sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
7f09f8e1436f4b93bca120cc76007c5ad92df1693a1e8f83c1e37190a80376f1 | JavaScript | 79,613 | 1 | Search.setIndex({"docnames": ["0.0_previous_MLSchool", "0.1_2023_MLSchool", "0.1_2023_MLSchool_AsiaPacific", "0.1_2024_MLSchool", "1.0_introduction", "2.0_prerequisites", "3.0.00_macos_users", "3.0.01_python_matlab", "3.0_gettingstarted", "4.0_mainmenu", "4.10_mainmenu_save_struct", "4.11_mainmenu_change_wd", "4.12_mai... |
ee05c73bdc3ed860118ed7926089b5e8ec2d75f0b8c1d476908a5350745912f2 | JavaScript | 80,813 | 3 | /*! For license information please see bootstrap.js.LICENSE.txt */
(()=>{"use strict";var t={d:(e,i)=>{for(var n in i)t.o(i,n)&&!t.o(e,n)&&Object.defineProperty(e,n,{enumerable:!0,get:i[n]})},o:(t,e)=>Object.prototype.hasOwnProperty.call(t,e),r:t=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(t... |
00df89d1103a914d2811841621b05d966ae22841eea9befeacb6adfcdf29603b | JavaScript | 85,625 | 1,860 | ////////////////////////////////////////////////
// MultiQC Report Toolbox Code
////////////////////////////////////////////////
let mqc_colours_idx = 0;
const mqc_colours = ["#e41a1c", "#377eb8", "#4daf4a", "#984ea3", "#ff7f00", "#a9a904", "#a65628", "#f781bf", "#999999"];
const zip_threshold = 8;
// Add these const... |
85556761a8800d14ced8fcd41a6b8b26bf012d44a318866c0d81a62092efd9bf | JavaScript | 86,709 | 4 | /*! jQuery v3.1.1 | (c) jQuery Foundation | jquery.org/license */
!function(a,b){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=a.document?b(a,!0):function(a){if(!a.document)throw new Error("jQuery requires a window with a document");return b(a)}:b(a)}("undefined"!=typeof window?wi... |
cd9db3b7d812139a4679a2566ea8f01b2a80159490a60b9e9e813e81b5e00a05 | JavaScript | 96,397 | 3 | /*! @algolia/autocomplete-js 1.19.1 | MIT License | © Algolia, Inc. and contributors | https://github.com/algolia/autocomplete */
!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||s... |
acc7e41455a80765b5fd9c7ee1b8078a6d160bbbca455aeae854de65c947d59e | JavaScript | 97,630 | 13 | /*!
JSZip v3.10.1 - A JavaScript class for generating and reading zip files
<http://stuartk.com/jszip>
(c) 2009-2016 Stuart Knightley <stuart [at] stuartk.com>
Dual licenced under the MIT license or GPLv3. See https://raw.github.com/Stuk/jszip/main/LICENSE.markdown.
JSZip uses the library pako released under the MIT... |
51e25d438f875f90c313e2f5a6ca1a458b4eca94e8f3dddd11f6ac3971f4da2f | JavaScript | 108,036 | 16 | "use strict";(()=>{var Wi=Object.create;var gr=Object.defineProperty;var Vi=Object.getOwnPropertyDescriptor;var Di=Object.getOwnPropertyNames,Vt=Object.getOwnPropertySymbols,zi=Object.getPrototypeOf,yr=Object.prototype.hasOwnProperty,ao=Object.prototype.propertyIsEnumerable;var io=(e,t,r)=>t in e?gr(e,t,{enumerable:!0,... |
10597790ff3cdc45601b58bd14ed25cf961a458e7821cadc97145f4baadc73e8 | JavaScript | 200,000 | 2 | /*! For license information please see molstar.js.LICENSE.txt */
!function(e,A){"object"==typeof exports&&"object"==typeof module?module.exports=A():"function"==typeof define&&define.amd?define([],A):"object"==typeof exports?exports.molstar=A():e.molstar=A()}(self,(()=>(()=>{var e={1944:()=>{"use strict";var e,A,t,r,n,... |
2bfe2138694543575950e6d761e8b317cea030f669c65c3ed3eb69560bd4024b | JavaScript | 200,000 | 6,083 | /**
* plotly.js v2.27.0
* Copyright 2012-2023, Plotly, Inc.
* All rights reserved.
* Licensed under the MIT license
*/
(function webpackUniversalModuleDefinition(root, factory) {
if(typeof exports === 'object' && typeof module === 'object')
module.exports = factory();
else if(typeof define === 'function' && define.... |
64f67ef4d2ebe567a89a4497af92973801d1ee54beb99fbe313d041e2cc1dca2 | JavaScript | 200,000 | 2 | /*! For license information please see main.187c56e6.js.LICENSE.txt */
(()=>{var __webpack_modules__={54371:(e,t,r)=>{"use strict";var n,a=Object.assign||function(e){for(var t=1;t<arguments.length;t++){var r=arguments[t];for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&(e[n]=r[n])}return e},o=r(65043),i=(n=o)&... |
fed9bb86c760320506f3cacdc9f051fdf617e390a08c780a75eb1952689f04f0 | JavaScript | 200,000 | 18 | /**
* plotly.js (custom - minified) v3.1.2
* Copyright 2012-2025, Plotly, Inc.
* All rights reserved.
* Licensed under the MIT license
*/
(
function(root, factory) {
if (typeof module === "object" && module.exports) {
module.exports = factory();
} else {
root.moduleName = factory();
}
} (typeof self !== "u... |
69889a12fa0f3440052245df98054193b09c8ef39d4cc9acc4b07728d857c9c6 | JavaScript | 200,002 | 8 | /**
* plotly.js v2.27.0
* Copyright 2012-2023, Plotly, Inc.
* All rights reserved.
* Licensed under the MIT license
*/
/*! For license information please see plotly.min.js.LICENSE.txt */
!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e... |
b8872c5a781614d23cb545015ec68c9803d55da6ccdde198c2d8ffbd4346cbc2 | JavaScript | 200,012 | 1 | var e=Object.defineProperty,t=(t,o)=>{let n={};for(var i in t)e(n,i,{get:t[i],enumerable:!0});return o||e(n,Symbol.toStringTag,{value:"Module"}),n},o="bottom",n="right",a="left",s="auto",r=["top",o,n,a],l="start",c="clippingParents",d="viewport",h="popper",u="reference",p=r.reduce(function(e,t){return e.concat([t+"-"+l... |
f025937817acbb227e7f6bdf17a1352d358f7574e04cff205a37ac2ca407ca0f | Julia | 368 | 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("ProgressMet... |
e5972e18ef1730c930c4106ceda31fa39f56191111962f3515fab6f31b4c64c1 | Julia | 737 | 48 | """
A package for fitting data from auditory evidence accumulation task (Poisson clicks task)
to evidence accumulation model.
"""
__precompile__()
module PBupsModel
# 3rd party
import Base.convert
using MAT
using ForwardDiff
using Optim
using GeneralUtils
import ForwardDiff.DiffBase
# using DiffBase
export
... |
a7a864b071818f28cad159282167a292a4102bada72ad1b6c7ac8c9581749a0d | Julia | 1,177 | 45 | # Rawdata Import
function LoadData(mpath, filename)
# data import
if contains(filename,".mat")
ratdata = matread(joinpath(mpath,filename))
else
ratdata = matread(joinpath(mpath,*(filename,".mat")))
end
println("rawdata from ", filename, " imported" )
# number of trials
n... |
5a3426c1c12fba28ef80f14f8a77219300940a1a8092893e984df59bdd1bf0eb | Julia | 1,405 | 42 | ### Kinetics and parameters for the DA model
# DA gating Functions
DA_boltz(V, A, B) = 1 / (1 + exp(-(V-A) / B))
tauX(V, A, B, D, E) = A - B / (1 + exp((V+D) / E))
# Initializing Nernst reversalPotential
DA_VNa = 60. # Sodium reversal potential
DA_VK = -85. # Potassium reversal potential
DA_VCa = 60. # Calcium reversa... |
42a96ae2bec8f7d8aa5cf7c65421ecbe46f0376246bc252f26c580665357e9e6 | Julia | 1,541 | 43 | #=
This file contains all STG model gating functions
=#
# Gating functions
boltz(V, A, B) = 1 / (1 + exp((V+A)/B))
tauX(V, A, B, D, E) = A - B / (1+exp((V+D)/E))
# Na-current (m = activation variable, h = inactivation variable)
mNa_inf(V) = boltz(V, 25.5, -5.29)
tau_mNa(V) = tauX(V, 1.32, 1.26, 120., -25.)
hNa_inf(V)... |
30b86ebaa1045005d8b0fae6e56506a476fd142611934f1ffb97cc45dd3dee1c | Julia | 1,715 | 54 | #=
This file contains all STG model gating functions
=#
# Gating functions
boltz(V, A, B) = 1 / (1 + exp((V+A)/B))
tauX(V, A, B, D, E) = A - B / (1+exp((V+D)/E))
# Na-current (m = activation variable, h = inactivation variable)
mNa_inf(V) = boltz(V, 25.5, -5.29)
tau_mNa(V) = tauX(V, 1.32, 1.26, 120., -25.)
hNa_inf(V)... |
dd333ac9aa8223543acce1533d093339eb4038d8f563331dfec431e44735508b | Julia | 1,864 | 40 | #=
This file contains all derivatives of STG model gating functions
=#
# Derivative of the gating function
dboltz(V, A, B) = -exp((A + V)/B) / (B * (exp((A + V)/B) + 1)^2) #boltz(V, A, B) = 1 / (1 + exp((V+A)/B))
# Na-current (m = activation variable, h = inactivation variable)
dmNa(V) = dboltz(V, 25.5, -5.29)
dhNa(V... |
ea731ca46d65a12b03e77a48fdc8c55d34cf4dfd2cc1531011507e954c548587 | Julia | 2,563 | 96 | using KissSmoothing
using Test
import Random
@testset "Goodness of Smoothing" begin
rn0 = Random.MersenneTwister(1337)
for N in [100, 200, 400, 800]
for α in [0.01, 0.05, 0.1]
x = LinRange(0, 1, N)
y = identity.(x)
n = randn(rn0, length(x)) .* α
yr = y .+... |
e7f3f773cf0a5801f6dabcbde4d587d73883a9f0b61c8ac7b65bb83983bf0574 | Julia | 2,973 | 73 | #=
This file generates an initial set of STG neurons using the STG model as well as
neuromodulating this set to tune its firing pattern
=#
# Include DICs computation
include("STG_DIC.jl") # Include the computation of DICs and compensation algorithms
## Functions to generate the initial set of STG neurons
# This funct... |
b23d08c2d77bc7c927927f2a1c085bdb963ae7c06573402cda5ac976e7f6b075 | Julia | 3,035 | 73 | #=
This file generates an initial set of STG neurons using the STG model as well as
neuromodulating this set to tune its firing pattern
=#
# Include DICs computation
include("network_STG_DIC.jl") # Include the computation of DICs and compensation algorithms
## Functions to generate the initial set of STG neurons
# Th... |
ae6871e5918cc08f498343b314d5a5076675583f3eb15f39a8178ae6bcda4060 | Julia | 3,240 | 87 | #=
This file contains large simulations scripts
=#
using DifferentialEquations, ProgressMeter
include("DA_kinetics.jl") # Include STG kinetics of gating variables
include("DA_ODE.jl") # Include STG model
include("STG_utils.jl") # Include some utils functions
# Moving average function
moving_average(vs, n, padding) =... |
2d6695581ebaab4d11d8155f9d32cc8453a9f16b4102e06bab54b5a8cbeb79e0 | Julia | 3,633 | 128 | #=
This file contains functions to extract characteristics of the firing pattern
as well as some functions to plot complicated graphs
=#
using Statistics, Plots, StatsPlots, LaTeXStrings, Printf
## Functions extracting characteristics of the firing pattern
# This function extracts the spiking frequency of a spiking ... |
abd7647ddfd06f0a588805b6b6c6c657b6f2db4b221bf5ca4b90a517bb017b1d | Julia | 3,758 | 95 | #=
This file contains large simulations scripts
=#
using DifferentialEquations, ProgressMeter
include("STG_kinetics.jl") # Include STG kinetics of gating variables
include("STG_models.jl") # Include STG model
include("STG_utils.jl") # Include some utils functions
include("STG_gs_derivatives.jl") # Include X_inf deriv... |
18745f42dbe2a5d4a740e6527b40a68d533030b2ffd01d5d941541d1a9694eaa | Julia | 4,081 | 124 | # for using keyword_vgh()
# change the order of parameters.
# function ComputeLL(LLs::SharedArray{Float64,1}, ratdata, ntrials::Int#, args, x::Vector{T})
function ComputeLL(LLs, ratdata, ntrials::Int#, args, x::Vector{T})
;kwargs...)
LL = 0.
@sync @parallel for i in 1:ntrials
RightClickTimes, Left... |
2858639e1ec3c874de5809a0be9d017d5325bb84a50028d17016810b9e2cff2a | Julia | 4,184 | 95 | #=
This file contains differential equations describing the STG model
=#
include("STG_kinetics.jl") # Include STG model gating functions
## STG model from Liu 1998 - current-clamp mode
function STG_homeo_leak_ODE(dx, x, p, t)
# Parameters
Iapp = p[1] # Amplitude of constant applied current
tau_Na ... |
b857e92864c6b6e126c81740df02c8223ad8ddcf2d440e23fdd49c2ba13ec22f | Julia | 4,242 | 105 | #=
This file generates an initial set of STG neurons using the STG model as well as
neuromodulating this set to tune its firing pattern
=#
# Include DICs computation
include("STG_DIC.jl") # Include the computation of DICs and compensation algorithms
## Functions to generate the initial set of STG neurons
# This funct... |
307ea53555b087933fe3840574cece192000c735d65b663bd5d443a5215e43b0 | Julia | 4,615 | 150 | # compute models in parallel: multiprocess
addprocs(Sys.CPU_CORES) # add a worker process per core
print_with_color(:white, "Setup:\n")
println(" > Using $(nprocs()-1) worker processes")
n_core = nprocs()-1#2
if nworkers() < n_core
addprocs(n_core-nworkers(); exeflags="--check-bounds=yes")
end
@assert nprocs() >... |
efb0f12a60f0ae96452edeb25486d7b6f7a7d4cc6c71dc4f5093c0e1adf742b3 | Julia | 4,651 | 94 | """
This script runs the sequences used in the AxCaliber paper (https://doi.org/10.1002/mrm.21577) on a single cylinder substrate we created before, it should be called with two arguments --radius and --MT (effective T2 in ms) provided. An optional argument is --n_spins which set the number of isochromats/spins for th... |
613f6085030cf17561bb3351e3d10fb401ad3631c31f8b7d9ac8462e8b9a5021 | Julia | 4,652 | 95 | """
This script runs the sequences used in the AxCaliber paper (https://doi.org/10.1002/mrm.21577) on a single cylinder substrate we created before, it should be called with two arguments --radius and --MT (effective T2 in ms) provided. An optional argument is --n_spins which set the number of isochromats/spins for th... |
5f6114bcf89d79ec8c70cfde9b167c9f90c0c8d81fbddb1b42b2fd2b25d3d1bd | Julia | 4,655 | 93 | """
This script runs the sequences used in the AxCaliber paper (https://doi.org/10.1002/mrm.21577) on a single cylinder substrate we created before, it should be called with two arguments --radius and --perm (permeability) provided. An optional argument is --n_spins which set the number of isochromats/spins for the si... |
ed31c5e1e34756f481023fe4a6529a619aae410286f28e3ae78274dde7cafa7f | Julia | 4,668 | 92 | """
This script runs the sequences used in the AxCaliber paper (https://doi.org/10.1002/mrm.21577) on a single cylinder substrate we created before, it should be called with two arguments --radius and --perm (permeability) provided. An optional argument is --n_spins which set the number of isochromats/spins for the si... |
9d701c471656fa47aee761781ed4549044972b72fe853a977f04779c9081a487 | Julia | 5,102 | 133 | """
repel_fixed_radius(radius, pos_in, repeatsize; maxiter=1000, repulsion_strength=0.01)
This function adds repulsion between randomly distributed cylinders generated by the base MCMRSimulator and obtains a more equally distributed configuration. It takes the fixed radius value, cylinder position output of random_posi... |
b295a62667f269689c1a50d5e39fda12b593d56128b8f7c0f2caac24e23aab7b | Julia | 5,408 | 199 | using Graphs, Distributions, Plots, GraphRecipes, LaTeXStrings, Random
function GaussianFluc(Mean,Std,NN,T)
d = Normal(0,Std)
return Mean .+ rand(d,T,NN)
end
rg1 = Random.seed!(123)
function Steady_randAmpl(a,b,NN)
return (a .+ rand(rg1,NN).*(b-a)) # nA
end
function PoissonStim(f,T,NN)
... |
851c06c56e3a1eeaa9ca357857fa8bc427bb51dc4f5245cf73c38a9d16a7e24c | Julia | 5,467 | 131 | #=
This file contains differential equations describing the STG model
=#
include("STG_kinetics.jl") # Include STG model gating functions
## STG model from Liu 1998 - current-clamp mode
function simple_PI_homeo_leak_STG_ODE(dx, x, p, t)
# Parameters
Iapp = p[1] # Amplitude of constant applied current
... |
485032e28283b11a88ee329f735a69d22f1d60cbe6ff94ce0d78e8ce37e91824 | Julia | 6,045 | 144 | #=
This file contains large simulations scripts
=#
using DifferentialEquations, ProgressMeter
include("STG_kinetics.jl") # Include STG kinetics of gating variables
include("STG_models.jl") # Include STG model
include("STG_utils.jl") # Include some utils functions
include("STG_gs_derivatives.jl") # Include X_inf deriv... |
5e25304242f7f5a8af887cb85e147d4faf0d08e874308a9ad45dfa138b3075cb | Julia | 6,511 | 163 | #=
This file contains differential equations describing the controlled CB model of interest
=#
# Function that outputs values of variables derivatives
function DA_ODE_PI_homeo(dx, x, p, t)
# Parameters
Iapp = p[1](t) # Time dependent applied current
tau_Na = p[2] # Sodium current time constant
t... |
21737f3100925d34b63480e5784d4e0a132e63be63cd4530613048a0e6f55a2f | Julia | 6,928 | 175 | #=
This file contains large simulations scripts
=#
using DifferentialEquations, ProgressMeter
include("STG_kinetics.jl") # Include STG kinetics of gating variables
include("STG_models.jl") # Include STG model
include("STG_utils.jl") # Include some utils functions
include("STG_gs_derivatives.jl") # Include X_inf deriv... |
2123ea5ccf328ec611396990bd0c505a3fc19e256ba0e71754b96b879860402b | Julia | 7,178 | 298 | """
KissSmoothing: Easily smooth your data!
exports a single function `denoise`, for further help look at it's docstring.
"""
module KissSmoothing
using FFTW: dct, idct
using Statistics: mean
using LinearAlgebra: factorize, I
using SparseArrays: sparse
"""
denoise(V::Array; factor=1.0, rtol=1e-12, dims=ndims(... |
5f71789ede44f605d59f338cfb7f788aeadb4b41ad1a1bb101d6c7d50725fcf7 | Julia | 8,260 | 228 | # updating for generalizing the model.
# user will give args to init parameters
# use 'dictionary' -> each parameter has different range
# utilize make_dict and defaults.
function InitParams(args, seed_mode=1)
# Parameters (match the parameter order with original code)
# --> order doesn't matter now, it wi... |
618199bbabac26122a7bf262a3f7d4fbf3dc2f15071efcee2e63049e8c7de6cc | Julia | 9,930 | 229 |
"""
function x, cost, iters_used, last_Delta_x = one_d_minimizer(seed, func; tol=1e-5, maxiter=100, start_eta=0.1)
Minimizes a 1-d function using constrained Hessian minimization.
We don't trust the long-range info from the Hessian too much, meaning that there's
a given (adaptive) step size. If Newton's method sugges... |
2a445c147a07b784ff439b746530bd747a9420314c83b0975909d7f41f2ddbd5 | Julia | 12,965 | 302 | #=
This file contains differential equations describing the STG model
=#
include("STG_kinetics.jl") # Include STG model gating functions
## STG model from Liu 1998 - current-clamp mode
function STG_ODE(du, u, p, t)
# Parameters
Iapp = p[1] # Amplitude of constant applied current
gNa = p[2] # Sodium c... |
a34da0ae0874667815187b6d23f0c1501a1b45f6d7a91490a7fd8eddf7da1e55 | Julia | 13,597 | 347 | #=
This file animates the simple PI on 1 neuron
=#
using Plots, LaTeXStrings, Random, GLMakie
using DataStructures: CircularBuffer
include("network_STG_kinetics.jl") # Loading of STG kinetics of gating variables
include("network_STG_models.jl") # Loading of STG model
include("network_STG_utils.jl") # Loading of some u... |
bd53512ec1170f2f59c74974d310a3507f3c65d22970c493065a9d4500e954ef | Julia | 13,622 | 368 | #=
This file contains differential equations describing the STG model
=#
include("network_STG_kinetics.jl") # Include STG model gating functions
using ProgressMeter
# STG ODEs
function dV(V, mNa, hNa, mCaT, hCaT, mCaS, hCaS, mA, hA, mKCa, mKd, mH,
Ca, Iapp, gNa, gCaT, gCaS, gA, gKCa, gKd, gH, gleak)
(dt... |
5a9b583d0a6044154120ea72e1991ee885478c4c8781b30b9fcf86824af970fd | Julia | 13,951 | 409 | # Global variables
const epsilon = 10.0^(-10);
const dx = 0.25;
const dt = 0.02;
const total_rate = 40;
# === Upgrading from ForwardDiff v0.1 to v0.2
# instead of ForwardDiff.GradientNumber and ForwardDiff.HessianNumber,
# we will use ForwardDiff.Dual
convert(::Type{Float64}, x::ForwardDiff.Dual) = Float64(x.value)
f... |
42ea78ade365f50a5e07cdaa6e586491ac73f0c2e114a1b84d626535a125d5eb | Julia | 15,715 | 429 | using GeneralUtils
using MAT
using JLD
include("constrained_parabolic_minimization.jl")
"""
pdict = wallwrap(bdict, pdict)
Given bdict, a dictionary of symbols to [minval, maxval] vectors, and pdict, a dictionary of symbols
to values (or, alternatively, an Array of (Symbol, value) tuples], goes through each of the sy... |
092b110ae6bbd458eabeaac5bcb74225b42b8c4f90db8a0c5c359c5cd4e41cbc | Julia | 16,126 | 247 | #=
This file computes the dynamic input conductances of the STG model as well as
applying the compensation algorithm for a set of maximal conductances (inputs of the algorithm)
=#
include("STG_kinetics.jl") # Include model gating functions
include("STG_gs_derivatives.jl") # Include derivatives of model gating function... |
d27294cc95fc6a8e544a99b112bc51524f93c6b4b22edca82b72671f803747a0 | Julia | 17,362 | 420 | #=
This file contains large simulations scripts
=#
using DifferentialEquations, ProgressMeter
include("STG_kinetics.jl") # Include STG kinetics of gating variables
include("STG_models.jl") # Include STG model
include("STG_utils.jl") # Include some utils functions
include("STG_gs_derivatives.jl") # Include X_inf deriv... |
e7dbe25779f1f8f33a820ab6a2d2226985034588005a6d4840e88a8e71eb4295 | Julia | 20,218 | 302 | #=
This file computes the dynamic input conductances of the STG model as well as
applying the compensation algorithm for a set of maximal conductances (inputs of the algorithm)
=#
include("STG_kinetics.jl") # Include model gating functions
include("STG_gs_derivatives.jl") # Include derivatives of model gating function... |
a86bc3ee1e33dd85855c6ed21197d52a5b9f74881c782164675196125fcc6603 | Julia | 22,228 | 469 | #=
This file contains large simulations scripts
=#
using DifferentialEquations, ProgressMeter
include("STG_kinetics.jl") # Include STG kinetics of gating variables
include("STG_models.jl") # Include STG model
include("STG_utils.jl") # Include some utils functions
include("STG_gs_derivatives.jl") # Include X_inf deriv... |
8d0c3d3ca41478f2b81778fe5f54a9a1fa16d9050e8891ca56bad5bbe6d0c0f9 | Julia | 25,262 | 386 | #=
This file computes the dynamic input conductances of the STG model as well as
applying the compensation algorithm for a set of maximal conductances (inputs of the algorithm)
=#
include("network_STG_kinetics.jl") # Include model gating functions
include("network_STG_gs_derivatives.jl") # Include derivatives of model... |
4c9d1fd5e9b27dcf64b12edb22034c33c5ed424e35af6c0181f28963faa1d289 | Julia | 25,772 | 607 | #=
This file contains differential equations describing the STG model
=#
include("STG_kinetics.jl") # Include STG model gating functions
## STG model from Liu 1998 - current-clamp mode
function simple_PI_homeo_leak_STG_ODE(dx, x, p, t)
# Parameters
Iapp = p[1] # Amplitude of constant applied current
... |
8e1595dab16fad066db13aeff3664c8b3d70a6cfe52317da6f3a0a31032b35a6 | Julia | 28,440 | 647 | #=
This file contains differential equations describing the STG model
=#
include("STG_kinetics.jl") # Include STG model gating functions
## STG model from Liu 1998 - current-clamp mode
function STG_ODE(du, u, p, t)
# Parameters
Iapp = p[1] # Amplitude of constant applied current
gNa = p[2] # Sodium c... |
b084936d22316a321e45bd0fbf11561d3ef69e31c2dc897dcf52a5bd74191b0f | Jupyter | 44 | 5 | # %%
import numpy as np
# %%
best_sigma =
|
0e5ecd2a1b53d2388827212f00ff298edcd505aac019b49f8dd1f4e1ec0fec36 | Jupyter | 70 | 3 | # %% [markdown]
# # Estimating connectivity models
# To be provided
|
2f879c22bcdc3530bceecb3de17650b543c70c28cee85904338d222a3d2fc968 | Jupyter | 128 | 9 | # %%
import numpy as np
# %%
y_fed = np.load('/data/LLMs/data_processed/fedorenko/dataset/y_fedorenko.npy')
y_fed.shape
# %%
|
e2eeb101687e8ed0c667722549c3722d95410b02aa3f8c889d49e97985a586f6 | Jupyter | 226 | 10 | # %%
import pandas as pd
# %%
df = pd.read_csv("/g/korbel2/weber/MosaiCatcher_output/HGSVC_HJ/counts/HWWKWAFXY_HG03065x02_19s004569-1-1/TEST_PLOIDY.txt", sep="\t")
df
# %%
df.groupby("#chrom")["ploidy_estimate"].describe()
|
0ead70ab1e789562ba0d3626824714bd4d7891c2a8f5ff5fa3283c79e002f4f7 | Jupyter | 243 | 18 | # %%
from test_plots import *
import pandas
import plotly.express as px
# %%
X = np.random.normal(0,1,(30,5))
plotscatter(X,0,1,2)
# %%
fig = px.scatter_3d(x=X[:,0],y=X[:,1],z=X[:,2],opacity=0.3,template='plotly_white')
fig.show()
# %%
|
2c75e2511e9bfb5396f9feff573b1cfda2be048d252e3a08627b675856f6bd84 | Jupyter | 310 | 13 | # %%
import numpy as np
# %%
best_sigma = np.load('/home3/ebrahim2/beyond-brainscore/analyze_results/figures_code/best_layer_sigma_info/best_sigma.npz')
OASM_best_sigma_384 = best_sigma['pereira_384_pearson_r_contig_noL2']
OASM_best_sigma_384
# %%
np.load('pereira_OASM-all-sigma_3.4_1_noL2_384.npz')
# %%
|
be42932a4e6b73f43849984dad22137e0052c26cce2424a9eeb813cbd87bd49c | Jupyter | 481 | 17 | # %%
import numpy as np
# %%
# should be of shape 627
pereira_acts_gpt2_xl = np.load('/data/LLMs/data_processed/pereira/acts/X_gpt2-xl.npz')
pereira_acts_gpt2_xl['layer_1'].shape
# %%
# should be of shape 416
fed_acts_gpt2_xl = np.load('/data/LLMs/data_processed/fedorenko/acts/X_gpt2-xl.npz')
fed_acts_gpt2_xl['layer_... |
d9e6bd875889c1363c8afdac55623efdefa33dc05eee100b0950aca5bd14fead | Jupyter | 544 | 21 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
import cortico_cereb_connectivity.globals as gl
import cortico_cereb_connectivity.run_model as rm
import Functional_Fusion.dataset as fdata
import glob
import matplotlib.pyplot as plt
# %% [markdown]
# ## Scaling vs. no-scaling comparison
# %%
df=rm.... |
30f485256d33b40e5b7f694b62a10c7afa1cf37c9917b04b0af63576018da129 | Jupyter | 576 | 13 | # %% [markdown]
# # Datasheets for Datasets (D4D) Agent Demo
#
# This notebook uses Datasheets for Datasets (D4D) agent in Aurelian to extract structured metadata from dataset documentation. The agent can analyze one or more web pages / PDF documents describing datasets.
# %% [markdown]
# ## Run D4D Agent
# %%
! OPE... |
58e7806eca610c849b32189118e54b167e5d537e0687e3320454c565b4253a27 | Jupyter | 592 | 33 | # %%
import pandas as pd
# %%
df = pd.read_csv("/scratch/tweber/TMP/ALICE/G4.csv", sep=",")
df = df.drop(df.columns[0], axis=1)
df
# %%
counts_file = pd.read_csv("/scratch/tweber/TMP/ALICE/c_G4.csv")
counts_file
# %%
counts_file.groupby("chrom")["end"].max()
# %%
bin_df = counts_file[["chrom", "start", "end"]]
bin_... |
1ae1a24bf05e2535a8906df4616a46b85612f8307173732738f78158d26615e7 | Jupyter | 654 | 21 | # %%
import numpy as np
from matplotlib import pyplot as plt
# %%
SP_SL_minus_GPT2XLU_sp_243 = np.load('/data/LLMs/brainscore/results_pereira/glass_brain_plots/SP+SL-GPT2-XLU-sp_out_of_sample_r2_243.npz')['426']
SP_SL_minus_GPT2XLU_sp_384 = np.load('/data/LLMs/brainscore/results_pereira/glass_brain_plots/SP+SL-GPT2-XL... |
c646d2eb961772abccd82e064dff2b5f0583013e0874d913eed7305a3e334d09 | Jupyter | 667 | 28 | # %%
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
def annotate_axes(fig):
for i, ax in enumerate(fig.axes):
ax.text(0.5, 0.5, "ax%d" % (i+1), va="center", ha="center")
ax.tick_params(labelbottom=False, labelleft=False)
plt.style.use("default")
fig = plt.figure()
fig.... |
c693d60cf1d73e4c20c0063bba3543609c758cde4107887278bfd47bba36ce08 | Jupyter | 698 | 21 | # %%
import os, sys
import pandas as pd
import random
import numpy as np
# %%
range(df.shape[0])
# %%
selected_folder = "/scratch/tweber/DATA/MC_DATA/EVA_scNOVA_comparison_090523/GM19705iTRU3C/selected"
np.random.seed = 0
df = pd.DataFrame(sorted([e.replace(".sort.mdup.bam", "") for e in os.listdir(selected_folder)... |
25d633b4e6299f2b72fe34f512587ce0443890404df698feb87b7cee0bf9613a | Jupyter | 739 | 41 | # %%
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
# Define our letters
letter_O = [
[1, 1, 1],
[1, 0, 1],
[1, 0, 1],
[1, 0, 1],
[1, 1, 1]
]
letter_P = [
[1, 1, 1],
[1, 0, 1],
[1, 1, 1],
[1, 0, 0],
[1, 0, 0]
]
# Create an empty 16... |
a6bfeedffc3cdc4379fbbdac45af6c84eb840ec707c6d8b8565a7147ec222deb | Jupyter | 746 | 35 | # %% [markdown]
# ## Welcome to gene2map!
# This is an interactive notebook for plotting cortical expression maps for genes of interest from the MAGICC dataset
# %%
#import one or two necessary packages
#press shift enter or run to run each cell
!pip install matplotlib-surface-plotting
%matplotlib inline
!git clone... |
6cc1a9b2e81e62c1b1506318bfe9dfe316bcdc750c7772be0b1133133facf1d2 | Jupyter | 773 | 22 | # %%
import pandas as pd
import numpy as np
# %%
# %%
ploidy_detailed = pd.read_csv("/g/korbel2/weber/workspace/mosaicatcher-update/.tests/output_CHR21_selected/ploidy/RPE-BM510/ploidy_detailled.txt", sep="\t")
ploidy_detailed = ploidy_detailed.loc[ploidy_detailed["#chrom"] != 'genome']
ploidy_detailed
m_f = "M"
# ... |
36ada7b444069dac75c2048df4690242e6c80fc78f41aea307d9ec684ac360c2 | Jupyter | 788 | 34 | # %%
import numpy as np
# %%
yhat = np.load('/data/LLMs/brainscore/results_pereira/shuffled/pereira_gpt2-xl-mp_layer_20_1_384.npz')['y_hat']
ytest_ordered = np.load('/data/LLMs/results_pereira/y_test_ordered_384.npy')
mse_intercept = np.load('/data/LLMs/results_pereira/mse_intercept_384.npy')
# %%
def compute_r2(y_tr... |
00d725eab0cc33dcf94c057a80d44b63932d394a5f47d7f690b6b884873b5a4e | Jupyter | 882 | 49 | # %%
from subprocess import CalledProcessError
from pydantic_ai import RunContext
from aurelian.agents.github.github_config import get_config, GitHubDependencies
cfg = get_config()
# %%
cfg.workdir = "/tmp/aurelian/gh-test"
# %%
!mkdir -p /tmp/aurelian/gh-test
# %%
ctx = RunContext[GitHubDependencies](deps=cfg, ... |
e1e278cb0278f83d8b014f78d5f8014bc375b847c78b408693cb4d3ef9c62638 | Jupyter | 896 | 33 | # %%
import tensorflow as tf
# %%
tf.__version__
# %%
import tensorflow as tf
# Load the saved model
saved_model_path = "/g/korbel2/weber/workspace/StrandSeq_workspace/STABLE/mosaicatcher-pipeline-old/workflow/data/scNOVA/models_CNN/DNN_train20_chr1.h5"
model = tf.keras.models.load_model(saved_model_path)
print(mode... |
c49a1ed9f17efa3140ae23b1046a9b75453e3997e8ba5650b60e32775438e591 | Jupyter | 910 | 47 | # %%
from scipy._lib._array_api import (
_asarray,
array_namespace,
)
import numpy as np
# %%
import numpy as np
X = np.random.randn(150,600)
# %%
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_normalized = scaler.fit_transform(X)
# %%
X_train = X_normalized[:100]
X_test = X_... |
2b1f360b728e1eb62b346148a9e74caf81fdfd245c5c82b7d3c0f6d260a88206 | Jupyter | 914 | 43 | # %%
!pip install graphviz -U --quiet
# %%
from graphviz import Source
# %%
s = Source("""
digraph G {
graph [rankdir=LR];
node [shape=plaintext];
a[label="input features"];
b[label="neural network", shape=rect];
d[label="decision forest", shape=rect];
e[label="predictions"];
a -> b;
d -> e;
b -> d ... |
83148e0c05d74dd7a787414b4631fb8364da3e39035355c72e247fbf3888445c | Jupyter | 944 | 40 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
import cortico_cereb_connectivity.globals as gl
import cortico_cereb_connectivity.run_model as rm
import matplotlib.pyplot as plt
import scipy.stats as stats
# %% [markdown]
# ## Evaluate different model fusion
# %%
dff = rm.comb_eval(models=['Fu'])
... |
f77a66b33e0778d24240d45dfdcf056cfb16b1bfecb62e6b700096c7d1afc2fb | Jupyter | 986 | 41 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
import cortico_cereb_connectivity.globals as gl
import cortico_cereb_connectivity.run_model as rm
import cortico_cereb_connectivity.model as cm
import cortico_cereb_connectivity.evaluation as ev
import matplotlib.pyplot as plt
# %% [markdown]
# ## Sim... |
adf182267579e93552131de89a245c9be1eb00dfbabac36cd2b232e721a03995 | Jupyter | 1,039 | 43 | # %%
import os # to handle path information
import nibabel as nb
import numpy as np
import h5py
import pandas as pd
import surfAnalysisPy as surf
import matplotlib.pyplot as plt
# %%
hem = ['L','R']
hem_name = ['CortexLeft','CortexRight']
# %%
baseDir = '/Users/jdiedrichsen/Data/fs_LR_32'
flatsurf = []
inflsurf = [] ... |
4bebdce86ec98c4e2133b61c1a8e3dad2cc6b118006af01494f5d8832064cef1 | Jupyter | 1,075 | 42 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
import cortico_cereb_connectivity.globals as gl
import glob
# %% [markdown]
# ## Look at crossvalidation to pick the best model for each of the training datasets
# %%
def check_Rcv(train_data=['MDTB','Somatotopic','Demand','IBC','WMFS','HCP'],
... |
a2a417a1ea11eb7bd14a3b5da3acd174d45aaa4365dcd3707a96c7c3b3344d03 | Jupyter | 1,091 | 42 | # %%
import numpy as np
%matplotlib inline
import matplotlib.pylab as plt
from cinnabar import plotting, stats
# %%
class Result(object):
def __init__(self,ligandA,ligandB,exp_DDG,exp_dDDG,calc_DDG,mbar_error,other_error):
self.ligandA = str(ligandA)
self.ligandB = str(ligandB)
self.exp_D... |
3f85b34d6d3ace343ef3adefd4a80a3b92dfb139bfc6a9dd230b1e36d0b92258 | Jupyter | 1,127 | 38 | # %%
import numpy as np
# %%
simple_model = np.random.uniform(0,1,1000)
banded_model = simple_model + np.random.uniform(0,0.1,1000)
# %%
gpt2_bl_perf, GPT2XL_se_full, GPT2XL_se = load_perf(f"/data/LLMs/brainscore/results_{d}/{d}_trained-var-par{exp}{fe}_gpt2xl_1{exp}.npz", perf, return_SE=True,
... |
788432a6802870509fcfffeefbac1036c59118f694979f9f6f1173d896082b67 | Jupyter | 1,175 | 35 | # %%
import numpy as np
%matplotlib inline
import matplotlib.pylab as plt
import pandas as pd
from cinnabar import plotting, stats, FEMap, plotlying
# %%
network = FEMap.from_csv('../cinnabar/data/example.csv')
# %%
plotting.plot_DDGs(network.to_legacy_graph(), method_name='softwarename',target_name='made up protei... |
c464099dadb6a3c27d7ded542baa469b21881974f58e21a6c328d005ba1f8dcf | Jupyter | 1,256 | 43 | # %% [markdown]
# # Xarray
# [](https://colab.research.google.com/github/google/yggdrasil-decision-forests/blob/main/documentation/public/docs/tutorial/xarray.ipynb)
# %% [markdown]
# ## Setup
# %%
pip install ydf xarray -U
# %%
import ydf
imp... |
4bb88f3c718ab1f6491ac9b173f4ce204d69586fe4460f872be3bb36452be454 | Jupyter | 1,272 | 34 | # %%
import os
import numpy as np
import matplotlib.pyplot as plt
# %%
res_path_fbirn = "/data/users4/xli/interpolation/results/dfnc_sz/vae/hypopt/layer7/seed1"
loss_fbirn = np.load(os.path.join(res_path_fbirn, "loss_l2_list.npy"))
vae_loss_fbirn = np.load(os.path.join(res_path_fbirn, "vae_loss_l2_list.npy"))
res_pat... |
c3de58998f20d06ecd6f64995fc2df7fd1bc81df965022fd383f6aedf8c8902f | Jupyter | 1,283 | 46 | # %%
import pandas as pd
# import plotly.express as px
data = pd.read_csv("/g/korbel/STOCKS_WF/mosaicatcher_pipeline_summary.tsv", sep="\t").reset_index()
data.columns = ["flowcell", "sample", "folder", "size", "_", "size%"]
data = data.drop(columns="_")
data = data.dropna()
data
# %%
import numpy as np
def convert_... |
6fa9bfde1e161d753547bde88e85f8111e4d630cae1a87627d2509b7fdf0adee | Jupyter | 1,299 | 44 | # %% [markdown]
# # Usage example
# [](https://colab.research.google.com/github/google/yggdrasil-decision-forests/blob/main/documentation/public/docs/tutorial/usage_example.ipynb)
#
# This page shows the minimal usage on the YDF frontpage. See t... |
15660cb8a56ff56f6873c85f6bcebd176adeb4d4c77ab470324c4383789649ba | Jupyter | 1,341 | 51 | # %% [markdown]
# # <font color=black> Spinal cord networks: iCAPs </font>
# <hr style="border:1px solid black">
# %% [markdown]
# ### Imports
# %%
import sys,json
import glob
import pandas as pd
import numpy as np
import nibabel as nib
import seaborn as sns
import os
from matplotlib import pyplot as plt
sys.path.a... |
b242e2db760bab50ad13168570e373d0aae192cc2390a2a7a744a8fbb2a3e4bb | Jupyter | 1,392 | 53 | # %%
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
import brainio
# %%
fed_data = brainio.assemblies.DataAssembly.from_files("no_share/fedorenko.nc")
# %%
np.save('FED_SUBJ', fed_data.subject_UID)
# %%
figurePath = '/home3/ebrahim/what-is-brainscore/figures/'
# %%
res_full = dict(np.... |
5421722b258abcce194ff39a8ba90914a820ddc90905a3b3f64d6231da917c20 | Jupyter | 1,435 | 36 | # %% [markdown]
# # Cross-Validation
# [](https://colab.research.google.com/github/google/yggdrasil-decision-forests/blob/main/documentation/public/docs/tutorial/cross_validation.ipynb)
# %% [markdown]
# ## Setup
# %%
pip install ydf -U
# %% [... |
c93f09f05094b38cdface907f2cf7fadd091ca8b6830d4a19e1eca95b01fecc5 | Jupyter | 1,479 | 50 | # %%
"""For each subject in a subject list, export the subject's average value of
a modulator over both of their runs."""
import sys
import numpy as np
import pandas as pd
from pathlib import Path
sys.path.append(str([p for p in [Path.cwd()]+list(Path.cwd().parents) if p.name=='scripts'][0]))
from paths import DATA... |
ac624e14fb3a3aa13fc2c8005b2d12f8e7920bab09dda795b22cc5f7a5d2d3ba | Jupyter | 1,520 | 51 | # %%
import os
import papermill as pm
# Root folder where all sub-XX files are located
ROOT_DIR = r"C:/Users/jorge/OneDrive/Documents/Doctorado en Tec. Monterrey/Data Motor Task without tES/BIDS/Data Motor Task without tES/sourcedata/"
OUTPUT_DIR = r"C:/Testing_Notebooks_Output/"
# Paths to your processing notebooks
... |
1de30a1037e3144b4adab9da28d59be5870840f05e7c66c823f20475563a18e5 | Jupyter | 1,540 | 51 | # %% [markdown]
# ## Evaluate different model fusion
#
# This notebook compares different fusion connectivity models, considered for Nettekoven et al. (2023).
# The different models are:
#
# Fusion4: ['Demand','HCP','MDTB'],
#
# Fusion5: ['Demand','HCP','IBC','MDTB','Somatotopic','WMFS','Nishimoto'],
#
# Fusion6:... |
cf8c0ed0cea7e217a2139994385ffa4baf136fb04d1828b768b64ed7a8f5de1e | Jupyter | 1,591 | 52 | # %%
%reset -f
%matplotlib inline
import matplotlib.pyplot as plt
import scipy.signal as signal
import lib.io.stan
import numpy as np
from matplotlib.lines import Line2D
import os
# %%
results_dir = 'results/exp10/exp10.20'
data_dir = 'datasets/id002_cj'
os.makedirs(f'{results_dir}/Rfiles', exist_ok=True)
os.makedirs(... |
9f5b308926a0be057bb54637185450cd031e696b113666df66d103e88a519ecc | Jupyter | 1,610 | 85 | # %%
! hostname
# %%
! nvidia-smi
# %%
# enable autoreload
%load_ext autoreload
%autoreload 2
# %%
import os
import sys
import scanpy as sc
import numpy as np
import pandas as pd
import torch
from anndata import AnnData
import anndata
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.f... |
62954f303b92f1ce59d0b00c75af1cbd9c813b104182cda4eb6eb0a5e4edadd4 | Jupyter | 1,634 | 44 | # %%
import numpy as np
import sys
sys.path.append('analyze_results/figures_code/')
# %%
feature_extraction = ['', '-mp', '-sp']
# %%
best_sigma_dict = np.load('/home3/ebrahim2/beyond-brainscore/analyze_results/figures_code/best_layer_sigma_info/best_sigma.npz')
best_gpt2xl_layer_dict = np.load('/home3/ebrahim2/beyon... |
a6f3ae308fa153877617f610b1a3815344a7ddaacd71617db80e613a048d953c | Jupyter | 1,653 | 54 | # %%
import os
os.chdir('/Users/justintorok/Documents/MATLAB/Nexis')
import numpy as np
from scipy.io import savemat
import Python.Nexis as Nx
# %%
loadinst = Nx.loading_matfiles()
endmstuff = loadinst.load_file('eNDM_mousedata.mat')
C = endmstuff['Networks'][0,0]['ret']
C = C / np.max(C[:])
endmstuff_kauf = loadinst.... |
a1341169b561442a41f3aa5602f676eda7404843429ff74be8588e5bea3ff7f0 | Jupyter | 1,655 | 79 | # %% [markdown]
# Unstable fixed point
# %%
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
I = 3.1
x0 = -1.8
tau0 = 10.0
x = np.r_[-3:1:0.01]
z = np.r_[2:5:0.01]
xx, zz = np.meshgrid(x,z)
dx = np.zeros([len(z),len(x)])
dz = np.zeros([len(z),len(x)])
for i in range(len(z)):
for j in range... |
cf13dadcba5a365806b95d3195888c48b8a97bf5a1b75c2042bb6c06f92bd9f2 | Jupyter | 1,705 | 51 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
import cortico_cereb_connectivity.globals as gl
import glob
# %% [markdown]
# ## MDTB as training dataset
# MDTB dataset is used for training the models. X and Y are crossed across ses-s1 and ses-s2. In other words, cortical data from ses-s2 was used t... |
e4913f4d863b7897f676691c036155b78962ba5cbdd151c6380058dc71c0d49d | Jupyter | 1,734 | 59 | # %%
"""
13 MARCH 2024
Theo Gauvrit
Testing if the tuning of the neurons for amplitude is different between genotype
"""
import numpy as np
import pandas as pd
import percephone.core.recording as pc
import os
import percephone.plts.behavior as pbh
import matplotlib
import matplotlib.pyplot as plt
import percephone.p... |
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