamp=20... |
bd99d0806af86eee8924418083f324aa912106349973fa2530c170176d70667a | Julia | 2,524 | 79 | ### em options
parallel = true # Run on multiple CPUs.
full = true # Maintain full covariance matrix (vs a diagional one) a the group level
emtol = 1e-3 # stopping condition (relative change) for EM
first_run = true;
# are we at work computer or laptop?
work_computer = false;
# set up parallel if using
using Di... |
06e2ce29fa83f1532263a24b3704e97337d0c0c51244ed390ad7d68308cce11f | Julia | 2,610 | 55 | # Test of fit! in toy data
## global seed
rng = Xoshiro(1234)
## simulating 10 4x4 adjacency matrices
X = [rand(rng,Bernoulli(0.5),4,4)]
for i in 1:9
push!(X,rand(rng,Bernoulli(0.5),4,4))
end
y = ones(size(X,1))*12 + rand(rng, Normal(0,2),size(X,1))
R = 5
truth = load("data/gen_test_results.jld2")
res = Bay... |
f4624a3bb20108b6cdd9f2b981dc825cb751c2ecd28658b776c446cccf39c4fd | Julia | 2,785 | 89 | using MRIgeneralizedBloch
using MRIgeneralizedBloch:propagator_linear_inversion_pulse
using StaticArrays
using Test
##
T = 500e-6
T2s = 10e-6
R2slT = precompute_R2sl()
R2sl = R2slT[1](T, π, 1, T2s)
function simulate_inversion_pulse(M0, ωz, B1, T, m0s, R1f, R2f, Rx, R1s, R2sl)
Δ = π * (-1 + .0001:.0001:1) # * 2.1... |
f2d91431094fded9887c51ec4946ee6ee730aa635bc02cf3b28d0760c5f832a7 | Julia | 2,844 | 101 | using Distributions,Random,TickTock,CSV,ArgParse,Printf,RCall
using BayesianNetworkRegression,DataFrames,StatsBase
include("../old-src/plot_output.jl")
function parse_CL_args()
args = ArgParseSettings()
@add_arg_table! args begin
"--case", "-c"
help="Case number, 1 or 2 controls sparsity p... |
1788a70f37a09a108d33a598ac7915e0425bc17bab1b09936a43288c85909d9e | Julia | 2,846 | 97 | function calc_CRB(s, w)
s = reshape(s, size(s,1)*size(s,2), size(s,3))
real.(w * diag(inv(s' * s)))
end
function grad_ω1_fd(w, ω1, TRF, TR, ω0, B1, m0s, R1f, R2f, Rx, R1s, T2s, R2slT, grad_list)
Δω1 = 1e-2
_grad_ω1 = similar(ω1)
α = ω1 .* TRF
s0 = calculatesignal_linearapprox(α, TRF, TR, ω0, B... |
34f490eecca4a4bafe163aeddd1f952164f899c35b86cfb52fe8ad73dd0addca | Julia | 2,969 | 90 | using MRIgeneralizedBloch
using MRIgeneralizedBloch:propagator_linear_crushed_pulse
using StaticArrays
using Test
##
T = 500e-6
T2s = 10e-6
M0 = 0.85
R2slT = precompute_R2sl()
R2sl = R2slT[1](T, π, 1, T2s)
function simulate_inversion_pulse(m0_v, ωz, B1, T, M0, m0s, R1f, R2f, Rex, R1s, R2sl)
Δ = π * (-1 + .0001:.... |
5329b0656bb2e7e7b821eb43a0bb343c0873d95caa610e49940fe8165e4dcac8 | Julia | 3,003 | 83 | using DifferentialEquations
using Test
using FiniteDifferences
using MRIgeneralizedBloch
using MRIgeneralizedBloch: apply_hamiltonian_freeprecession!, apply_hamiltonian_gbloch!
## set parameters
ω0 = 200
m0s = 0.15
R1f = 0.3
R2f = 1 / 65e-3
R1s = 2
T2s = 10e-6
Rex = 30
TE = 3.5e-3
mfun = (p, t; idxs=nothing) -> typeo... |
22ad3648bb8b2c3231e7914a316edd8f54c74c8aa0f87aeb845f1be825c56e8b | Julia | 3,016 | 118 |
using TDMSReader, Gtk, Plots, StatsPlots
struct SingleRead
TestParams
ImpedanceData
GainData
CurrentTimeDomain
VoltageTimeDomain
end
struct FreqSweepRead
FreqVec
ZMagVec
ZPhaseVec
GainData
TestParams
end
struct StatsRead
ImpedanceData
ZPhaseArray
ZMagArray
Gai... |
329ab9e5fa0d1dfc0fbc431719f053860a271578ce9a294a9d5649b345208fb6 | Julia | 3,042 | 113 | # julia EM model fitting, Nathaniel Daw 8/2019
# what's startx?
function optimizesubject_orig(likfun, startx)
#a = optimize(likfun, startx, NewtonTrustRegion(); autodiff=:forward)
a = optimize(likfun, startx, LBFGS(); autodiff=:forward)
return(a.minimum,a.minimizer)
end
function generate_start_vals(cost_fun, npa... |
bccfe7798768e682b70bca7e0a7e9b64932a58796285410f0c87c4a02091797a | Julia | 3,062 | 59 | # ============================================================================
# Deprecated function names — will be removed in a future release
# ============================================================================
# Renamed in v0.11: calculatesignal_* → simulate_*
# Updated in v0.12: insert M0=1 since the ol... |
6fa8414e0a37e3443ff5a20f7eb4ce10bb59090eab657bfa0adce2dc00b498c0 | Julia | 3,113 | 94 | using Pkg
Pkg.activate("docs")
Pkg.develop(PackageSpec(path=pwd()))
Pkg.instantiate()
using MRIgeneralizedBloch
using Documenter
using Literate
using Plots
# HTML Plotting Functionality
struct HTMLPlot
p # :: Plots.Plot
end
const ROOT_DIR = joinpath(@__DIR__, "build")
const PLOT_DIR = joinpath(ROOT_DIR, "plots")
... |
ed37620c24d69872256ba9174bb835a5a7c43edc8840dae833e4af488621cdda | Julia | 3,176 | 77 | using DifferentialEquations
using Test
using FiniteDifferences
using MRIgeneralizedBloch
using MRIgeneralizedBloch: apply_hamiltonian_graham_superlorentzian!, apply_hamiltonian_linear!
ode_solver = Tsit5()
## set parameters
ω1 = π / 500e-6
ω0 = 200
B1 = 1
m0s = 0.15
R1f = 0.3
R1s = 2
R2f = 1 / 65e-3
T2s = 10e-6
Rex = ... |
6e30b916611bc072c76b0339cca96a8780358309e00caea8e08cbce45a163698 | Julia | 3,188 | 92 | ### em options
parallel = true # Run on multiple CPUs.
full = true # Maintain full covariance matrix (vs a diagional one) a the group level
emtol = 1e-3 # stopping condition (relative change) for EM
first_run = false;
split_on_rt = false;
# are we at work computer or laptop?
work_computer = false;
# set up para... |
910a24a9855bc8c24de4bca1fe47ab2cd10f3e8c0feba59dc55cd2e6048a19a9 | Julia | 3,191 | 74 | using DifferentialEquations
using BenchmarkTools
using MRIgeneralizedBloch
using MRIgeneralizedBloch: apply_hamiltonian_gbloch!, hamiltonian_linear
using Test
## set variables
α = (.01:.01:1) * π
TRF = 500e-6 # s
ω1 = α ./ TRF # rad/s
m0s = 0.1
m0f = 1-m0s
R1f = 0.3 # 1/s
R1s = 2.0 # 1/s
R2f = 1 / 50e-3 # 1/s
T2s = 10... |
0522b4f36226dc9cbf2e987e622a2da6b99c79193382b2c09d1d804c4dc032ce | Julia | 3,214 | 116 | using DrWatson,DataFrames,CSV,Statistics
function fill_B(B,ξ,t,μₛ,σₛ,rng)
for i in 1:t
for j in (i+1):t
if (ξ[i] == 1 && ξ[j] == 1)
B[i,j] = B[j,i] = rand(rng,Normal(μₛ,σₛ))
end
end
end
end
function output_data(saveinfo,out_df,ξ,A,B,jcon,simtype)
if... |
c6ce9851331f3a940d291aac3e8484f2389dbd495846845e4303bb8e23ee3382 | Julia | 3,234 | 95 | using Pkg
Pkg.activate(Base.source_dir())
Pkg.develop(PackageSpec(path=pwd()))
Pkg.instantiate()
using MRIgeneralizedBloch
using Documenter
using Literate
using Plots
# HTML Plotting Functionality
struct HTMLPlot
p # :: Plots.Plot
end
const ROOT_DIR = joinpath(@__DIR__, "build")
const PLOT_DIR = joinpath(ROOT_DIR... |
6a21fc304103515e26a2ca0e01c09ec9d6d2fb83346095d79c6c93e8e89795ff | Julia | 3,242 | 86 | using MRIgeneralizedBloch
using Test
using LinearAlgebra
using StaticArrays
include("OCT_finite_difference_gradients.jl")
##
Npulse = 100
α = abs.(π/2 * sin.(π/2 * ((0:Npulse-1) / Npulse) .+ π/2))
TRF = 300e-6 .+ 200e-6 * cos.(π * (1:Npulse) / Npulse)
α[1] = π
TRF[1] = 500e-6
ω1 = α ./ TRF
isInversionPulse = α .≈ π
R... |
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