sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 17k | content stringlengths 1 200k |
|---|---|---|---|---|
cc246b74335bfbe4b2219130d1ab059cd35457bf69a696bf7f386fc2f2f54400 | MATLAB | 1,629 | 87 |
%% plot N50 from various assemblers at coverage;
clear all
hold on
N50 = load('n50s.txt.cov');
x = N50(:,1);
y = mean(N50(:,2:11)');
e = std(N50(:,2:11)');
errorbar(x,y',e','or-');
N50 = load('n50r.txt.cov');
x = N50(:,1);
y = mean(N50(:,2:11)');
e = std(N50(:,2:11)');
errorbar(x,y',e','.r--');
N50 = load('n50tec... |
e44935d8fd3e0068c66548e111aba159f0253677b5eab9b640b7c891746c3b7b | MATLAB | 1,629 | 38 | function [classes,classes_grouped] = classify_clusters(classes,coefficient_of_variation,method)
if isequal(method,'linkage')
parameters = classes(:,3);
parameters = vertcat(parameters{:});
normalized_parameters = zscore(parameters);
[link,~,~] = shape_classification_finding_linkage(normalized_param... |
e28d9cbfe9ececcdd5082bf14662d8fe06069d4b2a05ffb0c25b7f56370b6da5 | MATLAB | 1,636 | 46 | function [Hpos,Hneg] = diversity_coef_sign(W, Ci)
%DIVERSITY_COEF_SIGN Shannon-entropy based diversity coefficient
%
% [Hpos Hneg] = diversity_coef_sign(W,Ci);
%
% The Shannon-entropy based diversity coefficient measures the diversity
% of intermodular connections of individual nodes and ranges from 0 t... |
2a7ce92af01dafad2097637e6ac99206bd269cec09271c8ff2cde6978ab0be2b | MATLAB | 1,637 | 51 | function labels(obj,varargin)
% Sets hemisphere labels for plot_hemispheres. Labeltext is taken from
% obj.labeltext. Text name-value pairs can be provided. If none are
% provided, then the defaults are:
% Rotation: 90
% Units: Normalized
% HorizontalAlignment: 'Center'
% FontName: DroidSans
% FontSize 18
% ... |
7e4d5eb5588b593c968ae11492af7340259df7bec76ef61c728685e94bb1e10b | MATLAB | 1,645 | 47 | function drawDesignMtx(design_mtx, labels)
%DRAWDESIGNMTX Display design matrix in new figure
% design_mtx: NxN design matrix
% labels: Nx1 cell array, name labels of each variable
% column-wise normalize the design matrix
design_mtx_norm = (design_mtx - min(design_mtx)) ./ (max(design_mtx)... |
9a863862022d93ef265325f16723bcc559513998d602af76da586228155a015f | MATLAB | 1,646 | 50 | clear variables
addpath '/Volumes/Samsung_T5/Milan_DA/RGS14_Ephys_data'
cd('/Volumes/Samsung_T5/Milan_DA/RGS14_Ephys_da/Data_RGS14_Downsampled_First_Session')
%% Get the study day folders
rat_folder=getfolder;
prompt = {'Enter the rat index'};
dlgtitle = 'Rat Index';
k = str2double(inputdlg(prompt,dlgtitle));
cd(rat_fo... |
acfacd31aa793f6f123af4f2b165e2bc42058eb30101158fd87036e944a157eb | MATLAB | 1,649 | 43 | %% Fig 3D-F - Plot longitudinal trends from saved data
if ~exist('DATA_ROOT','var'), run(fullfile(pwd, 'matlab', 'config.m')); end
load(fullfile(DATA_ROOT, 'fig3def_plot_data.mat'));
% Apply current filter (already NaN in data, just use as-is)
metrics = {mergedDFF, mergedTrendsOnset, mergedTrendsDur};
metricNames = {'... |
03af56c947290b880da9dddd341fb0d35f3675fd6c7e6cea11b6f1a79f374a48 | MATLAB | 1,650 | 45 | function [EC,ec,degij] = edge_nei_overlap_bu(CIJ)
% EDGE_NEI_OVERLAP_BU Overlap amongst neighbors of two adjacent nodes
%
% [EC,ec,degij] = edge_nei_bu(CIJ);
%
% This function determines the neighbors of two nodes that are linked by
% an edge, and then computes their overlap. Connection matrix must... |
590ed1ad7ae4574df31e9f24e8b8e1d73ef8c39931a4ebbc5b0fff541bf73a41 | MATLAB | 1,653 | 71 | function [IDs,ARA_LIST]=name2structureID(names,ARA_LIST,quiet)
% Convert a list of ARA (Allen Reference Atlas) area names to a vector of structure IDs
%
% function [IDs,ARA_LIST]=name2structureID(names,ARA_LIST,quiet)
%
% Purpose
% Each Allen Reference Atlas (ARA) brain area is associated with a unique
% number ... |
f97028720e25d8d7d6643d974531e40309b3864c4abf3e53ccbd4815da32322c | MATLAB | 1,653 | 78 | %% plot
numberSubjects=68;
load distance_beta.mat
load('trait_score.mat')
%% figure 5b
true=distance_beta(:,:,1,2);
[r1,p1]=corr(true,trait_score);
x=-100:10:740;
figure;
suptitle('Association between Reward-Seq. Representation and trait Anxiety')
subplot(1,2,1)
plot(x,r1', 'k','LineWidth',1)
hold on
x0=x(1):10:x(85... |
d68bb2ebe1dafb3febc30143ff7ceadfbaee121afdbca2fbf5762bf15391b976 | MATLAB | 1,654 | 44 | function varargout = process_nst_cpt_fluences_om( varargin )
% @=============================================================================
% This software is part of the Brainstorm software:
% http://neuroimage.usc.edu/brainstorm
%
% Copyright (c)2000-2013 Brainstorm by the University of Southern California
% This ... |
1e1d3a5daafeeefc26a096e48a4e24ee20fb87d0b6ab4966d3b32f9bcc97a010 | MATLAB | 1,655 | 56 | function BCCT_SCN_GUI
SCN.fig = figure('unit','norm',...
'pos',[0.4,0.4,0.3,0.2],...
'menubar','none',...
'Name','Structural Covariance Network(SCN)');
SCN.Mappb = uicontrol('unit','norm',...
'pos',[0.125,0.3,0.15,0.4],'style','pushbutton','string','Map(Volume)');
SCN.Matpb = uicontrol('unit','... |
af87dd160ec6daefeb183ba9405159dfaa8edf3dedd65341a07c669c47cb8bfb | MATLAB | 1,658 | 41 | function TrimerStrengths = ExtractTrimerStrengths(MC, entries_list)
% FUNCTION TrimerStrengths = ExtractTrimerStrengths(MC, nregions, entries_list);
%
% This function takes as compulsory input: i) a MC matrix; ii) a number of
% regions nregions
% As optional argument one can also give a list of entries to limit the
% c... |
64be56389891ecd151f5e6ee91ab641827a2bf49fab1fb49d0a4c112fbd6679f | MATLAB | 1,662 | 58 | function [ data, vol ] = SurfStatAvVol( filenames, fun, Nan );
%Average, minimum or maximum of MINC, ANALYZE, NIFTI or AFNI volumes.
%
% Usage: [ data, vol ] = SurfStatAvVol( filenames [, fun [, Nan ]]);
%
% filenames = file name with extension .mnc, .img, .nii or .brik as above
% (n=1), or n x 1 c... |
d57270e935677f9e26697b2e65970514c9e396d111d5d594020df4b7a5b69ee1 | MATLAB | 1,665 | 57 | ls %% read data
[cu,~,~]=xlsread('L:\Elite\gaute\test\CDS_CU_EntrezID.xls');
cu(isnan(cu)) = 0.5 ; % cleanup and replace mssing vals
%% clusters
corrcu=corrcoef(cu)
cg=clustergram(cu(:,2:65), 'Colormap', redbluecmap,'ImputeFun','knnimpute')
%% pick interesting categories via uniprot search and ID mapping tool... |
1d2cec5115dc2ee51f563b3e4f7bbb587a07f83cd79d7e04665432c19196d46a | MATLAB | 1,669 | 58 | function BCCT_Modulate_GUI
MOD.fig = figure('unit','norm',...
'pos',[0.4,0.4,0.3,0.2],...
'menubar','none',...
'Name','Modulation on Covariance Connectivity');
MOD.Mappb = uicontrol('unit','norm',...
'pos',[0.125,0.3,0.15,0.4],'style','pushbutton','string','Map(Volume)');
MOD.Matpb = uicontro... |
de97c792e4f94599a378ed602f9b4db3b7624bcfc790e35c0b32289f2b3d1373 | MATLAB | 1,669 | 26 | function fn = digestNphosphorylate(s,e,m,l,dct,tct)
fn=[s,'.',e,'.',int2str(m),'.',int2str(l),'.',int2str(dct),'.',int2str(tct),'.txt'];
fprintf('Sequence %s\t Enzyme %s\t Missed Cleavage %d\t Minimum Length %d 2+Threshold %d\t 3+Threshold %d\n',s,e,m,l,dct,tct);
em=0.0005485799094;
pm=1.00727646681... |
548eff7353b8850f40155a35156bea0079fa1cfc65e1170fec6549bfcd705f0f | MATLAB | 1,680 | 44 | function varargout = process_nst_import_head_model_om( varargin )
% @=============================================================================
% This software is part of the Brainstorm software:
% http://neuroimage.usc.edu/brainstorm
%
% Copyright (c)2000-2013 Brainstorm by the University of Southern California
%... |
9d6efcd1884df4bcf55dd75f3cab9ba0889a67401b38958c105c1d303d93392f | MATLAB | 1,680 | 41 | %--------------------------------------------------------------------------
% Till Habersetzer, 08.07.2025
% Communication Acoustics, CvO University Oldenburg
% till.habersetzer@uol.de
%--------------------------------------------------------------------------
% ensure that we don't mix up settings
clear settings
% ... |
d15c65a571ac5053e417a123af5c138f91df893df5c296a2006658cc74c11224 | MATLAB | 1,687 | 66 | function qval = SurfStatQ( slm, mask );
%Q-values for False Discovey Rate of resels.
%
% Usage: qval = SurfStatQ( slm [, mask] );
%
% slm.t = 1 x v vector of test statistics, v=#vertices.
% slm.df = degrees of freedom.
% slm.dfs = 1 x v vector of optional effective degrees of freedom.
% slm.k = #vari... |
e1d8790e0a782ea6af69e962d6f684b66c5186246fc27b4f29ede4ff26b76cc2 | MATLAB | 1,691 | 60 | function D = agreement(ci,buffsz)
%AGREEMENT Agreement matrix from clusters
%
% D = AGREEMENT(CI) takes as input a set of vertex partitions CI of
% dimensions [vertex x partition]. Each column in CI contains the
% assignments of each vertex to a class/community/module. This function
% aggregates the ... |
83ba11e136281181dc02bb58f3c9c604a7b1d07d7138b5d497741a91296177e1 | MATLAB | 1,696 | 51 | sizeV1=800;
sizeV2=200;
sizeV3=500;
V1coords=rand(sizeV1,3);
V2coords=rand(sizeV2,3)+ones(sizeV2,1)*[1,0,0];
V3coords=rand(sizeV3,3)+ones(sizeV3,1)*[2,0,0];
powV1=sort(rand(sizeV1,1));
powV2=sort(rand(sizeV2,1));
powV3=sort(rand(sizeV3,1));
k=10;
f=@(distance,power) power./(1+exp(-k.*distance+k/2));
distsV12=pdist2... |
dff31a9ecb2457c4755875c25901b921be47e5f67b0c1da4699d1fd7104e92de | MATLAB | 1,701 | 43 | function varargout = process_nst_extract_sensitivity_from_head_model_om( varargin )
% @=============================================================================
% This software is part of the Brainstorm software:
% http://neuroimage.usc.edu/brainstorm
%
% Copyright (c)2000-2013 Brainstorm by the University of Sou... |
945b6f061d18c52b99f6ee56de5b155511fa2a4f5e3faa1c72859462a9252a20 | MATLAB | 1,703 | 55 | classdef MultiContrast < nla.edge.result.Base
properties
contrastNames %cell array of names of each contrast
contrastValues % m x p matrix. Each row is the vector of values for one contrast
contrastResultsMap % containers.Map object that stores each contrast with the key of its name
... |
9c2f4a622d1d4530a943edfb41022d270b660f1b1811aea3140bae9a1d808153 | MATLAB | 1,703 | 45 | function W = threshold_proportional(W, p)
%THRESHOLD_PROPORTIONAL Proportional thresholding
%
% W_thr = threshold_proportional(W, p);
%
% This function "thresholds" the connectivity matrix by preserving a
% proportion p (0<p<1) of the strongest weights. All other weights, and
% all weights on the mai... |
a8de4df14f60315fe283084b3206491c0b21f582fb762ba18b5ffb4f306fca1f | MATLAB | 1,703 | 55 | %PDF2EPS Convert a pdf file to eps format using pdftops
%
% Examples:
% pdf2eps source dest
%
% This function converts a pdf file to eps format.
%
% This function requires that you have pdftops, from the Xpdf suite of
% functions, installed on your system. This can be downloaded from:
% http://xpdfreader.co... |
bfcbfd0608862861c07cb4806b5397094526a7c0da0dfd14d51a435375df29a2 | MATLAB | 1,705 | 63 | function XYT = localfitt(x,y,t,winx,winy,wint,how_many,MINt,MAXt)
% function XYT=localfit(x,y,t,winx,winy,wint,how_many,MINt,MAXt)
% Use local fits to get velocity
% Parameters
% x,y,t: locations of activity in space and time
% win: size of region to fit (x, y, and t)
% how_many: number of points needed for g... |
4fcf952b1801d15c2db499a2e83d6ea708143a0a7f7c79fb570044026f77377b | MATLAB | 1,706 | 97 | function pno = mipp_pnam_convert(pni, mode)
if nargin < 2
mode = 1;
end
switch mode
case 0 % QTI
switch pni
case 's0'
pno = '\it{S}\rm{_0}';
case 'D'
pno = '{\itD}';
case 'Vi'
pno = '{\itV}\rm{_i}';
cas... |
3367329ed0a06b7adb44d37fbc1e5c5fe020951a698a0843357e98cb05fea5ab | MATLAB | 1,707 | 53 | function [isrc, idet, measure, channel_type] = nst_unformat_channel(channel_label, warn_bad_channel)
% NST_UNFORMAT_CHANNEL extract source, dectector and measure information from channel label.
%
% [ISRC, IDET, MEAS, CHAN_TYPE] = NST_UNFORMAT_CHANNEL(CHANNEL_LABEL)
% CHANNEL_LABEL (str):
% formatted ... |
8e87028e9d2ff924a864bcc152503396bd7c1ee316438e141b4c97ce9525ded1 | MATLAB | 1,707 | 50 | classdef Heteroskedastic < nla.helpers.stdError.AbstractSwEStdErrStrategy
properties (SetAccess = protected)
REQUIRES_GROUP = true;
end
methods
function contrastStdErr = calculate(obj, sweStdErrInput)
FORCE_USE_FAST_ALGO = true;
if FORCE_USE_FAST_ALGO
... |
e09d738941e3a0e3e7c8ab35921022469fd772385efb235d091bfd1bd620b408 | MATLAB | 1,711 | 58 | function data_load = load_stack_tiff_file_STORM()
[file_name,path] = uigetfile({'*.tif';'*.tiff'},'Select TIFF File(s)','MultiSelect','on');
if isequal(file_name,0)
data_load=[];
else
file_name=cellstr(file_name);
input_values = inputdlg({'steps'},'',1,{'100'});
if isempty(input_values)==1
... |
1add1163d83ddb8bbbf3ec8c0214158b8f7d6958cb085d3f32424119bc0f2b57 | MATLAB | 1,713 | 36 | function [ Z_corr_a, Z_corr_b ] = MatchZ_corr_coeff( dT, dZ, GapsAllowed )
%MATCHZ_CORR_PARAMS
% Match the closest a,b coefficients for the Z-correction.
% Use kd-tree to efficiently search the 2-dimensional space for the
% coefficients for the Z-correction.
% These coefficients were generated from Monte Carlo... |
59d0390078faf166b0561ee74bb3dce3f334bbd7b552f9414c49fc4316cab031 | MATLAB | 1,718 | 44 | function [loc_assort_pos,loc_assort_neg] = local_assortativity_wu_sign(W)
%LOCAL_ASSORTATIVITY_WU_SIGN Local Assortativity
%
% [loc_assort_pos,loc_assort_neg] = local_assortativity_wu_sign(W);
%
% Local Assortativity measures the extent to which nodes are connected to
% nodes of similar strength (vs. higher o... |
f45984d7e583261fe8124e1adb6bdf482b81f73643af3049cdf5d6155bfd6d74 | MATLAB | 1,721 | 45 | % Verify NIFTI header extension to make sure that each extension section
% must be an integer multiple of 16 byte long that includes the first 8
% bytes of esize and ecode. If the length of extension section is not the
% above mentioned case, edata should be padded with all 0.
%
% Usage: [ext, esize_total] = ... |
653a1d0eb8d6d154b1d6f775f3467193c478c6776d649a089e853b4733e88bd6 | MATLAB | 1,722 | 71 | function [IDs,ARA_LIST]=acronym2structureID(acronyms,ARA_LIST,quiet)
% Convert a list of ARA (Allen Reference Atlas) acronyms to a vector of structure IDs
%
% function [IDs,ARA_LIST]=acronym2structureID(acronyms,ARA_LIST,quiet)
%
% Purpose
% Each Allen Reference Atlas (ARA) brain area is associated with a unique
... |
794c93fc234fc85575fe6129346b5864a9d08dbedef11db007b60323cc9423ef | MATLAB | 1,728 | 49 | function [classes,size_classes] = shape_classification_iterative_clustering(classes,coefficient_of_variation,method)
i = 0;
while i<2
i = i+1;
size_one = size(classes,1);
disp(['please wait...',num2str(size_one)])
[classes,size_classes{i}] = classify_iter(classes,coefficient_of_variation,method);
... |
d018014eba66d58e6d115d4a52d058dc38bbefa0b84cc849f205b1bdb1437e53 | MATLAB | 1,729 | 56 | %% Generation of CFTd surrogates - useful to test the PDC - VERSION FOR STRICTLY CAUSAL MVAR MODEL
%%% ABSENCE OF DIRECT CAUSALITY FROM yj TO yi - Aij coefficients are forced to zero
% Note: the length of the series should be not be odd
%%% input:
% Y, M*N matrix of time series (each time series is in a row)
% A... |
4af000a01c69fb0ef5baa96de455255def64c113825df945651f2fd9acf6f1aa | MATLAB | 1,732 | 59 | function fnl = mipp_run_preprocess(maindir, srchstr)
fnl = find_files_under_folder([maindir filesep srchstr], 1, 'detail');
disp(['Found ' num2str(numel(fnl)) ' files to process.'])
opt = mdm_opt;
opt.mio.coreg.assume_las = 0;
for i = 1:numel(fnl)
try
this_nii = fnl{i};
this_dir = msf_fileparts... |
b26e917bdc5daa59d4cef1845bf28a06cdff98be44c03faa87cdd87baee4c81d | MATLAB | 1,735 | 57 | function [comps,comp_sizes] = get_components(adj)
% GET_COMPONENTS Connected components
%
% [comps,comp_sizes] = get_components(adj);
%
% Returns the components of an undirected graph specified by the binary and
% undirected adjacency matrix adj. Components and their constitutent nodes are
% assigned t... |
8b8f021e5e63a2a4e031bf828d1299ebfb92605fab4ce5369526c6b50c017777 | MATLAB | 1,739 | 62 | function blinks_combined(pp)
%combines detected blinks by EEG?ICA and eye tracker. Prioritizes
%eye-tracker, uses EEG when eye tracker data is bad.
sj=['S' num2str(pp)];
load(['/project/3018037.01/Experiment3.2_ERC/AnalysisFolder/subjectData/' sj '/6_EEG/blinks_ICA.mat']);
if pp>52
nb_eeg=no_blinks;
clear no_bli... |
e897c48597b814e5a0523ece0c98fafaf22c72dc1f606f2927b8ef8936c2ce39 | MATLAB | 1,739 | 54 | % use random seed
rng(42)
% setup
n_trials = 180;
n_cond = 2;
n_voxel = 250;
left = randn(n_voxel, 1);
right = randn(n_voxel, 1);
common = randn(n_voxel, 1);
layers = rand(n_voxel, 1);
% new raw data
new_signal = @(cond) randn(n_voxel, 1) + common + left * cond + right * abs(cond - 1);
% functions used
regress_... |
ac2e69afc7b80fb7fc06f7b7e4bb0851f85737784c82b4dd95302e953c0c5c59 | MATLAB | 1,752 | 48 | function [EC,ec,degij] = edge_nei_overlap_bd(CIJ)
% EDGE_NEI_OVERLAP_BD Overlap amongst neighbors of two adjacent nodes
%
% [EC,ec,degij] = edge_nei_bd(CIJ);
%
% This function determines the neighbors of two nodes that are linked by
% an edge, and then computes their overlap. Connection matrix must be... |
d4574629af5727685afdff9cdf2f269f3a162dfc2c3cd85cd694af87c8424d5a | MATLAB | 1,752 | 56 | function BCCT_CaSCN_GUI
CaSCN.fig = figure('unit','norm',...
'pos',[0.4,0.4,0.3,0.2],...
'menubar','none',...
'Name','Causal network of Structural Covariance Connectivity(CaSCN)');
CaSCN.Mappb = uicontrol('unit','norm',...
'pos',[0.125,0.3,0.15,0.4],'style','pushbutton','string','Map(Volume)');
... |
b30682b6c6abda911661b5b61d9abcb7a19b46f120b8655ea2eedacd48e3b4f2 | MATLAB | 1,757 | 73 | %% read
d=xlsread('X:\Elite\LARS\2013\April\T Slordahl\Multiconsensus from 3 ReportsMH.xlsx')
p0=xlsread('X:\Elite\LARS\2013\mars\tobias\Multiconsensus from 3 Reports 0t MH.xlsx')
p5=xlsread('X:\Elite\LARS\2013\April\T Slordahl\Multiconsensus from 3 Reports 5t MH.xlsx')
p12=xlsread('X:\Elite\LARS\2013\April\T Slord... |
2b86899f304d02f3810e1b8e6f29489838121ba2ae95e6379bb5ca17a8911f53 | MATLAB | 1,759 | 47 | function [pair_chan_indexes, pair_sd_ids] = nst_get_pair_indexes_from_names(channel_labels)
% NST_GET_PAIR_INDEXES_FROM_NAMES groups channels by optode pair
%
% [PAIR_CHAN_INDEXES, PAIR_SD_IDS] = NST_GET_PAIR_INDEXES_FROM_NAMES(CHANNEL_LABELS)
% CHANNEL_LABELS (cell array of str):
% each str is forma... |
381d9182285532b528f7ebd0466600daba0e57bad09480077c25fa3ad97fc435 | MATLAB | 1,760 | 45 | function nst_bst_set_template_anatomy(template_name, iSubject, confirm_download)
%NST_BST_SET_TEMPLATE_ANATOMY Use given template to set the anatomy.
% Set the anatomy of the given subject (default is 0) to the given
% Nirstorm template, for the currently loaded brainstorm protocol.
%
% WARNING: It does not hand... |
ab5a168f1516ab14f70cff20d0fed4ce19e85ac3870b244dddf0eb62f3717560 | MATLAB | 1,761 | 28 | function scaleSelector(src, bounds, plot_figure, parameters, chord_type, callback_function)
original_figure = src.Parent.Parent;
modal = figure('WindowStyle', 'normal', 'Units', 'pixels', 'Position',...
[original_figure.Position(1), original_figure.Position(2), original_figure.Position(3) / 3, original_... |
5b7bb429320ea94fadd63ffc0446be88fabea3bddef08820d2f7606d23e132a7 | MATLAB | 1,763 | 58 | function [CIJkcore,kn,peelorder,peellevel] = kcore_bu(CIJ,k)
%KCORE_BU K-core
%
% [CIJkcore,kn,peelorder,peellevel] = kcore_bu(CIJ,k);
%
% The k-core is the largest subnetwork comprising nodes of degree at
% least k. This function computes the k-core for a given binary
% undirected connection matrix by re... |
e2ac73d177c946b72d2a53a2f4b96b26c3f762fb49e877590b81195436cd86e1 | MATLAB | 1,766 | 66 | function [MI, H, P1, P2, P12] = Mutual_Information_Labels(S1, S2)
% FUNCTION [MI, H, P1, P2, P12] = Mutual_Information_Labels(S1, S2)
% This function takes as input two sequences of integer labels S1 and S2
% (should have the same length) and returns as main output the Shannon mutual
% information between the two sequ... |
ed9d2e971507d399737d182785844790832b551c28b71a3ed08e524956d5b50c | MATLAB | 1,766 | 41 | function T=transitivity_bd(A)
%TRANSITIVITY_BD Transitivity
%
% T = transitivity_bd(A);
%
% Transitivity is the ratio of 'triangles to triplets' in the network.
% (A classical version of the clustering coefficient).
%
% Input: A binary directed connection matrix
%
% Output: T ... |
7a34cefd7793cf7d8532f7344717f58198fe19aec21bbf8ed66a54b529a4b4d4 | MATLAB | 1,771 | 50 | %% Fig 3I - dF/Fmax distribution early vs late
if ~exist('DATA_ROOT','var'), run(fullfile(pwd, 'matlab', 'config.m')); end
load(fullfile(DATA_ROOT, 'fig3def_plot_data.mat'));
% Pool all data points from weeks 0-1 and weeks 3-4
earlyVals = reshape(mergedDFF(:,1:2), 1, []);
earlyVals = earlyVals(isfinite(earlyVals));
l... |
98be7e3264a7172ace9435840741c084a714d1a71e4d7c7f500ab1e8cdf9f2cd | MATLAB | 1,773 | 62 | function [distance,branch] = breadth(CIJ,source)
%BREADTH Auxiliary function for breadthdist.m
%
% [distance,branch] = breadth(CIJ,source);
%
% Implementation of breadth-first search.
%
% Input: CIJ, binary (directed/undirected) connection matrix
% source, source verte... |
f0c53b068c9babd58d5bac204daf9f5391638a12df8bfac421df76e9fff93064 | MATLAB | 1,779 | 39 | function vor = loc_list_construct_voronoi_structure(x,y,counter)
data_to_unique(:,1) = x;
data_to_unique(:,2) = y;
data_to_unique = unique(data_to_unique,'rows');
x = data_to_unique(:,1);
y = data_to_unique(:,2);
f = waitbar(0,['Constructing Delauny Tirangle...',num2str(counter(1)),'/',num2str(counter(2))]);
d... |
b61d5c1ca0e0d3786b2287bcf3a7f6e5f68a2292c250940a84d8105efc7bd1fb | MATLAB | 1,784 | 59 | function [CIJkcore,kn,peelorder,peellevel] = kcore_bd(CIJ,k)
%KCORE_BD K-core
%
% [CIJkcore,kn,peelorder,peellevel] = kcore_bd(CIJ,k);
%
% The k-core is the largest subnetwork comprising nodes of degree at
% least k. This function computes the k-core for a given binary directed
% connection matrix by recu... |
0330243d078fea7d35039671a128c846fcfbf9bcb1fa7d3c4e94798fe5d12dab | MATLAB | 1,793 | 64 | function [MATreordered,MATindices,MATcost] = reorderMAT(MAT,H,cost)
%REORDERMAT Reorder matrix for visualization
%
% [MATreordered,MATindices,MATcost] = reorderMAT(MAT,H,cost);
%
% This function reorders the connectivity matrix in order to place more
% edges closer to the diagonal. This often helps ... |
4d5b2cd79f2f7044c190c672fb7e91c795ac9ec5823f0af029e1eac30998ec58 | MATLAB | 1,793 | 47 | function T=transitivity_wd(W)
%TRANSITIVITY_WD Transitivity
%
% T = transitivity_wd(W);
%
% Transitivity is the ratio of 'triangles to triplets' in the network.
% (A classical version of the clustering coefficient).
%
% Input: W weighted directed connection matrix
%
% Output: T ... |
8951b94a7972131517e085e638f9106dfaec32c1b280f991bb35d36bc9a97f29 | MATLAB | 1,802 | 36 | function clusters = NND_cluster(clusters)
number_of_clusters = length(clusters.clusters_areas);
clusters_center = clusters.clusters_centers;
if number_of_clusters>2
dt = delaunayTriangulation(clusters_center(:,1),clusters_center(:,2));
connectivity_list = dt.ConnectivityList;
attached_triangles = vert... |
0859b436487f0b5cfea2b6ecca0633716423a2d25efa967e13a8d7bc3d3d3df7 | MATLAB | 1,806 | 64 |
function [map]=fluo_map(framerate,maskedimage,imagestack,tfilt,avbeat)
% function for map showing signal levels across tissue
% Chris O'Shea and Ting Yue Yu, University of Birmingham
% Maintained by Chris O'Shea - Email CXO531@bham.ac.uk for any queries
% Release Date -
% For licence information, Please see ... |
2335a53e6f2d4d891481a21945be27f0e9fda00b9fb7f7b80d699fc92bc214d9 | MATLAB | 1,807 | 63 | function [file_names, file_data_types] = nst_get_bst_func_files(subject_name, condition_name, item_name, data_types, protocol_name, sStudy)
file_names = {};
file_data_types = {};
if nargin < 4
data_types = nst_get_bst_data_fields();
else
if ischar(data_types)
data_types = {data_types};
end
end
if... |
f3f4db6688b94f6610cd31f41859a5fbb2790092121d1c7b6d85a04f50589827 | MATLAB | 1,809 | 63 | function shape_classification_pca_analysis(data)
parameters = data.classes(:,2);
parameters = vertcat(parameters{:});
parameters = zscore(parameters);
pairwise_correlatioin = corr(parameters,parameters);
figure()
set(gcf,'color','w')
imagesc(pairwise_correlatioin);
xlabel('Parameters')
ylabel('Parameters')
... |
ad8501ff25774735cdbec233159702753539c170294289c00215c9413da2c34f | MATLAB | 1,812 | 53 | function BC=betweenness_bin(G)
%BETWEENNESS_BIN Node betweenness centrality
%
% BC = betweenness_bin(A);
%
% Node betweenness centrality is the fraction of all shortest paths in
% the network that contain a given node. Nodes with high values of
% betweenness centrality participate in a large number ... |
06b95322b178b4775b48ec22e877a6c4d5d4ab413d7f1ce7f61d6849804855f2 | MATLAB | 1,821 | 50 | function [data_filter,data_filter_below] = loc_list_clusters_filter_random_selection(data)
answer = inputdlg({'Enter Number of Clusters to Randomly Select:'},'Input',[1 50],{'1000'});
if isempty(answer)~=1
N = str2double(answer{1});
f = waitbar(0,'Filtering Based on Number of Locs...');
for i=1:length(... |
c2d02a0ce2119920d47f248946ccf3056dc47a455f9e28bb2ba82115ccd1edfd | MATLAB | 1,822 | 57 | function [x2ycoef,y2xcoef,x2y_T,y2x_T,dfx2y,dfy2x] = gca_coef_sub(AROITimeCourse,ABrain4D,i,theCovariables,Order,nDim4)
xsig = AROITimeCourse;ysig = ABrain4D(:,i);
if std(ysig)~=0
Y_TimeCourse = ABrain4D(:,i);
for k = 1:Order,%set order
AX(:,k)=AROITimeCourse(k:nDim4-Order+k-1);
BY(:,k)=Y_Time... |
88652ea6451313da99e7e7269c2a4bad85fa38158d03503ca879bfef09ec38ea | MATLAB | 1,824 | 42 | % This function returns the path to the DL+Direct output folder
% If the path doesn't exist or the folder is empty, it will create the path
% and run the algorithm.
% The name of the folder is always: DLdirect_output
function DLdirect_path = DLdirect(data_path,varargin)
if nargin < 2
% No optional argumen... |
12909eb399a49836f6253ab6fa4db0551dcec1740fcd99fd75cac65407ff3f15 | MATLAB | 1,827 | 52 | function Plot_K_matrix(data_dir,save_dir,selectedK)
%
% Plot the centroids in matrix format
%
% INPUT:
% data_dir directory where LEiDA results are stored
% save_dir directory to save results for selected optimal K
% selectedK K defined by the user
%
% OUTPUT:
% .fig/.png Plot of centroids rendered in... |
74af2c801ca3038ebd36686ddadd0f8246ece341d28f4b69073f9f7f68d68e70 | MATLAB | 1,827 | 43 | close all; clear vars; clc;
pathdata='D:\Tanks\Feb 2022\';
pathout=fullfile('D:\L6b_Sleep\2022_JanFeb\OutputSignals24h\');mkdir(pathout);
TOOLSPATH='C:\TDT\TDTMatlabSDK\TDTSDK\'; addpath(genpath(TOOLSPATH));
RESAMPATH='C:\Users\meije\Dropbox\Matlab_Scripts\'; addpath(genpath(RESAMPATH));
recordingdates='022... |
1bceccf479ea3a42a3069769847240a4e8bc2e9e7c8ceb07637a891050fe7c47 | MATLAB | 1,832 | 58 | % Copyright 2016-2020 Biomedical Imaging Group Rotterdam, Departments of
% Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the Licen... |
7fd452e8395c5a26802992420078e8f779a6b3568c16491dcdf15c22f6aea50f | MATLAB | 1,833 | 73 | function [indices] = resampling( weights, nParticles, alg )
% Narayan Subramaniyam, 07-2016
% narayan.subramaniyam@aalto.fi
% License
%
% 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, ... |
46eaf2a95454babb57d3cbffc412f1406b5e3b2e9395b19589d4229591a85246 | MATLAB | 1,835 | 46 | function [mesh_l, mesh_r] = anatToMesh(anat, ctx, view_pos)
import nla.gfx.ViewPos nla.gfx.MeshType
%ANATTOMESH generate cortex hemisphere mesh from anatomy
% anat: anatomy struct, contained in cortex mesh files
% ctx: MeshType value, what inflation level of mesh to use
% view_pos: ViewPos va... |
8122be01e45eaba24085e31729c2a9cc79dcf3ec0a155ff12b26aafdebb0a24d | MATLAB | 1,840 | 42 | function loc_list_three_channel(data,scatter_size,scatter_num)
if length(data)~=3
msgbox('Number of files selected should be equal to 3')
else
figure()
set(gcf,'name','Montage Plot','NumberTitle','off','color',[0.1,0.1,0.1],'units','normalized','position',[0.15 0.2 0.7 0.6],'menubar','none','toolbar','... |
04e6aed1d588bb7423e1b4caba2f293d9c1efef41822b8966cf4c35d423f82e7 | MATLAB | 1,841 | 44 | function O = decoding(S)
O = [];
addpath('/projects/MINDLAB2017_MEG-LearningBach/scripts/MITDecodingToServer/Decoding')
addpath('/projects/MINDLAB2017_MEG-LearningBach/scripts/MITDecodingToServer/Decoding/scilearnlab')
addpath('/projects/MINDLAB2017_MEG-LearningBach/scripts/MITDecodingToServer/Decoding/scilearnlab/ex... |
38e78d07b81660794be2a459223bd69e663fb90bd2e40d964c4664e565b547ea | MATLAB | 1,848 | 48 | function append_tag(data_dir,info_file,tag_col,save_dir)
%
% Example script to append the condition tag of each participant to the
% filename to % enable running the script LEiDA_Start.
%
% INPUT:
% data_dir directory where data files are stored
% info_file path to file with the phenotypic data from each subje... |
4c3316f9a480517dba6b0fafc26687f7ef1546ac9972cb2e8ce5882c94883f56 | MATLAB | 1,850 | 50 | classdef PrefixMatrixTest < matlab.unittest.TestCase
properties
tmp_dir
end
methods(TestMethodSetup)
function setup(testCase)
tmpd = tempname;
mkdir(tmpd);
testCase.tmp_dir = tmpd;
utest_bst_setup();
end
end
meth... |
2f94610b7fb675fefbd16776a41947703e86413ee863fc31b3a32e500927a64d | MATLAB | 1,853 | 57 | function loc_list_voronoi_plot(data)
answer = inputdlg({'Down Sample Size:'},'Input',[1 50],{'100000'});
if isempty(answer)~=1
down_sample_size = str2double(answer{1});
x = data.x_data;
y = data.y_data;
data_to_unique(:,1) = x;
data_to_unique(:,2) = y;
data_to_unique = unique(data_... |
df59d0bc9b1ee5e424c0baf7360bcce040cfc1f597fe1afa9df8b237461f9a7b | MATLAB | 1,854 | 57 | if ~(exist('mainpath','var') && exist('thisSubject','var'))
clearvars
% current working directory needs to be /path/to/scripts
mainpath = pwd;
thisSubject = 'S1';
else
clearvars -except mainpath thisSubject
end
addpath([mainpath filesep 'fmriRegAnalysis'])
addpath([mainpath filesep 'toolboxes' file... |
ee1a85b70bf248b3b2c888acdb0697c1ec285d309cb4005fc78c6ec33230a8f7 | MATLAB | 1,854 | 47 | function betas = e_fmri_betas_feature_signal(parameters, delete_files)
%% betas = d_betas_feature_signal(mainpath, subject, delete_files)
%mainpath = '/project/3018037.01/Experiment3.2_ERC/AnalysisFolder/scripts/scriptTemplates/..';
tc_struct2ws('caller', parameters.main);
%% parameters
tc_struct2ws('caller', paramete... |
08852552a633168dfc403eb2e28b5abe8232bbe8c3863a486e5a9c4b35476223 | MATLAB | 1,855 | 78 | function [state, params, LL_complete] = demo_saem_meg(Y, model)
% Y is the MEG data (real or simulated from ECoG)
% model struct contains
% G - lead field matrix
% included_verts - included source points
% pnts_e - mesh points xyz in mm
path_ = pwd;
addpath(genpath(strcat(path_,'/joint_est')));
%scalin... |
52ce0ece26133cbcde1a39643025a49383530931c817ab018eb5e8d50c7ad62d | MATLAB | 1,873 | 73 | function s=mtimes(m1,m2)
if (~isa(m1,'term') && ~isa(m1,'random') && numel(m1)>1) || ...
(~isa(m2,'term') && ~isa(m2,'random') && numel(m2)>1)
warning('If you don''t convert vectors to terms you can get unexpected results :-(')
end
if ~isa(m1,'random')
m1=random([],m1,[],inputname(1));
end
if ~isa... |
a664c34bfce2e0a0b1059cabbc356c115af05958b27b5e6c799ad8a76431858f | MATLAB | 1,878 | 40 | function [thr_max] = find_threshold(Time_freqx,Time_freqy,Time_freqz,timeVec,freqVec,t_pre,t_basel_on,t_basel_off);
%%% Time_freqx structure, with size 1 x number of trials; Time_freqx.B
%%% contains the TFR time x frequency along the x direction of the vector source activity
%%% Time_freqy structure, wi... |
20634d5c03492751b4d75bf2a64d6611da973187e9bfa9076f62897964d13456 | MATLAB | 1,881 | 60 | function[A,F] = DFA_fun(data,pts,order)
% -----------------------------------------------------
% DESCRIPTION:
% Function for the DFA analysis.
% INPUTS:
% data: a one-dimensional data vector.
% pts: sizes of the windows/bins at which to evaluate the fluctuation
% order: (optional) order of the polynomial for the loca... |
6809a3210d1dceabf68c98b25e6597830911705f37cabfba38bd21546bb23e69 | MATLAB | 1,897 | 71 | function [lmse,msd] = run_LFA(data_ts,n_lag, exp_var_lim)
%RUN_LFA Summary of this function goes here
% Detailed explanation goes here
%
%
% Input:
% data_ts = 3d matrix(double); variable x time x subj/trial/run
%
% n_lag = Number of future timepoints to predict (scalar int)
%
% exp_var_lim = percentage of expl... |
9ec6a5b5e92f499d701630bf6ff2823eb103f15c6a5ceabd59ca4bf4632f38fd | MATLAB | 1,902 | 59 | function Histo = BuildSpeedHistogram(Speeds,color)
% FUNCTION [BinCenters, BinCounts, BinCounts_low, Bincounts_high] = BuildSpeedHistogram(Speeds, Bins, plotting)
% This function takes as compulsory input: Speeds, a temporally ordered or
% disordered time-serie of dFC_Speeds and constructs an histogram of the speed
% ... |
1afeb3d9efbb858ed57301f27149147914bab8f4a88b0922344cf6789d15a4f0 | MATLAB | 1,906 | 48 | function [data_filter_above,data_filter_below] = loc_list_clusters_filter_area(data)
answer = inputdlg({'Enter Minimum Area per Cluster:'},'Input',[1 50],{'0.2'});
if isempty(answer)~=1
min_area = str2double(answer{1});
f = waitbar(0,'Filtering Based On Area...');
for i=1:length(data)
... |
4b6b80b0655164ab339530fb0c2749008db29a6362ca856968d797f1c4bbed65 | MATLAB | 1,907 | 83 | function [Y, GT, sigma_meas] = simu_meg_from_ecog(model, meas_snr, bio_snr)
% subs array
path = '/m/nbe/scratch/braintrack/net_neurosc_codes';
addpath(strcat(path, '/EcoG_simu'));
addpath(strcat(path,'/external/mvar_functions'))
addpath(strcat(path, '/external/mvar_functions/external/arfit'))
addpath(strcat(path,'/mi... |
9465a4f9fd82a323bd116a4eb14243ffa6e182832fe43c4dce893678aefdf5ad | MATLAB | 1,909 | 41 | function loc_list_delauny_segmentation(data)
answer = inputdlg({'maximum number of localizations for down sampling'},'Input',1,{'1000'});
if isempty(answer)~=1
num_points = str2double(answer{1});
for i=1:length(data)
m = length(data{i}.x_data);
data_sample(:,1) = data{i}.x_data;
... |
760969ebb96fcacfab5c7fde4e2ba9ec329207c6c82d397e969383aa82a561f8 | MATLAB | 1,912 | 75 | function [CIJ] = makeevenCIJ(N,K,sz_cl)
%MAKEEVENCIJ Synthetic modular small-world network
%
% CIJ = makeevenCIJ(N,K,sz_cl);
%
% This function generates a random, directed network with a specified
% number of fully connected modules linked together by evenly distributed
% remaining random connections.
... |
843e14f9d8f306c2ea799c8e116ab6b3ec5cba6a32d8b43f97a9789be9383a69 | MATLAB | 1,912 | 70 | function [v, a, Ter] = tc_ez_diffusion(pc, mrt, vrt, s)
if nargin < 4 || isempty(s), s = 0.1; end
% make sure inputs are column vectors for consistent broadcasting
pc = pc(:);
mrt = mrt(:);
vrt = vrt(:);
% check sizes
n = numel(pc);
if ~(numel(mrt)==n && numel(vrt)==n)
error('pc, mrt and vrt must have the same n... |
04000065ae09fb62afbee9cc622976a1dc719f8f81de156504d9831bb3588479 | MATLAB | 1,916 | 48 | function PlotSelectedNonPerm(inArg)
% inArg will be a struct with 2 fields:
% net_result is the net_result object selected by the user in the
% NLA_Result window
% test_method is the type of test (no_permutations, full_connectome, within_network_pair)
% net pair) of the selected test.
plot_... |
53a32f54623d7b992a73bb03f1e47bf8dcfae4ead5f6573b9135b522eb9f9209 | MATLAB | 1,918 | 75 | clear variables
addpath(genpath('/Volumes/Samsung_T5/Milan_DA/OS_ephys_da/CorticoHippocampal'))
addpath ('/Volumes/Samsung_T5/Milan_DA/OS_ephys_da/ADRITOOLS')
cd('/Volumes/Samsung_T5/Milan_DA/GCsd')
rat_folder = getfolder;
k = 8;
cd(rat_folder{k});
path = cd;
dinfo = dir(path);
dinfo = {dinfo.name};
dinfo(1) = [];
din... |
b9858ff48e04c87c2a20ee4fa6f666034444a1ca601fc971ded7be88209788d4 | MATLAB | 1,925 | 82 | function [ Y, Yav ] = SurfStatNorm( Y, mask, subdiv );
%Normalizes by subtracting the global mean, or dividing by it.
%
% Usage: [ Y, Yav ] = SurfStatNorm( Y [,mask [,subdiv ] ] );
%
% Y = n x v matrix or n x v x k array of data, v=#vertices,
% or memory map of same.
% mask = 1 x v logical vec... |
603a68ac1866012c42ccd778a476c28b59c90f0110c17e72b333e842b1be539b | MATLAB | 1,928 | 58 | function jds_rippleRateSleepState(animalprefixlist)
%JDS_RIPPLERATESLEEPSTATE Compare cortical ripple rates in NREM vs REM.
% jds_rippleRateSleepState(animalprefixlist) computes the cortical ripple
% rate (events/s) within NREM and REM for each sleep epoch, compares the
% two states (rank-sum), and plots a boxplo... |
e76572bf072f8857f7c201732ce2a03368a418ec579c5050c7249681ebf8aef6 | MATLAB | 1,928 | 63 | %% load files
% Loading PD-1.tif
[PD1_file, PD1_path] = uigetfile('*.tif'); % PD-1_1.tif
tiffread(PD1_file)
PD1 = ans.data;
clear ans
% Loading masking file
[neuron_file, neuron_path] = uigetfile('*.tif'); % Neuron_1.tif
tiffread(neuron_file)
neuron = ans.data;
clear ans
[microglia_file, microglia_p... |
aef4ce5024a8699da6ad5cb741c1d3d5e972e9b92d4c3f0abc45b22fd4619df5 | MATLAB | 1,931 | 58 | %% Generation of CFTd surrogates - useful to test the PDC - VERSION FOR EXTENDED MVAR MODEL!
%%% ABSENCE OF DIRECT CAUSALITY FROM yj TO yi - Bij coefficients are forced to zero
% Note: the length of the series should be not be odd
%%% input:
% Y, M*N matrix of time series (each time series is in a row)
% Bm: ext... |
80b1eccb3ce6d9b3d80e74e2a215950d904a203736454438d82626ee10299813 | MATLAB | 1,934 | 67 | function sFile = nst_save_table_in_bst(t, subject_name, condition, comment, extra, displayUnits)
% Save a table as a matrix brainstorm item:
% Field Std is not used
% Fields Time and Description are not used because their interpretation by
% brainstorm is not straightfoward.
% Description sometimes refer to row... |
7caa7b717aba26ba8bd4e9b71124832996a742ea537e36c3f3a2492af301fb7f | MATLAB | 1,936 | 70 | function [typSpeed, Speeds] = dFC_Speeds(dFCstream, vstep)
% FUNCTION [typSpeed, Speeds] = dFC_Speeds(dFCstream, vstep)
% takes dFCstream ('2D' or '3D') generated by TS2dFCstream function and vstep
% as input, telling the distance in terms of stream steps over which to
% compute a speed of variation (via correlation d... |
ffb5ffd87c206678d7c61d398ce6fc0865607ecb0df30843990d28b369fac12a | MATLAB | 1,937 | 45 | function MFPT = mean_first_passage_time(adj)
% MEAN_FIRST_PASSAGE_TIME Mean first passage time
%
% MFPT = mean_first_passage_time(adj)
%
% The first passage time (MFPT) from i to j is the expected number of
% steps it takes a random walker starting at node i to arrive for the
% first time at node j. T... |
653c6f92193c845e9017482029bb1375b660972488115f20468d6dc5bf48803a | MATLAB | 1,938 | 46 | clear;clc;close all
%-------------input parameters for voronoi tesselation--------------%
minimum_number_of_cells_per_cluster = 5; %voronoi-cells will be identified as clusters if at leat M-many cells are connected
clear;clc;close all
[file_name,path] = uigetfile('*.bin','Select .txt File(s)','MultiSelect','on');
... |
c482862c2fc5e0a02002b8be7ff819de815e7f9b8c60b9eac07b385fb6af8a1e | MATLAB | 1,939 | 63 | classdef nst_math_WelfordVariance < handle
properties (SetAccess = private)
Count = 0 % Total number of matrix samples processed
N = 0 % Expected total number of sample
Mean % Matrix of running means [M x N]
end
properties (Access = private)
M2 ... |
f4b64490f7979b426806bf9a1b7ee48aa169bff947a0a26f3f8ef3339d0d8257 | MATLAB | 1,939 | 45 | function [P_singlesub] = computing_morletwavelet(freqq, delta_f, path, indexes, compp, srate,baseline, norm)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Calls the function morlet_transform_singlesub and compute the transform
% for each subject.
%
% INPUT:
% -freqq = (array) all the fr... |
bdbb9faf7b5d280469e23875270da730fb3e26a03c93008aa11c6b58ea1bfc76 | MATLAB | 1,942 | 56 | function G = surface_to_graph(S,distance,mask,removeDegreeZero)
% SURFACE_TO_GRAPH Converts a surface to a graph.
%
% G = SURFACE_TO_GRAPH(S,distance) converts surface
% S into graph G. Distance can either be "mesh", where edge lengths are 1
% if a connection exists and 0 otherwise, or "geodesic", where edge
% ... |
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