sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
bb77b3b5e8a76b90b67365466c2dc822d73b308078171be22794b9c84e58fa1e | MATLAB | 20,970 | 473 | % Copyright (c) 2009, Peder Axensten
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * Redistributions of source code must retain the above copyright
% notice, this list of condi... |
f05dc4e60bb2ba8f5631eb46d27d78b43e4eaf136f77c60c1c91e9877df15894 | MATLAB | 21,031 | 708 | % AUTHORSHIP
% Primary Developer: Stephen Meehan <swmeehan@stanford.edu>
% Math Lead & Secondary Developer: Connor Meehan <connor.gw.meehan@gmail.com>
% Bioinformatics Lead: Wayne Moore <wmoore@stanford.edu>
% Provided by the Herzenberg Lab at Stanford University
% License: BSD 3 clause
%
classdef Stri... |
a958be83301d61fc756d099e0f3c2553bea712ca3ea20487ac583027e981cd36 | MATLAB | 21,064 | 441 | function [spectrum,ntaper,freqoi,timeoi] = ft_specest_mtmconvol(dat, time, varargin)
% FT_SPECEST_MTMCONVOL performs wavelet convolution in the time domain by
% multiplication in the frequency domain.
%
% Use as
% [spectrum,ntaper,freqoi,timeoi] = ft_specest_mtmconvol(dat,time,...)
% where input
% dat = matr... |
cb81635b23abe958e0f87f485acc5697207f0da877fa78e4a16f4e247f20dd8f | MATLAB | 21,098 | 797 | function [] = VBA_spm_dcm_buid(DCM)
% build GUI for DCM results reviewing
if ~nargin
[P, sts] = VBA_spm_select(1,'^DCM.*\.mat$','select DCM_???.mat');
if ~sts, return; end
end
if ~isstruct(P)
try
load(P);
DCM.filename = P;
catch
error('Cannot load DCM file "%s".',P);
end
el... |
7d831507af70a098f49a4871396d3ffed05b5694d47d0e29ea4a7d45f6018618 | MATLAB | 21,100 | 392 | function [] = ground_truth_log_odd_analysis_place_field_randomised(folders,timebin,BAYSESIAN_NORMALIZED_ACROSS_TRACKS)
% Input:
% Folders: Each Folder is data for a session
% Timebin: 0.02 or 1
% 0.02 -> 20ms time bin
% 1 -> 1 event time bin
% posbin: [] or 1
% [] -> 20 position bin per track (will load extracted_place... |
99851810a1fa76fc9ba5ef1ed13c9dc8c4fa823f881a378b45ff4de3098b9a96 | MATLAB | 21,112 | 367 | %% Before running this script - manual stages:
% 1. Compute ANOVA across all subjects with the six spatial scales conditions, to identify regions with scale sensitivity ("ANCOVA random effects analysis" dialog); Change threshold to q=0.01 (FDR-corrected); Save the results to a VMP file - "anova_results.vmp"
subject... |
98bc8907b8ba70fd88c1e7e8ae7a0a522a76d0c46cbf102cd63fff3419ba5abc | MATLAB | 21,139 | 512 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
disp(['Making Figure: ',FigName])
figure('name',FigName)
set(g... |
0423d699f5b875616de38fb3bc3995b12821173a9ffb06641ee6368a0ad9ea19 | MATLAB | 21,177 | 441 | % PLSR_Simplified.m
% Sarah West
% 6/6/22
% A wrapper that runs the Matlab function plsregress_fullcode.m
function [parameters] = PLSR_Simplified(parameters)
% for notes purposes:
% n = number of observations
% m = number of explanatory variables
% j = number of response variables
% *** Inputs ... |
80d02a93af1a7d1c5ab03c02bb8a9612b74a855a29ca74ee4d70df26122aefce | MATLAB | 21,181 | 635 | function tfrqview(tfr,sig,t,method,p1,p2,p3,p4,p5);
%TFRQVIEW Quick visualization of time-frequency representations.
% TFRQVIEW(TFR,SIG,T,METHOD,P1,P2,P3,P4,P5) allows a quick
% visualization of a time-frequency representation.
%
% TFR : time-frequency representation (MxN).
% SIG : sign... |
96d3b9f24388409143d7b4d0696d5e7e6ce3f0d08a2815e6f337c2d239fbb0f7 | MATLAB | 21,190 | 533 | %% Supervised learning using discriminative models in pmtk3
% _This page was auto-generated by publishing_
% tutDiscrimClassif.m
%
% Discriminative models for classification/ regression are
% conditional models of the
% outputs given the inputs
% of the form $p(y|x,\theta)$.
% We consider various examples belo... |
ba00df71f93f1acef7181199f8b96fd41faf42cae1a76f13dc2fe775e05c5af3 | MATLAB | 21,195 | 619 | function varargout = bbApply( action, varargin )
% Functions for manipulating bounding boxes (bb).
%
% A bounding box (bb) is also known as a position vector or a rectangle
% object. It is a four element vector with the fields: [x y w h]. A set of
% n bbs can be stores as an [nx4] array, most funcitons below can handle... |
991bf1a10266845e42057b13bddc8256ff48cda9b93e6f3abf0b2bc80a326969 | MATLAB | 21,203 | 424 | function varargout = palm_help(varargin)
% Shows a help text. Call palm_help('logo') to show just the logo.
% _____________________________________
% Anderson M. Winkler
% FMRIB / Univ. of Oxford
% Mar/2014
% http://brainder.org
% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% PALM -- Permut... |
409632081cf21defc690f3c3ebd8a28621e4de0aaa7bd7f23c15ce825f4b00dc | MATLAB | 21,244 | 564 | function [input] = ft_apply_montage(input, montage, varargin)
% FT_APPLY_MONTAGE changes the montage of an electrode or gradiometer array. A
% montage can be used for EEG rereferencing, MEG synthetic gradients, MEG
% planar gradients or unmixing using ICA. This function applies the montage
% to the input EEG or MEG se... |
68fcd126f21b5b35179199b7596ca44a1391d6c31215aed9cb2c8466e3075bcf | MATLAB | 21,255 | 577 | classdef APT
properties (Constant)
Root = APT.getRoot_()
MANIFESTFILE = 'Manifest.txt'
SnapshotScript = fullfile(APT.Root,'matlab','repo_snapshot.sh')
BUILDSNAPSHOTFILE = 'build.snapshot';
BUILDSNAPSHOTFULLFILE = fullfile(APT.Root,APT.BUILDSNAPSHOTFILE);
BUILDMCCFILE = 'build.mcc'... |
bb4075530c66d74d3350d7e32b951d11d8b3501fc5464f6526ab03de32f254c6 | MATLAB | 21,256 | 507 | %% Calculates and plots several statistics of simulated Place fields
% gridAnalysisPlaceFieldStats.m
%
% Meak peak rate, KS-stat, PF specificity, PF spatial info., etc.
%% Choose which simulation results to analyze
decodeFileID = "mainPaperGrid"; % Select which decoding file result ID to analyze
%% Set up and load... |
329cb11f7bccc8eed114130a22dd160d2ef83b8724abf706d39cc8152cce8cc8 | MATLAB | 21,262 | 512 | function [x, cost, info, options] = rlbfgs(problem, x0, options)
% Riemannian limited memory BFGS solver for smooth objective functions.
%
% function [x, cost, info, options] = rlbfgs(problem)
% function [x, cost, info, options] = rlbfgs(problem, x0)
% function [x, cost, info, options] = rlbfgs(problem, x0, option... |
c07e6d2b0c4859487cf3de46ce126bc8ec8bcb5cfffb72b53cbd1702b0232991 | MATLAB | 21,267 | 845 | %% MyRobustCrust
%
% Copyright (c) 2008, Luigi Giaccari
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * Redistributions of source code must retain the above copyright
% noti... |
141a4e10dc501969fa25022b46d3d08f158a64bee141508e9e4f9da03dc03aa7 | MATLAB | 21,316 | 553 | function [freq] = freq_mtmconvol_lan(cfg, LAN, texto)
% v.0.1.2
% analysis multipater for LAN structur
% adapted of filetrip scripts
%
%
%
%
%
% Original HELP por freqanalysis_mtmconvol.m
%
% FREQANALYSIS_MTMCONVOL performs time-frequency analysis on any time series trial data
% using the 'multitaper method' (MTM) bas... |
18a2681477a19b7fca05d785c70243a775a7ec75800f1d718475aa1d9e59c42d | MATLAB | 21,329 | 610 | function PPI = spm_peb_ppi_Nosave(varargin)
% Bold deconvolution to create physio- or psycho-physiologic interactions
% FORMAT PPI = spm_peb_ppi(SPMname,ppiflag,VOI,Uu,ppiname,showGraphics)
%
% SPM - Structure containing generic details about the analysis or
% the fully qualified filename of suc... |
e3f99ce575a49d2d06e258a9fa4c17f7952a218014d5aea1acbcc5547fc34ccc | MATLAB | 21,346 | 538 | % AUTHORSHIP
% Primary Developer: Stephen Meehan <swmeehan@stanford.edu>
% Bioinformatics Lead: Wayne Moore <wmoore@stanford.edu>
% Provided by the Herzenberg Lab at Stanford University
% License: BSD 3 clause
%
classdef DensityBiased<handle
properties(SetAccess=private)
gridPoints;
gri... |
32b543d77acc1c000ca4ffc01fc4735c2ac7e407e13527b5e2824a02e9e738a1 | MATLAB | 21,357 | 438 | function DataStructure=PowerFit_ZN(Xdata,Ydata,FitOption)
if size(Xdata,1)<=1&&size(Xdata,2)<=1&&size(Ydata,1)<=1&&size(Ydata,2)<=1
warning('Need to provide more than one data point to fit!')
DataStructure=[];
elseif size(Xdata,1)~=size(Ydata,1)||size(Xdata,2)~=size(Ydata,2)
warning('Nee... |
e4ea089fd05276bc4676e8b68be18b1033ee7d0fea386487a97acbc903e4777b | MATLAB | 21,376 | 652 | function [A,fH,figNames] = meg_plotGroupITPC(data,sessionDir,p,selectedChannels,selectedFreq)
% MEG_PLOTGROUPITPC(data,selectedChannels,plotSingleTrial,plotAvgTrial)
%
% Karen Tian
% July 2020
%% setup
condNames = {'all'}; % {'cueT1','cueT2'};
p = meg_params('TANoise_ITPC');
[sessionNames,subjectNames,ITPCsubject,... |
0dbd8ac6895d7367682742cc0f4895570d41f41f68fe2ec16a7508fec42fea41 | MATLAB | 21,407 | 567 | function [input] = ft_apply_montage(input, montage, varargin)
% FT_APPLY_MONTAGE changes the montage of an electrode or gradiometer array. A
% montage can be used for EEG rereferencing, MEG synthetic gradients, MEG
% planar gradients or unmixing using ICA. This function applies the montage
% to the input EEG or MEG se... |
80e8a6d6fcf0b50953a03ac3e79ec5002b2a9232b311fd8e89b0c21717a0cf98 | MATLAB | 21,422 | 730 | clear
clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
%% Motion Data
MotionData = MotionData.MotionData;
% Joint Angles
SHLa = MotionData.GenericCat.Angles.AngShou.Total*(pi/180);
ELBa = MotionData.Ge... |
20985bc1e8f4b9f08a6f7f861d19b317c91af82890f5c609f2dc336e1815d69e | MATLAB | 21,446 | 543 | function varargout = DPABI_Standardization(varargin)
% DPABI_Standardization MATLAB code for DPABI_Standardization.fig
% DPABI_Standardization, by itself, creates a new DPABI_Standardization or raises the existing
% singleton*.
%
% H = DPABI_Standardization returns the handle to a new DPABI_Standard... |
565848457f550539d15320e62fd49ef879b95c3c261505cd8d9922d122119d0f | MATLAB | 21,450 | 570 | % function [slope, intercept,MSE, R2, S] = logfit(x,y,varargin)
% This function plots the data with a power law, logarithmic, exponential
% or linear fit.
%
% logfit(X,Y,graphType), where X is a vector and Y is a vector or a
% matrix will plot the data with the axis scaling determined
% ... |
37042dbf48462692b2d145fffe85f316958714b8c566c20999415f354101dd35 | MATLAB | 21,460 | 745 |
% load data
% contact Lead Author for data
%% PERCENTAGE COMMON-UNIQUE CELLS
% panel 1I-J
prcOfCells = nan(numel(folders), 4);
% 1 - unique audioVR cells / total audioVR cells
% 2 - common audioVR cells / total audioVR cells
% 3 - common nosound cells / total nosound cells
% 4 - unique nosound cel... |
324f04441e785e0f8b9ddb2aa22522b18350e4632c9c8e4ab16ff33e5e77d0a8 | MATLAB | 21,470 | 684 | %% Load Unit Data
datRoot = '/Volumes/LaCie/Farries recordings/';
load([datRoot 'unitDat_final_culled.mat'])
uName = arrayfun(@(u) strsplit(u.name), unit, 'UniformOutput', false);
uRat = cellfun(@(u) u{1}, uName, 'UniformOutput', false);
uSes = cellfun(@(u) u{2}, uName, 'UniformOutput', false);
uTrode = cellfun(@(u) ... |
7cf9652a91cbaa44173b36690f907b7c3896e2fed8a121120b6a1a6cc8a3f994 | MATLAB | 21,471 | 538 | function [autoparams,vizdata,autodescr] = compute_auto_params(lobj,varargin)
% Compute suggested ("auto-tuned") training parameters from the labels.
% autoparams is a containers.Map from parameter path to a bare
% numeric/logical suggested value. autodescr is a parallel
% containers.Map from parameter path... |
5276c5d75476b75979a18c47364a55a50c74dc3bd1649162836a1c380dac8c40 | MATLAB | 21,534 | 570 | % function [slope, intercept,MSE, R2, S] = logfit(x,y,varargin)
% This function plots the data with a power law, logarithmic, exponential
% or linear fit.
%
% logfit(X,Y,graphType), where X is a vector and Y is a vector or a
% matrix will plot the data with the axis scaling determined
% ... |
ce72c496addc574a809bd46c307615543f682e30c71ae069965adf1f8373b072 | MATLAB | 21,534 | 1 | % TSLOADSESSIONS Load Session data into Experiment structure.
% TSLOADSESSIONS(NEWDIR) load the sessions from a directory specified by
% NEWDIR. NEWDIR denotes the directory to find files which contain
% session data. Modified by CRG 10/20/09. Also loads MedPC code files
% into the Experiment.Programs field.... |
ae50c3fccecf52e11a18f09df730015ac6e4f853b7bfe105b2fab9e53ca507b5 | MATLAB | 21,567 | 681 |
% TO-DO List As of 11/13/15
% 1. Save final positions, not initialized positions!
% 2. Save .tif filenames in order of instance and group name in Scoring
% Data
% 3. Transients Color Scheme
% 4. Readable ROI Color + Appearance (Dots, Larger Font, Clear Colors)
% 5. Improved note functionality
% 6. Retreive syn... |
604974059718bec3bd4dc44bb0e466fa664ee99d994d0102763464f70a34c96c | MATLAB | 21,571 | 582 | % create_PLSR_comparisons_level1_categorical.m
% Sarah West
% 6/9/22
% A script that creates a structure of information to run comparisons
% between different subsets of data for the Random Motorized Treadmill
% experiments. Each comparison will be run per mouse, eventually will have
% a second-level comparison across... |
308eacb256eaa0b3d6bd810ca70240cd90369bc23a8a9a813a54dc01fd88416f | MATLAB | 21,572 | 647 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
cd935bbee764047c2d622f7c6414ea803022968443e0720edf98bad3ab90d42b | MATLAB | 21,572 | 498 | function [xPoints, yPoints] = plotSpikeRaster(spikes,varargin)
% PLOTSPIKERASTER Create raster plot from binary spike data or spike times
% Efficiently creates raster plots with formatting support. Faster than
% common implementations. Multiple plot types and parameters available!
% Look at Parameters section bel... |
1e976a419098d2d0b39c1aebb967e0a1308e8b2c280531fe4fdf5b93332e46bf | MATLAB | 21,587 | 626 | function [simulated] = ft_connectivitysimulation(cfg)
% FT_CONNECTIVITYSIMULATION simulates channel-level time-series data with a
% specified connectivity structure. This function returns an output data
% structure that resembles the output of FT_PREPROCESSING.
%
% Use as
% [data] = ft_connectivitysimulation(cfg)
% ... |
2e1798d56c05dce05f1e46d7b8d56e5c4c2dbc244aa5e8f6aeea5643398f49d0 | MATLAB | 21,631 | 735 | clear
clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
%% Motion Data
MotionData = MotionData.MotionData;
% Joint Angles
SHLa = MotionData.GenericCat.Angles.AngShou.Total*(pi/180);
ELBa = MotionData.Ge... |
169430e3853f203665cad3f5b64cec8c9e333c07915cf26d01976f31d0b9c7ce | MATLAB | 21,728 | 635 | function handles = plotSpread(varargin)
%PLOTSPREAD plots distributions of points by spreading them around the y-axis
%
% SYNOPSIS: handles = plotSpread(data, propertyName, propertyValue, ...)
% handles = plotSpread(ah, ...
% deprecated:
% handles = plotSpread(data,binWidth,spreadFcn,xName... |
25f576460e136b4ac52e4a684902735edea2a03d664f71e4a41c1d06646719e5 | MATLAB | 21,768 | 630 | function [simulated] = ft_connectivitysimulation(cfg)
% FT_CONNECTIVITYSIMULATION simulates channel-level time-series data with a
% specified connectivity structure. This function returns an output data
% structure that resembles the output of FT_PREPROCESSING.
%
% Use as
% [data] = ft_connectivitysimulation(cfg)
%
... |
7052832d9a8a1fa6fd83611347168833d7359878851ce1cb9b9d5ba8c04c7acd | MATLAB | 21,769 | 519 | % AUTHORSHIP
% Primary Developer: Stephen Meehan <swmeehan@stanford.edu>
% Math Lead & Secondary Developer: Connor Meehan <connor.gw.meehan@gmail.com>
% Bioinformatics Lead: Wayne Moore <wmoore@stanford.edu>
% Provided by the Herzenberg Lab at Stanford University
% License: BSD 3 clause
%
classdef SuhM... |
d20922b5b65b4fe6d6268dffb0b12365a1b7911cc9cb0a1ea7289e7d87ee6830 | MATLAB | 21,769 | 476 | function shape_classification_plot(data)
global minX maxX minY maxY AutoAxesVal AdrianaAxesVal SizeWindow
AutoAxesVal = 1;
AdrianaAxesVal = 0;
SizeWindow = 25;
fig = clf(figure());
set(gcf,'name','Shape Classification','NumberTitle','off','color','w','units','normalized','position',[0.3 0.2 0.4 0.6],'menubar'... |
af2340da378bb1bbfd286fe79fab3bf97bd2652f2c2a72a86cec00edc28f231d | MATLAB | 21,833 | 639 | function handles = plotSpread(faceColor,varargin)
%PLOTSPREAD plots distributions of points by spreading them around the y-axis
%
% SYNOPSIS: handles = plotSpread(data, propertyName, propertyValue, ...)
% handles = plotSpread(ah, ...
% deprecated:
% handles = plotSpread(data,binWidth,sprea... |
50ac83a86b49c393dc4622083862f93652587aa3af60170b593c24eb4fe787a3 | MATLAB | 21,857 | 575 | function varargout = DataPrepareSetting(varargin)
% DATAPREPARESETTING Prepare FC, ROISignals, and coordinate files for SFC Toolbox.
% Usage:
% result = DataPrepareSetting(previousSettings)
% The GUIDE figure is used for the layout. This file binds callbacks and runs
% the data preparation after the user presses OK.
... |
2b8f4583678c7b87d6f6be2f7c8211f32c362a56b309dfb7b6e3df615a1896e3 | MATLAB | 21,906 | 404 | %%%
% This script counts the number of mRNA inside cells previously segmented in 3D using LimeSeg
% (ImageJ): Macado et al. BMC Bioinformatics, 2019 (Jan 3;20(1):2.doi: 10.1186/s12859-018-2471-0),
% LimeSeg: a coarse-grained lipid membrane simulation for 3D image segmentation.
% To run this script, you need:
% ... |
d04ab48adc600ec24691b461852e4089ac145a9981f84fccad166d98ccbb429e | MATLAB | 21,912 | 744 | clear
clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
%% Motion Data
MotionData = MotionData.MotionData;
% Joint Angles
SHLa = MotionData.GenericCat.Angles.AngShou.Total*(pi/180);
ELBa = MotionData.Ge... |
08cefd3d54ca45f9fab46395ad7ef9c2c0739e5a3bd668f784ab54b80ab43489 | MATLAB | 21,915 | 747 | clear
clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
%% Motion Data
MotionData = MotionData.MotionData;
% Joint Angles
SHLa = MotionData.GenericCat.Angles.AngShou.Total*(pi/180);
ELBa = MotionData.Ge... |
038f1836885b6184bd617ec86612f0ab881e1834d4808ec0c5fce2a67f7d64ff | MATLAB | 21,922 | 340 | function [C_BrainHF_all, C_BrainLF_all, C_ROI_BrainHF_all, C_ROI_BrainLF_all] = NVR_C_BrainHeart(mov_cond, c_style, ROI, path_data)
% This function computes Brain-Heart couplings, using the model
% developped by Catrambone et al. (2019).
% Also select timepoints corresponding to low (LA) and high (HA) emotional
% arous... |
e19c466d4069fe482b652c1b8d6485b665274f86f583fa0a6118bb536a2124bf | MATLAB | 21,933 | 541 | function count = cprintf(style,format,varargin)
% CPRINTF displays styled formatted text in the Command Window
%
% Syntax:
% count = cprintf(style,format,...)
%
% Description:
% CPRINTF processes the specified text using the exact same FORMAT
% arguments accepted by the built-in SPRINTF and FPRINTF functions.
... |
58c0b437814dedf35deab384c31f4114fbaf2a1f6b60d918c24512a1e53ba9ac | MATLAB | 21,945 | 751 | clear
clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
%% Motion Data
MotionData = MotionData.MotionData;
% Joint Angles
SHLa = MotionData.GenericCat.Angles.AngShou.Total*(pi/180);
ELBa = MotionData.Ge... |
64a979fb30facef607a1e5ebd5ac1734ffbbbb2f4082e5d6de87cf63aebaf8ca | MATLAB | 21,980 | 501 | function [] = plot_ground_truth_compare_one_shuffle(folders,option,method)
total_number(1:3) = 0;
for nfolder = 1:length(folders)
cd(folders{nfolder})
load decoded_replay_events
[~,time_range]=sort_replay_events([],'wcorr');
for event = 1:length(decoded_replay_events(1).replay_events)
... |
88f56109d7194315b13cac172d515a47f30eea46cd7d311ba0e5ba6c69902f67 | MATLAB | 22,014 | 585 | function TSplotcumrecs(varargin)
% Graphics helper function for TSapplystat. MUST BE CALLED BY TSAPPLYSTAT!
% Plots one or more cumulative records per panel. If more than one, then
% they must be against a common x axis and against either one or two
% ordinates (left and right y axes, using Matlab's plotyy command). Wh... |
6f33852c8b695812a76bcaf817a5ade4ea7c7fbfd60be6f1fd0933ab365fad6e | MATLAB | 22,037 | 452 | function NMJ_Region_Selector(StatusFig,StackSaveName,BoutonSelectionDisplayImages, dilateSize, min_pixels, fudgeFactor,StackFileName_PeakArray,ImagesPerSequence, PeakImage, DeltaX_First, DeltaY_First,AllBoutonsRegion1)
OS=computer;
if strcmp(OS,'MACI64')
dc='/';
else
dc='\';
end
pwd;... |
d74034efd6fa78253928439082e28d4ea2a08ce5f631c60632ef57298315c062 | MATLAB | 22,074 | 452 | % motorFindBehaviorPeriods.m
% Sarah West
% 11/16/21
% Makes time ranges of different behavior periods output-ed by the Arduino
% program controlling the random motor stages.
% Activity tag list of possible periods
% * 1 = motor accelerating
% * 2 = motor decelerating
% * 3 = motor maintaining
% * 4 = motor... |
7bcaa8411788523122b45a0cacb80a817d61b454109c65c78274cf6b160df013 | MATLAB | 22,092 | 551 | %MP 2021
%This is the master script used for presenting behavioral stimuli and
%recording responses in the memory-guided saccade task described in
%Panichello, Jonikaitis, Oh, Zhu, Trepka, & Moore, 2024
%Due to the very large number of dependencies (eg., eyelink), it is not practical to run
%this code on machi... |
f405f31872289f48d5e20202cdbe9819c640d0731f8418222a29a0a2b3d99102 | MATLAB | 22,107 | 753 | clear all
close all
clc
% Use this script to generate figures similar to those in Figure 7 and Figure 8 of:
%Luna, R., Li, J., Bauer, R., & van Leeuwen, C. (2023). Retinal waves in adaptive
%rewiring networks orchestrate convergence and divergence in the visual system.
%Network Neuroscience
%Figures 1 to ... |
507c0e58c451c52a0c849cc6002fca848e888c18e722ce3a6fc5d19a671f0847 | MATLAB | 22,132 | 543 | function [stat, cfg] = clusterstat(cfg, statrnd, statobs, varargin)
% SUBFUNCTION for computing cluster statistic for N-D volumetric source data
% or for channel-freq-time data
%
% This function uses
% cfg.dim
% cfg.inside (only for source data)
% cfg.tail = -1, 0, 1
% cfg.multivariate = no, yes
% cfg.ordere... |
a66d7329de65aa8b19b7ff4e0b6f5de123b2535f7894fe89209ed56921d2dfd5 | MATLAB | 22,142 | 315 | function create_sdm_with_other_factors(original_allconds_prt, original_scales_sdm, logfile_for_RT, excel_file_questionnaire_allsubjs, subject_name, output_file_scales_confounds)
% create_sdm_with_other_factors(original_allconds_prt, original_scales_sdm, logfile_for_RT, excel_file_questionnaire_allsubjs, subject_name, ... |
3824ba937eb7633aa570640c3f23d899b54e28e67b044a009fc749618832c84e | MATLAB | 22,188 | 563 | function [cfg] = ft_rejectartifact(cfg, data)
% FT_REJECTARTIFACT removes data segments containing artifacts. It returns a
% configuration structure with a modified trial definition which can be used for
% preprocessing of only the clean data.
%
% You should start by detecting the artifacts in the data using the funct... |
da5d91fd5c111c2e9360d6b12c56c350814f9f8318dda2cb8a4d94a08a96b994 | MATLAB | 22,188 | 563 | function [cfg] = ft_rejectartifact(cfg, data)
% FT_REJECTARTIFACT removes data segments containing artifacts. It returns a
% configuration structure with a modified trial definition which can be used for
% preprocessing of only the clean data.
%
% You should start by detecting the artifacts in the data using the funct... |
1e100022ca8520fd0dcf1f72c7262e5880cff9424924a99c8859c3e843068a58 | MATLAB | 22,198 | 396 | function [] = ground_truth_log_odd_analysis_spike_train_randomised(folders,timebin,BAYSESIAN_NORMALIZED_ACROSS_TRACKS)
% Input:
% Folders: Each Folder is data for a session
% Timebin: 0.02 or 1
% 0.02 -> 20ms time bin
% 1 -> 1 event time bin
% posbin: [] or 1
% [] -> 20 position bin per track (will load extracted_place... |
c453dc29d1620f8083b60a88fc9798f8a4ed88eec107a50871fe5b834302dfd1 | MATLAB | 22,203 | 747 | classdef TrackingVisualizerMT < TrackingVisualizerBase
% TrackingVisualizerMT
% Like TrackingVisualizer, but can handles/display results for many
% targets at once.
%
% Non-gobject model state lives on the associated TrackingVisualizerMTModel
% (accessed via obj.tvm_). This class only owns graphics handle... |
bc1c1ccb93780c034bf05bd1d102241878df7032d227ab0edd9b9d4b6b113e99 | MATLAB | 22,212 | 733 | % clear
% clc
NineMuscleMorphParaInitial = load('NineMuscleMorphParaInitial.mat');
NineMuscleMorphParaInitial = NineMuscleMorphParaInitial.NineMuscleMorphParaInitial;
G = 1; % Muscle Group Number (G = 1 to 9)
x0(1) = NineMuscleMorphParaInitial{G, 1}.MorphologicalParameters.a1;
x0(2) = NineMuscleMorphParaInitial{G, ... |
646640076f877103db36b50c621e6e2ae9a64a6327243bd84d471971af8505fb | MATLAB | 22,215 | 633 | % mediation_zeta_control
calc_zeta = 0;
if calc_zeta == 1
load('sig_allSessions',...
'sigSL')
S = sigSL;
% Analysis parameters
max_coh = 0.1;
minRT = 0.67;
maxRT = 2;
start_t = 0.39;
end_t = 0.5;
session = S.session;
times = S.t;
dt = times(2... |
0b4cc3f4eb7b1413000992ed2fc03f822a48948803781bb478dfe8e5b84256bf | MATLAB | 22,233 | 655 |
load(fullfile(saveLoc, 'cosine_sim'), 'cosine_sim')
load(fullfile(saveLoc, 'resCorr_wiTrial'), 'all_zrMean', 'resCorr');
load(fullfile(saveLoc, 'cosine_sim_randperm'), 'cosine_sim_randperm') % cosine_sim_randperm(sess, d1, d2, :)
load(fullfile(saveLoc, 'resCorr_wiTrial_rand_gauss'), 'par_sess', 'filtW50b', 'rz_ses... |
5f5d82a70f56c12684d67bbbee0bf1afe2778706a79e99844c870770e4f5c350 | MATLAB | 22,249 | 604 | classdef Path
% apt.Path - Platform-aware path representation and manipulation
%
% This value class represents file system paths as lists of components with
% platform-specific formatting and conversion capabilities. It handles the
% differences between Windows (drive letters, backslashes) and POSIX
% (forw... |
da929d8d3bdde856ca8665f4805dad3aaac9dab5fa079e3e04caf4eafb587a38 | MATLAB | 22,253 | 599 | function [lh_labels, rh_labels] = CBIG_MSHBM_generate_individual_parcellation( ...
project_dir, mesh, num_sess, num_clusters, subid, w, c, subject_set)
% [lh_labels, rh_labels] = CBIG_MSHBM_generate_individual_parcellation(project_dir,mesh,num_sess,num_clusters,subid,w,c)
%
% This script will estimate the indi... |
ed8063c8b4253981b959a70e6577392f47a1c3e4053633c1c6448cefdf9161f4 | MATLAB | 22,269 | 473 | function [sens] = ft_datatype_sens(sens, varargin)
% FT_DATATYPE_SENS describes the FieldTrip structure that represents an EEG, ECoG, or
% MEG sensor array. This structure is commonly called "elec" for EEG, "grad" for MEG,
% "opto" for NIRS, or general "sens" for either one.
%
% For all sensor types a distinction shou... |
9906e12054cd9ba737beae1ce9e7876aad543bf9323acec06dab4c61b74e4615 | MATLAB | 22,285 | 528 | function [] = plotWalkingChamberAnalysis(dataset,params,dataset2Comp,figureFile)
close all
WT_Thresh = 45;
WT_angle_Thresh = 10;
wingExt_Thresh = 35;
nGenotype = numel(dataset);
allPitchData_lin_before = cell(1,nGenotype);
allPitchData_lin_during = cell(1,nGenotype);
allTheta_during = cell(1,nGenotype);
for f = 1:14... |
7b8bb3e548bc8eadf8508dc8461d22ba84e2883d8d1e48e3126899bb3268467a | MATLAB | 22,306 | 670 | function dimord = getdimord(data, field, varargin)
% GETDIMORD
%
% Use as
% dimord = getdimord(data, field)
%
% See also GETDIMSIZ, GETDATFIELD
% Please note that this function is called from many other FT functions. To avoid
% unwanted recursion, you should avoid (where possible) calling other FT functions
% insid... |
446729f932c71b2c5f48154733d1d6db51a8c2dbe9c138f90a3306688e867bd0 | MATLAB | 22,338 | 474 | function [sens] = ft_datatype_sens(sens, varargin)
% FT_DATATYPE_SENS describes the FieldTrip structure that represents an EEG, ECoG, or
% MEG sensor array. This structure is commonly called "elec" for EEG, "grad" for MEG,
% "opto" for NIRS, or general "sens" for either one.
%
% For all sensor types a distinction shou... |
54d50e641134b267c741c878fb85eab4fee8080f49bbff9784712b99bb45aef8 | MATLAB | 22,390 | 739 | % clear
% clc
NineMuscleMorphParaInitial = load('NineMuscleMorphParaInitial.mat');
NineMuscleMorphParaInitial = NineMuscleMorphParaInitial.NineMuscleMorphParaInitial;
G = 4; % Muscle Group Number (G = 1 to 9)
x0(1) = NineMuscleMorphParaInitial{G, 1}.MorphologicalParameters.a1;
x0(2) = NineMuscleMorphParaInitial{G, ... |
9683bd34397007a6c993a015d9cc6696566961126fc62e0052c9557e99581b27 | MATLAB | 22,394 | 608 | function [amplifier_data, board_dig_in_data, frequency_parameters] = read_Intan_RHD2000_file_(file, path)
% read_Intan_RHD2000_file
%
% Version 1.3, 10 December 2013
%
% Reads Intan Technologies RHD2000 data file generated by evaluation board
% GUI. Data are parsed and placed into variables that appear in the ... |
2d283f35cb3ae79ee134e852de4e15707551a7b8cd60ea18b731aa8859294719 | MATLAB | 22,411 | 574 | function varargout = MouseVocSettings(varargin)
% MOUSEVOCSETTINGS MATLAB code for MouseVocSettings.fig
% MOUSEVOCSETTINGS, by itself, creates a new MOUSEVOCSETTINGS or raises the existing
% singleton*.
%
% H = MOUSEVOCSETTINGS returns the handle to a new MOUSEVOCSETTINGS or the handle to
% the exis... |
2f5e6b52b573c82375a2497ba0d5b649219d143fcac557d61292dce0be589062 | MATLAB | 22,415 | 741 | % clear
% clc
NineMuscleMorphParaInitial = load('NineMuscleMorphParaInitial.mat');
NineMuscleMorphParaInitial = NineMuscleMorphParaInitial.NineMuscleMorphParaInitial;
G = 8; % Muscle Group Number (G = 1 to 9)
x0(1) = NineMuscleMorphParaInitial{G, 1}.MorphologicalParameters.a1;
x0(2) = NineMuscleMorphParaInitial{G, ... |
749c61cbb9d310baa6200a1322e8e61d85c375059c7a31a08c381f13994258d4 | MATLAB | 22,423 | 561 | %% Example 1 Overview
%
% GLMsingle is new tool that provides efficient, scalable, and accurate
% single-trial fMRI response estimates.
%
% The purpose of this Example 1 notebook is to guide the user through basic
% calls to GLMsingle, using a representative, small-scale test dataset (in
% this case, an example session... |
b00327e4c12fac9278abd37befff7f62520cca1513ba966b89e407f7d688a173 | MATLAB | 22,429 | 754 | % clear
% clc
NineMuscleMorphParaInitial = load('NineMuscleMorphParaInitial.mat');
NineMuscleMorphParaInitial = NineMuscleMorphParaInitial.NineMuscleMorphParaInitial;
G = 1; % Muscle Group Number (G = 1 to 9)
x0(1) = NineMuscleMorphParaInitial{G, 1}.MorphologicalParameters.a1;
x0(2) = NineMuscleMorphParaInitial{G, ... |
81a0050cc177cf41dea32b8f06746e599914c54aa8348bcef918626fa6146a68 | MATLAB | 22,431 | 501 | function [accuracy, stim] = rd_analyzeTADetectDiscrimOneRun(dataDir, stimDir, stimfile, run, plotLevel)
dataFile = dir(sprintf('%s/*%s%d.mat', dataDir, stimfile, run));
dd = load(sprintf('%s/%s', dataDir, dataFile(end).name));
stim = load(sprintf('%s/%s%d.mat', stimDir, stimfile, run));
trialCount = length(stim.p.blo... |
633c0b33c05acd19d512009f377149b4bd3295de47d3e9b6c4e2b1959b11835c | MATLAB | 22,439 | 588 | % AUTHORSHIP
% Primary Developer: Stephen Meehan <swmeehan@stanford.edu>
% Math Lead & Secondary Developer: Connor Meehan <connor.gw.meehan@gmail.com>
% Bioinformatics Lead: Wayne Moore <wmoore@stanford.edu>
% Provided by the Herzenberg Lab at Stanford University
% License: BSD 3 clause
%
classdef Proba... |
b56e9bcc6caaf19b17400469f6d32208eb81bbed777e37e4a85c88ce19119bd9 | MATLAB | 22,451 | 337 | % RSA analysis in ROIs
%% Loading the ROIs
% Getting one of the statistical maps, to resample all of the other images to its resolution
filename_for_resampling = 'C:\Users\michaelpeer1\Google Drive\bar_mord_shared_files\Experiment_results\group_analysis_RSA_searchlight_facebook_percentcommonfriends_radius3_ite... |
d93a80145ffab2e332de402150c95a189df2a1af0807639da0c397cb45c70e79 | MATLAB | 22,469 | 628 | function [varargout] = ft_stratify(cfg, varargin)
% FT_STRATIFY tries to reduce the variance in a specific feature in the data
% that is not related to an effect in two or multiple conditions, but where
% that feature may confound the analysis. Stratification is implemented by
% randomly removing elements from the dat... |
33e3ffdb902d4abd189a46e12f1a0eee73e5d953bb34c6f14cc0ad709004afba | MATLAB | 22,473 | 394 | function estimated_position = bayesian_decoding_ground_truth(place_fields_BAYESIAN,bayesian_spike_count,option,BAYSESIAN_NORMALIZED_ACROSS_TRACKS,save_option)
% INPUTS:
% place fields: matrix or []. Place fields that want to be used as a template in the decoding. If empty, it will load place fields for the whole ses... |
0c4c2f99ac280baabf90b0e2944a8a6f17c1dc1e6f87a8993dcbab2399479099 | MATLAB | 22,507 | 745 | % clear
% clc
NineMuscleMorphParaInitial = load('NineMuscleMorphParaInitial.mat');
NineMuscleMorphParaInitial = NineMuscleMorphParaInitial.NineMuscleMorphParaInitial;
G = 2; % Muscle Group Number (G = 1 to 9)
x0(1) = NineMuscleMorphParaInitial{G, 1}.MorphologicalParameters.a1;
x0(2) = NineMuscleMorphParaInitial{G, ... |
571ce856e59d7acfb5a8f7ae54b784015eb7cdb06fb39ffa3dee14715096ab1e | MATLAB | 22,508 | 411 | function [] = ground_truth_log_odd_analysis_cross_experiment_randomised(folders,timebin,BAYSESIAN_NORMALIZED_ACROSS_TRACKS)
% Input:
% Folders: Each Folder is data for a session
% Timebin: 0.02 or 1
% 0.02 -> 20ms time bin
% 1 -> 1 event time bin
% posbin: [] or 1
% [] -> 20 position bin per track (will load extracted_... |
4ebe27ccc4b392b94ada40ac3198d89ce3ea4d0ced8ce8efc4d59985883ea9f7 | MATLAB | 22,512 | 694 | % onsetsim
corepath = pwd;
addpath([corepath,'/functions'])
ath = 0.05; % arbitrary threshold value for ttest
pth = 1 - ath; % permutation threshold
Nperm = 2000;
%% Make template
% true onset = 160 ms, F=81, max at F=126
true_onset = 160;
Xf = 0:2:500; % 500 Hz
Nf = length(Xf);
temp1 = zeros(1,Nf);
erp = normpdf(li... |
75a7d0ba250a409aa370d9b0d1d9157d5329e3a5bbc225e5a4f72aa08ee54a7d | MATLAB | 22,529 | 710 | function colormapeditor(fig)
%COLORMAPEDITOR starts colormap editor ui
%
% When started the colormap editor displays the current figure's colormap
% as a strip of rectangluar cells. Nodes, displayed as rectangles(end
% nodes) or carrots below the strip, separate regions of uniform slope in
% R,G and B. The gr... |
2263e57f90f85d94955e6aa576b5c7d177a0cd974cc084cebda4791d3f670ef0 | MATLAB | 22,536 | 419 | function Behavior = LickTaskBehavior_ctrl(seizurePaths, seizureCell, params, varargin)
% Created By: Xinyuan Zheng - xinyuan.zheng@outlook.com
% Modified Abdo's LickTaskBehavior() for control periods
%**************************************************************************
%% Parse in optional arguments
savePath = p... |
892bb29de3e834e9ef72da47bac634a30eeca1af31146a304c1db60a5e416e7d | MATLAB | 22,588 | 515 | function [ likelihood, results] = CBIG_gwMRF_graph_cut_clustering_split_newkappa_prod(x,prams,hemisphere)
% This function performs the actual clustering. This version is for the premulitiplied input data.
%
% Input
% - x = input data (premultiplied timeseries data)
% - prams = a struct containi... |
731d3577e1fa7092db947d59e193c40507ae4e75a31458c2f1e775868e042e45 | MATLAB | 22,603 | 789 | clear
clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
%% Motion Data
MotionData = MotionData.MotionData;
% Joint Angles
SHLa = MotionData.GenericCat.Angles.AngShou.Total*(pi/180);
ELBa = MotionData.Ge... |
abea2a8c16636a06b808c36a2f9f1a699b58d4f417aae638f91d07720467e3be | MATLAB | 22,605 | 495 | function [xPoints, yPoints] = plotSpikeRaster(spikes,varargin)
% PLOTSPIKERASTER Create raster plot from binary spike data or spike times
% Efficiently creates raster plots with formatting support. Faster than
% common implementations. Multiple plot types and parameters available!
% Look at Parameters section... |
af209feadbbfc9a9298eb5854222ae0f8d857392f943384a0152d31a1f41d09b | MATLAB | 22,609 | 760 | % clear
% clc
NineMuscleMorphParaInitial = load('NineMuscleMorphParaInitial.mat');
NineMuscleMorphParaInitial = NineMuscleMorphParaInitial.NineMuscleMorphParaInitial;
G = 4; % Muscle Group Number (G = 1 to 9)
x0(1) = NineMuscleMorphParaInitial{G, 1}.MorphologicalParameters.a1;
x0(2) = NineMuscleMorphParaInitial{G, ... |
eb71e830d40ef169d4b79bba5ef0b6864cb166e29e33fb67627bf042cd1615fa | MATLAB | 22,623 | 527 | function conf = spm_cfg_deformations
% Configuration file for deformation jobs
%_______________________________________________________________________
% Copyright (C) 2008-2016 Wellcome Trust Centre for Neuroimaging
% John Ashburner
% $Id: spm_cfg_deformations.m 7700 2019-11-21 17:09:15Z john $
hsummary = {
'Utility... |
399b0f3205065eebc5fcf6c84df15004a70613db8a1f5c9b984fc969a616e57a | MATLAB | 22,633 | 761 | % clear
% clc
NineMuscleMorphParaInitial = load('NineMuscleMorphParaInitial.mat');
NineMuscleMorphParaInitial = NineMuscleMorphParaInitial.NineMuscleMorphParaInitial;
G = 8; % Muscle Group Number (G = 1 to 9)
x0(1) = NineMuscleMorphParaInitial{G, 1}.MorphologicalParameters.a1;
x0(2) = NineMuscleMorphParaInitial{G, ... |
8d0db229c696b588ea0aca13d2f7f9d92fb9cd9fbe21343f8609b7349e434729 | MATLAB | 22,721 | 766 | % clear
% clc
NineMuscleMorphParaInitial = load('NineMuscleMorphParaInitial.mat');
NineMuscleMorphParaInitial = NineMuscleMorphParaInitial.NineMuscleMorphParaInitial;
G = 2; % Muscle Group Number (G = 1 to 9)
x0(1) = NineMuscleMorphParaInitial{G, 1}.MorphologicalParameters.a1;
x0(2) = NineMuscleMorphParaInitial{G, ... |
4ab1b7f76f1fc35878f6f2daee053c26d765844ffe3cb22d2c18528977dd2107 | MATLAB | 22,725 | 533 | %% plot settings
set(groot,'DefaultAxesTickDir', 'out')
set(groot,'DefaultAxesTickDirMode', 'manual');
set(groot,'DefaultAxesFontWeight', 'normal');
set(groot,'DefaultAxesFontName', 'Arial');
set(groot,'DefaultAxesFontSizeMode', 'manual')
set(groot,'DefaultAxesFontSize', 12)
set(groot,'DefaultAxesTitleFontSizeMu... |
efd1d05c2b899d6692012696d7398036bb4192bbe1f4af737682301511ceaa57 | MATLAB | 22,726 | 717 | function varargout = playfmf(varargin)
% PLAYFMF M-file for playfmf.fig
% PLAYFMF, by itself, creates a new PLAYFMF or raises the existing
% singleton*.
%
% H = PLAYFMF returns the handle to a new PLAYFMF or the handle to
% the existing singleton*.
%
% PLAYFMF('CALLBACK',hObject,eventData,handl... |
851069d082b6b3e6b4a9c437da55d1523e939f6066bfc0a1c006c7a2e011ce78 | MATLAB | 22,758 | 410 | function [outputFilenameMatrix, maskMatrix, MPs, subjIds] = filenameParser(parentFolder,inputFormat, rsfcTaskNames, outputFolder, OnlyTheseIDs, varargin)
% Depending on inputFormat, rsfcTaskNames can be a single name (in the case
% of fmriprep) or an enumerated list in the case of HCP
outputFolder ... |
dac3a8613cc182e4e1700298d48339c852620be1f992fb8c3dc2b2fd4ab354e9 | MATLAB | 22,861 | 586 | function varargout = DPABI_ROISignalExtracter(varargin)
% DPABI_ROISignalExtracter MATLAB code for DPABI_ROISignalExtracter.fig
% DPABI_ROISignalExtracter, by itself, creates a new DPABI_ROISignalExtracter or raises the existing
% singleton*.
%
% H = DPABI_ROISignalExtracter returns the handle to a new D... |
999cb6997f7041d9e4d5b5454f9439dadfc3489b0e1306600c623ff6270d02ff | MATLAB | 22,871 | 679 | function [optim_priors] = VBA_optimPriors(M,u,partition,density, priors2optim,Nsim)
% This functions performs the optimization over priors of models in order
% to minimize misclassifications within partitions of the set of models
% considered.
% E[e(y)] using either MCMC or Laplace
%
% INPUT
% - M : structure containi... |
d59d24862f9efcea3d3a3b6521fa54fa0af996573124a4a56b94b5e1f1d0738c | MATLAB | 22,881 | 651 | %--------------------------------------------------------------------------
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% CBIG_MFMem_rfMRI_estimation_main.m
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% This is the main execution script. The purpose of the skript is to use
% given functional connectivity matrix (FC) and emprical funct... |
9e5e0523c9d2c8cf8e4b2dd3c535ee6acde1b681108eaa5c929c6e912bf39c9a | MATLAB | 22,887 | 564 | function [stat, cfg] = clusterstat(cfg, statrnd, statobs, varargin)
% SUBFUNCTION for computing cluster statistic for N-D volumetric source data
% or for channel-freq-time data
%
% This function uses
% cfg.dim
% cfg.inside (only for source data)
% cfg.tail = -1, 0, 1
% cfg.multivariate = no, yes
% cfg.ordere... |
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