sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
c9035a2744c1ba1e4f9a0e9325f8c3b7f61ab29bd8bf575647b5aa501c325b50 | MATLAB | 15,806 | 563 | function [AllBeads,ChromaticCorrections,Scale]=mnl_MergeBeadData
%% Input Channel Data
prompt='How many channels are used for imaging?';
nChan=input(prompt);
LaserEx=nan(nChan,1);
for i=1:nChan
LaserQ=sprintf('%s%d%s','Please enter the excitation laser wavelength for channel ',i,' (nm)');
prompt=LaserQ;
La... |
9c2f5a64e3da5d3fae9f77ca30bb5966db43c164fd2eda5c3cceece77bc22172 | MATLAB | 15,847 | 505 | close all
clearvars -except Behav_path Path_figures Path_variables Code_path MEG_path
% plot Figure 3 - 2
SubjectID={'S01' 'S02' 'S04' 'S05' 'S06' 'S07' 'S08' 'S10' 'S11' 'S12' 'S15' 'S17' 'S18' 'S19' 'S20' 'S22' 'S24','S21','S25','S23'};
Drug=[3 1 1 2 3 2 3; 3 1 3 2 1 2 0; 1 3 2 1 3 2 0; 2 1 1 3 2 3 0; 2 1 3 1 2 3 ... |
018bbfdfa5a193c3176b0d4f9a456ad8235946009158b0f578704f9fca5b8a3f | MATLAB | 15,859 | 515 | %% eria stats for all_psthT
%load all psthT
clear
clc
cd('I:\My Drive\ELYASHIV\PFC_paper_results_after_revision\using 0.9 probability')
all_psthT = importdata('all_cells_PSTH.mat');
T2 = readtable("cells_coordinates_map.csv");
T = importdata('all_cells_structers_probabilty.mat');
%% add structure prob... |
219dd44f17da779859deae7e98d8c5fe37e97e07b3d8ffb3396d0396d7b54d92 | MATLAB | 15,878 | 325 | function Data = Connect_Vertices(Im,Data,Ax)
% TODO: I'm currently not using the collision condition.
Plot0 = 1;
Plot1 = 0;
Plot2 = 0;
Messages = 0;
if(Messages)
assignin('base','Workspace_Trace_0',Data);
end
if(Plot0)
% imshow(Im,'Parent',Ax);
% set(Ax,'YDir','normal');
h = animatedline(Ax,'Lin... |
9524b9c506b559d1c8119f14f7d724363c636beb5886658d67653360c13df0e1 | MATLAB | 15,910 | 417 | %% response shape similarity using Procrustes analysis
%% load data
sessionTags = {'M22027', '20220517';...
'M22029', '20220607';...
'M22031', '20220524';
'M22032', '20220621';
'M22033', '20220706'};
dataDir = 'C:\Users\edward.horrocks\Documents\Code\V1Dynamics\Data\basic_111022';
s = struct;
for is... |
f07e9b941ffbc2b5291325ed95249f60fccf809157c734e9b2e2ae0bd0e9fe0b | MATLAB | 15,931 | 483 | % groupERP Analysis
%
% Group level analysis of Random & Two-Phase ERP data
%
% 2021-04-08 scott.bressler@elemindtech.com
% Updated 9-15-2023 tylor.harlow@uconn.edu
%% Housekeeping
clear;
close all;
clc;
%% Load Data
% Select subjects to exclude from group analysis
exSub = {'Sub_151','Sub_152','126'};... |
c7b9142f076b86c6656af25f1888633c6b0b15663bc08f52e062e197d9d5f32b | MATLAB | 15,971 | 289 | classdef Process
methods(Static)
function summary_firstHit = GetFirstHitInfo(se)
% Input
% MSessionExplorer
% Output
% Get the trial number of first hit trial in each block after block switch
% D... |
a15ac16d34208d95fbdb280be15bcd5c8c1ef244cb60710565797d55cb3c1ec8 | MATLAB | 15,986 | 482 | %% Figure 5
% Focus on EC012 for now
% YOU MUST LOAD IN ec012data.mat BEFORE RUNNING
work = whos('EC012_*'); % files are named EC012_1, EC012_2, etc.
work2 = whos('ec012ec*');
outstr = struct('velo',[]);
% Initialize PSDs
restTF12 = zeros(77,1);
ctr12 = 0;
movementTF12 = zeros(77,1);
ctm12 = 0;
% Initialize aperiodic... |
847f2c931b67e46e85f385b7e24d0d7bf7c4df8bedd6ee14de9a6769e6985edf | MATLAB | 15,994 | 271 | % A script to test EOG cancellation from EEG followed by event related potential extraction using Robust Weighted Synchronous Averaging
%
% Reference:
% Sameni, R. and Gouy-Pailler, C. (2014). An iterative subspace denoising
% algorithm for removing electroencephalogram ocular artifacts. Journal of
% neuroscience... |
d160fb5ba46cd7810476935d27ff9adabfcd0e7771704d7facf4e20a87067aec | MATLAB | 16,027 | 498 | function RLR20190604JS
global ACresult ACresultW CCresultDrinkOn SpikeArray TurnMarkerTime RpegTouchallWon LpegTouchallWon...
RpNum LpNum locsP1time locsP2time locsP1time2 locsP2time2 pksA1 pksA2 CCresultRtouchWon CCresultLtouchWon dpath3...
StartTime FinishTime DiffMaxMinR DiffMaxMinL fname tfile OneTurn... |
6a19bd88701c70ea3c737cd508545dc081f61590f86dd388e0bfb37cfe661572 | MATLAB | 16,063 | 462 |
%{
----------------------------------------------------------------------------
First_take number of frames in each tif files
Analyzing imaging data simply
At least for the correct rate
----------------------------------------------------------------------------
%}
function Task_kaiseki_tokyo_encode_20230915_G... |
895fdd1478a29e0bf8ff701b28346914b8747185bc8687de0397cbf844021cf7 | MATLAB | 16,086 | 446 | %% FIBER PHOTOMETRY ANALYSIS - EVENT WINDOW (PROGRESSIVE RATIO TEST)
% Code written by Carrie Ann Stine
% Michael Bruchas Lab - UW
% Analysis code used for Progressive Ratio Test results in NOPLight manuscript
% Script used to calculate the z-score of a fiber photometry trace over
% multiple event windows
% NOTE FO... |
79769b839bd9f5d1b1aa56bb6d69b717d47c168a0807a8926ed29edfbdb736c2 | MATLAB | 16,105 | 420 | function [PAC,areaK,deltaK] = consensusSelection_v3(cMat,varargin)
% [PAC,areaK,deltaK] = consensusSelection(cMat,kRange,varargin)
% ------------------------------------------------------------------------
% Select a good clustering solution based on consensus across many
% monte-carlo style permutations (see consensus... |
19dfcbc80d6e851c5c9b5b9bf530ca914d4b7efc41766918d35407b55d6700b2 | MATLAB | 16,123 | 346 | function pv_responsesorter_stats(cellids,issave,response_resdir,responsespec)
%PV_RESPONSESORTER_STATS Peri-event time histogram.
% PV_RESPONSESORTER(CELLIDS,ISSAVE) calculates non-adaptive PSTHs for a
% set of cells (see ULTIMATE_PSTH) aligned to stimulus onset and feedback
% delivery. Statistical tests ar... |
ea64f44c4405ba9084872fea302c45040d27baba1a07de1bc5af456175ad126e | MATLAB | 16,150 | 370 | function PegTouchAnalysis24ch(R_PegTouchCell,L_PegTouchCell,SliderTouch,TurnMarkerTime,OneTurnTime,RpegTimeArray2D,LpegTimeArray2D,FTselect)
%'PegTouch'ÍsensorÌîñð¾½¾¯B12ch·×ĪRpeg12{ÉèÄçêÄ¢éÆ«ÉÍPegTouch¾¯Åàæ©Á½B
%µ©µARLÅ6ch¸Âgp·éÛÉÍA1-6A7-12ðèÄéKvª ... |
c1fb35ef869329efc0f0179dbcee9ce817663341620ee7833289bdb1d2a8821c | MATLAB | 16,152 | 535 | function processSession_decoding(inputFileName,outputFileName,dataDir)
%% Decoding - training and testing in the same window
%% load data
load(fullfile(dataDir,inputFileName))
%% pre-process data - classify trials by behavioural state
% remove any trials that didnt run for correcgt amount of time
trialDur = 1;
to... |
9811e9231a86c6e894ce69a0d5e7e1f980de4f2fb45a4d5e6c61c924daec9920 | MATLAB | 16,199 | 394 | function [grid_size_hor, grid_size_ver] = ecg_gridest_spectral(img, varargin)
% ecg_gridest_spectral Estimates grid size in ECG images.
%
% This function analyzes an ECG image to estimate the grid size in both
% horizontal and vertical directions using a spectral approach. The image
% is segmentized into smaller regula... |
7d33ff90d566a5ab6e379919f33c55707a93a88b2bae2e5696d866cbc04c667b | MATLAB | 16,231 | 416 | % **** This is for Supplementary figure***
%
%
% 1. no difference between comparisons two conditions on motion artefacts
% 2. visualization on results.
% transgenic
% - motion xyz TPH
% - motion rot TPH
% - motion std xyz TPH
% - motion std rot TPH
% WT
% - motion xyz TPH
% - motion rot TPH
% - mot... |
b0682146fb3bf666240993b3b32d44dff8ffe721f2bc1de4e352fb52ad9ce3b5 | MATLAB | 16,235 | 451 | function [net, state] = DnCNN_train(sigma_min,sigma_max, net, varargin)
% The function automatically restarts after each training epoch by
% checkpointing.
%
% The function supports training on CPU or on one or more GPUs
% (specify the list of GPU IDs in the `gpus` option).
% Copyright (C) 2014-16 Andrea ... |
07dc53110c644d3ef1a2f085d6583e074fb056b355980b68c0b3e521a4f25cea | MATLAB | 16,238 | 450 | %function [pval, t_orig, crit_t, est_alpha, seed_state]=mult_comp_perm_t2(data1,data2,n_perm,tail,alpha_level,mu,t_stat,reports,seed_state)
%
% mult_comp_perm_t2-Independent sample permutation test based on a
% t-statistic. This function can perform the test on one variable or
% simultaneously on multiple variables. ... |
e917db2bee72fc3527c787774c415a3bfbca84bd92c3878ae25a221f9595592c | MATLAB | 16,253 | 358 | function PegTouchAnalysis24ch(R_PegTouchCell,L_PegTouchCell,SliderTouch,TurnMarkerTime,OneTurnTime,RpegTimeArray2D,LpegTimeArray2D,FTselect)
%'PegTouch'ÍsensorÌîñð¾½¾¯B12ch·×ĪRpeg12{ÉèÄçêÄ¢éÆ«ÉÍPegTouch¾¯Åàæ©Á½B
%µ©µARLÅ6ch¸Âgp·éÛÉÍA1-6A7-12ðèÄéKvª ... |
374712936d25f1b8f8fead8917a65a04b92b0abe3fbf24ad6aa8b7077a6a8c2b | MATLAB | 16,260 | 363 | function files_out = spmup_hrf_boost(varargin)
% this function takes a SPM.mat and related files to create boosted beta
% parameters and con images if the hrf doesn't fit properly the data, some
% information can be recovered using the 1st and 2nd derivatives - most of
% the time this boosts the hrf value but it can a... |
84df6f2fd19072777324a7bee7ac5028631c34af809ec0963b40feb9f581fce1 | MATLAB | 16,266 | 477 |
%{
----------------------------------------------------------------------------
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... |
3735a23f2591ee4ff6e607d7cdd0322cad8300111488c95125935d3a2d018a56 | MATLAB | 16,268 | 469 | function ModulationHeatMaprelative20191105
% Fs = 1000; % TvOüg
% t = 0:1/Fs:1; % ÔxNg
% x = sin(2*pi*5*t) .* sin(2*pi*100*t); % Ô²M
%
% F = fft(2*abs(x).^2);
% f = Fs * [0:length(F)-1] / length(F);
% F( f>10 & f< (Fs-10) ) = 0;
% y = ifft(F);
% plot(t,x,t,sqrt(y))
path=['C:\Users... |
21710223c68fc7c236ee704026c9f0c23bf3688f9831657fad827419e6a7d04f | MATLAB | 16,376 | 404 | function avg_psth_expectation_PV(pvcells, responsetype, ispart, resdir, data_type)
%AVG_PSTH_EXPECTATION_PV Average, normalized peri-event time-histograms
%comparing neuronal response to expected and unexpected stimuli.
%
% AVG_PSTH_EXPECTATION_PV(PVCELLS, RESPONSETYPE, ISLICK, RESDIR) calculates
% normalized... |
54f9b0ac0f2119d0d304265a17482578ad0e6d2124b166afe07aed430b0d293a | MATLAB | 16,495 | 512 | function RLR20190604
%¼àSCTFRt©çLtÜÅÌÔALt©çRtÜÅÌÔðp¢½\ª
global ACresult ACresultW CCresultDrinkOn SpikeArray TurnMarkerTime RpegTouchallWon LpegTouchallWon...
RpNum LpNum locsP1time locsP2time locsP1time2 locsP2time2 pksA1 pksA2 CCresultRtouchWon CCresultLtouchWon dpath3...
Sta... |
dfd191a7d35d5463914bcbc51cb2668a9c2e397496784f5c357cde3c1515e800 | MATLAB | 16,531 | 153 | function [FigA, FigB]=TouchSpikeAnalysis20170307(SpikeArray,RPegTouchAll,LPegTouchAll,fname,TrimStr)
% clear all;
% close all;
Bin=9;
% global StartTime FinishTime RPegTouchAll LPegTouchAll
%20170303 7-5
% StartTime=2459;
% FinishTime=167473;
% LpegTimeArray=[2829;3328;3833;4329;4837;5339;5843;6346;6847;7... |
06df3de96dfe5f4d69e8ebe13c889de7909a1621275e39279847770109d74353 | MATLAB | 16,534 | 486 | % plot outcome of task performance, to compare PVT and LAT
clear
clc
close all
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% parameters
Parameters = analysisParameters();
Paths = Parameters.Paths;
Task = Parameters.Task;
Participants = Parameters.Participants;
MinTrialCount = Parame... |
cebcf6b461dca862291c95e342b988f0b6e04b7cabff496e857bdda9597d1c57 | MATLAB | 16,557 | 262 |
function create_report(app)
tmp = sort(app.lstbx_report.Value);
idx = nan(length(tmp),2);
for i = 1: length(tmp)
tmp_idx = [find(tmp{i} =='[',1),find(tmp{i} =='-',1), find(tmp{i} ==']',1)];
idx(i,1) = int16(str2num(tmp{i}(tmp_idx(1)+1:tmp_idx(2)-1)));
idx(i,2) = int16(str2num(tmp{i}(tmp_idx(2)+1:tmp_idx(... |
aabe3e1f0dcbe8b4089805525b5249e8a112ec58efd2b1ad1090e1a9e64e7297 | MATLAB | 16,558 | 426 | function RLphase191024mean
%¼àPF¶E»ê¼êÌXpCNÌÊðp¢½\ª
%y=figureÌsubplotRgAEg@QOQOOPQU
%RLphase190415mean©çÌÏX_F1000rɼ·Æ±ëjMaxÌlð¬æêÊÜÅÉÏXAqxgÏ·ðgíÈ¢æ¤Éµ½
global ACresult ACresultW CCresultDrinkOn SpikeArrayWon TurnMarkerTi... |
67109753b0d6bdd042eef827e798a768bf19d8b94ec2a412b069aef86a6fb090 | MATLAB | 16,584 | 444 | filelistdata2 = dir('filepath')
MaxIterations=10000; %set number of iterations
%TreadmillVersion=1; % Choose 0 for old treadmill (pre 01Jan2021) datasets A and B , choose 1 for new treadmill, dataset C
FrameoutPresent=0; % If == 0 there is not frameout data, if == 1 framout data is present
for files=1:siz... |
661e6a9007e30d6565a77f71a339f4ddab1186fb3e144e9837f0c8ea678ebea5 | MATLAB | 16,588 | 375 | %%%%%% Analysis workflow:
% cell_f_extraction_2P_doublecheckplanes_24012022a.m
% ExportPreprocesseddata_2PGlusensstim08022022.m
% Combine_2Pdata0802.m
% Analysis_combined_data_2P.m
% Locate2Pcells.m
% Script for 2P image stack processing after FIJI macro:
% 1) Open motioncorrected tiff stacks one by one and comp... |
3a35933fb42f933fd677ba29870f3d8cbb4181f30f1b3b85d14012758be22a53 | MATLAB | 16,594 | 487 |
function varargout = Randomizer_Design(varargin)
% RANDOMIZER_DESIGN M-file for Randomizer_Design.fig
% RANDOMIZER_DESIGN, by itself, creates a new RANDOMIZER_DESIGN or raises the existing
% singleton*.
%
% H = RANDOMIZER_DESIGN returns the handle to a new RANDOMIZER_DESIGN or the handle to
% the e... |
52bebd59fc5112267ca8a95ba1edacf72cbb51a8d49d5a83d8a41e4694287040 | MATLAB | 16,636 | 484 | % this script calculates FWF in WM ROIs extracted using the atlas
% normalized to the subject's space
clearvars
close all
addpath(genpath('/storage/stella/MATLAB_toolboxes/'));
addpath('/storage/stella/Analysis/matlab_scripts/');
addpath('/storage/stella/Analysis/DTI_FreeWaterFraction_May2022/SCRIPTS_MATLAB/');
load(... |
b4c7d989e328eab22bd527d6b60c1263d31b213cc37b18cc1c48aa0ff71522e1 | MATLAB | 16,649 | 488 | function processSession_crossTimeDecoding(inputFileName,outputFileName,dataDir)
%% Decoding - training and testing in the same window
%% load data
wheel=struct; % to prevent matlab func issue
load(fullfile(dataDir,inputFileName))
%% pre-process data - classify trials by behavioural state
% remove any trials that... |
c77e93af8b2b2492a50fae482c2ed0e2086aa24cb3b3b2feeea4364071de0f73 | MATLAB | 16,663 | 363 | %%Prerequest
%1.neural index data generated from
%'evaluate_valenced_selectivity_byBAKS.m' and 'evaluate_cue_selectivity_byBAKS.m'
%make sure the save order
%'Neuron_index'>'valence'>'wsq'(session)>'IC'(brain region) is correct
%2.spike data and behavioral data (make sure the part name (end-16:end-4) of
%spike da... |
1af34afc4a0cee8ad090bc2a55b3e6446600f80dc9c7f2639022d0adc9580d6b | MATLAB | 16,669 | 355 | function PegTouchAnalysis24ch_SSanalysis(R_PegTouchCell,L_PegTouchCell,TurnMarkerTime,OneTurnTime,RpegTimeArray2D,LpegTimeArray2D,FTselect)
%'PegTouch'ÍsensorÌîñð¾½¾¯B12ch·×ĪRpeg12{ÉèÄçêÄ¢éÆ«ÉÍPegTouch¾¯Åàæ©Á½B
%µ©µARLÅ6ch¸Âgp·éÛÉÍA1-6A7-12ðèÄéKvª ... |
7909b6dab475d6eae7839306dc4ee721bebdef43155302d0384f3ece7138b1c5 | MATLAB | 16,674 | 355 | function PegTouchAnalysis24ch(R_PegTouchCell,L_PegTouchCell,SliderTouch,TurnMarkerTime,OneTurnTime,RpegTimeArray2D,LpegTimeArray2D,FTselect)
%'PegTouch'ÍsensorÌîñð¾½¾¯B12ch·×ĪRpeg12{ÉèÄçêÄ¢éÆ«ÉÍPegTouch¾¯Åàæ©Á½B
%µ©µARLÅ6ch¸Âgp·éÛÉÍA1-6A7-12ðèÄéKvª ... |
0d033c216595478dd3d1bf7a79cfbf325b175916f32f29707a8598a003108005 | MATLAB | 16,675 | 448 | function update_lag_axis(app, action,varargin)
load = false;
plt = false;
update_sig = false;
update_sldr = false;
update_ov = false;
calc_corr = false;
app.settings.inverse_lagsig = false;
switch action
case 'init'
load = true;
plt = true;
case 'sig'
load = true;
upd... |
cfb89c443c5eb46a2fb05be66a1d4726866cbebbad4f564c27cef8414042d0a5 | MATLAB | 16,749 | 364 | function [myMap N_state N_action N_transition]=Model_Map_Init2(map_name,mapopt)
%% use mapopt='sangwan2012c' only. the other maps are testing version.
% transition prob. for the left(1,1)/right(1,2) action
switch lower(map_name)
%% [random decision(action selection) mode] - (s,a)
case {'daw2005... |
a577d9cf725cb80794ea01d138e3a2e461fa97f35ec89d5cf00edd334b70cadb | MATLAB | 16,782 | 265 |
function create_report(app)
tmp = sort(app.lstbx_report.Value);
idx = nan(length(tmp),2);
for i = 1: length(tmp)
tmp_idx = [find(tmp{i} =='[',1),find(tmp{i} =='-',1), find(tmp{i} ==']',1)];
idx(i,1) = int16(str2num(tmp{i}(tmp_idx(1)+1:tmp_idx(2)-1)));
idx(i,2) = int16(str2num(tmp{i}(tmp_idx(2)+1:tmp_idx(... |
356eccbfe7a02d1f4f580f6e44c73c2a607dcc878103ce02716ea734285e216a | MATLAB | 16,785 | 409 | function Lt_Rt_Pattern1_and_CV__W3_Touch_sub20200310
path=['C:\Users\C238\Desktop\touchcellLtRt_terashita'];
cd(path);
LS=ls;
PositiveTouchesPointArray=zeros(1,6);
FRCVLPWTouches=[];
FRCVRPWTouches=[];
for i=1:length(LS(:,1))
TrimFolder=strtrim(LS(i,:));
if ~strcmp(TrimFolder,'.') && ~strcmp(TrimFo... |
901b52ec5ef410564c920b5cc2d9db8846bab4c82949cd6df2b576897cf86dd4 | MATLAB | 16,852 | 442 | function tSNR = spmup_temporalSNR(time_series,masks,varargin)
% Computes the temporal SNR of the time_series input in the different
% compartments provided by the masks images = mean signal / std over time
% The routine recapitulates tSNR as described in Thomas Liu (2016),
% Caesar Caballero-Gaudes and Richard C. Rey... |
780963dccdd7f5e3719cff32954493a71391e57bd825546e142089870f6eeb89 | MATLAB | 16,865 | 383 | function [grid_size_hor, grid_size_ver, peak_gaps_hor, peak_gaps_ver, peak_amps_hor, peak_amps_ver] = ecg_gridest_margdist(img, varargin)
% ecg_gridest_margdist Estimates grid size in ECG images.
%
% This function analyzes an ECG image to estimate the grid size in both
% horizontal and vertical directions using the ave... |
10b408fcd10e610384b7d2293fcb15f68c421130a7d7e8147c21d99eb42ec954 | MATLAB | 16,922 | 472 | function [projs mse like lat] = cvreducedims(D, alg, dims, handles)
% [projs mse like] = cvreducedims(D,alg, dims, handles)
%
% CVREDUCEDIMS a dimensionality reduction tool
%
% Input:
% D (struct) --- high-d data to be reduced
% alg (integer) --- which red. algorithm is used
% dims (array) --- array (ascending... |
cf96e76fe84598993c3ab6748334dbb9ad32fb707fc269d160e85bdf525240c0 | MATLAB | 16,937 | 547 | % Created 2021-05-25 Tylor J Harlow - tylor.harlow@uconn.edu
%% Housekeeping
tic;
clear;
close all;
clc;
svpath = '/Applications/Toolbox/MATLAB/Figures/Plots wout 126';
savePlots = 1;
updateTable = 0;
% Load current data table
datafilename = '/Applications/Toolbox/MATLAB/TwoPhaseExp_table.mat';
load(da... |
9c357ea074238cbebc189e324a960aad61934e7e79bc62b67e7950318ef7c0fb | MATLAB | 16,943 | 304 | function outbytes = cifti_write_xml(cifti, keep_metadata)
if ~(nargin > 1 && keep_metadata)
prov = cifti_metadata_get(cifti.metadata, 'Provenance');
cifti.metadata = struct('key', 'Provenance', 'value', 'cifti_write_xml.m v1.0');
if ~isempty(prov)
cifti.metadata = cifti_metadata_... |
79b97eb91e039444106e8a600628c82ae4db2bdb5f335285e4ec2e9d72506293 | MATLAB | 16,964 | 567 | function RRR(varargin)
currentDir = pwd;
cd('PATH_to_/Distributed_and_dynamical_communication/analysis/sub_space')
% replace the above path with your path to 'Distributed_and_dynamical_communication/analysis/sub_space'.
addpath('.')
startup
cd(currentDir) %
rng(1)
save_fig = 1;
numDimsUsedForPrediction = 1:10;... |
53f01504725909827a57894ab0a2ababc09ad712f9a60b0a3fda99cd0b4b7855 | MATLAB | 16,972 | 412 | function [ footIndex, systolicIndex, notchIndex, dicroticIndex, time, bpwaveform ] = bp_dect( inWaveform, inFs, verbose, varargin )
%% [ footIndex, systolicIndex, notchIndex, dicroticIndex, time, bpwaveform ] = BP_annotate( inWaveform, inFs, verbose, Units, isClean )
% Implementation of a feature detection algorithm fo... |
f1ae875dc472a16d73db8f031003d3dbd6e2a6fbd288c4866e88e1dda5a94ed6 | MATLAB | 17,054 | 437 |
%{
----------------------------------------------------------------------------
First_take number of frames in each tif files
Analyzing imaging data simply
At least for the correct rate
----------------------------------------------------------------------------
%}
function norm_Task_kaiseki_tokyo1_20220316_so... |
5c838461b806154c9645f6c6f5742011481c18d0f0dd0fbe8ba22e9324df7189 | MATLAB | 17,056 | 415 | function [ footIndex, systolicIndex, notchIndex, dicroticIndex, time, bpwaveform ] = bp_dect( inWaveform, inFs, verbose, varargin )
%% [ footIndex, systolicIndex, notchIndex, dicroticIndex, time, bpwaveform ] = BP_annotate( inWaveform, inFs, verbose, Units, isClean )
% Implementation of a feature detection algorithm fo... |
e3c4de216f8caa3c663cc6d46174ebb766b3037b9347428edc054b3489d0d95f | MATLAB | 17,063 | 467 | %{
----------------------------------------------------------------------------
Analyzing behavioral data
At least for the correct rate
----------------------------------------------------------------------------
%}
function Dual_ML_CumGauss_Fit_20220114
[filename1, pathname1]=uigetfile('*.mat','Block_mat,',... |
2dd7185a83e1f27660a7bbb1bf6762707a9294d30704cc39b71d6f996db04743 | MATLAB | 17,099 | 416 | function [ClusteringMetrics]=mnl_ComparingClusters(efPxTrace)
%set the random seed
rng(1)
%% Extract the imput matrix and weights (magnitude)
dim=efPxTrace(1).dim;
nTrace=size(efPxTrace,2);
nXFP=size(efPxTrace(1).VecNormMean,2);
InputWeights=nan(nTrace,1);
InputMatrix=nan(nTrace,nXFP); % Done this way so if a value is ... |
0298b071b094438a8f122449d787d602f95c7563655736a7a788b771a876fc83 | MATLAB | 17,116 | 417 | function [timingReport, tempNameRoots] = performHostGRAPPArecon2D_toDisk(twix_obj,tempDir,counterStruct, nSliceNeighbours, combinePars)
%
% function [timingReport, tempNameRoots] = performHostGRAPPArecon2D_toDisk(twix_obj,tempDir,counterStruct, nSliceNeighbours, combinePars)
%
% Applies GRAPPA to the host data, which h... |
eaaf295605e1611c4678a641c4571916853b4298836353d149c88bcf24c4f613 | MATLAB | 17,128 | 445 | function nwbData = nwb(hClust, outPath)
%NWBEXPORT exports jrclust data to NWB
% Assumes matnwb is in path and generateCore is called
% hClust is a regular hClust object with certain additions for config data
% outFileName is the destination file name produced by NWB.
% Should use the .nwb e... |
fb71d6f5d85bedf404a15389465ee2a87fbd0aaea2b71b1913c9ac9df1e5360a | MATLAB | 17,135 | 351 | %% 2P glucose +sensory stim analysis draft
%%%%%% Analysis workflow:
% cell_f_extraction_2P_doublecheckplanes_24012022a.m
% ExportPreprocesseddata_2PGlusensstim08022022.m
% Combine_2Pdata0802.m
% Analysis_combined_data_2P.m
% Locate2Pcells.m
clear all
clc
fid = 'P:\Alexander\Summer 2019\To_do\Sensory_stim_exp'... |
c54ccab7a35a706c70479cce6927e303469ca926fa138be95427975326d9c479 | MATLAB | 17,156 | 372 | %% mean-field stuff. We use parameters andto estimate firing rates
%% NOTE! mean Vs are now autonomus from single cell simulations
% Input: C_EE,C_EI,C_IE,C_II connectivity matrices
% S_EE,S_EI,S_IE,S_II synaptic strength
% S_EL6,S_IL6,rE_L6,rI_L6 L6 Input
% p_EEFail Synaptic failure pro... |
042bd1f54a20f605c200ef91855ab756daac1e045ca6b3b57eee40a4266ad6b4 | MATLAB | 17,159 | 369 | %% mean-field stuff. We use parameters andto estimate firing rates
%% NOTE! mean Vs are now autonomus from single cell simulations
% Input: C_EE,C_EI,C_IE,C_II connectivity matrices
% S_EE,S_EI,S_IE,S_II synaptic strength
% S_EL6,S_IL6,rE_L6,rI_L6 L6 Input
% p_EEFail Synaptic failure pro... |
33b547dc1d25c7336afe85f1f3984bb29005121faa7aa2b21701d7e382efd995 | MATLAB | 17,163 | 500 |
%{
----------------------------------------------------------------------------
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... |
6f54c176bf87da47891d75c79abfe2f89875e0170813fd6f8b8e492c8228ade3 | MATLAB | 17,166 | 500 |
%{
----------------------------------------------------------------------------
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... |
321cbe140e44457d2ca7384f121732db219ea6f0eb4acf802bddaa47753b2bd8 | MATLAB | 17,186 | 365 | function PegTouchAnalysis24ch20190226(R_PegTouchCell,L_PegTouchCell,SliderTouch,TurnMarkerTime,OneTurnTime,RpegTimeArray2D,LpegTimeArray2D,FTselect)
%'PegTouch'ÍsensorÌîñð¾½¾¯B12ch·×ĪRpeg12{ÉèÄçêÄ¢éÆ«ÉÍPegTouch¾¯Åàæ©Á½B
%µ©µARLÅ6ch¸Âgp·éÛÉÍA1-6A7-12ðèÄé... |
c7cafcceb5d6c2ca63937c4a55796695c772f929022fc50e595a02ddc5470ca9 | MATLAB | 17,187 | 441 | function [data_posterior_est, data_prior_est, n_var] = ECGPhaseDomainMAPFilterFullCovariances(data, peaks, params)
% % % [phase, ~] = PhaseCalculation(peaks); % phase calculation
if ~isfield(params, 'bins') % number of phase bins
params.bins = median(diff(find(peaks))); % use median of RR-intervals in samples by ... |
17da78c9dbbbf35bbafee5b8846d3d1260700aa1a52ea1d6b7a367e8ad8ab8a9 | MATLAB | 17,212 | 373 | %% mean-field stuff. We use parameters andto estimate firing rates
%% NOTE! mean Vs are now autonomus from single cell simulations
% Input: C_EE,C_EI,C_IE,C_II connectivity matrices
% S_EE,S_EI,S_IE,S_II synaptic strength
% S_EL6,S_IL6,rE_L6,rI_L6 L6 Input
% p_EEFail Synaptic failure pro... |
de3ff3c5f90d8a1be16ab56391742c0fc9e074e7727d04138929ade0de424980 | MATLAB | 17,220 | 348 | %% mean-field stuff. We use parameters andto estimate firing rates
%% NOTE! mean Vs are now autonomus from single cell simulations
% Input: C_EE,C_EI,C_IE,C_II connectivity matrices
% S_EE,S_EI,S_IE,S_II synaptic strength
% p_EEFail Synaptic failure prob
% lambda_E lambda_I LGN input
%... |
22daad753cfc8d97d79f4dc2b9948d0edeaf410866aef0968243d9eb8765068b | MATLAB | 17,226 | 373 | %% mean-field stuff. We use parameters andto estimate firing rates
%% NOTE! mean Vs are now autonomus from single cell simulations
% Input: C_EE,C_EI,C_IE,C_II connectivity matrices
% S_EE,S_EI,S_IE,S_II synaptic strength
% S_EL6,S_IL6,rE_L6,rI_L6 L6 Input
% p_EEFail Synaptic failure pro... |
da18c63b40643f69df4d3cdc6578b246b08e5eae626aa0c8ff9df14f8656aed8 | MATLAB | 17,270 | 480 | function P = spm12_realign_parfor(P,flags)
% Estimation of within modality rigid body movement parameters
% FORMAT P = spm_realign(P,flags)
%
% P - char array of filenames
% All operations are performed relative to the first image.
% ie. Coregistration is to the first image, and resampling
% ... |
fcf053c107500921f573484d9fc1a33928ea25423332981eb32e93a65a0cf2d6 | MATLAB | 17,274 | 497 | function CircularNetPilgrimNxRsA20160822
%1200j
[A1000ÂÍGroup~æèA200ÂÌInhÍÇÁÅ_
%SrndðÁ³¹ÄMIððÍ, SnetÍêè
%Neuron, RefPeriodSet, SFLðÝèµÄcdANet¼ð²®µÄÀs
close all;
clear all;
% MI=0;
% global Vreset dt tau_m tmax Matrix
global Vreset Vth dt tau_m tmax Matrix
... |
8e71dad2fbd6b88ddf004e40a7b212788bdd2fc0298b836a3c908f6f4df98cc8 | MATLAB | 17,275 | 392 | function save_spike_results(app)
[int_idxs,~] = listdlg('PromptString',{'Please select intervals ',...
'to be plotted/saved.',''},...
'SelectionMode','multiple','ListString',app.popup_int_spike.Items);
[form_idxs,~] = listdlg('PromptString',{'Please select fileformat ',...
''},...
'SelectionMode','mul... |
473bdc3b70c15aaaa4318ac7477cd1e04a6ccd60553a7b7dadf12245df635d82 | MATLAB | 17,316 | 474 | %{
----------------------------------------------------------------------------
Analyzing behavioral data
At least for the correct rate
----------------------------------------------------------------------------
%}
function Dual_ML_CumGauss_Fit_20220114_lapse
[filename1, pathname1]=uigetfile('*.mat','Block_... |
9c234b3a59778753a9408ddf7fd2e3dafe9a69f0ff35eee3369d1b350fe48116 | MATLAB | 17,348 | 462 | function [despiked,filtered,spmup_despike_log] = spmup_despike(varargin)
%
% SPM UP routine to 'despike' fMRI time-series.
% Note is requires the statistics toolbox (nansum, icdf are called)
%
% FORMAT spmup_despike
% spmup_despike(P)
% spmup_despike(P,M)
% spmup_despike(P,M,flags)
% spmup... |
9f5635c4b4bee6fb5c269400c8c7954818b1085532fe3184d812e60641a9b5e2 | MATLAB | 17,351 | 482 | classdef spikeRasterPlot < matlab.graphics.chartcontainer.ChartContainer & ...
matlab.graphics.chartcontainer.mixin.Legend
% spikeRasterPlot Create a spike raster plot
% spikeRasterPlot(spikeTimes) create a spike raster plot with the
% specified spike timestamps. spikeTimes must be a vector of d... |
995d05a6526b249d04d08c379f45de76b9382c4d51a9ecc2f0940a25ef7b7981 | MATLAB | 17,373 | 484 | function [result] = writeNex5File(nexFile, fileName, writeContVarsAsFloats)
% [result] = writeNex5File(nexFile, fileName) -- write nexFile structure
% to the specified .nex5 file. returns 1 if succeeded, 0 if failed.
%
% INPUT:
% nexFile - a structure containing .nex file data
%
% SOME FIELDS OF THI... |
d2800732ebbc26e59b9b81aa4762726f56980e8ec52cbdc83bddba906771d841 | MATLAB | 17,438 | 477 | function [ClusterIDs,Centroids]=mnl_WeightedEuclideanCrawler_v2(InputMatrix,InputWeights,EuThresh)
%% Step 1 - Initial Crawl
[~,nCol]=size(InputMatrix);
%disp('Performing the initial crawl...')
[Centroids,ClusterIDs]=mnl_EuclideanCrawl_Weighted(InputMatrix,InputWeights,EuThresh);
ClusterNum=size(Centroids,1);
%fprintf(... |
b69c873782aa986a7ab22113b9b4253c9faf7d2c2b5a44265b15ff68f85b0f8e | MATLAB | 17,458 | 467 | function saveas(this,filename,format)
% Save GIfTI object in external file format
% FORMAT saveas(this,filename,format)
% this - GIfTI object
% filename - name of file to be created [Default: 'untitled.vtk']
% format - optional argument to specify encoding format, among
% VTK (.vtk,.vtp), Col... |
8aa0147e39c50b45199584cc6308a9afdca6393310435d6877214e44e73d43ce | MATLAB | 17,476 | 343 | % ----------------------------------------------------------------------- %
% Function 'SC_plot' plots a topographical map of the spinal cord
% desired points given by 'ch_list' and their assigned 'values'. %
% %
% Input parameters: ... |
c99d17660efb04a1d6631dd55e2291741c39dd322b4a0d3375fe903b597eeac0 | MATLAB | 17,523 | 452 | function tree = xml_parser(xmlstr)
% XML (eXtensible Markup Language) Processor
% FORMAT tree = xml_parser(xmlstr)
%
% xmlstr - XML string to parse
% tree - tree structure corresponding to the XML file
%__________________________________________________________________________
%
% xm... |
c6c920e4bfdf3c9b8e6c60ab28d5b1e462555fba1215b7004d1dc4190e717eb4 | MATLAB | 17,525 | 454 | function FigureS1b_NtrialBlockChange
parent='G:/Ishizu_data';
workpath='/Revise_ishizu';
outpath='/Revise_ishizu/output/NtrialBlockChange';
% hulistic parameter %
xTrial=[-5,8];% range of plotting / xTrial=0: Block change timing
useES =2;% 1:All evidence strength / 2:moderate+difficult / 3:difficult
figsaveT... |
8a274544c2ab8cb8a3417db2c79ab35a370591363952314d7b4cb2cb72bf18a7 | MATLAB | 17,531 | 380 | %% mean-field stuff. We use parameters andto estimate firing rates
%% NOTE! mean Vs are now autonomus from single cell simulations
% Input: C_EE,C_EI,C_IE,C_II connectivity matrices
% S_EE,S_EI,S_IE,S_II synaptic strength
% S_EL6,S_IL6,rE_L6,rI_L6 L6 Input
% p_EEFail Synaptic failure pro... |
a89b0007ef8ade48578eb6470a994f0b3e9ad99c061f287605c8d904d52aadf0 | MATLAB | 17,572 | 344 | % ----------------------------------------------------------------------- %
% Function 'plot_topography' plots a topographical map of the head over the
% desired points given by 'ch_list' and their assigned 'values'. %
% %
% Input pa... |
2701e2408dddd32a45b4f72986f225fa95ef9667aa59c7d7a1cb958c2cc53433 | MATLAB | 17,575 | 387 | function [RpegTouchMatrix LpegTouchMatrix RpegTouchTurnMatrix LpegTouchTurnMatrix...
RpegTouchCell LpegTouchCell RpegTouchTurn LpegTouchTurn...
RpegTouchMatrixSorted LpegTouchMatrixSorted...
RLpegTouchTurnSorted RLpegTouchCellSorted RLpegTouchMatrixSorted RLpegTouchTurnMatrixSorted...
]=PegTouchAnal... |
a4780aabdecdb905b958a6464e783e0b074c7102dd3e8a2f0122481fe4fa222f | MATLAB | 17,613 | 468 | % Programmed by: D. Bolger Date: June 2022
% Most recent version of the Segmentation Tool for the EEGVR_l2Learning project.
% A few things to note:
% - You have to first load in the excel file "VERB_TRIGS.xlsx" - a dialogue box opens automatically allowing you manually search for this file.
% - Then you have t... |
ecba7f032df27dd86ba01db5eb52069f4e21f43dc123b90dc0f479104303cfba | MATLAB | 17,614 | 468 | % Programmed by: D. Bolger Date: June 2022
% Most recent version of the Segmentation Tool for the EEGVR_l2Learning project.
% A few things to note:
% - You have to first load in the excel file "VERB_TRIGS.xlsx" - a dialogue box opens automatically allowing you manually search for this file.
% - Then you have t... |
11e3af0fd2b07858763c5c401154cb7572096a69910e32c25c5a3108f5e6e126 | MATLAB | 17,620 | 453 | function [data_posterior_est, data_prior_est, n_var] = ECGPhaseDomainGPFilter(data, peaks, params)
% % % [phase, ~] = PhaseCalculation(peaks); % phase calculation
if ~isfield(params, 'bins') % number of phase bins
params.bins = median(diff(find(peaks))); % use median of RR-intervals in samples by default
end
if ... |
0a4ba364393a9d38a8eadf9bc6be46f52fb644a090e55b49835780c03841b094 | MATLAB | 17,625 | 416 |
%{
----------------------------------------------------------------------------
First_take number of frames in each tif files
Analyzing imaging data simply
At least for the correct rate
----------------------------------------------------------------------------
%}
function Task_kaiseki_tokyo1_20220912_sound_c... |
8953070ec063c106d77cd5642b6dec0491e9e3316c8dfc66b5bdc19031711636 | MATLAB | 17,669 | 456 | function RLphase191024meanfigure
%RLphase191024meanÌfigureì¬p
%¼àPF¶E»ê¼êÌXpCNÌÊðp¢½\ª
%RLphase190415mean©çÌÏX_F1000rɼ·Æ±ëjMaxÌlð¬æêÊÜÅÉÏXAqxgÏ·ðgíÈ¢æ¤Éµ½
global ACresult ACresultW CCresultDrinkOn SpikeArrayWon TurnMarkerTime RpegTouch... |
2e7051df234e782b2dc1677a9e74b5ac1436c75205d4688e8ba06e3faab05001 | MATLAB | 17,681 | 505 |
%{
----------------------------------------------------------------------------
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... |
426c5298328ca5842b5fa69b5239bb4296c87b71561ee873fd8608cbc26a7915 | MATLAB | 17,685 | 355 | %æsyOiyOP|UjÌOÉXC_ðÝè·éB
function varargout = TouchDivider(varargin)
% TOUCHDIVIDER M-file for TouchDivider.fig
% TOUCHDIVIDER, by itself, creates a new TOUCHDIVIDER or raises the existing
% singleton*.
%
% H = TOUCHDIVIDER returns the handle to a new TOUCHDIVIDER or the handle ... |
17b04d8908f84a0a4f8ef8be65cdc87adcf317bb7ee3deca9d2afca653c9678f | MATLAB | 17,686 | 505 |
%{
----------------------------------------------------------------------------
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... |
58039f718c151b57d983d0c3a2b7c3782b72818da037fee9d3f50ff807ad3e43 | MATLAB | 17,720 | 381 | %% mean-field stuff. We use parameters andto estimate firing rates
%% NOTE! mean Vs are now autonomus from single cell simulations
% Input: C_EE,C_EI,C_IE,C_II connectivity matrices
% S_EE,S_EI,S_IE,S_II synaptic strength
% S_EL6,S_IL6,rE_L6,rI_L6 L6 Input
% p_EEFail Synaptic failure pro... |
8126b3d7273814aafe4f9699b8103307b950655b113d5faf1ea05e6f4080bed0 | MATLAB | 17,728 | 414 | %% mean-field stuff. We use parameters andto estimate firing rates
%% NOTE! mean Vs are now autonomus from single cell simulations
% Input: C_EE,C_EI,C_IE,C_II connectivity matrices
% S_EE,S_EI,S_IE,S_II synaptic strength
% S_EL6,S_IL6,rE_L6,rI_L6 L6 Input
% p_EEFail Synaptic failure pro... |
da2d9cb02a611f82647b1dc6d6fc90286893bdf500996e4ee38ca25079487361 | MATLAB | 17,728 | 505 |
%{
----------------------------------------------------------------------------
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... |
bba7aafdc44758d04a91fa495683a6ae207c5a1b1e2d07965c943b6252e2d1fa | MATLAB | 17,731 | 505 |
%{
----------------------------------------------------------------------------
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... |
c8603a29a6f46fdb5f041eafe835d23f86838a3d1870070e78d0fa1704106f50 | MATLAB | 17,742 | 446 | close all
clear all
idx_single =[2];
spine = {[1:10]};
idx_sum = 11;
idx_SLM = 1;
branch_no =1;
path = 'E:\data\dendritic patch\082723\cell3';
preflix = '230827_001';
%%
filename_single = sprintf('%s.single_spine_series.%d.wcp',preflix, idx_single(1));
%filename_sum_SLM = sprintf('%s.summation.%d.wcp',preflix, idx_s... |
1c6e9bfbc724b706ef47a53692fe42839ed94110161e350140d63bf3201cf22b | MATLAB | 17,747 | 423 | classdef (Hidden) channel < handle
%
% Class:
% adi.channel
%
% This class only holds information regarding the channel.
properties
id %Internal number (1 based)
name
%These properties are on a per record basis ...
%-------------------------... |
b8c248a3138fcf0d5293d8696c76ef8b0a158fc821eb78dda0b66f16a9d176ae | MATLAB | 17,784 | 491 | function [projs mse like lat] = cvreducedims_edd(D, alg, dims, handles)
% [projs mse like] = cvreducedims(D,alg, dims, handles)
%
% CVREDUCEDIMS a dimensionality reduction tool
%
% Input:
% D (struct) --- high-d data to be reduced
% alg (integer) --- which red. algorithm is used
% dims (array) --- array (ascen... |
971afdf10eff6ab507695614bd4f5fa1acd4662f0ea66ef156d9f94b21248472 | MATLAB | 17,807 | 475 | function [sens] = ft_read_sens(filename, varargin)
% FT_READ_SENS read sensor positions from various manufacturer specific files. See
% further down for the list of file types that are supported.
%
% Use as
% grad = ft_read_sens(filename, ...) % for gradiometers
% elec = ft_read_sens(filename, ...) % for electro... |
60cbf1f0a7792269730d477f88815c3d964f23d58c576b88b0d0ea0455e982ab | MATLAB | 17,817 | 486 | function [ClusterIDs,Centroids]=mnl_Weighted_dCrawler(InputMatrix,InputWeights,EuThresh)
% Inputs
% InputMatrix - the list of points, each row = point, column = values
% InputWeights - the weight assigned to each point (higher number equals
% greater importance)
% EuThresh - the euclidean threshold (d) you want to ... |
5009eb838b6567f4d8a439266db53c8344c922041a276b3b6e944581fde42ef6 | MATLAB | 17,827 | 589 | function [dip,xl,xu, ifault, gcm, lcm, mn, mj] = HartigansDipTest(xpdf)
% % function [dip,xl,xu, ifault, gcm, lcm, mn, mj]=HartigansDipTest(xpdf)
% %
% % This is a direct translation by F. Mechler (August 27 2002)
% % into MATLAB from the original FORTRAN code of Hartigan's Subroutine DIPTST algorithm
% % Ref: Algorith... |
bd2bfa5f61d9baa2e9b4274e1f410995b636f9f430df710167957830f8b0757c | MATLAB | 17,853 | 708 | % demo_superbar
%
% In this tutorial, we demonstrate how to use SUPERBAR. We begin with the
% most basic usage, and show how the same code for BAR can be achieved
% using SUPERBAR.
% Make a figure to put the example plots into
figure;
%% ========================================================================
%. (1)... |
b165e4773dbf0c63ae6cc04932d82e8d6511797571def19890bd3f7ccb4311b2 | MATLAB | 17,897 | 479 | function varargout = RaguMSParameters(varargin)
% RAGUMSPARAMETERS M-file for RaguMSParameters.fig
% RAGUMSPARAMETERS, by itself, creates a new RAGUMSPARAMETERS or raises the existing
% singleton*.
%
% H = RAGUMSPARAMETERS returns the handle to a new RAGUMSPARAMETERS or the handle to
% the existing ... |
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