sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
9d10ebea9d29f8d90d77c6e447d9cbbb6ea43930ff8843bda880d54ed98fd692 | MATLAB | 5,962 | 157 | function fear_getIcaCoactTimeHighThreshold(basename,varargin)
% clear
% basename='~/data/Fear/triple/hoegaarden181115/hoegaarden181115';
% basename='~/data/Fear/triple/achel180320/achel180320';
param.thrshold=25; %in (z-score)^2
load([basename '.basicMetaData.mat'])
load([basicMetaData.AnalysesName '-icaReac.mat'])
... |
d17299dd5f7dfc54a51d1cdf2077b622225f8724d7b5e0cf9d9f9ba4a9f17c6b | MATLAB | 5,962 | 160 | 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 enough
% ... |
4bf56644e41c60035c25f83a248f4c36beb1c8fdd2a6d80f62e9d222e74a10ff | MATLAB | 5,965 | 208 | function [OUT] = cluster_overlap_npm(cl1,cl2,varargin)
% function [OUT] = cluster_overlap_npm(cl1,cl2,[mask_fname],[iterations])
% tor wager
% takes 2 clusters structures and determines overlap
% - then runs Monte Carlo simulation randomizing cluster
% centers within a mask
%
% Mask and input files must have the same... |
f066361a4bb99fe716b3ca530a783ca71d0f88664dc869ade3b82bd488366763 | MATLAB | 5,965 | 149 | function lme = plotSummary(quantVar, subj, useNum, probeInfo, exSubj, quantVarStyle, colAni)
% remove nan days
quantVec = quantVar(~isnan(quantVar(:)));
uses = useNum(~isnan(quantVar(:)));
subjVec = subj(~isnan(quantVar(:))); % Is this wrong?
probesVec = cellfun(@(y) y{2}, cellfun(@(x) strsplit(x,'... |
8e759ce1abe0521dd3d18542451f23213701cb2ce365aad6c877e2ca3126e1ac | MATLAB | 5,967 | 156 | % SLIDER - add slider to a figure
%
% Usage:
% >> slider( handler, horiz, vert, horizmag, vertmag);
%
% Inputs:
% handler - figure handler (for the current figure, use gcf)
% horiz - [0|1] add a horizontal slider
% vert - [0|1] add a horizontal slider
% horizmag - magnify the width of the figure befo... |
dda24d3f1bf7676b422f6b7389908f19f7536ed3e2724dcbad658c5a7d8fe2fc | MATLAB | 5,975 | 155 | function shuffle_icaReacCCG_chamber(basename)
[rootPath,sesName,~]=fileparts(basename);
fprintf('%s load files for %s\n',datestr(now),sesName);
load(fullfile(rootPath,'data',[sesName '-icaReac.mat']))
load(fullfile(rootPath,'data',[sesName '.sleepstate.states.mat']))
load(fullfile(rootPath,'data',[sesName '.sessions.e... |
fbf19f71cc8bde0fef447f87f76aa4974810965fb75b2d9a76539ea1c540ee06 | MATLAB | 5,975 | 170 | function [err, ErrMessage, Info, V] = ResliceBrick (BrickName, NewCode, Opt)
%
% [err, ErrMessage, Info, M] = ResliceBrick (BrickName, NewCode, [Opt])
%
%Purpose:
% Reslices an AFNI brick kinda a la 3daxialize but in any orientation desired
% No interpolation or shananigans are done
%
%Input Parameters:
% BrickNa... |
fd4295d8a94152653fe730ab15b7fc506d5462610d8a1583f4714207a6d08570 | MATLAB | 5,978 | 116 | function [post, nlZ, dnlZ] = infVB(hyp, mean, cov, lik, x, y, opt)
% Variational approximation to the posterior Gaussian process.
%
% Compute a parametrization of the posterior, the negative log marginal
% likelihood and its derivatives w.r.t. the hyperparameters. The function takes
% a specified covariance function (... |
d0cd248dbb7f12ea664e677f77e8c13241ed86b5d8705ddeb51928c61bf6bd8f | MATLAB | 5,985 | 225 | function [bw] = Bullwinkle(detector_out, params)
%function [bw] = Bullwinkle(detector_out, params)
%
% Score a detector image with Bullwinkle
%
% Bullwinkle is a per-pixel scoring method, made to complement
% the blob-based scoring method ROCKY
%
% to complain about the bad pun, email bjm@wossamotta.edu
%
% 8/14/2012... |
4b94c07c93801e43417a6de19eee7e878c8ef094116373835ffbdad782f3b2c7 | MATLAB | 5,986 | 158 | function out = CO_PosNegAsymmetry(y)
% CO_PosNegAsymmetry Asymmetry of local dynamics between positive and negative regimes.
%
% Splits the time series by the sign of each value (assumes y is z-scored, so
% the split is around the mean) and asks whether the one-step-ahead dynamics
% differ between the two regimes: is... |
51b545cb9cff5e24244d34cc7ae90859d6915e2d03edf8eb9ed86e514d31f38c | MATLAB | 5,986 | 170 | function [G, gainArray, normFactor] = tapas_physio_rescale_gradient_gain_fluctuations(G, ...
minStepDistanceSamples, verbose, varargin)
% Removes infrequent gain changes in gradient time courses (i.e. steps)
%
% G = tapas_physio_rescale_gradient_gain_fluctuations(G, ...
% minStepDistanceSamples, doPlot, var... |
13c112be9037e4957ffce04c6498d5edc6472a79ecd178b4b061ab8e38495816 | MATLAB | 5,987 | 185 | function BpRF_run_pRF_simulation(subj_id, session, mod_name, ana_name, sim_file, avg, conf, voxels)
% _
% Estimate Voxel-Wise Population Receptive Field Model
% FORMAT BpRF_run_pRF_simulation(subj_id, session, mod_name, ana_name, sim_file, avg, conf, voxels)
%
% subj_id - a string, subject ID (e.g. "EDY7")
% ... |
bf55805dc33690dd1bbeaca956ddc4be9c0021f984dd60a5abf256cce73d5b5a | MATLAB | 5,992 | 151 | %% BipolarCorr = FastBipolarCorrect(Bipolar,unwrap)
%
% Input
% --------------
% bipolarCplxME : complex-valued multi-echo GRE data with bipolar readout
% unwrap : phase unwrapped method (optional)
%
% Output
% --------------
% bipolarCorr : eddy current corrected data
%
% Description: correct the inconsistent... |
d6abcc22fabe49df4f5592003f97d47c5c7d4c38a337b1acf962ce68d12e2454 | MATLAB | 5,997 | 154 | %% %%%%%%%% Determine alignment between HRT2 and T2 and warp labels
for subjix=1:8
% setup
nsdsetup;
outputdir = sprintf('%s/ppdata/%s-structurals',nsddir,allnsdids{subjix});
% define
file1 = sprintf('~/nsddata/ppdata/subj%02d/anat/T2_0pt5_masked.nii.gz',subjix); % official T2 0.5-mm masked volume
file2... |
1c86788f36d64fb35601a4d85b9329da1775701c3e79741b3e9e9b910973af8d | MATLAB | 6,004 | 200 | function plotTrials_yy(params, trialsData)
% double y-axis plots, plotyy mode (2 y-axis)
% plots values in 'trialsData' struct with fields:
% yVals_1: [sample x ch]
% yErrs_1: [sample x ch x clz] -> optional (plotband)
% xVals_1: x-Axis values (vector), typically time in [s]
% and
% yVals_2: [sample x ch]
%... |
f1336f6cbe72c4d986d1bff806f7db9c9e4c3ab5733e5873ecb99559af60bed5 | MATLAB | 6,004 | 186 | % EEG_EVENTHIST - return or plot histogram of event or urevent field values.
% If NO output args, plots the histogram. If the field values
% are not numbers or strings, no histogram is computed.
% Usage:
% >> figure; eeg_eventhist(EEG.event,'field',bins); % plot histogram
%... |
d5c07877cf4ad56e6a171a1efe4451a00739f5368a9d1bcd1843d3f7edd410d2 | MATLAB | 6,005 | 171 | function [info] = decode_fif(orig)
% DECODE_FIF is a helper function for real-time processing of Neuromag data. This
% function is used to decode the content of the optional neuromag_fif chunk(s).
%
% See also DECODE_RES4, DECODE_NIFTI1, SAP2MATLAB
% Copyright (C) 2013 Arjen Stolk & Robert Oostenveld
%
% This file is... |
b6eda56e6fe0994aa2fd8a35a1b8904e505be7352695c9871004274cc03b6c77 | MATLAB | 6,006 | 129 | % =============== parameters section ===============
% Required args:
% zmat_names_array: array of file names with univariate GWAS results from the MOSTest analysis
% out: output file prefix
if ~exist('zmat_names_array', 'var'), error('zmat files array is required'); end
if ~exist... |
8d5507e615e030dea1d051f29c10d86fb509c825015e2d216f2c320f5b6421d5 | MATLAB | 6,008 | 175 | function [nel, comm] = ToyTalk()
% ToyTalk()
%
% A sample function to illustrate how matlab can listen to communication from
% SUMA.
%
%Dependency:
%-----------
%The TCP communication is based on pnet.m by:
% Peter Rydesäter,
% Mitthögskolan(Mid Sweden University) campus Östersund, SWEDEN
%pnet is... |
d12d0e384551763eb04537e0968284863e23b887520227bbcbc201fe70eae261 | MATLAB | 6,008 | 179 | % ICAPROJ - project ICA component activations through the
% associated weight matrices to reconstitute the
% observed data using only the selected ICA components.
% Usage:
% >> [icaprojdata] = icaproj(data,weights,compindex,[datameans],chansout);
%
% Inputs:
% data - data matrix (chan... |
1c2fa3a6df4871b1580d64fedc93ed4f0f473fddc654a9798b8b9a00c87d6d01 | MATLAB | 6,009 | 134 | function out = SC_HiguchiFD(y, kmax)
% SC_HiguchiFD Higuchi's fractal dimension of a time series
%
% Estimates the fractal dimension of the time series' waveform (its graph in
% the (index,value) plane) via Higuchi's curve-length method: at each scale
% k, the series is split into k interleaved subsequences, each sub... |
cbacb76a9a2c1e63b76b46aa12e5b0f053b7c44b7459e1bc9cb61d07f08bb7e7 | MATLAB | 6,009 | 213 | function [ DCM ] = tapas_rdcm_to_spm_struct(output)
% [ DCM ] = tapas_rdcm_to_spm_struct(output)
%
% Converts the output structure from rDCM into a DCM structure that SPM12
% can read.
%
% Input:
% output - rDCM output structure
%
% Output:
% DCM - DCM structure (for integrability... |
05e87576dba773ff677d4ad16616ac7eae58e4edc1b5252abf6616d441e1efde | MATLAB | 6,013 | 183 | %% Linlin Data
close all
clear all
filename = 'C:\Users\Harnettlab\OneDrive\Desktop\Voltage_Data\Sq_camera.bin';
xpixels = 96;
ypixels = 200;
mov = readBinMov(filename, xpixels, ypixels);
fft_tts = fft2(mean(mov(:, :, 1:50), 3));
clear mov
IPv4 = '127.0.0.1';
port = 30001; % Port number used for communica... |
e19604460dfe01b3286e3065c03f5d14550a6a91f1e1afc5b2d5f5015a6d20f6 | MATLAB | 6,017 | 172 | function isSimilar = compareLFPPeaks(peak1_time, peak1_volt, peak2_time, peak2_volt, options)
arguments
peak1_time (1,:) double {mustBeReal, mustBeVector, mustBeNonempty}
peak1_volt (1,:) double {mustBeReal, mustBeVector, mustBeNonempty}
peak2_time (1,:) double {mustBeReal, mustBeVector, mustBeNonem... |
ebe768bcc146fd07cf611c8b1389d00afdc09d98c217a23597fedadf387f6376 | MATLAB | 6,018 | 168 | function featureCorrelation(nodeLevelData, recordingLevelData, Params, subset_lag, plotSaveFolder)
% Looks at the correlation of features at the node level and the recording
% level
% Parameters
% ----------
% nodeLevelData :
% recordingLevelData :
% Params :
% subset_lag : int
% Returns
% -------
useFullName = 1... |
4af1f58ec34be7c8f848b43d4ee68bfdb1d2b13db12abde91bffc37330c625d7 | MATLAB | 6,024 | 154 | %% Main lag script used to compute time delay matrix and lag projection
% Version 2:
% In this version, I modified the computation of probability flow
% projection map. The positive and negative value in the map will be
% normalized to 1, respectively.
%
% This script was orignally obtained from Github (https://g... |
4cf347711ae3a0e9f70ad7f68b059f9831d26bd84fc8456e032f060426b07a60 | MATLAB | 6,024 | 205 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% FIRST, DEAL WITH VIDEO FEED
% files are in nsd/rawdata (both .edf and .mp4 files)
% ignore the following:
% eyevideo-20191013-NSD134-nsdsyntheticpilot.mp4
% eyevideo-20191103-NSD134-nsdimageryFAILED.mp4
% .edf recorded only for actual NSD (experiment) runs, not resting st... |
7f4b496001d255c1a6c233302964f90b9087f56a5159438f6e60b5323614347e | MATLAB | 6,024 | 154 | %% Main lag script used to compute time delay matrix and lag projection
% Version 2:
% In this version, I modified the computation of probability flow
% projection map. The positive and negative value in the map will be
% normalized to 1, respectively.
%
% This script was orignally obtained from Github (https://g... |
11cea14f3b308e50cbe2d8bad42397b21e4167e48598c81c75da7ae0d10f220a | MATLAB | 6,026 | 201 | %% This script generates Figures 2, 3, and 4
% Note: SP stands for sulcal span and SD stands for sulcal depth.
clear; clc; close all;
% Uncomment the parameter you want to run:
%param = 'AREA';
%param = 'SULC';
%param = 'R1';
%param = 'THICK';
param = 'CURV';
% Set axis limits for figures
switch param
ca... |
4f1e2b07abde31bc771124cbb5a7940031bbb4c637bdc3107d16a2944b00b9ee | MATLAB | 6,026 | 154 | %% Main lag script used to compute time delay matrix and lag projection
% Version 2:
% In this version, I modified the computation of probability flow
% projection map. The positive and negative value in the map will be
% normalized to 1, respectively.
%
% This script was orignally obtained from Github (https://g... |
53c0d65cacb4eddc8aef07f8ebc448e6546f44cfaed427426a3318eeedf6a0be | MATLAB | 6,032 | 160 | function obj = test(obj, test_data, varargin)
% obj = test(obj, test_data, varargin)
%
% test_data should be voxels x maps matrix of test data
% can be either binary (i.e., meta-analysis peaks)
% or continuous (e.g., single-subject t- or contrast maps)
% Different methods are used for each data type.
%
%
dobinarize ... |
df660cbabeea853b4f516669a4274e37233f474b9f48eedf9ac153028e021e50 | MATLAB | 6,032 | 144 | function pctFilename = convert(inputFilename, options)
%CONVERT Convert PETRA image to pseudo-CT.
%
% DESCRIPTION:
% convert converts a PETRA image to a pseudo-CT using the following
% steps:
%
% 1. Debiasing the image using N4ITK MRI bias correction
% 2. Applying histogram normalisation to shif... |
858ac5e3ac684e80ec647253550bc2161b37ba19d34ddba2d2bbcaa63ac2a98d | MATLAB | 6,033 | 125 | % STD_READCUSTOM - Read custom data structure for file save on disk.
%
% Usage:
% >> data = std_readcustom(STUDY, ALLEEG, fileext, 'key', 'val', ...);
%
% Required inputs:
% STUDY - an EEGLAB STUDY set of loaded EEG structures
% ALLEEG - ALLEEG vector of one or more loaded EEG dataset structures
% ... |
a85565cdb038c26e456458ed3c0ff3bcdbbe1509cd1d1d4c41106abc11d35ec2 | MATLAB | 6,034 | 137 | % Same as example 3 but uses different sessions and runs
%
% Arnaud Delorme - May 2022
data = [];
p =fileparts(which('eeglab'));
data(end+1).file = { fullfile(p, 'sample_data', 'eeglab_data.set') fullfile(p, 'sample_data', 'eeglab_data.set') fullfile(p, 'sample_data', 'eeglab_data.set') fullfile(p, 'sample_data', 'ee... |
35e5df07ebd07698bfe2d8ad067d2c4c94e4e861ee7f62aa44f9ad9f03453230 | MATLAB | 6,038 | 173 | A = MVAR.MCA.class_avg_indicator(:, 6);
B = MVAR.MCA.class_avg_indicator(:, 9);
%%
clear z
% Make table z of counts.
% Note: A and B should be 0 or 1
% table in same format as Patel 2006 paper
% A's activity = columns, active then inactive
% B's activity = rows, active then inactive
n = size(A,1); % number of ... |
b17dfa4fbae7f261276add01423d45ed5888f2cbae5532f7e0c93807317dff72 | MATLAB | 6,038 | 128 | function MDSConditions(RDMs, userOptions, localOptions)
% MDSConditions(RDMs, userOptions[, localOptions])
% This function performs first-order MDS, visualising the dissimilarities
% between brain response patterns. If specified in userOptions, a coloured
% dot will be drawn for each condition and a text label is disp... |
ee1a964a40aa71acba9126d4692f39b714ec8074f6853d60eab8abacb2c68485 | MATLAB | 6,040 | 98 | function cat12_seg(filename,spm_path)
try
if exist('spm_path','var')
addpath(spm_path);
else
% get SPM12 root directory from matlab search path
spm_path = what('spm12').path;
end
% set spm default parameters
spm('defaults','FMRI');... |
803acae097e560c2e29d433822be430e204d18a76c04273e3cdf963f1663a196 | MATLAB | 6,042 | 167 | %{
rf.Motion3D (imported) # motion in 3D computed from a ministack
-> rf.MiniStack
-----
anchor_slice : smallint # slice that best matched the average movie frame
motion_x : longblob # (um) movement x component
motion_y : longblob # (um) movement y component
motion_z : longblob # (um) movement z component
%}
... |
88d5d9275aa555b365ba4cc686cf4e670c6e217523809575762e5aaef774fa0f | MATLAB | 6,044 | 115 | function [mdlnlm fitresult gofit rp]=Curve_fitting_nlm(initialmd,modelnlmreal,modelfitreal,Lowerboundary,Upperboundary,ylabelname,name,Plotornot,betainitial)
tbl=initialmd.Variables;%Using fit to constrain the lower boundary of beta2 above ZERO (Or the model will be failed).
if(size(tbl,2)==5)
Data_rremain=tbl.data_... |
ef71f7ce5b7a5cbf27fff839fc84b821239f38e0969b3b1c43413ddaf6cbf1ee | MATLAB | 6,045 | 165 | %%% NUMERICAL METHOD TO SOLVE THE 2D Diffusion Equation for multiple release points at the same time %%%
function [C_store]=NumericalSolver2D_multiple(M,ds2,dt,nt,Diff,Source_file,Q,kapa,tp,rate,sc,Mask,Cb, BC,freq)
tic
%Extract source coordinates
Mx=M(2);
My=M(1);
dy2=ds2(1);
dx2=ds2(2)... |
677cedde1679077986462188657ea0ee85f7b9ea45f041f9456a0fd8c22178f0 | MATLAB | 6,046 | 134 | % Regenerates the node-cartography ground-truth fixture used by
% test_pipeline_cartography.py. Loads a real thresholded adjacency matrix,
% subsets to active nodes (same logic as gen_step4_reference.m), runs
% MATLAB's own modularity clustering ONCE to get a real Ci, then calls
% MATLAB's own participation_coef_norm /... |
65f0324fa7850a543d2f1f0de627f1b78af7f2bab329bbcfd3eff86d49629295 | MATLAB | 6,048 | 193 | function [idx,p_age,p_sex] = nf_reg_match_ratio(regA,regB,ratio,strategy,p_cutoff,depth)
%[idx,p_age,p_sex] = nf_reg_match_ratio(regA,regB,ratio,strategy,p_cutoff,depth)
% Function to automatically match two groups by age+sex
%
% regA = regressor of reference group, i.e. the group that is matched to
% regB = regress... |
6ae12293dd5667a4d394d0284b9dc8f9c51a71c689cbeb310fe84e6b90cf10aa | MATLAB | 6,048 | 137 | % --- Executes on button press in recordAudio.
function recordAudio_Callback(hObject, eventdata, handles)
% hObject handle to recordAudio (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
get(hObject,'Value');
handles = guidata(hObject);
%Check if detection file has changed to save file... |
7bf5f799e6d6ab192df80ef0647e24e82126e12cbad2804d3d057312dfbbb88b | MATLAB | 6,048 | 142 | %{
vis.MonetLookup (lookup) # cached noise maps to save computation time
moving_noise_version : smallint # algorithm version; increment when code changes
moving_noise_paramhash : char(10) # hash of the lookup parameters
---
params : blob # cell ar... |
fd5c6c4c6d79bbbd62fea9cda8b40386ebcc463ae361670dfd718bd4acc122f3 | MATLAB | 6,050 | 142 | function [d, Sig] = distanceLDCraw(Y,SPM,conditionVec,varargin)
% function d=rsa.spm.distanceLDCraw(Y,SPM,conditionVec,varargin);
% First, gets the regression coefficent from the SPM, and prewhitens them.
% Prewhiten can be controlled using different methods (run-wise or overall).
% The same code is used in rsa.spm.noi... |
59c319b56c365a78d8f96d1fa4632479b74d8fbf4fc4929e95aa9a2e96be2be1 | MATLAB | 6,051 | 228 | function f02_applySmoothing(subjectLabel, projectRoot)
% f02_applySmoothing
%
% Apply 6-mm FWHM spatial smoothing to four preprocessed BOLD runs
% for one participant using SPM.
%
% Associated article:
% Mano, Y. et al. (2026).
% Neural mechanisms underlying decisions to express or suppress emotions.
% Social Cognitive... |
2da6af7cca3e203b751d87bf297c5c094f3b36d6781feed6c35d3bfd58a237eb | MATLAB | 6,052 | 120 | %% load libraries
run('set_library_paths.m');
addpath(SPM_PATH); % per spm docs, do not genpath it
addpath(genpath(CANLABTOOLS_PATH));
%% VARIABLES THAT MUST BE DEFINED BEFORE THE SCRIPT IS SOURCED
% THIS SCRIPT IS DESIGNED TO RUN ACROSS MULTIPLE SUBJECTS FOR A SINGLE ENCODING MODEL
% with multiple runs per subject
% ... |
6a893a368c57d59a0f033f78ba596c9d53fb305833404eebf9bffa4199ae6b40 | MATLAB | 6,054 | 172 | % op_plotfid.m
% Jamie Near, McGill University 2015.
%
% USAGE:
% out=op_plotfid(in,tmax,xlab,ylab,tit);
%
% DESCRIPTION:
% Plot the spectrum in the time domain.
%
% INPUTS:
% in = input data in matlab structure format. This argument can be a
% MATLAB structure (formatted according to the FID-A structu... |
b8e6429070ca787f9ab45d6263d81dc935f0ff6a09ccbe49d7d91f4e46379ab5 | MATLAB | 6,054 | 140 | function TOL_fig_ritchie1948(config,params,dat)
% TOL_fig_ritchie1948, figure of Ritchie's (1948) apparatus and design
% Figure S6 for: Duvelle and Grieves (2026) Tolman's Sunburst Maze 80
% years on: A meta-analysis reveals poor replicability and little evidence for
% shortcutting. European Journal of Neuroscience. ... |
dbc50fd4ed477d54ca26444885a7a533c5d8a9516aad54ee39a2266b45f3f6d8 | MATLAB | 6,055 | 128 |
main_path='Pathway';
lim_gc=1000;
bin_time=linspace(-0.05,30,121);
lim_cons=0.8;
list_patterns=["20_over","40_over","60_over","80_over"];
for i_data=1:4
path_data=strcat(main_path,list_patterns(i_data));
list_patterns(i_data)
cd (path_data)
for itera=1:50
%itera
% Loading Da... |
ecdeffe16e8eb0e4d5a604a838eb59cc5a020ad92101beba8eaad0eb98b687ec | MATLAB | 6,055 | 170 | function saveEphysStats(ExpName, Params)
% Saves data related to spike and bursts to two csv files
% One csv contains node level metrics : NeuronalActivity_NodeLevel
% One csv contains network level metrics : NeuronalActivity_RecordingLevel
% INPUT
% -----
% ExpName : cell array
% cell array with size N x 1 whe... |
665dff863c887f05ef0415fb27022bcbd8e46f7949789c6ce3745f4917dc702a | MATLAB | 6,058 | 118 | % # (JM) MODIFIED FROM ORIGINAL FUNCTION 'assembly_patterns'
function AssemblyTemplates = assembly_patterns(SpikeCount,opts)
% Patterns = assembly_patterns(Activitymatrix,opts): extracts assembly patterns from the spike matrix.
%
% Description of inputs:
% Activitymatrix: spike matrix. Rows represent neur... |
b1d252fa49503e30647c42acea61de4107df8713e9199d3157e4ffc42f6fde4d | MATLAB | 6,059 | 196 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Setup
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% What I need to do - convolve each person's task timeseries with the HRF.
% I can read in each peron's EV text file, make a boxcar function with it,
% convolve it with the HRF, the... |
b994836479e8de32aa76a9a6b4edc3f0369bfa0b1c88da90f5dfc81e9c118b6b | MATLAB | 6,060 | 163 | % SNAPREAD - Read data in Snap-Master Standard Binary Data File Format
% Reads Snap-Master header and data matrix (nchans,nframes).
% Ref: Users Guide, Snap-Master for Windows (1997) p. 4-19
% Usage:
% >> data = snapread(filename); % read .SMA file data
% >> [data,params,e... |
3309a0f61390c9275773650b5672956f00980244fd0821126e725c4821fc28da | MATLAB | 6,062 | 148 | function [B_avg, B_std] = stftBaseline_fromRS(params)
% determines baseline from resting state (RS)
% - baseline for normalization (normalization factors) -> B_avg (& B_std)
% - baseline activity -> BA_avg & BA_std (saves 'baseData' struct to cache file)
%
% (1) find RS sessions (based on gameType)
% (2) compute ST... |
1d7c7b34a916a44bb56ece4b403ac4cbde749a024d735d459f55ea2a6a8866ec | MATLAB | 6,063 | 186 | % function lineStyles = linspecer(N)
% This function creates an Nx3 array of N [R B G] colors
% These can be used to plot lots of lines with distinguishable and nice
% looking colors.
%
% lineStyles = linspecer(N); makes N colors for you to use: lineStyles(ii,:)
%
% colormap(linspecer); set your colormap to h... |
2cdeebf201636114eabea7727336548fa40af1b4cce30f3abe45498e3e7c1878 | MATLAB | 6,063 | 159 | % EEG_INSERTBOUND - insert boundary event in an EEG event structure.
%
% Usage:
% >> [eventout newind] = eeg_insertbound( eventin, pnts, ...
% abslatency, duration);
% Required Inputs:
% eventin - EEGLAB event structure (EEG.event)
% pnts - data points i... |
6c4d0feebbfac62e84503f283479cbd69b96ba45e6adf3411a31e3ccab185677 | MATLAB | 6,065 | 196 | function outputImage = tapas_uniqc_folder2image(pathFolder, savedVariable, filePrefix, ...
dimensionLabels)
% Collects mat-files of slices (e.g. from Recon-code), and combines to
% MrImage
%
%`outputImage = tapas_uniqc_folder2image(pathFolder, savedVariable, filePrefix, ...
% dimensionLabels)
%
% IN
% pathFold... |
a833c2731bbb50db18e0560540f2e8ea50d1dea20947764f72a9f2a12809740b | MATLAB | 6,066 | 187 | function [output, options] = tapas_rdcm_estimate(DCM, type, options, methods)
% [output, options] = tapas_rdcm_estimate(DCM, type, options, methods)
%
% Main analysis function, which calls the subfunctions necessary to run a
% regression DCM (rDCM) analysis.
%
% Input:
% DCM - either model str... |
623bc48bbf11f761c54dd9723426efa63fe2e8910ba11d183a9a73d16b617618 | MATLAB | 6,067 | 209 | %%
clear;
working_dir = 'G:\CEST-fMRI'; % the actual directory to put the codes
gen_figure_path = [working_dir,'\gen_figures'];
addpath(genpath(gen_figure_path));
SPM_modified_path = [working_dir,'\scripts\SPM_modified'];
addpath(SPM_modified_path);
ds_dir = [working_dir,'\datasets\exp3']; % directory of datase... |
75d849e08a1078640cf30e3a3194dc0c80b00842e8be0d36d6e8032f4adc36c7 | MATLAB | 6,067 | 200 | function M = stiefelgeneralizedfactory_HS_KA(n, p, B)
% Returns a manifold structure of "scaled" orthonormal matrices.
%
% function M = stiefelgeneralizedfactory(n, p)
% function M = stiefelgeneralizedfactory(n, p, B)
%
% The generalized Stiefel manifold is the set of "scaled" orthonormal
% nxp matrices X such that X'... |
c8ec773a8245ef39dbc008eae32f82c68e2b0888a275cc7ec468f33b4dc3dc2a | MATLAB | 6,067 | 199 | % The symmetry breaking Area is calculated for the left and the right
% antenna
%
% The code plots the x coordinate for tip of the antenna and then detects
% the peak.The peak is used for detecting the beginning of the swimming
% cycle. Then the symmetry breaking area is calculated for each swimming
% cycle for ... |
8abc9f0a1cee1c5e1786b2d855a40f56ee446e74cc030c3cccf04c6612ac4afb | MATLAB | 6,068 | 165 | function fieldtrip2besa(filename, data, varargin)
% FIELDTRIP2BESA saves a FieldTrip data structures to a corresponding BESA file. This
% export function is based on documentation that was provided by Todor Jordanov of
% BESA.
%
% Use as
% fieldtrip2besa(filename, data)
% with data as obtained from FT_PREPROCESSING ... |
03875647a97139a9907c52db2e114e48d9108b881ff340cae006291cc5b5ccdc | MATLAB | 6,070 | 144 | function [svolpulse, spulse, spulse_per_vol, verbose] = tapas_physio_get_onsets_from_locs(t, VOLLOCS, LOCS, sqpar, verbose)
% creates timing vectors of found
%
% [svolpulse, spulse, spulse_per_vol] = tapas_physio_get_onsets_from_locs(t, VOLLOCS,
% LOCS, sqpar, verbose);
%
% I... |
0e6a5329631ca109721bc09e5ee6527f2463db9b9d329f09c8308d729ff187c6 | MATLAB | 6,072 | 153 | function out = SY_LocalGlobal(y, subsetHow, n, randomSeed)
% SY_LocalGlobal Compares local statistics to global statistics of a time series.
%
% ---INPUTS:
% y, the time series to analyze
%
% subsetHow, the local subset of time series to study:
% (i) 'l': the first n points in a time series,
% ... |
80cf95346f08571f6e7f4a6fe8f75461945fb65c36960cd57dad46d7a10f779c | MATLAB | 6,074 | 179 | % STD_LM_SETEEGFIELDS - set limo fields in eeg sets
%
% Usage:
% >> EEG = std_lm_seteegfields(STUDY,EEG,index,'datatype','Channels','format', 'cell')
%
% Inputs:
% STUDY - studyset structure containing some or all files in EEG
% EEG - EEG dataset
% index - index of dataset in STUDY.dataset... |
ac202a9b57557d5338154227aabcd90963cee2fc5dc2c80942c7029946652af4 | MATLAB | 6,074 | 109 | function plot_task_performance_APP_mice_6_9_months
close all
clear all
clc
% Plot task performance for 6-month-old and 9-month-old APP-KI mice.
% Select a folder containing data.
folder_name = uigetdir;
cd(folder_name)
% APP.
load('behavior_APP.mat')
behavior = behavior_APP;
clear behavior_APP
animal_6_months_ID = ... |
96fd921c7449fe2c8ca31565c03a917327403a497c380e7a91da51c195a29199 | MATLAB | 6,075 | 137 | %% h = sepia_handle_panel_EchoCombine_medi_nonlinear_fit_bipolar(hParent,h,position)
%
% Input
% --------------
% hParent : parent handle of this panel
% h : global structure contains all handles
% position : position of this panel
%
% Output
% --------------
% h : global structure co... |
f56796dfea49d29718159949bd1deb35a495435668a3c5f48be9951397311e3d | MATLAB | 6,075 | 170 | function ARTwarp_Create_Figure
global NET DATA
h0 = findobj('Tag','ARTwarp'); %activate the ARTwarp window and clear the figure (clf)
clf
numCols = round(NET.maxNumCategories^0.5);
numRows = numCols+1;
rowHeight = (0.98 - 0.01*numRows)/numRows;
colWidth = (0.98 - 0.01*numCols)/(numCols+1);
%generate the same menu i... |
6d91d1acc708525dd4a8f906b915d1e79bd0105b5f27a6782895527075575764 | MATLAB | 6,076 | 142 | function [MRSCont] = osp_fitHERCULES(MRSCont)
%% [MRSCont] = osp_fitHERCULES(MRSCont)
% This function performs spectral fitting of HERMES MRS data.
%
% USAGE:
% [MRSCont] = osp_fitHERCULES(MRSCont);
%
% INPUTS:
% MRSCont = Osprey MRS data container.
%
% OUTPUTS:
% MRSCont = Osprey MRS ... |
1baecfb863bc93c8f09aa11d8e16aed11cdb4e54eb99956a3e03ab9fa83a4195 | MATLAB | 6,088 | 145 | function out = NL_LocalDensity(y, NNR, past, embedParams)
% NL_LocalDensity Local density estimates in the time-delay embedding space
%
% Computes a standard k-nearest-neighbor local density estimate at each
% point of the time-delay embedding: density(i) is proportional to
% 1/r_NNR(i)^m, where r_NNR(i) is the dis... |
53bb896a8ebd29428eb81ad30af7170ca6c80dcdeb1218488ad52b18c99afe46 | MATLAB | 6,088 | 115 | function [mdlnlm fitresult gofit rp]=Curve_fitting_nlm_multicenter(initialmd,modelnlmreal,modelfitreal,Lowerboundary,Upperboundary,ylabelname,name,Plotornot,betainitial,Site_info)
tbl=initialmd.Variables;%Using fit to constrain the lower boundary of beta2 above ZERO (Or the model will be failed).
if(size(tbl,2)==5)
... |
041adb3f30c6fe61ec79e734efe054ddcc30d7958c53585918ea6b020a222a3b | MATLAB | 6,089 | 157 | function [ output_args ] = ASSEMBLIES_COLLECTION( CALCIUM_FLUORESCENCE_file )
%ASSEMBLIES_COLLECTION( CALCIUM_FLUORESCENCE_file )
%
%
% === Jan Moelter, The University of Queensland, 2018 ===================
%
narginchk(1,1);
fprintf( 1 , [ '\n' ] );
timerVal = tic;
%% > PRINT FUNCTION-CALL
if( isdeployed )
p... |
e78165bddd9e0e8a5b13c534f81e36f4970f289ab667ee2206d38d809d05f072 | MATLAB | 6,091 | 147 | function [ifeat, testStat, trainErr, testErr, testClass] = TS_ForwardFS(whatData,iTrain,criterion,cvFolds,numFeatSelect,annotateParams)
% TS_ForwardFS Greedy forward feature selection
%
% --NOT FULLY FUNCTIONAL CURRENTLY--
%
% Uses the sequentialfs function from Matlab's Statistics Toolbox.
% After selecting the featu... |
abcf9469ad6dd759a9ee10f2418703ee4b7ee4286efef02244096817a5041cc9 | MATLAB | 6,094 | 131 | % Main script for sampling volumetric SANDI maps onto inferred equivolume
% cortical surfaces to obtain supragranular and infragranular layer-specific intensities
%
% Reference:
% Lee H, ..., Huang S, Communications Biology, 2026
%
% Requirements:
% - FreeSurfer installed and configured
% - FreeSurfer recon-all complet... |
a22c2182f7228ba871e30567d2222b9dfbff380a8c7f2774e78c03c3ecc1747f | MATLAB | 6,097 | 135 | % notes:
% - only consider cases of 3 trials
% - use all the data for the main version (also do split-half for the split1 and split2 versions)
% - be careful about missing data (invalid voxels)
% - translate the vmetric to SNR
%
% history:
% - 2021/07/08 - add _split1 and _split2 versions.
% - 2020/07/14 - now use sq, ... |
b54f67224a28bc139b5e4614e1cf3b54865559049cbc2247d401382ab9addbfa | MATLAB | 6,101 | 178 | % EEG_STORE - store specified EEG dataset(s) in the ALLEEG 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);
%... |
6764c22ae88e243e37f1350af070ac0d9f75bfa16e232e270413dd0c27680eb4 | MATLAB | 6,102 | 182 | function [ obj ] = optional_inputs( obj, varargin )
% Parse and check name-value pair arguments for constructor tapas_Huge and
% estimate method.
%
% This is a protected method of the tapas_Huge class. It cannot be called
% from outside the class.
%
% Author: Yu Yao (yao@biomed.ee.ethz.ch)
% Copyright (C) 2019 Trans... |
92ca4a268bff60664e5570cc26060c0ff480cae37341fac47dca3e9c16069f28 | MATLAB | 6,106 | 166 | classdef model < handle
properties
% Model hyper-parameters
dot = brca.dot.euclidean(); % Dot product (< brca.dot.base)
M = Inf; % Number of principal components
lat = []; % Lattice dimensions
A0 = 1; % Prior expected latent precision
... |
3a8579f6bce21953ce677ad257b6372d5efd0b68b69d75b919cd2346d047ffca | MATLAB | 6,108 | 143 | function [outputImage, selectedSlices, selectedVolumes] = ...
select4D(this, varargin)
% Creates new image from selected data range, allows interactive picking of
% volumes/slices via clicking on montage
%
% Y = MrImage()
% Y.select4D(inputs)
%
% This is a method of class MrImage.
%
% IN
%
% varargin 'Para... |
7e44018fc9c0c3dbb06ab3a305124af66656a16356849f20a65423c0092c981e | MATLAB | 6,108 | 152 | function [C2,cfg] = mahal_twoclass_trialwise_xtime(dat,labels,cfg)
% [C2,cfg] = mahal_twoclass_trialwise_xtime(dat,labels,cfg)
% C2: output, nClass x nTrials x NTrainTime x NTestTime
% authors: M Stokes, E Spaak, N Myers
if nargin < 2
help mahal_twoclass_trialwise
error('missing input arguments!') ... |
289aac9dcfcb3f6f44f3122e48afbcaf86eeb832f673a2d0b72a1c394ca2f773 | MATLAB | 6,109 | 166 | function out = CO_Embed2(y, tau)
% CO_Embed2 Statistics of the time series in a 2-dimensional embedding space
%
% Embeds the (z-scored) time series in a two-dimensional time-delay
% embedding space with a given time-delay, tau, and outputs a set of
% statistics about the structure in this space, including angular
% ... |
2b42c70852be27a252a82a3619447ad964b5c2666d5c83e7338ac774432de6e5 | MATLAB | 6,112 | 145 | % GENERATE_ADVDIFF_DATA Solve the linear advection-diffusion equation
% using spectral discretization + ode45.
%
% NOTE: This example is NOT included in the paper. It is provided as
% an additional benchmark to demonstrate TASC-DMD on a PDE with
% analytically known eigenvalues.
%
% Equa... |
35afa39120174e9a1103b066e098d53c7ac4aa8dda754c4441362ecdc3183d0b | MATLAB | 6,112 | 139 | %% h = sepia_handle_panel_EchoCombine_medi_nonlinear_fit(hParent,h,position)
%
% Input
% --------------
% hParent : parent handle of this panel
% h : global structure contains all handles
% position : position of this panel
%
% Output
% --------------
% h : global structure contains a... |
3dff5be9be995062a7e14d45df8051528cbcf01f6f3f33bd4d5d39b4561ee96b | MATLAB | 6,112 | 213 | classdef ParameterServer < handle
properties
params
paramsRegister
method
prefix
memoryMapFile
memoryMap
pinnedMemory
otherLabs
inplace
tmoveOpts
end
methods
function obj = ParameterServer(varargin)
obj.prefix = 'mcn' ;
obj.params = struct('name', {}, 'shap... |
667f2d7be11adcd5ab84beaa704bb682cbd63a2283081acb38505006a314bac0 | MATLAB | 6,113 | 118 | function [post nlZ dnlZ] = infPrior(inf, prior, hyp, varargin)
% Inference with hyperparameter prior. Any inference method can be used.
% The prior specification is done by means of the prior parameter that has
% the same structure as hyp. Examples:
% 1) >> prior.cov{2} = {@priorLaplace,mu,s2};
% put a Laplace p... |
6e353a79611c6b2f0c67e25e7ff9a3f46aa8c9d8c929fb6980b67f9546cabe01 | MATLAB | 6,114 | 157 | % fit_setExpectValues.m
% Georg Oeltzschner, Johns Hopkins University 2020.
%
% USAGE:
% [EXT2, SDT2, SDSH] = fit_setExpectValues(dataToFit, basisSet)
%
% DESCRIPTION:
% This function determines the expectation and standard deviation values
% for the delta (1/T2) parameters describing the Lorentzian linebroadening
%... |
7db6a3573488d10052c112a358843becf89119af1b158941a0bea2cd1eba4c27 | MATLAB | 6,117 | 135 | % genFrames - Generate frames for the scan
%
% Syntax
% frames = genFrames(p, t)
%
% Input Arguments
% p – structure containing scan parameters
% t – structure containing timing parameters
%
% Output Arguments
% frames – structure containing frames
%
function frames = genFrames(p, t)
... |
641f05430a94d2d3e0951337c4bfd14c4d35ad3998e09534329850e93f1566ec | MATLAB | 6,118 | 168 | function checkdiff(problem, x, d, force_gradient)
% Checks the consistency of the cost function and directional derivatives.
%
% function checkdiff(problem)
% function checkdiff(problem, x)
% function checkdiff(problem, x, d)
%
% checkdiff performs a numerical test to check that the directional
% derivatives defined in... |
e5107f4b1bbb541617ec1e508d0f99f0c00c5c8d337124ca1af2436f76e184a8 | MATLAB | 6,118 | 198 | function [f,p,ci,stats,tbl,dist] = permuanova1(x,group,varargin)
%PERMUANOVA1 One-way permutation-based analysis of variance (ANOVA).
% F = PERMUANOVA1(X) performs a one-way permutation-based ANOVA for
% comparing the means of two or more groups of data in matrix X, and
% returns the test statistic. The columns ... |
a74d4b0e16c895a239c722570b389f58698b2cfe2aaca971d4b065f2db0f967c | MATLAB | 6,121 | 141 | function [datafit,Amps,freqs,Fval,sig]=fitlinesc(data,params,p,plt,f0,fscanbw)
% fits significant sine waves to data (continuous data).
%
% Usage: [datafit,Amps,freqs,Fval,sig]=fitlinesc(data,params,p,plt,f0)
%
% Inputs:
% Note that units of Fs, fpass have to be consistent.
% data (data in [N,C] i.e. time... |
e01b6e60889eb3be5e33f23069f8c271325aeea16a1829da82d682cc62dba05d | MATLAB | 6,126 | 141 | function out = CO_Oversampling(y)
% CO_Oversampling Detects temporal oversampling relative to a series' own dynamics.
%
% Implements the oversampling-detection statistic eta (and its downsampling
% correction) from the 'oversampling' stage of the Chaos Decision Tree
% Algorithm:
%
% Toker, D. et al. "A simple method ... |
28c4534fb9e850aeafcd67d3ec8da1b23d12416ac5e0906edd6cbc8af9e3209a | MATLAB | 6,131 | 134 | % STD_REJECTOUTLIERS - Commandline function, to reject outlier component(s) from clusters.
% Reassign the outlier component(s) to an outlier cluster specific to each cluster.
% Usage:
% >> [STUDY] = std_rejectoutliers(STUDY, ALLEEG, clusters, th);
% Inputs:
% STU... |
a4508e8f510c1f0da5a5d1fbedd0351b9a6ac467a6f08145ab1e9ea74e72256f | MATLAB | 6,131 | 187 | function [ listDcms, listConfounds ] = export( obj )
% Export results and data from HUGE object to SPM's DCM format.
%
% INPUTS:
% obj - A tapas_Huge object containing data.
%
% OUTPUTS:
% dcms - A cell array containing DCM structs in SPM's DCM format.
% confounds - An array containing group-level confounds... |
1f79ae68f17f6a9eaa7752eeddd6c37d4f4954d25ad4839a8eb2bc346704be7e | MATLAB | 6,134 | 158 |
%% this is for all-session version for osci
clear;
mypath = '/Users/bo/Documents/data_liujia_lab/analysis_liuP1_greeble';
for sub = 1:35
% access raw table
mypath_time_record = [mypath, '/sub', num2str(sub), '_mri_record_fft.txt'];
timeFile = fopen(mypath_time_record,'rt');
table_time = textscan... |
071beb36638c446f56317eee650ff41d85f1c7ca9d6762d74cc94fb8f1e3517a | MATLAB | 6,135 | 192 | function [R,S,T]=surfing_struct(varargin)
% Creates a struct from structs, pairs of keys and values, and/or cells
%
% R=surfing_struct(A1,A2,...)
%
% The "allowed form" of arguments A1,A2,... can be:
% - Ai, Ai+1 are a KEY, VALUE, pair, where KEY is a string (that can be
% used as a variable name) and VALUE can be o... |
c47cfc3b4f81720a0f625c07bf889b91c59354bc00b05a471f977e42f78e3d84 | MATLAB | 6,137 | 115 | function [varargout] = likLogistic(hyp, y, mu, s2, inf, i)
% likLogistic - logistic function for binary classification or logit regression.
% The expression for the likelihood is
% likLogistic(t) = 1./(1+exp(-t)).
%
% Several modes are provided, for computing likelihoods, derivatives and moments
% respectively, see... |
d5fcd15486f87eda8d575ec0d5baa6380cd6a9fc0c62a5e37c54e090dab9cd4d | MATLAB | 6,142 | 138 | function E = efficiency_wei(W, local)
%EFFICIENCY_WEI Global efficiency, local efficiency.
%
% Eglob = efficiency_wei(W);
% Eloc = efficiency_wei(W,2);
%
% The global efficiency is the average of inverse shortest path length,
% and is inversely related to the characteristic path length.
%
% The local effi... |
13f018495581e2ece159e651b4a33dbfb3ea6150dbf1f058934ea531283ad57a | MATLAB | 6,148 | 160 | function ripples = BatchFindRipples(session,channels, noisyChannel,excludeInterval)
% This is the algorithm for finding ripple events developed in May 2016 for
% Celine's data. The reasoning is to take the filtered (ripple-band) signal's
% envelope from multiple sources (carefully picked informative channels) togethe... |
1b0d5bb95e343e39d3b25789d28ff34953da3ac03a29a56584d72c5dc95bfff7 | MATLAB | 6,152 | 134 | function [peaktopeak,troughs, amplitude] = FindThetaCycles(lfp,varargin)
%FindThetaCycles - Find intervals that qualify as theta cycles from the lfp signal
% lfp should be in the [timestamps signal] format. Using CleanLFP before
% calling this function is recommended. The [start stop] intervals as well
% as the troug... |
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