sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
fe4b4850cf6d10e51536ecf8fce1c526b59aca8a283d18bcf8df667be597001b | MATLAB | 2,183 | 69 | function MOEA20_Liley(M,run_number,population_size)
% Script runs the Multiobjecitve evolutionary algorithm MOEA20. See paper
% for details.
% Load data
load('Control_EEG_data.mat')
data = Control_EEG_data(M,:)-mean(Control_EEG_data(M,:));
data = zscore(data);
data = filter_b_f(data, 2, 256,'high',4);% 2 Hz ... |
1d819d06bcc485c34efcc78f9f90a6cace3617669c9c03504830f716049141a1 | MATLAB | 2,185 | 58 | function [nobs, nexp] = pattEvolution(patterns, nt, nafter, nbefore)
% PATTEVOLUTION finds the number of transitions between pattern types.
% Calculates the number of times in a recording that transitions
% from pattern to pattern were observed (NOBS) and the expected number of
% transitions (NEXP) if patterns occurred... |
f7a957981b478491b34af28533a66019afabde139eca2767f8e71f77518fbdc7 | MATLAB | 2,187 | 99 | % PURPOSE: resolve user-writable and legacy ERPLAB storage paths
%
% FORMAT:
%
% paths = erplab_user_paths
function paths = erplab_user_paths
paths = struct();
pref_root = prefdir;
if isempty(pref_root)
pref_root = tempdir;
end
paths.root = fullfile(pref_root, 'ERPLAB');
if exist(paths.root, 'dir') ~= 7
try... |
d85bd1b330345911cedbf702b99133d0470bfd0e5ef636aa81c3fd693516901a | MATLAB | 2,191 | 62 | close all; clear all;
gt_dir = '/home/zxi/refinenet/datasets/eyth/SegmentationClass/testset';
pred_dir = '/home/zxi/refinenet/cache_data/Multiscale_evaluation_refinenet/eyth_on_eyth';
im_dir = '/home/zxi/refinenet/datasets/eyth/JPEGImages/testset';
dir1 = dir(fullfile(gt_dir));
%remove '.' and '..' from directories
d... |
401e536e3d6325347f0ea02e9c77e6900c8b1579e930ea1622fad8b0d2e8e8a7 | MATLAB | 2,196 | 65 |
function CreateConfigFile(self)
[folders, ~, ~] = fileparts(self.FilePath);
split = regexp(folders, '/', 'split');
projectName = split{end};
fileID = fopen(self.FilePath, 'w');
fprintf(fileID,'[Project]\n');
fprintf(fileID,' ProjectName=''%s''\n', projectName);
fprintf(fileID,' MultipleCameras=false\n');
fprintf(f... |
8d3a03f7fb15d3369febe1cd382a11c300859be19ebef753ea6927ca5d9cc73a | MATLAB | 2,197 | 59 |
function [rt,response]=sureOld_sureNew_response(Window,filePath,xCenter,yCenter,screenXpixels,screenYpixels,key,image_duration,font_size,instructions)
im=imread(filePath);
imagetexture=Screen('MakeTexture', Window, im);
framerect = RectOfMatrix(im);
[s1, s2, ~] = size(im);
lengthScreen=min(sc... |
0d493a8058dc1d5386f2477e71706dff51ced858c3535ae99e65daa96f5a9a59 | MATLAB | 2,199 | 81 | function parcellate
%
% Function to
% - Load BOLD data in MNI space
% - Load the AAL template
%
% Origianl code
% Joana Cabral
% joana.cabral@med.uminho.pt
%
% Adapted Fran Hancock
% fran.hancock@kcl.ac.uk
%
% November 2021
%
%
%%%%%%%
PROJECT='/Users/HDF/Dropbox/Fran/Academics/PhD/matlab/projects/COBRA_COMPLEXITY_P... |
7ac3e063b2194bea5cc1567c8c41999bbd8353c7196ca94ef8789731da69942d | MATLAB | 2,202 | 86 | function videoNames = GetVideoNames(self, varargin)
inclVideo = true;
inclFrames = true;
inclFeats = true;
inclAnnots = true;
if nargin == 2
if strcmpi(varargin{1}, 'videos')
inclFrames = false;
inclFeats = false;
inclAnnots = false;
end
if strcmpi(varargin{1}, 'frames')
in... |
f34e60a0bbdc9245fb1b8f4a7c9d8a1e391bb65b4973c58c4466899429a9d650 | MATLAB | 2,203 | 44 | function [data,cats,dur_tw] = video_lowlevelAudio(videofile,time_window,hop)
% This function utilizes MIRtoolbox to extract some predefined low-level
% features mainly important for fMRI/eye-tracking analysis
% INPUT
% videofile = myVideo.mp4
% time_window = temporal length of each time window (in seconds... |
c65482c6fea526b4a789507bc3a8fbcfb6cb3f097996eb3972c4c946ef72b91e | MATLAB | 2,204 | 80 |
%studyPath = '/path/to/study'
%Set this to the specific directory your study is.
studyPath = '.'
%The EEGLAB "study" file. Set this to whatever your study is.
%Default here for the EEGLAb "animal study dataset available from:
%https://sccn.ucsd.edu/eeglab/download/animal_study.zip
studyFilename = 'animal.s... |
f2a7788b9a90e99de44cf75fc08eb4bb81e9405b090e423b14bbea7ea5175fda | MATLAB | 2,208 | 66 | function SCM_wrapper(fpath,J,n_tasks_per_node,zeta_min,zeta_max,n_zeta,a_max,n_a,f0_min,f0_max,n_f0,f_stim,n_tr,N)
%%% Wrapper for computing the rotation number in the sine circle map with
%%% and without dithering. Designed to be used on a supercomputer - build
%%% using "make.txt", and see example batch file for slu... |
dfd39236038444810e6ebcafdef18cb4bd3359aa30e64596f5dd41ec91569026 | MATLAB | 2,210 | 72 | % part of https://github.com/LudgerS/MRSartifactRemoval
%
function plotSignalPeakEstimate(specAxisPpm, spectrum, centerEstimate, fwhmEstimate, artifactCenters)
close all
% display x-ranges
fullRange = specAxisPpm([1, end]);
zoomBorder = 15;
smallRange = [centerEstimate - zoomBorder, centerEstimate + zoom... |
31312b92e5a903d60d7b61c7840179b0ba3bdee71fb08dcd7c0dadd00600e2fe | MATLAB | 2,211 | 65 | % PURPOSE: subroutine for averager.m
% Explore artifact detection fields in EEGLAB.reject (epoch by epoch)
%
%
% FORMAT
%
% observa = eegartifacts(reject, fields4reject, j)
%
% INPUTS
%
% reject - EEG.reject structure
% fields4reject - EEG.reject's fields to be explore. e.g.... |
9c71effe04627a1433030c2840dce96b8cbf96b661ba849a5b0cb1c2917ee07c | MATLAB | 2,212 | 75 | function [sel1, sel2] = match_str(a, b);
% MATCH_STR looks for matching labels in two listst of strings
% and returns the indices into both the 1st and 2nd list of the matches.
% They will be ordered according to the first input argument.
%
% [sel1, sel2] = match_str(strlist1, strlist2)
%
% The strings can be stored a... |
22009b958f8894a60682af0cb1d544460abd135a7fb6d421aefe37b5ef594674 | MATLAB | 2,213 | 60 | %% Detect EMG activation
% This function provides the EMG analysis for detectiong EEG corrupted via
% EMG activity. Note that here we considered data from 11 subjects. After
% this analysis we dismissed EEG data of subject S13 given the highly
% number of EEG trials contaminated by EMG activity [1].
% Refere... |
67fcd5792be622c1b16e2935472696fbda418bb9c19fd63f0ed75577fe6e7ed7 | MATLAB | 2,214 | 69 | function [chan_idx, time_idx] = compute_CC(all_h, nT_data, all_names, all_t)
% Based on a h-mask, compute connected components of 1's accounting for
% channel topography.
% Called in TSL_plot_IO_fit.m
%
% Inputs
% all_h: (n_chan, nT_data) ;
%
% Outputs
% chan_idx: cell containing the channels indices ... |
9bbf38b61b6ac265e96de9d4f2b4d2e412198433ab145b8e5aa58a74a8c0b740 | MATLAB | 2,214 | 73 | function step3c_validation_pairwise_decoding_mne_to_cosmo(bids_dir, toolbox_dir, varargin)
%% Function that takes preprocessed MNE data and transforms it to a matlab-cosmo script
% Note: this script requires the MNE-matlab & cosmomvpa toolboxes
%
% @ Lina Teichmann, 2022
%
% Usage:
% step3c... |
7631daf589eeb2e4a4d5e3de5d30cd773a6134c093ac85f2fb049efd5dd83775 | MATLAB | 2,216 | 81 | function make_temperature_records_csv(file_path)
%
% Convert weird temp files from TC Central into table with same file name
% (but different extension)
%
% Parameters
% --------------
% file_path : str
% Directory containing text files with temperature records in the format
% saved by the Omega system
%
... |
bd2d0838e61f9b5c7655535e1bb1ca9a1fd697e43fec558a8c2443b7dbf0c51e | MATLAB | 2,217 | 100 | %%
tank = 'ZAZU_8';
block = 8;
%
emap = [17 31 19 29 21 27 23 25 18 32 20 30 22 28 24 26 1 15 3 13 5 11 7 9 2 16 4 14 6 12 8 10];
%
win = [0.0 0.05];
% win = [0.06 0.1];
denoise = true; % smooth FRFs
scaleall = false; % put all FRFs on the same scale
data = TDT2mat(tank,sprintf('Block-%d',block),'type',[2 3],'sil... |
8e9c7a12d1403e7526e1344345a3eda7bc0af28e92cdf02fbcffec75e5c23e06 | MATLAB | 2,220 | 91 | function Id = calc_pi_Idep_mvn(C, varsizes);
% calc_pi_Idep_mvn(C, varsizes)
% Calculate bivariate PID using Idep approach (James et al. 2017) for
% Gaussians (Kay & Ince 2018).
% C is the full joint covariance matrix with variables in order
% X0 (first predictor), X1 (second predictor), S (target)
% varsizes is a len... |
c3b8f389c9c30383101e0007a4233b1bec6b846f5d55018f3966707e8ddde58f | MATLAB | 2,220 | 107 | %% 7 Take a look at T1s with the pattern 'rm*00.nii'
% D = rdir('**\STRUCTURALS\rm*00.nii');
% P = {D.name}';
P = getFiles('**\STRUCTURALS\rmT1_00.nii');
Pt = fullfile(spm('Dir'),'canonical','avg152T1.nii');
Ptpm = fullfile(spm('Dir'),'tpm','TPM.nii');
spm_check_registration(char([{Pt}; {[Ptpm ',1']}; P]));
%% zero o... |
8d68057572edc5cb1a730b9195c7b887a579310150961fae506934ba786ae5de | MATLAB | 2,223 | 54 | function out=driftcorrectionshift(parameters, input)
% DRIFTCORRECTIONSHIFT - Perform a manual drift shift on existing drift correct shift
%
% DRIFTCORRECTBYCORRELATION(PARAMETERS, INPUT)
%
% PARAMETERS should be a structure with the following entries:
% warning_msg (0/1) Should we print the warn... |
aec424dfe6ff73799d396c9842fe97737805758a2532079e0e00fd906b2f85d9 | MATLAB | 2,223 | 60 | function dat = getBehFigData(db,P,behP)
P.focusOn = behP.focusOn;
%% get pupil part
% for each mouse
clear dat
for k = 1:P.nMice
if P.miceweye(k)
dattmp = getEyeData(db(k),P);
for dd = 1:numel(dattmp)
dataA = dattmp(dd).data... |
f742a0b99b62f453d249fc34f599c363b97dd7ca603a6a83b45485d3d2328369 | MATLAB | 2,225 | 75 | function ph = pp_make_phys_struct( epData, t1, t2 )
%
% pp_make_phys_struct( epData, t1, t2 )
% Sub-routine called by pp_prepare_format.
%
% Creates matrix of filtered ephys data. First, filters the entire data
% stream from epData. Then extracts a window of data around each trial
% onset.
%
%
% K... |
d04b49021d4db16c5fd0018c7604df4355fb92f08985a496533728d5bfe847ea | MATLAB | 2,226 | 66 | function [lmatrix,complist] = identify_frequency_patches(I,CBF)
% This function returns frequency patches given an input image I
% that shows frequencies as determined for a cilia carpet.
% This function assumes that the noise region is masked by NaN values.
% In addition, we require frequency patches to have minimum ... |
191a3ae27ac6cd7702cc14440a305e13df6d07983fc710dc39d8930349a91085 | MATLAB | 2,227 | 66 | function [eda,newfs] = eda_downsample(eda, fs, reqfs)
% EDA_DOWNSAMPLE EDA downsample
% [edaout newfs] = EDA_DOWNSAMPLE(edain, fs, reqfs)
%
% Required input arguments:
% eda - 1-by-n vector of EDA samples
% fs - old samplig frequency (Hz)
% reqfs - requested sampling frequency (Hz)
%
% Ou... |
e6039abc7ec96d823f3709d5473c3df1ce4e4d1d19bfcf601c0ad5c8ac9c86b9 | MATLAB | 2,229 | 82 | function rte = pred2path(P,s,t)
%PRED2PATH Convert predecessor indices to shortest paths from 's' to 't'.
% rte = pred2path(P,s,t)
% P = |s| x n matrix of predecessor indices (from DIJK)
% s = FROM node indices
% = [] (default), paths from all nodes
% t = TO node indices
% = [] (default... |
0a78cf3c33a5c9ce49be58ef04793267973b13f90ba371b0b9c0f8d08a8b4d94 | MATLAB | 2,230 | 69 | function [counts,t_inds,x_inds] = histxt(x, varargin)
% UltraMegaSort2000 by Hill DN, Mehta SB, & Kleinfeld D - 07/12/2010
%
% histxt - 2D histogram of set of waveforms
%
% Usage:
% [counts,t_inds,x_inds] = histxt(x, varargin)
%
% Description:
% COUNTS = HISTXT(X,D), where X is an M x T matrix returns a D x T
%... |
81e761fc48378f43bb48681a2af3cb1353be0f7268b6f0066e464173bcec4a3a | MATLAB | 2,231 | 41 | % Package GMLM_dmc for dimensionality reduction of neural data.
%
% References
% Kenneth Latimer & David Freeedman (2021). Low-dimensional encoding of
% decisions in parietal cortex reflects long-term training history.
% bioRxiv
%
% Copyright (c) 2021 Kenneth Latimer
%
% This software is distributed unde... |
95a1740bed8fc70c86faf6cd318d8d89280c28515197f3811f663a974120349f | MATLAB | 2,231 | 41 | function [C, audiovisuoCode, P, ...
eventTimes, sp, motSVD, videoTimestampsEphysTime_face, uMotMask, ...
pupilarea, pupilcom, blink, videoTimestampsEphysTime_eye] = loadONE(dataPath, expRef)
savePath = fullfile(dataPath,expRef);
%%
% trials
audiovisuoCode(1,:) = readNPY(fullfile(saveP... |
ebff1acb74a6b990b8acc77bb2e05f10f167909083cde6d8f7daa7e9528b8992 | MATLAB | 2,233 | 76 | function [dx_values, Nx] = set_up_dx(G)
increase_factor = G.increase_factor_x;
% Set up adaptive mesh
%% Intracellular space
dx_valuesI = [G.dx_min*ones(G.num_boundary_layer_dx, 1)];
x = G.num_boundary_layer_dx*G.dx_min + G.dx_min/2;
counter = 0;
if G.dx_max >= G.dx_min*increase_factor
current_dx = G.dx_min*inc... |
22b95925db1cb7be1222224c7d295595df4cff4bca79dcca8e9540f83ca27d82 | MATLAB | 2,236 | 115 | %%
tank = 'ZAZU_8';
block = 5;
%
emap = [17 31 19 29 21 27 23 25 18 32 20 30 22 28 24 26 1 15 3 13 5 11 7 9 2 16 4 14 6 12 8 10];
%
win = [-0.025 0.6];
data = TDT2mat(tank,sprintf('Block-%d',block),'type',[2 3],'silent',true);
%-------------------------------------------------
BuID = data.epocs.BuID.data;
uBuID = ... |
d576f3a81ffb78be855e55144e72a99285fcf6d9bdf4c5b7b81f3d5644a1534b | MATLAB | 2,240 | 71 | % Calculates bivariate van Rossum-Distance using kernels and markage vectors
% x_spikes and y_spikes are matrices with the same number of spike trains (rows)
% rsd_tc: exponential decay (time scale parameter)
%
% For a detailed description of the methods please refer to:
%
% Houghton C, Kreuz T:
% On the efficient calc... |
45c82a838daf29d142314e94b04e5adc2e2728e4dd0908fcb2c0945d82511590 | MATLAB | 2,241 | 92 | function [sigma_sim, sigma_sim_include_burnin, sigma_sim_sub] = ...
simulate_neuron_var(nNodes, tf, params, plotFlag)
% function [sigma_sim, sigma_sim_include_burnin, sigma_sim_sub] = ...
% simulate_neuron_var(nNodes, tf, params, plotFlag)
%
% Generate synthetic neuronal dynamics using Vector Autoregressive Mode... |
e787781e79b2a14a82690cd2a2d06df902122144ad36aeded131a5b4603fad84 | MATLAB | 2,242 | 76 | function LEiDA_for_stats
%%%%%%%
%
% Code to analyze the LEiDA clustering results
% Calculates the occupancy of and Lifetimes of Clusters obtained for each K
%
%%%%%%%
PROJECT='/Users/HDF/Dropbox/Fran/Academics/PhD/matlab/projects/COBRA_COMPLEXITY_PHASE/';
Rmax=2;
Tmax = 150;
TR = 2;
for run=1:Rmax
switch... |
a0102a27c50b82c4db17caf74730cbe132e9a48bdef88583deea04c612a78f3c | MATLAB | 2,244 | 77 | function [hubfrag80,hubfragAUC] = get_hub_fragility(hubs,hubprob,proprand)
%
% get the hub fragility, given hubs and hub probabilities. use function in
% tandem with hub_sweep
%
% INPUTS:
% hubs: Nx1 vector of hubs
% hubprob: Nxlength(proprand) matrix of hub probabilities
% proprand: 1xN vector of ra... |
ff6b5f3dc3e31428159f0989bf03b366c6d40bce2cda6552b4cd95e4b966c54f | MATLAB | 2,245 | 75 | function [EEG,com]=pop_continuous2epochs(EEG,varargin)
com = ''; % this initialization ensure that the function will return something
% if the user press the cancel button
% display help if not enough arguments
% ------------------------------------
if nargin < 1
help pop_continuous2epochs;
r... |
19d88e9829dde51cb78711241be44db7e276f5ec8889db519af9b6ea529fdc99 | MATLAB | 2,247 | 50 | function CVE=DecodeCVE_GPU(FR,evt0,reg)
% Leave-one out cross-validation error (CVE) Decoder
%
% Aleksander Domanski PhD UoB 2015
% aleks.domanski@bristol.ac.uk
%
% Reports binary classification strength between two outcomes across time
%
% Input Variables:
% FR: Firing rate matrix [time x no. trials]
% evt0: Event ... |
cab80e7ce9eab73adbd894ee2fdc7d08f8ffa4dc46738f9e44715114c19a4c2b | MATLAB | 2,247 | 93 | function ok = write_label(lindex, lxyz, lvals, labelfile, subjid,space)
% ok = write_label(lindex, lxzy, lvals, labelfile, <subjid>,<space name>)
%
% write_label.m
%
% Original Author: Doug Greve
% CVS Revision Info:
% $Author: fischl $
% $Date: 2014/10/09 21:51:47 $
% $Revision: 1.6 $
%
% Copyright © 2011 T... |
ff67b14a839f240ef52e968bd1b7c78fb2d159ad6ebb7fe373d73e2c1a56d730 | MATLAB | 2,248 | 67 | %% Look at LFP
localLFPFolder = '\\zserver\Lab\Share\Celian\audioVisLFP';
clear LFP CSD depthChan timebinsAll
for k = 1:P.nMice
rawFolder = fileparts(P.fileref.ksDir{k});
[~,recRef] = fileparts(rawFolder);
finalLFPFile = fullfile(localLFPFolder,recRef,[recRef, '_lfp_triggered.mat']);
load(finalLFPFile... |
85b406af006e0404872adeed213f3876e73224e299a8a3b337068a7a33eb505a | MATLAB | 2,252 | 89 | function proprio_stim(model, iPace, iMuscle, interpolate)
% Run without arguments to select model and parameters via GUI.
% iMuscle = 1 for medial, = 2 for lateral
addpath('matlab');
if nargin == 0
model = view_run();
iPace = listdlg('ListString', list_pace(), 'SelectionMode', 'single');
iMuscle = select_m... |
414930aecfba7404fe3a3167f98c626398b352edfdad610937e01a1942b8642f | MATLAB | 2,259 | 49 | function FracCorr=DecodePredErrCrossTemporal(FR,evt0,reg)
% CVE decoding on erroroneous behavioural trials
%
% (c) 2014, Daniel Durstewitz, Dept. Theoretical Neuroscience, CIMH
% Mannheim/ Heidelberg University
% Edited by Aleksander PF Domanski PhD UoB 2015
% aleks.domanski@bristol.ac.uk
%
% Input Variables:
... |
48334e2a15109f4bde3887d21ad5171d3049f7d581ad2c2a18f9f88fd99dd3d3 | MATLAB | 2,260 | 84 | % PURPOSE: subroutine for ploterpGUI.m
% Creates default Y ticks for ERPLAB's plotting GUI
%
% FORMAT
%
% [def miny maxy] = default_amp_ticks(ERP, yrange)
%
% INPUTS
%
% ERP - ERPset
% yrange - min and max ERP amplitudes
%
% OUTPUT
%
% def - tick values to show in Y axis
% miny - minimum E... |
3f70b2c69e9c7d89bbc33185de4de943e14d63494fc445f233f9a471dd941ec7 | MATLAB | 2,266 | 55 | function [branches_id] = path2idBranch(path)
% PATH2IDBRANCH Convert the path of the branches geometry in branch IDs to
% the endo geometry.
%
% [branches_id] = path2idBranch(path)
%
% Input: - path: matrix with all the cross-section branches adjacencies
% and paths
%
% Output: - br... |
eb2a4090ebe8ac79dd014dae14f5bdee4e4f90af7e8db6ac400cf018da1936f3 | MATLAB | 2,267 | 89 | function [Problem] = trace_norm_matrix_completion(A, mask, lambda)
% This file defines a matrix completion problem with trace (nuclear) norm minimization.
%
% Inputs:
% A Full observation matrix. A.*mask is to be completed.
% mask Linear operator to extract existing elements, denoted as P... |
fa7c31724dac93cd15a49de72a608a53f9112aea8a93406907fd8ecdff0a4428 | MATLAB | 2,267 | 49 | function CVE=DecodeCVE(FR,evt0,reg)
% Leave-one out cross-validation error (CVE) Decoder
%
% (c) 2014, Daniel Durstewitz, Dept. Theoretical Neuroscience, CIMH
% Mannheim/ Heidelberg University
% Edited by Aleksander PF Domanski PhD UoB 2015
% aleks.domanski@bristol.ac.uk
%
% Reports binary classification strength betw... |
b54da6f68f9a7f4ce546336ef3cb76d368181a7c7488459df5400f7bdf87a47c | MATLAB | 2,269 | 85 | function irepistruct = irepifit(irepistruct)
% irepistruct = irepifit(irepistruct)
% Performs LMS fit.
%
s = irepistruct; % copy into s for easy handling
% s = irepitiming(s); Need to do this if fitting seq params
% Need to be able to cache so not recomputing every slice
% Need this way too for incremental
s = irepi... |
0b09843be6dfd9ce4a67212a895923987e182fd169f30bcea62bcbc5f494e3dc | MATLAB | 2,272 | 67 | function [mappedX, mapping] = dr_pca(X, no_dims)
%PCA Perform the PCA algorithm
%
% [mappedX, mapping] = pca(X, no_dims)
%
% The function runs PCA on a set of datapoints X. The variable
% no_dims sets the number of dimensions of the feature points in the
% embedded feature space (no_dims >= 1, default = 2).
% For n... |
b1e14f248d0ed56f5a505832b9d897c8ae59e903e49ff2bd7947ecc55219e0f8 | MATLAB | 2,272 | 78 | function SOEA45_Liley(M,run_number,population_size)
% Script runs the sinlgeobjecitve evolutionary algorithm SOEA45. See paper
% for details.
% Load data
load('Control_EEG_data.mat')
data = Control_EEG_data(M,:)-mean(Control_EEG_data(M,:));
data = zscore(data);
data = filter_b_f(data, 2, 256,'high',4);% 2 ... |
32ca745e678ce3068f274f07c13d6fbcfec4b596657ce1279abbe6c07f75b131 | MATLAB | 2,274 | 65 | function [results_group] = GMLMprior_lever(params, results, groupNum, levPrior_setup)
if(isempty(params))
results_group = 1;
return;
end
params_group = params.Groups(groupNum);
results_group = results.Groups(groupNum);
if(numel(params_group.H) ~= 1)
error('Incorrect number of hyperparameters')
end
results... |
b4c3f494328c8529da85cadba3bd7b005e2a53721e91b171bc0b6944426b2233 | MATLAB | 2,278 | 76 | function params=tpreadconfig( tpdirname )
%TPREADCONFIG read twophoton/confocal experiment config file
%
%
% PARAMS. =
% params.Main.Total_cycles (total number of cycles)
% params.Main.Scanline_period__us_ (scanline period, in us)
% params.Main.Dwell_time__us_ (pixel dwell time, in us)
% params.Main.Frame_perio... |
6f4af914e00240c4ab7b0af85ce14490866c149155ab6934f70bd3a7716e3947 | MATLAB | 2,279 | 116 | %%
tank = 'ZAZU_2';
block = 13;
%
emap = [17 31 19 29 21 27 23 25 18 32 20 30 22 28 24 26 1 15 3 13 5 11 7 9 2 16 4 14 6 12 8 10];
%
win = [-0.025 0.6];
data = TDT2mat(tank,sprintf('Block-%d',block),'type',[2 3],'silent',true);
%-------------------------------------------------
Rate = data.epocs.Rate.data;
fpidx =... |
0690f7583b518b214205b0cdf50900be732eb2a6292da8711a70b8a9dc1e6d72 | MATLAB | 2,280 | 65 | function [results_group] = fixedGaussianPrior(params, results, groupNum, prior_setup)
if(isempty(params))
results_group = 1;
return;
end
params_group = params.Groups(groupNum);
results_group = results.Groups(groupNum);
if(numel(params_group.H) > 1)
error('Incorrect number of hyperparameters')
end
results_... |
7251c4e63d7faecde7c1fb736786e399f16e5a8092b70d5b7914d949d6c2e386 | MATLAB | 2,281 | 90 | function [] = test_group_lasso_cv()
clc;
clear;
close all;
%% prepare dataset
if 0
data = importdata('diabetes.txt');
b = data.data(:,11);
A = data.data(:,1:10);
[n,d] = size(A);
G = [1 1 2 2 3 3 3 3 3 3];
else
% load data
data =... |
4e16870f8d8e7282c00faa386daaa2339583025566470bfb442c87419a2c9811 | MATLAB | 2,283 | 57 | function [ DOM ] = AddToColladaVisualScene( DOM, Name, MaterialName, Translation )
%ADDTOCOLLADAVISUALSCENE Summary of this function goes here
% Detailed explanation goes here
DOMNode = DOM.getDocumentElement;
VisLibList = DOMNode.getElementsByTagName('library_visual_scenes');
if(isempty(VisLibList... |
5cbc2f8f9140b937b60a163793317df9929f1d052cc8770c016e6a5264602204 | MATLAB | 2,284 | 44 | % COMPILE compiles the c files and generates mex files.
%
%Copyright (c) 2000 RICE UNIVERSITY. All rights reserved.
%Created by Haitao Guo, Department of ECE, Rice University.
%
%This software is distributed and licensed to you on a non-exclusive
%basis, free-of-charge. Redistribution and use in source and binary... |
89c2849cd4d91ec64c38975af3eea7764a0df752d2ca17557fae5c76be22c9fb | MATLAB | 2,284 | 56 | function model = select_fibers(model, clusterCenters, minDiamIFibers, minDiamIIFibers, numIaFibers, numIbFibers, numAlphaFibers, numIIFibers, numIIIFibers)
% If clusterCenters is empty, assign randomly
fibers = model.fibers{model.fascIds == model.motorFasc};
if nargin < 3
minDiamIFibers = 12;
minDiamIIFibers ... |
fe91b670bf655804a5d7bb9f1efa716dea658833d32245ab3da94af65f668249 | MATLAB | 2,284 | 100 | function [Problem] = trace_norm_convex_clustering(X, L, W, lambda)
% This file defines the TV (Total Variation) problem with L1 norm.
%
% Inputs:
% X data matrix of size dxn.
% L difference matrix of size n(n-1)/2 * d.
% W similarity matrix for L of size n(n-1)/2 * d.
% ... |
490cdfaf21a019aa6deb8c02ef26b146cbcbdf58cde909d82b2c10a008702c1c | MATLAB | 2,286 | 61 | % Savitzky-Golay smoothing filters
% (also called digital smoothing polynomial filters or least-squares smoothing filters)
% are typically used to "smooth out" a noisy signal whose frequency span (without noise) is large.
%
% Author: Javier Lopez-Calderon
% Center for Mind and Brain
% University of California, Da... |
ce40f188b088adaae581f86736e2e529225162d0becb62833e1dfaa9faa65b3f | MATLAB | 2,286 | 73 | function [iv, ip] = peak_detect(data, fs)
% PEAK_DETECT Automatic peak-valley detection in data.
%
% Format:
% [iv, ip] = PEAK_DETECT(data, fs)
%
% Required Input arguments:
% data - 1-by-n vector of EDA data
% fs - sampling rate (Hz)
%
% Output arguments:
% iv - valleys index into eda input... |
1a42b4e1722728d2f396d6237f6f5b1194115dd0d5a322b5208ecf8a50245a7a | MATLAB | 2,287 | 67 | clc; clear; close all
Fig6c=figure();
N_mu=500; %There are \mu independent embedded patterns, 500 simulations (number of simulations)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%R=1
yline(1,'--k','Linewidth',2)
hold on
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
8ea4435bd13577f50aaf74e2a61ae7524a546c4df742dc169fb015db33adb442 | MATLAB | 2,287 | 84 | % PURPOSE: saves or loads the ERPLAB Studio working memory to another location
%
% FORMAT
%
% estudioworking_mem_save_load(save_or_load)
%
% INPUT
%
% save_or_load - 1 for save; 2 for load
%
% OUTPUT
%
% Mat-file .erpm written to disk, or memory structure loaded.
%
% *** This function is part of ERPLAB StudioT... |
e0b1a60cd61f69bca78658e0181a8f1a5fccc69a8f1f35f271a80ddf4308d121 | MATLAB | 2,289 | 77 | function [k,sigma,ks,w] = gpdfitnew(x)
%GPDFITNEW Estimate the paramaters for the Generalized Pareto Distribution
%
% Description
% [K,SIGMA] = GPDFITNEW(X) returns empirical Bayes estimate for the
% parameters k (ksi) and sigma of the two-parameter generalized Parato
% distribution (GPD) given the data in... |
f0f4929a87f22b8f325e6bd1b0e79a4d8e1654b439810d4907bd3de4fae176ac | MATLAB | 2,289 | 82 | function joinedStr = strjoin(c, aDelim)
%STRJOIN Join cell array of strings into single string
% S = STRJOIN(C) constructs the string S by linking each string within
% cell array of strings C together with a space.
%
% S = STRJOIN(C, DELIMITER) constructs S by linking each element of C
% with the elements of D... |
67ecb4a977e57e18122fed8cb5a530b02fedc5ccb19243617769e761dc999c3d | MATLAB | 2,292 | 37 | % This function generate masks for spliting data to train-validation-test sets from each participant.
% Each participant perform 6 trials, among them 4 trials have been implemented as the training set, 2 trials for validation and test set.
% This train-vali-test spit will be done 3 times so that each trial will be app... |
15eb803de6585eacd7a523a624734d116b429e8fe560e7201898a325caab6f5e | MATLAB | 2,294 | 87 | function [B,R2] = ridge_run_model(epi_file,X,lambda,mask,throw_away)
% Calculate first level ridge regression for given subject
% Inputs:
%
% epi_file = path to subject's preprocessed (and detrended)
% epi.nii -file
%
% X = model design matrix without cons... |
7cef83f6e45deb4dac6d89016230623fc96fe8093b4936deaf41793fab464a57 | MATLAB | 2,297 | 52 | function design = designMatrix_BH(cfg0, phi)
% [design] = designMatrix_BH(cfg, phi)
% Returns hypothetical channel responses given a presented orientation, cf. Brouwer & Heeger.
%
% phi Array of length N, where N is the number of trials, that specifies the presented
% orientation on each tr... |
faf4b9ff5c44c438c9e009797274de27e78d9b02ebaa27a4c462b4163c9f546e | MATLAB | 2,299 | 50 | function [tscounts, wfcounts, evcounts, contcounts] = plx_info(filename, fullread)
% plx_info(filename, fullread) -- read and display .plx file info
%
% [tscounts, wfcounts, evcounts, contcounts] = plx_info(filename, fullread)
%
% INPUT:
% filename - if empty string, will use File Open dialog
% fullread - i... |
f56b5099c2c4a7ab09382dfb3fad74a334fd5aef2ae9db42bc0787534243035e | MATLAB | 2,303 | 83 | function plot_thresholds(model, args)
arguments
model Model
args.SavePlot = true
end
[~, activeSites] = load_active_sites(model.paths);
%set(gca, 'XLimMode', 'auto', 'YLimMode', 'auto');
thresh10 = compute_fasc_thresh(model);
elecIds = unique([activeSites.elecId]);
if args.SavePlot
plotDir =... |
2488269f6b6f7f8aa86d3e3f0241c0aee4082257ca43411435648c9e9273d4ab | MATLAB | 2,305 | 32 | function Params=GetParams()
% Returns a structure containing the data paths and general parameters
% regarding the analsyis to be conducted.
%__________________________________________________________________________
% Copyright (C) 2022 Laboratory for Neuroimaging Research
% Written by A. Lutti, 2022.
% Labo... |
93dd15dd8a4b53a1bd39863a1ef9fdf981da283113b714a726aca5f8474c556c | MATLAB | 2,306 | 63 | function [cm,cm_angle,cm_eccen] = make_BensonColormap()
% This colormap was made using linspecer:
% Beautiful and distinguishable line colors + colormap
% version 1.4.0.0 (8.25 KB) by Jonathan C. Lansey
% and then made darker/lighter for a cortical surface
% and then shuffled to make sure adjacent maps differ in color... |
49499b61ff79d390405912b5de50b9d6984d0a732dd3a4b15de77f276a0513ed | MATLAB | 2,307 | 88 | function dtvariance_patch(path_list_file,varargin)
%% Internal variables
fs=filesep;
%% Define defaults
% Save in BIDS folder, default NO,
save_in_BIDS_folder=0;
output_folder=[pwd fs 'dtvariance_files'];
%
string_to_match=['*func' fs '*rest*bold*dtseries.nii*'];
%% Read additional arguments, if ... |
81097fed0e8c00175535e658b8595010dd4e7f7c029ea818c12d32ef5227cfb1 | MATLAB | 2,307 | 63 | function [params, precomputedPortion] = scaleWB5(params, spkHistPrior_setup, couplingGroup)
% i.i.d non-mean Gaussian over spike history coefficients & constant
K = size(params.B,1);
K2 = K/2;
w_mu_idx = 1;
w_sig_idx = 2;
H = double(params.H);
w_mu = H(w_mu_idx);
log_w_sig = H(w_sig_idx);
w = double(params.W);... |
4477c86e6bee072ea503d8721f96bb0f46c4ed6cc6f6aa9115b28af9dbf20f29 | MATLAB | 2,310 | 65 | function [B,Bnorm,commsizes,blockpairs] = comm_mat_und(W,ci,omitnan)
%COMM_MAT Returns the block matrix of the community structure of a graph W with given membership vector ci
%
% Inputs W, undirected weighted/binary network.
% ci, membership vector
%
% Outputs: B, block matrix of communi... |
4c8e275e05d21961830d6643fa755c35e23cd52414871db97543a12871f43835 | MATLAB | 2,314 | 57 | function fiberCenters = spread_fibers(fiberCenters, fiberRs, sections, strength, maxCount, thresh)
%SPREAD_FIBERS
% SECTION is an array of single region polyshapes
maxShift = 10; % um
for iter = 1:maxCount
initialFiberCenters = fiberCenters;
cumShift = 0;
for indFiber = 1:size(fiberCenters, 1)... |
d294e2edf79e6581361551702c78e8d6e72ce2ff62cd642e5391fe1c84d146b3 | MATLAB | 2,323 | 67 | clc; clear; close all
Fig6a=figure();
N_mu=500; %There are \mu independent embedded patterns, 500 simulations (number of simulations)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%R=1
yline(1,'--k','Linewidth',2)
hold on
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
afbe7afa46adae61185d1d5cf10549c4bb1cee2d273c9787870bc71eb8372cb8 | MATLAB | 2,324 | 93 | % TODO: fix descriptions of run_meshrefinement and add notes
clear all;
close all;
clc;
%% Input variables
% Data folder
basePaths = get_base_paths();
dirData = basePaths.data;
dirSol = ['D:\FEM_solutions\'];
% Model ID
modelId = '3_thin';
nerveModel = ['Subject' num2str(modelId)];
%% Elec ID
ele... |
ed60f9b14f3ff38d10237f8eac1473083cf67d2d4631f85e4de42b05c7e7d16f | MATLAB | 2,331 | 88 | function [lp_log_x, dlp_log_x, d2lp_log_x, dlp_log_scale] = halfTPrior(log_x, nu, scale, is_log)
% x = exp(log_x) is a standard deviation (or similary exp transformed positive parameters) with distribution half-t distribution of scale and nu degrees of
% freedom
%
%when nu = 1, is half cauchy
if(nargin < 3 || isempty(... |
b5608d7c4e2253bb29e6ac2ce3efbd9b602f1b896614e21f3c14adc3e7535274 | MATLAB | 2,333 | 50 | %
% Relabel the annotation labels to match the indices of a given (string) list of annotation areas
% reAnnotLabels = fsRelabelToAreas(fsAreas, annotColortable, annotVertexLabels)
%
% fsAreas = a string cell array of the areas to look up
% annotColortable = the color table, read in us... |
70484feac338bc1785e53dcfa36a979e5c5d107a9677a7381ca8ed7206a9289e | MATLAB | 2,337 | 77 | function signalGrid = interpolateDeadElectrodes(signalGrid, deadElectrodes, angleFlag)
% Interpolate dead electrodes to be the median value of surrounding nodes.
% Input signalGrid can be a 2D matrix with dimension [X Y] or a 3D matrix
% with dimension [X Y T], where t gives time. badElectrodes is
% an array containing... |
b03410f41d0b7b540c1b78e6f0cff93c13debfa3c6014d1e104ff83a7fdef935 | MATLAB | 2,338 | 78 | function [fstat, pvalue, rss_reduced, rss_full] = ...
measure_gc_bivariate(x1, x2, lags, plotFlag, constantFlag)
% [fstat, pvalue, rss_reduced, rss_full] = ...
% measure_gc_bivariate(x1, x2, lags, plotFlag, constantFlag, fstat_effect)
%
% Measure granger causality from x2 to x1
%
% Input: 1. x1: one-dimensio... |
ff59a51c3a43285d72b2a2f918b4f6ba0879208b953073a9c69b4251bca29be0 | MATLAB | 2,344 | 78 | function [accuracy, distanceMean] = decodeTemplate(UTrain_r, UTest_r, plt)
% will decode based on the template obtained on a train set
% NB: takes into account the strength of the projection on each pc (ie,
% the eigenvalues)
% UTrain_r and UTest_r should be nBins * nGroups * nPCs * nRep
if nargin < 3
plt = 0;
en... |
2972c68054202cce05c1b60722d03942f656ee4982230155dd27528d62959e37 | MATLAB | 2,348 | 60 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% MANN-KENDALL TEST ORIGINAL %%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function[H,p_value]=Mann_Kendall(V,alpha)
%%%%%%%%%%%%%%%%%
%%% Performs original Mann-Kendall test of the null hypothesis of trend
%%% absence in the vector V, against the alternative... |
e5fc7d41fdd3749753a930fe1fa6aaf5da486887038737daf8a3780d26455944 | MATLAB | 2,348 | 74 | function b = tpdriftexport(tpdirname, channel, exportfilename, varargin)
%TPDRIFTEXPORT - Export a TIFF stack of motion-corrected images from a 2-photon record
%
% B = TPDRIFTEXPORT(TPDIRNAME, CHANNEL, EXPORTFILENAME, ...)
%
% Exports a 'TIFF' stack movie of the 2-photon data in TPDIRNAME on
% channel CHANNEL. The d... |
7ed91b288fd952d7491f8bdb0504ce4b22d77d4e9a267fc6642ddf1964727090 | MATLAB | 2,349 | 86 | % ALPHA VERSION
% PURPOSE: get mean global field power from an ERP
%
% FORMAT
%
% MGFP = mgfperp(ERP, selch)
%
% inputs:
%
% ERP - ERP structure
% selch - array containing included channels
%
% output:
%
% MGFP = row array containing MGFP
%
% Author: Javier Lopez-Calderon
% Center fo... |
904ac4d5ce98c31f3b9bf8d36b085dc235a5b86535687f83e42d2c00ae08f2cc | MATLAB | 2,349 | 72 | % Loads and plots the sequence of correct and error trials from behavioural data and calculates the (Markov-esque) 1st orde transition matrix (sequence probabily matrix)
%
% Aleksander PF Domanski PhD UoB 2015
% aleks.domanski@bristol.ac.uk
%% Preamble: load files
clear
% start in 'raw'
% cd 'C:\Analysis\Ass... |
28171cff7e6ebb1196b27c902fccf6b00a0175d0b73531a5a72248556ce9c9f1 | MATLAB | 2,350 | 60 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% MANN-KENDALL TEST ORIGINAL %%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function[H,p_value,Z]=Mann_Kendall(V,alpha)
%%%%%%%%%%%%%%%%%
%%% Performs original Mann-Kendall test of the null hypothesis of trend
%%% absence in the vector V, against the alternati... |
7d3a70664507ae57d967b81746e08c9fafe289335d283840280c9b042ae0b473 | MATLAB | 2,352 | 47 | function out=driftcorrectionmanual(parameters, input)
% DRIFTCORRECTIONSHIFT - Perform a manual drift manual on existing drift correct manual
%
% DRIFTCORRECTBYCORRELATION(PARAMETERS, INPUT)
%
% PARAMETERS should be a structure with the following entries:
% instruction_msg (0/1) Should we print t... |
c7a52f9ca2a89312675e1ce60efb61ebcbf3038aedecfc85e8d69f4a194f36cd | MATLAB | 2,352 | 80 | %%
clear
close all
%% Loading & folder management
[start_folder] = uigetdir(pwd,'Select the NetworkBurstDetectionFiles folder');
if isempty(strfind(start_folder, 'NetworkBurstDetectionFiles'))
f = errordlg('Wrong folder', 'Folder Error');
return
end
disp(start_folder)
cd(start_folder);
[StartFolderPath,StartF... |
133cf655aff1d4674cf8dee395e2c7eabecd27ba7e69a09499adb91c9d1f5652 | MATLAB | 2,357 | 64 | function [paramStruct2] = saveSampleToFile(obj, samples_file, paramStruct, sample_idx, scaled_WB, scaled_VT, save_H, saveUnscaled)
paramStruct2 = paramStruct;
J = numel(paramStruct.Groups);
for jj = 1:J
if(save_H.Groups(jj).H)
samples_file.Data.(sprintf("G%d_H", jj))(:,sample_idx) = paramStruct.Groups... |
8cb13bf80456ef420897ef47e9646876da693116647cc93eabc4b39ee3e597b6 | MATLAB | 2,357 | 96 | function [] = test_lasso_cv()
clc;
clear;
close all;
%% prepare dataset
n = 1280;
d = 100;
k = 5;
noise_level = 0.01;
[A, b, ~, ~, lambda_max] = generate_lasso_data(n, d, k, noise_level);
%% set only ONE algorithm
% select from {'ADMM-LASSO','FISTA','PG-... |
3ee8920537ee9a65d68ce45c4d5176eb9a050e985de0c3bd19121736f142636f | MATLAB | 2,358 | 78 |
%% %% %% %% %% %% MASTER SCRIPT FOR GENERATING FIGURES %% %% %% %% %% %% %%% %
% %
% GLOBAL NONLINEAR APPAOCH FOR MAPPING PARAMETERS OF NEURAL MASS MODELS %
% ... |
77b6ae93447eb1ec001d0537af2ee3d00d09f9b273a8973e96fb19bef5b842f9 | MATLAB | 2,358 | 72 | function tH = ieeg_RenderGiftiLabels(g,vert_label,cmapInput,roiNames,varargin)
% function to render a gifti
%
% input:
% g: gifti file with faces and vertices
% vert_label
% cmapInput
% roiNames
% varargin{1}: sulcal_map, can be loaded with read_curv('lh/rh.sulc')
%
% output:
% th: returns trime... |
6742d8342112fc04b356789a41c5b8949f4b810634ef5eaea2b9236890adff04 | MATLAB | 2,359 | 73 | function plot_residuals( spikes, show )
% UltraMegaSort2000 by Hill DN, Mehta SB, & Kleinfeld D - 07/12/2010
%
% plot_residuals - plots standard deviation of waveforms for a group of events
%
% Usage:
% plot_residuals( spikes, show )
%
% Description:
% For the specified spikes, plot the standard deviation of ... |
117f4b9ca8dd5f950f5179f6e8276d47872669b073a04bd33b04a56bcbb4857f | MATLAB | 2,363 | 80 | function MOEA45_Liley(M,run_number,population_size)
% Script runs the Multiobjecitve evolutionary algorithm MOEA45. See paper
% for details.
% Load data
load('Control_EEG_data.mat')
data = Control_EEG_data(M,:)-mean(Control_EEG_data(M,:));
data = zscore(data);
data = filter_b_f(data, 2, 256,'high',4);% 2 Hz ... |
eb2c4ccaedee7e58d9510537e36fd62bb92e23873dddf7470f9b2f2a2357d349 | MATLAB | 2,364 | 81 |
clf
figure
grid on
space=0;
alpha=0.88;
sd=10;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Sloped surface- Flexion output of MoCap- right foot %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for i=1:3 % Iterate through various methods Linear Regression
for j=3
ah(1)=subplot(1,3,i);
data_r=results{j,i}{2}'; % getting data for... |
9ab76d864d45242e0bcdd8fd7124bc88a6947b68a339259444744837755ae25b | MATLAB | 2,365 | 69 | % DEPRECATED...
%
%
% *** This function is part of ERPLAB Toolbox ***
% Author: Javier Lopez-Calderon
% Center for Mind and Brain
% University of California, Davis,
% Davis, CA
% 2012
%b8d3721ed219e65100184c6b95db209bb8d3721ed219e65100184c6b95db209b
%
% ERPLAB Toolbox
% Copyright © 2007 The Regents of th... |
ee8825ef9d04d68e2b000572f63975cbc41cda525ffea56a38a5ed3209e30355 | MATLAB | 2,365 | 60 | % function MapMontage (Montage)
%
% This routine roughly maps the 2-D locations of an EEG montage (down view,
% nose on top, circle approximates Fpz-T7-Oz-T8 plane).
%
% Usage: MapMontage (Montage);
%
% Input argument: Montage cell structure returned by the CSD toolbox
% Ext... |
d01aef85715dd3acd8d9bca8a27d8110c5b4fb24c1c35c3fef57a507fee543f9 | MATLAB | 2,373 | 89 | % PURPOSE: subroutine for ploterpGUI.m
% Creates default X ticks for ERPLAB's plotting GUI
%
% FORMAT
%
% [def, stepx] = default_time_ticks_studio(ERP, trange)
%
% INPUTS
%
% ERP - ERPset (used to determine data type and epoch limits)
% trange - [tmin tmax] display window in ms (ERP) or Hz (spectrum)
... |
0fa43cc80959a9dc86b39f101038e858eaa8e1ce5631cb59f0456d614f9303ee | MATLAB | 2,376 | 79 | % *** This function is part of ERPLAB Toolbox ***
% Author: Javier Lopez-Calderon and Guanghui Zhang
% Center for Mind and Brain
% University of California, Davis,
% Davis, CA
% 2014 & 2022
%b8d3721ed219e65100184c6b95db209bb8d3721ed219e65100184c6b95db209b
%
% ERPLAB Toolbox
% Copyright © 2007 The Regents of the Univer... |
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