sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
0be7bc3394b42f88aff070f509e21cc380c92be861e186a85dff379af5631981 | MATLAB | 32,860 | 773 | function [segmented] = ft_volumesegment(cfg, mri)
% FT_VOLUMESEGMENT segments an anatomical MRI. The behaviour depends on the output requested. It can
% return probabilistic tissue maps of gray/white/csf compartments, a skull-stripped anatomy, or
% binary masks representing the brain surface, skull, or scalp surface.
... |
b2ee4e288897aba6a4a7a268c081f3899b238c03cac950e087d107e04d878245 | MATLAB | 32,864 | 621 | function Locs_Extraction_Module()
% Use data and listbox as global variables (access from anywhere).
global data listbox
% Make a new figure, and set its properties.
figure();
set(gcf,'name','Localization Extraction Module','NumberTitle','off','color','k','units','normalized','position',[0.25 0.2 0.5 0.6],'men... |
3134da6c4f35ac072d5cfacfd9c95fbe00cccaa87e716a5b3c26c9085c0ad949 | MATLAB | 32,887 | 774 | function [segmented] = ft_volumesegment(cfg, mri)
% FT_VOLUMESEGMENT segments an anatomical MRI. The behavior depends on the output requested. It can
% return probabilistic tissue maps of gray/white/csf compartments, a skull-stripped anatomy, or
% binary masks representing the brain surface, skull, or scalp surface.
%... |
f53474744ac53efaacf93fed98d0f39e31f884ae106274e787c510e41d5eb746 | MATLAB | 33,116 | 961 | function stack = tiffread(file_name, indices, varargin)
% tiffread, version 3.32 - November 23, 2018
%
% stack = tiffread;
% stack = tiffread(filename);
% stack = tiffread(filename, indices);
%
% Two options can be specified: 'ReadUnknownTags' and 'DistributeMetaData', eg:
% stack = tiffread(filename, [], 'ReadUnknown... |
b8950e69c4664207cf0ad60764ef72b56b4ad7c6d8827e79494acc7ddd54ce91 | MATLAB | 33,130 | 791 | function out = read_micromed(action, varargin)
% function out = read_micromed(action, varargin),
%
% case 'data',
% read data with calibration
% read_micromed('data', fullfilename, header, iSample, nSamplesToRead)
% same as dataNC ?
%
% case 'data_t1_t2',
% read calibrated data from t1 to t2
%
% case 'dataNC... |
0adaf82f3e0fc405c8f295607f4f074ea2ae759e97f2428006f3edeeb366f477 | MATLAB | 33,195 | 1,032 | classdef LandmarkSpecs < handle
% Properties that correspond to app components
properties (Access = public)
hFig
gl % uigridlayout container when embedded in a parent container
isStandAlone_ = true % false when embedded via 'hParent'
axSkel
axHT
axSwap
% GridLayout matlab.ui.... |
09b53ac227650009e03e6aaa0c529de091e1d1dbe9483edeb3fc4c0edcff9e6d | MATLAB | 33,198 | 445 | function significant_replay_events = number_of_significant_replays_ground_truth(p_value_threshold,ripple_zscore_threshold, method, no_of_shuffles,rexposure_option)
% Establish significance for the replay events that are above a set ripple power threshold.
% The output will therefore have less replay events than the st... |
c2b848b3e11319353f4d5d6b948e9fe17401a71fabacab24b3d684060a617fd3 | MATLAB | 33,220 | 409 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
4323482c5272f5168eb6cd9096eb845495ff7694514bb6db5d2d832e85073a6d | MATLAB | 33,245 | 810 | function varargout = DPABI_Standardization_Surf(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_Sta... |
6bd105249e834c4792edd349d4946c69f6e91072134492c82d37de19a8b203bd | MATLAB | 33,249 | 1,026 | % onsetsim_1overf
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 = nor... |
982eb0d6fb4ceb65c34cecfd397be0fc92dcac44cccefcc6c806a6a972a7df1e | MATLAB | 33,250 | 702 | function [data] = ft_preprocessing(cfg, data)
% FT_PREPROCESSING reads MEG and/or EEG data according to user-specified trials
% and applies several user-specified preprocessing steps to the signals.
%
% Use as
% [data] = ft_preprocessing(cfg)
% or
% [data] = ft_preprocessing(cfg, data)
%
% The first input argument... |
c4ab259dcd8801545748facd063a5859b46eec2e4531ee1fa5a17b8fe1cf0669 | MATLAB | 33,255 | 702 | function [data] = ft_preprocessing(cfg, data)
% FT_PREPROCESSING reads MEG and/or EEG data according to user-specified trials
% and applies several user-specified preprocessing steps to the signals.
%
% Use as
% [data] = ft_preprocessing(cfg)
% or
% [data] = ft_preprocessing(cfg, data)
%
% The first input argument... |
04738c4a9c90f590303ba2fdc09ab9b111b668af21daccd5feb5c28f89106d89 | MATLAB | 33,256 | 917 | function varargout = HumanOdorRep_venn(varargin)
%VENN Plot 2- or 3- circle area-proportional Venn diagram
%
% venn(A, I)
% venn(Z)
% venn(..., F)
% venn(..., 'ErrMinMode', MODE)
% H = venn(...)
% [H, S] = venn(...)
% [H, S] = venn(..., 'Plot', 'off')
% S = venn(..., 'Plot', 'off')
% [...] = venn(..., P1, V1... |
2248f7e9fa8373ad8b91836bf94746d5d24628afbd7341c856ad1af0698bf9ba | MATLAB | 33,321 | 754 | % encoderFindBehaviorPeriods.m
% Sarah West
% 10/11/21
function [parameters] = encoderFindBehaviorPeriods(parameters)
% Announce what stack you're on.
message = ['Finding '];
for dispi = 1:numel(parameters.values)/2
message = [message ', ' parameters.values{dispi}];
end
disp(message);
... |
935a881cd971601e4ff80976f99ba4f64a2656b5fa5cfd3a0d181d21efff8d90 | MATLAB | 33,336 | 799 | function varargout = DPABINet_Construction(varargin)
% DPABINET_CONSTRUCTION MATLAB code for DPABINet_Construction.fig
% DPABINET_CONSTRUCTION, by itself, creates a new DPABINET_CONSTRUCTION or raises the existing
% singleton*.
%
% H = DPABINET_CONSTRUCTION returns the handle to a new DPABINET_CONSTRUCTI... |
c282dcb326257b0bf7ff1c45ead8e6ba9391d060d6c072f82c226d158d587dba | MATLAB | 33,450 | 543 | function preproc = spm_cfg_preproc8
% Configuration file for 'Combined Segmentation and Spatial Normalisation'
%__________________________________________________________________________
% Copyright (C) 2008-2016 Wellcome Trust Centre for Neuroimaging
% John Ashburner
% $Id: spm_cfg_preproc8.m 7629 2019-06-27 12:35:45... |
0c2195fda395d2882b616f3ab615f3ccc2f49d029d2b548560b3c69c276dcb7e | MATLAB | 33,623 | 882 | function mesh_realigned = ft_meshrealign(cfg, mesh)
% FT_MESHREALIGN rotates, translates and optionally scales electrode positions. The
% different methods are described in detail below.
%
% INTERACTIVE - You can display the mesh surface together with axis coordinate
% system, and manually (using the graphical user in... |
25ce43b275a71539296507959ff3f172ab229ce6809545eb669c5f3dd89d835b | MATLAB | 33,625 | 882 | function [mesh_realigned] = ft_meshrealign(cfg, mesh)
% FT_MESHREALIGN rotates, translates and optionally scales electrode positions. The
% different methods are described in detail below.
%
% INTERACTIVE - You can display the mesh surface together with axis coordinate
% system, and manually (using the graphical user ... |
7f21b6549254db9ad0cdaeb02464d7571bcaae5548070c8ada13da42fca54b07 | MATLAB | 33,727 | 775 | function [ROC,log_odd,AUC_combined,AUC_session,Mean_zdata,SE_zdata,pval_average,pval_session] = plot_log_odd_new(ROC,log_odd,timebin,option,figure_option)
% colour_line={'g','k','b','r','m'};
% colour_symbol={'go','ko','bo','ro','mo'};
states=[-1 0 1 2]; % PRE, POST and RUN
% no_of_sessions = 5;
no_of_sessions = ma... |
3a21338ef24bae3b3265c57093ed1c20c2ab40154c84f8f8b6c478ad093754ab | MATLAB | 33,758 | 701 | function plot_ground_truth_compare_single_shuffle(folders,option,method)
total_number{1}(1:3) = 0;
total_number{2}(1:3) = 0;
for nfolder = 1:length(folders) % Get total number of replay events
for nshuffle = 1:2 % 1 is original and 2 is global remapped
session_total_number{nshuffle}(nfolder,1:3) = 0;
e... |
e28db9e4296c12da352d7e4c4c73f91ac1564b83284db002a789b37d0420d152 | MATLAB | 33,821 | 851 | classdef TableDataSource < dspace_data.DataSource
% A TableDataSource holds data in Dataspace. The concept of datasources
% is explained in broad terms here:
% <a href="matlab:dspace.help('dspace.resources.docs.Introduction_to_Dataspace')">Introduction to Dataspace (Dataspace Help)</a>.
%
% A T... |
b5432d64a2764c554d75440709da8275b27d02c3ba9c17d66cc7adc031ffaa25 | MATLAB | 33,883 | 657 | % readlocs() - read electrode location coordinates and other information from a file.
% Several standard file formats are supported. Users may also specify
% a custom column format. Defined format examples are given below
% (see File Formats).
% Usage:
% >> eloc = readlocs( ... |
ec84084050e74d40447c6563b3c8f1d1f10c00ca329a68182e4681e124ceb5f6 | MATLAB | 33,892 | 828 | classdef Map < handle_light
% Take elements in domain set to elements in image set
%---------------------------------------------------------------------
% license
%---------------------------------------------------------------------
% Copyright 2015 Idse Heemskerk and Sebastian Streichan
... |
9cfa3c801eb4b8a4353cc96c973a3d07039ff84d516d8d360d4dae6a103ed562 | MATLAB | 33,990 | 802 | function varargout = DPABI_STAT_TOOL(varargin)
% DPABI_STAT_TOOL MATLAB code for DPABI_STAT_TOOL.fig
% DPABI_STAT_TOOL, by itself, creates a new DPABI_STAT_TOOL or raises the existing
% singleton*.
%
% H = DPABI_STAT_TOOL returns the handle to a new DPABI_STAT_TOOL or the handle to
% the existing si... |
58f89f884c747e084b85cedd844ceb8aae37fec69f84c38ffe2ac8ae4dc7857a | MATLAB | 34,032 | 748 | function [x, y] = TrainingSection(obj, action, varargin)
%%%% TODO:
%%% training stages and substages : when nic grows etc. -> make explicit
%%% add the possibility to go back in training (???)
%manual
%%% reward more the harder task / make it easier???
%manual
%%% put some constant checks: bias -> update antibias ;... |
8aa8cd16f206b103672984005f918e1d7dfcfdc490ee90c08c3f36895f429b49 | MATLAB | 34,032 | 660 | %
% Input
%
% expInfo | experiment configuration (struct)
% subj | identifier of subject (string)
% condSet | conditions (cell array of strings)
%_______________________________________________________________________
%
% 170707 | updated palamedes to take in more inputs
% 170821 | adapted to state switch vari... |
c562f6428b399ff107060b3ebaac64cece8fd80ea6dc474ea2078e98173cb0ae | MATLAB | 34,046 | 885 | function varargout = bbGt( action, varargin )
% Bounding box (bb) annotations struct, evaluation and sampling routines.
%
% bbGt gives access to two types of routines:
% (1) Data structure for storing bb image annotations.
% (2) Routines for evaluating the Pascal criteria for object detection.
%
% The bb annotation sto... |
d1a06d20e6dc628cc715901ac40867930e6ad460dace21160db949ebc07a4c6f | MATLAB | 34,223 | 1,120 | % clear
% clc
NineMuscleMorphParaInitial = load('NineMuscleMorphParaInitial.mat');
NineMuscleMorphParaInitial = NineMuscleMorphParaInitial.NineMuscleMorphParaInitial;
G = 3; % Muscle Group Number (G = 1 to 9)
x0(1) = NineMuscleMorphParaInitial{G, 1}.MorphologicalParameters.a1;
x0(2) = NineMuscleMorphParaInitial{G, ... |
36abc44faa4e0a01b7c9b4496727e8c8aee3cd61644ad853454168d2049761ea | MATLAB | 34,238 | 1,124 | % clear
% clc
NineMuscleMorphParaInitial = load('NineMuscleMorphParaInitial.mat');
NineMuscleMorphParaInitial = NineMuscleMorphParaInitial.NineMuscleMorphParaInitial;
G = 9; % Muscle Group Number (G = 1 to 9)
x0(1) = NineMuscleMorphParaInitial{G, 1}.MorphologicalParameters.a1;
x0(2) = NineMuscleMorphParaInitial{G, ... |
bcc217ac04fe8ba184df921c225f02f0948804ccf30ddda3d86547dbfc2905c6 | MATLAB | 34,240 | 1,025 | classdef LabelCoreMultiViewCalibrated2Controller < LabelCoreController
% Multiview calibrated labeling controller
%
% Owns all graphics for multi-view calibrated labeling: point handles in
% multiple views, epipolar line handles, reconstructed point handles, 3D
% display handles, axis xlabels. Receives GUI callbacks, e... |
e8dc2bce86191e478439378625d11eb6c2da8f0954587878ae223f493cded061 | MATLAB | 34,240 | 411 | function [significant_replay_events sig_event_info] = number_of_significant_replays_ground_truth_backup(p_value_threshold,ripple_zscore_threshold, method, shuffles,rexposure_option)
% Establish significance for the replay events that are above a set ripple power threshold.
% The output will therefore have less replay ... |
030334d2509819f0f8ca5da666c004d24d304ade234023ade228d9998139e21f | MATLAB | 34,289 | 754 | classdef FeaturesPanel < TimelinePanel
properties
reporter
end
properties (Transient)
featureTypeLabels
featureTypeShadows
featureHandles
timeRangeRectangles
detectFeaturesInSelectionMenuItem
showReporterSettingsMenuItem
... |
9299c011d0776134e76fb91ffb730d78f7daefe9c18f0b8c6ebe367fad024ba5 | MATLAB | 34,345 | 1,138 | % clear
% clc
NineMuscleMorphParaInitial = load('NineMuscleMorphParaInitial.mat');
NineMuscleMorphParaInitial = NineMuscleMorphParaInitial.NineMuscleMorphParaInitial;
G = 3; % Muscle Group Number (G = 1 to 9)
x0(1) = NineMuscleMorphParaInitial{G, 1}.MorphologicalParameters.a1;
x0(2) = NineMuscleMorphParaInitial{G, ... |
d1a5d19d33397b2a8c2ab0ae3b79510d3633155e9baabb5a61844d4ba091e35e | MATLAB | 34,362 | 1,144 | % clear
% clc
NineMuscleMorphParaInitial = load('NineMuscleMorphParaInitial.mat');
NineMuscleMorphParaInitial = NineMuscleMorphParaInitial.NineMuscleMorphParaInitial;
G = 9; % Muscle Group Number (G = 1 to 9)
x0(1) = NineMuscleMorphParaInitial{G, 1}.MorphologicalParameters.a1;
x0(2) = NineMuscleMorphParaInitial{G, ... |
9b920d0e9ac0728037eb84c48ad8ccca9802fae1bc96598c6b99a24e6e99e9f2 | MATLAB | 34,363 | 794 | function output = read_nervus_header(filename)
% read_nervus_header Returns header information from Nicolet file.
%
% FILENAME is the file name of a file in the Natus/Nicolet/Nervus(TM)
% format (originally designed by Taugagreining HF in Iceland)
%
% Based on ieeg-portal/Nicolet-Reader
% at https://github.com... |
345ba0df0db989067d594d37a8d741e59de7e90a50a61647bcf0c1194b7bc26b | MATLAB | 34,382 | 855 | function BehaviorResults = analyzeBehavior_ctrl(Behavior, params, varargin)
% this function performs the behavioral analysis on the behavior struct
% outputted by LickTaskBehavior_ctrl and plots the relevant analyses.
% Inputs:
% - Behavior: the structure outputted by LickTaskBehavior
% - params: struct with analys... |
012c4b2e31ad6e1cf285de0f92bd2b53c963c1b5739790a20ffab7fd3fc6eee3 | MATLAB | 34,412 | 797 | function output = read_nervus_header(filename)
% read_nervus_header Returns header information from Nicolet file.
%
% FILENAME is the file name of a file in the Natus/Nicolet/Nervus(TM)
% format (originally designed by Taugagreining HF in Iceland)
%
% Based on ieeg-portal/Nicolet-Reader
% at https://github.com... |
5a3c5c1e1f460b7373b281204332bc6ba533616d412f08f50d3c0579cdde3df2 | MATLAB | 34,481 | 874 | function varargout = DPABI_Stability(varargin)
% DPABI_Stability MATLAB code for DPABI_Stability.fig
% DPABI_Stability, by itself, creates a new DPABI_Stability or raises the existing
% singleton*.
%
% H = DPABI_Stability returns the handle to a new DPABI_Stability or the handle to
% the existing si... |
e4ae571c42b69c91b1011f1ec4926310d0fd98f6a7b09da8196c5adafe2b70fd | MATLAB | 34,509 | 917 | classdef LandmarkColorsController < handle
% Controller for the "Landmark Cosmetics" dialog (keypoint appearance:
% per-landmark colors, marker/label properties, and skeleton cosmetics).
%
% This replaces the legacy GUIDE app LandmarkColors.m / LandmarkColors.fig.
% The figure is built programmatically in cre... |
46f4a96c48073391efaa5ac1c2a03124a3205f4511b0c2279a5095a2bd1cd1df | MATLAB | 34,534 | 820 | classdef SurfaceOfInterest < diffgeometry.Manifold2D
% SurfaceOfInterest extends Manifold, adding methods that go beyond the
% basic mathematical structure
%---------------------------------------------------------------------
% license
%----------------------------------------------------------... |
8d050852f5d0d5297e9845ff0d3ec7b08c30aa210d52e8dc5220c9c2afac8ed5 | MATLAB | 34,610 | 780 | function [x, cost, info, options] = trustregions(problem, x, options)
% Riemannian trust-regions solver for optimization on manifolds.
%
% function [x, cost, info, options] = trustregions(problem)
% function [x, cost, info, options] = trustregions(problem, x0)
% function [x, cost, info, options] = trustregions(pro... |
892aa55535e8c8cb3af501ab34bb4f41d14534687f10451c5993b4eeef416c0b | MATLAB | 34,694 | 1,190 | clear
clc
NineMuscleMorphParaInitial = load('NineMuscleMorphParaInitial.mat');
NineMuscleMorphParaInitial = NineMuscleMorphParaInitial.NineMuscleMorphParaInitial;
G = 7; % Muscle Group Number (G = 1 to 9)
x0(1) = NineMuscleMorphParaInitial{G, 1}.MorphologicalParameters.OW.a1OW;
x0(2) = NineMuscleMorphParaInitial{G,... |
400424130909bca53eaa7b32d623ed2f81bb22420b69325d17aa55d1bfa94ccf | MATLAB | 34,824 | 534 | %%%%%%%%%%%%%%%%%%%%%%%%%%% main func %%%%%%%%%%%%%%%%%%%%%%%%%%
function varargout = mFusionV3(varargin)
% MFUSIONV3 MATLAB code for mFusionV3.fig
% MFUSIONV3, by itself, creates a new MFUSIONV3 or raises the existing
% singleton*.
%
% H = MFUSIONV3 returns the handle to a new MFUSIONV3 or the handle to... |
03ee6d7952ba3414cd65dafc018483d0e198e185207a0461a611d391d4084376 | MATLAB | 34,842 | 642 | function [log_odd] = extract_ground_truth_info_backup(folders,option,method,shuffles,ripple_zscore_threshold)
% Input:
% Folders: Each Folder is data for a session
% method = 'linear' or 'wcorr' or 'spearman' or
% Output:
% log_odd: save the log odd - log(sum(probability of T1)/sum(probability of
% T2)) of the events... |
322022015c0f68a9affecd2a2645245dc19f16874f2ae0dbbe0e787b8cb8adf0 | MATLAB | 34,859 | 792 | function [sourcemodel, cfg] = ft_prepare_sourcemodel(cfg)
% FT_PREPARE_SOURCEMODEL constructs a source model, for example a 3D grid or a
% cortical sheet. The source model that can be used for source reconstruction,
% beamformer scanning, linear estimation and MEG interpolation.
%
% Use as
% sourcemodel = ft_prepare... |
72c152dafd4c085f666c50ea147e1bf89c200c6b2721dadc3ff337e2183d19c5 | MATLAB | 34,879 | 1,192 | clear
clc
NineMuscleMorphParaInitial = load('NineMuscleMorphParaInitial.mat');
NineMuscleMorphParaInitial = NineMuscleMorphParaInitial.NineMuscleMorphParaInitial;
G = 6; % Muscle Group Number (G = 1 to 9)
x0(1) = NineMuscleMorphParaInitial{G, 1}.MorphologicalParameters.OW.a1OW;
x0(2) = NineMuscleMorphParaInitial{G,... |
3eddd02136d1232087c30ce64ea1629ef4711ce258a127f1b1bcf02ed17a1e85 | MATLAB | 34,941 | 793 | function boney_segment_print_figure(Vo,Ym,Yc,Ybonemarrow, Si,St, out,job,Affine) % many things
%print_figure. Print final report figure with volumes and surfaces.
% _________________________________________________________________________
%
% Robert Dahnke & Polona Kalc
% Structural Brain Mapping Group (https://neuro-... |
eb9d46f49f65c6dfe393b2c0861cf36e69ad6b26a0564655a3f2443f48d55a82 | MATLAB | 35,161 | 1,224 | function [] = humanplots(s1, s2, ipage, varargin)
%humanplots Plots behavioral data sets from various compelled tasks
%
% [] = humanplots(s1, [s2], [ipage], varargin)
%
% This function displays
%
% (1) psychometric, chronometric and tachometric curves
% (2) Left/Right choice probability curves
% ... |
c9489b6c50cf464a5e9ffd04c99b818c307e6270d389788c7c4288154aa127db | MATLAB | 35,323 | 799 | function [grid, cfg] = ft_prepare_sourcemodel(cfg, headmodel, sens)
% FT_PREPARE_SOURCEMODEL constructs a source model, for example a 3D grid or a
% cortical sheet. The source model that can be used for source reconstruction,
% beamformer scanning, linear estimation and MEG interpolation.
%
% Use as
% grid = ft_prep... |
be2859bdb4bbcbe6b15a09aab11aa9d561c0fbd8586d74da8059a48aa16a424e | MATLAB | 35,407 | 682 | function [frames,expInfo, ExperimentProtocol,ResultMat,DisplayInfo,Timing] = ...
StateSwitch_trialPresentation_words_visual(screenInfo,expInfo,targets,indRun,indBlock,indTrial,ExperimentProtocol,ResultMat,DisplayInfo,Timing,setup,dio)
% run trial loop for word version of state switch task
% 170704 JQK | - added ... |
dbe9cdcee00535425023356772c871f8eb64bbf760cfe97f5712a4d39cf1f3c5 | MATLAB | 35,578 | 807 | function imageData = screencapture(varargin)
% screencapture - get a screen-capture of a figure frame, component handle, or screen area rectangle
%
% ScreenCapture gets a screen-capture of any Matlab GUI handle (including desktop,
% figure, axes, image or uicontrol), or a specified area rectangle located relative to
%... |
488038fba3213f11f5f887a266a72729ac27490d822ba819481353f5f452fa04 | MATLAB | 35,676 | 1,021 | function [shape] = ft_read_headshape(filename, varargin)
% FT_READ_HEADSHAPE reads the fiducials and/or the measured headshape from a variety
% of files (like CTF and Polhemus). The headshape and fiducials can for example be
% used for coregistration.
%
% Use as
% [shape] = ft_read_headshape(filename, ...)
% or
% ... |
88d0f6baa09ed4d0959c45b0f89742e92f3cf4e0d0d0c5c2c24ca27fcf87777e | MATLAB | 35,724 | 1,073 | % add_back_ICs_RandomMotorizedTreadmill.m
% Sarah West
% 4/29/22
% A script to add back ICs that you think are real but were taken out by
% regularize_ICs.
% Wil run as a script, saves to end of list of artifact_removed sources
% so it doesn't disrupt any previously saved artifact
% removals.
% All mice used:
% 500 ... |
454107587217aabecb1dc89bffb8b342d5499011f998b9f123844ceea93d0a23 | MATLAB | 35,756 | 780 | classdef OEphysdlg < matlab.apps.AppBase
% Properties that correspond to app components
properties (Access = public)
UIFigure matlab.ui.Figure
pathname matlab.ui.control.EditField
CheckBox_B1 matlab.ui.control.CheckBox
CheckBox_B2 matlab.ui.... |
5aafdd49422a88d5ed96ac828df59f3742cc2c61f4776147619ae648555b6781 | MATLAB | 35,770 | 908 | function cfg = topoplot_common(cfg, varargin)
% TOPOPLOT_COMMON is shared by FT_TOPOPLOTTFR, FT_TOPOPLOTER and FT_TOPOPLOTIC, which
% serve as placeholder for the documentation and for the pre/postamble.
% Copyright (C) 2005-2011, F.C. Donders Centre
%
% This file is part of FieldTrip, see http://www.fieldtriptoolbox... |
574c0028f67f26b7cbede6235ee72ccf1d87cbe3a242b7e201b1483a7189b005 | MATLAB | 35,806 | 422 | function [significant_replay_events sig_event_info] = number_of_significant_replays_ground_truth(p_value_threshold,ripple_zscore_threshold, method, shuffles,rexposure_option)
% Establish significance for the replay events that are above a set ripple power threshold.
% The output will therefore have less replay events ... |
10e5478c03fde39bbbc93296ce0f7211c9ad3b9baa21770c33afe041742da3b3 | MATLAB | 35,871 | 1,079 | function im = metamerSynthesisColor(params,seed,m,opts)
%
%-----------------------------------------
% metamerSynthesisColor(params,seed,m,opts)
%
% generates an image matched to a set of
% statistical parameters computed within
% a set of tiling regions defined by window functions
% (see metamerParams.m and metamerSy... |
d41f41b1aba74c41e380c4bf914d6b5aff2fb37a82c20ca98292a80dd1d7779b | MATLAB | 35,961 | 912 | function cfg = topoplot_common(cfg, varargin)
% TOPOPLOT_COMMON is shared by FT_TOPOPLOTTFR, FT_TOPOPLOTER and FT_TOPOPLOTIC, which
% serve as placeholder for the documentation and for the pre/postamble.
% Copyright (C) 2005-2011, F.C. Donders Centre
%
% This file is part of FieldTrip, see http://www.fieldtriptoolbox... |
abbe65feee5501eb33bd5a0425644379aba9562c0d31189ced5194ae68acf62d | MATLAB | 36,128 | 676 | % pipeline_PLSR_correlations_specials_Inverted_Intercepts.m
% Sarah West
% 6/13/23
% Sets up variables for & runs partial least squares regression analysis
% for Random Motorized Treadmill experiements---correlations for non-warning
% periods.
% This is the edits made after peer review from Nature Communications. T... |
07e1b91023cf451c42856a6752c053341d86f2bba865a65d9b1f84050d07b50a | MATLAB | 36,316 | 887 | function varargout = data_view(varargin)
% DATA_VIEW M-file for data_view.fig
% DATA_VIEW, by itself, creates a new DATA_VIEW or raises the existing
% singleton*.
%
% H = DATA_VIEW returns the handle to a new DATA_VIEW or the handle to
% the existing singleton*.
%
% DATA_VIEW('CALLBACK',hObject... |
e48190544b862906c7204cf9e341eff4c58ef4bc49a5f4419921c089abb1f1ee | MATLAB | 36,377 | 703 | %% pipeline_ICs_MotorizedTreadmill.m
% Sarah West
% 9/1/21
% After preprocessing data and SVD compressing all data from a mouse into one compression
% with the supercomputers, use this code to calculate and clean the ICs of each animal.
% **Use and run create_days_all.m before using this***
%% Initial Setup
clea... |
0de1bd586584d22565e6b0a328387fca88e590c368ed9cbeaa00f40dbcd3ac13 | MATLAB | 36,389 | 853 | classdef UstTest
%
% AUTHORSHIP
% Primary Developer: Stephen Meehan <swmeehan@stanford.edu>
% 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
prop... |
eb629cb4b76eaa54afec9e16c061880dbe0d2f1a5c17537784a097fa7e6bbb49 | MATLAB | 36,507 | 890 | classdef Manifold2D < handle_light
% 2+1 dimensional manifold: a surface in time
%
% Time dependence is dealt with by making arrays of Fields indexed by
% a time index (most of the time called ti). If the Fields representing
% the geometry (embedding and metric) are themselves time dependent
% ... |
43e8921ff1be7a9f44175223b0a20eca683cd022d0c348d28e28388c65f70048 | MATLAB | 36,647 | 866 | classdef tracking
% functions for processing tracking, either from raw DLC output or
% general behavior file
methods(Static)
% For processing behavior
function vec_out = scale_transform(vec,origin,scale_factor)
% translate coordiante vector relative to origin and scale
... |
7408b64828cbcc4d290c0d67b538284b0dd9ccf20358b87a60d6b26bd94cd416 | MATLAB | 36,944 | 859 | function [accuracy,accuracy2,responseData_all,responseData_labels, io] = TADetectDiscrim_analysis(subject, runs, dates, plotLevel,saveFile,saveFigs)
% function [accuracy,accuracy2,responseData_all,responseData_labels] = TADetectDiscrim_analysis(subject, runs, dates, plotLevel,saveFile,saveFigs)
%
% subject is the ... |
37195bc7c7b4d54d28c81ede3f7f4e4544172ce1f1b5975158282a521414441d | MATLAB | 37,027 | 752 | function [] = plot_ground_truth_compare_jump_distance(folders,option,method)
total_number{1}(1:3) = 0;
total_number{2}(1:3) = 0;
for nfolder = 1:length(folders) % Get total number of replay events
for nshuffle = 1:2 % 1 is original and 2 is global remapped
session_total_number{nshuffle}(nfolder,1:3) = 0;
... |
8390df0e2b30899cd81eed3dc296e7a2c6c1801b57777d462ac6cb3bfe210882 | MATLAB | 37,096 | 956 | function handles = distributionPlot(varargin)
%DISTRIBUTIONPLOT creates violin plots for convenient visualization of multiple distributions
%
% SYNOPSIS: handles = distributionPlot(data,propertyName,propertyValue,...)
% handles = distributionPlot(ah,...)
%
% INPUT data : m-by-nData array of values, or v... |
9ae7b26f87d8e4a71a8b43e749967226a035ecebc2161d67ba97dcfe47a3f32c | MATLAB | 37,132 | 910 | function handles = violinPlot(varargin)
%DISTRIBUTIONPLOT creates violin plots for convenient visualization of multiple distributions
%
% SYNOPSIS: handles = distributionPlot(data,propertyName,propertyValue,...)
% handles = distributionPlot(ah,...)
%
% INPUT data : m-by-nData array of values, or vector ... |
4d8ef3188d004a6509d86fd06bf953ea7890fbe357c46f30b317c0c61d47288f | MATLAB | 37,191 | 959 | function handles = distributionPlot(varargin)
%DISTRIBUTIONPLOT creates violin plots for convenient visualization of multiple distributions
%
% SYNOPSIS: handles = distributionPlot(data,propertyName,propertyValue,...)
% handles = distributionPlot(ah,...)
%
% INPUT data : m-by-nData array of values, or v... |
8f4f344ad3a0055074ee3bf3b7a5a0bb5134594038df1eafe8470f8dfbb41ec1 | MATLAB | 37,280 | 954 | % Analysis seizure data (mat) labelled by Anna
% spectrogram/psd plot's units is [dB]:
% 1. normalization (./) using baseline spectrogram/psd mean (across time)
% 2. mean of normed spectrogram/psd
% 3. 10log(mean)
% 4. plot
% bandpower is scatter-plot of dB-power:
% 1. P = 10log(bandpower)
% 2. mean(P), std(P)/sem(P... |
0197e12cbd8a05f846cafe82db80a96af3c984ed976622d63a2faf84aebb77aa | MATLAB | 37,309 | 1,134 | %******************************************************************************
% Calcium Photometry Analysis Use of Pmat from Parker Lab for some
% procedures DeltaF.m FastPrint
% Prabahan Janneteau code Fontanaud
% 03/08/2021 mise a jour du 01/09/2022
% procedure menu : https://fr.mathworks.com/matlabcentral/a... |
8e110eb34ab911fbe1c8d00b556363d7f12ad0f7d5111bfe4217329bab98d63d | MATLAB | 37,325 | 924 | function varargout = DPABI_Harmonization(varargin)
% DPABI_Harmonization MATLAB code for DPABI_Harmonization.fig
% DPABI_Harmonization, by itself, creates a new DPABI_Harmonization or raises the existing
% singleton*.
%
% H = DPABI_Harmonization returns the handle to a new DPABI_Harmonization or th... |
7890d034c5b9156bcc9cc90b2f5fd2f1e96e2e2f05b1be08072deab8a2e2dac3 | MATLAB | 37,366 | 949 | function SFC_Results = MainScript_SFC(SFC_Settings)
% MAINSCRIPT_SFC Main execution function for the SFC Toolbox.
% This function receives all settings from the GUI and coordinates the
% complete SFC analysis workflow.
%
% Required input structure fields:
% WorkDir, Participants, Initialization, StatCorr, Communicati... |
cd2a1f9ee7234124f660be75bda47cdb8f0c888f0939748e533265edcaf47503 | MATLAB | 37,366 | 1,135 | %******************************************************************************
% Calcium Photometry Analysis Use of Pmat from Parker Lab for some
% procedures DeltaF.m FastPrint
% Prabahan Janneteau code Fontanaud
% 03/08/2021 mise a jour du 01/09/2022
% procedure menu : https://fr.mathworks.com/matlabcentral/a... |
a723be9c045f2d9ccf06a3402ffc6c19de12816af9fb529c3b5c74c0abe969a9 | MATLAB | 37,372 | 724 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%... |
72520ad30e0b1e74021e50fe122efb37b5ffcaa9e3646390fb24eccf63ea45df | MATLAB | 37,471 | 1,188 | % Analyses using the codex data format (Albert Lin)
directory = 'DIRECTORY';
cd(directory)
%% load data from csvs
% neurons in neurons.csv and neuropil synapse table are in the same order,
% thresholded
data_directory = 'DATA_DIRECTORY';
opts = detectImportOptions(fullfile(data_directory,'neurons.csv'));
... |
b6f36a118730902afa0ed4959521f999cdbfb803a23deab0c7d9552e3eb9d67b | MATLAB | 37,486 | 783 | % The main script for analysis of ORCHID imaging and electrophysiology data
% To run this pipeline: add all folders to path, ensure your data folder
% structure is correct, set your anaylsis parameters, and run this script.
% Data input:
% For ORCHID imaging: typically a uManager tiff stack (.ome.tif) and
% a .wcp... |
040b13c7a02770ee0bb08f6b6c09f12e212b63fd1bbdb377864352d3cc404414 | MATLAB | 37,510 | 837 | function [stats,predictedY,predictedY0,predictedYD,predictedYD0,beta,YD] ...
= nets_predict5_kfold(Yin,Xin,family,parameters,varargin)
% nets_predict - elastic-net estimation, with two-stage feature selection,
% using (stratified) LOO and permutation testing
%
% Diego Vidaurre and Steve Smith
% Aarhus Uni / FMRIB ... |
8f6b946a9716a08187c564ea32e6ddf6f8ab6e64eb69b04fc19a80a8b4b807b7 | MATLAB | 37,637 | 972 | function cfg = topoplot_common(cfg, varargin)
% TOPOPLOT_COMMON is shared by FT_TOPOPLOTTFR, FT_TOPOPLOTER and FT_TOPOPLOTIC, which
% serve as placeholder for the documentation and for the pre/postamble.
% Copyright (C) 2005-2011, F.C. Donders Centre
%
% This file is part of FieldTrip, see http://www.fieldtriptoolbox... |
824791ceff655b45cb0a0be3c019031724e7d2e25ebb8fdb99d9673356e64844 | MATLAB | 37,667 | 775 | function [] = plot_ground_truth_jump_distance_replay_quality(folders,option,method)
total_number{1}(1:3) = 0;
total_number{2}(1:3) = 0;
for nfolder = 1:length(folders) % Get total number of replay events
for nshuffle = 1:2 % 1 is original and 2 is global remapped
session_total_number{nshuffle}(nfolder,1:3)... |
0613c1587f32eff8e9ef72ef42c1e547cbdb13e751922a038dc8cf53129ef633 | MATLAB | 37,681 | 791 | % The main script for analysis of ORCHID imaging and electrophysiology data
% To run this pipeline: add all folders to path, ensure your data folder
% structure is correct, set your anaylsis parameters, and run this script.
% Data input:
% For ORCHID imaging: typically a uManager tiff stack (.ome.tif) and
% a .wcp... |
d1f951927bb67e695f66565b6fcb6d1dc724561d915d33516576932bbeac93e5 | MATLAB | 37,987 | 827 | %% 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... |
748cb48db408cc8f6e4fe37394d6e21123e4ac4a9e862afc3ba76791b210e2f4 | MATLAB | 38,217 | 855 | function mesh = load_mesh(fn)
% LOAD_MESH Loads meshes from specified root file name.
%
% SYNTAX:
% MESH = LOAD_MESH(FN)
% LOAD_MESH(FN)
%
% [MESH] = LOAD_MESH(FN) Creates the MESH structure of FEM mesh in NIRFAST
% format. Data is loaded from the root file name FN.
% FN - the root file name of the mesh. The file... |
4531ea9f131db4e944aab6937ecfc9814d9a171b8f816b2e4993d1980106b563 | MATLAB | 38,295 | 855 | % readlocs() - read electrode location coordinates and other information from a file.
% Several standard file formats are supported. Users may also specify
% a custom column format. Defined format examples are given below
% (see File Formats).
% Usage:
% >> eloc = readlocs( ... |
adf2cf15c212efd85c09b035362bf9752b415b7a6dd728f543254dcfc0d2c98c | MATLAB | 38,307 | 862 | function [freq] = ft_freqanalysis(cfg, data)
% FT_FREQANALYSIS performs frequency and time-frequency analysis
% on time series data over multiple trials
%
% Use as
% [freq] = ft_freqanalysis(cfg, data)
%
% The input data should be organised in a structure as obtained from
% the FT_PREPROCESSING or the FT_MVARANALYSI... |
e5b1ee6ca41e4b55b7ecb388f92c514320f36f861778908a62930c7785199a54 | MATLAB | 38,512 | 786 | % pipeline_pupil_diameter.m
% Sarah West
% 6/14/22
% Use "create_mice_all.m" before using this.
%% Initial setup
% Put all needed paramters in a structure called "parameters", which you
% can then easily feed into your functions.
clear all;
% Output Directories
% Create the experiment name. This is used to name ... |
ccae11f4017d99823e07988befd1f33d24726abf4c2d669a15a28875bfd4e1f8 | MATLAB | 38,522 | 812 | function ret = w_spm_ov_roi(varargin)
% ROI tool - plugin for spm_orthviews
%
% With ROI tool it is possible to create new or modify existing mask images
% interactively. ROI tool can be launched via the spm_orthviews image
% context menu.
% While ROI tool is active, mouse buttons have the following functions:
% left ... |
4d10fdb63e879330d6e83f52c56d7a7ff93e48b5122d72872ea2c97e31cead92 | MATLAB | 38,538 | 740 | % Periods based spectrogram/psd/bandpower analysis
% Edited by Jerry. 09/23/2022
function [szDur,PeriodLO_SPGM,PeriodLO_PSD,PeriodLO_BP,...
PeriodHC_SPGM,PeriodHC_PSD,PeriodHC_BP]=...
AnalyzePeriodFrequencyV3_220923(params,szCell,szDir,szTiming,SaveDir)
% parse in the relevant parameters
sps = params.... |
6250b738779a4c732c6e5102aea8a6222dd3aae79afbf6ed47296bcc39b4ea50 | MATLAB | 38,996 | 575 | function out = boney_getBoneMeasures(groups,opt)
% The function returnes cranial bone measures by using the output of the SPM
% segmentation of the native tissue output of classes 1 to 5 (c#*.nii) and
% bias corrected images (m*.nii).
%
% out = getBoneMeasures(groups,opt)
%
% groups .. cell of cell with m*.ni
%
%In... |
0f032f843b5d6da4d7ce4c2f8434b4fb98d6e789ca26475b8ba07cc6ad8174a4 | MATLAB | 39,183 | 746 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
77d69deb6c56e714cdb8eb5bf3258588023158d1c9e5a0e7e32ef8a1188ceecb | MATLAB | 39,236 | 772 | function [] = plot_ground_truth_compare_global_remapped(folders,method,option)
total_number{1}(1:3) = 0;
total_number{2}(1:3) = 0;
for nfolder = 1:length(folders) % Get total number of replay events
for nshuffle = 1:2 % 1 is original and 2 is global remapped
session_total_number{nshuffle}(nfolder,1:3) = 0... |
a0de139aee1e9745dbd32dcaf3ea117eb5d821c341e4008a3425f400d54a32b0 | MATLAB | 39,262 | 748 | function [figure_handle, use_axis] = TSraster_v3(tsdata, trialdef,events, varargin)
% TSraster(TSDATA, TRIALDEF, EVENTS, ARG1, ARG2, ARG3, ARG4)
%
% Produces a raster plot of time stamped data. Trials and events must be identified and
% then the y axis indicates trial number (with the first trial on the bottom) ... |
10d963b5564edd2640ebd40225ec068abfd568cfb3df2aebd0c2ddc6f1d2b515 | MATLAB | 39,300 | 708 |
%To do
%Pause abort clear buttons
%Auto Sizing
%auto shut down when finished
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
run('Batching_Tracker_Initialization.m')
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%... |
2ab676c2130fb12a09b345a793d890d7516480f4ab54a1d873dfecff48a5e51a | MATLAB | 39,354 | 1,155 | % 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 Html... |
487a1de9dda2eb7cf1aff5212675d218142a77ee9520892895c06ecd2abd5f74 | MATLAB | 39,395 | 997 | function makeTADetectDiscrimStim(subjectID, run, displayName)
%% run setup
%run = 7;
saveStim = 1;
saveFigs = 0;
%% add paths
addpath(genpath('../../vistadisp'))
addpath('../TAPilot')
%% file i/o
stimDir = 'stimuli';
vistaStimPath = 'vistadisp/Applications2/Retinotopy/standard/storedImagesMatrices';
... |
3a9a3dab8d4ffff3223c3cb8e1335571e98f1d16da75977efb04526e3c4d6e9d | MATLAB | 39,454 | 895 | function [freq] = ft_freqanalysis(cfg, data)
% FT_FREQANALYSIS performs frequency and time-frequency analysis
% on time series data over multiple trials
%
% Use as
% [freq] = ft_freqanalysis(cfg, data)
%
% The input data should be organised in a structure as obtained from
% the FT_PREPROCESSING or the FT_MVARANALYSI... |
c40a11307503996dbf3bf652f834e1f6039f165eee5e0b58127a4776809af3de | MATLAB | 39,454 | 896 | %% Plot preplay decoding statistics across grid search
% gridAnalysisDecoding.m
%
%% Choose which simulation results to analyze
decodeFileID = "mainPaperGrid"; % Select which decoding file result ID to analyze
figSettings = 'manuscript0'; % manuscript0 for Figure 4/6, manuscript1 for figure R5
%% Set up and load d... |
19184176d6482d1985545c23f8f4e71f3d416f3df76728510d0d1dd7d0e8ea38 | MATLAB | 39,484 | 948 | %% 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) ... |
808339fb9818adec9ee80c9e4d758d851c289c2a7653b327a2cbab267cfded23 | MATLAB | 39,549 | 2,001 | function label = senslabel(type)
% SENLABEL returns a list of sensor labels given the MEEG system type
%
% Use as
% label = senslabel(type)
%
% The input type can be any of the following
% 'biosemi64'
% 'biosemi128'
% 'biosemi256'
% 'bti148'
% 'bti148_planar'
% 'bti248'
% 'bti248_planar'
% 'btiref'
... |
b5efe47aa00f411b9fb88355626e4c3129967b08f7e75a1e313d3764fa4ee92c | MATLAB | 39,910 | 910 | addpath('/data/users2/eswar/software/utilities/')
%addpath('/home/eswar/Documents/MATLAB/MANCOVAtb')
%addpath('/home/eswar/Documents/MATLAB/spm12/');
%addpath(genpath('/home/eswar/Documents/MATLAB/GroupICATv4.0b/'));
addpath(genpath('/data/users2/eswar/temp/MISA_analysis/MISA-master/'));
addpath('/data/users2/eswar/sof... |
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