sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
fffd1d007b058d7f1c2dc17d8750cef49268c0428fe9ea7537a43df0245c8b73 | MATLAB | 12,691 | 309 | function [x, cutvalue, cutvalue_upperbound, Y] = maxcut(L, r)
% Algorithm to (try to) compute a maximum cut of a graph, via SDP approach.
%
% function x = maxcut(L)
% function [x, cutvalue, cutvalue_upperbound, Y] = maxcut(L, r)
%
% L is the Laplacian matrix describing the graph to cut. The Laplacian of a
% gra... |
0c4772c939853d05162a355b7b61150bf2e4c4ed96edf15b77092bebc79fe1a9 | MATLAB | 12,707 | 322 | % history:
% 2016/05/02 - add support for <wantpushalt> and the case of <epifilenames> being NaN
% 2016/02/05 - add <epismoothfwhm>
% 2015/11/15 - implement special cell2 case of episliceorder
% 2014/04/30 - add binary saving for the extratrans {X} case
% 2014/04/17 - add fieldmapslicejump for DICOM case
% 2011/08/07 -... |
dab67f360f780a0083371f22c4312fa54a87f9d3813086c1eb8c71e2533574f2 | MATLAB | 12,710 | 389 | function headmodel = spm_cfg_eeg_inv_headmodel
% Configuration file for specifying the head model for source reconstruction
%__________________________________________________________________________
% Copyright (C) 2010-2016 Wellcome Trust Centre for Neuroimaging
% Vladimir Litvak
% $Id: spm_cfg_eeg_inv_headmodel.m 7... |
0cb5f5a27926c989bd34710b39970ee5082b1ae2b0e8037e5d48c2097206c2b6 | MATLAB | 12,713 | 337 | % This script shows how to run urOMT algorithm and Eulerian & Lagrangian post-processing on a rat brain dataset
% Parameters of urOMT are defined in getPamams.m
% Parameters of Eulerian post-processing are defined in paramInitEULApar.m
% Parameters of Lagrangian post-processing are defined in paramInitGLADpar.m
% One c... |
a1feea2ce088c351d156d00116c321fd4207042a152d9cf35fb22e73818a6e40 | MATLAB | 12,714 | 386 | function [P, P0, output] = cp_wopt(Z,W,R,varargin)
%CP_WOPT Fits a weighted CP model to a tensor via optimization.
%
% K = CP_WOPT(X,W,R) fits an R-component weighted CANDECOMP/PARAFAC
% (CP) model to the tensor X, where W is an indicator for missing
% data (0 = missing, 1 = present). The result K is a ktensor. I... |
fe63f275dd624a4b73e5a285989404cdd2648b32a3703777170ebb9baff6b0ee | MATLAB | 12,728 | 273 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% f12_charge_engine_lu.m -- Using FMM-LU, simulate an event given by %%%
%%% a source dipole. The signal to noise ratio is varied from 0 to a %%%
%%% maximum value, and from the maximum back to 0, in an %%%
%%% inverse-quad... |
59694bca01d7d890b1784431516c6a1fd73856c6f42bd940b59538d70b64fa29 | MATLAB | 12,729 | 332 | function [] = SMA1(obj, action)
GetSoloFunctionArgs;
switch action
case 'init',
feval(mfilename, obj, 'next_trial');
case 'next_trial',
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%% SETUP THE... |
dd0116da4454faead1439bc6b6dd3ce3f43b7275dd4b958f9a9e7af6b9d5fbd8 | MATLAB | 12,732 | 320 | function [Image, Space]=w_MontageImage(IndexM, type, curfig, flag)
% Written by Xindi WANG
% State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern
% Institute for Brain Research, Beijing Normal University, Beijing, China
% sandywang.rest@gmail.com
%================================================... |
6971a231d64e7ce588235a7def4886c2ff3926f57071b528162c4294cba32f49 | MATLAB | 12,770 | 413 | function dipfit = spm_cfg_eeg_dipfit
% Configuration file for configuring imaging source inversion
% reconstruction
%__________________________________________________________________________
% Copyright (C) 2010-2016 Wellcome Trust Centre for Neuroimaging
% Gareth Barnes
D = cfg_files;
D.tag = 'D';
D.name = 'M/EEG ... |
e3c1f0bb7b28bdb707648d173c5d5ab518e69d055e52a490d5e218cad0115e72 | MATLAB | 12,771 | 359 | classdef FeaturesReporter < handle
properties
name;
featuresColor = [0 0 1];
end
properties (Transient)
controller
featuresRange
end
properties (Transient, Access = private)
waitBarHandle
cachedFeatureTypes
end
... |
dacccda393d81bb9ff415507f6a9cae6f16e95feaaeebe81be1f3249d3129492 | MATLAB | 12,786 | 312 | function [SPM] = spm_fMRI_design(SPM,save_SPM)
% Assembles a design for fMRI studies
% FORMAT [SPM] = spm_fMRI_design(SPM)
%
% 1st level
%--------------------------------------------------------------------------
% SPM.
% xY: [1x1 struct] - data structure
% nscan: [1xs double] - nscan(s) = number of sc... |
7430e2a32a3e60fbbda2649b37b883c64d5d24ffb2bb1ed6f830ae6104cdfd8c | MATLAB | 12,800 | 298 | function write_bioimage_mgrid(filename, elec)
% --------------------------------------------------------
% WRITE_BIOIMAGE_MGRID writes BioImage Suite .mgrid files from a FieldTrip
% elec datatype structure
%
% Use as:
% write_bioimage_mgrid(filename, elec)
% where filename has an .mgrid file extension and elec has... |
f53dbad9f131b59708cfc48f2a3c58770a2943ebd4d76055c1748e9d82e997dc | MATLAB | 12,800 | 403 | function [INLP,ILP] = fminspleas(funlist,NLPstart,xdata,ydata,INLB,INUB,weights,options)
% pleas: partitioned nonlinear least squares estimation, using fminsearch
% usage: [INLP,ILP] = FMINSPLEAS(funlist,NLPstart,xdata,ydata)
% usage: [INLP,ILP] = FMINSPLEAS(funlist,NLPstart,xdata,ydata,INLB,INUB)
% usage: [INLP,ILP] =... |
82f3d57103cf56260c07ac9179b21bb44357bc618f86657555c210d23169862a | MATLAB | 12,801 | 281 | % Loads animal recordings information.
% Edited by Jerry, 09/20/2022
function [szCell,szDir,szTiming]=GetSeizureInfo_220919...
(AnimalID,FileName,DataDir,params,bilateral,SaveDir)
% FUNCTION INFORMATION: Lick task analysis pipeline
% Inputs:
% (1)AnimalID: name of the animal [string].
% (1)FileNa... |
7c08435fafe55c5875eaa89fea512d9713603b9dc534b10153c9342232cc688e | MATLAB | 12,808 | 320 | classdef AbstractFeatures < matlab.mixin.Copyable
% Baseclass for Features in Dataspace.
% Features are explained in broad terms here:
% <a href="matlab:dspace.help('dspace.resources.docs.Introduction_to_Dataspace')">Introduction to Dataspace (Dataspace Help)</a>.
%
% Features can have fixed di... |
d5f0ed1ce48212552b40364d15ea3e876a27cefbd7e1d0e62fa81ca341af2646 | MATLAB | 12,808 | 371 | function [cfg] = ft_topoplotCC(cfg, freq)
% FT_TOPOPLOTCC plots the coherence between channel pairs
%
% Use as
% ft_topoplotCC(cfg, freq)
%
% The configuration should contain:
% cfg.feedback = string (default = 'textbar')
% cfg.layout = specification of the layout, see FT_PREPARE_LAYOUT
% cfg.foi ... |
3a5098bd3a8ddaa7db8ce89d0a9355657f0fe4ec344a476629504baa81757364 | MATLAB | 12,829 | 296 | function ret = w_spm_ov_reorient(varargin)
% Reorient tool - plugin for y_spm_orthviews
%
% This tool provides the capabilities of the reorientation widget in SPMs
% "DISPLAY" for any image displayed within y_spm_orthviews. The control fields
% are drawn in the SPM interactive window and work as described in the
% Disp... |
da4c6b944966e14a6b815d800a765b2e514ef537a615c2fdf086323d58b3f7ad | MATLAB | 12,838 | 207 | %boney_extractBoneMeasures_printDetails. Batch to create a figure
%
% _________________________________________________________________________
%
% Robert Dahnke & Polona Kalc
% Structural Brain Mapping Group (https://neuro-jena.github.io)
% Departments of Neurology and Psychiatry
% Jena University Hospital
% _________... |
2db68f6a9ff8b23bb4afc9269839345f5477da49972ab8be2ddb1c941686f5ab | MATLAB | 12,839 | 352 | function [mdlFit, A4] = meg_time_crossValidation(expt, sessionDir, user)
% function meg_time_crossValidation(expt, sessionDir, user)
% split half cross validation per session by time (first half v second
% half)
% Inputs:
% expt: 'TANoise'
% sessionDir: eg: 'R0817_20171212', 'test' will create simulated data
% ... |
56b93e9d8ff86269d52874556e5b4ff9958652b700d983f1d0df1433d7deb227 | MATLAB | 12,845 | 359 | function data = ft_anonymizedata(cfg, data)
% FT_ANONYMIZEDATA clears the value of potentially identifying fields in
% the data and in the provenance information, i.e., it updates the data and
% the configuration structure and history that is maintained by FieldTrip
% in the cfg field.
%
% Use as
% output = ft_anony... |
729bf2d5552e0b0303e464dc5be6201f3f0209fced0a39014ca9801b5ca447f8 | MATLAB | 12,847 | 313 | function [data, varargout] = femdata_spec_FD(mesh, frequency, varargin)
% FEMDATA_SPEC_FD Calculates data (phase and amplitude) for a given
% spectral mesh at a given frequency (Hz). Outputs phase and amplitude in
% the structure DATA.
%
% SYNTAX:
% [DATA] = FEMDATA_SPEC_FD(MESH, FREQUENCY)
% [DATA] = FEMDATA_SP... |
8b064d993a9eb9c14fea5221ec44de7b8809bb22a02623ade70d77c183953f29 | MATLAB | 12,853 | 359 | function data = ft_anonimizedata(cfg, data)
% FT_ANONIMIZEDATA clears the value of potentially identifying fields in
% the data and in the provenance information, i.e., it updates the data and
% the configuration structure and history that is maintained by FieldTrip
% in the cfg field.
%
% Use as
% output = ft_anoni... |
861881f2b830c41da52a2dc9736cdb0ca2151e1fab2f01035cb06d9f4d1fb19f | MATLAB | 12,857 | 295 | clear
train = 0;
n_sources = 2;
load('../anatomy/fs_cortex_20k_inflated.mat')
load('../anatomy/fs_cortex_20k.mat')
load('../anatomy/fs_cortex_20k_region_mapping.mat');
load('../anatomy/dis_matrix_fs_20k.mat');
% when load mat in python, python cannot read nan properly, so use a magic number to represent nan when... |
b8f758e35dddd02477acf2c9f041906c8d4d62caa28db66170f1617358d84ba8 | MATLAB | 12,862 | 310 | %% rm_raincloud_cg - plots a raincloud of different groups and observations
% This function is based on the rm_raincloud function.m of
% github.com/RainCloudPlots/RaincloudPlots/
%
% Please see the publication Allen M, Poggliali D, Whitaker K et al.
% Raincloud plots: a multi-platform tool for robust data visualization... |
1f14260028e9e56a7fc92daffc3e10b9f51f4fe03268e0e4d599597c81f3a354 | MATLAB | 12,865 | 334 | function dataOut=fc_2group_Ttest_1tp_HSB(fc,IM,params)
% Performs the Welch-Satterthwaite-corrected t-test
% and enrichment analyses using Chi-square and Hypergeometric stats.
% fc is the Fisher-z fc-data; Nseeds-Nseeds-Nsubj
% IM is the ROI-to-module key.
% params.type determines the type of correlation {'Spear... |
70369e12c71cba618d5fe65d14b9750c0b52d6ca0f00c6d6b87643c3b8c19d4a | MATLAB | 12,874 | 305 | % AUTHORSHIP
% Math Lead & Primary Developer: Connor Meehan <connor.gw.meehan@gmail.com>
% Secondary Developer: Stephen Meehan <swmeehan@stanford.edu>
% Bioinformatics Lead: Wayne Moore <wmoore@stanford.edu>
% Funded by the Herzenberg Lab at Stanford University
% License: BSD 3 clause
%
classdef UmapPyt... |
7eadf9744c43d3e8f95f5fb3fe1a74bdc7d2a95f6ae4e1930b2cc3d447a57c7c | MATLAB | 12,881 | 257 | function [sorted_replay,time_range]=sort_replay_events(option,varargin)
% option is [] if no reexposure, and a vector for reexposures, e.g. [ 1 3 ; 2 4 ]
% varargin can be 'wcorr', 'spearman' ,'control_fixed_spike' or 'rate_detection_control'
if isempty(varargin)
load significant_replay_events;
else
switch var... |
0d3bf781ad5660446d68b600d1f703a2180ca6828002a7b109ac3788ac8a7411 | MATLAB | 12,893 | 252 | function PARAMS = initializeSession( subjectName, subjectID, deviceName )
% (c) Coraline Rinn Iordan 10/2017, 02/2018, 03/2018, 04/2018, 06/2018
if nargin < 2, error( 'Must provide subject name and ID.' ); end
if nargin < 3, deviceName = 'SKYRA.CurrentDesign'; end
initStartTime = GetSecs;
%%%%%%%%%%%%%
%%% SETUP %%... |
2ddc8b2db6d226349abbf66cd209e462462b7fef78c1dfd955b47dfda6ee52e7 | MATLAB | 12,893 | 354 | function FilterIntanSiProbeTest(datDir, varargin)
% 8/28/2018 M. Farries
% currently works on single-channel-per-file-format only
% usage: FilterIntan( datDir, varargin )
% This function wavelet filters data and performs common average
% referencing (CAR). The result is saved in a single .hsd file.
% It also cre... |
0157e12a2a5b63e42dd735231a4882d589eefe1f171b74e5677af318ce2a79dc | MATLAB | 12,895 | 296 | function h_updateInfo3(handles)
[currentInd, handles, currentStruct, currentStructName] = h_getCurrendInd3(handles);
currentFilename = get(handles.currentFileName,'String');
[pname, fname, fExt] = fileparts(currentFilename);
pos = get(handles.h_imstack3, 'position');% adjust font size accordingly
defaultHeight... |
3b66f38bd984415b7f6933634e2f4e311266be0ffc580e1ee9e139e459bc9180 | MATLAB | 12,900 | 302 | %{
Copyright (c) 2025 Axion BioSystems, Inc.
Contact: support@axion-biosystems.com
All Rights Reserved
%}
classdef LoadArgs < handle
%LOADARGS parser for file loading arguments
properties (GetAccess = private, Constant = true)
wellVal = 1;
electrodeVal = 2;
timespanVal = 3;
... |
3839ce9c56ba4fe60e656f56256d9f67871e190729bda4e69d7ed9888dea97f0 | MATLAB | 12,901 | 393 | function ConvertPhyKilo2Neurosuite(varargin)
%
% ###
% .
% +-- _folder(basename)
% | +-- basename.dat
% | +-- basename.xml
% | +-- _Kilosort_date_time
% | | +-- spike_clusters.npy
% | | +-- spike_times.npy
% | | +-- cluster_group.tsv
% | | +-- pc_features.npy
% | | +-- templates.npy
% | | ... |
bddd206a36bf5136e20baa8ea204a6c6d956d9a842ddbe135161f6282a8dfeaf | MATLAB | 12,903 | 379 | classdef LabelCoreSeqController < LabelCoreController
% Sequential labeling controller
%
% Owns all graphics for sequential labeling mode. Receives GUI callbacks,
% extracts GUI state (mouse position, modifiers), delegates data logic
% to LabelCoreSeqModel, and syncs graphics in response to model events.
properties
... |
794c13d294e84d906f509e7b1feb4df0dea375a24a21d5a9b73bab200666203a | MATLAB | 12,913 | 400 | function [labels,info]=isosplit5(X,opts)
%% Default parameters and self test
if nargin<1, test_isosplit5; return; end;
if nargin<2, opts=struct; end;
if ~isfield(opts,'isocut_threshold') opts.isocut_threshold=1; end;
if ~isfield(opts,'min_cluster_size') opts.min_cluster_size=10; end;
if ~isfield(opts,'K_init') opts.K_... |
3ed2c0778652c178ffc778f1328b2ea8a4d6d3085386dae817b46e566a6c1931 | MATLAB | 12,926 | 359 | % MARA() - Automatic classification of multiple artifact components
% Classies artifactual ICs based on 6 features from the time domain,
% the frequency domain, and the pattern
%
% Usage:
% >> [artcomps, info] = MARA(EEG);
%
% Inputs:
% EEG - input EEG structure
%
% Outputs:
... |
6b251087bf7d3f9e9e49087d886d02c75008a0e1c732bed52b7c11b4760761ee | MATLAB | 12,927 | 368 | function [cfg, artifact] = ft_artifact_ecg(cfg, data)
% FT_ARTIFACT_ECG performs a peak-detection on the ECG-channel and identifies the windows
% around the QRS peak as artifacts. Using FT_REJECTARTIFACT you can remove these windows from
% your data, or using FT_REMOVETEMPLATEARTIFACT you can subtract an averaged temp... |
471e2297f6c75e2444d49e75b6389f00d747790308a11757629e7d076822a3d5 | MATLAB | 12,936 | 390 | function [predictedY,predictedYD,YD,stats] = predictPhenotype_kernels_kfolds(Yin,Din,options,varargin)
%
% Kernel ridge regression estimation using a distance matrix using (stratified) LOO.
% Using this means that the HMM was run once, out of the cross-validation loop
% adapted for using either precomputed kernels or ... |
f5f27f9cde2c24071c6da417a69ad9a413cb5e5180a9ade94a2595c7d58b78ec | MATLAB | 12,942 | 369 | function [cfg, artifact] = ft_artifact_ecg(cfg, data)
% FT_ARTIFACT_ECG performs a peak-detection on the ECG-channel and identifies the
% windows around the QRS peak as artifacts. Using FT_REJECTARTIFACT you can remove
% these windows from your data, or using FT_REMOVETEMPLATEARTIFACT you can subtract
% an averaged te... |
25bf38a6eaec067a15698c5dee79027136297fd19f802249a1255c7fedebb3ba | MATLAB | 12,957 | 320 | function seizureInfo = getControlTimingInfo(seizurePath, seizureCell, params)
% This function reads a seizure's mat file and extracts information about
% the baseline times, control times, postcontrol times, recovery times.
% It also extracts information about lick and click times.
% INPUTS:
% - seizurePath: path t... |
659a3c69e0639de8a7179e4d9b2b42a4c9f7a4c30d195a3da03fbe55895da57c | MATLAB | 12,963 | 270 | function [tismri, Ybraindist0] = boney_segment_evalSPMseg(Yo,Ym,Yc,Ymsk,vx_vol, fast, job, seg8t, tis)
%evalSPMseg. Evaluation of the SPM segmentation.
% This function extracts tissue intensities by the segmentation Yc from the
% intensity scaled image Ym.
%
% [tismri, Ybraindist0] = boney_segment_evalSPMseg(Yo,Ym,Y... |
fe01fadaeec5e55b15d5a577e7253d9cd58a6adb88052d899c0639a7d6617b94 | MATLAB | 12,967 | 167 | GaussTestFig=figure;
set(GaussTestFig,'position',[100,100,1700,800]);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
AreaSize=100;
Amp=1;
mu1 = [30 30];
Sigma1 = [ 50 20;
20 50];
% Sigma = [Variance1, C... |
de67ba8a0e3db7bb11596ec419cedc063c1452cac684bc3c57baafca5b616973 | MATLAB | 12,981 | 466 | function varargout = boundedline(varargin)
%BOUNDEDLINE Plot a line with shaded error/confidence bounds
%
% [hl, hp] = boundedline(x, y, b)
% [hl, hp] = boundedline(x, y, b, linespec)
% [hl, hp] = boundedline(x1, y1, b1, linespec1, x2, y2, b2, linespec2)
% [hl, hp] = boundedline(..., 'alpha')
% [hl, hp] = boundedline(... |
f625a38bbe23df254c8a709207a3c39b8786fd13f86da2cbd1b1a7ede53c23f4 | MATLAB | 12,983 | 330 | %%MergeEvents
function [events,noise]=MergeEvents(posevents,negevents,noise,HowToMerge,DealWithNoise,EventCutoff,NoiseCutoff,EventGlue)
%Combines lists of positive, and negative events, as well as noise into one using either intersection, union, or another method
%
%Input: 'posevents' matrix of all positive even... |
dd7f0355890773e50c0614c40072e6ade9f0ddca9a18d396020c5d27ac124c33 | MATLAB | 12,992 | 363 | function headmodel = ft_headmodel_interpolate(filename, sens, grid, varargin)
% FT_HEADMODEL_INTERPOLATE describes a volume conduction model of the head in which
% subsequent leadfield computations can be performed using a simple interpolation
% scheme.
%
% Use as
% headmodel = ft_headmodel_interpolate(filename, sen... |
8338787e035ec0fa34fc77d98e55ed4a632e22f65cd9584239792ae7f7a9cd05 | MATLAB | 12,997 | 293 | function SingleUnitAnalysis(config,varargin)
%%
% DEtermine whether to use single windows or finescale windows
% opts..(FN{i})=opts..(FN{i});
if strcmp(mfilename,'') || nargin==0
opts=Analyze.Libet.Config_BasicLinearModel;
else
opts=feval(config,varargin{:});
end
WindowType=Utilities.getInput... |
d04e565ab61fc4e7b2d507a69a3512dbdf0a7c1399bbf25f8eeee5490c7fdef7 | MATLAB | 13,030 | 310 | function [StandardizedBrain_LH, StandardizedBrain_RH, Header_LH, Header_RH, OutNameList_LH, OutNameList_RH] = y_Standardization_Surf(ImgCells_LH, ImgCells_RH, MaskData_LH, MaskData_RH, MethodType, OutputDir, Suffix)
% [StandardizedBrain_LH, StandardizedBrain_RH, Header_LH, Header_RH, OutNameList_LH, OutNameList_RH] = y... |
bf47cd2a73ab6f2b7fce033f757146446850a9a21d51d4245b2ffd46bd1da1d9 | MATLAB | 13,033 | 463 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
60af318be4d51fc553afd61c07c5571d14667f7b8c4f74f73f996c242dc40eb2 | MATLAB | 13,041 | 357 | function FilterIntanSiProbe(datDir, varargin)
% 8/28/2018 M. Farries
% currently works on single-channel-per-file-format only
% usage: FilterIntan( datDir, varargin )
% This function wavelet filters data and performs common average
% referencing (CAR). The result is saved in a single .hsd file.
% It also creates... |
6fc55fb8239f60210d3c6d3cecc2188a1e725e7d9f779e67178342e148c0efba | MATLAB | 13,045 | 305 | %% ANOVA-N: look at ants, vowels, sub-classes
if(run_ANOVA==1)
DIRresultsGammaAnova = [DIRresultsRipple, '/anova_gamma_multiple'];
mkdir(DIRresultsGammaAnova);
% running multiple multiple-way ANOVAs on the words vs non vs noise data
for k=1:length(task_data(:,1))
if(multi_se... |
a042eaa65669d85df1c71a99fdb2d0efd1423ea55390c73c90c1475762783bab | MATLAB | 13,049 | 401 | % add dependencies
addpath(genpath('C:/Code/wjn_toolbox-master'))
addpath(genpath('C:/Code/wjn_toolbox'))
addpath(genpath('C:/Code/leaddbs'))
addpath(genpath('C:/Code/spm12'))
%% Parcellate and export conmaps
input_images = {'alpha_functional_network.nii', 'alpha_str_network_n388.nii','beta_str_network_n982.nii', 'bet... |
e2168f7dbc1ea3a27be1d6a90038a4c51a74bdff3628a87f135c95752fc4b880 | MATLAB | 13,050 | 305 | clear; clc;
% This analysis script can be used to run a parellelised version of
% connectome-based predictive modelling with LOOCV or repeated k-fold CV.
% CPM package could be dowdload from https://github.com/rorytboyle/flexible_cpm/
% Authors: Rory Boyle & Yihe Weng
% Emails: rorytboyle@gmail.com & wengy@tcd.ie
% Da... |
ea7645078d796cfdffd2b658a47250bec029dd7092a3b1e6f22600dea4c1e6c4 | MATLAB | 13,051 | 329 | function [options,u,dim] = VBA_check(y,u,f_fname,g_fname,dim,options)
% VBA_CHECK
% [options,u,dim] = VBA_check(y,u,f_fname,g_fname,dim,options)
% Checks (and fills in default) optional inputs to VBA_NLStateSpaceModel.m
% function [options,u,dim] = VBA_check(y,u,f_fname,g_fname,dim,options)
% This function checks the ... |
0f202711b07feb3bac208f987a3bde9d63690deae0ddff7ca99b86039cbd0ab3 | MATLAB | 13,068 | 495 | function ok = CheckEEGMatData(EEG, kb, varargin)
% function ok = CheckEEGMatData(EEG, kb, varargin)
% checks the dates of the EEG file, mat file, eyelink files all match
% checks these against the records
% will give errors/warnings if there are mismatches
% Input:
% EEG = eeglab structure, with EEG.result = resu... |
c73bc6e34d56c73bfa303bd8d042b525b82a6c2de9d9d0c2b8bff2036018950a | MATLAB | 13,069 | 381 | % Author: Birgit Nierula
% nierula@cbs.mpg.de
function stats_late_seps(epo_esg, epo_control, condition, srmr_nr)
%% stats late SEPs
addpath('/data/pt_02296/toolboxes/fieldtrip-20200331');
% paths
cfg_path = getenv('CFGDIR');
ga_path = getenv('GADIR');
% condition info
cond_info = get_conditionInfo(condition, srmr_nr... |
5f448d98f303933ee912e997ed1059fe547c08ed9bcc2dca7b1fef2403880218 | MATLAB | 13,070 | 330 | function draw_figure2AB(p,decRes)
%
% This code is for drawing figure 2AB
%
%
%% settings
% score index
dimIdx = 1:14;
catIdx = 15:48;
% get roi Idx
hcpIdx = ~cellfun(@isempty,strfind(p.roiDescrip,'hcp180'));
subIdx = ismember(p.roiDescrip,{'thalamus','hyppocampus','hypothalamus','pallidum','brainstem','caudate','puta... |
7f62820c80e72db7496ebdeec65bc61b9751eec1bb8e1fa13171d57fefe2f3e3 | MATLAB | 13,073 | 392 | % This script separates the images for each freqband
addpath C:\code\spm12
addpath C:\Users\ICN_guest\Charité - Universitätsmedizin Berlin\Interventional Cognitive Neuromodulation - PROJECT ECoG Atlas Connectomics (1)\functions;
addpath C:\code\wjn_toolbox
spm('defaults','eeg')
%% Load Table
clear all
%%
load('E:\ECo... |
e68cc0b5f1a2ee44cb5e366a7196c63da4907a3628ed368ddb6e91f80717a7a0 | MATLAB | 13,075 | 259 | function DatsMetadata = bz_DatFileMetadata(basepath)
% Store basic info about the original dat files: names and bytes
% Brendon Watson 2016-8
%% Input and directory handling
if ~exist('basepath','var')
basepath = cd;
elseif isempty(basepath)
basepath = cd;
end
basename = bz_BasenameFromBasepath(basepath);
... |
19e426151f29455efb66f0ebbe11d2aefadc6a8ffd8c982be0cfc679a7601ed9 | MATLAB | 13,091 | 364 | function [dipout] = beamformer_pcc(dip, grad, headmodel, dat, Cf, varargin)
% BEAMFORMER_PCC implements an experimental beamformer based on partial
% canonical correlations or coherences. Dipole locations that are outside
% the head will return a NaN value.
%
% Use as
% [dipout] = beamformer_pcc(dipin, grad, headmod... |
c297082cae4f12434954373fb42d3f817293800872886bb2e71e9870d30c7e85 | MATLAB | 13,093 | 363 | function [varargout] = polarPcolor(R,theta,Z,varargin)
% [h,c] = polarPcolor1(R,theta,Z,varargin) is a pseudocolor plot of matrix
% Z for a vector radius R and a vector angle theta.
% The elements of Z specify the color in each cell of the
% plot. The goal is to apply pcolor function with a polar grid, which
% provides... |
516d5fa601dac9b6ac5f2bce5ed8c4e2b6aeb04250d286175fde9ddda99b2faa | MATLAB | 13,095 | 213 | function info = display_Training( subjectName, subjectID, scanNum, currentRun )
% (c) Coraline Rinn Iordan 09/2017, 10/2017, 02/2018, 04/2018
%
% Flips on given timing! Flips TR-locked!
% Total Length: 10m32 sec = 632 secs = 316 TRs
if nargin < 4, error( 'Must provide subject name, subject ID, scan run number, and cur... |
2ad56d3f7441d1a341974b0439ae2054e8ff4bec3a848d244d7173204442f653 | MATLAB | 13,096 | 367 |
% run_mediation_all2all
load(fullfile(saveLoc, 'sig_allSessions'),...
'sigSL')
% Analysis parameters
max_coh = 0.1;
minRT = 0.67;
maxRT = 2;
start_t = 0;
end_t = 0.6;
S = sigSL;
S.MinI_minus_C = S.MinI - S.MinC;
S.Int_MinC_minus_I = cumsum(S.MinC - S.MinI, 2);
session = S.session;
times = S... |
f018bd770d02c08c260744cf3d4616345bb206896800f0250f91164d6ff20821 | MATLAB | 13,110 | 325 | function [e, thresh] = canny(im, varargin)
%CANNY is an implementation of the Canny edge detector
% E = CANNY(IM) takes a 2-D grey-level image or a 3-D array representing
% a volume and returns a 2-D or 3-D logical edge map using centred
% differences and non-maximum suppression. No smoothing or thresholding
... |
9273ef8a1de2f467d3834762451e9f9375b5073365138b8c7370a4ac41bc3dc4 | MATLAB | 13,116 | 382 | function [px] = projective (x, t, d, m, r)
% PROJECTIVE
%
% CALL: [px] = projective (x, t, d, m, r);
%
% INPUTS:
% x: the vector containing the scalar observations
% t: delay parameter used in embedding
% d: embedding dimension to consider
% m: dimensiona of null space
% r: number of nearest neighbors to c... |
3d466a963d2513c092178e2fe868cfdcea7f4de298b581818ea32a57c28ef2a7 | MATLAB | 13,132 | 309 | function varargout = h_CASpineDetectionGUI3(varargin)
% H_CASPINEDETECTIONGUI3 M-file for h_CASpineDetectionGUI3.fig
% H_CASPINEDETECTIONGUI3, by itself, creates a new H_CASPINEDETECTIONGUI3 or raises the existing
% singleton*.
%
% H = H_CASPINEDETECTIONGUI3 returns the handle to a new H_CASPINEDETECTION... |
d31d77f38566efd3567302245ece0c4fc4d0f305ee056f72172b7700de924cd7 | MATLAB | 13,135 | 347 | function [neighbours, cfg] = ft_prepare_neighbours(cfg, data)
% FT_PREPARE_NEIGHBOURS finds the neighbours of the channels based on three
% different methods. Using the 'distance'-method, prepare_neighbours is
% based on a minimum neighbourhood distance (in cfg.neighbourdist). The
% 'triangulation'-method calculates a... |
097694a75b82eb3c9be501607c7369ae81f3cee8cfef97107cdb7fc68eda318e | MATLAB | 13,138 | 362 | function [t,I]=image_difference(V,U,type,Mask,MaskNum)
% This function gives a registration error and error image I between the two
% images or volumes.
%
% [t,I]=image_difference(I1,I2,type,Mask)
%
% inputs,
% I1: Input image 1
% I2: Input image 2
% type: Type of similarity / error measure
% (optional)... |
1c1754f5ba3c6500f39abaa2c40a9706123eab87ed6d3bf5fac1f92c755b72aa | MATLAB | 13,150 | 457 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Mus... |
b679c67a7c0a4712942090932d8983147a7db0426973c7c2fc53b3e14e6f9b2c | MATLAB | 13,154 | 492 | clear all
close all
clc
% Use this script to generate figures similar to those in Figure 5 and Figure 6 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
cd '..\scr... |
d13ca3b2b1ac64bf452fdf4c0bcc6eef0fc27aaf0a353b8f4e45044d5cf61e56 | MATLAB | 13,159 | 373 | % create_periods_nametable_forPLSR.m
% Sarah West
% 6/4/22
% Script that organizes periods_nametable for easy creation of response
% variables for partial least squares regression for Random Motorized
% Treadmill experiment.
clear all;
% Create the experiment name.
parameters.experiment_name='Random Motorized Tread... |
e93d680b43380a6d76d1e0f2501f9f78f501d7f7de41fa85bf76161e201108e2 | MATLAB | 13,164 | 450 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
6b787cc70f014de8c9887a066c03e022427dd5bf505b00919158b32d59da5dff | MATLAB | 13,171 | 457 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Mus... |
a2caf79807f94855985cbefd16cb753d11940816ae1d4373ecd3be3fb375cdab | MATLAB | 13,180 | 325 | function model = plotMCmatlabMC(model,varargin)
% Displays (if calculated)
% Absorbed power
% Fluence rate of all photons
% Example paths of some of the photons
% Distribution of escaped photons in the far field
% Fluence rates (intensities) on all boundaries
% And, if a light collecto... |
c17ac873117c4249f1625f7e21abf55498948154830ff66589b5e5d0c1326273 | MATLAB | 13,182 | 456 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
7e990a380466304d5f13dfb54a5a7196ba066288d1493a45a149578faf2ce46e | MATLAB | 13,184 | 453 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
6fcfbe8c7c25e4b5238a43d80ed112f6641baa111c4618909d5a9e7b0484a65f | MATLAB | 13,192 | 449 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (q.fang <at> neu.edu)
% created on 2013/08/01
%
% $Id$
%
% input:
% fname: input file... |
f08a08c4f8667c71fbbe8a074e02bab60575c6121aee21c95e46398a27e24abe | MATLAB | 13,196 | 358 | function Stroop_170907(setup, expInfo)
% 170904 JQK | adapted from http://peterscarfe.com/stroopdemo.html
% | merged with parts of BasicSoundInputDemo for audio recording
% | removed parts related to RT encoding by button press
% 170904 JQK | added EEG triggers
%% create individual directory
St... |
07b0dcceacdcd21b19b8698248c93d9099cf26adeede8a1331c7227c134c7b11 | MATLAB | 13,203 | 363 | % Author: Birgit Nierula
% nierula@cbs.mpg.de
function ga_amplitudeAndLatency(subjects, condition, srmr_nr)
savepath_ga = getenv('GADIR'); if ~exist(savepath_ga,'dir'), mkdir(savepath_ga); end
% get condition info
[cond_info] = get_conditionInfo(condition, srmr_nr);
cond_name1 = cond_info.cond_name;
nerve = cond_inf... |
3e96d9bfc6f0dd2dbed3736b957f40bf5a9475cbbba8ef45763dea29d1b49392 | MATLAB | 13,203 | 385 | function [cfg] = ft_connectivityplot(cfg, varargin)
% FT_CONNECTIVITYPLOT plots channel-level frequency resolved connectivity. The
% data are rendered in a square grid of subplots, each subplot containing the
% connectivity spectrum between the two respective channels.
%
% Use as
% ft_connectivityplot(cfg, data)
% w... |
e7d9c06d335724e75cf3ef1ab698a1f88535c5370c43989c62879103e8c98a57 | MATLAB | 13,208 | 295 | function DataStructure=ExpFit_ZN(Xdata,Ydata)
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('Need to provide... |
da2526d5ec7518bd1c5d5683fa3ce4145c37b76bcd2b19831c95b8b41f4fdbf8 | MATLAB | 13,209 | 246 | % TSTRIALSTAT Compute within-trial statistics
% ER = TSTRIALSTAT(STATNAME,FUNHAND,VARARGIN) creates a stat called STATNAME
% that is determined by running the function FUNHAND (passed in as a
% function handle or a string) on the current trial's active data (usually TSData)
% and the remaining arguments given ... |
17091c574ea703111d9ca1cad75e01e3a108d7c06ae7e3c508baf4337088173b | MATLAB | 13,211 | 363 | function headmodel = ft_headmodel_interpolate(filename, sens, sourcemodel, varargin)
% FT_HEADMODEL_INTERPOLATE describes a volume conduction model of the head in which
% subsequent leadfield computations can be performed using a simple interpolation
% scheme.
%
% Use as
% headmodel = ft_headmodel_interpolate(filena... |
9fa68106613caa5cfdd3d0e9e71d2f9e609b15e1b97d9b8296c42a449d88924c | MATLAB | 13,211 | 461 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
b4fb7f40cbffe15cd86ead865095d8604411b6f7fc2fbaf88346292851acf047 | MATLAB | 13,222 | 353 | function bz_ConcatenateDats(basepath,deleteoriginaldatsbool,sortFiles)
% bz_ConcatenateDats - Concatenate raw .dat files found in a session folder
% - for intan type recordings
%
% ALGORITHM OUTLINE: looks for .dat files in a folder (or in subfolders) to
% concatenate together. The concatenation happens via system c... |
fbc495bd817cd20d83c371861e5891d27745f76f8e386a26c7f51e8082406637 | MATLAB | 13,228 | 459 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
ccb7188ec56be4724c52eeeac003c55a02f4a57dffee3c54cc5b00af21ad81eb | MATLAB | 13,229 | 320 | classdef cylinderDetector < surfaceDetection.surfaceDetector
% Detect surface by maximal cylindrical radial derivative
% as opposed to fast cylinder detector, the center for the radial
% derivative is estimated separately for each z-position based on
% thresholding
%-----------------------------... |
6cd6174db0a5a127081358ecd10c1a4242cf25b778a087a91eda5daef60bbc94 | MATLAB | 13,263 | 460 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Mus... |
77a6e7df5c3ccd5456dacfbfc3b2dcfb14227222caabb1fa865039a5bc40e023 | MATLAB | 13,264 | 469 | function [LAN] = lan_read_file(filename,type)
% <*LAN)<] toolbox
% v.0.3
% read file usig Fieldtrip toolbox
% -- addpting for reading micromed .trc file
%
% Pablo Billeke
% 03.05.2017 add Brain Amp option
% 12.04.2016 add CNT particular option to not use MEX file from FILEIO
% use EEGLAB fun... |
08ec0dda3533f4d6ac57c73f222e0bbf77f88a9d88ff0df6a653f1e9e818a871 | MATLAB | 13,266 | 322 | function [data] = ft_regressconfound(cfg, datain)
% FT_REGRESSCONFOUND estimates the regression weight of a set of confounds
% using a General Linear Model (GLM) and removes the estimated contribution
% from the single-trial data.
%
% Use as
% timelock = ft_regressconfound(cfg, timelock)
% or as
% freq = ft_re... |
94e21b2564bedf790c5a022da56e9d0274cf526cfe9f2afc112fce19ae5aa6fd | MATLAB | 13,269 | 334 | function [r_coupling, beta_weights, pc_variance, predictor_outputs, CommModel_SFC_Parameters] = ...
fcn_calculate_CommModel_SFC_RegionalPCA_wy(results_dir, ids, cs, coords, opts)
% FCN_CALCULATE_COMMMODEL_SFC_REGIONALPCA_WY
% Compute regional SFC using selected communication-model predictors.
%
% This toolbox versi... |
422362a1cdfee695a344c0c16da630ebd8ea36c2eac5102b5adf6a1b44167a31 | MATLAB | 13,281 | 345 | function [trl, event] = rest_trialfun(cfg)
% FT_TRIALFUN_GENERAL determines trials/segments in the data that are
% interesting for analysis, using the general event structure returned
% by read_event. This function is independent of the dataformat
%
% The trialdef structure can contain the following specifications
% ... |
581861cf7997d59008be4b9979227b50bd860ffad28cebd9f1e759558897f9ef | MATLAB | 13,284 | 313 | function varargout = h_proteinGelAnalysisGUI3(varargin)
% H_PROTEINGELANALYSISGUI3 M-file for h_proteinGelAnalysisGUI3.fig
% H_PROTEINGELANALYSISGUI3, by itself, creates a new H_PROTEINGELANALYSISGUI3 or raises the existing
% singleton*.
%
% H = H_PROTEINGELANALYSISGUI3 returns the handle to a new H_PROT... |
07ef49d363329b49fbdc106f0bdedfa43164fe31e715153362ac506e4c623dde | MATLAB | 13,286 | 326 | classdef SurfacesReader < SurpassObjectReader
% SurfacesReader Read Imaris ims file Surfaces data
%
% © 2015–2016, Peter Beemiller (pbeemiller@gmail.com)
%
% See also ImarisReader | SurpassObjectReader
properties (SetAccess = 'immutable', GetAccess = 'public', Transient = true)
... |
c23c166a781f74da7b413af8768ac5c8b639f30bc05ef2f2e43475000be92ed0 | MATLAB | 13,286 | 345 | function [trl, event] = ft_trialfun_general(cfg)
% FT_TRIALFUN_GENERAL determines trials/segments in the data that are
% interesting for analysis, using the general event structure returned
% by read_event. This function is independent of the dataformat
%
% The trialdef structure can contain the following specificatio... |
f7f7298ebec21fb5d43056d03e4c5f05d7b6b27aaf0035840d4d41a3666280a7 | MATLAB | 13,288 | 345 | function [trl, event] = ft_trialfun_general(cfg)
% FT_TRIALFUN_GENERAL determines trials/segments in the data that are
% interesting for analysis, using the general event structure returned
% by read_event. This function is independent of the dataformat
%
% The trialdef structure can contain the following specificatio... |
7bc231628b3a182f741fbdd295a71e9e908746482ba854691ebad3a1bc569c98 | MATLAB | 13,299 | 338 | function [imgPair] = imageRegistrationWorkflowParfor(imgPair,imgSize, doNotSection)
% imgPair must contain at least the fields moving.input and static.input,
% which are the matrices containing input images
% output imgPair is a structure with all relevant results from operations in
% this function
% moving.mask is... |
185d917ebb0c50650962981c71fe0dad478b7489afe48b894932616d14abc27c | MATLAB | 13,300 | 248 | % MATLAB code provided by Seyed Mohammad Ali Rahmati
% Affiliation: Biomechanics and Motor Control Lab, Department of Biological Sciences, Georgia Institute of Technology, Atlanta, GA.
%
% Description:
% This script processes and analyzes sensory feedbacks in cat locomotion.
% In order to compute sensory feedback (Ia, ... |
4d93c15afae88418402d98a3945594e5d8205e079d01427bf64212cd042cf8c4 | MATLAB | 13,303 | 462 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
e9b80772f2a414dd38956f09d5c4647912d95525ce3a1ff47d13f6269311f244 | MATLAB | 13,304 | 386 | function [Y,xY] = spm_regions_mask(job)
% VOI time-series extraction of adjusted data (& local eigenimage analysis)
% FORMAT [Y,xY] = spm_regions(xSPM,SPM,hReg,[xY])
%
% xSPM - structure containing specific SPM, distribution & filtering details
% SPM - structure containing generic analysis details
% hReg - Handl... |
92b0e2574c4177a4296470135831b98184cf289e956a9795443efe407052f055 | MATLAB | 13,305 | 464 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
bfbd1dc94c781a45bfa4c66b25991161916e31b3f944544efcf86ca6bab2582b | MATLAB | 13,305 | 480 | classdef TrackingVisualizerMTFast < TrackingVisualizerBase
% TrackingVisualizerMTFast
% Like TrackingVisualizerMT, but uses fewer graphics handles for speed.
%
% Non-gobject model state lives on the associated
% TrackingVisualizerMTFastModel (accessed via obj.tvm_).
properties
parent_ % LabelerControl... |
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