sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
2108fcdc165a45335a14f1c113b3a8fdd9fe266f0111e4f8ca733ec0e2367f4d | MATLAB | 4,164 | 148 | clear
close all
clc
% load ECG1
% s = data;
load ECG2
s = data(14, 1:end);
% s = randn(1, 10000); fs = 1000;
% load ECG3
% s = data(2, 1:end);
% s = resample(s, 0.3*fs, fs);
% fs = 0.3*fs;
s = LPFilter(s - LPFilter(s, 1.0/fs), 100.0/fs);
s = s(:)';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
280881ccd70702893938f19bb3e61f767bceb49fd967486690658e9be3357c0b | MATLAB | 4,167 | 152 | close all; clear mex;
funtype = 'gpu';
%funtype = 'cpu';
%funtype = 'matlab';
kCNNFolder = '..';
addpath(kCNNFolder);
addpath(fullfile(kCNNFolder, 'matlab'));
if (strcmp(funtype, 'gpu') || strcmp(funtype, 'cpu'))
kMexFolder = fullfile(kCNNFolder, 'c++', 'build');
kBuildFolder = fullfile(kMexFolder, funtyp... |
e83e54442b1d13c694d184b733a023e538714cbbf8e506a7bb89c171f78e9041 | MATLAB | 4,172 | 112 | function [sig] = digitalLIA(modSig,refSig,freq,Fs,lpCut,filtOrder)
%Digital Lock-In Amplifier Demodulation
%
% [sig,filtStruct] = digitalLIA(modSig,refSig,Fs,lpCut,filtOrder)
%
% Description: This function demodulates a signal using a phase sensitive
% detection method. The function takes two inputs: modula... |
7bad1aaba4956943d3271fd7c8ecdf27bc59cb4e5fd07c25037bb46506f9f610 | MATLAB | 4,173 | 154 | function controlfilename = createLCmodel_control_laser117T(metabname,l1,l2,l3,preproc_dir)
do_water_scaling=0;
doecc=0;
%basis_set_file_path = '/home/romain/tmp/basis/basis_LASER_TE_20_11T.BASIS';
basis_set_file_path = '/servernas/home/romain/data/spectro/Basis_set/laser_11T_mouse/basis_LASER_TE_20_117T_MM.BASIS'
... |
a1ac73669c649884cdde1c84739f4258d1e651daa048f1b98a2daecde349c5fb | MATLAB | 4,175 | 136 |
%{
----------------------------------------------------------------------------
Determine the time window for task relevant neurons
%Start of task
%Sound on
%Sound off
%Before choice
%After choice (0sec)
%After choice (1sec)
%After choice (2sec)
%p = 0.001
%Each epoch, predict the prior, sensory and choice... |
b22a2342d3d1642fb3a7b1a4cf23ae715ca64bb509d4c24d47295bdc05fc0dd1 | MATLAB | 4,185 | 118 | function seq = cosmoother_gpfa_viaOrth_fast(seq, params, mList)
%
% seq = cosmoother_gpfa_viaOrth_fast(seq, params, mList)
%
% Performs leave-neuron-out prediction for GPFA. This version takes
% advantage of R being diagonal for computational savings.
%
% INPUTS:
%
% seq - test data structure
% params - ... |
9d5bc6f48f61460470e1e4408a5c317c7928c1e807b8f326a4fc2a09d7b61e6f | MATLAB | 4,188 | 153 | function controlfilename = createLCmodel_control_mega_2HG(metabname,l1,l2,l3,preproc_dir)
nb=1;
do_water_scaling=0;
phantome=0;
basis_set_file_path = '/home/romain/data/spectro/Basis_set/MEGA5_inv/MEGA5_inv.BASIS';
%basis_set_file_path = '/home/romain/data/spectro/Basis_set/MEGA5_inv_no_gln/MEGA5.BASIS';
%basis_set_... |
1db31ebf4eacb14833b04dc3dd9b0ec848292478738e5716f8020d54200fbcad | MATLAB | 4,190 | 109 |
%{
----------------------------------------------------------------------------
Determine the time window for task relevant neurons
%Start of task
%Sound on
%Sound off
%Before choice
%After choice (0sec)
%After choice (1sec)
%After choice (2sec)
%p = 0.001
%Each epoch, predict the prior, sensory and choice... |
421fc209154148b763af0366d32e631bd6bff466128e8d2e76be952f1cdd8cb7 | MATLAB | 4,190 | 120 | function PVD_Generate_Dataset(Im_In,Im_Out,S)
% This function generates a training set made of input-output pairs of PVD patches from annotated grayscale images.
% Note:
% To generate samples with an even number of rows and columns, an asymmetric x-range and y-range are used around each pixel (-d:(d-1)).
% In... |
177a7a84b346eb1fe221b781b9ed55f3818a515dba57a7bf5179257dbc0e18d5 | MATLAB | 4,192 | 173 | % with reduced dimensions
clear;
close all;
fs = 1000;
data = load('s0010_re.txt');
% data = load('s0014lre.txt');
time = data(:,1)';
data = data(:,2:end)';
data = data - LPFilter(data,1.5/fs);
data = LPFilter(data,150/fs);
I = 1:10*fs;
data = data(:,I);
N = length(data);
% % % mn = mean(da... |
535ae6797254b5ce3e2be4330ef68b69d541f8b06504a490dd7dc16c8b410fef | MATLAB | 4,193 | 133 | function createBasisSet()
par.BasisSetDir = '/home/romain/data/spectro/Basis_set/Gosia/new_MEGA/basis_MEGA_edit_CENIR_SS/';
par.name_basis = 'basis_SS_inv';
par.element_basis_name={'NAA','GABA','GLU','MM'};
par.element_basis_name={'NAA','GABA','GLU','GLN','MM'};
par.element_basis_filename={'NAA','GABA','Glu','Gln','... |
4df772b56b503b22edaedc25f1342bd41ddc98fe32e991a5d73f47d015651b18 | MATLAB | 4,197 | 86 | function [rr_interval_corrected, rr_interval_differences, matched_peaks_indexes, smoothed_matched_output] = peak_det_matched_filter_pwr_env(x, matched_template, type, wlen, PP_diff_wlen, PP_diff_th, average_peak_det_rate, fs, varargin)
% peak_det_matched_filter_pwr_env - matched filter R-Peak detector
% and smoothed he... |
5eff597c8de2606e13a84bc19ff094826c7198dce0432c065d6c2058867fc1fb | MATLAB | 4,200 | 137 |
%{
----------------------------------------------------------------------------
First_take number of frames in each tif files
Analyzing imaging data simply
At least for the correct rate
----------------------------------------------------------------------------
%}
function Figure3c_Task_kaiseki_tokyo1_2022021... |
01486589d9dd4084eb15555993d22002ba8726c895eec7dcea9745b5c35265bc | MATLAB | 4,208 | 171 | % read .PRINT file and plot data
% valabregue 02/2012
function [met cormat] = readprint(filename)
%filename = '/home/romain/data/spectro/hipo/repro_study/lcmodel/9session_Naj_last_mean/BA_MM/REPRO_HIPO_01_mean.PRINT';
[pathstr,fname,ext] = fileparts(filename);
lcmodelresults.name=fname;
% open .COORD file
fprintf('... |
7857aaf785789bbd12620eb10c47117781cd40c3996b329b2fe3ee0948caaae9 | MATLAB | 4,211 | 136 | function [ch_table_new] = Bad_Channels(data_f,ch_table,fs)
%Plots channels and provides ui to remove bad channels
% data_f... nsamps x nchans matrix
% time...time vector
% ch_table...table containing columns name, status, with channel names and
% good/bad status
% hp/lp...filter cut off values for band pas... |
e0435d83f1977c9dbe65bd215c60dec9d02f9e40e8a2100e48140cc2b121f3cc | MATLAB | 4,211 | 150 | %This script will generate data and then fit pibdfc as an example.
%%
addpath('pibdfc_functions');
addpath('bdfc_functions');
rng(34);
%% Generating the data
run('create_gaussian_simset.m');
%% Concatonating dataset
y_dat = zeros(N*Subs,R);
true_states_long = reshape(true_states(:,1:Subs),N*Subs,1);
for i = 1:Subs
... |
14ec9ceb52b6ba3a67843454a8fb0925aebcd5568aee8330101fb6198c0e075a | MATLAB | 4,214 | 107 | function [BrightnessThresh]=mnl_DetermineBrightnessEffects_v4(FragmentValues,FragLength,EuclideanThreshold,ErrorThresh)
%This is the version 2 of determining what is an appropriate brightness
%threshold to use based on the data provided. It was initially designed to
%be compatible with mnl_ProcessTracesFromNeurolucida_... |
b3be7e78efabef64ea69bea4eebe17ed4b897cdc9f2b2d18b6b3069ac8fa9a57 | MATLAB | 4,214 | 176 | % with reduced dimensions
clear;
close all;
fs = 1000;
data = load('s0010_re.txt');
% data = load('s0014lre.txt');
time = data(:,1)';
data = data(:,2:end)';
data = data - LPFilter(data,1.5/fs);
data = LPFilter(data,150/fs);
I = 1:10*fs;
data = data(:,I);
N = length(data);
% % % % mn = mean(... |
a1666eb1d4aadfeaf117867b74fffb99b08dd0a0751a9ac46f854f476f5be7f1 | MATLAB | 4,230 | 118 | load(cleanupFile)
run([RETROMOCOBOX_PATH '/addRetroMoCoBoxToPath.m']);
%run([MIRT_PATH '/setup.m']);
addpath(SPM_PATH);
%%
fidHTML = fopen([htmlDir '/index.html'],'a');
%% And put the reconstructed images into the html
all_ims = rn([outDir '/GRE.nii']);
all_ims_corrected = rn([outDir '/GRE_corrected.nii']);
ov... |
2bfba94cca7217197989fc188095eb620ddef853b86f7a79654576604893b9ff | MATLAB | 4,234 | 107 | function Both_RLt_Pattern_4Seq_20200304
path=['C:\Users\C238\Desktop\touchcellLtRt_terashita'];
cd(path);
LS=ls;
RLtouchPoint=zeros(1,7);
RLtouchFire=zeros(1,7);
X=[];
RLtouchUnit=[];
RLtouchPointUnit=[];
for i=1:length(LS(:,1))
TrimFolder=strtrim(LS(i,:));
if ~strcmp(TrimFolder,'.') &&... |
968894d6820839eca15ff27ea11c5ae5fe15ed8649609bef1146d397531b8906 | MATLAB | 4,235 | 145 | function []=mnl_ViewXFPdistribution_Model(Dimension)
NumDim=size(Dimension,2);
figure
for i=1:NumDim
NumCopies=size(Dimension(i).Cells,2);
for j=1:NumCopies
VecNormMatrix=Dimension(i).Cells(j).NormXFPvals;
tn=sprintf('%d%s%d',i,' Dimensions, Copy Number - ',Dimension(i).Cells(j).CopyNumber);
... |
c5c733d582cfdd7b3060207e8d0178891f104875638b189acd5c04e8e5626d72 | MATLAB | 4,237 | 100 | function [data,numChan,labels,txt,fs,gain,prefiltering,ChanDim] = eeg_read_bdf(filename,SecLoad,reshape)
% this function loads at least "SecLoad" seconds of EEG data from the
% specified bdf-file "filename". If all available data is needed, specify
% 'all' instead of "SecLoad" like this:
% [...] = eeg_read_bdf('test.b... |
e8c0a8160e7334b599a40d8e0378045b869b0ef4a5a1f21b7638eae22f53dcb5 | MATLAB | 4,237 | 155 | function controlfilename = createLCmodel_control_laser(metabname,l1,l2,l3,preproc_dir)
do_water_scaling=1;
doecc=0;
TEMET=0;
basis_set_file_path = '/home/romain/data/spectro/Basis_set/sead/3T_SIM_SEAD_FRANCE_2011_11_23.BASIS';
[pathstr,filename ] = fileparts(metabname)
wd = fullfile(pathstr,preproc_dir)
if ~exist(... |
8a4dd70271bd68d7a89bf2f2fd6eb180aa1d6bc8d3d67c4c1db75725e81970d3 | MATLAB | 4,238 | 112 | %%
clear
[file, path] = uigetfile('*.mat');%When the user clicks the load data button, a window should open to enable the user to select a file.
datafile = fullfile(path, file); %save path
load(datafile)
close all
clc
delay = 1.16;
%%
%sort clusters by size
[~, I] = sort(clusters.num_of_cells, 'descend')
binsize = 0.1... |
0d21f1c392dc665457d23e7b119fb8ed53370f3c70183fa02a1366fb176e5ee4 | MATLAB | 4,240 | 112 | %new calculate premutation paeird t-test only on cimogenetics before and after cells
clc
clear
[file, path] = uigetfile('*.mat','MultiSelect','on');%When the user clicks the load psetion button, a window should open to enable the user to select a file.
cd(path)
datafile = fullfile(path, file); %save path
%%
if ~iscell(... |
7449c69247ed455c92efe1068ef6702e4baeb0f2ce735102692a867e1d27cc0d | MATLAB | 4,240 | 116 | function W = Adjust_Rect_Width_Rot_Generalized(Im,Rotation_Origin,Rect_Angle,Rect_Length,Rect_Width_Min_Max,Origin_Type,Smoothing_Parameter,Width_Ratio,Im_Rows,Im_Cols)
% Description:
% This function detects the local apparent width of the neuron.
% Calling functions: Find_Cell_Body, Add_CB_Branch, Trace1.
% In... |
14954277617bef087fc438b09051365bb01bc9d6b356709bff411eb3e2b0c2b0 | MATLAB | 4,241 | 109 | function [lead_fields,UnitMDip] = Forward_OPM_single_sphere(x,y,z,slots_all,normal_unit,sphere_origin)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
mean_sphere_origin = sphere_origin;
%% number of channels
Nch = size(slots_all,1);
%% voxel position in CTF and in metres
vox_pos_ctf = [x y ... |
d0f46d2db6d0efdfe3059dd17b449e2e5ef22c7f115ce7cfa00a4d637b448b39 | MATLAB | 4,245 | 108 | function [ecg, phi] = ecg_gen_stochastic(N, fs, varargin)
% ecg_gen_stochastic - Generate synthetic single-channel ECG with beat-wise stochastic deviations.
%
% Syntax:
% [ecg, phi] = ecg_gen_stochastic(N, fs, params)
% [ecg, phi] = ecg_gen_stochastic(N, fs, f, f_deviations, alpha, delta_alpha, b, delta_b, the... |
8b8669c41cc54fc3ec976dfb2fe2df987f50449fc798e513e0951ccb62bad1db | MATLAB | 4,250 | 133 | % A test script for Laplacian Eigen-maps for ECG signals
% Reza Sameni, 2020
% The The Open-Source Electrophysiological Toolbox
% https://github.com/alphanumericslab/OSET.git
clc
clear
close all;
load('SampleECG2.mat'); data = data(:,2:end)';
%/////////////////////////////////////////////////////////////////////////... |
8c048f1189510dcbc32dc93f194007a08c705c02130ac3af26ea883fda01e5a8 | MATLAB | 4,250 | 149 | function [Data, t] = simulate_periodic_eeg(CenterFrequency, BurstAmplitude, BurstDensity, BurstDuration, Duration, SampleRate, Plot)
arguments
CenterFrequency = 10;
BurstAmplitude = 20;
BurstDensity = .5;
BurstDuration = 1;
Duration = 10;
SampleRate = 250;
Plot = false;
end
% [Data, t] = sim... |
c4236e88e685db3004412dae0154fd4119ce16ffc5738af009ede9b941c6f01e | MATLAB | 4,255 | 126 | function [fid, dcmInfo_arr] = spec_read_all(dn, sortmethod)
% function to read spectroscopy time series data from a
% directory with sequentially named siemens .ima/.dcm files
% usage: [fid, dcmInfo_arr] = spec_read_all(dn, [sort])
% arguments dn = filename, [sort 0=Filename/Alphabetical, 1=InstanceNumber]
v... |
edd51af5bcc3ab917721ba82ea89cddc4c1cc9c49e226685a9a5ab4a078447eb | MATLAB | 4,257 | 89 | function [AD, readOut] = ArrayDesignerGRASP(AD)
%Book-keeping / unpacking for brevity
pathnameAD = AD.inputs.pathnameAD;
pathnameROI = AD.inputs.pathnameROI;
pathnameSolSpace = fullfile(AD.inputs.pathnameHeadModel,'Utils','scalpSolutionSpace_10_2p5.txt');
pathnameSolSpaceLabels = fullfile(AD.inputs.pathnameHeadModel,'... |
4d0eb61a744194ed524384b423864bb7f080f4c33d5f4a6f27d0d6174d31ed49 | MATLAB | 4,266 | 150 | function [peaks, peak_indexes] = peak_det_adaptive_hr(x, ff, fs, varargin)
% peak_det_adaptive_hr - R-peak detector based on max search over sliding window with adaptive width
% Syntax:
% [peaks, peak_indexes] = peak_det_adaptive_hr(x, ff, fs, flag, [th, th2], rejection_threshold)
%
% Method:
% detects R-pea... |
81d4d6db7a0de1321be238f66724b7b4ff00a2bf8aea9f68eae6bd5bc4f6b223 | MATLAB | 4,267 | 89 | function [x,v] = randfixedsum(n,m,s,a,b)
% [x,v] = randfixedsum(n,m,s,a,b)
%
% This generates an n by m array x, each of whose m columns
% contains n random values lying in the interval [a,b], but
% subject to the condition that their sum be equal to s. The
% scalar value s must accordingly satisfy n*a <= s <= n*b. ... |
ae6f8e11261eedbf180f11070094446c844fd614374fd924c84974fd9def66e1 | MATLAB | 4,268 | 121 | function Colors = pick_colors(N, Order, Color)
% Selects from colorblind friendly pallettes the requested colors.
% N can be either 1 or 2 values; the first indicates the number of hues,
% the second the number of luminance changes. if no second value is
% indicated, than a N x RGB matrix is returned. Otherwise, an N(2... |
1d7f47fe111956e722dc53728db62c62ee24fe8ba1c87ec7ef742f0c3aa55144 | MATLAB | 4,270 | 112 | function nnd_distance_map_2 = nnd_distance_map(skeleton_data, radii_data,target_resolution, atlas_resoultion, atlas_size)
asd = ones(atlas_size);
asd_x = asd .* [1:1:atlas_size(1)]'.* atlas_resoultion;
asd_y = asd .* [1:1:atlas_size(2)].* atlas_resoultion;
asd_z = asd .* permute([1:1:atlas_size(3)], [1,3,2]).* ... |
21c535e428287657d0bfee84400d02245dc51bf54b37f24c2f18882c61f22040 | MATLAB | 4,271 | 148 | clear
close all;
R1 = importdata('/Users/rsameni/Desktop/Seizure/kaggle/SourcesWithResultsBackup/testFreqDomainEnergyIncreaseRate01.txt');
R2 = importdata('/Users/rsameni/Desktop/Seizure/kaggle/SourcesWithResultsBackup/testICASubspaceAngles01.txt');
R3 = importdata('/Users/rsameni/Desktop/Seizure/kaggle/SourcesWithRes... |
c529ab43ffa74ca86f6ae2043ab58e4a518adedbc88f32c214f787a5381e5992 | MATLAB | 4,271 | 174 | function [spikeMatrix, peak_idxs, peakTimes_ms, classes, peak_vals, dip1, dip2 ]=comBIN12_v01(aaa,filename,NBINS_peak,NBINS_dip,NBINS_dip2,npointsbeforepeak,npointsafterpeak)
if nargin<6
npointsbeforepeak=20;
npointsafterpeak=20;
end
load(filename,'data','sr');
data=data';
templateMatchedSpikes=aaa.spikes;
peak... |
602a07134738cde2dfab4751bac239304f7431a844bd1343d0f00dda7ff53ca7 | MATLAB | 4,272 | 117 | function [ ripple_info ] = ripple_detection_apply_threshold( lfp_signal, lfp_threshold )
% Usage:
% [ ripple_info ] = ripple_detection_apply_threshold( lfp_signal, lfp_threshold )
% Input:
% lfp_signal Return value of ripple_detection_preprocess
% lfp_threshold Return value of ripple_detection_determi... |
02b2fe11d8d7f1d58fa57725a7398735be47fff05c322c90ab9bc4091c6dd8bc | MATLAB | 4,274 | 117 | % EM algorithm, bic and plot
% ------------------------------------------
% This file is part of the adaptative_threshold SPM plug-in
% Copyright (C) Cyril Pernet and Chris Gorgolewski 2012
classdef ExpectationMaximization < handle
properties
components
mix
end
methods
func... |
083163ef54d9ecb8243408a1b0c158efe6df123a0a3c871d3753d7937def5830 | MATLAB | 4,274 | 135 | function [CSIdata] = write_fid_to_nii(fids,rootdir,naming_convention)
if ~exist('rootdir')
rootdir=spm_select([1 Inf],'dir','Select directories to write nii','',pwd);
end
if ~exist('naming_convention')
naming_convention=3;
end
for k=1:length(fids)
f=fids(k);
CSIdata.dim = [1 1 1];
CSIdata.vox =... |
494a122b1dba22c67952f64a2289295711b1e33ec2f3235e9a49a11fccd07e63 | MATLAB | 4,275 | 175 | % Test semsitibity od different ECG segments to ICA dim reduction
% Reza Sameni, Copyright 2008
clear;
close all;
fs = 1000;
data = load('s0010_re.txt');
% data = load('s0014lre.txt');
time = data(:,1)';
data = data(:,2:end)';
data = data - LPFilter(data,1.5/fs);
data = LPFilter(data,150/fs);
I =... |
b9b53cb71db89bcbf387cc4f1583320704b20a92d7459fd324f3078450664e4a | MATLAB | 4,281 | 177 | function [spikeMatrix, peak_idxs, peakTimes_ms, classes, peak_vals, dip1, dip2 ]=comBIN12_v01(aaa,filename,NBINS_peak,NBINS_dip,NBINS_dip2,npointsbeforepeak,npointsafterpeak)
if nargin<6
npointsbeforepeak=20;
npointsafterpeak=20;
end
load(filename,'data','sr');
data=data';
sr=1e4;
templateMatchedSpikes=aaa.sp... |
42138de6467f4f713b814d2384dfd57a9c9650a7eeed665343eb6ee79dd9bc5c | MATLAB | 4,284 | 135 | load osCategorizedTable.mat
% NRvnTable = osCategorizedTable(strcmp(osCategorizedTable.condition,"NR"),:);
NRvnTable = osCategorizedTable(strcmp(osCategorizedTable.condition,"NR") | strcmp(osCategorizedTable.condition,"DR"),:);
dataTable = NRvnTable;
DistFromMainAxis = 0;
DistAlongMainAxis = 0;
%Only mainDi... |
3a5c5de8dfcb03cbf5034109f7de69b71768fc143c170ec11a3c18ae0125e94a | MATLAB | 4,286 | 134 | function samplesOut = bandpassFilter(samplesIn, hCfg)
%BANDPASSFILTER Summary of this function goes here
try
hCfg.updateLog('bpFilt', sprintf('Applying bandpass filter to %d samples', size(samplesIn, 1)), 1, 0);
samplesOut = filtfiltChain(single(samplesIn), hCfg);
catch ME
hCfg.updat... |
b553102665d7f3039ea2bf728a72e106184ee910181a7479854bbd10ebcc8593 | MATLAB | 4,286 | 120 | function flag = recover(obj, ask)
%RECOVER Inconsistent data recovery, with a hammer
%returns -1 if canceled by user,
% 0 if failed
% 1 if succeeded and spikeClusters DID NOT change
% 2 if succeeded and spikeClusters DID change
if nargin < 2
ask = 0;
end
... |
99b64ecc17f1d9ff92220254a84790c0c8c03c71ce23477a92ff6c2c91b8d796 | MATLAB | 4,299 | 148 | clear
close all;
R1 = importdata('/Users/rsameni/Desktop/Seizure/kaggle/SourcesWithResultsBackup/testFreqDomainEnergyIncreaseRate01.txt');
R2 = importdata('/Users/rsameni/Desktop/Seizure/kaggle/SourcesWithResultsBackup/testICASubspaceAngles01.txt');
R3 = importdata('/Users/rsameni/Desktop/Seizure/kaggle/SourcesWithRe... |
4ba67af06ded2027863b28ad83698fd103466bf633a7fd92f6d6eca14956351f | MATLAB | 4,304 | 145 | clear all
% this script contains code to reproduce Figure 3b, 3e-d
%% load data
% drag and drop corresponding file (S3 Data)
%% data content description
% D contains the region acronyms
% I_anes contains segemented and trial-averged data from anesthetized recordings (100 regions x 192 frames x 32 sessio... |
4cdc5d0f082e0f62ff5869a3066974fbdd09e99add6c7dabdb643bf98ebb82ab | MATLAB | 4,304 | 135 | function [clu, tree] = run_cluster(par, multi_files)
dim = par.inputs;
fname = par.fnamespc;
fname_in = par.fname_in;
% DELETE PREVIOUS FILES
if exist([fname '.dg_01.lab'],'file')
delete([fname '.dg_01.lab']);
delete([fname '.dg_01']);
end
dat = load(fname_in);
n = length(dat);
fid = fopen(sprintf('%s.run',fn... |
466e2cbed67d28df3ba31f4b699b88f617a8614d890b979af765e2d2957b0547 | MATLAB | 4,307 | 131 | function extract_cell_data(file, cell_num,ploting)
cell_name = ['cell_' cell_num];
load(file)
cluster_num = file(1:(end-4));
mkdir (cluster_num)
cd(cluster_num)
cell_data = all_data.(cell_name); %relevant_cell
response_window = 65:125;
mkdir(cell_name)
cd(cell_name)
% cell_data = clusters.cluster_cells.cluster_3{1,31}... |
b4d6eabb3f39761a681b50f09838c42b97e687a61ecc2993a218e13475500322 | MATLAB | 4,308 | 119 | % datarange = 1:length(data);
datarange = 137:139;
dt = 5e-5;
for i =datarange
if isempty(data(i).EPSP)
continue
end
data(i).amp_single = [];
data(i).halfwidth_single = [];
data(i).maxdv_single = [];
data(i).area_single = [];
data(i).risetime_single = [];
if isempty(data(i).EPSP)... |
958088862d85b8cee588a94d19365ab8d6b207dbe76c732763caf6c869a7ca25 | MATLAB | 4,309 | 121 | function varargout = wave_clus_aux(varargin)
% WAVE_CLUS_AUX M-file for wave_clus_aux.fig
% WAVE_CLUS_AUX, by itself, creates a new WAVE_CLUS_AUX or raises the existing
% singleton*.
%
% H = WAVE_CLUS_AUX returns the handle to a new WAVE_CLUS_AUX or the handle to
% the existing singleton*.
%
% ... |
9983bdb2490a3ec6bf38222c8ee6a79bf512b66fa536257bb327408b60b22bbd | MATLAB | 4,310 | 164 | % Return time frame of a NIFTI dataset. Support both *.nii and
% *.hdr/*.img file extension. If file extension is not provided,
% *.hdr/*.img will be used as default.
%
% It is a lightweighted "load_nii_hdr", and is equivalent to
% hdr.dime.dim(5)
%
% Usage: [ total_scan ] = get_nii_frame(filename)
%
% filen... |
ed8c736fdaae47c0ccf943a37c5af0f4bc7826035b844dbc3f00f4ded6150ab9 | MATLAB | 4,313 | 129 | load paper_indices; load demographic;
LABELS = group(inds==1);
del_inds = find(LABELS==-1); dup_inds = find(LABELS==1); con_inds = find(LABELS==0);
%load the feature matrices
load TBM_gray_down/features;
GM = features;
load TBM_white_down/features;
WM = features;
%%things to modify
GRP_INDS = 1:206;
numIter = 100;... |
9e10a42114e98b7fa5daa0099043a4631a4e8909acdfc6c354b605e0f073f495 | MATLAB | 4,314 | 135 | function [clu, tree] = run_cluster(par, multi_files)
dim = par.inputs;
fname = par.fnamespc;
fname_in = par.fname_in;
% DELETE PREVIOUS FILES
if exist([fname '.dg_01.lab'],'file')
delete([fname '.dg_01.lab']);
delete([fname '.dg_01']);
end
dat = load(fname_in);
n = length(dat);
fid = fopen(sprintf('%s.run',f... |
2e762ca319fb14506f736345f769cd4d11f67b7ccad07245098e14ba74b8483c | MATLAB | 4,321 | 127 | function [sqrl_all, Z, rho_all, eta] = multi_nhmm_pg_draw(xDat, sqrl_old, Z_old, Z_prior, rho_old, eta_old, eta_prior, s_assign, a0, b0)
%% This function is a Gibbs sampling step to sample new values for the log linear coefficients
%% in the Holsclaw et al. paper for sampling from heterogenous HMMs.
%% Inputs:
% Xi_o... |
53b739d28bb734d17aec50310fad6a85d7475643c3cdf257ef42489210d3c28b | MATLAB | 4,322 | 92 | %% Transfer Entropy Calculation
%Set-up - need installation of JIDT toolbox https://github.com/jlizier/jidt
%Add JIDT jar library to the path, and disable warnings that it's already there:
warning('off','MATLAB:Java:DuplicateClass');
%javaaddpath('C:\\Users\\natas\\Documents\\PhD\\Code\\infodynamics-dist-1.6.1\\inf... |
7ece6f750a114e24a4a55c7e0182edbe14a074d8e49ad9ac663ea8c5f20ff59b | MATLAB | 4,322 | 154 | close all;
clear;
clc;
% parameters
subject = 'Dog_2';
path = ['J:\Seizure\' subject '\'];
ch = 1:16; % desired channels
time = [7 10]*60; % in seconds
d = dir([path '*.mat']);
NumRecords = length(d);
mode = zeros(1, NumRecords);
powerradio = zeros(1, NumRecords);
A = zeros(1, NumRecords);
skew = zeros(1, NumRecords)... |
0f0f3b5dd45bef9ece25c53bb41010d6630478fd072adeadbe07c4da1dcc0a8f | MATLAB | 4,323 | 118 |
%{
----------------------------------------------------------------------------
Determine the time window for task relevant neurons
%Start of task
%Sound on
%Sound off
%Before choice
%After choice (0sec)
%After choice (1sec)
%After choice (2sec)
%p = 0.001
%Each epoch, predict the prior, sensory and choice... |
bbba8a6797b8c48b1ff072635721002fa3ab2bdaadf8b610805d5aebd5438841 | MATLAB | 4,326 | 120 | function lick_psth_summary_FF(sessions,resdir,issave)
%LICK_PSTH_SUMMARY_FF Average lick PETH for fiber photometry recordings.
% LICK_PSTH_SUMMARY_FF(SESSIONS,RESDIR,ISSAVE) plots average lick PETH
% (beam break time stamps aligned to an event). Session indicated in
% SESSIONS as pairs of animal and folder na... |
3fec28a282044c193accf39ef82bd20e41cce9e60cfa50a21d3f7f5b3353e699 | MATLAB | 4,327 | 152 | function intervalHist190507
%Ϊ Á½Æ±ëÌC^[oÌqXgOðìé
%ÔC^[o
% cd('C:\Users\C238\Desktop\CheetahWRLV20180729\119\2017-6-7_20-8-33 11901-51');
% dpath=('C:\Users\C238\Desktop\CheetahWRLV20180729\119\2017-6-7_20-8-33 11901-51\');
% dpath3=('C:\Users\C238\Desktop\CheetahWRLV... |
8036dae5a3cac6759b937da2808add61e943a30d2787164f863a2f197b3a2ac1 | MATLAB | 4,327 | 117 | %{
----------------------------------------------------------------------------
Determine the time window for task relevant neurons
%Start of task
%Sound on
%Sound off
%Before choice
%After choice (0sec)
%After choice (1sec)
%After choice (2sec)
%p = 0.001
%Each epoch, predict the prior, sensory and choice (... |
eeb0b45ef785e84c581166c6a4ea0473637e1370a70070821f597d20e250195d | MATLAB | 4,328 | 127 | function [sqrl_all, Z, rho_all, eta] = multi_nhmm_pg_draw_par(xDat, sqrl_old, Z_old, Z_prior, rho_old, eta_old, eta_prior, s_assign, a0, b0)
%% This function is a Gibbs sampling step to sample new values for the log linear coefficients
%% in the Holsclaw et al. paper for sampling from heterogenous HMMs.
%% Inputs:
% ... |
152dfafa7a01ee69fde9bc495e5d19780f7506342229223c80505644a2c0523b | MATLAB | 4,329 | 130 | function create_mat_file(filename,varargin)
%create_mat_file function to create .mat files readable by the MNG toolbox
%
% create_mat_file(filename,varargin) create a mat file named filename
% defined by varargin
% create_mat_file(__, Name, Values)
%
% Name-Values Arguments
%
% 'channel' - define channel w... |
69d80b6ffd244a4550b9187d818b04484e333f82388b73e7ac2ef637e00bb25a | MATLAB | 4,331 | 162 | %% plot av/err vs iti/rand for different number of iti transitions
[cols,alpha] = chooseColors;
figure
for g = 1:length(gaging_var)
subplot(2,3,g)
hold on
% plot av/err
dc_rep_mean = squeeze(mean(all_acc(:,g,1,:,:),4))*100;
sample_mean = mean(dc_rep_mean);
plot(sample_mean,'Color','k','LineWidt... |
c3545ae95c98e920ce56db49cda8424963ed18dec2958e6b3af63d4fc97bfd35 | MATLAB | 4,331 | 87 |
%% loading data
load('dataFig7.mat');
PLkIPDmax = mean(refractoryIPD.RTmaxPLk); PLkIPDmin = mean(refractoryIPD.RTminPLk);
PSpIPDmax = mean(refractoryIPD.RTmaxPSp); PSpIPDmin = mean(refractoryIPD.RTminPSp);
PExIPDmax = mean(refractoryIPD.RTmaxPEx); PExIPDmin = mean(refractoryIPD.RTminPEx);
PLkILDmax = mean(re... |
e4296324171528153a69607210125952d9d9b8089c61f14dfdc28356b881033f | MATLAB | 4,336 | 130 | function [class,distance] = rst_outlier(data,method)
% Allows to detect outliers
%
% FORMAT [class,distance] = rst_outlier(data,method)
%
% IMPUT data a vector of data points
% method = 1 - is the standard MAD Median rule
% = 2 - is the MAD Median rule with adustement for small samples
% ... |
cea55e76ad78295b6942a80501630de72729de642f458b441df14af664ed0a40 | MATLAB | 4,340 | 117 | %% Complete data array for each recSite
% Concatenate sessions
% Fill with nan
% Get firing rate average
% load data array
load('E:\Data_array\AllTrials_10bin_Smooth_lickOnset\data_array.mat')
% load('E:\Data_array\AllTrials_100bin_noSmooth\data_array.mat')
% Import setting
B = 2; % number of contexts: respond-t... |
f84d2ae929ffef3fd72a7c365b230f373b1de3e315a1e16763e69dc38c3d40d9 | MATLAB | 4,344 | 180 | function [fix, start_end_sac, peaks_sac, start_end_fix, peaks_fix, locss, amplitudesac] = fixsacepoch(TrackingData)
%FIXSACEPOCH Process tracking data to determine rapid head movements and fixation epochs
% fix= fixation timestamps
% start_end_sac= onset and offset timestamps of head movements
% peaks_sac= peak head m... |
5cd865caab7941c6bdf91c19448269923f6b5399ee32ee3b553cd62f5b33f86f | MATLAB | 4,346 | 92 | %% single cell simulation: to collect mean V (and firing rates)
function [v, meanV,fr] = MEanFieldEst_SingleCell_L6(NeuronType, f_EnI, ...
N_EE,N_EI,N_IE,N_II,...
S_EE,S_EI,S_IE,S_II,p_EEFail,...
... |
4cc645ad36f7fa8164740bae6bc384ceb3db919d7699e72a0491724c6b964ff7 | MATLAB | 4,356 | 92 | function save_regression_res(app)
[int_idxs,~] = listdlg('PromptString',{'Please select intervals ',...
'to be plotted/saved.',''},...
'SelectionMode','multiple','ListString',app.popup_int_entropy.Items);
[form_idxs,~] = listdlg('PromptString',{'Please select fileformat ',...
''},...
'SelectionMode','... |
58bce6c235aff2d1e8f7117ec4f425a6bbbba2924190b5fe3a6d9a4a34d1129d | MATLAB | 4,357 | 92 | function save_regression_res(app)
[int_idxs,~] = listdlg('PromptString',{'Please select intervals ',...
'to be plotted/saved.',''},...
'SelectionMode','multiple','ListString',app.popup_int_stepwise.Items);
[form_idxs,~] = listdlg('PromptString',{'Please select fileformat ',...
''},...
'SelectionMode',... |
80edeafade7fb82a4660300511c876a17f39d1ae2ce247ad38bd70e41ed9b0ef | MATLAB | 4,360 | 140 | classdef TemplateClustering < jrclust.interfaces.Clustering
%TEMPLATECLUSTERING A Kilosort(2) clustering
%% TEMPLATE-MATCHING PROPERTIES
properties (Dependent, SetObservable)
amplitudes; % extracted amplitude for each spike
templateFeatures; % matrix giving the magnitude of the... |
433d6523671079be5dfb2c8325430649facef33052baa2cfea13a304387e079e | MATLAB | 4,362 | 140 | clear
close all
clc
%%
[file, path] = uigetfile('*.mat','MultiSelect','off');%When the user clicks the load psetion button, a window should open to enable the user to select a file.
cd(path)
cell_num = (inputdlg("cell_numebr"));
cell_name = ['cell_' cell_num{1}];
%%
load(file)
cluster_num = file(1:(end-4));
mkdir (cl... |
37221cbbbce2d577a43ca8b7756aa1e4d1e8b25f16995e85070cdd505f2a5a40 | MATLAB | 4,365 | 124 | function spmup_smooth(P,Q,s,dtype,M)
% just like spm_smooth with additional mask to mask data before smoothing
% useful when applying brain mask
%
% 3 dimensional convolution of an image
% FORMAT spm_smooth(P,Q,s,dtype,M)
% P - image(s) to be smoothed (or 3D array)
% Q - filename for smoothed image (or 3D arra... |
52116e619a6b8a9779dccd854fc37ae31ac93d6aa905e4a1f4494237ead32eb7 | MATLAB | 4,365 | 135 | close all;
clear;
clc;
resultfname = 'results.txt';
% parameters
subject = {'Dog_1', 'Dog_2', 'Dog_3', 'Dog_4', 'Dog_5', 'Patient_1', 'Patient_2'};
f0 = 60; % powerline frequency Hz
Q = 30; % notch filter Q-factor
wlen = 5.0; % energy window lengths in seconds
bins = 0:.01:3;
epochlen = 15; % epoch length in seconds
e... |
679acb86d9a45f0f2b628e1189ae1929adc17f4244ec35e13966b84574443858 | MATLAB | 4,366 | 126 | function [Lead_fields, L_reshaped] = Forward_OPM_shell_FT(mri_file,sensor_info,sourcepos)
% Use FieldTrip to create a single shell lead field model
% mri_file = .mri file
% sensor_info = sensor positions (Nx3), orientations(Nx3), and labels (Nx1)
% skanect_file = where to save/load things to/from
%% Load OPM sen... |
eebca4e5223ecba75bb7df0bfb8816ba75339170cb7f2c05ed197aa4c08c0e6e | MATLAB | 4,366 | 107 | %Author: Tatjana Schmitt, 2019-2023
function [radial_vectors_fitted, borders] = fitting_BF_vectors(stats, idx_vectors, ...
radial_vectors_BF, radial_vectors_mean_BF_smoothed)
% fits BF course of vectors, calculates turning points, sets borders and
% calculates edges of auditory cortex
%% initialize variables... |
5576497553ff75d8a1dcbb3242ba145e70dac0d060e72b445ad6a999d088c44c | MATLAB | 4,367 | 141 | function [CSIdata] = write_fid_to_nii(fids,rootdir,naming_convention)
if ~exist('rootdir')
rootdir=spm_select([1 Inf],'dir','Select directories to write nii','',pwd);
end
if ~exist('naming_convention')
naming_convention=3;
end
for k=1:length(fids)
f=fids(k);
CSIdata.dim = [1 1 1];
CSIdata.vox =... |
da234ce7db0774cdb7aa001d64e87f48ef5db4184411416a11876a50ec87b35a | MATLAB | 4,375 | 174 | function [spikeMatrix, peak_idxs, peakTimes_ms, classes, peak_vals, dip1, dip2 ]=comBIN(aaa,data,sr,NBINS_peak,NBINS_dip,NBINS_dip2,npointsbeforepeak,npointsafterpeak)
%comBIN(spks,data,sr,1,1,1)
if nargin<7
npointsbeforepeak=20;
npo... |
26ac3c07766c6c0262f306df52ec0a23461d14793f3a71e7fe26f8b03d30e466 | MATLAB | 4,376 | 124 | function fit = rst_weibullfit(AFC, stimlevel, pc, CritY, labelx, estdelta, flag)
% Fit a cumulative Weibull function to relationship between vectors:
% stimlevel and pc, both obtained in N-alternative discrimination task.
% Set AFC to N.
%
% CritY = threshold proportion correct. default is halfway between chance
% and... |
786f8d85f756983b23acd07c2e4ee1231a4baa0baf1b6621173221d122bf2906 | MATLAB | 4,379 | 106 | % *************************************************************************
% * This code implements and illustrates three distributions, namely *
% * 'Rayleigh', 'Rician' and Phase distributions for envelope and phase *
% * introduced in [2]. *
% * >>> Refer to... |
c1d7a86d60d6594d9416474f4bf9a48b2c346fef25746b45b1cc5c6c77cd4697 | MATLAB | 4,379 | 147 | close all; clear mex;
funtype = 'gpu';
%funtype = 'cpu';
%funtype = 'matlab';
kCNNFolder = '..';
addpath(kCNNFolder);
addpath(fullfile(kCNNFolder, 'matlab'));
if (strcmp(funtype, 'gpu') || strcmp(funtype, 'cpu'))
kMexFolder = fullfile(kCNNFolder, 'c++', 'build');
kBuildFolder = fullfile(kMexFolder, funtyp... |
e22a0104b2ea2c8e98492ab93878eb8b134b2f657981304bd1a94d878b7ef185 | MATLAB | 4,379 | 112 | function Data = zScoreData(Data, Dimentions)
% z-scores a matrix across the first dimention (presumed participant) and
% seperately for the last dimention (presumed frequency), if option
% selected.
% 'last' indicates that last dimention is also to be kept separate
% 'last-1' indicates the second to last dimention is a... |
535d1ac9f120a6108d06258e3c34a20da8ace2df94097cfcffe098ffad23906a | MATLAB | 4,384 | 95 | function [ECG_mean, ECG_median, ECG_cov, ECG_mean_robust, ECG_median_robust, stacked_beats, beats_sqi] = ecg_features_time_domain(data, peaks, params)
% ecg_features_time_domain - Extracts time-domain ECG mean and median beats, and beat covariance matrix.
%
% Usage:
% [ECG_mean, ECG_median, ECG_cov, ECG_mean_robust, ... |
2b0eb022df07c3c9920537352d03eda738f105f5c893b6e8a8e9014c6f52ba46 | MATLAB | 4,387 | 148 | function OnePhaseSpike190514
%¶«Ì^b`ÌÔÌDZÅE^b`ª Á½©Ì¶«Ì^b`ÔÅDZÅXpCNª Á½©
% global
cd('C:\Users\C238\Desktop\CheetahWRLV20180729Part1\119\2017-6-7_20-8-33 11901-51');
dpath=('C:\Users\C238\Desktop\CheetahWRLV20180729Part1\119\2017-6-7_20-8-33 11901-51\');
dpath3=('C:\Use... |
c9d2358981c5f249a2de39823c90ef41e323dcc51f1d4683eb99c0b87a9490ee | MATLAB | 4,390 | 136 | %{
----------------------------------------------------------------------------
Determine the time window for task relevant neurons
%Start of task
%Sound on
%Sound off
%Before choice
%After choice (0sec)
%After choice (1sec)
%After choice (2sec)
%p = 0.001
%Each epoch, predict the prior, sensory and choice (... |
89ce0229d7d17ad86c5f8f8547373442e059dbf3a27d9e896156f3514c3a18ff | MATLAB | 4,397 | 45 | close all;
clear;
clc;
% testFreqDomainEnergyIncreaseRate01
%
% testICASubspaceAngles01
%
% testWeightedHistogramAveraging02
%
% testProcessSeizureEEG_EnergyDistributions01
% testProcessSeizureEEGAllSpectralFeatures
%
% AnalyzeResults11_WithCrossValidationResults
% AnalyzeResults13_SpectralFeaturesFunction('Anal... |
247f8d6dcfac6b2e6f695f5730258155482659c09f2b6f5ffec4f5cae0afacbf | MATLAB | 4,404 | 85 | % MARK CLUSTERS IN TEMPERATURE DIAGRAM
function mark_clusters_temperature_diagram(handles,tree,clustering_results)
handles.par.min.clus = clustering_results(1,5);
% creates cluster-temperature vector to plot in the temperature diagram
nclasses = max(clustering_results(:,2));
class_plot = [];
for i=1:nclasses
ind... |
9692ef1a3e95c74e48060739bbbdb49acec6f754c44bd71d145821809efbcdaf | MATLAB | 4,406 | 115 | %{
----------------------------------------------------------------------------
Analyzing behavioral data
At least for the correct rate
----------------------------------------------------------------------------
%}
function FigureS1d_Dual_ML_analysis_220818_para_all
analysis_folder = {
'G:\Ishizu_data\... |
43c998a27c2b77c171d4ce694a8d91e5b41ce1072d60430b8a93303df091a480 | MATLAB | 4,408 | 145 |
%{
----------------------------------------------------------------------------
First_take number of frames in each tif files
Analyzing imaging data simply
At least for the correct rate
----------------------------------------------------------------------------
%}
function Figure_c_Task_kaiseki_tokyo1_2022021... |
96ae26717355def66c231e9b3ab2cff36e0a371be1314754fb31e1772e79e165 | MATLAB | 4,410 | 111 | function Apply_PVD_CNN_Test_Set(My_CNN)
% This function gets a trained neural My_CNNwork and a grayscale image and produces a matrix of probabilities.
% Each pixel in the output matrix contains the probability of the corresponding pixel in the grayscale image of being
% a neuron pixel or a non-neuron pixel.
... |
9bec362207c6b3b3ad7f9b23292439388193ec29636d71b095abc486a25fa451 | MATLAB | 4,415 | 119 | classdef adi
%
% Class:
% adi
%
% This class is meant to hold simple helpers for the package.
methods (Static)
function view()
%
% adi.view(*file_path)
%
% This is
%TODO: Run the viewer
file_path = adi.... |
3acf782f07a6e81424dc2e83789c751dfa9eb9ad7ae6d9f0122026e8ce25c3ac | MATLAB | 4,418 | 116 | %%
% top: blcok types (respond-to-touch: blue; respond-to-light: red)
% middle: tactile stimuli
% bottom: visual stimuli
% right lick (green); left lick (orange); no lick (gray)
% block start (dash line)
% cue for block transition (star)
% setting
y_idx = [2 1];
lick_color = {[0.5 0.2 0.55] [1 0.4 0.15] [0... |
de12d2170e2e1e93431af0349992cf7255cb4bd67fc9b565295354948abde16c | MATLAB | 4,423 | 148 | function [threshold, index, par, spikes] = detect_spikes(data, sr, par)
%DETECT_SPIKES Summary of this function goes here
% Detailed explanation goes here
%default config
init_date = now;
threshold = [];
index = [];
spikes = [];
seglen = par.segments_length*sr*60;
num_seg = ceil (length(data)/(seglen)); %% process ... |
a301cb7ddef485c42e6fdf88d2ad169be3f740e5044ac6c6bd09261b158bb6c7 | MATLAB | 4,425 | 130 | %BOFRE AND AFTER
for i = 1:2
[pl2_file,pl2_path]=uigetfile('*.pl2','Select all individual .pl2 files','MultiSelect','on');
datafile = fullfile(pl2_path, pl2_file);
exp_name = inputdlg(pl2_path,pl2_path,[1 40],"after_OPN");
pl2_path1{i} = pl2_path;
datafile1{i} = datafile;
exp_name1{i} = exp_name... |
2af52a4798ddbc5eea02fe73b2f2b3789690539db430b031d6465bb9ca5b8814 | MATLAB | 4,426 | 133 |
%{
----------------------------------------------------------------------------
Determine the time window for task relevant neurons
%Start of task
%Sound on
%Sound off
%Before choice
%After choice (0sec)
%After choice (1sec)
%After choice (2sec)
%p = 0.001
%Each epoch, predict the prior, sensory and choice... |
12acd85f23c6716a96312ae47afd7c40d494846cdf217b839239ac9fdaeefded | MATLAB | 4,428 | 128 | function combined = combine_IQR(set1,set2)
% combine values from IQR analysis across animals, easier for statistics
for shuffle_idx= 1:2
if shuffle_idx ==1
shuffle_indicator = 'shuffled';
else
shuffle_indicator = 'not_shuffled';
end
... |
21885d6ecb3a8b7383cbb13e6e8d1e7d3192c7672a9a112e965f3e3af18bbca0 | MATLAB | 4,428 | 145 |
function [nCIJtree CIJtree mdeg globalcosteffmax costmax E ]=threshold_omst_gce_wd(CIJ,flag)
% threshold_omst_gce_w Optimizing the formula GE-C via orthogonal MSTs.
%
% [nCIJtree CIJtree mdeg degree vector cost ]=threshold_omst_gce_wu(CIJ)
%
% This function thresholds the connectivity matrix by absolut... |
af54c2fa84f0c1e11160ea08dd2271e01de2608cc0b202a26e3af36e0568659b | MATLAB | 4,429 | 124 | %% EAG_Analysis_ExampleDrift
% bla
%
% Version: 25-Mar-24 (R2023a)
% Clean up
clc; clear all; close all;
% Add paths:
% --- Current paths
addpath(genpath(pwd))
% --- Toolbox for exporting publication quality figures
% https://github.com/altmany/export_fig
addpath(genpath('...\GitHub\export_fig'))
mkdir('FIGs')
%%... |
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