sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
b56ce37507fc5fd9fd99180f67561ddb5349f5421f66d101601086b0e55e18a1 | MATLAB | 14,728 | 410 | %% Compile xv results over rounds
nE = 3015;
nH = 3019;
nPch = 39;
xverrE = nan(nE,10,nPch,0);
xverrH = nan(nH,10,nPch,0);
pchsE = cell(0,nPch);
pchsH = cell(0,nPch);
xvresE = cell(nPch,0);
xvresH = cell(nPch,0);
[xverrE(:,:,:,end+1),pchsE(end+1,:),xvresE(:,end+1)] = HPOptim.loadXVres(...
'pch00','easyfold2/rnd0res'... |
5418a22ffa2051b3092ec0d47ede049df7d01c78a4ead9de6ea555e584b5ade9 | MATLAB | 14,742 | 433 | % April 2020
% setup
expt = 'TA2';
user = 'karen';
slice = 'cue_subsample50'; % cueAcc_correct'; % cueAcc 'cueAcc_incorrect'
exptDir = meg_pathToTAMEG(expt, user);
% analStr = 'bietfp';
saveFigs = 1;
%% setup
% directory
figDir = sprintf('%s/Group/figures/%s_max/',exptDir,slice);
if ~exist(figDir,'dir')
mkd... |
974215eb8232380a23b9fe9972e41050b694cd2fc1520a2c1df4afcb870acb3d | MATLAB | 14,742 | 345 | % Cardiac model ToR-ORd-Land for acute and chronic myocardial infarction
% Copyright (C) 2024 Xin Zhou. Contact: xin.zhou.joy@hotmail.com
%
% 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 Softwa... |
c1a369d613ce4e6806db7645a08f341c5c4a4e50ed8ea74dc9b97add6c3aa59f | MATLAB | 14,746 | 348 | % Author: Milena Capiglioni, University of Bern
% Contact: milena.capiglioni@extern.insel.ch
% Last update: Nov.2023
% If you use/modify this code for your future publication, please cite the
% corresponding article: "Stimulus-Induced Rotary Saturation imaging of
%visually evoked neuroelectric response: preliminary r... |
daeb8b0eda6030967cd06f589dac5644ac0de27b5bf6b2c49aa9ff5c3709624c | MATLAB | 14,747 | 421 | %% Basics of pmtk3
%% Overall design of PMTK3
% PMTK3 has an object oriented design.
% That is, it can be thought of as defining a series of 'classes',
% representing different kinds of probabilistic models.
% Each class supports
% various 'methods', which perform certain operations.
% These methods are of... |
5001b2ef88b68e23589fca154869afb5bcc1021ccb46dbd9ec8ae45669c9a978 | MATLAB | 14,755 | 319 | clear; clc;
% This analysis script can be used to run a parellelised version of
% connectome-based predictive modelling with LOOCV or repeated k-fold CV.
% Authors: Rory Boyle & Yihe Weng
% Emails: rorytboyle@gmail.com & wengy@tcd.ie
% Date: 07/10/2021
% Date edited: 20/05/2022
%% 1) Load data and prepare variables
% ... |
e40850e90df0f3530c6fcba47923a2e8947c007d411cd8a4231a07158338a4f8 | MATLAB | 14,767 | 296 | % Double checked on Jun24, run again to make sure whole-cell analyses is
% complete. Don't separate into 3 phasor locations right now.
% loop to analyze 3 different features.each need different G/S
% map, Changed on Oct10, 2023, binning should after the feature
% segmentation--Lipo/ev/cell
% Finished running on Oc... |
6c5adddc294b5f7ca1b57bf1a6f6844bef7899041473c6c71f2da290747ab769 | MATLAB | 14,774 | 309 | %%% Figure 2 - Stability
addpath("HelperFunctions")
load(fullfile(DataPath, 'SurveyMainData_Processed.mat'))
load(fullfile(DataPath, 'Stability.mat'))
[palmar_mask, palmar_template, dorsal_mask, dorsal_template] = GetHandMasks();
[pbt_regions, dbt_regions] = GetHandSegments();
orig_size = [1200, 1050];
SetFont('Arial',... |
2b2d56ce829edbe33cd85b946a472ae46170cb3d5303fc1a73d392e38b1902a6 | MATLAB | 14,794 | 442 | function PlotPopulationERAs(config,varargin)
%%
if strcmp(mfilename,'') || nargin==0
opts=Analyze.Libet.opts_BasicLinearModel;
else
opts=feval(config,varargin{:});
end
if ~exist(opts.ERADir,'dir'); mkdir(opts.ERADir); end
% Get necessary fields from opts and stuff.
PanelCondNames = opts.ERAPanelC... |
1ca3c91e7ad58c87e4d477f39ff6d4e174f4a1b743b2a0a50d5bf4732cc91b53 | MATLAB | 14,808 | 412 |
dimLabelsCS = {'ramp', 'PC1', 'Tin^{con}_{in}', 'What-decoder', 'When-decoder'};
%% Stats on cosine similarities
load(fullfile(saveLoc, 'cosine_sim'), 'cosine_sim')
allpVals = [];
for d1 = 1 : length(dimLabelsCS)
for d2 = 1 : length(dimLabelsCS)
if d1 < d2
dVec = squeeze(cosin... |
d1f35f181e574ebc431490fcc7d9cfdff665bbef4f9d5951c5a6302331b0eff0 | MATLAB | 14,820 | 332 | function CrossDecodeAnalysisTrial(AllTrialData,Labels,DecodeLabels,TimeWindow,OutDir,Tag,varargin)
% Train on dim 1 to classify dim 2, test on how well it generalizes.
% e.g. train on nancy to diff bp, test how well bp diff'd in Tyson.
[~,PThresh] = Utilities.ProcVarargin(varargin,'PThresh','fdr');
% [~,NIter] =... |
21bbf7104f5341a3f99d239983ac549a85c6430c5a2f4bf5af1b2cbcc858d535 | MATLAB | 14,824 | 339 | function CBIG_KRR_workflow( setup_file, save_setup, varargin )
% CBIG_KRR_workflow( setup_file, save_setup, varargin )
%
% This function runs the whole workflow of kernel ridge regression
% algorithm to predict some target variables. All the input filenames and
% hyperparameters can be passed in through the setup_fil... |
3a2cdd45c4d75123674d1df4e12c44244766cd890cabdde36f50acbf20b23aa9 | MATLAB | 14,854 | 474 | classdef LabelCoreMultiViewCalibrated2Model < LabelCoreModel
% Multiview calibrated labeling model
%
% Owns all data state for multi-view calibrated labeling: point-to-axis
% mappings, point set mappings, working set, calibration rig, adjusted/
% stored flags, epipolar line visibility state, etc. Communicates state
% c... |
c325e291c178c1ea0292f18e7f9c3158fde170b01df443e05942b8868c2342f1 | MATLAB | 14,916 | 362 | classdef TransformableCP < diffgeometry.ComponentPatch
% ComponentPatch whose components obey a coordinate transformation law
% In particular tensors and the Christoffel connection, but not a
% Jacobian.
%---------------------------------------------------------------------
% license
%------... |
9027919166191dbf64172dd1f8083cc3457d47aa73be81477e8bd4b9b8627e65 | MATLAB | 14,917 | 406 | function TA = tbx_cfg_TextureAnalysis()
% Configuration file for Texture Analysis
addpath(fileparts(which(mfilename)));
%==========================================================================
%============================== GLCM TOP ==================================
%====================================... |
1011b018b01844d9620c94b9b91c3c022ff4671e5d1c75c68176aab8d75c6c2a | MATLAB | 14,930 | 408 | function Stack_Viewer_AVI_Player(MovieName,MovieDir)
warning on all
warning off verbose
warning off backtrace
ScaleFactor=4;
FrameJumpPer=0.1;
GridSpacing=50;
GridColor=[1,1,0];
MoviePlaybackControllerPos=[0 0.05 0.08 0.15];
global FINISHED_PLAYBACK
FINISHED_PLAYBACK=0;
... |
bc18df19626e75e7d22120914b597f55b2a6100d9327c008f47ffeb641c6edbf | MATLAB | 14,932 | 337 | %% Imports
addpath("HelperFunctions")
load(fullfile(DataPath, 'DigitLocalizationData'))
DigitLocalizationData = rmfield(DigitLocalizationData,...
{'Frequency', 'Images', 'Responses', 'TestLogComments', 'Paradigm'});
load(fullfile(DataPath, "RoboticDigitLocalization.mat"))
%% Make response tables
response_table_con... |
89b97bfe66821450c05ed3e9345a85fbc68900fdf620f4ec9dca12f0ef186da2 | MATLAB | 14,940 | 355 | function [pvals, pvalsgroup] = corrplot( data, varnames1, varnames2, groupvar, critp)
% mimics the corrplot function from the econometrics toolbox, which I sadly
% do not have. data has to be a table, and varnames the names of the variables
% that should be correlated. if present, will use varname_cilow and varname_cih... |
e21f591c9b4660a59b9b3b2712ca5812c1a49c6ee0309866e4771f9157248944 | MATLAB | 14,988 | 432 | function tracks = compute3Dfrom2DRomain(vscores,cRig)
% vscores should be a cell with dims TxYxXxPts
% Order of views is Left,Right and then Bottom for Romain
offx_front = 0;
offy_front = 0;
offx_side = 0;
offy_side = 0;
scale= 4;
nviews = numel(vscores);
thresh_nonmax_front = 0;
thresh_nonmax_side = 0;
thresh_per... |
f915cb19cf397b44443f1ac3ff290a4ed35f9d0209c4092742e3defadcca4780 | MATLAB | 14,990 | 311 | function get_circular_data(plate_image, plate_seg, data_location)
% Run circle Hough transform on inputted image
%% Parameters to change
% Isolated colony conditions (used for estimating colony sizes for a given image only)
% (getting data on exactly how large single colonies are in the images,
% with constraints so... |
74c4a720ff40bbcfe078d4298051c0748da59c1ebf873fd30fdb13080e70d043 | MATLAB | 15,012 | 389 | function [sens] = ft_read_sens(filename, varargin)
% FT_READ_SENS read sensor positions from various manufacturer specific files. The
% following acquisition system and analysis platform file formats are currently
% supported:
%
% asa_elc besa_elp besa_pos besa_sfp yokogawa_ave yokogawa_con yokogawa_raw 4d
% 4d_pd... |
890f97ee7c63cecc73a2fc708bead0010f6d32387652a1cfcbb1ee8c3c5c26e4 | MATLAB | 15,013 | 513 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
78655bcd03654658a6cc436de65541de7e743744db13b6b98f2233b6b6b4c26b | MATLAB | 15,023 | 469 | clear;
clc;
% close all;
if exist('subj_counts_fooof_ecog.mat','file')
else
error('Figure5_AB_CHO_v1: Run "Figure5_A_Fooof_v1.m" first');
end
srate = 400;
n_subjects = 8;
%%
warning('off');
band_labels = {'\delta (<3Hz)','\theta (3-6Hz)','\alpha (7-14Hz)','\beta (15-30Hz)','low \gamma (31-40Hz)'}... |
efccccec13965632a468ac57711029ec1c1de0abbe4f25229ca04e620f52f2f2 | MATLAB | 15,025 | 337 | function partest(x)
%This function calculate the performance, based on Bayes theorem, of a
%clinical test.
%
% Syntax: PARTEST(X)
%
% Input:
% X is the following 2x2 matrix.
%
%....................Affected(D+)..Healthy(D-)
% _______________________
%Positive Test(T+) ... |
0163987935689e256ac50c46d5f50de95b7e3f086c121d9362e1f5e875b19b6f | MATLAB | 15,031 | 302 | function perform_decoding_analysis(p)
%
% - This code is written for performing multiple ROI decoding analysis.
% - A total of 383 (HCP360, 13 individual ROIs, and 10 subcortical ROIs) are used.
%
%
% called from emotion2020_analysis_BATCH
%
%% initialization
warning off
%% start analysis
for scoritr = 1:length(p.scor... |
d5e9ff75063de7b1d394eb2b944a59263df570920d847bf9a03838a107a31600 | MATLAB | 15,045 | 410 | function FilterIntanMC(datDir, varargin)
% 8/25/2018 M. Farries
% This works on the "traditional Intan file format," where all channels are
% written to the same file, but a new file is created every N minutes. This
% is mainly--perhaps exclusively--used for silicon probe data. Tetrode data
% is normally saved in o... |
2e4dfa2fd7a6541ad87947cf2b581b2a98e0843d2ced4f6b493814aff2fed1dc | MATLAB | 15,063 | 515 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Mus... |
56be6296931ae122d98e8b17bc321dc53455e5bf247e84ba98d82165843d6d8a | MATLAB | 15,064 | 339 | function General_Region_Selector(ID_Name,SelectionDisplayImage, SelectionRegion, SelectionRegion_BorderLines, Additional_Markers,Additional_BorderLines)
OS=computer;
if strcmp(OS,'MACI64')
dc='/';
else
dc='\';
end
pwd;
currentFolder = pwd;
[upperPath, deepestFolder] =... |
cbe6e0fd3235ff8ca8e9a1a377cce4922027acdc02648de0821fda5e8f26f5cb | MATLAB | 15,065 | 562 | %load data: contact lead author
%%
fields=dfofUse;
%% %all pairs of fake reward zones: 4 bins per zone, any distance throught any location of the track
%get the pairs
%create locations of every 4 bins
totalBins=140;
% all4Bins=[0 0];
% for n=1:140/4;
% all4Bins(n+1,:)=[all4Bins(n,2)+1 n*4];
% end
% all4B... |
118205cc97f8eefa76a89585ec1584b40457efe75752f6b7ec74d22664803e67 | MATLAB | 15,066 | 397 | function Out=Dec_SubspaceProj(AllTrialData,Phase,unit,timeWindow,opts)
%%
% timeWindow=[-1.5 -.5];
% Task related Firing Rate
disp(timeWindow)
%%
LabelOrder={'Pinch' 'Press' 'Rub' 'Tap'};
cTrialData=Analyze.SubSelectTrials(AllTrialData,'Phase',Phase);
%%
% Conditions are basically {{Fc,Fs,Oc},{Fc,... |
1530752804d4f7aa1e2a6cd1fb1dbd43bba414c88b3f34ebda1ecba520cd9a3d | MATLAB | 15,066 | 419 | function [mdlFit A3 A4] = meg_singleTrial_crossValidation(expt, sessionDir, user)
% function meg_singleTrial_crossValidation(expt, sessionDir, user)
% split half cross validation per subject
% Inputs:
% expt: 'TANoise'
% sessionDir: eg: 'R0817_20171212', 'test' will create simulated data
% user = 'karen' 'scc' ... |
5d3464d8276bddc704bc4a52c2f49300e1a45f894d31b7162be89a74464941d6 | MATLAB | 15,066 | 295 | %--------------------------------------------------------------------------
% Title: Multi-Layer Dictionary Learning using residual-based cascaded kSVD
% Author: [Behrang Fazli Besheli]
% Email: FazliBesheli.Behrang@mayo.edu
% Institution: [Mayo Clinic, Department of Neurosurgery]
% Date: [09/07/2024]
%----------... |
cde1a0a42477db8d3a60357eff84049b08bd9883d01fb272a4b96a4f01930924 | MATLAB | 15,070 | 437 | function spkDat = ClusterSpikes(trodeFile, ops, varargin)
% 11/18/2016 M. Farries
% REQUIRED INPUTS:
% trodeFile - name of filtered hsd file to be processed (for just 1 tetrode/stereotrode)
% ops - structure containing spike sorting parameters and options (see ConfigSort script)
% OUTPUTS:
% spkDat - dat... |
c8470abf14c4f6fc8e67621a3277d07c45a7ab44248ce6f598fb92a0471c638e | MATLAB | 15,078 | 359 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
40c05e81bd6639b33bd9c94239866cdbfa50410e67976208d1e109d319c4c495 | MATLAB | 15,081 | 542 | function b = imfilter(varargin)
%IMFILTER N-D filtering of multidimensional images.
% B = IMFILTER(A,H) filters the multidimensional array A with the
% multidimensional filter H. A can be logical or it can be a
% nonsparse numeric array of any class and dimension. The result,
% B, has the same size and class ... |
d959835c652edfbbe4722b079a462bd295e2848aa6df21dc8241eae4dd563e5f | MATLAB | 15,081 | 272 | if ~exist(CurrentScratchDir)
mkdir(CurrentScratchDir)
end
CurrentAnalysisLabel='Registration Evaluation';
CurrentAnalysisLabelShort='Reg Eval';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
disp('============================================================')
try
FileSuffix='_DeltaFData... |
8cea40e38effb5d9e810320ed80a8caa4e6aecdb56d3a3295fb66719c538ddf5 | MATLAB | 15,104 | 387 | function varargout = eval_gradient(net, init_condition, v_u_t, m_target_t, forward_pass)
%% Setup.
% Network structure.
[n_Wru_v, n_Wrr_n, m_Wzr_n, n_x0_c, n_bx_1, m_bz_1] = unpackRNN(net, net.theta);
[V,T] = size(v_u_t);
[M,N] = size(m_Wzr_n);
% Transfer functions.
rec_transfun = net.layers(2).transfun;
out_transfun ... |
f889b31e185cdb192f9a7352f194a7b0986f77ee8437f725d321fa17d42cc0f6 | MATLAB | 15,104 | 531 | datRoot = '/Volumes/LaCie/Farries recordings/';
load([datRoot 'unitDat_final_culled.mat'])
uName = arrayfun(@(u) strsplit(u.name), unit, 'UniformOutput', false);
uRat = cellfun(@(u) u{1}, uName, 'UniformOutput', false);
uSes = cellfun(@(u) u{2}, uName, 'UniformOutput', false);
uTrode = cellfun(@(u) u{3}, uName, 'Unifo... |
7ccfe3c237d1bc95d68ea99e8bfa365307cd1236cad39f7fe249273b45d7106d | MATLAB | 15,105 | 409 | function CBH = cbunits(varargin)
%CBUNITS Adds units (and ISU prefixes) to the colorbar ticklabels.
%
% SYNTAX:
% cbunits ºC % Just an example for celsius degrees.
% cbunits ºC prefix % Uses ISU's prefix instead of 'x 10^y'.
% cbunits ºC off % Resets the ... |
d4e054e009594b156f122ec3956325571990927c2a1aa09cf48538b0c81082bd | MATLAB | 15,105 | 410 | function CBIG_ComputeCorrelationProfile(seed_mesh, target, output_file1, output_file2, threshold, varargin_text1, varargin_text2, outlier_text, split_data)
% CBIG_ComputeCorrelationProfile(seed_mesh, target, output_file1, output_file2, threshold, varargin_text1, varargin_text2, outlier_text)
%
% Compute surface correl... |
99dea66823ddcbc6b55abbb1e924e96dc9dabfa6f27b16b85b826a9d454bdc65 | MATLAB | 15,122 | 516 | classdef hdf5prop < handle
% HDF5PROP Class for transparent file data access.
% Matlab class to create and access HDF5 datasets transparently as Matlab
% variable. Data can be accessed and written with subscript referencing and
% assignment methods, just like a matlab variable, only size must be
% explicitly set or cha... |
632bc2247ddaa2ae1b4bdcabe61c367cc5c29f4dd62a92d5b25b3a2969c44c87 | MATLAB | 15,124 | 516 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
4c4fdd222e068a59abf54236f3f331bf31a41e6a6aa3baf5b8383f6b8dd8166c | MATLAB | 15,129 | 515 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
afb2863d1e649018b6b34d65b43b1dfb5c24c319a6f03e470bf4c16c71cc2a76 | MATLAB | 15,135 | 517 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
5be4140d9324f07c1fba1b866a232bf2f1868192bb4d965355469e311ae7facc | MATLAB | 15,136 | 517 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
812596072c120b31b0c198d15df59cc9c04349381dd89fced129340fe5fc3f95 | MATLAB | 15,142 | 272 | % start parallel MATLAB to speed up execution.
delete(gcp);
numworkers = 1; % keep low to prevent memory swapping!?
if parpool('local')==0
parpool('local',numworkers);
end
datadir = '/home/xianchige/YZY/20220523data/sub05/session01'; %设置数据目录
figuredir = [datadir '/preprocessfigures']; %设置结果输出路径
%%% CAS... |
a46696ae3c23ea076733a2d8e56f6c6f4467f425674bf47c02656942d9cf25f6 | MATLAB | 15,152 | 299 | % Double checked on Jun24, run again to make sure whole-cell analyses is
% complete. Don't separate into 3 phasor locations right now.
% loop to analyze 3 different features.each need different G/S
% map, Changed on Oct10, 2023, binning should after the feature
% segmentation--Lipo/ev/cell
% Finished running on Oc... |
4b55aee0f51d7aa53026ce01c3b33a5fef1718398df8b5762c89424959aa65df | MATLAB | 15,170 | 514 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
faab9cb12dd814d26a37d615c2954554cd4164088ecc05b040bb751c108ef190 | MATLAB | 15,171 | 400 | function [firings_out info]=ms_merge_across_channels(templates,firings,opts)
% MS_MERGE_ACROSS_CHANNELS - merge clusters from the same neuron but detected
% on different channels
%
% [firings_out info] = ms_merge_across_channels(templates,firings,opts)
%
% Inputs:
% templates - MxTxK array... |
ad87e5199720c766745b9d8b52f3e2f85f33d11a6b05b83a113673cfbf7868e8 | MATLAB | 15,173 | 524 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Mus... |
49fc611ea14339a6a070d3db07196064aa1f91b6ad567666d50fd56054ee661d | MATLAB | 15,177 | 348 | %% Imports
addpath("HelperFunctions")
load(fullfile(DataPath, "AmpDiscrimBioLin"))
load(fullfile(DataPath, "ForceEquivMulti.mat")) % Mech amp est
load(fullfile(DataPath, "AmpDiscrimMultiChanRaw")) % multi chan discrim
load(fullfile(DataPath, "AmpDiscrimMultiBioLin.mat")) % multi chan discrim bio vs lin
load(fullfile(D... |
755ea771a5a97717a1eed3802f33f70290283c69556eb9bfd303b5501fcac0c7 | MATLAB | 15,180 | 400 | function [data] = ft_resampledata(cfg, data)
% FT_RESAMPLEDATA performs a resampling or downsampling of the data
%
% Use as
% [data] = ft_resampledata(cfg, data)
%
% The data should be organised in a structure as obtained from the FT_PREPROCESSING
% function. The configuration should contain
% cfg.resamplefs ... |
06c5955a2cbc7c4073bbfa8c624467a155b79b7e9b357b70d89302fa3dd0e83c | MATLAB | 15,183 | 395 | function [dipout] = ft_sloreta(dip, grad, headmodel, dat, Cy, varargin)
% ft_sloreta scans on pre-defined dipole locations with a single dipole
% and returns the sLORETA spatial filter output for a dipole on every
% location. Dipole locations that are outside the head will return a
% NaN value. Adapted from beamformer... |
e87ac0963c17fb39bb0cfa037bf5f56868167ea2f0aea93ec6f05198b9c0126b | MATLAB | 15,183 | 432 | function [Gain, errMsg] = bst_duneuro_modLAN(cfg)
% BST_DUNEURO: Call Duneuro to compute a FEM solution for Brainstorm.
%
% USAGE: [Gain, errMsg] = bst_duneuro(cfg)
% @=============================================================================
% This function is part of the Brainstorm software:
% https://neuroimage... |
9c93b3083e413472a0f62f79a4bbdc3e8ca50266e4b5cb960661ef270a8c2511 | MATLAB | 15,185 | 395 | function [dipout] = ft_sloreta(dip, grad, headmodel, dat, Cy, varargin)
% ft_sloreta scans on pre-defined dipole locations with a single dipole
% and returns the sLORETA spatial filter output for a dipole on every
% location. Dipole locations that are outside the head will return a
% NaN value. Adapted from beamformer... |
258052612e325e1dcfd4fbff9da2abef4e517b79e7312299d352a1dbd6d27cef | MATLAB | 15,186 | 545 | function [pval , stats] = lan_nonparametric(data,cfg)
% v.0.6
% <*LAN)<] toolbox
%
% [pval , stats] = lan_nonparametric(data,cfg)
% Forms: nonparametric(data,cfg)
% dependencies : statistic toolbox:
%
% cfg.method = 'rank'
% cfg.paired = true or false
% cfg.cortest =... |
90254b7185fb032873fb561e26940361e7e40c7e4473dd4a709f1192c40ed3af | MATLAB | 15,188 | 510 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
79e40d42852113097675cfc146fa577b3fae95ef95e63173cb1b20498042f7d6 | MATLAB | 15,191 | 512 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Mus... |
fa5de5c247c9835ab6e77854b4504d175408e3d87ee11cbd67da11e2d69eb109 | MATLAB | 15,191 | 519 | % clear
% clc
MuscleMomentArmOW_time = [];
MuscleMomentArmWI_time = [];
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleClust... |
3243844446300036154630e4748dfd0a0a9a16e7962a46a3253ed2c2ef4c7ea0 | MATLAB | 15,193 | 554 | %load data: contact lead author
%%
cd('reward_useShuffleThresh2')
fields=dfofUse;
% load('fieldDistriUse.mat');
% dfofRF=fieldDistriUse;
% fields=dfofUse.*dfofRF;
d=[];%dfof within cues before the reward compared to other cues
r=[39:42 91:94];
nr=setdiff([1:1:140],r);
rdfof = fields(:, r); rdfof = nanmean(rdf... |
35ccc8e7f3ac9b91abe279382e009840ef0a8c6ab5ae4da2ea6289a25b7065e2 | MATLAB | 15,194 | 557 | function erp_glan(GLAN,roi,comp,chh)
% Remplazada por erp_plot.m ¡¡¡SIN MAS ACTUALIZACIONES!!!
% v.0.1.7
% erp_glan.m
%
% plotea erp por electrodo o grupos de electrodos (roi)
% roi = electrodos
% comp = indice de comparacion a graficar
% chh = 1 --> plotea significacia con correcciones
% chh = 0 --> plotea signific... |
d5cf3f489d3c1264dd056026270e42bc9b6728a09d23e8f17147788e8e59015f | MATLAB | 15,195 | 516 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
9c8996c5b6353eff40658a456ff684417dc4b18ec000d4606dcfa9f7cc59267f | MATLAB | 15,197 | 516 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
2aaa5ef4555b32543f77016ec68fbaaf96806281a3d30027d97a9f956bad16a2 | MATLAB | 15,199 | 516 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
e722fd44e49c64e8ba28d502d1ddf3aae61ed031ad9e2478c4567dbaf230fa97 | MATLAB | 15,202 | 512 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
86a25f6e3bb24228681cae17cbc89cf4c1bf5ff6d13a496b01d9fdb9bf3b5762 | MATLAB | 15,205 | 515 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Mus... |
38a1e57ede8cfa6d416445344073032df53a484383906b20236567fc12b3de9e | MATLAB | 15,207 | 517 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Mus... |
500ea276ab6ec0e8ef0b9c3f9c66698c35fd1b3aa300fa745484e4e7ec65fee5 | MATLAB | 15,208 | 333 | function [clickClasses, featureArray, numLicks, timeCourses] = analyzeClicks(clickArray, afterLastClick,...
lickTimes, binTimePts, params, varargin)
% this function classifies click events as hits (spared seizures) or
% misses(impaired seizures) based on the classification method of choice.
% also returns the total... |
0b2e388a73fb3c4ec3f8ae5e8510582f6ae33081df32837753aea2db651abd16 | MATLAB | 15,209 | 519 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Mus... |
6400fcbdd6d85688d152e44bcba4b66be69ddc06a53ebc3d2a6dd8ddf3702df5 | MATLAB | 15,211 | 520 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
f44c16c9f9c9b96f4821e1a1118ad54735f782724f90912ce2b17e9d70fa0dd2 | MATLAB | 15,215 | 515 | function RGB = cmapping(varargin)
%CMAPPING Colormap linear mapping/interpolation.
%
% SYNTAX:
% cmapping
% cmapping(U)
% cmapping(U,CMAP)
% cmapping(U,CMAP,...,CNAN)
% cmapping(U,CMAP,...,TYPE)
% cmapping(U,CMAP,...,MODE)
% cmapping(U,... |
14be5d342a07d6f498bc040650f91aec9bc4185a58ee1cb219152bd801500363 | MATLAB | 15,237 | 388 | function [normalised] = ft_volumenormalise(cfg, mri)
% FT_VOLUMENORMALISE normalises anatomical and functional volume data
% to a template anatomical MRI.
%
% Use as
% [mri] = ft_volumenormalise(cfg, mri)
% where the input mri should be a single anatomical volume that was for
% example read with FT_READ_MRI.
%
% Con... |
905a1cf31d46075ff91bb199c9232c68def5f34626289cbbc7a6b9e443c4a5bc | MATLAB | 15,238 | 509 | function exportfig(varargin)
%EXPORTFIG Export a figure to Encapsulated Postscript.
% EXPORTFIG(H, FILENAME) writes the figure H to FILENAME. H is
% a figure handle and FILENAME is a string that specifies the
% name of the output file.
%
% EXPORTFIG(...,PARAM1,VAL1,PARAM2,VAL2,...) specifies
% parameters th... |
e32fbfa04a4671ad2414338a7dc52dcffdb1936e493cf98615f6cec776c0c1fe | MATLAB | 15,253 | 345 | classdef FeatureLayout < matlab.mixin.Copyable
% Instances of this class determine how coordinates are plotted and
% describe and annotate various components or dimenions.
%
% Features with fixed dimensions are viewed as matrices or (sometimes)
% higher order tensors (i.e. n-d arrays with n>=3)... |
47d20c562834135dc38ec2fd719c6231a20b3c15f4a26c5ea85f71b4cabef107 | MATLAB | 15,254 | 334 | function varargout = placeFigures(varargin)
% Optimally lay out existing or new figures on the screen so that they
% don't overlap.
%
% Figures are laid out from top to bottom and left to right (just like a
% Matlab matrix), and will have width and height greater than minwidth, &
% minheight, ([150,150] pixels by defa... |
1006840fa80108ec30853e99950fa427619f2a7f5bc826ac59805a38a4c7a2a1 | MATLAB | 15,256 | 356 | function [resample] = resampledesign(cfg, design)
% RESAMPLEDESIGN returns a resampling matrix, in which each row can be
% used to resample either the original design matrix or the original data.
% The random resampling is done given user-specified constraints on the
% experimental design, e.g. to swap within paired o... |
5c31497230e2863086697b66b269ff3b3f9d6036bcb6d0be3be682576d405061 | MATLAB | 15,256 | 303 | % Double checked on Jun24, run again to make sure whole-cell analyses is
% complete. Don't separate into 3 phasor locations right now.
% loop to analyze 3 different features.each need different G/S
% map, Changed on Oct10, 2023, binning should after the feature
% segmentation--Lipo/ev/cell
% Finished running on Oc... |
635bb77f2d8ffdef4b2240d0cb57d4c02cbe055b57df1dde5ea329d8ade85324 | MATLAB | 15,256 | 356 | function [resample] = resampledesign(cfg, design)
% RESAMPLEDESIGN returns a resampling matrix, in which each row can be
% used to resample either the original design matrix or the original data.
% The random resampling is done given user-specified constraints on the
% experimental design, e.g. to swap within paired o... |
7f34bc3dd1cc526f1b3e51fc1a2cdb1a22c2fccda98213829e34a6dcf1b4820a | MATLAB | 15,275 | 525 | % clear
% clc
MuscleMomentArmOW_time = [];
MuscleMomentArmWI_time = [];
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleClust... |
30b7d7075cfa77f8389b3d2e3d907f35dd4b4fedb627893eef6555056abb022c | MATLAB | 15,280 | 344 | function [FCBrain_AllWindow, zFCBrain_AllWindow, Header] = y_SCA_Window(WindowSize, WindowStep, WindowType, AllVolume, ROIDef, OutputName, MaskData, IsMultipleLabel, ROISelectedIndex, IsNeedDetrend, Band, TR, TemporalMask, ScrubbingMethod, ScrubbingTiming, Header, CUTNUMBER)
% [FCBrain_AllWindow, zFCBrain_AllWindow, H... |
37d3e924d526cef6ec1aa8fffe2db2e032b3ff7bdf972b467c6e814159fa011f | MATLAB | 15,303 | 397 | function [coef,pval] = y_partialcorr(x,varargin)
%Adapted from MATLAB 2015a for back compatibility. YAN Chao-Gan, 20161001.
%PARTIALCORR Linear or rank partial correlation coefficients.
% RHO = PARTIALCORR(X) returns the sample linear partial correlation
% coefficients between pairs of variables in X, controlling f... |
2d28300a2385c371b9de205a3369dc6e288e51e3086d8481759c4791013e30e9 | MATLAB | 15,319 | 559 | function lan_erpplot(LAN,roi,comp)
% v.0.0.1
% <*LAN)<]
% from erp_glan (PB, FZ, RH)
%
%
% lan_erpplot
% plotea erp por electrodo o grupos de electrodos (roi)
% roi = electrodos
% comp = indice de comparacion a graficar
%
% 03.04.2012
% Pablo Billeke
% Rodrigo Henriquez
% Francisco Zamorano
if n... |
3662abd11c953e84ceb3c9b65f52c74107c3efacdd7e1b8a929bd4d9cd2083d3 | MATLAB | 15,333 | 463 | function [info,params] = create_problem(varargin)
%CREATE_PROBLEM Create test problems for tensor factorizations.
%
% INFO = CREATE_PROBLEM('Param',value,...) creates a tensor factorization
% test problem. It generates a solution corresponding to a ktensor or a
% ttensor, and then it generates an example data ten... |
0c9fae674e812a4da944fbbff839778715931e406a3a3a55b78d3c227b9e48b2 | MATLAB | 15,347 | 431 | function varargout = DPABI_ROIList(varargin)
% DPABI_ROILIST MATLAB code for DPABI_ROIList.fig
% DPABI_ROILIST, by itself, creates a new DPABI_ROILIST or raises the existing
% singleton*.
%
% H = DPABI_ROILIST returns the handle to a new DPABI_ROILIST or the handle to
% the existing singleton*.
%
% ... |
f0180d391c3c4bf1ef57b43f6135021a218a2e73505985f2edc3465bd62962a2 | MATLAB | 15,360 | 431 | function [stat, cfg] = ft_statistics_stats(cfg, dat, design)
% FT_STATISTICS_STATS performs a massive univariate statistical test using the
% MATLAB statistics toolbox. This function should not be called directly,
% instead you should call the function that is associated with the type of data
% on which you want to pe... |
e6f19af3d1edc84d0a458500560aac2539f2062278c752f2e8a4eb9bcf4c3f6f | MATLAB | 15,378 | 427 | % HIFDE2X Hierarchical interpolative factorization for differential equations
% in 2D.
%
% The hierarchical interpolative factorization is an extension of the
% standard multifrontal factorization with additional skeletonization to
% achieve improved complexities in 2D and above. Specifically, in 2D,... |
5c86d1e810d4361eaa5cd6102bc7a19013a9396984d90262fbd14625249fbf0c | MATLAB | 15,380 | 370 | function [dipout] = dipole_fit(dip, sens, headmodel, dat, varargin)
% DIPOLE_FIT performs an equivalent current dipole fit with a single
% or a small number of dipoles to explain an EEG or MEG scalp topography.
%
% Use as
% [dipout] = dipole_fit(dip, sens, headmodel, dat, ...)
%
% Additional input arguments should b... |
cd36cc377673bbda4a72bff9333f2c6eeeb2ab8ef3b40f17844e2099fbf3ec07 | MATLAB | 15,389 | 334 | % Author: Birgit Nierula
% nierula@cbs.mpg.de
function eeg_preprocessing_loops(srmr_nr, loop_number, subjects, conditions, ...
subject_idx, condition_idx, chan_name, display_time, iso_latency)
switch loop_number
case 1
%% loop 1
% 1) loads bids data
% 2) selects EEG + ECG + ... |
02bb8f88bc0a7de34d46825a1c7363ec31656b4ee00ffd1ea0f2188b44239d24 | MATLAB | 15,424 | 250 | function Split_Axis_Figure(FigName,FigDir,FigPosition,XVals_Mean,YVals_Mean,XVals_Error,YVals_Error
XMax=0;
XMin=100000;
YMax=0;
YMin=100000;
for BoutonCount=1:length(ExperimentSet_PooledData.Refined_Quantifications.ExperimentSet(Experiment).BoutonArray)
... |
2867111e8538df255917c930ee0c8aa6ca4f66b62f8cb23dabb2133baefa6580 | MATLAB | 15,443 | 299 | function C = fdct_wrapping(x, is_real, finest, nbscales, nbangles_coarse)
% fdct_wrapping.m - Fast Discrete Curvelet Transform via wedge wrapping - Version 1.0
%
% Inputs
% x M-by-N matrix
%
% Optional Inputs
% is_real Type of the transform
% 0: complex-valued curvelets
% ... |
f65b14a759d068d247b216f09c05dbe9798b35ec6ccb23d9fdbcbd5ad607a75c | MATLAB | 15,443 | 308 | function [] = Twave_personalisation_POMs_ToRORd_IKr_not_converged(nb_models, celltype, stimAmp, stimulus_function_str, result_dir)
% Population of models generation for T-wave personalisation
% Adapted from scriptDemonstration_2_ParameterComparison.m:
%% Setting parameters
param_baseline.bcl = 8... |
08284f18f29198c12c68f9fe3921b56fc5e1575a9601ec10ca272e0892f7c6f7 | MATLAB | 15,445 | 380 | % LoopGenerator.m
% Sarah West
% 2/25/22
% Creates list of data variables to cycle through. Is meant to make lab
% code abstractable across users with different data division levels.
% Inputs:
% loop_list - a cell array (? will maybe make a Map or something later, but order is immportant)
% with information about eac... |
ac569126d0a0998ff91fb90596ac6d4cc4d2ae613461ff64af268dbbd47f74f3 | MATLAB | 15,446 | 464 | %% Lots of C+P from nbGenSplits20180416
%%%%%%%%%
%% SH %%%
%%%%%%%%%
%% Test
tMain = loadSingleVariableMatfile('tMain4523.mat');
for eh={'easy' 'hard'},eh=eh{1}; %#ok<FXSET>
outerfname = sprintf('hpo_outer3_%s.mat',eh);
outer = loadSingleVariableMatfile(outerfname);
szassert(outer,[4523 3]);
for iOuter=1:3
... |
721edfbd5fe0eecbc8349b8a5b2bdccfca5ff3dfd77d06cda51ee1c098d6c526 | MATLAB | 15,496 | 378 | %% This compares dendritic responses during late wave (LW) and weak late wave (NLW) ephys trials
% shows that the dendritic responses is sharpened by the late wave
clear;clc
%% paths to analyzed dendritic data with simultaneous ephys
Fs = 2500;
Fr = 30;
dendritePath{1} = 'D:\Grids\Imaging\2022-10-08_Grids-De... |
b9765683414902fd5d1010930b6d70e47467133ecd1170cf1de44f8735aeac8d | MATLAB | 15,510 | 345 | function [FailedID WaitingID SuccessID]=y_ReRunfmriprepFailedSubjects(Cfg,WorkingDir,SubjectID)
% FORMAT [FailedID WaitingID SuccessID]=y_ReRunfmriprepFailedSubjects(Cfg,WorkingDir,SubjectID)
% Cfg - the parameters for auto data processing.
% WorkingDir - Define the working directory to replace the one defined ... |
16deb7c7574071128d4242d8509479e42dc8d1bb98ed683444c5adbd1150f698 | MATLAB | 15,534 | 446 | classdef HistEq
methods (Static)
function [hgramsel,info] = selMovCentralImHist(cntmat,varargin)
% Select a movie with a central/median imhist
%
% cntmat: [nbin x nmovset x nvw]. cntmat(:,imov,ivw) contains the
% imhist for the given movie/view.
% Typically this will be ... |
582b65a1c9e972a5032e23ad9ecca9861f9393a4748bef49b08bf06edba347ae | MATLAB | 15,544 | 309 | function [] = Twave_personalisation_POMs_ToRORd_IKs_Ito_not_converged(nb_models, celltype, stimAmp, stimulus_function_str, result_dir)
% Population of models generation for T-wave personalisation
% Adapted from scriptDemonstration_2_ParameterComparison.m:
%% Setting parameters
param_baseline.bcl... |
cd3e5012e48d3811e50832da093211b0b03a453f1308eab5dee2637431c5e23e | MATLAB | 15,544 | 349 | function [grid, cfg] = ft_prepare_leadfield(cfg, data)
% FT_PREPARE_LEADFIELD computes the forward model for many dipole locations
% on a regular 2D or 3D grid and stores it for efficient inverse modelling
%
% Use as
% [grid] = ft_prepare_leadfield(cfg, data);
%
% It is neccessary to input the data on which you want... |
dc90ad2ffd90e556c270c1703c4a8b9408f76737bb1ce959e5135e8face935aa | MATLAB | 15,551 | 457 | function [ output_args ] = PlotPCAs(AllTrialData,PlotConds,Phases,TimeWindows,OutDir,varargin)
%PLOTPCAS Plots projection of PCAs over time, separated by conditions.
% Units: Matrix of unit IDs (e.g. ASF). All by default
% PlotConds: Cell array containing subselection criteria use for
% extracting specifi... |
47e1d74168c7b80952581d57ab76c0938840ee6891753df9e7c14da4092f3f22 | MATLAB | 15,559 | 262 | %%
% ================= REACTION TIMES =================
%%
clear all; close all; clc;
addpath('C:\toolbox\edfImport1.0.5\')
%%
for s = 1:21
if s < 10
sub = strcat('P0',num2str(s));
else
sub = strcat('P',num2str(s));
end
dir_files = strcat('F:\SEM_mri_rawdata\'... |
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