sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a70fe9a279ff49338c51805d6e839abcfc47b9b651b78b86a35fce37658048fe | MATLAB | 1,903 | 53 | % Compute <sigma^2> analytically and from numerical simulations
% for continuous-time Ornstein-Uhelnback process through a small-world
% transition.
%
% This recreates the plots in Figure 1 of our paper.
%
%% Linear Sync Toolkit (linsync)
% Copyright (C) 2023 Joseph T. Lizier
% Distributed under GNU General Public Lice... |
67e17da588f1fb0f5814bed4b2e901b578138975b6b4c41e1ff865308fe6d3c3 | MATLAB | 1,906 | 66 | function out_reduced_interpolation_matrix = zef_kron_reduction( ...
in_interpolation_matrix, ...
in_schur_complement, ...
in_electrode_model, ...
in_source_model ...
)
% Documentation
%
% Performs a Kron reduction on a given interpolation matrix G. In
% practical terms, this might mean simplifying ... |
2c1dbf5e70cdfaf2967714784a21745916987976af02cb6317f6cf5428df3d57 | MATLAB | 1,913 | 65 | function [xy, q, qdt, q2dt, tau] = robot_data_generator(time_infor, noise_level, dt, properties)
% generate time series for training and validation
[m1, m2, l1, l2, lc1, lc2, I1, I2] = matsplit(properties);
section_len = time_infor.section_len;
time_length = time_infor.time_length;
% generate noise as the ... |
b5efa680c439d933ee966b91c317b7127bd2c818eed36ea3efdbc853a0c4e6dc | MATLAB | 1,915 | 46 |
%% make all reconstructions for data
%set all parameters!
% beamformer app needs to be open
%choose node to add to
%enjoy!
allHashes=fieldnames(zef.dataBank.tree);
allHashes=allHashes(startsWith(allHashes, 'node_1'));
for i=1:length(allHashes)
if strcmp(zef.dataBank.tree.(allHashes{i}).type, 'data')
ze... |
a9296acf5a2ce4e441bbaa19c3638c2b3050bbc33e3ca0ba5ad62a8889651217 | MATLAB | 1,918 | 45 | % Get location of the toolbox and specify data directory
toolbox_path = microstate.functions.toolbox_path ; % location where the toolbox is installed
datadir = fullfile(fileparts(toolbox_path),'tutorials','tutorial2_RestingSource_Group') ; % path to folder containing the data
addpath(datadir) % add this to the path
% ... |
25c2ce142ac309365634d33334f385701c6e5414682b47b058744491561c0480 | MATLAB | 1,921 | 27 | zef_data = zef_system_settings;
zef.fieldnames = fieldnames(zef_data);
for zef_i = 1:length(zef.fieldnames)
zef.(zef.fieldnames{zef_i}) = zef_data.(zef.fieldnames{zef_i});
end
set(zef.h_system_settings_table,'CellSelectionCallback',@zef_system_settings_table_selection);
zef.ini_cell = readcell([zef.program_path '... |
ef0cc0e8844873f11c14f5114c5f154867aca0208d948408c9ac9f0ed566753e | MATLAB | 1,921 | 47 | function [time_serie,time_var] = zef_generate_time_sequence(zef)
h = zef_waitbar(0,1,['Generate time sequence.']);
if nargin == 0
zef = evalin('base','zef');
end
%Sample rate as the number of samples per second
sampling_freq = eval( 'max(cell2mat(zef.inv_synth_sampling_frequency))');
%The time when peak of pulse oc... |
21752d0ba1cd54a6bf51c4d07618a543b575420aa2e15a03cb158f2ee5b13fd8 | MATLAB | 1,923 | 51 | %Copyright © 2021- Sampsa Pursiainen & GPU-ToRRe-3D Development Team
%See: https://github.com/sampsapursiainen/GPU-Torre-3D
load([torre_dir '/system_data/signal_configuration.mat']);
load([torre_dir '/system_data/mesh_1.mat']);
load([torre_dir '/signal_data/simulated_data_1.mat'])
system_setting_index = 1;
paramete... |
a4abb6383f1bd09d089122c306b3aacf258df5a20f564b86c448f49698f2ade7 | MATLAB | 1,923 | 69 | %Copyright © 2018- Sampsa Pursiainen & ZI Development Team
%See: https://github.com/sampsapursiainen/zeffiro_interface
function [z] = zef_evaluate_topography(zef)
h = zef_waitbar(0,1,['Topography.']);
sampling_freq = eval('zef.top_sampling_frequency');
high_pass = eval('zef.top_low_cut_frequency');
low_pass = eval('ze... |
b3d202a5fcc1f4781f23c183d236f06fcd7e8ccad82d4b5ddf9b15c53433e35b | MATLAB | 1,923 | 67 | function obj = stats_mutualinformation(obj,miseq,atpeaks)
% Calculate mutual information between microstate sequence and another sequence (2nd input)
if nargin < 3
atpeaks = false ;
end
% Check inputs
if isempty(obj.label)
error('To calculate microstate mutual information, prop... |
f038675805649affffd10ada9dbb3988aa58f1fbb82762fbab3492ca4f9455f3 | MATLAB | 1,924 | 44 | function [nse_mat] = zef_nse_matrices(nodes,tetra,rho,mu)
nse_mat.M = zef_volume_scalar_matrix_FF(nodes,tetra,rho);
nse_mat.L_11 = zef_volume_scalar_matrix_GG(nodes,tetra,1,1,mu);
nse_mat.L_22 = zef_volume_scalar_matrix_GG(nodes,tetra,2,2,mu);
nse_mat.L_33 = zef_volume_scalar_matrix_GG(nodes,tetra,3,3,mu);
nse_mat.L_... |
66728e121259fde137096b04e64caa20a9e0beb35cc0734ee3d4e6d69b7994e9 | MATLAB | 1,927 | 46 | function zef = zef_update_compartment_table_data(zef)
if nargin == 0
zef = evalin('base','zef');
end
zef.compartment_tags = fliplr(zef.compartment_tags);
for zef_i = 1 : length(zef.compartment_tags)
zef_j = zef_i;
zef_init_fields_compartment_table
zef_n = 0;
for zef_k = 1 : size(zef.paramete... |
9dc28540604156093671a363956a8138ce3e294d2af003ef8e1322dbfde33dcc | MATLAB | 1,927 | 76 | function [p,Dxy] = tanova(X,Y,modality,Nperm)
if nargin < 4
Nperm = 5000 ;
end
switch modality
case 'eeg'
X = X-mean(X) ; % re-reference to average
Y = Y-mean(Y) ;
distfun = @(X,Y) 1-abs( (X./vecnorm(X))' * (Y./vecnorm(Y)) )' ;
centfun = @(v1) v1 ;
case 'meg'
% ... |
c6f7209aa0516ad1787865e9f939ee12eab947ff238a6e82aa75273e2303a2fd | MATLAB | 1,927 | 51 | function plot_badchannels(params,EEG)
etc = {EEG.etc};
if range([EEG.nbchan]) ~= 0 % Check that all recordings have the same number of channels
error('Different number of total channels in at least one recording')
end
nChans = length(EEG(1).urchanlocs);
nRec = length(EEG);
recmask = cellfun(@(x) isfield(x, 'clean... |
d1f2bc3336b057924191796a178534ae4878353b04bb04d82c3f1b47453256f8 | MATLAB | 1,932 | 59 | function [cluster_centres, dipole_moments, index_vec, GMModel, MahalanobisD] = zef_cluster_reconstruction(zef)
n_clusters = zef.GMModel.max_n_clusters;
if iscell(zef.reconstruction)
rec_vec = zef.reconstruction{zef.GMModel.frame_number};
else
rec_vec = zef.reconstruction;
end
pos_data = zef.source_positions;
c... |
64773404491681481cbab2e6077e6ca9370dfd33b141dcff77feadec22f99e0e | MATLAB | 1,933 | 66 | function V = zef_tetra_volume(nodes, tetrahedra, take_absolute_value)
% VOLUME - Calculates a volume V from a given set of finite element nodes, the
% corresponding tetrahedra and suitable index matrix.
arguments
nodes (:,3) double {mustBeNonNan}
tetrahedra (:,4) double {mustBeInteger, mustBePositive}
take... |
0a0b5bb342983937872393af3c3e2f5f4ed140642a2c9372f422205684ea7337 | MATLAB | 1,936 | 52 | function compartment_settings = zef_bst_compartment_settings(zef_bst,surface_meshes)
n_compartments = length(surface_meshes);
compartment_settings = [repmat({'Compartment name'}, n_compartments,1), {surface_meshes.Name}',...
repmat({'Compartment type'}, n_compartments,1),{surface_meshes.Type}', ...
repmat({'R... |
2b6b75e6d84a320f1cc5ae7fb4705ae5ae60a82a914a5464c0f9e991c17f73d5 | MATLAB | 1,938 | 93 | function [x,fval,exitflag] = zef_mosek_linprog(f,A,b,Aeq,beq,lb,ub,options)
exitflag = -1;
fval = [];
x = [];
if nargin < 4
Aeq = [];
end
if nargin < 5
beq = [];
end
if nargin > 5
if not(isempty(lb))
model.lb = lb;
else
model.lb = -Inf*ones(size(A,2),1);
end
end
if nargin > 6
if not(isem... |
c29a8426bf22652a105fa31804453464418e62da011fc43a72b4bd79eed48c17 | MATLAB | 1,941 | 70 |
%**************************************************************************
%
% demo.m
%
% This demo file will load some example EEG data containing a period of
% EMG and a blink artefact. It will then use Fully automated and Online
% artifact Removal for brain-Computer interfacing (FORCe) to remove these
% ... |
169fbbadc69fe5e4d5f5a9db90ec2ba90233563f1a4a2eea75540703995020ff | MATLAB | 1,948 | 48 | function significant = spectsignificance(tests,alpha)
%
% From a estiamtion of p-values through the specttest function,
% It returns 0 or 1 for each power of coherence value, if the p-value is
% below the level of significance 'alpha'.
%
% INPUTS:
%
% tests The output of specttest, i.e. a cell
% ... |
4de3360bb5679eb8f1126734ef8a34bd44e89fd5e395937ea26c03f8b248e017 | MATLAB | 1,953 | 59 | function tensor_array = zef_dti_tensor_interpolate(reference_points, dti_tensor, scale_value, roi_radius, mode)
[nx, ny, nz, ~] = size(dti_tensor);
N = size(reference_points, 1);
tensor_array = zeros(N, 6);
for i = 1:N
c = reference_points(i, :);
ix = round(c(1));
iy = round(c(... |
7a2197c7b45aea73503988267110dc2f521f9b3922e0400a025777f00477254d | MATLAB | 1,954 | 75 | function self = initialize(self,L,f_data)
%
% initialization function
%
% Initialize recursively updated variables before the computation of
% the first time step.
%
% Inputs:
%
% - self
%
% An instance of IASInverter with the method-specific parameters.
%
% Outputs... |
e4b8a2bbc0553ae21fb6d05a1e7bc508eee69c06a01eb63ebba64acbc329ed5e | MATLAB | 1,954 | 50 | % tuto1_signal_filter_Process.m
% -------------------------------
% Author : Laurent Bonnet (INRIA)
% Date : 25 May 2012
%
% The function tuto1_signal_filter_Process is called at the clock frequency.
%
function box_out = tuto1_signal_filter_Process(box_in)
% we iterate over the pending chunks on input 2 (STIMU... |
093df49d871a34c8cc1b01708f62fe6ffecd38113573d2f0ec50850b983273c1 | MATLAB | 1,958 | 57 | function [B,E,C] = pcamar_decomp (X,T,options)
is_cell_strings = iscell(X) && ischar(X{1});
is_cell_matrices = iscell(X) && ~ischar(X{1});
is_struct = ~is_cell_strings && ~is_cell_matrices && isstruct(X);
orders = formorders(options.order,options.orderoffset,options.timelag,options.exptimelag);
if is_cell_strings |... |
9205701b5711a34d8ded4c19dc827130e0e27e5839317e0d79311192787207b7 | MATLAB | 1,958 | 72 | hmm = cell(2,1); Gamma = cell(2,1);
options = cell(2,1); options_s = cell(2,1);
X = randn(2000,2); T = [250 250 250 250 250 250 250 250];
N = length(T);
options{1} = struct();
options{1}.K = 2;
options{1}.order = 3;
options{1}.filter = [1 Inf];
options{1}.Fs = 100;
options{1}.inittype = 'random';
options{1}.cyc =1;... |
5c1f014e460c321b606dddd9d4434137b50294931cbf61a23fb13ceba9ddc8fd | MATLAB | 1,963 | 57 | function [V, V_and, V_or] = zef_GMM_resection_volume(res_zef, FB,D, GMM,GMM_ind)
%the GMM-dipole is inside the resection
%
%the GMM-dipole is outside the resection
%%
% d=nan(size(points,1),1);
% inside_index=zef_tetra_in_compartment(mesh_points, mesh_edges, points);
% not_inside=setdiff(1:size(points, 1), inside_ind... |
97002cc8b8f64f2eb0397be0623b12dee9d6bcef55ae509dc84a01c5fbdb2c6a | MATLAB | 1,966 | 69 | function x = LG_optimization(A,sigma,y,gamma,x,maxiter,estimation_type)
%MAP estimate using EM algorithm
%L1 norm minimization problem (LASSO):
%0.5|1/sigma(Lx-y)\|_2^2+0.5*\sum_i [gamma_i |x_i|]
% L : lead field matrix
% sigma: noise variance
% y: measurements
%for gamma->0 we have less sparse solutions...
%Output:... |
262230292015a2552596c39eec0bb95a77a5d814dc8ec174f39ef22c44bb01a5 | MATLAB | 1,970 | 66 | function obj = preprocess_filter(obj,flow,fhigh,varargin)
% Bandpass or bandstop filter data
% check inputs
options = microstate.functions.make_options(varargin) ;
% default options
defaults = {'type','pass' ; % pass or stop
'order',2 ; % 4th order
'demean... |
faba35dd306596640e8d3a6c8f1d4e1f039018af07c69e0b3f572b4b4d2329aa | MATLAB | 1,970 | 37 | function plot_node_performance_distribution(app)
cla(app.node_performance_distribution);
app.node_performance_distribution.NextPlot='replacechildren';
num_udf = app.StimulusEditField.Value;
node_performance = cell2mat(app.node_performance);
num_original_nodes = size(app.params.data,2);
node_performance = node_performa... |
54930525f80893add85d233f756c0308b2d77f29205dbfd22b0a46d5090183b1 | MATLAB | 1,972 | 47 | function [gamma, Xi] = hidden_state_inference_mx(B, Pi_0, P, order)
%HIDDEN_STATE_INFERENCE using foward-backward propagation
% [GAMMA,XI,SCALE] = HIDDEN_STATE_INFERENCE_MX(B, PI_0, P, order)
% returns the updated object SELF, together with probabilities
% of each state given all previous and future data points, ... |
6edf4705b3302a1ab4aa3c7e9fb9813f26e6c3ecada40d60c3930a8940d24d51 | MATLAB | 1,975 | 75 | % 2020_02s_05 motorlearningtask
%
%
%
%
clear;
clc;
isScreenMirrored = true;
pause(0.1);
Screen('Preference', 'SkipSyncTests', 1 );
% Open up a window on the screen and clear it.
whichScreen = max(Screen('Screens',0));
Screen('Resolution', whichScreen, 800,600);
PsychImaging('PrepareConfiguration');
PsychImaging('Ad... |
ea658b5064dee308c75a89d4992ca8dc2a5fdd88a210af7c74992e98de12faec | MATLAB | 1,975 | 29 | %Copyright © 2018- Sampsa Pursiainen & ZI Development Team
%See: https://github.com/sampsapursiainen/zeffiro_interface
zef.relax_iteration_type = get(zef.h_relax_iteration_type,'value');
zef.relax_preconditioner_type = get(zef.h_relax_preconditioner_type,'value');
zef.relax_db = str2num(get(zef.h_relax_db ,'value'));
... |
df39683e4ea5291eb7f61a501e4eeac7270386c4f7fe78ca55a6cb1f7c65008c | MATLAB | 1,977 | 55 | function [result,g] = Grad_of_my_GWNBR_likeli(x,u,N,w,G,y,i)
% Anotation:
% u y N G w i are the global coefficients,x is the parameters to be solved.
% w is weighted matrix,u is scRNA features,y is spatial matrix,
% N is the spots num,G is the genes num, i is target index,
% n is the nums of neighbors of the dest... |
4770a5cc457760b63a992937311fbe593826c6221def1a97ed9047ba1ce51141 | MATLAB | 1,979 | 58 | function [MEA120] = MEA120_lookuptable()
%UNTITLED Summary of this function goes here
% Detailed explanation goes here
clear all
clc
MEA120 = string(ones(120,2));
letter = 'A':'M';
letter(letter=='I')=[];
count = 1;
for i = 1:length(letter)
if letter(i) == 'A' | letter(i) == ... |
72ce289c97cde298a9ff61f372214438d76737e21fdfd225cb056c448b90ff7c | MATLAB | 1,979 | 51 | function cleaned = replaceOutliers( A, outliers )
% out is a logical array identifying outliers to limit search
tic
sx = size( A, 1 );
sy = size( A, 2 );
% sz = size( A, 3 );
cleaned = A; %zeros( sx, sy, sz, 'uint16');
meanround = zeros(1,size(A,3));
for i = 1 : sx
for j = 1 : ... |
c709df354be56ad34a6f730ba1df7960ca2b77572bcd1c107b3f1c37ba7044f1 | MATLAB | 1,979 | 67 | function [post_info, spike_info] = evaluate_posterior( ...
data, Model, parm)
% evaluate posterior
%
% [post_info, spike_info] = evaluate_posterior(data, Model, parm)
%
% --- Estimated spike info for overlap window
% post_info.Pspike(ns,n) = P(ns | yk) of n-th overlap window
% post_info.twin{n} = time index... |
8b69815658fdf7ac556fa1d3303af7c491153b03a039312c3173378665471a40 | MATLAB | 1,986 | 88 | function [reconstruction, sample_rate, time_step, column_title_vec] = reconstruction_from_edf_fn(path_to_file)
%
% reconstruction_from_edf_fn
%
% Reads an EDF or European Data Format file into a reconstruction
% compatible with Zeffiro Interface. Supposes that the sampling rate of
% each measurement has been constant,... |
c42bbf66eb455f96a34c4f74e90076088a13aa9c706eb49b103aab92da4c2d1b | MATLAB | 1,990 | 51 | function ROIstats = getROItraces2( imStack, h, fracPenumbra )
% replaces roiStats3D !
% h contains handles for ROI polygons, more crucially the masks!!!
% imstack is the raw data stack
% fracPenumbra is the fraction of the penumbral signal to subtract, 0.7
imStack = single( imStack );
nROIs = size( h, 2 ); % nu... |
36362af5cf2fc3ced60053336a91248e226c2bc8239add0f7de69f1a2fd1e180 | MATLAB | 1,992 | 59 | function [ax1,ax2] = plotEvents(t, ROIdata, analytes, iFile, iROI, pre, post)
% t is a tiledlayout
% ROIdata contains dF/F0 traces
% analytes contains detected event info
% ifile, iroi for choosing trace to display
% pre and post are the baseline and duration of the event display
dx=mean(diff(ROIdata(iFile).xdata... |
c8b28b6fad6dd76ce4285c1dfacd65791d4284d75799de30febb600688e55bf0 | MATLAB | 1,995 | 90 | % validate
if save_rend == 0
start_info.q=0;
start_info.qdt=0;
start_info.q2dt=0;
start_info.tau=0;
r_end = zeros(size(res_infor.res_net, 1), 1);
end
if exist('traj_frequency','var') == 0
if strcmp(traj_type, 'lorenz') == 1
traj_frequency = 100;
elseif strcmp(traj_type, 'cirlce') =... |
8158b4da0d02aa5aa654be273c697d1bc6b0256153f79cf981d707af83562aff | MATLAB | 2,000 | 56 | function perfusion_estimate = zef_nse_calculate_perfusion(nse_field,nodes,tetra,domain_labels,mvd_length)
mm_conversion = 0.001;
ml_min_conversion = 1e-6/60;
hgmm_conversion = 101325/760;
mvd_length = 1E6.*mvd_length(:,1);
c_ind_1_domain = find(ismember(domain_labels,nse_field.artery_domain_ind));
[v_1_nodes, v_1_te... |
9c9043b12e5e1bf09b154cea17c3ecd02c7fe71fa153e9ef8ec2f9ee39985d79 | MATLAB | 2,001 | 65 | % double check class
function check_and_change(varargin)
% convert to double the N x T matrices created in Read_fMRI and copied
% locally
p = inputParser;
pathExists = @(x) exist(x,'dir');
addRequired(p,'local_functional_HCP_folder', pathExists);
parse(p,varargin{:});
check_resting(p.... |
810fb76b1780eeb47325ad19317e8196b278aa40b23dc9531098fabc4ae513fd | MATLAB | 2,006 | 56 | function [X,opix] = ola(Y,H)
% X = ola(Y,H)
% Overlap-add columns of Y, offset by H.
% There's no windowing (i.e. scaling by a tapered window) in here.
% 2010-11-14 Dan Ellis dpwe@ee.columbia.edu
[W,nw] = size(Y);
% How long should X be in the end?
% one complete window, plus another hop's worth of points for ea... |
f9bb4f61189ac041a5e49c554d6c50f66cb463841cb00d6b461b559b6f6c1b1d | MATLAB | 2,006 | 53 | function zef = zef_update_labeling_priority(zef,update_type,labeling_priority_vec)
use_settings = 0;
if nargin < 2
update_type = [];
end
if nargin < 3
labeling_priority_vec = [];
use_settings = 1;
end
n_compartments = length(zef.reuna_mesh_ind);
if use_settings
item_selected = zef.h_labeling_prior... |
52854c72817e00895fc09b3365d2da69b051629bfd67da22a1e7fe572db42f24 | MATLAB | 2,010 | 75 | function varargout = fftfrequencies(data,fs,flag)
%FFTFREQUENCIES Frequencies corresponding to the output of Matlab fft function
%---
% function ff = fftfrequencies(data,fs[,'centered'])
% function [ff1 ff2] = fftfrequencies(data,fs[,'centered[+shift]'])
%---
% utility to get the vector of frequencies for which fft is ... |
d13d08be3e653daad3af4b735ab099660ec02ee4f34da13b72f9da056777ee8d | MATLAB | 2,010 | 56 | function [Lsamps,Rsamps,dur_real,IACt] = GetOSCORNoiseBand(params)
global ChanConfigInfo %This global variable is set by the user at startup, and determines which channel is considered "contra-lateral".... taking into account the recording side, and the connections between channels and ears (i.e., this should be rob... |
0ff395580f9ddee52da889dde7d8daba0dfda88822cfc18bc940b1fc493ff451 | MATLAB | 2,014 | 49 | function hmm = hmmperturb(hmm,epsilon)
if nargin<2, epsilon = 1; end
S = hmm.train.S==1; regressed = sum(S,1)>0;
p = hmm.train.lowrank;
K = length(hmm.state);
for k = 1:K
setstateoptions
if p > 0 % pcaHMM
for n = 1:size(hmm.state(k).W.S_W,2)
Cov = permute(hmm.state(k).W.S_W(n,:,:),[2 3 1])... |
aac96789d4e4d3abc9bfd21ea1fd1796cb8682b2a08c17c6982879fe0d137117 | MATLAB | 2,023 | 55 | function [processed_data] = zef_constant_epoching(f, first_epoch_point, epoch_step, number_of_epochs, start_time, end_time, sampling_frequency)
%Copyright © 2018- Sampsa Pursiainen & ZI Development Team
%See: https://github.com/sampsapursiainen/zeffiro_interface
%This function processes the N-by-M data array f for N ch... |
efa548859e2bcb2236c027fc934ccc902ce3a604547a7a99b443c3ac5cd2fa76 | MATLAB | 2,029 | 74 | function [Path,Xi] = hmmsdecode(Xin,T,hmm,type)
% Computes the state time courses or viterbi paths
%
% INPUTS
% Xin: cell with strings referring to the subject files
% T: cell of vectors, where each element has the length of each trial per
% hmm: the stochastic HMM structure
% type: 0, state time courses; 1, viterbi pa... |
2797a69cb466a002c88252ddfa7ea22a625c645518d2397e6e9cdbe39858b515 | MATLAB | 2,039 | 57 |
zef.aux_data_1 = cell(0);
zef_i = evalin('base','zef.current_sensor_name');
zef_init_sensors_parameter_profile;
if ismember(evalin('base',['zef.' zef.current_sensors '_imaging_method_name']),evalin('base',['zef.imaging_method_cell{1}']))
zef.aux_data_2 = {
{'X-coordinate','points','1'}
{'Y-coord... |
deaf1b6b2137b463f6433caadf0601216171b33706fad11dbab9a53c75f81ad0 | MATLAB | 2,040 | 91 | % [] = XXXX()
%
% -----------------------------Definition---------------------------------%
%
% description
%
% usage :
%
% -----------------------------Input--------------------------------------%
%
% XX : description + dimension
%
% -----------------------------Output-------------------------------------%
%
% XX : d... |
e6233484f22cae9187e1d7c2f2f33d7152f7d3f30b12726435df263713e52ac6 | MATLAB | 2,041 | 58 | function handleLines = plotGrid3D(handle)
%plotTube3D Plots 3D tube with specified color-range and width
%Syntax: handle = plotTube3D(vecX,vecY,vecZ,vecR,vecC,intPointsPerRadius)
%plot 3D tube
%{
vecX = [-1 -0.8 -0.3 -0.1 0.05 0.2 0.4 0.7 0.9 1.0];
vecY = [-0.9 -0.85 -0.2 -0.15 0.1 0.1 0.4 0.7 0.9 1.0];
vecZ =... |
dd09c1e287ec26fdb8f49abe3821919cdbeedcea1ec75f88acf78c517ca1f51a | MATLAB | 2,042 | 54 |
%--------------------------------------------------------------------------------
% phi_estimate.m
%
% Copyright (C) 2024 Takuya Isomura
% (RIKEN Center for Brain Science)
%
% 2024-10-28
%--------------------------------------------------------------------------------
function phi1 = phi_estimate(phi0,o,x,y,d,pos,isL... |
a87bcc60ce4349760a57e5878b7022f63f836b5ae6bfd2ce5613875d6cc0aec4 | MATLAB | 2,043 | 54 | zef = zef_start_dataBank(zef);
load data/exported/mesh.mat;
mesh.labels = max(mesh.labels) + 1 - mesh.labels;
[tetra,domain_labels] = zef_hexa_to_tetra(mesh.elements,mesh.labels);
nodes = mesh.nodes;
brain_ind = find(domain_labels == 2);
save data/converted/tetra_mesh.mat tetra domain_labels nodes brain_ind
clear mesh ... |
de8d6bf98aed712da165033faef58b3a8f5e54739977bc8c0a606b4367887dc8 | MATLAB | 2,046 | 59 | %% initialize
clear; clc;
currentPath = pwd;
cd ../temp
load('tempFileForAnalysis.mat');
cd(pathBIDS);
cd derivatives
pathDeriv = pwd;
cd(pathDeriv);
cd('spm-preproc'); % For saving preprocessed file from SPM
pathPrepFile = pwd;
prepSubjFolders = dir('sub*');
cd(pathDeriv);
cd('behavior');
pathBehav = pwd;
load('be... |
312f77c2e30d60cf260d7de1eca1c3ef8ef3528951620d733a44cc420c9ccebd | MATLAB | 2,048 | 57 | %Copyright © 2021- Sampsa Pursiainen & GPU-ToRRe-3D Development Team
%See: https://github.com/sampsapursiainen/GPU-Torre-3D
function [data_val_inphase,data_val_quad,amp_val] = qam_demod(wave_val,carrier_cycles_per_pulse_cycle,pulse_length,t_data,varargin)
equalize_components = 1;
if carrier_cycles_per_pulse_cycle >... |
483d23e840146490796e88d3f951a76719b567977c7e4d6c60beff524ad6da7d | MATLAB | 2,048 | 75 | % [daqData,headCt] = Fetch( myObj, streamID, start_scan, scan_ct, channel_subset, downsample_ratio )
%
% Get MxN matrix of stream data.
% M = scan_ct = max samples to fetch.
% N = channel count...
% If channel_subset is not specified, N = current
% SpikeGLX save-channel subset.
% Fetchin... |
4b6524d36109a315cea955075116eded31586715d07a14796c44d8c2b8a15c94 | MATLAB | 2,049 | 59 | function zef = zef_update_transform(zef)
if nargin == 0
zef = evalin('base','zef');
end
zef.aux_field_1 = zef.h_transform_table.Data;
zef.aux_field_2 = [];
zef.aux_field_3 = [];
zef.aux_field_4 = {'transform_name','scaling','x_correction','y_correction','z_correction','xy_rotation','yz_rotation','zx_rotation','af... |
ce0b51b81ad012f10845f5a0b3ce515bc4533ffbd2adf96af049eadf2a166778 | MATLAB | 2,049 | 60 | function approxgrad = getApproxGradient(problem, x, storedb, key)
% Computes an approximation of the gradient of the cost function at x.
%
% function approxgrad = getApproxGradient(problem, x)
% function approxgrad = getApproxGradient(problem, x, storedb)
% function approxgrad = getApproxGradient(problem, x, storedb, k... |
1e69fc9c5c89fa73f7f63ffc4a83e6d0612101f855081b177628f4f5a8d5d67a | MATLAB | 2,053 | 66 | function zef_toggle_figure_controls
toggle_mode = 'unlocked';
current_figure = gcf;
tgb = findobj(get(gcf,'Children'),'Tag','togglecontrolsbutton');
toggle_status = tgb.UserData;
if isempty(toggle_status)
toggle_status = 1;
tgb.UserData = 2;
elseif isequal(toggle_status,1)
tgb.UserData = 2;
elseif isequal(... |
e875b286cc8ec22661af97f6789cfcb63611d544118202910aa352c64113268a | MATLAB | 2,055 | 83 | function D = computeDistMatrix_AVFC (data,T,options)
%
% It uses KL-divergence to compute a subject-by-subject distance matrix
% of average FC matrices (on fMRI).
%
% INPUT
% data observations; in this case it has to be a cell, each with
% the data for one subject
% T length of serie... |
456b4c92a02a9a2a3795886c777b378f4eb9a74ed05d88937e13c3757796d2cf | MATLAB | 2,058 | 54 | function [covmat,corrmat,icovmat] = getFuncConn(hmm,k,verbose)
% Get the covariance, correlation and precision matrices for state k,
% from the estimated model hmm
% If order==0 (Gaussian distribution), these purely represents functional
% connectivity
% If order>0 (MAR), these refer to the covariance matrix of th... |
ef977ee09fec1ba609310664820ecd2cfdf771123a9435683fab82bda116865d | MATLAB | 2,059 | 59 | function [matPET,vecWindowC] = doPET(vecTime,vecVals,vecEvents,vecWindow)
%doPET Calculates peri-event time-series for variable-rate data, averaging withins bins and interpolating over empty bins
%syntax: [matPET,vecWindowC] = doPET(vecTime,vecVals,vecEvents,vecWindow)
% input:
% - vecTime; timestamps of data in ve... |
275662fc2cd7bcd4eac26504964a13376350192d1a290a9ea4da192d26d86a4b | MATLAB | 2,060 | 32 | function plot_model_decoding_evaluation(app)
v = app.StimulusEM.Value;
app.model_decoding_evaluation.NextPlot = 'replacechildren';
app.model_decoding_evaluation.Color=[0.94 0.94 0.94];
performance = app.model_performance.training_performance;
if app.ROC_Decoding.Value == 1
plot(app.model_decoding_evaluation,perf... |
3057da1253f1be2cddf95f965913c67176d36537d05f18dc5e145c1ac77a49b1 | MATLAB | 2,060 | 62 | function ord = plot_Gamma (Gamma,T,continuous,order_trials,behaviour,cm)
if nargin < 3, continuous = false; end % show it as continuoys data?
if nargin < 4, order_trials = false; end % order the trials?
if nargin < 5, behaviour = []; end % behaviour with respect to which order the trials
if nargin < 6, cm = colormap... |
f1d9572040699cd696f80e580765eeaaa139f51a172a9c3e8d7457f320ab8d0c | MATLAB | 2,060 | 55 | function [ classifier, model, parm, feature, label ] = ha_time_train( data, parm, model )
% Descriptions for the ha_time_train
% + Input
% - data: training data
% - data.t [1 x Nt]: sampling time vector
% - data.y [1 x Nt]: observed Ca response
% - data.spike_time [1 x Ns]: ground-truth spik... |
2208606060f039bedc58fd1d07ec84d8f09fdd8b47dfe4e3ff63b747df0af806 | MATLAB | 2,061 | 34 | function [optd]= sw_delta_settings
% funtion to call to set delta detection settings
% add check for calling local script?
optd.detect = 'delta'; % detect slow wave/delta events
optd.verbose = 0; % verbose on|off [0|1]; eg: opt.verbose=0;
optd.again = 0; % do data filtering, r... |
5530e2a554b95357b0e8f1a24d77d2ee86add6a23373376ffb8cfbcf34d76c8d | MATLAB | 2,063 | 71 | function treeStruct = zef_cbtreenode_delete_node(nodeObj)
%ZEF_CBTREENODE_DELETE_NODE Delete a UITree node and all its children.
%
% treeStruct = zef_cbtreenode_delete_node(nodeObj)
%
% nodeObj must be a matlab.ui.container.TreeNode (a selected node).
% The function deletes the node (which deletes the entire subtree)... |
db4aaf5b0ae8a1f0913f1650b06299017e91ac4548f6fef3e108b15061fdc1e6 | MATLAB | 2,066 | 63 | %APPEND_PDFS Appends/concatenates multiple PDF files
%
% Example:
% append_pdfs(output, input1, input2, ...)
% append_pdfs(output, input_list{:})
% append_pdfs test.pdf temp1.pdf temp2.pdf
%
% This function appends multiple PDF files to an existing PDF file, or
% concatenates them into a PDF file if the output fi... |
8cecbbbe41fdcc987c6ce16dbcbbed8a9a93f2fcb959bfe3dc583a88d93f3195 | MATLAB | 2,067 | 54 |
%--------------------------------------------------------------------------------
% mdp_learning.m
%
% This demo is included in
% Canonical neural networks perform active inference
% Takuya Isomura, Hideaki Shimazaki, Karl J. Friston
%
% The MATLAB scripts are available at
% https://github.com/takuyaisomura/reverse_en... |
60eb1cc52884ec1f4c00ff43243f52f16371d7b205bd8c665e47162b5df49f30 | MATLAB | 2,069 | 66 | function [gMatVid,imReflection] = ET_ImPrep(gMatVid,gMatFilt,sglReflT,objSE,boolInvertImage)
%ET_ImPrep Summary of this function goes here
% [gMatVid,imReflection] = ET_ImPrep(gMatVid,gMatFilt,sglReflT,objSE,boolInvertImage)
%% prep
%global
global sETC;
%move to GPU and rescale
if ~ismatrix(gMatVid)
gMat... |
a9d0e655afaf0608c6fc32a26079dbff98386b63abda46344c46e0e83506b754 | MATLAB | 2,069 | 61 | function [] = compute_voxel_based_functional_connectivity(subject,part)
% This script is meant to be the continuation of the
% extract_voxel_based_BOLD_signal.m script, and its purpose is
% to take the BOLD output .mat file and output the subject's voxel-based
% functional connectivity matrix as the Pearson's correl... |
aa6c800558246cc934476140374208ecacf9b241097d0c551b2b4ce48f67d370 | MATLAB | 2,076 | 90 | function [A, I, J] = zef_adjacency_matrix(nodes, tetra)
% Constructs a sparse square adjacency matrix or stensil A for a given set
% of nodes and tetrahedra constructed from them. Also returns the nonzero
% indices I and J of A.
arguments
nodes (:,3) double {mustBeNonNan}
tetra (:,4) double {mustBeInteger, mu... |
e26176f98a19895c4600f6e664f8168953cb29a2a913f4a2cebd064765b21d5a | MATLAB | 2,080 | 69 | %% Parses varargin into the relevant parameters
% either based on a structure, filename, or
% each parameter supplied individually.
%
%% Linear Sync Toolkit (linsync)
% Copyright (C) 2023 Joseph T. Lizier
% Distributed under GNU General Public License v3
if (length(varargin) == 1)
% User has provided a parameters ... |
d24b94f96bb9af1d8dfc37794bd9903fdacd9d1bcd795c5c19d7bde47918f63e | MATLAB | 2,081 | 68 | function state = state_snew(rate,shape,gram,m,covtype,Sind)
% does basically the same than updateW and updateOmega, but approximately,
% and using the accumulated sufficient statistics
state = struct();
state.W = struct();
ndim = size(m,2); npred = size(gram,1);
fullcovmat = strcmp(covtype,'full') || strcmp(covtype,'u... |
b97d73391567e52af4fb0383ebdcdcc087e5b6b84019e83792101b2442249e52 | MATLAB | 2,096 | 54 | function [Gamma,D] = checkGammaOverfitting(Gamma,residuals,hmm,T,orders)
% Check whether a given timecourse is overfitting to the data by assigning
% anticorrelated state timecourses to different conditions. If overfitting
% is occuring for a particular label, that label's timecourse is reset to
% the global mean value... |
f47f613fd80c01d519d5132ed9eb0fcab32fc76f8a03f60c743eb58a5440620b | MATLAB | 2,101 | 51 | function zef = zef_init_sensors_name_table(zef)
if nargin == 0
zef = evalin('base','zef');
end
zef.aux_data_1 = cell(0);
zef.aux_data_2 = eval(['zef.' zef.current_sensors '_name_list']);
zef.aux_data_3 = eval(['zef.' zef.current_sensors '_points']);
for zef_i = length(zef.aux_data_2) + 1 : size(zef.aux_data_3,1)
... |
32ad57721325213b120d19fe27873af5132ba8d81ea6eff5a4aaea26ac24c393 | MATLAB | 2,107 | 74 |
loop_indx = 1;
ms_after_stimulus = [120];
for frame = [84]
frame = num2str(frame);
ms = num2str(ms_after_stimulus(loop_indx));
full_address = 'exportImage';
file_name = ['KalmanSL' ms 'ms' 'Auditory'];
file_full = fullfile(full_address, file_name);
%%
zef.h_reconstruction_type.Value = 7;
... |
09c75b7242cd62285b450021dc3e6b8a224fcba53078e03ecde3037d75418900 | MATLAB | 2,115 | 52 | function bsliang_bootstrap_anl()
load ..\data\bootstrap
% indexs={'slp_nonorm','slp_norm','pse_norm','pse_nonorm'};
indexs={'slp_nonorm','pse_nonorm'};
regions={'Larynx','Tongue'};
conds={'left_clear','right_clear','left_noise','right_noise'};
tasks={'tone_iTBS','tone_cTBS','tone_icTBS','phon_iTBS','phon_cTBS','p... |
ab240b101858d15303b306917cc3ad09763471f3e05b84b09ebba25447cc4252 | MATLAB | 2,124 | 39 | %Copyright © 2018- Joonas Lahtinen, Sampsa Pursiainen & ZI Development Team
%See: https://github.com/sampsapursiainen/zeffiro_interface
%Script for starting exporting window for GMM.
%don't allow to open multiple app window because it cause ether App
%Designer or app's functionalities to crash or both of them
if isfi... |
8dbe2a8d99a3c1bec538ed5b68561232df340c6cc94006ec9791d00b52f817be | MATLAB | 2,125 | 47 | % eegplugin_clean_rawdata() - a wrapper to plug-in Christian's clean_artifact() into EEGLAB. .
%
% Usage:
% >> eegplugin_firstPassOutlierTrimmer(fig,try_strings,catch_strings);
%
% see also: clean_artifacts
% Author: Makoto Miyakoshi and Christian Kothe, SCCN,INC,UCSD 2013
% History:
% 05/13/2014 ver 0.30 by Chri... |
b4708305a8f7740c8e57b2511473f36a4564b0ed92a0785c750335447a538d27 | MATLAB | 2,126 | 83 | function zef = zef_start(zef)
if nargin == 0
zef = evalin('base','zef');
end
zef_aux = zef;
zef = zef_apply_system_settings(zef);
fieldnames_aux = fieldnames(zef_aux);
for i = 1 : length(fieldnames_aux)
zef.(fieldnames_aux{i}) = zef_aux.(fieldnames_aux{i});
end
clear zef_aux fieldnames_aux;
if isequal(zef.ze... |
6d4f497b6d6cb494470326a2ad7b93bc4c1515fa2a02410bc68ece668aa07e56 | MATLAB | 2,129 | 90 | function stat = spike_state_statics(tau, data, p)
% spike_state_statics for spike window data
% stat = spike_state_statics(tau, data)
% ---
% data.a : spike amplitude
% data.b : bias
% data.y : y(k,n)
% data.Dt : Dt(k,n) = t(k,n) - T(k)
% : (# of pattarn) x (# of time sample) x (# of spi... |
9e8a99f6684716a353a6132a81f6b710c6a0f14fd0acaa96816a48be67f24457 | MATLAB | 2,130 | 96 | function dti_streamlines = zef_dti_streamlines(dti_directions,dti_anisotropy,seed_point,roi_radius,n_dir,step_size,max_steps,fa_thresh);
if nargin < 4
roi_radius = 15;
end
if nargin < 5
n_dir = 10000;
end
if nargin < 6
step_size = 1;
end
if nargin < 7
max_steps = 1000;
end
if nargin < 8
fa_thresh = 0.15;
end
nx =... |
460552e7f80956ac6676e1aba6f1d93caf63aaa1aa7d232b4d542bb47df1f3e2 | MATLAB | 2,132 | 62 | function [vecIdx,vecDur,vecEnergy] = getSustainedPeaks(vecT,vecData,vecSlope,dblCutOffZ)
%% get durations
intN = numel(vecData);
vecPeakDuration = nan(intN-1,1);
for i=1:(intN-1)
if vecSlope(i) > 0
intPeakEnd = find(vecData(i) > vecData((i+1):end),1);
else
intPeakEnd = find(vecData(i) < vecData((i+1):end)... |
4bee863b07a335b556c5b101697803d541ddb5f0f9a72227c3678f178827c650 | MATLAB | 2,133 | 62 | function [processed_data] = zef_threshold_epoching(f, threshold_value, number_of_epochs, start_time, end_time, sampling_frequency)
%Copyright © 2018- Sampsa Pursiainen & ZI Development Team
%See: https://github.com/sampsapursiainen/zeffiro_interface
%This function processes the N-by-M data array f for N channels and M ... |
c39be70b1561a4f313009255ccd24a9d4a5e3215988dfe3e14d171eda7e2044c | MATLAB | 2,136 | 64 | function bsliang_rawplot_conds(IDraw_G,LR_ind)
% LR_ind: ±»ÊÔ×óÓÒÄÔ
% ¸ù¾ÝÌõ¼þ»³örawÇúÏßµÄͼ£¨¾ùÖµºÍ±ê×¼Î󣩣¬Ö±¹Û½øÐбȽϣ¬Í¬Ê±Ò²ÊÇΪÁËÑé֤ģÐÍÄâºÏµÄ½á¹û
% IDraw_G
% step(5)*block(4)*turn(3)*subjs
% DATA structure:
% turn: L T S
% block:
% Id_Di BLOCK NAME NUMBER
% Id A T_CLEAR_ID ... |
38669f516d388ed7e6fd64ffebe9c469abd314508da4efc63a655f0dc7f14d95 | MATLAB | 2,138 | 77 | % [daqData,headCt] = FastFetch( myObj, streamID, start_scan, scan_ct, channel_subset, downsample_ratio )
%
% Get MxN matrix of stream data.
% M = scan_ct = max samples to fetch.
% N = channel count...
% If channel_subset is not specified, N = current
% SpikeGLX save-channel subset.
% Fet... |
4a1736a692d7df1f403f58bb723e124360ca62909c4921ae46318e6ccc9ebf5b | MATLAB | 2,139 | 63 | K = length(hmm.state);
if exist('Gamma','var') && ~isempty(Gamma)
XXGXX = cell(K,1);
end
if ~exist('B','var')
if isfield(hmm.train,'B'), B = hmm.train.B;
else B = [];
end
end
if ~exist('V','var')
if isfield(hmm.train,'V'), V = hmm.train.V;
else V = [];
end
end
if ~exist('XX','var') || (size(... |
ad7b3c75b025c4c332fa2d1d5ec61c6387f091fee8d2fa5c2bc742f34a820790 | MATLAB | 2,142 | 71 | function [W_est Ms crit]=uwedge(M,W_est0)
%
% Uniformly Weighted Exhaustive Diagonalization using Gauss itEration (U-WEDGE)
%
% Coded by Petr Tichavsky, March 2008, updated July 2008
%
% Please cite
%
% P. Tichavsky, A. Yeredor and J. Nielsen,
% "A Fast Approximate Joint Diagonalization Algorithm
% Using a Cr... |
72b1e3545b68ece1101af9489300a65afdafc274200bb3b04708d990c58068f8 | MATLAB | 2,144 | 61 | function erp_chi2_plot(info_chi2,plottype)
if nargin<2
plottype = 'chi2vstime' ;
end
latency = find(~isnan(info_chi2.chi2)) ;
info_chi2.time = info_chi2.time(latency) ;
info_chi2.chi2 = info_chi2.chi2(latency) ;
info_chi2.uncorrected_pvals = info_chi2.uncorrected_pvals(latency) ;
info_chi2.pearsonR... |
c8d9bc0547e78f41dc13d9e922a804b44ca575e3709ba882b0c73a46c92623ef | MATLAB | 2,145 | 41 | %% Result 3 : Long-Range Connections for Balancing Performance and Wiring Cost (Figure 4)
%% Changes of classification accuracy and the total connection length during the training (Figure 4b)
%% The ratio of LRCs converged to the nonzero value (red curve) with length penalty. (Figure 4d)
disp('====================... |
fcb2d93008b0e2ac7fc70f5734714da0281a2eccfd0db8b2beb5f4511435b771 | MATLAB | 2,147 | 63 | function approxhess = getApproxHessian(problem, x, d, storedb, key)
% Computes an approximation of the Hessian of the cost fun. at x along d.
%
% function approxhess = getApproxHessian(problem, x, d)
% function approxhess = getApproxHessian(problem, x, d, storedb)
% function approxhess = getApproxHessian(problem, x, d,... |
7e4a3d6a76efcfd1b867de3915aa9e592b7df07a86527c00cbbd8021c59022cc | MATLAB | 2,150 | 95 | function figures_from_folder_without_colorbars(folder, filetypes, resolution)
%
% figures_from_folder_without_colorbars
%
% Collects the figures from a given folder, disables their colorbars and saves
% them using utilities.figure_without_colorbar_fn.
%
% Inputs:
%
% - folder (1,1) string { mustBeFolder }
%
% The fol... |
b925b8885edeb2e6454f5df903eccd013b5185b789378bd4a3c3cacc7c461331 | MATLAB | 2,156 | 54 | function [Wout, r_end] = func_reservoir_train(data_reservoir, time_infor, input_infor, res_infor, dim_in, dim_out)
rng('shuffle');
% train the reservoir computing by given the input and output.
xy=data_reservoir.xy;
q=data_reservoir.q;
qdt=data_reservoir.qdt;
q2dt=data_reservoir.q2dt;
tau=data_reservoir.tau;
... |
0d7a1b7a40f5220a9d5c3de89f482beed1862ccb74819a5a2375b1ec1689e184 | MATLAB | 2,157 | 83 | function nearest_list = zef_nearest_points( ...
points, ...
neighbour_points, ...
quantity, ...
quantity_interpretation ...
)
% Documentation
%
% Find the nearest neighbours either within a single set of points, or
% between two point clouds. In the first case, simply pass in the same set
% of poin... |
14a2cead4df8e2b7dcd8e79f08b2219635f0f91dd0ddcf10837a884423d110e3 | MATLAB | 2,159 | 50 | function plot_structure_connectivity(app, uval, maxD, meanD, medianD, rmsD, complexityD)
%% Max D
newcolors = app.newcolors;
app.max_degree_structural_learning.ColorOrder = newcolors;
app.max_degree_structural_learning.NextPlot = 'replacechildren';
area(app.max_degree_structural_learning, uval, maxD);
hold(app.m... |
8c4d4efe4d650d28b08f47717516b92d4748eed4f6115dda7da14d2cebd52c13 | MATLAB | 2,159 | 82 | clearvars -except thepath h;
tic
% Construct full paths for the input files
y_file = fullfile(thepath, 'y.txt');
u_file = fullfile(thepath, 'u.txt');
N_file = fullfile(thepath, 'N.txt');
label_file = fullfile(thepath, 'label.txt');
coord_file = fullfile(thepath, 'coord.txt');
% Import data using the constru... |
2180a6add94ac3c577614f848ac73f9ca522415029a3f66f3e419d62924d0bc6 | MATLAB | 2,163 | 77 | %% initialize
clear; clc;
pathCurrent = pwd;
cd ../temp
load('tempFileForAnalysis.mat');
cd(pathBIDS);
cd derivatives
pathDeriv = pwd;
cd(pathDeriv);
cd('spm-preproc'); % For saving preprocessed file from SPM
pathPrepFile = pwd;
prepSubjFolders = dir('sub*');
cd(pathDeriv);
mkdir('conn-art'); cd('conn-art');
pathCo... |
79d5cd9c3bdf01b08aa0766e465f0cc265a0d99c5c61ee0c9a46bd421b5c7125 | MATLAB | 2,169 | 54 | function plot_one_udf_all_ens_auc(app)
udf = app.StimulusEV.Value;
dataset = app.ViewEnsPerfButtonGroup.SelectedObject.Text;
newcolors = app.newcolors;
num_udf = app.params.num_udf;
all_udf = [1:app.params.num_udf];
all_udf = setdiff(all_udf, udf);
legend_string = cell(1+num_udf,1);
legend_string{1} = app.params.udf... |
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