sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
68ec84b5dfcaed54c1d23eeca686c3420a84a7740b46a48b3c05cc2051be679a | MATLAB | 3,135 | 102 | function M = clique_communities(A, cq_thr)
% CLIQUE_COMMUNITIES Overlapping community structure via clique percolation
%
% M = clique_communities(A, cq_thr)
%
% The optimal community structure is a subdivision of the network into
% groups of nodes which have a high number of within-group connections
% and a... |
ee6b05d17bd6db6a989d8c5c9e81518d269211fa2e4fc819e165e022eaa4d92d | MATLAB | 3,137 | 109 | function [opt_hE, opt_E, opt_F, opt_D] = nk_DiversityMax(E, L, EnsStrat)
global VERBOSE
ind0 = L~=0; E = E(ind0,:); L=L(ind0);
[m,k] = size(E);
% Compute initial ensemble performance
if EnsStrat.Metric == 2, T = sign(E); else, T = E; end
opt_hE = nk_EnsPerf(E, L); orig_hE = opt_hE;
opt_D = nk_Entropy(T,[-1 1], m, k)... |
2b7fa1c1d3d042b42b3e076768416228d284900cbb35a9ebd98f76346cf26893 | MATLAB | 3,139 | 104 | function updrs_bursting_corr(avgBI, figdir)
%UPDRS_BEHAV_CORR calculates correlation between UPDRS scores and bursting index
% UPDRS_BEHAV_CORR calculates Pearson's correalation between preop and postop (DBS-on)
% UPDRS scores (tested both with and without medication) and bursting
% index of units.
% Creat... |
a3efe06d4e9d37bd6f2e6412e473172adb1690b324e131627cda400f064cf667 | MATLAB | 3,140 | 134 | function result=halfspacedepth(u,v,x,y)
%HALFSPACEDEPTH computes the halfspace depth of a (two-dimensional) point theta
% relative to a bivariate data set.
%
% The algoritm is described in:
% Rousseeuw, P., Ruts, I. (1996),
% "AS 307: Bivariate Location Depth",
% Applied Statistics (JRSS-C), 45, 516-526.
%... |
d798f8fe07ca2b4de2a527166a0872bf4ebc0ece3563f86ce3c9e0568ee6c946 | MATLAB | 3,140 | 99 | function [mappedX, mapping] = lpp(X, no_dims, k, sigma, eig_impl)
%LPP Perform linearity preserving projection
%
% [mappedX, mapping] = lpp(X, no_dims, k, sigma, eig_impl)
%
% Perform the Linearity Preserving Projection on dataset X to reduce it to
% dimensionality no_dims. The number of neighbors that is used by LP... |
b78d027ed23c2153a4ceb16f204ee5d4069fc466e3dc555de22a7e87bec40320 | MATLAB | 3,141 | 74 | function setupOptickaCI()
%> setupOptickaCI — Configure the MATLAB path for CI without PTB's interactive
%> SetupPsychtoolbox (which fails on CI).
%>
%> This script is the CI equivalent of:
%> 1. Cloning/installing Psychtoolbox
%> 2. Running SetupPsychtoolbox(1)
%> 3. Running addOptickaToPath(true) — but s... |
06d9b941aa91016b2b38ca9c72ac9bdc60a9cd56c2a566bc25d03530fa2c6a90 | MATLAB | 3,150 | 117 | function res = JuSpace_noGUI_2D(list1,atlas,options,PET_list,image_for_size,dir_tool)
% addpath(genpath('/opt/matlab/JuSpace_v1.3/'));
list2 = [];
if ~ischar(image_for_size)
image_for_size = char(image_for_size);
end
% list1: cell with filenames
% list2 (options
% JuSpace directory
% dir_tool = fileparts(whic... |
d34d896a485610f3a1d6fa87d51e811710848368c8788435bc978dae95b8a9ac | MATLAB | 3,152 | 75 | function load_selModelMeasures(handles)
l=1;
switch handles.modeflag
case 'classification'
popupstr{l} = 'Classification plot'; l=l+1;
case 'regression'
popupstr{l} = 'Regression plot'; l=l+1;
end
predind = get(handles.popupmenu1,'Value');
predstr = get(handles.popupmenu1,'String');
if strcmp... |
b719d6bb993150d96557f0dc7cb67fd2c1531a46e887f93cfaa84cdd38ba84ea | MATLAB | 3,156 | 88 | function param = nk_RGS_config(param, defaultsfl)
% function param = nk_RGS_config(param, setupfl)
%
% Setup parameters for the RGS algorithm (kNN-regression)
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% (c) N. Koutsouleris 04/2015
epochs = 1;
num_starts = 2;
verbose = 1;
if ~exist('d... |
eceeda990c2a05e39cecf536ddfda4c209fd13d9a0dc2798e1f2475795056790 | MATLAB | 3,156 | 115 | function [tform,Corr] = ManualAlignSessions(refFrame, frame,i,j )
%ManualAlignSessions Summary of this function goes here
%
%
hLarge = fspecial('average', 40);
hSmall = fspecial('average', 3);
refFrame1 = refFrame;
frame1 = frame;
clf
set(gcf,'units','normalized','outerposition',[0 0 1 1])
subplot(2,3,1)
imagesc(refFr... |
b5dc805ecf163040d4dee915ac8e9445cacdad071a51686efd2cdf63b52c2862 | MATLAB | 3,158 | 95 | function [Y,numIter,tElapsed]=convexnmfruletest(X,outTrain)
% map the test/unknown samples into the convex-NMF feature space, this
% function is called by function featureExtractionTest
% X: matrix, test/unknown set, each column is a sample, each row is a feature.
% outTrain: struct, related options in the training ste... |
d3d740cd083796e20656156749a85e3ed49bc3ee84e98e898191ed9ad2a408b5 | MATLAB | 3,159 | 75 | function [dims, PercMode, act] = nk_ExtDim_config(RedMode, PercMode, dims, defaultsfl, parentstr)
global NM
if ~exist('defaultsfl','var') || isempty(defaultsfl); defaultsfl = false; end
if ~defaultsfl
if (~exist('dims','var') || isempty(dims)) || (~exist('PercMode','var') || isempty(PercMode))
[PercM... |
a82b19de147e9fa37a3ecc227ca3ccfd2c5b062d851cc881906c0ad7bb38feef | MATLAB | 3,168 | 53 | function [act, SEQOPT ] = nk_SEQOPT_config(SEQOPT, defaultsfl)
if ~exist('defaultsfl','var') || isempty(defaultsfl), defaultsfl = false; end
if ~defaultsfl
if ~isfield(SEQOPT,'C'), SEQOPT.C = NaN; end
if ~isfield(SEQOPT,'PerfMode'), SEQOPT.PerfMode = 1; end
if ~isfield(SEQOPT,'AnchorT... |
0a4398d50efb9ad15e71e8247f49d562ebfaeb34067d8b5a4fc54268dca09af1 | MATLAB | 3,170 | 103 | function [p, R] = polypredcicall_mod(x,y,lev,str,res)
%POLYPREDCICALL Plot regression line with confidence interval.
% POLYPREDCICALL(X,Y,LEV) calls POLYPREDCI with input coordinates X and
% Y. Regression line is overlayed on scatter plot. Confidence interval is
% plotted at a confidence level defined by LE... |
bf63a3631ecc23bfd009a7feb01f2d22ec1760c727a08e54b8f087cb9989bed6 | MATLAB | 3,173 | 80 | % =========================================================================
% FORMAT param = nk_OOCV_config(param, res)
% =========================================================================
% NeuroMiner menu configurator for the OOCV runtime parameters
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
e9f0b9fdd1e13a82cdefa61681c422b51429979c6451aed4ad6680c722c4ba3a | MATLAB | 3,174 | 86 | function [mappedX, mapping] = charting(X, no_dims, no_analyzers, max_iterations, eig_impl)
%CHARTING Performs manifold charting on dataset X
%
% [mappedX, mapping] = charting(X, no_dims, no_analyzers, max_iterations, eig_impl)
%
% Performs manifold charting on dataset X to reduce its dimensionality to
% no_dims dime... |
13fea449a1c71721ae08f49d4868c2c8b1c2b8c00d26195d6b97d089b5902c46 | MATLAB | 3,177 | 117 | function [selection, OK] = nonmodal_listdlg(varargin)
% [selection, OK] = nonmodal_listdlg(varargin)
%
% variables are chosen to match Matlab's listdlg box
% code is modified from Matlab's listdlg code version 2012a
ListString = [];
SelectionMode = 'multiple';
ListSize = [160 300];
InitialValue = [1];
Name = '';
Prom... |
71641dabd026a56a6989e00ddc101693a25450c57079909258b089946600aa52 | MATLAB | 3,177 | 98 | function [mappedX, mapping] = landmark_isomap(X, no_dims, k, percentage)
%ISOMAP Runs the Isomap algorithm
%
% [mappedX, mapping] = landmark_isomap(X, no_dims, k, percentage);
%
% The functions runs the Landmark Isomap algorithm on dataset X to reduce the
% dimensionality of the dataset to no_dims. The number of nei... |
b868e38a9794c5e39cc402a4f60b2ff7697fca5f07dec802cf580df4b0898f37 | MATLAB | 3,181 | 111 | function [x, infos] = rnmf(V, rank, in_options)
% Robust non-negative matrix factorization (NMF) with outliers (RNMF) algorithm.
%
% Inputs:
% matrix V
% rank rank
% options options
% Output:
% w solution of w
% infos information
%
% References:
% Naiy... |
a4a80accb9bd12ae1880528c23d35ad2f9f986b2f3bfc256adb05ffb1511303e | MATLAB | 3,182 | 94 | function robpcareg=robpcaregres(x,y,w,cutoffWeights)
%ROBPCAREGRES is a function for robust multivariate regression, based on
% the output from RSIMPLS and ROBPCA.
%
% Required input arguments:
% x : Data matrix of the explanatory variables
% (n observations in rows, p variables in columns... |
0e855d0eeac9e88f50b245e25e308136a5dddf2133f2237d6b0467389f8dba3d | MATLAB | 3,183 | 120 | % create mex files all
% %% list all c files
% cd 'trainpredict'
% trainpred_c = dir('**/*.c');
% trainpred_cpp = dir('**/*.cpp');
%
% cd 'preproc'
% preproc_c = dir('**/*.c');
% preproc_cpp = dir('**/*.cpp');
%
% %% trainpredict c
% error_count_trainpredict_c = 0;
% for i= 1:size(trainpred_c,1)
% cur_c = tr... |
1eb6b2ff5e3a5c67b60c113928cf0bdb521713e1e040d4bb9c28471de85c5598 | MATLAB | 3,183 | 88 | function W = weight_conversion(W, wcm)
% WEIGHT_CONVERSION Conversion of weights in input matrix
%
% W_bin = weight_conversion(W, 'binarize');
% W_nrm = weight_conversion(W, 'normalize');
% L = weight_conversion(W, 'lengths');
% W_fix = weight_conversion(W, 'autofix');
%
% This function may either binarize... |
912a99ffa5dfd91f7f1a95cead8fb5bcb5a84a92402207804111707b7391a2bd | MATLAB | 3,204 | 93 | % =========================================================================
% FORMAT param = ALLPARAM(expected, predicted, invflag, tiedfun)
% =========================================================================
% Compute all model performance parameters
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
3c67b84cb1db20318a2f9753fa970e67d90fc4165d6507b4fe69b2dcfca3615b | MATLAB | 3,219 | 85 | function [TimeStamps, WaveForms] = inodisc(data,ts,sr,thr,varargin)
%INODISC Unit discrimination.
% [T W] = INODISC(DATA,TS,SR,THR) performs threshold discrimination of
% continuous timestamped (TS) unit data (DATA) sampled at the rate SR
% using the specified threshold (THR). Peak times (T, 'TimeStamps') a... |
c374f4478ebd239ba57cd856c7ef6b00b413535015471bf29fbb93d1e4dce4e0 | MATLAB | 3,220 | 88 | function [sY, IN] = nk_PerfNormObj(Y, IN)
% =========================================================================
% FORMAT function [sY, IN] = nk_PerfNormObj(Y, IN)
% =========================================================================
% Percentage scaling of single-subject data to the group mean. The group is... |
69e3e122011484f82e9d32834043f8bed67d0b7e71865ba68dd30b359dad0daf | MATLAB | 3,225 | 90 | function [f_fitted,P_fitted,idx,prob] = db_fit(exp_f,exp_P,db_data,constraint_data)
% Fit a power spectrum to a parameter combination already in a database
% Arguments are
% f - experimental frequencies
% P - experimental power
% pp_f - database frequencies
% pp - matrix of all power spectra in ... |
20bd896de56459139988bf7fb031b6ac0d717c52acaa97309a4860127160f8be | MATLAB | 3,226 | 93 | function [ydata, mpp] = tsne(X, no_dims, perplexity)
%TSNE Performs symmetric t-SNE on dataset X
%
% mappedX = tsne(X, labels, no_dims, initial_dims, perplexity)
% mappedX = tsne(X, labels, initial_solution, perplexity)
%
% The function performs symmetric t-SNE on the NxD dataset X to reduce its
% dimensionality t... |
45a8f66cb38908258b8844ce26120d312a824bf9b58d03d1107c365d7cbc9cd1 | MATLAB | 3,228 | 85 | function Y = sym_sne(X, d, perplexity)
%SNE Implementation of symmetric Stochastic Neighbor Embedding
%
% mappedX = sym_sne(X, no_dims, perplexity)
%
% Runs the symmetric Stochastic Neighbor Embedding algorithm. The
% high-dimensional datapoints are specified by X. The target dimensionality
% is specified in no_dims... |
8a955c1438838668c154a97da1d4a3d637ee066516961dc6b5dd06dc85c2e8b6 | MATLAB | 3,229 | 121 | <<<<<<< HEAD
function write_surf(fname, vert, face)
% write_surf - FreeSurfer I/O function to write a surface file
%
% write_surf(fname, vert, face)
%
% writes a surface triangulation into a binary file
% fname - name of file to write
% vert - Nx3 matrix of vertex coordinates
% face - Mx3 matrix of face t... |
e1aa8cabc20998abc588b7e635d4fa8f65f16102180805984b096ab642a5d827 | MATLAB | 3,233 | 84 | function Y = sne(X, d, perplexity)
%SNE Implementation of Stochastic Neighbor Embedding
%
% mappedX = sne(X, no_dims, perplexity)
%
% Runs the Stochastic Neighbor Embedding algorithm using a Gaussian kernel
% with fixed perplexity. The high-dimensional datapoints are specified by X.
% The target dimensionality is s... |
0b616d62d0394e4b6babe1dabea41ba67ce85af95a2f438e6244b4c73762f4bb | MATLAB | 3,234 | 68 | function [yl, ylb, yfun, ylbshort] = nk_GetScaleYAxisLabel(param)
global PREPROC NM MULTILABEL
if ~exist('param','var')
OPTCRIT = NM.TrainParam.SVM.GridParam;
elseif isnumeric(param)
OPTCRIT = param;
else
OPTCRIT = param.GridParam;
end
switch OPTCRIT
case 1
yl = [0 100]; ylb = 'Accuracy ... |
736d7bdeb689ff725076a7e2f161a4f3b18475988a1a8b1ecc681b9611ffbe74 | MATLAB | 3,237 | 81 | function [OOCVres, cases] = nk_ExtractPredictions(dat, analysis, dim)
flgcv = nk_input('Display the CV data, too?',0,'yes|no',[1,0],1);
labels = dat.label_oocv;
cases = dat.cases_oocv;
if flgcv
if isfield(analysis{dim}.OOCV,'BinResults')
frmwrk = 1;
nclass = leng... |
a3335643d8a2da46636755ed5bf14ec9d23bb4e963a8987edc986ff906e280b0 | MATLAB | 3,238 | 86 | function network = backprop(network, X, T, max_iter, noise, lambda)
%BACKPROP Trains a network on a dataset using backpropagation
%
% network = backprop(network, X, T, max_iter, noise, lambda)
%
% The function trains the specified network using backpropagation on
% dataset X with targets T for max_iter iterations. Th... |
468944093e0998a1d3328f6fe119994cd8e2e8b46b3ef1186427602942fed8bb | MATLAB | 3,242 | 92 | clear
%% reorg behaviors to odor average for overall fig
% deltas index
load('ProcessedData\tables_compiled.mat');
final_output = cell(5,1);
animals = unique(animal_ID);
for i = 1:length(tables_compiled)
day_now = tables_compiled{i,1};
trials_all = unique(day_now.TrialN);
pull_day = zeros(length(tr... |
04a52a58bf42e65d0b61adb80ee670abbbe13231e39f52521c47145e55138dfb | MATLAB | 3,248 | 119 | % load face file and compute SVD of motion and/or movie
function h = computeSVDmotion(h)
%%
np = cumsum([0 h.npix]);
tp = cumsum([0 h.tpix]);
npix = h.npix;
tpix = h.tpix;
nframes = h.nframes;
ntime = h.vr{1}.Duration;
[nviews, nvids] = size(h.vr);
wvids = [];
for k = 1:nviews
kmotion = h.avgmotion(tp(k) + [1:tpix... |
89ef6390bdb8a777792b11ef2c6b5539526beb9ccf73199b94d698aa35a006cd | MATLAB | 3,260 | 80 | function [I,Q,F]=motif3struct_wei(W)
%MOTIF3STRUCT_WEI Intensity and coherence of structural class-3 motifs
%
% [I,Q,F] = motif3struct_wei(W);
%
% Structural motifs are patterns of local connectivity in complex
% networks. Such patterns are particularly diverse in directed networks.
% The motif frequency ... |
7a6ea7010e5c732654acee53310451795333351b218b9b8c274b7b898cb00dc7 | MATLAB | 3,262 | 91 | function runMsCluster(path)
%% Define parameters
% path
eval(['cd ' path])
%p = parpool('local',8);
%% msRun2018
% Version 1.0 GE
% Updated version of the msRun script originally proposed by Daniel B
% Aharoni to analyse miniscope 1p calcium imaging data.
% This version is build on top of the original package to max... |
c705bee0cb8042cb1cadd5ccf734d0fa4fbca71e8ce18b01b8ec5bf13c49df7f | MATLAB | 3,264 | 91 | function [A,Y,numIter,tElapsed,finalResidual]=seminmfnnls(X,k,option)
% Semi-NMF based on NNLS: X=AY, s.t. Y>0.
% Definition:
% [A,Y,numIter,tElapsed,finalResidual]=seminmfnnls(X,k)
% [A,Y,numIter,tElapsed,finalResidual]=seminmfnnls(X,k,option)
% X: matrix of mixed signs, dataset to factorize, each column is a ... |
83ffe148c547f15f69486873d9c2d2150d6eb667f9d2ae76599aec3fd9fffc25 | MATLAB | 3,273 | 97 | %% file example_SRMTL.m
% this file shows the usage of Least_SRMTL.m function
% and study the graph regularization
%
%% OBJECTIVE
% argmin_W { sum_i^t (0.5 * norm (Y{i} - X{i}' * W(:, i))^2)
% + rho1 * norm(W*R, 'fro')^2 + rho2 * \|W\|_1}
%
%% LICENSE
% This program is free software: you can redistribut... |
45ee4a797af51ecf1d0e614dc73371d8a3f6fc166df3e3010b9fceef886df73d | MATLAB | 3,274 | 58 | function [act, param] = nk_SA_config(param, defaultsfl, parentstr)
SA.c = 0.01; % Sparsity constant
SA.T = 1; % Starting temperature
SA.T_stop = 0.005; % Stopping temperature
SA.alpha = 0.9;
SA.itt_max = 2000; % Max number of iterations
SA.Rep_T_max ... |
d62f3bd2bfbbd23b2a715c7f501c867893364ebfc138193c0115ffd4fc07670c | MATLAB | 3,274 | 92 | function [P_pos, P_neg, mindist_pos, mindist_neg, marginList, g_dists] = gflipHelperGpu(X, Y, g_dists, changed_feat, added, labelsSet)
% [P_pos, P_neg, mindist_pos, mindist_neg, marginList, dists] = gflipHelper(X, X_square, Y, alphas, inds, dists, add_feat, rem_feat);
%
% This function finds the nearhit and nearmiss ... |
13bf728e2512b9e275dd5c9b510c90ab59d8dfd45ae43bf348d11041187ef284 | MATLAB | 3,289 | 74 | function runConcatStep1(path,equipment)
%% Pipeline for the proper concatenation of miniscope data across sessions
% Developed by Daniel Almeida Filho Mar/2020 (SilvaLab - UCLA)
% If you have any questions, please send an email to
% almeidafilhodg@ucla.edu
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
3c14aa6542a10e623c2493b7560184f58e679c0828ee85e8010869ee685c18f4 | MATLAB | 3,291 | 110 | classdef ctsd
% ctsd object
%
% Rebuilt ADR 2011/12
% contains:
% T0 - starting time
% dt - timestep
% D - data: n x .. array of data
%
% ctsd objects assume that
% -> T is sorted in ascending order
% -> dt is stored in seconds
% -> D is in order and complete
... |
2ac55981717db0818f70659c863f55139712f875b4cb3f1135f9b3f8fdc3c024 | MATLAB | 3,295 | 107 | function [On,Wr] = reorder_mod(W,M)
%REORDER_MOD Reorder connectivity matrix by modular structure
%
% On = reorder_mod(W,M);
% [On Wr] = reorder_mod(W,M);
%
% This function reorders the connectivity matrix by modular structure and
% may consequently be useful in visualization of modular structure.
%
% ... |
4989f946882ad1b90cbfb0566179c68193ca1a49e358a17dca22f7f889e11ee9 | MATLAB | 3,296 | 126 | function out = nk_Cluster(W, ACT)
global VERBOSE
% Find clusters (connected areas) in an image and return new image containing the
% number for each cluster sorted by size. The largest cluster has number
% one and so on.
%spm_progress_bar('Init',n,'Cluster',' ');
if VERBOSE,
fprintf('\n\n========================... |
cac2f68701f3e402c9ec710f1631e97b3eb143cad0da9716a92523e10f8bf17f | MATLAB | 3,297 | 113 | % choose which movies to process and how to process them
function [filename, folders, namef] = chooseFiles(filename, folders, namef)
filename0{1} = [];
folders0 = {};
namef0 = {};
figpos = get(0,'DefaultFigurePosition');
figunits = get(0,'DefaultFigureUnits');
set(0,'DefaultFigurePosition',[1000 500 500 150]... |
d06f2be7be94a29c3d0330f1ebda9b73da04f16a861453bc5cce3132cd5baf48 | MATLAB | 3,297 | 88 | %% ================================================================
% Script: compute_foca_matrix.m
% Purpose:
% Construct Functional Topography Covariance (FOCA) matrices
% based on individualized functional topographies.
%
% Description:
% - Loads subject-level individualized topography maps
% - Comp... |
55c8218d27320ddc5385c90dc95094040ca2ce274817884d564d76e7ceeb9a5d | MATLAB | 3,304 | 133 | function demo(func, k);
% demo;
% demo(func);
% demo(func, k);
%
% Demo for using the algorithms in this directory, developed or used in
%
% A. Navot, L. Shpigelman, N. Tishby, E. Vaadia. Nearest Neighbor Based Feature Selection for Regression and its
% Application to Neural Activity. Submitted to NIPS 2005.
%
% the d... |
302c639bfe781651ad4ff1e3fceaeed246c611493cae45871f8f64368bcc95b4 | MATLAB | 3,310 | 90 | function [Xk,Uk]=l1QP(H,C,lambda)
% solve 0.5*tr(X^THX) + tr(CX) + lambda|X|_1
% Yifeng Li, Feb. 21, 2012
option.tol=2^(-32);
nV=size(H,1); % number of variables
nP=size(C,2);% number of problems
% Xk=H\(-C);%pinv(H)*(-C);
Xk=zeros(size(C));
Uk=abs(Xk);
Zk=[Xk;Uk];
W01=false(nV,nP);
W02=false(nV,nP);
W01(Xk>=0)=true;... |
31444004b9ce06d8cff3f023d658b6062f514d51a3b3a2962af8c4e198802b93 | MATLAB | 3,313 | 87 | % This script extracts frequency (and related) data from the "click" data
% that came from digitizing spectrograms from xeno-canto.org
% Define the path to your folder if necessary
folder_root = ".\";
% Uncomment the animal category for which you want to extract data
folder_rel = "Birds";
% folder_rel = "Frog... |
3a9b5819c40e6afc54f250294652e87fad7f235685c1daad7e2e0b0b420591bb | MATLAB | 3,315 | 105 | function [mappedX, mapping] = lltsa(X, no_dims, k, eig_impl)
%LLTSA Runs the linear local tangent space alignment algorithm
%
% [mappedX, mapping] = lltsa(X, no_dims, k, eig_impl)
%
% The function runs the linear local tangent space alignment algorithm on
% dataset% X, reducing the data to dimensionality d. The numb... |
5cdca059c204924b61727bdec78b1412ec1c6ed22f0b175322f8d038f211b456 | MATLAB | 3,315 | 106 | %data = randn(1000,10);
%batch = [1 1 1 1 1 2 2 2 2 2];
%mod = [1 2 1 2 1 2 1 2 1 2]';
function bayesdata = combat(dat, batch, mod)
[sds] = std(dat')';
wh = find(sds==0);
[ns,ms] = size(wh);
if ns>0
error('Error. There are rows with constant values across samples. Remove these rows and rerun ... |
c4d821db685b0805880b44eb1b06e74c53e91d0411c3dcaefc737f6cf623e735 | MATLAB | 3,328 | 95 | % Produce Figure S2
% ( 10 s excerpts of firefly and cricket signals)
% File to analyze:
InputVideo = 'cricket_and_firefly_video.mp4';
firefly_data_file = 'firefly_flash_matrix.csv';
% Load info about frames per second (FPS) etc from video
v = VideoReader(InputVideo);
Nframes = v.NumFrames;
Fs_vid = v.FrameRate; %... |
e40ae29ff63565a0e6c9be70dd8595fb49437dca67b87c15876f31302908396c | MATLAB | 3,336 | 100 | function [selectedFeatures] = feast(criteria,numToSelect,data,labels,varargin)
%function [selectedFeatures] = feast(criteria,numToSelect,data,labels,varargin)
%
%Provides access to the feature selection algorithms in FSToolboxMex
%
%Expects the features to be columns of the data matrix, and
%requires that both the feat... |
27d8a0c410164bbfdd8f4c9bea15fae3e28e544d2f416862de52271fe99b9d6c | MATLAB | 3,338 | 98 | function cuepair_trialtypes_2Evinx(sess2analyse,EventTypes)
%CUEPAIR_TRIALTYPES_2EVINX saves trial/epoch indeces of trials with different level of conflict.
% CUEPAIR_TRIALTYPES_2EVINX sorts trials into following groups based on cue pair:
%
% - ordered, i.e. numbers are in ascendent order: 12, 23 (level of
% confl... |
cd37379b93bfc8affb09d9bc67678e7d923abeb85180ea979fffcf6df8812efd | MATLAB | 3,341 | 114 | function unit_sorter(EventTypes,EvTy)
%UNIT_SORTER Responsive/ predictive units
% UNIT_SORTER(EventTypes,EvTy)
% -Tests activation/ inhibition of units around events
% -Statistically significant activation/ inhibition following an event
% (responsive units)
% or significant difference between partitioned ... |
0f874deb7453802ffea9e7159d92a777d1c5d3cc35636a7d72399d9cf947ada6 | MATLAB | 3,349 | 107 | function [A,Y,numIter,tElapsed,finalResidual]=sparseNMFNNQP(X,k,option)
% linear sparse NMF based on NNQP: X=AY, s.t. A,Y>0.
% Definition:
% [Ak,A,Y,numIter,tElapsed,finalResidual]=kernelnmfrule(X,k)
% [Ak,A,Y,numIter,tElapsed,finalResidual]=kernelnmfrule(X,k,option)
% X: matrix of mixed signs, dataset to facto... |
42e066a97bef0a90e97dcb437b4edc6afd9c08cc209e367a888a96e90dea08c1 | MATLAB | 3,350 | 94 | clear
load('ProcessedData\tables_compiled_noShift.mat');
%% classifier code; runs on increasingly larger random subsets of cells
% % reorg main table if needed
% re_org = vertcat(tables_compiled{:});
% tables_compiled = cell(1,1);
% tables_compiled{1,1} = re_org;
tic
% run within days by iter; params for n... |
3e402bdd36f98f7a028495064cfc6ee3fea310a8a7d7a78abe82759589d94165 | MATLAB | 3,353 | 98 | function estY = kNNreg(X_test, X_train, Y_train, weights, extra_param, sorted_dists, NNs);
% Implementation of kNN regression algorithm, as used in
%
% A. Navot, L. Shpigelman, N. Tishby, E. Vaadia. Nearest Neighbor Based Feature Selection for Regression and its
% Application to Neural Activity. Submitted to NIPS 2005... |
2649196dc5114dcbfbd6d29995b44215e3bcf3074077099ce1c1cfa97dfd0e54 | MATLAB | 3,358 | 85 | classdef spatial_express_xyz_all < bt.model.template_spatial
% This is a template function that allows rapid prototyping of new spatial
% fitting by leveraging model.params.spatial_spectrum with absolutely
% no regard for performance
properties
% No new properties required by this derived class
end
methods
... |
30a9e6c61ae7e6ff3e08982b3bd642091935daae86bfe46493f70582076a036c | MATLAB | 3,361 | 101 | % =========================================================================
% FORMAT [R, nsigma, nlambda] = imreliefmain(Y, labels, SortInd)
% =========================================================================
% This function provides an interface between NeuroMiner and the IMRelief
% feature selection function.... |
e0cadcdbdb78f5a4f58ffa40c9a5b1467a443f9c1744450fea93b74900b9a5fd | MATLAB | 3,363 | 152 | function [S,P,t,kmax,med]= rstep(X,k,center,r);
%RSTEP is an auxiliary function for 'rapca.m'.
%
% This function is part of LIBRA: the Matlab Library for Robust Analysis,
% available at:
% http://wis.kuleuven.be/stat/robust.html
%
% Created by Sabine Verboven and Mia Hubert (October 2000)
% Part of the ... |
c2f03af076b95e30ed8ffe88feb1bd6f45af65c462f6263667dbe8eae7669bb4 | MATLAB | 3,365 | 88 | clear
%% load PreProcessed file
load('PreProcessed.mat');
%% Re-org deltas and drop lost trials so dataset has no missing values
% intialize empties and parameters
tables_compiled = cell(1,5);
trial_cutoff = 8; % number trials to keep
norm_toggle = 0; % 0 to not, 1 to normalize as below
Odors_New = ["1","2","3","4",... |
531949e062a942d4e8ed8609bca73db7b7a431b5b286aad09fa58b5d50e1c6b1 | MATLAB | 3,366 | 94 | function RT_allgroupin(allpat_cntr,conditions,allpats,whichRT)
%RT_ALLGROUPSIN1 draws boxplots of RT/SSDp0.5/SSRT values
% RT_ALLGROUPSIN1(allpat_cntr,conditions,allpats,whichRT) plots
% RT/SSDp0.5/SSRT values (ALLPAT_CNTR)of type WHICHRT, for each task
% condition (CONDITIONS), including all patients inclu... |
6fc4eba9cd3f16d35a19abbd728b8f191bb70312d6a52838f58d13f6cf67325f | MATLAB | 3,366 | 122 | function result=mscalelogist(x,varargin)
%MSCALELOGIST is an M-estimator of scale with psi-function equal to
% (exp(x)-1)/(exp(x)+1) and with auxiliary location estimate.
% It is iteratively computed. It can resist 50% outliers.
% If x is a matrix, the scale estimate is computed on the columns of x. The
% result is t... |
1241f9412b5bac99e663955b657151a85aa736b4a3210663f07c30311d8ec88b | MATLAB | 3,367 | 101 | function selected_features = fwd_sel(X_train, Y_train, extra_param);
if ~isfield(extra_param, 'verbose'), extra_param.verbose=0; end
if ~isfield(extra_param, 'k'), extra_param.k = ceil(log2(size(X_train,1))); if extra_param.verbose, disp(['k = ' num2str(extra_param.k)]); end; end
k = extra_param.k;
if ~isfield(extra... |
14f40c68f0551fcc1d5c2511f2988bf5f4a4f77eaf98a90aa813ccb6d2f8ec65 | MATLAB | 3,368 | 103 | %% file example_SRMTL.m
% this file shows the usage of Least_SRMTL.m function
% and study the graph regularization
%
%% OBJECTIVE
% argmin_W { sum_i^t (0.5 * norm (Y{i} - X{i}' * W(:, i))^2)
% + rho1 * norm(W*R, 'fro')^2 + rho2 * \|W\|_1}
%
%% LICENSE
% This program is free software: you can redistribut... |
668b95e340601783f1624fd3feb37b7e551561f37ac8ac24b176f795c518feeb | MATLAB | 3,373 | 75 | %
% Edited so that eigenvalues can be extracted to separate aligned(+ve eigenvalues) and liberal(-ve eigenvalues) eigenvectors
% Then we calculate the number of eigenvectors (k) that can give us 80%, 90% and 95% variance explained
% Do this for all subjects then find group-level average number of k for 80%, 90% and 95... |
b384b9d2e082344145ed50ef713c6c341a10c61551d94c6b9f26d8fe29945c65 | MATLAB | 3,375 | 106 | % This is an example of how to draw learning curves
% Copyright (C) <2012> <Yifeng Li>
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% any later version... |
18db6820d3ba811697f71511350351c8d8aa29655a9495757a752a21742f47a7 | MATLAB | 3,379 | 89 | %%% script for plotting spectrograms of the cricket chirping (audio signal)
%%% and the firefly flashing (video) from our field recordings
% File to analyze:
InputVideo = 'cricket_and_firefly_video.mp4';
firefly_data_file = 'firefly_flash_matrix.csv';
% Load info about frames per second (FPS) etc from vide... |
a02c28df2c009a6a934a289c3f4ce48fbc0bbaec16591a8ad39a550c38aab3de | MATLAB | 3,383 | 53 | function [act, WBLCOX ] = nk_WBLCOX_config(NM, WBLCOX, defaultsfl)
if ~exist('defaultsfl','var') || isempty(defaultsfl), defaultsfl = false; end
if ~defaultsfl
if ~isfield(WBLCOX,'interval'), WBLCOX.interval = median(NM.time); end
if ~isfield(WBLCOX,'MaxIter'), WBLCOX.MaxIter = 10000; end
... |
b8f8315a04ebf8b00ad6ca63f29f34e6251f03a22d88dd9b5d03410a2fc807f1 | MATLAB | 3,383 | 88 | clear
%% load PreProcessed file
load('PreProcessed_peakMag.mat');
%% Re-org deltas and drop lost trials so dataset has no missing values
% intialize empties and parameters
tables_compiled = cell(1,5);
trial_cutoff = 8; % number trials to keep
norm_toggle = 0; % 0 to not, 1 to normalize as below
Odors_New = ["1","2","... |
2c9a908fe8e455e90dd7985d524129b2e54dfa5424026e59261ea4bd3bb8fa65 | MATLAB | 3,385 | 86 | function sigArray = periEventSigArray(sig,behEvtTbl,fps)
% This functions organizes ca signals into a periEvent array
% Inputs:
% sig: calcium signal matrix: nFrame x nCell
% behEvtTbl: behavior event table: nEvent x nFeature
% fps: calcium signal frame rate per second
% Outputs:
% sigArray:... |
0bee1820134b9b4f7f674c94ad5c98d590e325abee56de2d5bf36ed531b3ba45 | MATLAB | 3,395 | 94 | function [A,Y,numIter,tElapsed,finalResidual]=nmfnnls(X,k,option)
% NMF based on NNLS: X=AY, s.t. X,A,Y>=0.
% Definition:
% [A,Y,numIter,tElapsed,finalResidual]=nmfnnls(X,k)
% [A,Y,numIter,tElapsed,finalResidual]=nmfnnls(X,k,option)
% X: non-negative matrix, dataset to factorize, each column is a sample, and ea... |
94c9a608b5bb63c60c458181e362780a29f85322bf9f70875ae0367e05f350d2 | MATLAB | 3,397 | 104 | function mappedX = ltsa(X, no_dims, k, eig_impl)
%LTSA Runs the local tangent space alignment algorithm
%
% mappedX = ltsa(X, no_dims, k, eig_impl)
%
% The function runs the local tangent space alignment algorithm on dataset
% X, reducing the data to dimensionality d. The number of neighbors is
% specified by k.
%
%
... |
b9d79ebb54adea7757e5ba3b558b0069d45216e9b6da57556f6014476da97171 | MATLAB | 3,398 | 91 | function [OpenedFileName, Version, Freq, Comment, Trodalness, NPW, PreTresh, SpikePeakV, SpikeADResBits, SlowPeakV, SlowADResBits, Duration, DateTime] = plx_information(filename)
% plx_information(filename) -- read extended header infromation from a .plx or .pl2 file
%
% [OpenedFileName, Version, Freq, Comment, Trodal... |
b4200a06c737f83982e21583d1d609cbd0618789e7ae400a9fac0098c44ad478 | MATLAB | 3,400 | 63 | function [GridParam, modeflag] = nk_EvalFunc_config(res, param, parentstr, modeflag)
global EXPERT
if ~exist('modeflag', 'var') || isempty(modeflag), modeflag = res.modeflag; end
deffunc = 1;
if exist('param','var') && ~isempty(param)
if isfield(param,'evalfunc')
deffunc = param.evalfunc;
elseif isfi... |
25a8ef604557d0832a3c9ac8a5ef00c47957057616cbb5473665d512a7c7bc17 | MATLAB | 3,405 | 84 | function [param, act] = nk_DiscSym_config(param, parentstr)
dscBinStart = 0;
dscBinStep = 0.5;
dscBinStop = 4;
symMinBin = 3;
symMaxBin = 25;
symSeqLength = 4;
symStdNum = 3;
cmd = 'discretize';
methods = {'discretize', 'symbolize'};
if isfield(param,'DISCRET') &... |
c9d665b517fa1652c34dd068c0cd3475ceff741be795da65a481531c7fbf57b4 | MATLAB | 3,406 | 106 | function joinActivity(path,order,flag)
% Function to join the raw trace and the calculated activity of each cell
% across all the sessions concatenated.This function is part of the
% pipeline ConcatMiniscope.
%
% INPUTS:
% path: path of the folder containing the dataset to be treated. Outputs
% will be loaded from ... |
9a46b25fb1f68550e022d09bbf121b3df8d22b2d1172f10a1e5bdf47f1e8e023 | MATLAB | 3,411 | 109 | % ========================================================================
%> @brief [WIP] staircaseManager links a taskSequence to Palamedes staircase
%>
%> Use a taskSequence to randomise a variable, then for each value of
%> that variable, run a Palamedes staricase
%>
%>
%> Copyright ©2014-2022 Ian Max Andolina — r... |
27c243d88df7c854dfd02d8d9a8d08eef8aebf6879703ebbd79ff6db3c57e993 | MATLAB | 3,419 | 95 | function OUT = DetrendFoldPerm(IN, OUT)
global SVM MODEFL EVALFUNC
if isfield(SVM,'Post') && isfield(SVM.Post,'Detrend') && SVM.Post.Detrend
OUT.detrend.flag = true;
else
OUT.detrend.flag = false;
end
if OUT.detrend.flag
PermVec = 1:IN.nperms;
FoldVec = 1:IN.nfolds;
ClassVec = 1:IN.nclass;
... |
1b0fda0fce224d30fb18c714f593e222c07d810d9d5a8d656271b88690e9582a | MATLAB | 3,429 | 153 | function regresdiagplot3d(sdist,odist,rdist,cutoffsd,cutoffod,cutoffrd,k,class,multi,labsd,labod,labresd,labels)
%REGRESDIAGPLOT3D is a 3D-outlier map which visualizes the orthogonal distance,
% the score distance and the residual distance calculated in a PCR or PLSR analysis.
%
% I/O: regresdiagplot3D(sdist,odist,rd... |
1d7d722f81558b01f9e87ce4f9be2c337166a0713776ac055f257a708119ea17 | MATLAB | 3,436 | 80 | function [ act, PLS, PX ] = nk_PLS_config(NM, PLS, PX, defaultsfl, parentstr)
algostr = 'pls';
UseLabel = true;
V = nk_MakeDummyVariables(NM.label);
cu = 1; cv = 1;
act=0;
if ~defaultsfl
if isempty(PLS), [~, PLS ] = nk_PLS_config(NM, [], [], true); end
algostr = PLS.algostr;
cu = PLS.cu;
cv = PLS... |
a3e7de5434ad0b356210c1f35a6a1c095926ce93b1868e452a2a68060934d67f | MATLAB | 3,441 | 108 | %%% plotting arnold tongues for our system
clear;
colormap default;
Nneurons = 6; % group size
NumSeeds = 2; % number of random seeds to average over
% making one neuron the driver of the system, with different frequencies omegas
omega_0 = 2; % Hz
NumOmegas = 71; %71 % number of detunings to explore (between freq ... |
0d0c905dabaef8a7eb6ccbd1ee6842b5fd879965f64e64a3999a99e59a315e42 | MATLAB | 3,444 | 87 | function [key qflag] = kbhit(cmd)
% Returns the last key pressed in the command window
%
% kbhit('init') : initialise listening
% key = kbhit : last character typed
% key = kbhit('struct'): struct for last character typed (including ctrl,
% shift, alt and ascii code information)
%... |
38ee4bf0745908dc995a5bfea8673d62e8dfad5ed233b0ccbd686eb85ae48e3a | MATLAB | 3,447 | 64 | function [ LABELIMPUTE, act ] = nk_LabelImpute_config(LABELIMPUTE, parentstr, defaultsfl)
IMPUTEDEF_K = 7; IMPUTEDEF_METH = 'none' ;
if ~exist('defaultsfl','var') || isempty(defaultsfl); defaultsfl = false; end
if ~defaultsfl
if isfield(LABELIMPUTE,'method'), IMPUTEDEF_METH = LABELIMPUTE.method; end... |
30afd7e04439b0cf0dc92b67c2a2068fb3d573af0ebb99f9bdfe9a4be3ed500c | MATLAB | 3,451 | 73 | function [W_ACT, PX] = nk_WAction_config(W_ACT, PX, datadesc, brainmask, defaultsfl, parentstr)
if ~exist('defaultsfl','var') || isempty(defaultsfl), defaultsfl = 0; end
softflag = 1;
threshvec = [25 50 75];
absvec = [ 0.25 0.50 0.75 ];
clustfl = 2;
exppar =... |
72b0c2496418bb595436dbdc0abef2c5bcbaa5155d93f1f85d7695e35f602fa9 | MATLAB | 3,455 | 103 | function [mappedX, mapping] = em_pca(X, no_dims, max_iter)
%EMPCA Run an EM-based implementation of (probabilistic) PCA
%
% [mappedX, mapping] = em_pca(X, no_dims)
%
% Performs probabilistic PCA on dataset X in order to reduce its
% dimensionality to no_dims. The dimensionality reduction is performed by
% means of an... |
158dcc6449e3c600968666601289a92f7d886b4d2bf477953a8bd92ca3f6dbdb | MATLAB | 3,456 | 92 | function weights = RGS(X_train, Y_train, extra_param)
% weights = RGS(X_train, Y_train, extra_param);
%
% Implementation of RGS algorithm, described in
%
% A. Navot, L. Shpigelman, N. Tishby, E. Vaadia. Nearest Neighbor Based Feature Selection for Regression and its
% Application to Neural Activity. Submitted to NIPS... |
dc55d72fba19bcf06f1378ca6eb651ba389f4b8ff55937f132cf863c6acfe448 | MATLAB | 3,462 | 88 | function [t, wv] = LoadTT_openephys(fn,records_to_get,record_units)
%LOADTT_OPENEPHYS MClust loading engine for open ephys data.
% LOADTT_OPENEPHYS loads open ephys data and fullfils all requirement for
% MClust loading engines (see MClust 3.5 documentation for details on
% MClust loading engines).
%
% Syntax... |
7c2982f81ae69004a8d46a9acc68bbd841a0a02caeb06890974654ff63cfcedf | MATLAB | 3,466 | 96 | classdef spatial_express_superphase < bt.model.template_spatial
% This is a template function that allows rapid prototyping of new spatial
% fitting by leveraging model.params.spatial_spectrum with absolutely
% no regard for performance
properties
% No new properties required by this derived class
end
methods... |
d31edd21271236cd96db7a02a7835c6f7d180b65c77c8538c4c388126f9d76b9 | MATLAB | 3,466 | 126 | classdef ListboxPair
% A pair of listboxes, when an entry is selected it is moved from one
% box to the other. (Typically used as a set of "available" and "use"
% boxes.
properties(Constant)
leftTitlePosition = [0.05 0.85 0.4 0.1];
leftBoxPosition = [0.05 0.05 0.4 0.80];
rightTitlePosition = [0.55 0.85... |
d7f50fd292459088316823e243a3442368a15e6d7e2d2aa249bbffa140956601 | MATLAB | 3,471 | 126 | % ========================================================================
classdef joystickManager < optickaCore
%> @class joystickManager
%> @brief Manages the Simia Joystick
%>
%> Copyright ©2014-2023 Ian Max Andolina — released: LGPL3, see LIv12345c12345CENCE.md
% ===================================================... |
27ca8a49c9d53cc8f31e42f43f3e65a8250d4fb07efb96631f366340d305c997 | MATLAB | 3,473 | 107 | function [R,eff] = randmio_und_connected(R, ITER)
%RANDMIO_UND_CONNECTED Random graph with preserved degree distribution
%
% R = randmio_und_connected(W,ITER);
% [R eff] = randmio_und_connected(W, ITER);
%
% This function randomizes an undirected network, while preserving the
% degree distribution. The func... |
8a7c8dd33b9706ee99ea313b520a50fdb61afb6f72bdc20920ca0767fa3c43e9 | MATLAB | 3,475 | 120 | function [eigvector, eigvalue, elapse, Y] = KPCA(options, data, ReducedDim)
%KPCA Kernel Principal Component Analysis
%
% Usage:
% [eigvector, eigvalue] = KPCA(options, data, ReducedDim)
% [eigvector, eigvalue] = KPCA(options, data)
%
% Input:
% options - Struct value in Matlab. T... |
1e65017d445dc11434a35ec32a366872344cafa1827c2c24b946da41dfe2d5f9 | MATLAB | 3,478 | 104 | function [w_Brain, model_All] = W_Calculate_SVR_PCA(Subjects_Data, Subjects_Scores, Pre_Method, C_Parameter, Netnum,Netfea,Voxelfea,Indicatorfea,Reduction_Method,randNet,ResultantFolder)
%
% Subject_Data:
% m*n matrix
% m is the number of subjects
% n is the number of features
%
% Subject_... |
9e924fd0297cc26cc02af0f6c5d1bf63ecb49eebf02f86be88d532a31adbee25 | MATLAB | 3,479 | 90 | %% ================================================================
% Script: behavior_prediction_neonatal_FOCA_GLM.m
% Purpose:
% Test whether neonatal internetwork covariance (FOCA matrix)
% predicts 18-month developmental outcomes (Bayley-III: Cognitive,
% Language, Motor).
%
% Description:
% - Each ... |
01cfb38239a986fbffe0108139c4ce61d99d36e4c98ca10a77563a2ab0b8abc0 | MATLAB | 3,480 | 107 | function jzmqBatchServer(cfg)
%> jzmqBatchServer — headless ØMQ server driven by JzmqCrossProcessTest.
%>
%> Runs in a secondary MATLAB instance (`matlab -batch`) and executes
%> three sequential phases, each controlled by handshake files created
%> inside cfg.tmpDir by the test process:
%>
%> 1. REP echo server boun... |
77fb72f15448b9fc7a7a2ffedf8b6937798d9b3ca18692682e18e641703bb153 | MATLAB | 3,485 | 92 | function nk_OOCV_batch(paramfile)
global NM
% Read parameter file
% ===========================
if ~exist(paramfile,'file')
error([paramfile 'not found. Abort job!']);
end
fid = fopen(paramfile);
params = textscan(fid, '%s');
fclose(fid);
% Parse parameters in params
% ==========================================... |
0b6806cff2aa83f9fad3f1951d2e91ed712e1a6eeba1a7bc3d4979f82002cb79 | MATLAB | 3,486 | 114 | classdef awsManager < handle
% Use aws CLI command from MATLAB
% Can install cross-platform with pixi:
% > pixi global install awscli
%
% Secrets can be kept locally using setSecret('AWS_ID')
% and setSecret('AWS_KEY'), then passed with getSecret
% e.g.
% aws=awsManager(getSecret("AWS_ID"),getSecret("AWS_KEY"... |
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