sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 17k | content stringlengths 1 200k |
|---|---|---|---|---|
d6b1897107d2d54f9ee43248d0b41307e23ec505a2323a252f45a57664f27681 | MATLAB | 1,067 | 43 | function t=emab(I,NI, m1, s1, m2, s2)
w0=0.001;
w1=0.6;
w2=0.399;
N=sum(NI);
%disp([m1, s1, m2, s2, w0, w1, w2]);
%while (dm1>.1 || dm2>.1 || ds1>.1 || ds2>.1)
iter=0;
while iter<80,
p0=1/I(end);
p1=exp(-1/2*((I-m1)/s1).^2)/sqrt(2*pi*s1^2);
p2=exp(-1/2*((I-m2)/s2).^2)/sqrt(2*pi*s2^2);
p0old=w0*p... |
37c17696292490ab9d2423fe23b33571daeb7e57f91db63d8efa334257ea646d | MATLAB | 1,068 | 31 | classdef PermutationMethodsTest < matlab.unittest.TestCase
properties
variables
end
methods (TestClassSetup)
function loadTestData(testCase)
testCase.variables = load("edgeTestInputStruct.mat");
end
end
methods (TestClassTeardown)
function clearTestData(... |
8f1a910171b40c44b6e90ba4b726ddd15d1d134f10eb9436853177dee4526cf8 | MATLAB | 1,072 | 16 | function w = widthOfString(str, h)
% Approximate the display width of a given text string
c_total = strlength(str);
c1 = count(str, ["i", "j", "l", "I"]);
c2 = count(str, ["t"]);
c3 = count(str, ["f", "r", "-", "(", ")", "*"]);
c4 = count(str, ["a","c","d", "e", "g", "h", "k", "n", "o", "p", "q"... |
8febf060fc6a0a8b47cbd435e59cf6d7c67577c0f52405fd460ef69bf1159b6a | MATLAB | 1,073 | 39 | function fmriLayerMakeMovie(subject)
%%
% current working directory needs to be /path/to/scripts
mainpath = pwd;
addpath([mainpath filesep 'toolboxes' filesep 'OpenFmriAnalysis'])
addpath([mainpath filesep 'toolboxes' filesep 'spm12'])
tvm_installOpenFmriAnalysisToolbox
basepath = [mainpath '/../subjectData/S%d'];
%%
... |
de634af82fc89f5c62dacac52e2116ecf808767aa408ef796768ab1cdab29d06 | MATLAB | 1,074 | 29 | function squareFc = reshapeFlatFcIntoSquares(flatFc)
[numSubj, numFcEdges] = size(flatFc);
fcEdgeSize = (1 + sqrt(1 + 8*numFcEdges)) / 2;
if mod(fcEdgeSize,1) ~= 0
error(['\nFirst dimension of input flat FC is not consistent with size of a square fc.\n',...
' %i unique edges res... |
8d2e3b9c12bb4815931da429ec81be7fdbc971ab63ba306c7d5c0c57afe6adee | MATLAB | 1,078 | 33 | function data_load = load_SpotOn()
[files,path] = uigetfile('*.mat','select SpotOn .mat Files(s)','MultiSelect','on');
if path~=0
if iscell(files)
f = waitbar(0,'Loading Files');
for i=1:size(files,2)
trackedPar = load([path,files{i}],'trackedPar');
data_lo... |
2c45bb41d2cc5db0a0c4c4315cd4dea049618ed423ef0cfc6977bf765cf433a8 | MATLAB | 1,081 | 34 | function [mapping] = check_diploc(pos)
%--------------------------------------------------------------------------
% Till Habersetzer (25.01.2022)
% Communication Acoustics, CvO University Oldenburg
% till.habersetzer@uol.de
%
% Dipole locations of a 2-dipol-fit are checked in terms of their mappings
% to the left and... |
b537289e7bb9bd6741c516f41b9d68e0f68778ee4af192f11e152d92cdf9e860 | MATLAB | 1,081 | 21 | function arc = genArcSegmentHandlePoorlyDefined(arc_origin, arc_origin_rad, arc_radius, r1_center, r2_center, n)
%GENARCSEGMENTHANDLEPOORLYDEFINED Wrapper around genArcSegment that
% handles poorly-defined arc segments - those with radius = 0 or arc of
% infinite radius (aka. straight line segment)
% fr... |
1c1bbe2259d3281c05fe6498eb0554ce2e659006a7b7cfcc6701628fd8a96d45 | MATLAB | 1,083 | 28 | function shape_classification_filter_aspect_ratio(data)
classes = data.classes;
input_values = inputdlg({'Major Axis to Minor Axis Aspect Ratio Threshold:'},'',1,{'1.5'});
if isempty(input_values)~=1
min_aspect_ratio = str2double(input_values{1});
for i=1:size(classes,1)
for j = 1:length(class... |
ff2fe967e15395d602c8db958f6f2499738c11c70edc7a57d93a886ce5ccedf4 | MATLAB | 1,087 | 28 | function modified_data = change_erp(x)
% function to change ERP by randomly shifting time and amplitude.
latency_range = -30:30;
amplitude_range = 0.75:0.01:1.25;
data = permute(x, [3,1,2]);
[trials, channels, timepoints] = size(data);
modified_data = data;
for trial = 1:trials
latency_shift = latency_range(r... |
4709dd6a737c42356f75c1543e47af2c78ce81db7808de7b0199f669a086883f | MATLAB | 1,088 | 44 | function [R,T,p,df] = dcor_uc(X,Y)
% [R,T,p,df] = dcor_uc(X,Y)
% Computes the U-centered (bias corrected) distance correlation between X and Y. Also outputs the
% associated t-value (T) and p-value (p). Rows represent the examples, and columns the variables.
% Based on: http://www.mathworks.com/matlabcentral/fileexcha... |
c1e4dc4329497f3853cc43378f92612980c22dbb258684e41978a2b80651fe82 | MATLAB | 1,088 | 38 | function c = redblue(m)
%REDBLUE Shades of red and blue color map
% REDBLUE(M), is an M-by-3 matrix that defines a colormap.
% The colors begin with bright blue, range through shades of
% blue to white, and then through shades of red to bright red.
% REDBLUE, by itself, is the same length as the current... |
2bb61bcdced8f396913997f47884178b319b5ddcb9575928d42fba5b7806d6fc | MATLAB | 1,090 | 45 | function [CIJ] = makelatticeCIJ(N,K)
%MAKELATTICECIJ Synthetic lattice network
%
% CIJ = makelatticeCIJ(N,K);
%
% This function generates a directed lattice network without toroidal
% boundary counditions (i.e. no ring-like "wrapping around").
%
% Inputs: N, number of vertices
% K, ... |
9c31c38e104f37a4cbc5567f334e65e2d5813aa0c7d4e93f76b04e7b031f2341 | MATLAB | 1,090 | 41 | function [Wq,twalk,wlq] = findwalks(CIJ)
%FINDWALKS Network walks
%
% [Wq,twalk,wlq] = findwalks(CIJ);
%
% Walks are sequences of linked nodes, that may visit a single node more
% than once. This function finds the number of walks of a given length,
% between any two nodes.
%
% Input: CIJ b... |
43e165002a015738d4d07a71a4566b6448cc006838da7262f71f77b881a9a8a4 | MATLAB | 1,094 | 39 | function ox=adjph(x)
%ADJPH Normalization of columns of a complex matrix.
%
% Given a complex matrix X, OX=ADJPH(X) returns the complex matrix OX
% that is obtained from X by multiplying column vectors of X with
% phase factors exp(i*phi) such that the real part and the imaginary
% part of each column vector of OX ... |
8f6a73dc8c55eb40c0fa3a3f5ece719e8a1a42dd55f4658b198c628413da6585 | MATLAB | 1,096 | 26 | function tests = genTests(subpackage)
%GENTESTS Generate cell array containing all tests in given subpackage
% subpackage: dot-seperated subpackage name within NLA namespace, eg.
% 'net.test' for net-level tests
root_path = nla.findRootPath();
subfolders = strsplit(subpackage,'.');
%Firs... |
02efb77fd8d74e67bf29e16171c83abf5cbd96e594b42199c280822146a327d8 | MATLAB | 1,099 | 39 | function coord = SurfStatInd2Coord( ind, surf );
%Converts a vertex index to x,y,z coordinates.
%
% Usage: coord = SurfStatInd2Coord( ind, surf );
%
% ind = 1 x c vector of indices of vertex, 1-based.
% surf.coord = 3 x v matrix of coordinates.
% or
% surf.lat = 3D logical array, 1=in, 0=out.
... |
25f2bca1aaa227483eee48909dc3290fe19f16721bc497ce91cbc59aab60a47d | MATLAB | 1,100 | 36 | %USING_HG2 Determine if the HG2 graphics engine is used
%
% tf = using_hg2(fig)
%
%IN:
% fig - handle to the figure in question.
%
%OUT:
% tf - boolean indicating whether the HG2 graphics engine is being used
% (true) or not (false).
% 19/06/2015 - Suppress warning in R2015b; cache result for i... |
bafa7cb2b1ff3cedd8f30dc3a3a7e6f7bdf53b41648201661109023abf89a578 | MATLAB | 1,100 | 23 | function [dataA, dataB] = get_eegfmri_all_subject_data(all_res, freq, dtype, raise_to, ROI)
dataA = [];
dataB = [];
area = sprintf('V%d', ROI);
for time = fieldnames(all_res.lh.(area).(freq))'
if contains(dtype, '-')
dtypes = strsplit(dtype, '-');
dataA = cat(4, dataA, ...
permute(... |
12728231661fa6cfdd30705ddb1e94184cab0882a83d2d8e4af5a90d5dd47dc5 | MATLAB | 1,102 | 37 | %für MNI:
db_id = "db4";
subj_id = "20";
subj_name = "sub-" + subj_id + "Slicer";
if (db_id == "db4")
file_path = "/mnt/data1/lmu_or_reconstructions/";
file_path += db_id + "/" + subj_name + "/derivatives/leaddbs/sub-";
file_path += subj_name + "Slicer/reconstruction/sub-";
file_path += subj_name + "Slicer_desc-r... |
a26ba3de586ee2a6bdcf3fa04b459bebf428987884be7f0b16b5215fe94e9751 | MATLAB | 1,104 | 58 | clear
analdir = [path_ludisc '\common/DATA\MIPCART pilot\Analysis\'];
load([analdir 'ParTable_250625.mat']);
[T, m_vol] = mipp_time_2_commonTime_v2(T, 105);
T = T(contains(T.dps_fn, 'resQ_nocov_dps.mat'), :);
%%
clf
uID = unique(T.ID);
X = [];
Y = [];
for i = 1:numel(uID)
ind = T.ID == uID(i);
x = T.t_shif... |
876c6ebd1421cf2ebe455643cfe2ce2ccfcb5dd3cbc6c1f0c2185181dcd6d745 | MATLAB | 1,107 | 29 | %% Data paths
% Set REPO_ROOT to the root of the icms-plasticity-code repository.
REPO_ROOT = fileparts(fileparts(mfilename('fullpath')));
if isempty(REPO_ROOT)
% Fallback if mfilename fails (e.g., running from editor temp dir)
REPO_ROOT = pwd;
end
DATA_ROOT = fullfile(REPO_ROOT, 'data', 'matlab');
% Fig 2 / ... |
9a07f8749de9a3750e99894ad3c7cb7de3913de50fb92cd70943e99ac52cffa5 | MATLAB | 1,112 | 35 | % current working directory needs to be /path/to/scripts
mainpath = pwd;
addpath([mainpath filesep 'toolboxes' filesep 'analyzePRF'])
addpath([mainpath filesep 'toolboxes' filesep 'analyzePRF' filesep 'utilities'])
addpath([mainpath filesep 'toolboxes' filesep 'knkutils'])
addpath([mainpath filesep 'toolboxes' filesep ... |
afd884e5bcfd822d77cb28bb85819e5d24bd96bcc61ec44d615936ddb7c7f002 | MATLAB | 1,115 | 44 | function data = mipp_table2par_byID(T, pn)
if nargin < 2
pn = {'s0', ...
'D', 'Vi','Va','Vt', ...
'D_lo', ...
'D_hi', 'Vi_hi','Va_hi','Vt_hi', ...
'D_delta','Vi_delta','Va_delta','Vt_delta', ...
'MKi', 'MKa', 'MKt', 'ufa', ...
'MKi_hi', 'MKa_hi', 'MKt_hi', 'ufa_hi', ... |
cbaef75494e780fe2c6c195d48f579802ea6d2dc8986f83328a7045a52ed7c41 | MATLAB | 1,115 | 34 | function [coreness,kn] = kcoreness_centrality_bu(CIJ)
%KCORENESS_CENTRALITY_BU K-coreness centrality
%
% [coreness,kn] = kcoreness_centrality_bu(CIJ)
%
% The k-core is the largest subgraph comprising nodes of degree at least
% k. The coreness of a node is k if the node belongs to the k-core but
% ... |
d37e3b49836c0948adc91051e6fa9b862a7dc78810e102e496d03b3772cb4ff3 | MATLAB | 1,117 | 57 | function [A,fint] = glm_phi (phi,dt,fb)
% Estimate connectivity parameters using GLM/EMA method
% FORMAT [A,fint] = glm_phi (phi,dt,fb)
%
% phi [N x Nr] matrix of phase time series
% (N time points, Nr regions)
% dt sample period
% fb bandwidth parameter
%
% A [Nr x Nr] normalised ... |
54c50724f8812a60e4990dc3705f0660603e8952d9ad7c400ce08ebb1b3a2f68 | MATLAB | 1,118 | 33 | function t_stats = computeTStatsAcrossTrials(inputData)
% Extracts trial data from a struct and computes t-stats
% inputData: struct with fields trial1, trial2, ..., trialN
% Each trial: [nSensors x nTimepoints]
% Returns: t_stats [nSensors x nTimepoints]
if isstruct(inputData)
trialNames =... |
4582179e4263615bb41dac27d100a8fa8c7992f43d3ee896661460e6382b1fa1 | MATLAB | 1,120 | 38 | function BCCT_CON_Surf_GUI
D.fig = figure('unit','norm',...
'pos',[0.4,0.4,0.2,0.2],...
'name','Structural Covariance Surface-based Map',...
'menubar','none');
D.Freesurfer = uicontrol('parent',D.fig,...
'unit','norm',...
'pos',[0.1,0.3,0.3,0.4],...
'style','pushbutton',...
'string'... |
4ec709efb605c2fa21390b67659af1e336ecd927f4b35fb9d07a5c2e85e8c48d | MATLAB | 1,121 | 42 | function [loc_est, id_est, G_mcmv, data_cov_inv] = MCMV_beamformer_localizer_reg(Y, noise_cov, G, pnts, included_sp, num_sources)
%addpath('/m/nbe/scratch/braintrack/pnas_mne_results')
num_trials = size(Y,3);
T = size(Y,2);
data_cov = compute_cov(Y, num_trials, T);
%%%%%%%%%%%reg%%%%%%%%%%%%%%%%%%%%%
reg=0.05;
alpha=... |
5531b4b567d289c78db1be41d82f0fd59a8e1bbf67203119b173037858c2063a | MATLAB | 1,122 | 38 | %% create and read file
%awk '{print ">Pep"NR;print $0}' peppho.txt
%http://www.uniprot.org/uniprot/P05198
prot=fastaread('L:\Elite\LARS\2014\november\Lars Inger Ane Serum\kng1prot.fasta')
peps=fastaread('L:\Elite\LARS\2014\november\Lars Inger Ane Serum\kng1pep.fasta')
%% map
plot([1 length(prot.Sequence)],[1 ... |
2276abe13ff44513bd83f3264725d8ada9cd4a1ad028b086fb7f70ada31ccb80 | MATLAB | 1,123 | 30 | function [surf_lh,surf_rh] = load_conte69(name)
% LOAD_CONTE69 loads conte69 surfaces.
%
% [surf_lh,surf_rh] = LOAD_CONTE69(name) loads conte69-32k surfaces of
% the left (surf_lh) and right (surf_rh) hemispheres. Name can be set to
% 'surfaces' for cortical surface or 'spheres' for corresponding spheres.
%
% ... |
4f04e9b82bc626c2b7239591825d7ff7697fdd0c93181a0a6a05d976cdeba3c9 | MATLAB | 1,123 | 33 | %% Dijkstra's algorithm
% To evaluate the performance of Dijkstra's algorithm, we pick
graphdir = '../graphs/';
graphs = {'cs-stanford', 'tapir'};
profile off;
if exist('prof','var') && prof, profile on; end
nrep=15; ntests=10; mex_fast=0; mat_fast=0; mex_std=0; mat_std=0;
for rep=1:nrep
for gi=1:length(graphs)
... |
ca7720e17dbe47c5ab4eada16999c49baf5903054ec5e58eb502b7cb8db31eff | MATLAB | 1,123 | 48 | function demo_mcmv(sim,type)
path_ = pwd;
addpath(genpath(strcat(path_,'/MVAR_simu')));
addpath(genpath(strcat(path_,'/two_step_est')));
addpath(genpath(strcat(path_,'/misc')))
meas_snr=5;
bio_snr_vals=[1 3 5 10];
bio_snr_arr=repmat(bio_snr_vals, [50 1]);
T=5000;
num_bio_ns=500;
num_sources=3;
dist_thr = 30;
%load h... |
d0ba73c2a97422d1eafbbdc14c7e9db7be6434a2833f8938f9bfd60759d1cd6d | MATLAB | 1,125 | 51 | function R_lp_d = WongWangFluc(Gm,Gn)
% inputs:
% Gm: connectivity baseline
% Gr: reversion rate
dt = .0001; % 1ms
t_run = 1500; % run time in seconds
tpts = 0:dt:t_run;
gamma =.641;
a = 270;
b = 108;
d = .154;
jn = .2609;
sigma = 0.06;
w = 1.0;
I = .32;
tau = .1;
load('AAL_matrices.mat','C');
C = C./max(C);
N = size(C... |
3c3f87e198171d94e5d57f1717eb966af46e6e5b6f6e0dc481463527a7f1fd3d | MATLAB | 1,127 | 29 | %% threshold
[data,id,~]=xlsread('L:\Elite\kamila\SILAC1p25.mRNA1p25all.xlsx');
corrcoef(data(:,11)>0,data(:,12))
axis equal
plot(data(:,11),data(:,12),'r.')
grid on
xlabel('SILAC')
ylabel('mRNA')
upup=sum(data(:,11)>0&data(:,12)>0)
updn=sum(data(:,11)>0&data(:,12)<0)
dnup=sum(data(:,11)<0&data(:,12)>0)
dnd... |
c87191b73d6ff57e53ee6e281ce49d7d391a6ba424fc4b6f08d7a79c182f3915 | MATLAB | 1,128 | 36 | %% Model Order Selection for identification strictly causal MVAR model
%%% input:
% Y, M*N matrix of time series (each time series is in a row)
% pmax, maximum tested model order
% idMode, determines estimation algorithm (0:builtin least squares, else other methods [see mvar.m from biosig package])
%%% output:... |
18748f432672fb22a18fb1b0519d38e6b2af5de9fdbb73e35799af51816620ff | MATLAB | 1,130 | 33 | function [GEdiff,Ediff] = diffusion_efficiency(adj)
% DIFFUSION_EFFICIENCY Global mean and pair-wise diffusion efficiency
%
% [GEdiff,Ediff] = diffusion_efficiency(adj);
%
% The diffusion efficiency between nodes i and j is the inverse of the
% mean first passage time from i to j, that is the expected ... |
bb54620c35661d6a4e73fddb889516f3195489bef6829dd259967e0c46e73a3b | MATLAB | 1,130 | 34 | function [complete_linkage,complete_distances] = find_complete_linkage(link,no_of_files)
clear global complete_data complete_dist
global complete_data complete_dist
distances = link(:,3);
complete_distances = distances(1);
complete_distances(2) = complete_distances(1);
distances(1) = [];
link(:,3) = [];
link(... |
ef356cdaf14a3259e2101751d089846fa56b43bc4b226e67cf63b992b8a06d09 | MATLAB | 1,135 | 45 | function image(m)
[sx,sv]=char(m);
[x,v]=double(m);
d1=0.66;
d2=0.88;
clf;
if ~isempty(x)
[n,p]=size(x);
minx=min(x);
maxx=max(x);
x=(x-ones(n,1)*minx)./(ones(n,1)*(maxx-minx+(maxx==minx))) ...
+ ones(n,1)*(maxx==minx);
axes('position',[0.06 0.06 0.2 d1]);
imagesc(x); colo... |
d9d58db846d3c03673bcbc6f45ce829e49330d23c0665329b395ab9522f22190 | MATLAB | 1,136 | 41 | function Z=module_degree_zscore(W,Ci,flag)
%MODULE_DEGREE_ZSCORE Within-module degree z-score
%
% Z=module_degree_zscore(W,Ci,flag);
%
% The within-module degree z-score is a within-module version of degree
% centrality.
%
% Inputs: W, binary/weighted, directed/undirected connection matri... |
1dd7be45c7f2499fd3a67100d10d3cb88da91013c2d11e216454720e0a4fb3bd | MATLAB | 1,138 | 58 | bsad=csvread('X:\Elite\Mohmd\bsad.csv')
bsat=csvread('X:\Elite\Mohmd\bsat.csv')
%hist(bsat(bsat(:,1)>0,1),[100])
hist(bsat(:,1),[100])
hold
%hist(bsad(bsad(:,1)>0,1),[100])
hist(bsad(:,1),[100])
h = findobj(gca,'Type','patch')
display(h)
set(h(1),'FaceColor','b','EdgeColor','k','facealpha',0.5);
set... |
d8fe0034c90674239177cc93415a000439f797b6f6fb64963acd780287fece0a | MATLAB | 1,138 | 43 | % function adapted from the LiNGAM package
% complete software may be downloaded from http://www.cs.helsinki.fi/group/neuroinf/lingam/
function [Bopt,optperm,bestval] = permslowertriagbrutal( B )
% questa è per strictly lower triangular su B0!
% Finds the best identical row and column permutation in terms o... |
f3fa4bd9bf4ede2d33337badcd71d3d8689b7f348381ebe8af462b2ec8dab8d5 | MATLAB | 1,140 | 28 | function colorlimits(obj,limits)
% Sets color limits for plot_hemispheres. Limits must be a 2-element
% numeric vector or n-by-2 matrix where n is the number of columns in
% obj.data. The first column of limits denotes the minimum color limit and
% the second the maximum. When limits is a 2-element vector, then the
% l... |
5f388e1218a24f48690b3fd953bb19447c4dca5f59e3dfccf91e48c6bde49fb1 | MATLAB | 1,144 | 38 | function BCCT_MOD_Surf_GUI
D.fig = figure('unit','norm',...
'pos',[0.4,0.4,0.2,0.2],...
'name','Modulate effect of Structural Covariance Surface-based Map',...
'menubar','none');
D.Freesurfer = uicontrol('parent',D.fig,...
'unit','norm',...
'pos',[0.1,0.3,0.3,0.4],...
'style','pushbutton... |
617f6bb46c7cd0ddbfbf4fdb34d24d27b2160b74c5416666a65f799965f43312 | MATLAB | 1,145 | 55 | function fn = now_write_wf(r, p, o_dir, fn)
% function fn = now_write_wf(r, p, o_dir, fn)
% Write result from NOW_RUN to a .txt file.
% If the file names (fn) are not defined, a name will be created based on
% the problem definition.
if nargin < 3
o_dir = pwd;
end
if nargin < 4
fn = now_problem_to_name(p);
e... |
64ab171baa7d098edf06f24b309f839909a67fc984b202100937735978103f07 | MATLAB | 1,151 | 32 | function voxel_neigh = tc_check_voxel_neighborhood(layer_perc,label, mask, shape)
%% voxel_neigh = tc_check_voxel_neighborhood(layer_perc, mask, shape)
%
% checks and plots stats on the neighborhood of selected voxel
voxel_sel = find(mask);
overlapping_perc = [];
for comp = 1:size(layer_perc, 2)
overlapping_perc ... |
0b2e6667d8bb5e8cfad1a92219261e5b1cab0230b839ce0d2155cfd71a7faecb | MATLAB | 1,154 | 36 | function image_crop_enter_values(data)
for i=1:length(data)
max_x(i) = max(size(data{i}.image,2));
max_y(i) = max(size(data{i}.image,1));
end
input_values = inputdlg({'X1:','X2:','Y1:','Y2:'},'',1, {num2str(1),num2str(max(max_x)),num2str(1),num2str(max(max_y))});
if isempty(input_values)==1
retur... |
2aa1b745982f8e0299fc599a99061d1de7c9deb49a5d4246492002e6609f8a13 | MATLAB | 1,157 | 40 | function [CIJscore,sn] = score_wu(CIJ,s)
%SCORE_WU S-score
%
% [CIJscore,sn] = score_wu(CIJ,s);
%
% The s-core is the largest subnetwork comprising nodes of strength at
% least s. This function computes the s-core for a given weighted
% undirected connection matrix. Computation is analogous to the more
% ... |
0726d7bf1b8f8b7e87b70de3dec06fbb71d46debe7dfafe98c1bccbe89cb9e54 | MATLAB | 1,159 | 46 | classdef Label < nla.inputField.InputField
properties
name
display_name
end
properties (Access = protected)
field = false
end
methods
function obj = Label(name, display_name)
obj.name = name;
obj.display_name = display_name;
obj.s... |
ab76af8b2a4d9c685cbc3dc540ac879a7bbea32cff6a5c6d503fa48bcdd3d80d | MATLAB | 1,163 | 32 | function bst_events = utest_get_test_bst_events()
% Return a BST event struct with corner cases:
% - multiple conditions
% - some have empty notes / channels tags
% - some have non-empty notes with weird chars
% - some selected, some not
%
% Maintain this function everytime bst event structure changes
% -> to a... |
6d95108a965323c421a39f4eb4475c77c7f81f4b1bc75b7ae9c52928a72bcfe0 | MATLAB | 1,166 | 45 | function s=mtimes(t1,t2)
if (~isa(t1,'term') && numel(t1)>1) | (~isa(t2,'term') && numel(t2)>1)
warning('If you don''t convert vectors to terms you can get unexpected results :-(')
end
t1=term(t1,inputname(1));
t2=term(t2,inputname(2));
if isempty(t1) | isempty(t2)
s=term;
return
end
n1=size(t1.m... |
e972fa3e8b6faaa93b1024f41cf8cab8df01015a46bba9a30a3ad8b35c687880 | MATLAB | 1,175 | 38 | function BCCT_CaSCN_Surf_GUI
D.fig = figure('unit','norm',...
'pos',[0.4,0.4,0.2,0.2],...
'name','Causal network of Structural Covaraince Connectivity(Seed to Whole Brain, Surface-based)',...
'menubar','none');
D.Freesurfer = uicontrol('parent',D.fig,...
'unit','norm',...
'pos',[0.1,0.3,0.3,0... |
9582ac7fc4d6795f1d7558d0ad93e0cde649dfa7e8b26600d1ad3c9c8a9d1421 | MATLAB | 1,178 | 28 | function conn_matrices = load_group_mpc(name,parcel_number)
% LOAD_GROUP_MPC loads group level microstructural profile covariance matrices.
%
% conn_matrices = LOAD_GROUP_MPC(name, parcel_number) loads sample
% microstructural profile covariance matrices of the HCP dataset. Name
% can be set to 'vosdewael' for ... |
4c511ecfb660d39f2568745d51921148d2ad7bf29cd0a368d8b61e577a91c891 | MATLAB | 1,179 | 54 | %%%% EXTERNAL FUNCTION
% function taken from the LiNGAM package
% complete software may be downloaded from http://www.cs.helsinki.fi/group/neuroinf/lingam/
function p = slttestperm( B )
% slttestperm - tests if we can permute B to strict lower triangularity
%
% If we can, then we return the permutation in p, ... |
5148472918533998ae82d90c49d5fc755682819337a37f6fffadd9244a6aa264 | MATLAB | 1,179 | 52 | % Demonstration of generative model functions.
%
% See GENERATIVE_MODEL and EVALUATE_GENERATIVE_MODEL for further details
% and interpretation.
clear
close all
clc
data = load('demo_generative_models_data');
A = data.A;
Aseed = data.Aseed;
D = data.D;
% get cardinality of network
n = length(A);
% set model ... |
4ed11fe26f5ca9d9c43ae0ca26f379f3d77a62239fbda80cf85b3b26daeb70bc | MATLAB | 1,180 | 46 | function [status]=write4dfp(filename, info, data)
%Processing filename
[path,name1,ext1]=fileparts(filename);
[dum,name2,ext2]=fileparts(name1);
if (isempty(ext1))
name=[name1 '.4dfp.img'];
nameroot=name1;
elseif (isempty(ext2)&&strcmp(ext1,'.4dfp'))
name=[name1 '.4dfp.img'];
nameroot=name1;
elseif (... |
e8e008085521f93f4b8e90ed9d06045c1f7d87e341cacf3509286ca7c3505c35 | MATLAB | 1,185 | 47 | function [CIJ] = makeringlatticeCIJ(N,K)
%MAKERINGLATTICECIJ Synthetic lattice network
%
% CIJ = makeringlatticeCIJ(N,K);
%
% This function generates a directed lattice network with toroidal
% boundary counditions (i.e. with ring-like "wrapping around").
%
% Inputs: N, number of vertices
% ... |
560ea9ab5d76c38f02bcb534880a05f0957ea02a2733ea0879edb830833ed1c7 | MATLAB | 1,186 | 31 | %% read
[data,id,~]=xlsread('L:\Qexactive\Linda\MaxLFQall.xlsx');
protab = tblread('c:\users\animeshs\Desktop\MCRshalinitonje.txt','\t')
protab = tblread('L:\Qexactive\Linda\LFQALLseries\proteinGroups.txt','\t')
[data,id,~]=xlsread('L:\Elite\Ani\lfq.xlsx');
lfq=data(:,10:33);
%% extract LFQ
lfq=protab(:,[103:1... |
a4bf4ec2644a95b055bf53dd2945db5d224e162c687137e4a2e0288468446d84 | MATLAB | 1,187 | 61 |
%% plot figure 3a
clear;clc;close all;
figure;
load('decode_accur_mean.mat')
% suptitle('Functional Localizer')
mean_accur_mean=squeeze(mean(decode_accur_mean,1));
subplot(1,2,1)
x=10:10:750;
y=10:10:750;
clims = [0.15 0.25]
imagesc(x,y,mean_accur_mean,clims)
ylabel('Train Time Post Onset (ms)')
xlabel('Test Time Po... |
fa35069f7074ce8544d03de8e9fccd3703ca6f0cb7aa0d79e32f9fd62f28427a | MATLAB | 1,188 | 37 | function data_cluster = loc_list_extract_clusters(data)
for i=1:length(data)
data_cluster{i} = extracting_clusters(data{i});
end
data_cluster = horzcat(data_cluster{:});
loc_list_plot(data_cluster)
end
function clusters = extracting_clusters(data)
f = waitbar(0,'extracting clusters');
area = data... |
3d06454a0ff7a59e858a50a9cf845e7d8ad9c4f2edef89f3816db1bf984d1e94 | MATLAB | 1,192 | 30 | function data_rotate = loc_list_rotate_cm(data)
input_values = inputdlg({'Enter the degrees with which you want to rotate the image'},'Rotation input',1,{'180'});
degrees = str2double(input_values{1});
data_rotate = cell(1,length(data));
for i = 1:length(data)
data_rotate{i} = loc_list_rotate(data{i},degre... |
bf3d755949e31c3b71d4fdaeaeb641bd91cc38c645214b174bddfe44913b6b90 | MATLAB | 1,193 | 40 | function [channel_label, measure] = nst_format_channel(isrc, idet, measure)
% NST_FORMAT_CHANNEL make channel label from source, dectector and measure information.
%
% CHANNEL_LABEL = NST_FORMAT_CHANNEL(ISRC, IDET, MEAS)
%
% ISRC (int >= 0): source index
% IDET (int >= 0): extracted detector index
% ... |
097b279efbf6aa302846f7b0c8ee5efcabbf3d37eb0145877240cefb166901eb | MATLAB | 1,196 | 41 | function SurfStatWriteData( filename, data, ab )
%Writes data (e.g. thickness) to a single .txt or FreeSurfer file.
%
% Usage: SurfStatWriteData( filename, data [, ab] );
%
% filename = .txt or FS file name, either new version binary or ASCII.
% ab = 'a' for ASCII or 'b' for binary.
%
% data = k x v ... |
5bed0b6581e84ac56c0ec45fef257e61b8c99b6fbf30d73b4dd1dddff5b4a664 | MATLAB | 1,196 | 40 | function noise=mkpinknoise(n,m);
% makes m channels of pink noise
% of length n. Each column is one
% channel
%
% Guido Nolte, 2012-2015
% g.nolte@uke.de
% If you use this code for a publication, please ask Guido Nolte for the
% correct reference to cite.
% License
%
% This program is free software: ... |
0d7b3a722edc54cfa027240945283a5d2bc1be700fc2f8128f0642ebde3dc62a | MATLAB | 1,198 | 30 | function [aN] = compute_aN_gpu(r_pf_t, r_pf, q_kf, P_kf, V_r, ...
omega_t, lambda_t, w, N, CONST, log_det)
np=size(q_kf,1);
gamma = zeros(np, np,N);
logw_rb = zeros(N,1);
I=eye(np);
for i=1:1:N
gamma(:,:,i) = chol(P_kf(1:np,1:np,i));
end
q_kf_r (:,1,:) = q_kf;
q_kf_gpu = gpuArray(q_kf_r);
gamma_gpu = gpuArray(gamm... |
83b08ec6379c56b0db471b069e3f6034e396bbe2ce99801c9f2e7acd52372a01 | MATLAB | 1,200 | 42 | function [FDfilt mvm_filt] = filter_motion(TR,mvm)
% [FDfilt mvm_filt] = filter_motion(TR,mvm)
% a function that filters the motion parameters and calculates a filtered
% FD (FDfilt)
%
% Inputs:
% TR = TR in seconds
% mvm = time X 6 motion parameters
%
% Outputs:
% FDfilt = array with filtered FD values
% mvm_filt = t... |
8078576c82b13025dc14c33b57fe8061cb361b18fb271b49f0b9db2c1fae16ab | MATLAB | 1,202 | 36 | function transitions = find_transitions(A, min_state_length, transition_threshold)
nwin = size(A,1);
if nwin < 2*min_state_length
errordlg('Error using ''transitions'' function: duration must be at least 2 times the minimum state length')
end
transitions = zeros(0,1);
last = nwin-2... |
10ceedb34d6b6c1f0510ee43a40a289e71e2bbb5d111aa2be7a2b629ac4cca8b | MATLAB | 1,204 | 27 | function [status,cmdout] = tc_wrapper_nifti2surf(subjectsDir, i_filename, o_filename, r_filename, varargin)
additional_command = '';
if ~isempty(varargin)
if varargin{1}
additional_command = [';mri_surf2surf --srcsubject "0_freesurfer" --trgsubject fsaverage --hemi $hemi ' '--sval ' strrep(o_filename, '.mgh', '.$h... |
2b12f138795ad1e5e896635e4e4c2732378e19e2482a574a640f79847ba75cf2 | MATLAB | 1,204 | 37 | function path = retrieve_shortest_path(s,t,hops,Pmat)
% RETRIEVE_SHORTEST_PATH Retrieval of shortest path
%
% This function finds the sequence of nodes that comprise the shortest
% path between a given source and target node.
%
% Inputs:
% s,
% Source node: i.e. node where the short... |
45e6b5b68b26e743aca494b4303605adecc6ca5b2d3d6cc8f1c7481186743609 | MATLAB | 1,206 | 27 | function [vor, dist] = dg_voronoi(img, voxsize, seeds, distance, aniso)
% Geodesic Discrete Voronoi Diagram
% FORMAT [vor dist] = dg_voronoi(img, voxsize, seeds, distance, aniso)
%
% img - binary image (2D or 3D) with : > 0 : inside
% <= 0 : outside
% voxsize - siz... |
5c01e565026762d09fa14b5ec97bfd16120f68e736b0bed4822553325732a7e1 | MATLAB | 1,206 | 31 | %% Method generate a atlas with all parental regions of the given xlsx file
% getParentalARA('./annotation_label_IDs_valid.xlsx','./annotation/annotation.nii.gz')
function getParentalARA(xml_file,atlasNii_file)
addpath('./AllenBrainAPI-master/');
labelsStrArray = char(readXML_Lables(xml_file));
atlasData = load_ni... |
d88945776a4a492f9adef9bf661a9fd2d5c6544d4032dc19f632c0212451faea | MATLAB | 1,206 | 40 | function loc_list_voronoi_plot(data)
answer = inputdlg({'Maximum Number of Localizations for Down Sampling'},'Input',1,{'1000'});
if isempty(answer)~=1
num_points = str2double(answer{1});
m = length(data.x_data);
xy(:,1) = data.x_data;
xy(:,2) = data.y_data;
if m>num_points
xy = data... |
7957643d7aca3e0692246bc937b0333b24f68192be8ab68e63dce82e059f5bc0 | MATLAB | 1,211 | 26 | function shape_classification_network_graph(classes)
if size(classes,1)<500
parameters = shape_classification_normalized_parameters(classes);
disp('Finding Nodes and Edges')
number_of_nodes = size(parameters,1);
[x,y] = meshgrid(1:number_of_nodes);
x = triu(x,1);
y = triu(y,1); ... |
115605a8d6ad8e4e7716d68ff5f6b0444f477453898ea735ad03892ffbe3153e | MATLAB | 1,213 | 51 | for ica = 1:70
ICAS=num2str(ica);
if(ica<10)
ICAS=strcat('00',ICAS);
else
ICAS=strcat('0',ICAS);
end
%strcat('ica','_',ICAS)=[];
%ica_mat=[];
%icamat(ica)=[];
for s = 1:66
SS=num2str(s);
if(s<10)
SS=strcat('00',SS);
... |
f2e75c9490644c6aaa6e79c850bbf08a54fff3b139f4282a27891be4bdd03745 | MATLAB | 1,217 | 24 | function [data_above,data_below] = loc_list_voronoi_seperate_based_on_value(data)
answer = inputdlg({'Voronoi Area Threshold Value:'},'Input',[1 50],{'0.1'});
if isempty(answer)~=1
threshold = str2double(answer{1});
for i = 1:length(data)
I_below = data{i}.vor.voronoi_areas<threshold;
I_ab... |
e905eb5499d474578d28414bb2c8990f0e016e82c6ac03371a1721eb855c982d | MATLAB | 1,219 | 34 | function data_k_means = loc_list_k_means(data)
answer = inputdlg({'k-Value:'},'Input',[1 50],{'5'});
if isempty(answer)~=1
k = str2double(answer{1});
for i=1:length(data)
counter(1) = i;
counter(2) = length(data);
data_k_means{i} = loc_list_k_means_inside(data{i},k,counter); ... |
3da8a153e109c00281d979ad5cd19ba0d5d65b4b718188b2f906c49552c217cf | MATLAB | 1,221 | 40 | % Undo the flipping and rotations performed by xform_nii; spit back only
% the raw img data block. Initial cut will only deal with 3D volumes
% strongly assume we have called xform_nii to write down the steps used
% in xform_nii.
%
% Usage: a = load_nii('original_name');
% manipulate a.img to make... |
dcf65f4b8edf50d8d3d7882cf6d9f3481045682d051426f44b1892a2fc1dbcb8 | MATLAB | 1,226 | 29 | function parcel_data = load_parcellation(name,parcel_number)
% LOAD_PARCELLATION loads parcellation vectors.
%
% parcel_data = LOAD_PARCELLATION(name) loads parcellations on
% conte69-32k surfaces. Name can be set to 'vosdewael' for a
% subparcellation of the Desikan-Killiany atlas, or 'schaefer' for a
% func... |
92a17a12f411ff39543f578d608e275b670014d1eb8e018fb9518703902f49e1 | MATLAB | 1,227 | 48 | function shape_classification_save_results_bin(data)
classes = data.classes;
path = uigetdir();
if path~=0
mass = classes(:,3);
mass = vertcat(mass{:});
mass = mass(:,1);
[~,I] = mink(mass,size(classes,1));
classes = classes(I,:);
for p=1:size(classes,1)
mkdir(fullfile(... |
2dfae60502aee633c485f52e969a95e0b20f622e3e47415e3f4f4db41cfcf2f2 | MATLAB | 1,229 | 41 | function report_stat(csvPath, panel, comparison, vals1, vals2)
% REPORT_STAT Append one row of Mann-Whitney U stats to a CSV file.
% report_stat(csvPath, panel, comparison, vals1, vals2)
%
% Columns: panel, comparison, n1, n2, median1, median2, test, U, p, r
n1 = numel(vals1);
n2 = numel(vals2);
med1 ... |
71d0b48356cc0c895ea82cc1ff133b46d79c4bab6670dad2adc82fc883cf0771 | MATLAB | 1,230 | 36 | function [interp_cmap]=interpolate_cbrewer(cbrew_init, interp_method, ncolors)
%
% INTERPOLATE_CBREWER - interpolate a colorbrewer map to ncolors levels
%
% INPUT:
% - cbrew_init: the initial colormap with format N*3
% - interp_method: interpolation method, which can be the following:
% 'neare... |
99d7cae6bdc142cac8374ecae217e89d79181b83468eb03141aa0871fb140905 | MATLAB | 1,231 | 44 | function SurfStatWriteVol( filenames, data, vol );
%wtites volumetric data to files in MINC, ANALYZE, NIFTI or AFNI format.
%
% Usage: SurfStatWriteVol( filenames, data, vol );
%
% filenames = single file name with extension .mnc, .img, .nii or .brik as
% above (3D if k=1, 4D if k>1), or cell ar... |
a8be9b04297dcfd83d1b3c51552c647abc035dc468a0668d4a1fc83525feecee | MATLAB | 1,234 | 30 | function data_load = spt_simulate_directed_brownian_motion()
input_values = inputdlg({'number of particles:','time step:','number of time steps:','diffusion coefficient: (um^2/s)','size:','mean velocity (um/s)'},'',1,{'10','0.05','100','0.001','2','0.05'});
if isempty(input_values)==1
data_load = [];
else
... |
a58536c829540307ba8a6be869f95ac0d16f90c02d94b9a6835e05414eae88ae | MATLAB | 1,236 | 32 | function knn_data = loc_list_knn_density_map(data)
answer = inputdlg({'KNN k value:','Maximum Number of Localizations for Down Sampling:'},'Input',[1 50],{'10','10000'});
if isempty(answer)~=1
k = str2double(answer{1});
num_points = str2double(answer{2});
knn_data = cell(1,length(data));
for i... |
52450265dfb5b6c2fec54e91bdfaf6a15f1289ecd0d82c63693fa71b7211f9ac | MATLAB | 1,237 | 49 | %%
% current working directory needs to be /path/to/scripts
mainpath= pwd;
addpath([mainpath filesep 'toolboxes' filesep 'tc_functions'])
addpath([mainpath filesep 'fmriRegAnalysis'])
addpath([mainpath filesep 'toolboxes' filesep 'fieldtrip'])
addpath([mainpath filesep 'toolboxes' filesep 'OpenFmriAnalysis'])
tvm_inst... |
730bfb6ef272a2c446bd236bdfca3389b7d5f643fad69a85b66f2a8bc729304e | MATLAB | 1,239 | 52 | % Demonstration of generative model functions.
%
% See GENERATIVE_MODEL and EVALUATE_GENERATIVE_MODEL for further details
% and interpretation.
clear
close all
clc
data = load('demo_generative_models_data');
A = data.A;
Aseed = data.Aseed;
D = data.D;
% get cardinality of network
n = length(A);
% set model ... |
1a9d8c061f3a1f49f74949a9072428a78148b64c0ae6ec80d6357111bb9cf122 | MATLAB | 1,240 | 30 | % Example code for how to run the spin test
% Medial wall removal is now included
% SMW 07/31/2020
% Step 1: SpinPermuFs.m to obtain 'spins' of the data
% (or use SpinPermuCIVET.m):
% left and right surfaces (group-averaged at every vertex):
readleft = '/path/to/left_data1.csv';
readright = '/path/to/right_data1.csv... |
7cc1ec97cd571b555ef85f5cac05fbfb6d87ceed0949000ce0e2f46a627230c1 | MATLAB | 1,248 | 40 | function fn_l = now_problem_to_name(p)
% function fn_l = now_problem_to_name(p)
% Get a set of reasonable names for the waveform files based on the problem
% definition. This function may be replaced with header information stored
% in the file one such headers are supported by the sequence code.
gampstr = sprintf('%0... |
b08ce417e8ae224f96863744ac65cf40c96706776681fda75a37126ac91df097 | MATLAB | 1,251 | 37 | function [co_vec1, co_vec2, count_co_vec1, count_co_vec2]=cooccurrence_vec(a_s,a_e,n_s, n_e)
%%% This function finds co occurrences between event 1 and event 2
% input:
% a_s, a_e: start and stop of event 1
% n_s, n_e: start and stop of event 2
% output:,
% co_vec1 : index of cooccur... |
b54e662bbded32c8b3e6f6908462a2a7aa2fa138bb6ab776888c588fb8d4a27f | MATLAB | 1,258 | 39 | function [data_filter_above,data_filter_below,I] = filter_area(data,min_area)
clusters = extract_clusters(data);
areas = cellfun(@(x) x(1,3),clusters);
I = areas > min_area;
clusters_above = clusters(I);
if ~isempty(clusters_above)
clusters_above = vertcat(clusters_above{:});
data_filter_above.x_... |
e60b8e900112c8b7d4e65e954da5c1a06a11cc92505bfcf7eabe8a83a1452011 | MATLAB | 1,259 | 31 | function vor = construct_voronoi_structure(x,y)
disp('=========voronoi structure calculation==========')
disp('calculating delaunay triangles')
dt = delaunayTriangulation(x,y);
disp('calculating voronoi vertices and connections')
[vertices,connections] = voronoiDiagram(dt);
disp('finding voronoi cells coordin... |
ab31db327d3a37df2f2e91a5b1418b2c52c093fc0b6066b4932378601ee88af1 | MATLAB | 1,263 | 37 | function v = eigenvector_centrality_und(CIJ)
%EIGENVECTOR_CENTRALITY_UND Spectral measure of centrality
%
% v = eigenvector_centrality_und(CIJ)
%
% Eigenector centrality is a self-referential measure of centrality:
% nodes have high eigenvector centrality if they connect to other nodes
% that have... |
a0897bbacc6a74b193e94c580c39107de96ab55fdc2baa8d80abbe44bf2d731e | MATLAB | 1,268 | 35 | function head_global = reconstruct_head_from_chest(newChest3, head_positions_rel_to_chest)
% Input:
% newChest3 - 3x3 matrix of new chest sensor positions (must be in same order as original)
% head_positions_rel_to_chest - Nx3 matrix of head sensor positions in chest frame
%
% Output:
% he... |
4b52ffe002815f26ad497095bdd319d7f10b30a0e278c9432595d1eb24888fa3 | MATLAB | 1,269 | 36 | fo='L:\promec\USERS\Synnøve\20200825_3samples\QE\1stRun\combined\txt-rabit-noMBR-1\proteinGroups.txt';
testtype=' WSRT';
data=readtable(fo);
IDX=[1 7 8];%Uniprots, Gene Name, Fasta header
sdx=108;
edx=119;
rep=3;
log2data=log2(table2array(data(:,sdx:edx))+1);
log2ctr=log2data(:,[1:ceil((edx-sdx+1)/rep):size(log... |
f798722628055d214e1bf465cbe6ac4e1a2d1a1a87d56c99cc7ffe5c91bf49d2 | MATLAB | 1,271 | 24 | function reg_middle_volume = tc_reg_middle_volume_index(num_trials, num_volumes_per_trial, skip_first_N_volumes, TR, pseudo_TR, TEEG)
%% reg_middle_volume = tc_reg_middle_volume_index(num_trials, num_volumes_per_trial, skip_first_N_volumes, TR, pseudo_TR, TEEG)
%
% Rene, you clearly messed this up :/ we must check
%
% ... |
3491ed5ccf6c5745decd1509ce4343acb8938c4ee9c8a001a55223872c209bd3 | MATLAB | 1,280 | 30 | function Show_GC_res_subfun(R,P3,NROI,colormapshow,enhanind,outdir,outname,width1,width2)
Hsize = get(0,'ScreenSize');
Bsize = min(Hsize(3),Hsize(4))*0.9;
Bsize2 = Bsize;
POSm1 = [20,20,Bsize2,Bsize2];
Hm1 = figure('pos',POSm1);
maxv = max(R(:));
axHm1_1 = axes('parent',Hm1,'unit','norm','pos',[0.1 0.1 0.8 0.8],... |
7e48af1f6a3d8324ce02b38838fabc2025bda4f9b1ab3e178cdd1805ac7d1a6f | MATLAB | 1,289 | 50 | function ColormapOut = AFNICOLORMAP(k)
if mod(k,2)
k = k+1;
end
mk = k/2;
ColorMapOrig = [1,1,0;1,0.8,0;1,0.6,0;1,0.4118,0;1,0.2667,0;1,0,0;0,0,1;0,0.2667,1;0,0.4118,1;0,0.6,1;0,0.8,1;0,1,1;];
ColorMapMid = ColorMapOrig(:,2);
switch k
case 2,
ColorMap=[0,1,1;
1,0.8,0;];
case 4... |
5830cdba2586c709c4fa8bbd1d448efd8aac0e171c9733c23f28f82aae4c754c | MATLAB | 1,298 | 40 | % Common settings for stats and figures
%
% Other m-files required:
% brewermap: https://github.com/DrosteEffect/BrewerMap
% subtightplot
% Author: Cameron Hassall, Department of Psychiatry, University of Oxford
% email address: cameron.hassall@psych.ox.ac.uk
% Website: http://www.cameronhassall.com
% Participants t... |
1c6321ebf480782db90fba0dae4f9e310a1537b0e635facf7aa4c59c010dfb34 | MATLAB | 1,299 | 45 | function D=distance_bin(A)
%DISTANCE_BIN Distance matrix
%
% D = distance_bin(A);
%
% The distance matrix contains lengths of shortest paths between all
% pairs of nodes. An entry (u,v) represents the length of shortest path
% from node u to node v. The average shortest path length is the
% ch... |
59964b55dcfd36929fae3d6d1e63be7cca8fe8857a1d40eea9ee4e5fe5ecbfa0 | MATLAB | 1,301 | 60 | function [freq1,freq2,subs_freq,zlim]=spectrogram_automation(input1,input2,channel,freqrange)
bottom=min([size(input1,1) size(input2,1)]);
input1=input1(randperm(length(input1)));
input1=input1(1:bottom);
input2=input2(randperm(length(input2)));
input2=input2(1:bottom);
%% input1
clear Data
fn=1000;
leng=length(input... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.