sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
eeab08701f6939077b44c3079b6c62ed9f16d05cf3667ed54a62b13a1c91795a | MATLAB | 5,413 | 152 | %% empirical_var_to_pwcgc
%
% Calculate sampling distribution for pairwise-conditional time-domain MVGCs
% from generated time series data for a specified VAR model
%
% <matlab:open('empirical_var_to_pwcgc.m') code>
%
%% Syntax
%
% FE = empirical_var_to_pwcgc(A,SIG,m,N,H0,nsamps,mtrunc,decayfac,regmode,acmaxlags,ac... |
500d06645be5f478932d3dc333beb5a8e02f440487c0dcad85a6dc2b7c46aabe | MATLAB | 5,414 | 164 | function [s, cfg] = ft_statfun_roc(cfg, dat, design)
% FT_STATFUN_ROC computes the area under the curve (AUC) of the Receiver Operator
% Characteristic (ROC). This is a measure of the separability of the data divided
% over two conditions. The AUC can be used to test statistical significance of being
% able to predict... |
2618796777c5499035178e3c5d1922c8aee1834046157dacb0c18627c50e102c | MATLAB | 5,415 | 110 | function [hdr] = read_nifti2_hdr(filename)
% READ_NIFTI2_HDR
%
% Use as
% [hdr] = read_nifti2_hdr(filename)
% where
% filename = string
%
% This implements the format as described at
% http://www.nitrc.org/forum/forum.php?thread_id=2148&forum_id=1941
%
% Please note that it is different from the suggested f... |
42940c1bdb46b3ba3df22cd782887e75134a47325cf640fe9ec4c8eea639304d | MATLAB | 5,419 | 165 | clear
%
figure
set(gcf,'color','w');
set(gcf, 'Position', [230,430,200,400])
load('position_info.mat')
for line = 1:2
if line == 1
load('20240207f_00005_analysis.mat', 'Dnum', 'data_compressed','stimuli_start_time','total_trials','RF_weight','p_values','total_ROI')
load('data_20240207f_... |
57a88dd9fb65d8b34aaa863864f6978592a5b0c135c7b90687a6f584402fe0a8 | MATLAB | 5,419 | 195 | %% function mypca
% [mu,pc_vec,pc_val,pc_rec,pc_coeff,err] = MYPCA(data,...)
%
% This function performs principal component analysis on the input data. It uses
% the Turk and Pentland trick to speed up computation if the dimensionality is
% larger than the number of samples.
%
% Input:
% data: data is a matrix of an... |
fa3ae7bac781b76c87df8d48dba0d0b45d3f4a7506e168e296ec1ce92ee7bbff | MATLAB | 5,419 | 151 | %% Plot expected and perceived pain like (Jepma et al., 2018) Fig 1c)
function figureOut = PlotLikeJepma2018Fig1c(xhatStats, xhat_Stats, ...
maxVAS, figNr, varargin)
% Plot xhat and xhat_ (avg + SE) as a function of stimulus temperature and
% cue type (blue for low cue, red for high cue) to all... |
171f1c21bd1298ff7e155b85a2332e7278b8e3cfff23da830011a1857786a2a0 | MATLAB | 5,420 | 181 | flynum = zeros(1,numel(lpos));
lbl_count = zeros(1,numel(lpos));
for ndx = 1:numel(lpos)
[~,~,~,~,mtch] = regexp(movf{ndx,1},'fly_*(\d+)_');
flynum(ndx) = str2double(mtch{1}{1});
qq = lpos{ndx};
nlabels = nnz(any(any(~isnan(qq),1),2));
lbl_count(ndx) = nlabels;
end
%%
zz = unique(flynum);
nflies = numel(zz... |
fbbe50eb0b5a15e1dc0b165c92aef80aa8a72a761089b5d0587abe11a5ff5bfc | MATLAB | 5,420 | 165 | function M = mvtk_read(filename)
% Read VTK formatted data from disk
% FORMAT M = mvtk_read(filename)
%
% filename - VTK-formatted file name
% M - data structure
%__________________________________________________________________________
%
% VTK File Formats Specifications:
% http://www.vtk.org/VTK/img/file-for... |
5c9bdd5cb3d196996a8e025eb72b5543ff231593c74fbddd69364f3d424bbb1a | MATLAB | 5,421 | 179 | function [Data, VoxelSize, FileList, Header] = Gradient_ReadAll(InputName)
% Gradient_ReadAll
% Read .nii/.nii.gz/.gii/.mat/.xlsx/.csv/.tsv/.txt files.
VoxelSize = [];
Header = struct();
if iscell(InputName)
if size(InputName,1) == 1
InputName = InputName';
end
FileList = InputName;
elseif exist(... |
053c605b4d37097eb660a4baf98021c68e044bc112a1ac90ef4dfbb9ba44e248 | MATLAB | 5,422 | 151 | clear P t
% This script performs mesh refinement in a selected domain using
% barycentric triangle subdivision (1:4). The input mesh must be 2 manifold
% Copyright SNM 2012-2018
% The Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General
% Hospital & ECE Dept., Worcester Polytechnic Inst.... |
a965f5151a926ae5a5eca5ede605b17fde06029fd3cf4ac9f1f4627716daedba | MATLAB | 5,424 | 168 | % eeg_store() - store specified EEG dataset(s) in the ALLEG variable
% containing all current datasets, after first checking
% dataset consistency using eeg_checkset().
%
% Usage: >> [ALLEEG EEG index] = eeg_store(ALLEEG, EEG);
% >> [ALLEEG EEG index] = eeg_store(ALLEEG, EEG, index)... |
ad6d5f3fc9e13e5ab62631dba7944707eb63b0af9a8de5135f4d9ef16a103730 | MATLAB | 5,425 | 144 | function vtkwrite_pathlines( filename,dataType,varargin )
% VTKWRITE Writes 3D Matlab pathlines into VTK file format.
% vtkwrite(filename, 'polydata', 'lines', x, y, z) exports a 3D line where
% x,y,z are coordinates of the points that make the line. x, y, z are
% vectors containing the coordinates of points of the... |
99e8cdfbda6f28512e40cc01135ee0ad0d374ab8dc261a16931d7b2fcf6db22b | MATLAB | 5,428 | 146 | function [figHandle, pValMat] = makePvalMatrix(eventRs, shuffleRs, eventJD, shuffleJD, varargin)
% [figHandle, pValMat] = utils.makePvalMatrix();
%
% Generates the preplay p-value matrix, as in Farooq et al., 2019.
%
% Input:
% eventRs, column vector of actual event weighted correlations
% shuffleRs, column cell ar... |
fdd723920961cac1cd60c441a2833dee7b9a764d6af91134553d8cff95e76772 | MATLAB | 5,428 | 151 | classdef SuhLeafRaker < handle
properties(SetAccess=private)
epp;
topKey;
leafKeys;
leafIds;
sizes;
figTable;
table;
data;
args;
cbBrowser;
end
methods
function this=SuhLeafRaker(epp, topKey)
this.epp=ep... |
ffa2fb7aa90f6b4bcf8725bce84aba21ebf76371895d82124d4cc5d85cbcdf54 | MATLAB | 5,428 | 114 | function [h_f,h_a] = plotFarField(nFig,axisValues,data,axisLabels)
% arguments
% nFig (1,1) double {mustBeInteger} % An integer denoting which figure number to make the plot in
% axisValues (1,:) cell % A (1,N) cell array, each element containing a 1D array of the values of the indepen... |
507632fc2ae1ac140b33ce6a95fc331734122d153baa9541441a2d19374731b0 | MATLAB | 5,429 | 150 | function colorcorrection_3D_poc(roundnum)
loadParameters;
if isfield(params,'COLOR_CORRECTION_MAX_THREADS')
maxNumCompThreads(params.COLOR_CORRECTION_MAX_THREADS);
end
if params.DO_DOWNSAMPLE
FILEROOT_NAME = sprintf('%s-downsample',params.FILE_BASENAME);
else
FILEROOT_NAME = sprintf('%s',params.FILE_BAS... |
6bbc573ea1ed78013c71d510e7ab7bb2875fe297cb89e5ab9e91a4682d8b2120 | MATLAB | 5,431 | 146 | function [M, L, Y, C] = lmnn(X, labels)
%LMNN Learns a metric using large-margin nearest neighbor metric learning
%
% [M, L, Y, C] = lmnn(X, labels)
%
% The function uses large-margin nearest neighbor (LMNN) metric learning to
% learn a metric on the data set specified by the NxD matrix X and the
% corresponding Nx1 ... |
f4d291b0e820a508049e9648d191e05470d9b6fd999387f6db2fbe8f69210427 | MATLAB | 5,432 | 170 | function [output extra_edges]=TailToHead(orig_edges,projected_nodes_coords,...
projected_nodes,aa,bb,cc,dd,EdgeOrder)
% [output extra_edges]=TailToHead(orig_edges,projected_nodes_coords,...
% projected_nodes,aa,bb,cc,dd,EdgeOrder)
% Orders edges in orig_edges ... |
fd9bcb6143099f18b82da9ff7c43a8fb4b9d346919582ed47effdabed96fe7c0 | MATLAB | 5,432 | 217 | % 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 2 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,... |
7a9bac6bb31b8287359ae7672657e801603c9eefacb7644cd9f2a5dd2692b6b8 | MATLAB | 5,433 | 146 | function hessfun = approxhessianFD(problem, options)
% Hessian approx. fnctn handle based on finite differences of the gradient.
%
% function hessfun = approxhessianFD(problem)
% function hessfun = approxhessianFD(problem, options)
%
% Input:
%
% A Manopt problem structure (already containing the manifold and e... |
d1b5c3b1f2b3e943286e9d2d777903cdf3876a53d23136c73a3fc42e4b760b46 | MATLAB | 5,434 | 153 | function oimg = loadtiff(path, sFrame, num2read)
% Copyright (c) 2012, YoonOh Tak
% All rights reserved.
% Modified by Eftychios Pnevmatikakis, 01/2017.
% Added into the ExSeqProcessing repo on 09/2017
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that ... |
09d730fed081b8b9484bd280bb4e9493dba7509e3192b88f341a9871e3254db4 | MATLAB | 5,438 | 171 | function CBIG_bandpass_vol(input_vol, output_vol, low_f, high_f, detrend, retrend, censor_file, sample_period)
% CBIG_bandpass_vol(input_vol, output_vol, low_f, high_f, detrend, retrend, censor_file, sample_period)
%
% Given an input 4D volume file name (input_vol): nifti (.nii) or cifti
% (.dtseries.nii), and sample... |
ea4f1eaaf8f624d6f50c81e147ad66781e54e0fbed6cf00a73ac0b8a33f031a1 | MATLAB | 5,438 | 186 | function [hF,hB] = imoverlay_timeseries(B,F,T,cmap,alpha,range)
ALPHADEFAULT = 0.4; % Default transparency value
CMAPDEFAULT = 'parula';
% Check image sizes
if ~isequal(size(B),size(F))
fprintf('Warning! Image sizes unequal. Undesired scaling may occur.\n');
end
% Check arguments
if nargin < 6 || isempty(range)... |
e483b3e81a13c1c4754c8a0f5a187d2a800df7f53c87a95b7c503c2f783b8289 | MATLAB | 5,439 | 174 |
function [MMT_Shoulder, MMT_Elbow, MMT_Wrist, MMT, Force_MT ] = Results_Function (xopt)
% MATLAB code provided by Seyed Mohammad Ali Rahmati
% Affiliation: Biomechanics and Motor Control Lab, Department of Biological Sciences, Georgia Institute of Technology, Atlanta, GA.
%
% xopt is activation for 40 muscles
% Act: ... |
01d96f7a5470bb5bb054388d4548e8750b03280302731ea7c08c01c0be58a5e5 | MATLAB | 5,441 | 224 | %% fnc_getSobolSequence: give a set of sobol quasi-random
%
% Usage:
% X = fnc_getSobolSequence(dim, N, dbpath)
%
% Inputs:
% dim number of variables, the MAX number of variables is 40
% N number of samples
%
% Output:
% X matrix [N x dim] with the quasti-rand... |
5ebfb0313aed81741fb4278c4e967f2e5330c8160abbb454df2fba86fd568daa | MATLAB | 5,441 | 181 | %% tsdata_to_cpsd
%
% Estimate cross-power spectral density from time series data
%
% <matlab:open('tsdata_to_cpsd.m') code>
%
%% Syntax
%
% S = tsdata_to_cpsd(X,fres,method,window,noverlap,nw,ntapers)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% X ... |
d52ba2bfb8d54eb5b42f30f9b43485df39256a554d97bc9e3e3980f5c05629bc | MATLAB | 5,443 | 135 | function [MEC_PET] = MEC_PET_atlas(PET_scan,MRI_atlas)
save_imgs = 0;
%% Slices
% Column 1 - left
regions = cell(15,2);
regions{1,1} = [78 30;84 30;89 32;93 35;95 40;93 45;85 43;78 39;75 34;]; %slice=67
regions{2,1} = [77 29;83 29;89 32;93 35;94 40;91 45;83 44;76 39;73 34]; %slice=68
regions{3,1} = [76 29;83 29... |
55613a1c517ee61ae2fd89b879c29b67468792f95d68080054822ace3eefba42 | MATLAB | 5,444 | 117 | function h_frameAnalysisGroupCalc3(handles)
% global FV_img
[currentInd, handles, currentStruct, currentStructName] = h_getCurrendInd3(handles);
set(handles.frameAnalysisPauseGrpCalc,'Enable','on');
currentFilename = get(handles.currentFileName,'String');
if isfield(currentStruct.state, 'frameAnalysisGrpCa... |
a98fc4a3e89561cc68da9f87efdd7d24d22e501a4d2d63ab885388f55b81176a | MATLAB | 5,446 | 185 | function [H, unitIdxLIP_Tin, unitIdxLIP_Tout, t, RT, include, coh, direction, choice, correct, ...
unitIdxLIP_dotsInRF, pulse_on, pulse_off, pulse_size] = get_data(dt_ms, neurons_to_include, dataset,par,varargin)
% AZ 2022 modified by NST 2023
dt_nans_before_RT = 0.05; % in seconds
for i=1:2:length(varargin)
... |
deee6fdd112384ff7c709167fef72e4afb5d9905fdd89872ff5394a5487b994a | MATLAB | 5,446 | 176 | %% WRAPPER for processing multi-ExR synapses data and converting to data table format%%
% Accompanies the manuscript by Kang*, Schroeder* et al., 2023
% Last modified by Margaret Schroeder on 6/3/23
% Note: you can run this section-by-section or all at once.
clear all
% Enter directory where your .mat files (outp... |
43a620d8057117e0c3f7806cb535bdeeee40eed1a9c25e320d02ef70409abe24 | MATLAB | 5,447 | 152 | % Author: Javier Lopez-Calderon & Steven Luck
% Center for Mind and Brain
% University of California, Davis,
% Davis, CA
% 2009
%b8d3721ed219e65100184c6b95db209bb8d3721ed219e65100184c6b95db209b
%
% ERPLAB Toolbox
% Copyright © 2007 The Regents of the University of California
% Created by Javier Lopez-Caldero... |
b6a879786b752a609a9cd080caa5927bb7cf59b9e669aac54b9c1b699efc3a19 | MATLAB | 5,447 | 156 | function M = multinomialfactory(n, m)
% Manifold of n-by-m column-stochastic matrices with positive entries.
%
% function M = multinomialfactory(n)
% function M = multinomialfactory(n, m)
%
% The returned structure M is a Manopt manifold structure to optimize over
% the set of n-by-m matrices with (strictly) pos... |
d04ce4dca33e7ffc69d1ad05fc5bf533d382bf7c8784707a617c8ebf21f7a62f | MATLAB | 5,448 | 200 | addpath /groups/branson/home/bransonk/behavioranalysis/code/Jdetect/Jdetect/filehandling;
addpath /groups/branson/home/bransonk/behavioranalysis/code/Jdetect/Jdetect/misc;
addpath /groups/branson/bransonlab/projects/flyHeadTracking/code/
addpath /groups/branson/home/bransonk/tracking/code/Ctrax/matlab/netlab
%%
J = ... |
8a505a377ed1c9c1120fc4d74d701c7ca895c9cd65535163b871a8d805c9bb68 | MATLAB | 5,449 | 132 | clear all
addpath(genpath('/gpfs51/dors2/gpc/JamesBooth/JBooth-Lab/BDL/LabTools/nifti'));
direc = '/gpfs51/dors2/gpc/JamesBooth/JBooth-Lab/BDL/jinwang/PlausGram_9-10/preproc';
direcOutput = '/gpfs51/dors2/gpc/JamesBooth/JBooth-Lab/BDL/jinwang/PlausGram_9-10/ConjunctionAnalysis';
contrasts = [10 13]; %[3 6];
%001 Gram_v... |
d0742cfdb2d4e69d54a960d7eb05dde5699aa7dcefa131515468311bed716b89 | MATLAB | 5,450 | 122 | function [PCs] = y_CompCor_PC(ADataDir,Nuisance_MaskFilename, OutputName, PCNum, IsNeedDetrend, Band, TR, IsVarianceNormalization)
% FORMAT [PCs] = y_CompCor_PC(ADataDir,Nuisance_MaskFilename, OutputName, PCNum, IsNeedDetrend, Band, TR, IsVarianceNormalization)
% Input:
% ADataDir - The data direcotry
% Nuisanc... |
1679563b71796a06d6311dec4f342a63f9a04f9487e8157b25a64e46716d871a | MATLAB | 5,451 | 162 |
% Calculate the strength of phase-amplitude coupling between two LFPs,
% which is shown by modulation index (Tort et al. 2010 J Neurophysiol)
% Calculate coupling for each of segmented epochs from the state to analyze
% (whole session, freezing or non-freezing) and take average over all epochs
clc; clear;
% ---... |
82d657db613d14861657e4a9d850075e7ff60ec36a4691cbd5b98796c23ffe5c | MATLAB | 5,451 | 135 | clear all
trialCOUNT = 40;
%% DATA with rule biased towards depression (with no plastic F -> VIP)
external_stimulation = 3; %0: neither CS nor US; 1: only CS; 2: only US; 3: US+CS
condition = 1; % 0: before FC; 1: during FC; 2: after FC;
%experiment = 0; %0: PV and SOM; 1: without SOM; 2: without PV; 3: neither PV ... |
8656c1946e3ab8e001ef8dafbd897f31293e0a92536a05a854282accb3c6a139 | MATLAB | 5,451 | 163 | function [ ] = bz_plotEphys( lfp,varargin )
%bz_MultiLFPPlot(lfp). This function plots multiple lfp channels from a
%buzcode lfp structure with the channels appropriately spaced.
%
%USAGE
% figure
% bz_MultiLFPPlot(lfp)
%
%INPUT
% lfp a buzcode lfp structure. channels are assumed to be ordered
% ... |
c2e13b255dfb88579a0ee443f2415250bf6dcd707d4bd5d7ba3169ebe516c72a | MATLAB | 5,454 | 138 | %% Calc Prediction Error
% For use within the SDO Analysis Toolkit.
% Generate the differences between the predicted t1 state and the observed
% t1 state for all cases of predictions shown.
%
% Error rates are only running (error x position) or cumulative
% Summed running error == total error.
% Terminal cumulative e... |
0b08d27a0b60ab8bdfad9be75c62c2eec7378097a1e919c17b297dfadf072d3c | MATLAB | 5,455 | 185 | function [waf,tau,theta] = ambifuwb(X,fmin,fmax,N,trace);
%AMBIFUWB Wide-band ambiguity function.
% [WAF,TAU,THETA]=AMBIFUWB(X,FMIN,FMAX,N,TRACE) calculates
% the asymetric wide-band ambiguity function.
%
% X : signal (in time) to be analyzed (the analytic associated
% signal is considered), of length Nx.
... |
217147b8fcd0a8fd1c6d3cab59bf0b258509ac0bd06edf9af1383b7be7b8c03d | MATLAB | 5,458 | 188 | % meg_plotSubjectBehav
% MEG_PLOTSUBJECTBEHAV
%
% Karen Tian
% August 2020
%% input
expt = 'TANoise';
saveFigs = 1;
figDir = '/Users/kantian/Dropbox/Data/TANoise/MEG/Group/figures/ITPCUpDown';
%% setup
[sessionNames,subjectNames,ITPCsubject,ITPCsession] = meg_sessions(expt); % session & subject info
p = meg_para... |
1f7785a0f2585bf6abbf3f1ebbeaa83428d0d42534568ac744d3a49340041d21 | MATLAB | 5,461 | 113 | function CBIG_AuthorTopicEM_RunEMwithGibbsInitExample
% CBIG_AuthorTopicEM_RunEMwithGibbsInitExample
%
% Wrapper function to unit-test a coordinate-based meta-analysis with
% the author-topic model and expectation-maximization (EM) algorithm.
% The inference was performed with the following hard-coded hyperpa... |
522ff8bfbfdc04005e1ce291069603ec8a32944ce4841c546b887f7b72905b80 | MATLAB | 5,464 | 140 | % Author: Birgit Nierula
% nierula@cbs.mpg.de
function ga_detection_stats(subjects, condition, srmr_nr, stat_number, has_allsubj)
%% set variables
savepath_ga = getenv('GADIR');
% get condition info
[cond_info] = get_conditionInfo(condition, srmr_nr);
cond_name = cond_info.cond_name;
nerve = cond_info.nerve;
str_st... |
5f270886e371f53e93373702f1e87087a4d6298469335d51475d834feaeed73d | MATLAB | 5,464 | 106 | %--------------------------------------------------------------------------
% Title: Initial Dual-band HFO detection with pseudo-HFO elimination
% Author: [Behrang Fazli Besheli]
% Email: FazliBesheli.Behrang@mayo.edu
% Institution: [Mayo Clinic, Department of Neurosurgery]
% Date: [09/07/2024]
%----------------... |
c9f879e70267498d9dc4f4bae083b00963ab1ed3280e0611183b959b5277321e | MATLAB | 5,466 | 150 | function [Y, problem, S] = elliptope_SDP(A, p, Y0)
% Solver for semidefinite programs (SDP's) with unit diagonal constraints.
%
% function [Y, problem, S] = elliptope_SDP(A)
% function [Y, problem, S] = elliptope_SDP(A, p)
% function [Y, problem, S] = elliptope_SDP(A, p, Y0)
%
% A is a real, symmetric matrix of... |
43f23f571f096867f5b6ac62314ff5fcd74bda2d61e0d172130ce993a471e156 | MATLAB | 5,473 | 172 |
function [ csd, lfpAvg ] = bz_eventCSD (lfp, events, varargin)
% [ CSD ] = bz_eventCSD (lfp, events, varargin)
% Calculates event-triggered (i.e. SWRs) CSD map from a linear array of LFPs
% INPUT
% lfp a buzcode structure with fields lfp.data,
% lfp.tim... |
461ac57d8fa6160e47e0b1128fd5a878ed4193299003d2a9c1a735d26e23fd15 | MATLAB | 5,475 | 130 | function [ titleStr, settings, results ] = samplingTool( dataview, settings )
% The samplingTool serves to subsample data. Based on one or several binning
% variables (settings.binningVars), datapoints are selected such that the
% number of datapoints falling into each bin is either limited by some uppe... |
d0bfd98671a930cbf86da5f8a40d0b498202464c15e05b5781b298ede2876810 | MATLAB | 5,480 | 141 | function [phi] = semi_infinite_FD(mua, musp, n, frequency, rsd, varargin)
% SEMI_INFINITE_FD Calculates photon fluence rate at any given point of
% semin-infinite medium assuming global absorption, scattering and
% refractive index.
%
% SYNTAX:
% [PHI] = SEMI_INFINITE_FD(MUA, MUSP, N, FREQUENCY, RSD)
% [PHI] =... |
e69cbf75be2f64178f535efab3ae60fd58a722f20cbc6b57aaa7d226fe67cd21 | MATLAB | 5,480 | 168 |
function [MMT_Shoulder, MMT_Elbow, MMT_Wrist, MMT, Force_MT ] = Results_Function (xopt)
% xopt is activation for 9 muscles
% MMT is a function of muscle activation (xopt) & time step (TS); TS has
% been loaded
% Act: muscles activations (9 muscles) at time step of TS
% TS: time step (example: 0 to 101)
% Motion res... |
605576a37ec8875ae747e316268068b160995107bbf10955c97ec57555ef2d58 | MATLAB | 5,484 | 133 | function [SNR_overall,SNR_synapses,nsynapses] = analyze_mExR_validation(fov,params)
% Analyzes validation data, with same proteins stained in consecutive rounds
% Outputs
% SNR: the overall image SNR in each round for each channel
% SNR_synapses: the SNR for each synapse over each round
% nsynapses: the number of syna... |
a7406d4d007cdce0635841e811a628a940276fe415ad2ec6058f3ed4e934daa0 | MATLAB | 5,484 | 174 | function [xymax,smax,xymin,smin] = extrema2(xy,varargin)
%EXTREMA2 Gets the extrema points from a surface.
% [XMAX,IMAX,XMIN,IMIN] = EXTREMA2(X) returns the maxima and minima
% elements of the matriz X ignoring NaN's, where
% XMAX - maxima points in descending order (the bigger first and so on)
% IMAX... |
bbe1162ac54a904fbb2310d54158ef4c271c33bfd4548c1e5af3814d387d0192 | MATLAB | 5,486 | 108 | format compact;
FLIM_main_folders = ''; % Main working directory
ShortNames = {'R1_6hr', 'R1_24hr', 'R1_72hr', 'R2_72hr', 'R2_24hr', 'R2_6hr'}; % Folder names for experiments
FLIM_Results_folders = {}; % Folders to save the results
FLIM_RawImage_folders = {'Folder1', 'Folder2', 'Folder3', 'Folder4', 'Folder5', 'Fol... |
e2f52a52b3487d84b6b86ed8cbff2e3dc72f814ae088cdc3455e24f7302bbd6a | MATLAB | 5,488 | 172 | %% Plot Figure 6
function figureOut = PlotFigure6( R1, R2, n1, n2, trueAs, iEvent, ...
xhatArr, AhatArr, BhatArr, figNr)
% Plot Ahat, Bhat and R (R1 for n1 time points and R2 for n2 time points) in
% upper subplots and pain (xhat) in lower subplots, for different values of
% Aha... |
87e2139530958c79b3d3915e65d162c150316a45b9a34b63784281bffcb39dda | MATLAB | 5,489 | 172 | % FADE-SAME: Figure S6
% clear
% close all
%%% Step 1: load data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% set results files
FADE_file = '../FADE_scores/FADE_SAME_scores_2021_01_11_FADE_yFADE.xls';
covs_file = 'covariates/covariates.xls';
% load results files
[num, txt, raw1] = xlsread(FADE_file);
[nu... |
daa05fe8580a5a835dc30dc3fda854f7939ec8312460ab65940d39b7b57fdde5 | MATLAB | 5,499 | 149 | function [Y, problem, S] = elliptope_SDP_complex(A, p, Y0)
% Solver for complex semidefinite programs (SDP's) with unit diagonal.
%
% function [Y, problem, S] = elliptope_SDP_complex(A)
% function [Y, problem, S] = elliptope_SDP_complex(A, p)
% function [Y, problem, S] = elliptope_SDP_complex(A, p, Y0)
%
% A is... |
870a1f14d384c6f1efd973a0263233e9202003c3481a1c4fab85063f2aa0e3e2 | MATLAB | 5,500 | 144 | function y_ICC_MSb_Image(Rate1Dir,Rate2Dir,OutputName,MaskFile)
% function y_ICC_MSb_Image(Rate1Dir,Rate2Dir,OutputName,MaskFile)
% Calculate the between-subject mean square (MSb, a component of Intraclass correlation coefficient) for brain images.
% Input:
% Group1Dir - Cell, directory of the first group. Ta... |
4dd4535a00d414010abf955b77ac7ad77fbca31db10df912775cf43ec79f678c | MATLAB | 5,501 | 145 | function [Image_Contrasted,cMap_Contrasted,Image_Contrasted_Color,cMap_Contrasted_Color,ContrastHigh,ContrastLow,cMap]=...
ImageContraster(RawImage,Label,ContrastHigh,ContrastLow,ChannelColor,BitDepth,TurboMode)
%jheapcl
% RawImage=BrpImage_Crop;
% Label=[];
% ContrastHigh=[];
% ContrastLow=[];
% BitDepth=16;
% Ch... |
6419fc650ae90f1f5cb805b37b131dc7e716a5fd81839279bc9945c8a2102434 | MATLAB | 5,504 | 166 |
%
% Trial-concatenation across the datasets. Allows for trials to be combined
% together; Handled at the level of xtDataCell and ppDataCell;
function obj = concatenateTrials(obj, useTrials)
arguments
obj
useTrials = 1:obj.nTrials;
end
% Concatenate data from given trials;
% Combin... |
a59374c0ec474a3dbb6ce7ec278911dd0c4086ed14c51eb7ff312edc6bad3a71 | MATLAB | 5,508 | 176 | % MF3 Multifrontal factorization for nearest-neighbor interactions on a
% regular mesh in 3D.
%
% This is an optimization of MFX for the special case of nearest-neighbor
% interactions on a regular mesh in 2D. A given mesh node is allowed to
% interact only with itself and its 26 immediate neighbors, thu... |
402d5b7f633ef183dbc7f0ec718535c9f59fdaf44d31d19fe57400d2644246ee | MATLAB | 5,511 | 156 | ti = 0;
vs = 1;
tauk = 50; %Cambiar también dentro de la funcion de histeresis_model
tf = 10;
tr1 = linspace(ti, tf, 10000);
gH = 0.7*10^-6*ones(size(tr1));
gL = 0.1*10^-6*ones(size(tr1));
Cm = 0.01*10^-6*ones(size(tr1)); %Cm = 0.01 para los pulsos inductivos
Vm = 0.1;
V1 = 2;
lamda_ini = [0,0];
Rs = 2000000... |
bbc38e6ac50b7afc2ac0af75400f247d45f6bbc342d3ad2069f4d68bbd53c6fc | MATLAB | 5,511 | 144 | function GridDim = determine_griddim(elec)
% DETERMINE_GRIDDIM uses the labels and positions of electrodes in elec to
% determine the dimensions of each set of electrodes (i.e., electrodes with
% the same string, but different numbers)
%
% use as:
% GridDim = determine_griddim(elec)
% where elec is a structure th... |
f1c21238f1927c1da27334987841deff8ee42a24bfb2a9cc83a0d78b9b252b93 | MATLAB | 5,513 | 212 | function LAN = lan_rm_TMS(LAN,cfg)
% v.0.5 en prueba
%
% cfg.events = [x1 x2 ... ] % evente labels that indicated TMS pusle
% cfg.times = [s1 s2] % time to interpolated betweeen TMS pulse
% cfg.npulse = n % number o oulse to be remove togetehr (number of pulse of a tren)
% cfg.edge = s3;
% cfg.tim... |
0110b1f1b7a53f53a0273c76cada06b1115ff0f62e2e46b82c7dc4802767c4f3 | MATLAB | 5,514 | 211 | function glm_fit = run_glm_neuron(behav,ephys)
global glm
if(size(ephys.neural_data,3)==0)
error('no neurons')
end
if(size(ephys.neural_data,3)>1)
error('more than one neuron')
end
glm.n_neur=1;
disp('running for a single neuron');
prep_glm;
x = build_matrix_regressors(behav,ephys);
glm_fit.x = x;
... |
20e271edb5fa1a1845a7aa6f43b851cf80f2be40daaecbe2238dba43c6d33bb6 | MATLAB | 5,514 | 206 | %%% INPUT THE IMAGE FILE NAME:
if ~exist('fc')|~exist('cc')|~exist('kc')|~exist('alpha_c'),
fprintf(1,'No intrinsic camera parameters available.\n');
return;
end;
KK = [fc(1) alpha_c*fc(1) cc(1);0 fc(2) cc(2) ; 0 0 1];
%%% Compute the new KK matrix to fit as much data in the image (in order to
%%% accomodate... |
1c290db5793856d780b89a3cb695972f6c33f629c8bf4afd40b5a6ec0c35f77e | MATLAB | 5,516 | 158 | function ie_square(n,occ,p,rank_or_tol,method)
% IE_SQUARE An example usage of strong skeletonization, solving a
% first-kind integral equation (Laplace single-layer potential) on the
% unit square. Sane defaults are provided for all parameters.
%
% IE_SQUARE(N,OCC,P,RANK_OR_TOL,METHOD) runs the example on a regu... |
8ee819afc207b302f690f34737f2e863be93e162f4a497b93f8e8ec86b0500d4 | MATLAB | 5,516 | 144 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% MAIN SCRIPT %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% To generate all figure panels from the paper, run the following cells in order.
% Please... |
7de5c4da50062cfc5fcd0d8f4e70e64551c98488c96360017ec748c436183687 | MATLAB | 5,517 | 116 | function params = CBIG_EM_AT(corpus, paradigm_by_exp, params)
% params = CBIG_EM_AT(corpus, paradigm_by_exp, params)
%
% Runs EM algorithm to estimate parameters of the Author-Topic model
% Note that compared to CBIG_EM_AT_vol.m, this function assumes the Author-Topic model is applied to activation
% foci on the brain... |
043bc57400dd03936e250907b3068b0ea5283dbe06603a692119f8f760c339b6 | MATLAB | 5,518 | 116 | function output = Downsampling(filename)
%IMPORTFILE ÅؽºÆ® ÆÄÀÏÀÇ ¼ýÀÚÇü µ¥ÀÌÅ͸¦ Çà·Ä·Î °¡Á®¿É´Ï´Ù.
% SUC1465 = IMPORTFILE(FILENAME) µðÆúÆ® ¼±Åà Ç׸ñÀÇ ÅؽºÆ® ÆÄÀÏ FILENAME¿¡¼ µ¥ÀÌÅ͸¦ ÀнÀ´Ï´Ù.
%
% SUC1465 = IMPORTFILE(FILENAME, STARTROW, ENDROW) ÅؽºÆ® ÆÄÀÏ FILENAMEÀÇ
% STARTROW Çà¿¡¼ ENDROW Çà±îÁö µ¥À... |
a3ce4af42cb42eaab4674dc82b6c167d61594766c0a311d30058c43ec78c82c9 | MATLAB | 5,519 | 140 | function [s, cfg] = ft_statfun_depsamplesT(cfg, dat, design)
% FT_STATFUN_DEPSAMPLEST calculates the dependent samples T-statistic on the
% biological data in dat (the dependent variable), using the information on the
% independent variable (ivar) in design.
%
% Use this function by calling one of the high-level stati... |
b8db874f64e2189c86b15a0af3bf280c3036dd698d8af0b4736c55f4a620224f | MATLAB | 5,520 | 143 | function y_ICC_MSw_Image(Rate1Dir,Rate2Dir,OutputName,MaskFile)
% function y_ICC_MSw_Image(Rate1Dir,Rate2Dir,OutputName,MaskFile)
% Calculate the within-subject mean square (MSw, a component of Intraclass correlation coefficient) for brain images.
% Input:
% Group1Dir - Cell, directory of the first group. Tak... |
edc3878a75dbecd8e38575ebd10d39e4373090f9ba514418882b825f40b26d4b | MATLAB | 5,521 | 189 | function CPD = condMixGaussTiedCpdCreate(mu, Sigma, M, varargin)
%% Create a local CPD representing a tied mixture of Gaussians
% where the state of the hidden parent influences only the mixing weights,
% not mu and Sigma. When used as the observation model for an HMM, it is
% called a semi-continuous or tied-mixture H... |
fcdf144638bfd92e47dfc67cf59a56a191e5cc80e028a39a8a46e43bcabd7b76 | MATLAB | 5,523 | 151 | % Five-point stencil on the unit square, constant-coefficient Poisson equation,
% Dirichlet boundary conditions.
%
% This example solves the Poisson equation on the unit square with Dirichlet
% boundary conditions. The system is discretized using the standard five-point
% stencil; the resulting matrix is square, real, ... |
fbf61b9d3cfc687216ca33411f7bd68036cd16dd5e1542d9c84b2757fda638fa | MATLAB | 5,524 | 168 | %% MVGC permutation test demo
%
% Demonstrates permutation significance testing with MVGC on generated VAR data
% for a 5-node network with known causal structure (see <var5_test.html
% |var5_test|>). Pairwise-conditional Granger causalities are estimated and
% significance tested using both the theoretical and permuta... |
0bdefce7c129ada9021a9ae215176033a02f641a87eed15394bf41657313189e | MATLAB | 5,525 | 128 | function y_Call_DPABISurf_VIEW_FromVolume(BrainFile,OutputName,NMin,PMin,Mask,Mask_Rule,ClusterSize,ConnectivityCriterion,Space,SurfUnderlay,ColorMap,NMax,PMax)
% function y_Call_DPABISurf_VIEW_FromVolume(BrainFile,OutputName,NMin,PMin,Mask,Mask_Rule,ClusterSize,ConnectivityCriterion,Space,SurfUnderlay,ColorMap,NMax,PM... |
83dd1b20bef3b57866c713927e0a664b494eb3ac5d6ee61c95d76137cde19aec | MATLAB | 5,525 | 214 | % [mu,cost,idx,threshx] = onedimkmeans(x,k,varargin)
%
% the optimal k-clustering of n 1-dimensional points is computed in
% time O(n^2*k), space O(n^2+n*k)
%
% inputs:
% x: n x 1 vector of one-dimensional data points
% k: number of clusters
% optional:
% 'issorted': true/false, assumes the data is sorted from smalle... |
c1143809081fd506283ad4d26b3f81feae7541562a762c14790eb89400bde53c | MATLAB | 5,526 | 168 | function headmodel = ft_headmodel_fns(seg, varargin)
% FT_HEADMODEL_FNS creates the volume conduction structure to be used
% in the FNS forward solver.
%
% Use as
% headmodel = ft_headmodel_fns(seg, ...)
%
% Optional input arguments should be specified in key-value pairs and
% can include
% tissuecond = matr... |
24b74f763143b94f1fd3ee6d90818b202c3b402604d8756bcde0b7fa46391b0d | MATLAB | 5,529 | 202 | function M = spherefactory(n, m)
% Returns a manifold struct to optimize over unit-norm vectors or matrices.
%
% function M = spherefactory(n)
% function M = spherefactory(n, m)
%
% Manifold of n-by-m real matrices of unit Frobenius norm.
% By default, m = 1, which corresponds to the unit sphere in R^n. The
% m... |
2f6d3c30e47c8c236ae0a0ddf6c17af4c3100c5a5f06b764e919f1a492299dc2 | MATLAB | 5,529 | 140 | % CheckSizes.m
% Sarah West
% 6/21/22
% Function that searches through your iterators for the inputted file
% names. Does NOT use RunAnalysis, but has similar input structure.
function [] = CheckSizes(parameters)
% Make a loop list from iterators.
looping_output_list = LoopGenerator(parameters.loop_list, pa... |
1585c476f1be7a25ccb9e756e2c8a5b41b6d4bdf7d020de814ef3622a73266a9 | MATLAB | 5,533 | 140 |
puncta_present = ones(N,readlength);
%Get the total number of voxels involved for the puncta
%used to initialize data_cols
total_voxels = 0;
for n = 1:N
total_voxels = total_voxels + length(puncta_set_cell_filtered{1}{n});
end
puncta_set_normalized = puncta_set_cell;
%Create a clims object for each round
%This ... |
32ee475fb0c6f5de3ba747561bffbdbe45b6c817f0bf79954ded8413e1ec336f | MATLAB | 5,533 | 113 | function plot_all_sessions(varargin)
cd 'X:\BendorLab\Drobo\Neural and Behavioural Data\Rate remapping\Data'
load folders_to_process_remapping
parameters = list_of_parameters;
f1 = figure;
data_across_tracks(1).place_field_diff=[];
data_across_tracks(1).mean_spike_diff=[];
data_across_tracks(1).mean_spike... |
f50bb7b0c5738dd76e96711a85f0493b8ea69494d2fcc3ca06c42df573f8d4cb | MATLAB | 5,533 | 139 | function [dat] = read_plexon_ddt(filename, begsample, endsample)
% READ_PLEXON_DDT reads header or data from a Plexon *.ddt file,
% which is a Plexon continuous data file optimized for continuous
% (streaming) recording where every channel is continuously recorded
% without gaps and the recording includes any dead tim... |
f6bb4e27d2998d6d57432b73919723a668a8b24aaf905aa0954087e04abb82b6 | MATLAB | 5,535 | 140 | classdef PathTruncationUtils
% Utility class for truncating long file paths in GUI components
% Provides methods to calculate field widths and truncate paths with ellipses
methods (Static)
function truncated = truncateFilePath(filepath, varargin)
% Helper function to truncate long f... |
41a0ffa109f4acfcd4fe0568625658316473b49f85cb625f5899e51c9cfff9fe | MATLAB | 5,537 | 127 | % Evaluate estimated sources based on saved resource after performing otsu
% threshold.
clear
dataset_name = '_sample_source2';
fname = '_sample_source2';
model_id = '148';
gt = load(['../source/Simulation/test' dataset_name '.mat']); % ground truth
num_source = size(gt.selected_region, 2);
load(['.... |
634f6411a84de486d6264e69a91927e1530bdbee25b0b39c24461f58f96147be | MATLAB | 5,538 | 158 | function [h_sites,h_steps,food_start_ph,ax] = plot_arena(ax,sites,simParam,colorMapSites,colorMapSteps,minmaxSiteMarkerSize,minmaxStepLineWidth,siteArgs,stepArgs)
if isempty(ax)
fh = figure;
ax = axes;
end
if (nargin < 4) || isempty(colorMapSites) % if set to false, then does not plot sites
... |
e7e48a6e5ad05a885772d35f7e75d90783d25abf2974b804028e7942783d6ca3 | MATLAB | 5,539 | 156 | %code snippet to demonstrate how to get 2D head position data from 3D data
%saved in Kine data structure.
%% getting data files
[f,p] = uigetfile('*.mat','Please select Kine file containing HEAD position data');
headDataFname = [p,f];
bodyDataFname = [p,f];%quick hack, some videos don't contain body data
load(hea... |
569cf81f95bd7b94cdb58522193c4bf1c0cb7442ef70c8e86477d9ac1ab83221 | MATLAB | 5,541 | 204 | function [stats,st,a] = lme_RgFSfit(X,Zcols,Y,ni,Theta0,Dist,model,e)
% [stats,st,a] = lme_RgFSfit(X,Zcols,Y,ni,Theta0,Dist,model,e)
%
% Linear mixed-effects estimation by the Fisher scoring algorithm for a
% whole region. This function is intended to only be called from other
% functions to perform region-wise mass-... |
8c83968f12cd326a916a4aab57a79d178b5243c13f2a878f272ee8dc45fe5559 | MATLAB | 5,541 | 158 | function out = read_nervus_data(nrvHdr, segment, range, chIdx)
% read_nervus_data Returns data from Nicolet file.
%
% OUT = GETDATA(NRVHDR, SEGMENT, RANGE, CHIDX) returns data in an n x m array of
% doubles where n is the number of datapoints and m is the number
% of channels.
%
% NRVHDR is a header from the f... |
d66ba88792b9b26e6804fc7d2b924c92850b932705321fd294df125513eabc31 | MATLAB | 5,541 | 141 | %% Overview: MVGC computational pathways
%
% The diagram below, adapted from the MVGC Toolbox reference document> [1]
% (Section 3) illustrates key computational pathways implemented in the toolbox.
%
% <<mvgcschema.png>>
%
% A great deal of effort has gone into identifying useful, accurate and
% numerically efficient ... |
f9e623063fb6ce2ae95b56cded284d8209cecd3812acc5667318cad8045ee3ee | MATLAB | 5,542 | 158 | function out = read_nervus_data(nrvHdr, segment, range, chIdx)
% read_nervus_data Returns data from Nicolet file.
%
% OUT = GETDATA(NRVHDR, SEGMENT, RANGE, CHIDX) returns data in an n x m array of
% doubles where n is the number of datapoints and m is the number
% of channels.
%
% NRVHDR is a header from the f... |
db64ac4a00ce7e7e5947a1a2945d8fe6fad7553fc0abb2d693fdfdf50e526fbe | MATLAB | 5,543 | 149 | % Second-kind integral equation on the unit square, Helmholtz single-layer.
%
% This is the same as IE_SQUARE3 but using HIFIE2X.
%
% Inputs (defaults are used if not provided or set empty):
%
% - N: number of discretization points in each dimension (default: N = 128)
% - K: wavenumber (default: K = 2*PI*4)
% - O... |
6c3159ccea53b4818329991d32a5b224d9bcbe5ea89b534a42877b3347fd74ad | MATLAB | 5,544 | 219 | function [averages, stderrs, data, indices] = selective_average(y, onsets, varargin)
% Purpose: Get a selective average of values of data vector y, given
% onsets specified in onsets. Onsets can be fractional; in this case,
% linear interpolation is used.
%
% :Usage:
% ::
%
% [averages, stderrs, data, indices] = se... |
3b8bc8eea879197da6ed7809935620dd3552f2787aca606bd8eb6828e7f64977 | MATLAB | 5,545 | 181 | scrsz = get(0,'ScreenSize');
scrWidth = scrsz(3);
scrHeight = scrsz(4);
datRoot = '/Volumes/LaCie/Farries recordings/';
% choose rat
rat = 'IM1056';
sesList = dir([datRoot rat '/20*']);
sesList = arrayfun(@(x) x.name, sesList, 'UniformOutput', false)
% choose session
iSes = 2;
sesList{iSes}
datDir = [datRoot rat '/' ... |
d478a1fab99703ab91bc39a5292824ba0f1a7f38905c9b4475c98faa28fba4f9 | MATLAB | 5,548 | 131 | function [AllVolumeBrain, Header_Out] = y_Scrubbing(AllVolume, OutputName, MaskData, TemporalMask, ScrubbingMethod, ScrubbingTiming, IsNeedDetrend, Band, TR, Header, CUTNUMBER)
% [AllVolumeBrain, Header_Out] = y_Scrubbing(AllVolume, OutputName, MaskData, TemporalMask, ScrubbingMethod, ScrubbingTiming, IsNeedDetrend, Ba... |
a4fb68c0b50a3fdd2656a38f01eb53d2eddeed385679e28116459d179cab5a2e | MATLAB | 5,553 | 158 | function obj = TLreadLogs(obj,participant_TL_task_log_path)
%set up
[a,b,c]=fileparts(participant_TL_task_log_path);
obj.td.logs.fnames={[b c]};
obj.td.logs.copied=true;
%specify name of dataset with arbitrary name
obj.par.name = 'dataset';
if ~isempty(obj.td) && isfield(obj.td,'logs') &... |
6ed1dee130628d0353d326daab6a5709eb5e89f29d93e0aabf8c88af4b141c78 | MATLAB | 5,555 | 226 | function A = meg_decode(data, classLabels, classNames, p)
% function A = meg_decode(data, classLabels, classNames, p)
%
% INPUTS
% data
% data matrix, time x channels x trials
%
% classLabels
% vector of numerical class labels, trials x 1
% NaN values are ignored.
%
% classNames
% cell array of class names (o... |
75f2743b8af8ac43652644fa2796e9636f9f078ef636494991f75464690374eb | MATLAB | 5,556 | 122 | function [lpfdvCensoredVolumes] = lpfDVcensoredvolumes(timeSeries,brainMask,TR,DV_GEV_d)
% This function returns a logical vector denoting which volumes are
% targeted for removal using GEV-DV based on input time series, a gray
% matter mask in the same space as the input time series, the TR (for
% low-pas... |
247e1825e7588efab990d3501e1fe9a9829e1f4fa28fcf673385799836b91105 | MATLAB | 5,558 | 135 | % ConcatenateData.m
% Sarah West
% 4/9/22
% Concatenates data together.
% Input: parameters.data,
% parameters.concatenated_data - previously concatenated data that
% you're adding this data to.
% parameters.concatDim -- the dimension the data should be
% concatenated across.
% Output:
... |
381b0cf288fceb3c5cad039dbaff45e82f0d756aade78edc9f5d2d23360de7b2 | MATLAB | 5,558 | 167 | function [source] = ft_source2sparse(source)
% FT_SOURCE2SPARSE removes the grid locations outside the brain from the source
% reconstruction, thereby saving memory.
%
% This invalidates the fields that describe the grid, and also makes it
% more difficult to make a plot of each of the slices of the source volume.
% ... |
a689270ef4a4ce08ae5202caa1560fa4821622bfada086bd9a69b01ea7ea19e6 | MATLAB | 5,558 | 136 | function [corr_LxLy_task,pvals_corr_LxLy_task] = CBIG_VK2019_validation_taskFC...
(Y_rest,U_rest,V_rest,taskFC_dir,signif_LC,nRois,names_subj,grouping,nPerms,normalization_img)
%
% This function aims to validate PLS latent components (LCs) obtained with
% RSFC on task FC. The RSFC saliences are projected onto the ... |
60da6be99ab78229d9930cc3c61d32b5cae56c7e8b24c4673e8aaa80116cfac2 | MATLAB | 5,560 | 162 | function [dataout] = ft_channelnormalise(cfg, data)
% FT_CHANNELNORMALISE shifts and scales all channels of the the input data.
% The default behavior is to subtract each channel's mean, and scale to a
% standard deviation of 1, for each channel individually.
%
% Use as
% [dataout] = ft_channelnormalise(cfg, data)
%... |
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