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im... |
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* Bootstrap v4.0.0-beta (https://getbootstrap.com)
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* jQuery JavaScript Library v1.7.1
* http://jquery.com/
*
* Copyright 2011, John Resig
* Dual licensed under the MIT or GPL Version 2 licenses.
* http://jquery.org/license
*
* Includes Sizzle.js
* http://sizzlejs.com/
* Copyright 2011, The Dojo Foundation
* Released under the MIT, BSD, and GPL ... |
ba439bc1fbf27cd926243a12a8600a272f3cc5bea4d6ed0b4bc696d7c82621e2 | JavaScript | 168,953 | 6,450 | /*!
* Select2 4.0.3
* https://select2.github.io
*
* Released under the MIT license
* https://github.com/select2/select2/blob/master/LICENSE.md
*/
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define(['jquery'], factory);
} e... |
9aff8da67eb9c3f258dfc60179ee2df51cdfc8a385f9b5d9f4d8e74bfdc25bbe | JavaScript | 168,983 | 4 | parcelRequire=function(e,r,n,t){var i="function"==typeof parcelRequire&&parcelRequire,o="function"==typeof require&&require;function u(n,t){if(!r[n]){if(!e[n]){var f="function"==typeof parcelRequire&&parcelRequire;if(!t&&f)return f(n,!0);if(i)return i(n,!0);if(o&&"string"==typeof n)return o(n);var c=new Error("Cannot f... |
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3316324bf8575d6ae6942aa7c35b4d4579d68b1c577ab424be362ff0cebf7723 | JavaScript | 200,044 | 7,408 | (function(f){if(typeof exports==="object"&&typeof module!=="undefined"){module.exports=f()}else if(typeof define==="function"&&define.amd){define([],f)}else{var g;if(typeof window!=="undefined"){g=window}else if(typeof global!=="undefined"){g=global}else if(typeof self!=="undefined"){g=self}else{g=this}g.parsemd = f()}... |
c13c1c5c81d0e613630f4db56fa58d41d47332056e46d31c0e6597422ad21377 | Julia | 53 | 5 | KONECT_SYM = 1;
KONECT_ASYM = 2;
KONECT_BIP = 3;
|
7c61890d3cee62c763a6c62a08a25650e44755b3ad9220321612a9bcdb748d44 | Julia | 85 | 6 | using sEMTor
using Test
@testset "sEMTor.jl" begin
# Write your tests here.
end
|
cc15fe6fe3113110c4b0707e28fbd9acc43c913eea227e5311e7ec10b5c86555 | Julia | 195 | 8 | using Pkg
# activate the environment defined by `Manifest.toml` and `Project.toml`
Pkg.activate(joinpath(@__DIR__))
# Download and precompile the dependencies
Pkg.instantiate()
Pkg.precompile() |
d61df6846c04fc5d3a2abc9f88d366649e0cac8f6a172fea6998472a25df0751 | Julia | 280 | 17 | #
# Read a statistic from a file.
#
# PARAMETERS
# statistic Statistic name
# network Network name
#
# INPUT FILES
# dat/statistic.$statistic.$network
#
function read_statistic(statistic, network)
data = readdlm("dat/statistic.$statistic.$network");
return data;
end
|
bdf64f53b3e36471b9866e01d5a88edc391757fe21489995d2a7345c51f67550 | Julia | 282 | 11 | using NBInclude
@nbinclude("Fig2.ipynb")
@nbinclude("Fig3A.ipynb")
@nbinclude("Fig3B.ipynb")
@nbinclude("Fig4B.ipynb")
@nbinclude("Fig4C-D.ipynb")
@nbinclude("Fig5.ipynb")
@nbinclude("Fig6A.ipynb")
@nbinclude("Fig6B.ipynb")
@nbinclude("Fig6C-F.ipynb")
@nbinclude("Fig4Supp1D.ipynb") |
efbd2e9381a9d14bf68330d763958c07cafe0c3bbe32adde8aca028db80dbd41 | Julia | 384 | 16 | # This file aims at installing all used packages in this work
using Pkg
Pkg.add("IJulia")
Pkg.add("DifferentialEquations")
Pkg.add("Plots")
Pkg.add("Polynomials")
Pkg.add("LaTeXStrings")
Pkg.add("ColorSchemes")
Pkg.add("DelimitedFiles")
Pkg.add("Statistics")
Pkg.add("StatsPlots")
Pkg.add("Random")
Pkg.add... |
384bb4eec3e27ebd0567e7595123fa4fa70da3cd0e4de8c7a661bed9f5d55c7c | Julia | 390 | 27 | #
# Draw a "full" step plot.
#
# PARAMETERS
#
# x (n) Values
#
function step_full(x)
n = length(x);
sort!(x);
step(x,
(n:-1:1) / n,
linestyle="-");
# If the Y axis was not logarithmic, we show also add the point
# (x_sorted[end], 0) to the plot, making x_sorted[end] be... |
ad11a8e359a29a2f13f104a83c6f52342ca26742e97121fefd1f70371fac6f8d | Julia | 403 | 20 | using SEMTor
p = StaticParameters()
ens = simulate_ensemble(p)
plot_state(ens[1][1], p)
st = compute_statistics(ens, p)
# example analysis
using CairoMakie
df = st.stats_mean["tissue"]
T = df.time
X = df.apical_height_mean
X_std = df.apical_height_std
lines(T, X, axis = (xlabel = "time [hr]", ylabel = "tissue he... |
1b9f9fd76c343e925d3d4f470e73cff816c01bc2d20bfa0022e8f30a76caceb9 | Julia | 487 | 22 | module Force
using LinearAlgebra
export FORCE_feedback_current, FORCE_learn
function FORCE_feedback_current(z_out::Real, eta::Array, Q::Real, N::Integer, NE::Integer, N_out::Integer)
I_add = zeros(N)
I_add[1+NE-N_out:NE] = Q * z_out .* eta
I_add
end
function FORCE_learn(W_out::Array, PW::Matrix, err::Re... |
0e67d62cc651d0e2d12e544e242bb324683bab61deeb8a8e2677f93088e3d63c | Julia | 518 | 27 | using Test, Random, PulseInputDDM, LinearAlgebra, Flatten, Parameters
@testset "PulseInputDDM" begin
#check that random number generator from Base julia has not changed
Random.seed!(1)
@test isapprox(sum(randn(10)), 2.86; atol=0.01)
@testset "choice_model" begin
include("choice_model_tes... |
58d695b471c9b42f0e441c5d6baafe93243a2c35cb45ee56527e78a5519f7526 | Julia | 518 | 23 | using SEMTor
using Documenter
DocMeta.setdocmeta!(SEMTor, :DocTestSetup, :(using SEMTor); recursive=true)
makedocs(;
modules=[SEMTor],
authors="Steffen Plunder <steffen.plunder@web.de> and contributors",
sitename="SEMTor.jl",
format=Documenter.HTML(;
canonical="https://SteffenPL.github.io/SEMT... |
7a0f4bd02e70d9b2575f26a4f47af0cd52483efaa56dfdddfd9338cc2264e0f7 | Julia | 559 | 17 | module DePasqualeElife2024
using Flatten, Parameters, Distributions, PulseInputDDM, Distributed
import PulseInputDDM: nθparams, optimize, diffLR, DDM, DDMθ, neuraldata
import PulseInputDDM: initialize_latent_model, choice_likelihood!, adapt_clicks, latent_one_step!
import StatsFuns: logistic
import Optim: converged, m... |
feebfede13371262aae85de0c7aa9882c5871e06a4581486b8494aadd4394335 | Julia | 591 | 43 | abstract type DDM end
abstract type DDMdata end
abstract type DDMθ end
abstract type DDMf end
"""
"""
@with_kw mutable struct θz{T<:Real} @deftype T
σ2_i = 0.5
B = 15.
λ = -0.5; @assert λ != 0.
σ2_a = 50.
σ2_s = 1.5
ϕ = 0.8; @assert ϕ != 1.
τ_ϕ = 0.05
end
"""
"""
@with_kw struct clicks
... |
30f2b8cd57bcb087b3c0fe84439c9865d78e314a6a398bc467a4c40dde87fb6e | Julia | 608 | 30 | module Math
export pick_from_array, trapz, struct_is_same, clip_range
function pick_from_array(arr, n)
[a[n] for a in arr]
end
function trapz(x, y, a, b)
vals = (x.>=a) .& (x.<b)
xp = x[vals]
yp = y[vals]
sum([(xp[i+1] - xp[i]) * (yp[i] + yp[i+1]) / 2.0 for i=1:(length(xp)-1)])
end
function str... |
aed691b2f7ff8b98e3d79da9894fd37f15013c3c57882c4dbf21fa25b95b24d8 | Julia | 711 | 29 | module Types
export RecurrentNetworkModelParams,
RecurrentNetworkModelWeights,
RecurrentNetworkModelMetadata, @add_metadata_fields,
RecurrentNetworkModelTypes
abstract type RecurrentNetworkModelParams end
abstract type RecurrentNetworkModelWeights end
macro add_metadata_fields()
return esc(:(
model::Str... |
3df4bfed8912f9365d17f8191a14c683e8c5b217f30c51a13262fa373f0fff7e | Julia | 750 | 25 | function reset_cell_cycle(s::State{d}, p::StaticParameters, i::Int64) where {d}
cell1 = new_cell_copy_startimes(s, p, i)
cell1.age = 0.0
cell1.birth_time = s.t
type = get_type(s,i)
if type.life_span.min < type.life_span.max
cell1.division_time = rand(Uniform(type.life_span.min, type.life_s... |
bbd8be8b05b89ca6dd6a12ef6e1bca4b190cc4c1a3776586622d1b6db5e6f419 | Julia | 843 | 50 | #
# Plot inter-event distributions.
#
# This is the first Julia code in KONECT, and therefore can serve as an
# example of how to do it.
#
# PARAMETERS
# $network Network name
#
# INPUT FILES
# dat/out2.$network
#
# OUTPUT FILES
# plot/inter.$type.$network.png
# $type:
# a Overall distribution, log-log
# al Overa... |
a7ef766bdedc86a26867eb573a39f05f7efabf41e1f773f0496d360f9e70e39c | Julia | 995 | 33 | ntrials = 2
θ = θchoice(θz=θz(σ2_i = 0.5, B = 15., λ = -0.5, σ2_a = 50., σ2_s = 1.5,
ϕ = 0.8, τ_ϕ = 0.05),
bias=1., lapse=0.05)
θ, data = synthetic_data(;θ=θ, ntrials=ntrials, rng=1)
model_gen = choiceDDM(θ=θ)
choices = getfield.(data, :choice)
@test all(choices .== vcat(true, false))
@time @test... |
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