sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
56a70c59880dc0d89bfaff1f256cd4fd89d7e22d25da5c1a06e9d05df6d739d7 | MATLAB | 3,057 | 81 | function [ F, J ] = gauss2dfun( params, xy)
%GAUSSIAN2DFUN evaluates modified gaussian with params at x
% Output:
% F: function value
% J: Jacobian of the parameters
%Written by Dimos.
%==========================================================================
%Unwrap inputs
%===========================... |
5baae0359d2498aeb78bb66320f159054b37853d98ab10fd81ea41955871dd7c | MATLAB | 3,057 | 129 | function [p_BPA] = VBA_BPA(priors0,posteriors0)
% performs Bayesian Parameters Averaging (BPA)
% function [p,o] = VBA_BPA(posterior,F)
% IN:
% - priors0: a Kx1 cell-array of VBA prior structures (where K is the number of subjects)
% - posteriors0: a Kx1 cell-array of VBA posterior structures (where K is the number ... |
c2f3eabc2bbbcb0347bcf22f8bf56a4425de96e47b5cee40b3e9f8ad554fcf4b | MATLAB | 3,057 | 86 | function [stop, reason] = stoppingcriterion(problem, x, options, info, last)
% Checks for standard stopping criteria, as a helper to solvers.
%
% function [stop, reason] = stoppingcriterion(problem, x, options, info, last)
%
% Executes standard stopping criterion checks, based on what is defined in
% the info(las... |
225d40c799f53f7fd5e925467fd0727fd2ea2d763fcbc543a77cb6d5d3a5794a | MATLAB | 3,058 | 98 | function SpikeSortDU(datDir, varargin)
verbose = true;
saveDir = datDir;
preprocessRange = [0 600];
timeRange = [0 Inf];
lfp = true;
lowpass = -1;
for i = 1:2:length(varargin)
switch lower(varargin{i})
case 'verbose'
verbose = varargin{i+1};
case 'savedir'
saveDir = vararg... |
87094166cd74c963e997e9af3a168b597a0eefcddcd577c6799ccf0bcff52a94 | MATLAB | 3,058 | 89 | % Use corrected S
S = Scorr;
% run the first block of main_DIAN_centrality_anal_signed first
BrainViewCmap = jet(101);%jet(100);% hot(100);
% BrainViewCmap = interp1(linspace(0,100,10),Plot.redbluecmap(10),linspace(0,100,100));
T = readtable('DIAN_Seitzman_246.xlsx');
load('IM_13nets_246_newcolor_MNI.mat'); % load p... |
e0cc1120553b5839dea06fd0a90c0def404c58a45d4accd1e2196b50f906c558 | MATLAB | 3,058 | 114 | function [uniquedays,muperday,Sperday,nperday,hax,hfig] = PlotLabeledReachesPerMouse(labeldata,varargin)
[hax,hfig,mousename,sepdays] = myparse(varargin,'hax',[],'hfig',[],'mousename','','sepdays',true);
if isempty(hax),
if isempty(hfig),
hfig = figure;
else
figure(hfig);
end
hax = gca;
end
for i = 1... |
50487706a2d19ebba14755f5024e30f02f8d482a3d2133adee48f63c933af339 | MATLAB | 3,059 | 143 | clear
clc
global glm
%%%%% SET HYPERPARAMETERS
%bin width
resolution=2;
glm.res=resolution/100;
%pulse duration
duration=65;
%ensure duration can be divided by resolution
if(mod(duration,resolution)~=0)
duration=duration+1;
end
glm.pulsedur=duration/100;
glm.keep_regression_values=0;
% only consider pulse... |
572a040066338218a1bf0ae98ca607cee69bc825fd230b5c6db21a69147259ba | MATLAB | 3,059 | 57 | function cMap=UniversalColorMap(ColorCode,TempContrastLow,TempContrastHigh)
if (TempContrastHigh-TempContrastLow)<=0
warning on
warning('Provide UniversalColorMap with separated contrast values!')
error('Provide UniversalColorMap with separated contrast values!')
end
if ischar(... |
d7f5ca04d6ce0fe80aaa32bb3b18cf9493448d19f4cbb4378452c2b2e4853d96 | MATLAB | 3,059 | 104 | function [ripple_stats, chan_labels, lab, data] = chanSelect(sub, ripple_stats, lab, data)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Thalamic spindles and Up states coordinate cortical and hippocampal
% co-ripples in humans
%
% Dickey et al. 2024
%
% Sub-select good channels for ... |
5ee324ed19e87c90417c6b27c16fa1243a79264bafff9af11762d461c0964ece | MATLAB | 3,061 | 91 | function ft_write_sens(filename, sens, varargin)
% FT_WRITE_SENS writes electrode information to an external file for further processing in external software.
%
% Use as
% ft_write_sens(filename, sens, ...)
%
% The specified filename can already contain the filename extention,
% but that is not required since it will... |
e293e1ba3164231e6587154de242c3911dacf642d772523ff1e4f42a192f530e | MATLAB | 3,061 | 70 | % JPSTH calculates jpsth for two spike counts aligned around an event
% jpsth.m
%
% Copyright 2008 Vanderbilt University. All rights reserved.
% John Haitas, Jeremiah Cohen, and Jeff Schall
%
% This file is part of JPSTH Toolbox.
%
% JPSTH Toolbox is free software: you can redistribute it and/or modify
% it under ... |
33a42a4034799eb44c08834ed4bc892722a1543e5f36d01cb005ef7b898c5578 | MATLAB | 3,062 | 73 | function cPPI_with_deconv_FIR(stat_path,exp_folder,N,N_ROIs,q_level,ground_truth)
% ========================================================================
% Ruslan Masharipov, October, 2023
% email: ruslan.s.masharipov@gmail.com
% ========================================================================
tic
for subj... |
9149ab76812ac97ceda8394b5bf7a0b6b4c379b3316fb9457c6913131ba0ab5d | MATLAB | 3,062 | 94 | function costs = surfprofile(problem, x, d1, d2, t1, t2)
% Plot the cost function as a surface over a 2-dimensional subspace.
%
% function surfprofile(problem, x, d1, d2, t1, t2)
% function costs = surfprofile(problem, x, d1, d2, t1, t2)
%
% Evaluates the cost function at points
%
% gamma(t1, t2) = exponentia... |
7640a56ec1cb26091251e810c8594e301a18aef57d712276ec8542e2e0ec7ea6 | MATLAB | 3,063 | 71 | function EEG = DS_Zscore(EEG,StartEEG,EndEEG,~)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Normalize EEG signal using sliding window
%
% Part of detect_spindles toolbox:
%
% Authors: Stephane Sockeel, PhD, University of Montreal
% Stuart Fogel, PhD, University of Ottawa
... |
92b7078dc23a4aec3afa9c45401006b8f62ecfb5a6253d71b762fe9154d3f27c | MATLAB | 3,063 | 77 | function varargout = handleVarargin(optionalInputs, optionalVariables, defaultValues)
% assignedInputs = assignDefaultValues(optionalInputs, optionalVariables, defaultValues)
% This function can be used to when you 'varargin'. It will assing
% varargin{1, 2, ...} to their corresponding variables (you don't need to
% d... |
73d47e2fc10843d92ae307e0874a210cd944789082959d948bb7ae60927c2b86 | MATLAB | 3,064 | 79 | classdef Workspace < handle
properties
images
TCA
params
end
methods
function ws = Workspace(def_file)
addpath('./lib');
%load def file
load(def_file);
%number of temporally concatenated runs
... |
9ba54f673334bc88948b442b297695798909121bf26e40963b53c68370e9ef6e | MATLAB | 3,064 | 95 | function hfigs = PlotExampleLabelsAndPreds(lbld,predfns,movieidx,frames,varargin)
[hfigbase,naxperfig,landmarkcolors,figpos,prednames,err] = ...
myparse(varargin,'hfigbase',100,'naxperfig',5,...
'landmarkcolors',[],'figpos',[10,10,2100,1500],...
'prednames',{},'err',[]);
nviews = size(lbld.movieFilesAll,2);
nla... |
ffbc1204edb25932859fabc16ad210e2a98fd112daa73fb2bbce7b174d0709a2 | MATLAB | 3,064 | 106 | function GND = FillEmptyGND(data, chanInds, EEG, binNames, nGoodTrials, nBadTrials, expDesc, fNames)
% function GND = FillEmptyGND(data, chanInds, EEG, condNames, expDesc, fNames)
% Fill up an empty GND structure with data and other fields (will call
% MakeEmptyGND subfunction)
%
% Inputs:
% data = [nPP nTimes nBins... |
5bf914faec7f1b58cfdb6159f706d6ccabb4af73916077d33c9f8b4c96b03427 | MATLAB | 3,066 | 122 | %% Illustrate benefit of using T distribution instead of plugin estimate
% We use a naive Bayes classifier with 2 Gaussian features
%%
% This file is from pmtk3.googlecode.com
function naiveBayesBayesianDemo()
setSeed(0);
C = 3;
[Xtrain, Ytrain] = makeDataHelper([2 5 8]*1);
[Xtest, Ytest] = makeDataHelper([2 5 8]*20)... |
d967fa9b8096140b2104b8249cb8ef139c1b118d25daabb13d323d33bb7800fc | MATLAB | 3,066 | 89 | % Copyright 2015 Idse Heemskerk and Sebastian Streichan
%
% This file is part of ImSAnE.
%
% ImSAnE is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any l... |
17d2a7798f107920461780de5c02556344dcd28e6994c80814ef4be5974dcb49 | MATLAB | 3,067 | 75 | function J = convBox( I, r, s, nomex )
% Extremely fast 2D image convolution with a box filter.
%
% Convolves an image by a F=ones(2*r+1,2*r+1)/(2*r+1)^2 filter. The
% convolution can be performed in constant time per-pixel, independent of
% the radius r. In fact the implementation is nearly optimal, with the
% convolu... |
6e6fbf0447a35214c547d4b01a6ab2db6307f74cf764a18bab1bc974d441f892 | MATLAB | 3,068 | 85 | function HMM = fit_HMM_simcv(datadir, hmmdir, HMM_name, n_train, n_test, betwgroup_diff, Y_noise, k, cv)
% HMM = fit_HMM_simcv(n_train, n_test, betwgroup_diff, Y_noise, type)
%
% fit a group-level HMM to simulated timecourses using
% simulate_cv_generatetc, either only to the training set or to all
% subjects.
% This ... |
939caa89ccc28042f91221046a16d2e0792aa4937a8a5888254cfc8647c04bb5 | MATLAB | 3,068 | 98 | function [tfr,t,f] = tfrstft(x,t,N,h,trace);
%TFRSTFT Short time Fourier transform.
% [TFR,T,F]=TFRSTFT(X,T,N,H,TRACE) computes the short-time Fourier
% transform of a discrete-time signal X.
%
% X : signal.
% T : time instant(s) (default : 1:length(X)).
% N : number of frequency bins (default :... |
e074f4de44bd652e76a5c87eb1847e136adf0d3c94399e083e296ee22accb8e3 | MATLAB | 3,068 | 97 | function [MergedStack] = StackMerge_wBorders_noLoading(Stack1_Input,Stack2_Input,Borders,Limits)
%this will load in two stacks and merge them vertically and help avoid
%memory loading issues if large stacks
if length(Limits)~=1
Stack1=Stack1_Input;
Stack1_Name = 'Stack 1';
disp(['Stack1_Name = ',Stack... |
83d3daee5ad9f46f695f9963a5ba453484c024bbc9455adbfc24a820eb0628e8 | MATLAB | 3,069 | 107 | function p = redgreencolormap(m,varargin)
%REDGREENCMAP creates a red and green colormap.
%
% REDGREENCMAP(M) returns an M-by-3 matrix containing a red and green
% colormap. Low values are bright green, values in the center of the map
% are black, and high values are red. If M is empty, the length of the
% map ... |
912aedc902d59fd018414058bc212106f1e4b3f11902773f6253ffc4d7236f61 | MATLAB | 3,069 | 95 | function [cs,index] = sort_nat(c,mode)
%sort_nat: Natural order sort of cell array of strings.
% usage: [S,INDEX] = sort_nat(C)
%
% where,
% C is a cell array (vector) of strings to be sorted.
% S is C, sorted in natural order.
% INDEX is the sort order such that S = C(INDEX);
%
% Natural order sorti... |
3fafaa47a05944602316216c86f3e634803324d92fe389b147ef17c0a1bdcd59 | MATLAB | 3,070 | 114 | function [o]=approxbrunnermunzel(x, y, sidedness)
% sidedness = 1 -> two sided; = 2 -> greater ; = 3 -> less
% ported from
% https://github.com/toshi-ara/brunnermunzel/blob/master/src/bm_test.f
alpha = 0.05; % for computing 95% CI
TOLER = 10^(-10);
P1 = [0.0, 1.0, 0.0];
P0 = [0.0, 0.0, 1.0];
% columnize d... |
60044f9ff20716daf062efbee1c127b9cbe28a37f56a521e8d63ecd34faf985a | MATLAB | 3,070 | 80 | function Log3DPlotZeroCorrection(TempXData,TempYData,TempZData,Input_Color_Map,MarkerSize_3D,AdjustRatio)
warning on all
warning off verbose
warning off backtrace
if length(size(Input_Color_Map))>2
for i=1:size(TempXData,2)
Color_Map(i,:)=horzcat(Input_Color_Map(1,i,:));
... |
5df78455754f8dc49ead09bfc5d06d54ebf3246cd5a080708a3299cf2004b00e | MATLAB | 3,071 | 109 | %% this script generates a distribution of LED -> PD spreads for each of the three layers
%% this section shows the ideal spreads are not too far off from the real LED -> PD position spread
led = generate_spread_values('wurth_led');
pd = generate_spread_values('osram_3710');
ds = [3 27];
folder = 'D:\data\22... |
fd8bb9b1cff13b22a507a8803e1053df09c831fad36e693cd188e95d85b5c714 | MATLAB | 3,071 | 90 | function headmodel = ft_headmodel_singlesphere(mesh, varargin)
% FT_HEADMODEL_SINGLESPHERE creates a volume conduction model of the
% head by fitting a spherical model to a set of points that describe
% the head surface.
%
% For MEG this implements Cuffin BN, Cohen D. "Magnetic fields of
% a dipole in special volume ... |
70e49278fcc4c39be1621e40a7b2a4357fe0646ae15341f1f89b30958c0dea4d | MATLAB | 3,072 | 114 | %% plotSDO_shearSDO
%
% plot diagonal Band of SDO which contain the majority of the SDO effects,
% sheared, such that the diagonal is on the horizonal axis.
%
% PREREQUISITES:
% computeSDO()
% INPUT PARAMETERS
% sdoMatrix:
% - The target spike-triggered SDO to plot
% OPTIONAL POSITIONAL ARGUMENTS
... |
21ba897474e55d8fa24b1c0d4c4ddeb02ed85183c2d02794b1d28f6241a51d44 | MATLAB | 3,073 | 87 | function publishMethod(mfile,methodName,evalCode)
% Publish a class method or a subfuncion of an mfile to html.
%
% INPUTS:
%
% mfile - the name of the mfile containing the function/method without the .m
% methodName - the name of the method/function
% evalCode - [true] | false if true, the code is evaluated an... |
36bf7e52bee9ae068be76de683764cae25aa00fb0401e9ac0a7c7b1f8164e0fe | MATLAB | 3,073 | 61 | function [spmat] = helm_near_corr(S,zpars,eps)
npts = S.npts;
npatches = S.npatches;
ipatch_id = zeros(npts,1);
uvs_targ = zeros(2,npts);
norders = S.norders;
ixyzs = S.ixyzs;
iptype = S.iptype;
srcvals = S.srcvals;
srccoefs = S.srccoefs;
npp1 = npatches+1;
n3 = 3;
n9 = ... |
2016aa798fd05ea1e4fd58b33783169bc2c1b3aa0e6dbd8f6c2d5a182ca5616d | MATLAB | 3,074 | 97 | function [tfr,t,f] = tfrppage(x,t,N,h,trace);
%TFRPPAGE Pseudo Page time-frequency distribution.
% [TFR,T,F]=TFRPPAGE(X,T,N,H,TRACE) computes the Pseudo Page
% distribution of a discrete-time signal X, or the
% cross Pseudo Page representation between two signals.
%
% X : signal if auto-PPage, or [X1,X2] if cros... |
dfdbbc0c4c618788344108d263412d52ba2c4181ac875275af98625799a09bd2 | MATLAB | 3,074 | 85 | %% NS prepare pipeline
%This main file will call the the neccessary routines to generate the NS
%results of the publication: "Stimulus-Induced Rotary Saturation imaging of
%visually evoked neuroelectric response: preliminary results and data
%analysis" (currently under review)
% Author: Milena Capiglioni, University o... |
f864a129c89dd1fbb46f799c89b49a4d7cac247d36b7c828b67551ecaf113e84 | MATLAB | 3,074 | 93 | function cfg = dataset2files(cfg)
% Helper function that converts a dataset into headerfile and datafile
% if necessary. This is used in PREPROCESSING and DEFINETRIAL
%
% This function operates only on
% cfg.dataset
% cfg.datafile
% cfg.headerfile
% and returns the updated cfg.
% Copyright (C) 2004, Robert Oost... |
24c1e56615461cf25b9ad0bf03de3663a518db61b358e99bb0e37d9dbb154161 | MATLAB | 3,075 | 97 | function [nfft,tableout]=nextfastfft(n)
%NEXTFASTFFT Next higher number with a fast FFT
% Usage: nfft=nextfastfft(n);
%
% `nextfastfft(n)` returns the next number greater than or equal to *n*,
% for which the computation of a FFT is fast. Such a number is solely
% comprised of small pri... |
2ca4ccbe55845721938f75f81bcaff3506da580f724c6f6fb6db2f865e350bc9 | MATLAB | 3,075 | 93 | function cfg = dataset2files(cfg)
% Helper function that converts a dataset into headerfile and datafile
% if neccessary. This is used in PREPROCESSING and DEFINETRIAL
%
% This function operates only on
% cfg.dataset
% cfg.datafile
% cfg.headerfile
% and returns the updated cfg.
% Copyright (C) 2004, Robert Oos... |
aed5d32aff64b84fe0d8871a9191a1fb730fc7b94abc1a9f2c043b35d2d92c56 | MATLAB | 3,075 | 75 | function varargout = jbm_quickInfoGUI3(varargin)
% JBM_QUICKINFOGUI3 M-file for jbm_quickInfoGUI3.fig
% JBM_QUICKINFOGUI3, by itself, creates a new JBM_QUICKINFOGUI3 or raises the existing
% singleton*.
%
% H = JBM_QUICKINFOGUI3 returns the handle to a new JBM_QUICKINFOGUI3 or the handle to
% ... |
5732c24652dfd27fb6da7273495766465aece30d2a9014bbb58ad2e5f0a51a68 | MATLAB | 3,076 | 84 | %Accuracy 49.44% 20printer with 70 and 30 svm+full image fc7
%Accuracy 51.67% 20printer with 80 and 20 svm+full image fc7
%Accuracy 59.77% 20printer with 80 and 20 svm+full image fc6 and 51.11
%with 70% train
clc
close all
clear
%D:\Doaa_PHD\PHD2020\DataSets\ck\CK48وو
%D:\Doaa_PHD\PHD2020\DataSets\jaffedba... |
469db836286fe0fb8ac8d24b8c4359f6e3d30d238b19d9b264e6f3910f0a13de | MATLAB | 3,077 | 171 | function [] = compute_logistic_regression(ratto)
nreps=50;
load(['data/Sessions_trials/' ratto '_sessions.mat']);
minperf=.7;
minbeta=.7;
mintri=100;
ind=find([sessions.hit]>=minperf & (([sessions.bdir]+[sessions.bfreq])/2)>=minbeta & ...
[sessions.valid]>=mintri & [sessions.good]==1);
sessions=sessions(ind... |
84f80f414aed8e2e335416455e7877e5a4e0a1787ef54765b0bbb84716f20c8e | MATLAB | 3,077 | 96 | function [file] = read_ctf_ascii(filename)
% READ_CTF_ASCII reads general data from an CTF configuration file
%
% The file should be formatted like
% Group
% {
% item1 : value1a value1b value1c
% item2 : value2a value2b value2c
% item3 : value3a value3b value3c
% item4 : value4a value4b value... |
d29da765b9b1068f183eb20784b3f56f26a19d9552c2120d6e0570b2b048bc94 | MATLAB | 3,078 | 103 | %% var_to_tsdata
%
% Generate random multi-trial Gaussian VAR time series
%
% <matlab:open('var_to_tsdata.m') code>
%
%% Syntax
%
% [X,E,mtrunc] = var_to_tsdata(A,SIG,m,N,mtrunc,decayfac)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% A VAR coef... |
ea3d7c17580216beda9cf477774708760f53c9545d80e0687af993c882a46f20 | MATLAB | 3,078 | 63 | function CBIG_ASDf_FC2doc_estFactors_wrapper(corrMat_ASD_path, corrMat_con_path, sub_info_file, output_dir)
% CBIG_ASDf_FC2doc_estFactors_wrapper(corrMat_ASD_path, corrMat_con_path, sub_info_file, output_dir)
%
% Wrapper function to compute z-normalized, discretized FC and write into
% word documents that will be used ... |
deb3d5e0310e6c99fb1331b9593b5a7216494c74fe8f869f485b63fcfb05d410 | MATLAB | 3,079 | 105 | function tucker_me_test
%TUCKER_ME_TEST Very simple tests of tucker_me.
% Code by Tamara Kolda and Jimeng Sun, 2008.
%
% Based on the paper:
% T. G. Kolda and J. Sun. Scalable Tensor Decompositions for Multi-aspect
% Data Mining. In: ICDM 2008: Proceedings of the 8th IEEE International
% Conference on Data M... |
d5bafba9d3cb31863fdd323dcba0d4881ecf0419c04914cf1469700fd8894b14 | MATLAB | 3,080 | 112 | % demo for binary data classification
close all
clear variables
dim.p = 32;
dim.n_t = 1;
dim.n_phi = 16;
dim.n_theta = 0;
dim.n = 0;
g_fname = @g_classif;
options.sources = struct('type',1 ,'out', 1); % one binomial observation;
options.priors.muPhi = zeros(dim.n_phi,1);
options.priors.SigmaPhi = 1e0*eye(dim.n_phi)... |
f68241c0f8546fabc53ab00b4820d9e0c1a46c002683f04a05831ed209163708 | MATLAB | 3,080 | 90 | function [C,time,g,s,k, phi, sta, stb] = minTimeGradient(C,rv, g0, gfin, gmax, smax,T, ds,show)
% [C,time,g,s,k, phi, sta, stb] = minTimeGradient(C, RIV/RV, g0, gfin, gmax, smax,T, ds, show)
% [C,time,g,s,k, phi, sta, stb] = minTimeGradient(C, [], [], [], gmax, smax, T, [], [])
%
% Given a k-space trajectory C... |
3d8ef021331e250c54bf097a805e8d01685300911c1101ebb68d83ed24a23e01 | MATLAB | 3,081 | 92 | function [depth] = ft_sourcedepth(dippos, headmodel)
% FT_SOURCEDEPTH computes the distance from the source to the surface of
% the source compartment (usually the brain) in the volume conduction model.
%
% Use as
% depth = ft_sourcedepth(dippos, headmodel);
% where
% dippos = Nx3 matrix with the position of N... |
d9eb101d562b2513c121981f5cf047c4c5c9480442f366ea6df2a6f8f0407f3c | MATLAB | 3,081 | 131 | %% Interpolate some data using a joint Gaussian
% Based on p140 of "Introduction to Bayesian scientific computation"
% by Calvetti and Somersalo
% See also https://users.oden.utexas.edu/~tanbui/PublishedPapers/BayesianTutorial.pdf
function main()
demo(0.1)
demo(0.01)
end
function demo(priorVar)
setSeed(1);
Nobs = 1... |
fa07632b62d6988917ccb4b2c4e12788b712cc37cde5d53666f4df3921daab54 | MATLAB | 3,081 | 83 | % plot_rawICs.m
% Sarah West
% 9/16/21
% Plots raw ICs from calculate_ICs.m for looking at/inspection.
function []=plot_rawICs(parameters)
% Return parameters to individual names.
mice_all = parameters.mice_all;
num_sources = parameters.num_sources;
yDim = parameters.yDim;
xDim = parameters.xD... |
29e056381166ed55e851dcfc58f95a310d43cafecf3fd3b8c1a94f8db7d1ff1a | MATLAB | 3,083 | 126 | function elasticDistortionsDemo()
%% Illustrate elastic deformations
%PMTKauthor Kevin Swersky
%load goodsix.mat
loadData('pmtkImages', 'isMatFile', false)
I = imread('digit6.png');
setSeed(2);
%rand('seed',30);
cm = ones(64,3);
cm(:,1:2) = repmat(linspace(1,0,size(cm,1))',1,2);
X = repmat(1:size(I,2),siz... |
d2bc487b411f8d10c5559f5a6cb1f6c24e4fd6d451067afff58204ea1aba4783 | MATLAB | 3,083 | 76 | function shorten_ufmf(input_file_name, output_file_name, desired_frame_count)
% read in the header
input_header = ufmf_read_header(input_file_name) ;
input_frame_count = input_header.nframes ;
% find the end of the header
headerstart = 0;
% loc of something after the header
headerend = min(min(... |
7438a420807c2dc43f202d4adcc8fa665ffad92e880fea6f1d1b2576d714fb03 | MATLAB | 3,084 | 73 | function [P_all,w_all] = DeleteSample3DPointsRomain(r,c,S,w_in,cRig,K)
nviews = numel(r);
cNames = {'L','R','B'}; % for now.
P_all = nan(3,numel(r{1})+1,numel(r{2})+1,numel(r{3})+1,3);
w_all = zeros(numel(r{1})+1,numel(r{2})+1,numel(r{3})+1,3);
for view = 1:numel(r),
fview = view;... |
afb8bad25c1a048e32fc139a4cea0728daf07efd86e51584645ea83ebbe315af | MATLAB | 3,084 | 97 | function generators = getGaussGeneratorsFixMovie(experiment, ...
imEnsemble, runningfixations, modelparams, xmar, ymar)
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
Nx = experiment.projector.screen(2);
Ny = experiment.projector.screen(1);
Nyx = Ny * Nx;
[Nyim, Nxim, Ni... |
fb953e53264e30925ebc7b5ab97ccbd68585047889df332f0233e248ba74d869 | MATLAB | 3,084 | 88 | function [x,iflaw]=fmpar(N,p1,p2,p3)
%FMPAR Parabolic frequency modulated signal.
% [X,IFLAW]=FMPAR(N,P1,P2,P3) generates a signal with
% parabolic frequency modulation law.
% X(T) = exp(j*2*pi(A0.T + A1/2.T^2 +A2/3.T^3))
%
% N : the number of points in time
% P1 : if NARGIN=2, P1 is a vector containing the three
% ... |
c089fdcba2516639e09da91d8da4a4b4ba01790f7310fb53a3d61709b0fa4a73 | MATLAB | 3,085 | 83 | function [W, output] = logregBoundOptL2overrelaxed(X, Y, lambda)
% Fit the weights for L2 penalized multinomial logistic regression via
% overrelaxed bound optimization in O((cd)^3) where c is the number of
% classes and d is the number of dimensions.
%
% Inputs:
%
% X nexamples-by-ndimensions
% Y ... |
073148edc8be111305f60dd77b2f8e4f78bbac2097a73b958676b9c2dec642dd | MATLAB | 3,087 | 68 | function Aout = h_routinesForInVivoAnalysis(flag, varargin)
for ii = 1:length(flag)
switch(flag(ii))
case 1 % 1) spine number syntax: Aout = h_routinesForInVivoAnalysis(1, synapseAnalysisData, figFlag)
presenceMatrix = varargin{1}.presenceMatrix;
synapseNum = sum(presenceMatri... |
5f072426e7b5f6b98ef30a86db739b48273bfe7ccf8087d0923d17a5bd36d0f3 | MATLAB | 3,087 | 84 | % fooof() - Fit the FOOOF model on a neural power spectrum.
%
% Usage:
% >> fooof_results = fooof(freqs, power_spectrum, f_range, settings, return_model);
%
% Inputs:
% freqs = row vector of frequency values
% power_spectrum = row vector of power values
% f_range = fitting range (Hz)
% sett... |
7282c42813b53501b4faf30cb7add3de63c843a817be74056a18e65f7537df42 | MATLAB | 3,087 | 109 |
% Demo for Balloon model.
% This demo inverts a balloon model of the HRF.
close all
clear all
% Choose basic settings for simulations
TR = 2e0; % sampling time interval (in sec)
n_t = 60/TR; % number of time samples (over 40 sec)
deltat = 5e-2; % micro-time resoluti... |
c91be2b3e6d5359d3fec6ef524b5fae4452d0f925748c3f807d09ef1920ab54a | MATLAB | 3,090 | 91 | function [depth] = ft_sourcedepth(dippos, headmodel)
% FT_SOURCEDEPTH computes the distance from the source to the surface of
% the source compartment (usually the brain) in the volume conduction model.
%
% Use as
% depth = ft_sourcedepth(dippos, headmodel);
% where
% dippos = Nx3 matrix with the position of N... |
51f7a7da9fca07732a707897f3b7b2e469b9271699faa721432b138906e5243a | MATLAB | 3,091 | 136 | function coor = coords_txt2coords_mat(name,pos,mni)
if nargin ==0
% names !!!!
realk= [];
[FileName,PathName,FilterIndex] = uigetfile({'*.txt','electrode names';},'open electrode names file','xx_Name.txt');
if isequal(FileName,0) || isequal(PathName,0)
disp('User selected Cancel')
... |
f3e5397331eb062e8a4ad3f7c376461ec3bc0e720b2dc96bf3fe49dbf9ce96a6 | MATLAB | 3,092 | 116 | function [hyp,p,z] = mengz(varargin)
% This function implements Meng's z-test for correlated correlations (Meng,
% Rubin, & Rosenthal (1992), Comparing Correlated Correlation Coefficients,
% Psych Bulletin 111(1), 172-175.)
%
% mengz(r1, r2, r12, n) compares two correlations r1 and r2:
% r1: correlation between X and Y... |
8e6581234c8dca23fc2f74147ef766ba462cc2e391718b647ae899da827b1797 | MATLAB | 3,093 | 91 | function [params, s_new] = denoise_artifact(params, s, state)
% DENOISE_ARTIFACT can be used for denoising source separation (DSS)
% during component analysis
%
% See also COMPONENTANALYSIS
% Copyright (C) 2005, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.fieldtriptoolbox.org
% for the docume... |
ac42faaba10523439d1d3b96ac770d8d7626ad898afb3cb747cbe3021af61742 | MATLAB | 3,093 | 101 | function model = linregRobustStudentFitConstr(X, y, dof, sigma2, includeOffset)
% Constrained gradient-based optimization of linear regression with Student T noise model
% We assume X is an N*D matrix; we will optionall add a column of 1s internally.
% Set dof=[] or 0 (or omit it) if it is unknown.
% Set sigma2=[] or 0... |
41aa939cc928e95d2935191782a3bf3ad2b124ef65560bfaf8be9ccba9bb8f69 | MATLAB | 3,094 | 121 | function [Delta490] = DeltaF(t,Ch490,Ch405,varargin)
% Smooth and process 490 channel and control channel data for fiber
% photometry.
%Inputs:
% 1--Ch490-GCamp Channel
% 2--Ch405-isosbestic control channel
% 3--Start time- Set a specific sample to start at
% 4--End time-specify a specific ending sample
%mdi... |
8383abf64f2f50b9bb8c4b049168170d06062df8a6d5d018291016d7c314d816 | MATLAB | 3,094 | 83 | function stims = InsertTriggerCodes(trigChannel, sampleTime, trigDeltaThresh, ET, triggerScaling)
% stims = InsertTriggerCodes(trigChannel, sampleTime, trigDeltaThresh)
% finds trigger values based on threshold for diff()
% gets the delay between on&off as the trigger value, rounds to nearest
% integer
% Inputs: trig... |
c29f4d243e15f6522065555a21df5b1af86a132a4cd73d710d4cba07375294e3 | MATLAB | 3,094 | 109 | function unitTest()
windowMin = -10;
windowMax = 30;
window = [windowMin windowMax];
binWidth = 10;
signal1 = [-10 0 20; -25 -10 20; -45 30 45];
signal2 = [-11 0 20; -22 -10 23; -45 30 43];
expectedSpikes1 = [1 0 1 0; 0 0 1 0; 0 0 0 0];
expectedSpikes2 = [1 0 1 0; 0 0 0 1; 0 0 0 0];
expectedPSTH1 = [... |
ccafdd82d6417e21b1c567063471ba9bf2f847e318c8e8e44f1298f0d3074576 | MATLAB | 3,094 | 116 | function catFAtest()
% Categorical factor analysis - check the code is syntactically correct
% We create some synthetic data with NaNs, fit the model,
% infer the latents, and impute the missing entries
clear all
setSeed(16)
% generate data
[trainData,testData,simParams] = makeSimDataMixedDataFA(100);
nClass = simPara... |
33a026c1764d851d1f2fe9d20399cf250b006344086e8dbf5d6b5674f491fbee | MATLAB | 3,095 | 78 | function [R_pos,R_neg,R_combined,pred_pos,pred_neg,pred_combined,pos_mask,neg_mask]...
=General_cpm(train_group,test_group,train_mats,train_behav,train_covars,...
test_mats,test_behav,test_covars,adjust_stage,thresh_type,thresh,id_pd)
%% CPM analysis
no_node = size(train_mats,1);
test_no_covars = si... |
b77c66b3a3b240984c205070401d2b877eb0d547e1261a1e31a66369ef3f9a9b | MATLAB | 3,095 | 86 | %% The following script is used for constructing a model to reconstruct muscle activity from MCx and afferent activities in a training data
currentFolder = pwd;
addpath(currentFolder)
cd([currentFolder, '\data']);
datafile = 'DRG_M1_PM_EMG';
load(datafile);
[Model, Info, parm, W] = testjob(xdata, ydata, xtes... |
da12d28a9b94015d08501a1fcf4d2827bdd5a26f24c36d53bd72e84a03795f68 | MATLAB | 3,096 | 99 | function [testVals,trainVals,order,RAW,AUC]=ComputeRankOrderList(Data,Condition,NumTests,varargin)
% Test to see if one condition (column) is reliably the larger then all
% other columns across random 50/5-0 NumTests in the data
% Data : Number of samples X number of conditions.
% labels Optional condition name... |
aabcb92363e114c9b3d5c00752298b5cd0aa4fa282c5870aaa73cbf866c87ef1 | MATLAB | 3,097 | 139 | %clear
%close all
%% Load current data
%load('WinRest')
%load('Tr1b')
%load('Cells')
%load('MovT')
%load('Speed')
Tr1b=Tr1; % to check
%% Detect small calcium transients
[NCell,Nz] = size(Tr1b);
% Savitzky-Golay filter
Tr1b = sgolayfilt(Tr1b',3,5)';
figure
for i = 1:NCell
plot(MovT,Tr1b(i,:)+i-1)
hold on
e... |
223fee26af779e04b29c5b6a0d6174d84b686313016f51e4c1a7ee8976c83872 | MATLAB | 3,098 | 130 | % Demo of the TrueSkill model
%PMTKauthor Carl Rasmussen and Joaquin Quinonero-Candela,
%PMTKurl http://mlg.eng.cam.ac.uk/teaching/4f13/1112
%PMTKmodified Kevin Murphy
function trueskillDemo()
setSeed(0);
% Let us assume the following partial order on players
% where higher up is better
% 1
% / \
% 2 3... |
551b2b5d5ba62b49de5f5639c5b4780dffed77ec4e072087f9d944fef75246bd | MATLAB | 3,098 | 72 | function Obj=SOFAconvertTUBerlinBRIR2SOFA(irs)
% OBJ=SOFAconvertTUBerlin2SOFA(irs) converts the HRTFs described in irs
% (see TU-Berlin HRTF format) to a SOFA object, using the MultiSpeakerBRIR
% Convention.
%
% SOFA API - demo script
% Copyright (C) 2012-2013 Acoustics Research Institute - Austrian Academy of Science... |
96a68fbd7e4013cc92e8bf17232f89893cecf21d0ffbf9a7b3d8defdfee0e074 | MATLAB | 3,098 | 79 | function [Vertices_sm, A] = tess_smooth_bs(Vertices, a, nIterations, VertConn, isKeepSize)
% TESS_SMOOTH: Smooths a surface.
%
% USAGE: [Vertices_sm, A] = tess_smooth(Vertices, a, nIterations, VertConn, isKeepSize)
% [Vertices_sm, A] = tess_smooth(Vertices, a, nIterations, VertConn)
%
% INPUT:
% - Vertic... |
769aad563314f3594a1fa68886da569932d2f24e654bd49747c2c4fc1b11e14e | MATLAB | 3,099 | 89 | function jump_distance = calculate_jump_distance(folders,option)
if strcmp(option,'global_remapped')
data_folders = 'global_remapped';
elseif strcmp(option,'place_field_shifted')
data_folders = 'place_field';
elseif strcmp(option,'spike_train_shifted')
data_folders = 'spike_train';
elseif strcmp(option,'cro... |
b1a046def4b39d721510de501f1b0a18d91e351f84671093648f856069d5dbbd | MATLAB | 3,099 | 92 | function gaussEvec
lambda1 = 9;
lambda2 = 1;
tx1 = 5;
tx2 = 4;
theta = 30 * pi / 180;
rot = [cos(theta), -sin(theta);
sin(theta), cos(theta)];
[x1, x2] = generate_ellipse(lambda1, lambda2);
v = rot * [x1; x2] + repmat([tx1;tx2], 1, size(x1,2));
% compu... |
84b90df33a38bd17eedd482bb2aa18684f8daeca03ae1c18dc3bd46818165f14 | MATLAB | 3,100 | 105 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% wrapper_full_calibration.m -- A wrapper for the calibration runtime. %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function []=wrapper_3shell(pat_ix, model, inverse_model)
pat_cell = {'110411','117122'... |
a6dc869ae8ecb73b52a924872e18a276c68b8a13377b37598f8d744d55a21daf | MATLAB | 3,100 | 92 | function [sigma,shrinkage]=covCor(x,shrink)
% x (t*n): t iid observations on n random variables
% sigma (n*n): invertible covariance matrix estimator
%
% Shrinks towards constant correlation matrix
% if shrink is specified, then this constant is used for shrinkage
% The notation follows Ledoit and Wolf (2003, 2004)
%... |
b34c527741fe4248faa9bf86db9e369d72bf767dc25123a4831ccf430cd0a51b | MATLAB | 3,104 | 88 | function [W, output] = boundOptL2overrelaxed(X, Y, lambda)
% Fit the weights for L2 penalized multinomial logistic regression via
% overrelaxed bound optimization in O((cd)^3) where c is the number of
% classes and d is the number of dimensions.
%
% Inputs:
%
% X nexamples-by-ndimensions
% Y ne... |
44a08468757de0642e78b19e2cc7f8d39f8e3dbc412cf24b0b2d93f8e7748c81 | MATLAB | 3,105 | 96 | % This script can be used to try out the experiment behaviorally (defaults to word version)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% enter subject ID & task variant %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
prompt = {'Enter subject ID:','Enter paradigm (dynamic/words/visual):'};
dlg_title = 'StateSwitch Input';
num_l... |
23de2e2ed06fda1f4f90265be23ef925ccb30b457edb602e606da6c01fb01b44 | MATLAB | 3,106 | 82 | clear all
%%
%Define default Parameters
[opt] = ParameterOptions();
%%
%Estimate diffusion attenuation as function of flip angle (1-180o)
FlipArray=[1:179/99:180];
for k=1:length(FlipArray)
S0_Flip(k)=FreedDWSSFP(opt.G,opt.tau,opt.TR,FlipArray(k),0,opt.T1,opt.T2);
S_Flip(k)=FreedDWSSFP(opt.G,opt.tau,opt.TR,Flip... |
dbb6bd9de93b28181736d67e79f4dfdd56b36c9fdcf612f5091f02b94be5e2e7 | MATLAB | 3,109 | 86 |
function [smoothed_array] = affine_diffusion_3d(original_array,t_tot,dt,aff_flag,verbose)
% affine_diffusion_3d smooth an image using the affine-invariant
% mean_curvature flow. Runtime is a bit restrictive.
%
% t_tot = total evolution time, in "seconds"
% dt = numerical evolution parameter
% aff_flag =... |
f031eb65ebfc7039a35f4bcd4d7f2d816bdd69fe0922aecf451798e9e15f534a | MATLAB | 3,110 | 52 |
ind = 1;
data = dir('/DATAPOOL/ERRORMONITORING/SEM_mri_data/P*');
%%
for i = 1:size(data,1)
data_anat = [data(i).folder, '/', data(i).name, '/mri/SEM_', data(i).name, '/nifti/anat'];
fprintf(data(i).name);
anat_image = dir(fullfile(data_anat, "*.nii"));
anat_image = [anat_image.folder, '/', anat... |
f52726ac4103b60f70b4a509561bf50894746c55da06a208adfe3292f54021ec | MATLAB | 3,110 | 86 | function [Gm,k] = calc_significant_path(sites_jk,stddev_level,avg_type,invert_y)
% returns only the gradient vectors that have stddev < stddev_level
% for each site of the lattice
%
% each row in the resulting Gm mean gradient vectors corresponds to the position
% [y,x] = ind2sub(simParam.L,(1:numel(prod(simParam.L)))'... |
2c81e6ab2dc61dadbddfcddeb6fa2cc7d1e22cc18b1797eacf96a0b928465329 | MATLAB | 3,111 | 100 | function imLabel( labels, position, S, pvPairs )
% Improved method for labeling figure axes.
%
% USAGE
% imLabel( labels, position, [S], [pvPairs] )
%
% INPUTS
% labels - cell array of strings, labels for display
% position - 'left', 'right', 'bottom', or 'top'
% S - [] rotation for to... |
b13ba6c28643be745491c30ed5de752a5bf54a8c77bf42272f766b0605a869e4 | MATLAB | 3,116 | 71 | function best_seed = CBIG_AuthorTopicEM_ComputeBestSolution(root_dir, K, ...
seeds, alpha, eta)
% best_seed = CBIG_AuthorTopicEM_ComputeBestSolution(root_dir, K, ...
% seeds, alpha, eta)
%
% Return the index of the initialization (seed) of the Expectation-
% Maximizaiton (EM) inference algorithm run that returned... |
4f51015f4ef5be868806be7adea4cbae4c8c6d896c1cb92d55308679907518d6 | MATLAB | 3,117 | 92 | function [dat] = ft_preproc_padding(dat, padtype, prepadlength, postpadlength)
% FT_PREPROC_PADDING performs padding on the data, i.e. adds or removes samples
% to or from the data matrix.
%
% Use as
% [dat] = ft_preproc_padding(dat, padtype, padlength)
% or as
% [dat] = ft_preproc_padding(dat, padtype, prepadleng... |
778410bd78b00245a1996e29a6f5923ed647a9ae0e16060f3c19e5147a8a77d2 | MATLAB | 3,120 | 82 | function [X0,H0,X1,H1] = demoGenData1(n0,n1,k,d,sep,ecc,frc)
% Generate data drawn form a mixture of Gaussians.
%
% For definitions of separation and eccentricity see:
% Sanjoy Dasgupta, "Learning Mixtures of Gaussians", FOCS, 1999.
% http://cseweb.ucsd.edu/~dasgupta/papers/mog.pdf
%
% USAGE
% [X0,H0,X1,H1] = demoGe... |
0ee5251b5c41c946d7781528dbbafe6a8f1ab94772444f038ceca577145b5a04 | MATLAB | 3,122 | 104 | function c = ttv(a,v,dims)
%TTV Sparse tensor times vector.
%
% Y = TTV(X,V,N) computes the product of a sparse tensor X with a
% (column) vector V. The integer N specifies the dimension in X
% along which V is multiplied. If size(V) = [I,1], then X must have
% size(X,N) = I. Note that ndims(Y) = ndims(X) - ... |
e9220cd4a736e7fef58f9af6de9f06523ff507903051eac11da21c69725a58c5 | MATLAB | 3,122 | 93 | function scatterHistDiff(x, y, xeb, yeb, colors)
% takes paired data x and y, optional arguments for errorbars around
% individual datapoints (see ploterr) and plots a scatterplot as well as a
% rotated histogram of the differences between the two.
% requires http://nl.mathworks.com/matlabcentral/fileexchange/22216-pl... |
eb513cd7ec5fc95d6ddc6fadc10eceae13b0b2c686d670f011c230f97744981a | MATLAB | 3,122 | 112 | % note: change current folder to figure "number"
clear all;close all;clc;
rawPath=pwd; %displays the current working directory - set it to the figure "number"
savePath=rawPath;
addpath Panels/
addpath Functions/
load("Strycnhine_Population_All")
load("colors")
%% Figure 6
dataPath = [rawPath,'/Panels']; %raw data
fi... |
4d64f1f227eda011fa34422eed51a4eddca694d3cb86f7c6f445d78b76b6192b | MATLAB | 3,123 | 106 | function status = makessense(data, field)
% MAKESSENSE determines whether an existing field in the data structure makes sense
%
% Use as
% status = makessense(data, field)
%
% See also GETDIMSIZ, GETDATFIELD
% Copyright (C) 2018, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.fieldtriptoolbox.... |
d1212bd22215d5e5f06cf578dff54a21f0ca36c4824d2436ff1fbee817006ccf | MATLAB | 3,123 | 117 | % This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 2 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,... |
6254783f9277a27a5f141395761a7898bed186e10005937c4b7eb54bc9082f0b | MATLAB | 3,124 | 80 | % calc_wiTrialCorr
% Used in Figure 4e
clear r_conv50b
for sess = 1 : 8
isSess = find(sigSL.session == sess); % All trials in this session
sig_coh_sess = sigSL.sig_coh(isSess); % signed coherences of all trials in this session
cohs = unique(sig_coh_sess);
for d = 1 : length(useD)
dim =... |
426033369bc452c91356066857012b8b8f0b602aee4ba0bda89446c498565afa | MATLAB | 3,125 | 73 | function ax = plot_time_to_food(si_exp,si_th,si_th_param,normalize_run_trials,cMap)
if (nargin < 4) || isempty(normalize_run_trials)
normalize_run_trials = true;
end
if (nargin < 5) || isempty(cMap)
cMap = @winter;
end
assert(isfield(si_th(1),si_th_param),'si_th_param must be a nume... |
419d8b00505110c40d9693dd5a4d097100235160d8b7bfbb13ca249ce2fd5d33 | MATLAB | 3,126 | 106 |
loadParameters;
params.basecallingResultsDir = '/Users/Goody/Neuro/ExSeq/exseq20170524/6_base-calling';
%% Load Ground truth information
%load(fullfile(params.basecallingResultsDir,'groundtruth_codes.mat'));
%Turn the Nx3 array into a set of strings
%gtlabels = {};
%for i = 1:size(groundtruth_codes,1)
% transcript_... |
1b25df9fb0aa6665e02805990b39e54f40eb751ee9f551cb37cce9dc7413d859 | MATLAB | 3,127 | 116 | % Author: Birgit Nierula
% nierula@cbs.mpg.de
function other_prepro_loop1(subject, condition, srmr_nr, sampling_rate)
%% eng preprocessing
%% set variables
subject_id = sprintf('sub-%03i', subject);
analysis_path = [getenv('OTHERDIR') subject_id '/other/prepro/'];
if ~exist(analysis_path,'dir'); mkdir(analysis_path... |
afaf234af86e6131429822771461f62d61c3d2356f1a72a20cef40ccdb972ec3 | MATLAB | 3,127 | 95 | function SpikeSort(datDir, varargin)
verbose = true;
saveDir = datDir;
preprocessRange = [0 600];
timeRange = [0 Inf];
lfp = true;
lowpass = -1;
for i = 1:2:length(varargin)
switch lower(varargin{i})
case 'verbose'
verbose = varargin{i+1};
case 'savedir'
saveDir = varargin... |
623d7acec5f7aabe5a32f093b119aac14da96891f76e08cbcfeef96f75e65418 | MATLAB | 3,128 | 79 | function vers = eegplugin_detect_spindles(fig, trystrs, catchstrs)
% eegplugin_detect_spindles() - EEGLAB plugin for sleep spindle detection
%
% Usage:
% >> eegplugin_detect_spindles(fig, trystrs, catchstrs)
%
% Inputs:
% fig - [integer] EEGLAB figure.
% trystrs - [struct] "try" strings for menu callba... |
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