sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
11ed33c7d0c69fe9fe2aaf6a178f7ef80be6eec5152060a745ae9defb45bc010 | MATLAB | 1,398 | 47 | load('DataTrain.mat')
computeStatsTrain
meanParamsP=squeeze(nanmean(blobDataNan(y>0,:,:),1));
stdParamsP=squeeze(nanstd(blobDataNan(y>0,:,:),1));
meanParamsN=squeeze(nanmean(blobDataNan(y<=0,:,:),1));
stdParamsN=squeeze(nanstd(blobDataNan(y<=0,:,:),1));
cLP=meanParamsP-2*stdParamsP;
cHP=meanParamsP+2*stdPa... |
c46c9db69b61ff9e359d99868da46be955ed77c770d9354661820b2b2abfbf13 | MATLAB | 1,398 | 50 | function rc = psychlasterror(varargin)
% psychlasterror - Replacement for Matlab-7 'lasterror'.
% This is hopefully useful for older Matlab installations and
% for the Octave port:
%
% If the 'lasterror' variable, M-File or builtin function
% is supported on your Matlab installation, this function
% will return it.
%
%... |
4014ee3299f1d310cf63b300e4c6c08f43ba61e10dd6c0217eae4eb9b4aaeba8 | MATLAB | 1,401 | 72 | function p = peaks(tsa, varargin)
% Get peaks in data
%
% USAGE:
% p = peaks(tsa, OptionName, OptionValue, ...)
%
% INPUTS:
% tsa - a tsd object
%
% OUTPUTS:
% p - a tsd containing the peaks of tsa
%
% OPTIONS:
% 'Extremes' - the kind of peaks:
% 'MaxMin' - both max and mins are ret... |
d08eb943bee650a4514da9370d0ced898857d477ac51ded73d055d58060f0383 | MATLAB | 1,402 | 59 | %% does adding an effect for movement reduce the omega for other variables?
clear all
T = 10;
N_TRIAL = 80; %average per condition
effect_size = [0:20];
var_vals = 40;
N_CELL = 80;
t_response = [3:5];
match_d = [];
for d=1:8
match_d = [match_d; d*ones(N_TRIAL/8,1)];
end
match_p = [ones(N_TRIAL/2,1);2*ones(N_TRI... |
04b76a4bc51ec557f01c8fd8e7e46b2368e824fe9ead43f5e020d5527d5d6842 | MATLAB | 1,403 | 51 | %% Plots V4 noise correlations (raw and MI) in cue epoch at cued and cue-opposite locations
%% Code
load('V4_rsc_MI.mat')
cue_onset_win = [500 2500]; epoch = num2str(cue_onset_win(1))+"_to_"+num2str(cue_onset_win(2));
plot_flag = 1; % to plot or not to plot
for bin_flag = 12 % > 5 spikes/s geometric mean
t... |
631bf789f006582accbdddbfaa856c216d2efd6217b5d562957f0eec1ce6fd3c | MATLAB | 1,403 | 40 | function refreshDiary(this, actigraphy)
% REFRESHDIARY Update diary elements from ACTIGRAPHY
%
% $Author: Peng Li
% $Date: Feb 29, 2026
%
ax = actigraphy.actiAxis;
ptH = actigraphy.Decoration.YAdjust;
yrange = actigraphy.Decoration.YLim;
delete(findobj(ax, 'Tag', 'DiaryPatch'));
delete(findobj(ax, '... |
944533da07ebbb8979d13d8613cc4a44f43691d278d627fa5b9ebb1380c43e6d | MATLAB | 1,403 | 55 | %% Plotting L2 Norm results
load('L2_norm_RFTrials.mat')
theta_actual.G = best_thet_est.G{1}{1}(1,:);
theta_actual.B = best_thet_est.B{1}{1}(1,:);
for iLambda = 2:11
mb_sse.G(iLambda-1,:) = sum((theta_actual.G(end-15:end) - best_thet_est.G{iLambda}{1}(1,end-15:end)).^2);
mb_sse.B(iLambda-... |
0e5651b887962cd95cd7b264d671dcdbb4321e13d40897d242b76c3a8d0bfd81 | MATLAB | 1,404 | 36 | % This script validates caner healthy vs non healthy is in agreement with
% cancer name
libraryNames = {'LC','LD','LE','LF','LG','LH'};
st = loadStatusReportByLibrary(libraryNames);
LxC = cellfun(@(x)(contains(x,'LGC') | contains(x,'LHC')),st.subjectNames);
IsHealthVsLxCConflict = (st.isSampleHealthy == LxC)... |
5ed9d9f71543023a7f5ced646ea2e640b6e00f82e999c9a2dfac96c0542a052e | MATLAB | 1,404 | 45 |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% This script is used to generate figure for the mean map of gray matter
% volume on brainnectome 246 atlas across participants
%
% 7-22-2022
% Jin Liu
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear,clc
... |
7938a98853a40dca20d9507432cacdaf2f6f63ca7c5b0e0853f3fa90a0dae6de | MATLAB | 1,404 | 43 | function net = gpinit(net, tr_in, tr_targets, prior)
%GPINIT Initialise Gaussian Process model.
%
% Description
% NET = GPINIT(NET, TRIN, TRTARGETS) takes a Gaussian Process data
% structure NET together with a matrix TRIN of training input vectors
% and a matrix TRTARGETS of training target vectors, and stores them... |
e84477a93d4221c1c2a464fb68064fd4fd58166cb0d7c1dc088dcfd09b4dd4f0 | MATLAB | 1,404 | 73 | function p = peaks(tsa, varargin)
% Get peaks in data
%
% USAGE:
% p = peaks(tsa, OptionName, OptionValue, ...)
%
% INPUTS:
% tsa - a tsd object
%
% OUTPUTS:
% p - a tsd containing the peaks of tsa
%
% OPTIONS:
% 'Extremes' - the kind of peaks:
% 'MaxMin' - both max and mins are ret... |
bf48b4c2bf83b434320f4ba475558de051584d8aacd513b22561c1e9acf2a721 | MATLAB | 1,405 | 55 | %% Behavior figure
MaestroPath = 'C:\Users\Owner\Desktop\DATA\albert\';
supPath = 'C:\noga\TD complex spike analysis\Data\albert\speed_2_dir_0,50,100';
load ('C:\noga\TD complex spike analysis\task_info');
req_params.grade = 10;
req_params.cell_type = 'CRB|PC';
req_params.task = 'speed_2_dir_0,50,100';
req_params.ID ... |
cdcec19fc675a48dd23a18f8ab30efc677b4b346bffb428ecfa7dcf9a260ea14 | MATLAB | 1,405 | 50 | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of th... |
8b5c8139d7a99cf1bbe08a0e2f3e8eb950ea9acc235b910634737c757fa3a8ac | MATLAB | 1,406 | 32 | function data_epo = data_eeglab2epo(data_eeglab, event_eeglab,classname)
% EPO struct
% .fs sampling rate [samples per second]
% .x multichannel signals (DOUBLE [T #channels #epochs]) where T is the number of samples within one epoch
% .clab channel labels (CELL {1 #channels})
% .y c... |
a8678d9330f903464fc0c9934d7ae8ddcb7493221421679dc64ed5f0d6f67aad | MATLAB | 1,406 | 66 | function b = isequal_prec(x1,x2,n)
% Usage:
%
% b = isequal_prec(x1,x2,n)
% b = isequal_prec(x1,x2)
%
% Description:
%
% Compare two numbers for equality based on the nth decimal place.
% If the 3rd argument is not supplied then x1 and x2 have to be preceisely
% equal for the function to return true... |
c8b42e264f278eb0db0b60611fd91fcfaf74feb615c7f80e3f3f6593f549f67a | MATLAB | 1,407 | 58 | function [ blockmem ] = random_blocks(Y, M)
% Creates a vector of 1:N split into M random blocks.
% License:
%=====================================================================
%
% This is part of the Princeton MVPA toolbox, released under
% the GPL. See http://www.csbmb.princeton.edu/mvpa for more
% information.
%... |
67f920f816350d38ea27d3702f7947e19954f912f790b571fef55add94114e9a | MATLAB | 1,408 | 45 | function [cc,pv,center_point] = phase_correlation_rotation( pl, cl, center_point, varargin )
% *WAVE*
%
% PHASE CORRELATION ROTATION correlation of phase with rotation angle
% theta (circular-circular), given the curl
% of the phase gradient vector ... |
f428dd1df490701356a07e81ca2dfaa3708714ca1dab85a4e4d531d88003f235 | MATLAB | 1,410 | 36 | #import <UnityAds/UnityMonetization.h>
#import "UnityJsonAdditions.h"
const void* UnityMonetizationCreateNativePromoAdapter(const void* pPlacementContent) {
if (pPlacementContent) {
UMONPromoAdPlacementContent* placementContent = (__bridge UMONPromoAdPlacementContent*)pPlacementContent;
UMONNativeP... |
6d82043b200bf09ed3a0fe21438d36d63c8a123746ad58f0fd4318812b3ecd7e | MATLAB | 1,411 | 55 | function [fh,dArea] = PETH(S,center,TStart,TEnd, varargin)
% USAGE
%
% H = PETH(S,center,TStart,TEnd, 'BinSize',binsize)
% S : ts
% center : ts of reference times
% TSart/TEnd : bounds of PETH
% optional:
% 'BinSize' : size of bins in 10^-4s
% Adrien Peyrache 2007
font_name = 'Arial';
font_siz... |
e77e823cc2786e22d80e48c6232231e4f4d6d0ac8d3ea7051f14a04d9c0f2ea3 | MATLAB | 1,415 | 52 | function ep = LoadEpoch(fbasename,epname)
%ep = LoadEpoch(fbasename,epname)
%Adrien Peyrache, 2012
Fs = 1250;
%try
if exist([fbasename '-states.mat'],'file');
load([fbasename '-states.mat'])
states = states(:);
t = [0:length(states)-1]';
switch lower(epname)
case 'sw... |
d4d7884f4f8fcb0b59dff8a5dc347a8118d274626abfb890532223d7288924a8 | MATLAB | 1,416 | 33 | function varargout = neighborhoodloop(varargin)
% NEIGHBORHOODLOOP Implicit loop over neighborhoods.
%
% Example:
% Let f be a matrix. Then
% h = neighborhoodloop(2, [3 5], f, ones(size(f)), @(f,c) median(f(c>0)));
% implements a median filter with 3x5 kernel and proper median computations
% near the borders.
%
% This ... |
27a86438cbfcd145c59a2ad6c93c762387ada4ad81e8a655cf16a4335fee3c15 | MATLAB | 1,417 | 46 | function awsSaveMatlabFigure(figureHandle, pathToSave, isMaximizeBeforeSave, isCloseFigureAfterSaving)
% This function saves a matlab figure handle as png to the cloud / local
% drive.
% INPUTS:
% - figureHandle - use gcf for current matlab figure handle.
% - pathToSave - s3 or local path to save figure to.
%... |
678719a294813da9a3e36a0074155b72920e5a3187c71804dee751cab17a4f26 | MATLAB | 1,417 | 38 | function y=fProcess1(targetdata,targetpath,savepath,calendar)
realtargetpath = targetpath;
realtargetdata = targetdata;
calendartoday=calendar;
realsavepath = savepath;
for cnt = 1:length(realtargetdata)
cd(realsavepath);
data0 = realtargetdata(cnt).name;
load([data0 '_prepare_save.mat']);
cale... |
d1b5af39bf85035b256a92801d7801edaa144f0b631d93823493b0de7b06262e | MATLAB | 1,417 | 33 | function [] = runBreak(taskStruct, dispStruct)
% instruction #
Screen('TextSize', dispStruct.wPtr, 40);
Screen('TextColor', dispStruct.wPtr, dispStruct.textColor);
DrawFormattedText(dispStruct.wPtr, '- Summary -', 'center', 50);
text1 = ['So far you''ve played ' num2str(taskStruct.trialTotal) ... |
14cf23281afd15e2f51814498336d4a9d2ed054caa5859ec45230fd789581c13 | MATLAB | 1,418 | 43 | function smp_map_data = bv_createsmpmap(vmp,reco_srf)
% function smp_map_data = bv_createsmpmap(vmp,reco_srf)
%
% Given a vmp (stats in the volume) and a reconstructed surface, create an
% smp stat map by sampling the stat volume at the vertices.
%
% History:
%
% 2004.03.10 bds wrote it
% 2004.06.10 ... |
81dd68eaaa18cf3fca6d76883e0dd8b6e159e185a8c1b259421c33a378c7448f | MATLAB | 1,419 | 32 | function L = crossCorrModel_Intensity(preTrain,postTrain,preTest,postTest,binT,params)
% Computes the cross-validated log-likelihood of a spike train given a (predictor) spike train in a
% gaussian temporal window of a given s.d. and mean.
%
% USAGE
%
% L = crossCorrModel_Likelihood(preTrain,postTrain,preTest,pos... |
90415458227c8fd78aba1f75dbd51a809bef6762545ca75c1e79bc5b99c10e4c | MATLAB | 1,420 | 35 | function [maskSeries, maskWindow, maskLength] = doMask2(dataLength, epochInSec, dataStartTime, maskStartTimestr, maskEndTimestr)
%DOMASK2 generate masking series based on start and end time
%
% $Author: Peng Li
% $Date: Jan 09, 2020
% $Modif.: Dec 02, 2020
% relocation
% May 17, 2021
% ... |
4642f915cc1ad202af9bd8e7955f015bdba95d3c4984224e5b92ef5b4748d31c | MATLAB | 1,421 | 73 | function [errmsgs warnmsgs] = test_smooth_predictions(varargin)
% USAGE :[ERRMSGS WARNMSGS] = TEST_(type)
%
% This is a script that tests temporal smoothing using the
% prediction function. It mostly tests the parameter handling of
% the function.
%
% INPUT ARGUMENTS:
%
% STDOUT (optional, default = true) If true, p... |
811bcb10552d1becdc4003ffe046425b61cd8f5790ae393b8dc28699ad5be899 | MATLAB | 1,421 | 51 | function [e, edata, eprior] = gperr(net, x, t)
%GPERR Evaluate error function for Gaussian Process.
%
% Description
% E = GPERR(NET, X, T) takes a Gaussian Process data structure NET
% together with a matrix X of input vectors and a matrix T of target
% vectors, and evaluates the error function E. Each row of X
% corr... |
b326c38d20f3d0d412eced46e081f21210a63bdc933d6784d178f22dfc2d9b3e | MATLAB | 1,422 | 63 | function data = Product(transformer, data)
%
% Computes the row-wise product of two or more columns.
%
%
% Arguments:
%
% :param Input: **mandatory**. Names of two or more columns to compute the product of.
% :type Input: array
%
% :param Output: **mandatory**. Name of the newly generated column.
... |
0756d610c583d31955ad2ff469fa1a0addcde1f16e43b1d2e26b5ab8333b3022 | MATLAB | 1,423 | 50 | function [xf] = generate_plane_wave( image_size, dt, T, ...
frequency, lambda, orientation, varargin )
% *WAVE*
%
% GENERATE PLANE WAVE TEST DATA generates a simple test dataset for
% use with the functions in WAVE
%
% dat... |
f4ba8cef8cc89d093b17431c34179b2a222c8c174144e28826c5ddcfdaf8ccf6 | MATLAB | 1,426 | 52 | function [d] = cross_entropy_deriv(code, e, x, perf, pp)
% Derivative of cross-entropy
%
% [D] = CROSS_ENTROPY_DERIV(CODE, E, X, PERF, PP)
%
% See CROSS_ENTROPY.M
% This is part of the Princeton MVPA toolbox, released under the
% GPL. See http://www.csbmb.princeton.edu/mvpa for more
% information.
% License:
%======... |
448e41a9fb16b821aef89a03df0786047eee0f4c8a59509e59efbfee64c7eb01 | MATLAB | 1,427 | 40 | % Y = spharm_fast(theta,phi,l,m,p0)
%
% Orthonormal spherical harmonic function (Arfken, p. 681) for quantum
% numbers l and m. Angles theta and phi should be given in radians.
% p0 contains the values of the Legendre polynomial
%
function Y = spharm_fast(theta,phi,l,m,p0)
% Copyright (c) 2016, Elekta Oy
% -----------... |
968825f8a02fd460006442baa26a8df3defe2897cc869ef5f2301514008ed555 | MATLAB | 1,428 | 49 | function [] = check_1ofn_regressors_subj(subj,regsname,xval_selname)
% [] = CHECK_1OFN_REGRESSORS_SUBJ(SUBJ,REGSNAME,XVAL_SELNAME)
%
% wrapper for check_1ofn_regressors that also takes in a
% group of xvalid selector indices, to confirm that your
% regressors won't elicit 1-of-n warnings
% License:
%=================... |
d6c08ea11be2ba1d99af6c70023f2274b473f4225582ab12427211a6669f91c4 | MATLAB | 1,428 | 58 | function [] = mvpa_add_paths()
% Adds the paths necessary to use the MVPA Toolbox. Assumes
% that you've already got this script in your path, and so
% it uses the location of this script to determine the root
% directory of the rest of the MVPA scripts. That way, it
% doesn't need an argument.
%
% Usage: MVPA_ADD_PAT... |
253559a11f8f68d6b445d7e1e7a1daa7e86fa74b88184a7df973ae6a5cd366d1 | MATLAB | 1,429 | 43 | function [LL, LF] = bhvr_LL(y, z, r, mu, ka, Ik)
% _
% Log-Likelihood Function for a von Mises Mixture Model
% FORMAT LL = ME_vMMM_LL(y, z, r, mu, ka, Ik)
%
% y - an N x 1 vector of response errors (domain: -pi <= y_i <= +pi)
% z - an N x 1 vector of swap errors (domain: -pi <= y_i <= +pi)
% r - a 1 x ... |
6627749dd3b980f8bad7cbbd83eab11843eb474c15f79461e1c4ab311bba205a | MATLAB | 1,430 | 40 | function string=implode(pieces,delimiter)
%IMPLODE Joins strings with delimiter in between.
% IMPLODE(PIECES,DELIMITER) returns a string containing all the
% strings in PIECES joined with the DELIMITER string in between.
%
% Input arguments:
% PIECES - the pieces of string to join (cell array), each... |
cec36de270b11420a92f84197dc1f41ecd083786397d2fbd4e60ae073ba63572 | MATLAB | 1,430 | 49 | function msRastorPlot(vidObj,segmentRange,offset,showFiring, color)
%MSRASTORPLOT Summary of this function goes here
% Detailed explanation goes here
if (isempty(color))
index = [1 4 7 10 3 6 9 2 5 8];
cmap = colormap(jet(256));
numColors = 10;
colors = 0.8*cmap(ceil(2... |
5d3c85f3cea466e5a3b99457896c6e8724cb903b14134a0daa79e3213a5ccc3e | MATLAB | 1,432 | 47 | % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Authors : Farad KHOYRATEE, Romain BEAUBOIS
% Create Date : 04 Mar 2018
% Update Date : 10 Aug 2021
%
% Description :
%
% Revision :
% > Revision 0.01 - File created
% > Revision 0.02 - Integrate sum in function
%
% Addit... |
e1de9bca618824d99ca699ee22cabb80400254378c3b8bdc3478305250854055 | MATLAB | 1,432 | 76 | function [biasmean1, biasstd1, bias_rp1] = CrossCorrBootstrap2(Sdata, S, E)
nbins = 10;
binsize = 10;
n_cells = length(Sdata);
bias = ones(n_cells,n_cells, 50) * NaN;
bias_rp = ones(n_cells,n_cells) * NaN;
for l = 1:50
[SM, EM] = NonRippleTimes(Data(S), Data(E));
for i = 1:n_cells
Sdatam{i} = Restrict(Sdat... |
8911b403718db7c280f9ef40ac7ef4d6bf368b1771f3df3d581efc041f23a832 | MATLAB | 1,433 | 33 | function [] = runPause(taskStruct, dispStruct)
% instruction #
Screen('TextSize', dispStruct.wPtr, 40);
Screen('TextColor', dispStruct.wPtr, dispStruct.textColor);
DrawFormattedText(dispStruct.wPtr, '- Pause -', 'center', 50);
text1 = ['So far you''ve played ' num2str(taskStruct.trialTotal) ' ... |
68a262e8815a284f2d2ce16baa15d72607b4c66a42103ffae8bb75a97b952917 | MATLAB | 1,434 | 47 | function [coefficients levels] = db4dwt(X, N)
% Imitates the wavedec function in wavelet toolbox.
%
% [coefficients levels] = db4dwt(X, N)
%
% This is hard coded with values of the 'db4' wavelet taken from
% online resources.
% License:
%=====================================================================
%
% This is... |
cd1a94c9b2e42b8a1975d1be8dc80134b2daab14d47c868b3c508c63e490b3cd | MATLAB | 1,434 | 52 | function [res, fmt] = detAlpha(actigraphy, epoch, varargin)
%DETALPHA do detrended fluctuation analysis and fit results
%
% [RES, FMT] = DETALPHA(ACTIGRAPHY, EPOCH, REGION, FILENAME, STARTTIME, FILEPATH, QUALITY)
% performs detrended fluctuation analysis on ACTIGRAPHY of epoch
% length EPOCH seco... |
b05cd0136ecb1e4308abc66f299aa32d79b2b1073c430925f271cd3cb06eaf06 | MATLAB | 1,435 | 33 | function [coeffs] = CalculateT1Map(T1raw_reordered_reshaped,tvalues,ivalues)
%UNTITLED Summary of this function goes here
% Detailed explanation goes here
opts = optimoptions(@lsqcurvefit,'Algorithm','levenberg-marquardt');
tmax = max(tvalues);
if isnan(ivalues(1))
fun = @(coeffs,tva... |
42aad3bd933c436e6d7d8df5480aa80692cc913638609d9c845481baad5a27b8 | MATLAB | 1,438 | 39 | function varargout = read_header(software,filename,varargin)
% function varargout = read_header(software,filename,varargin)
% IN
% software: String with software to read header in cfg.software (e.g. 'SPM8')
% filename: name of volume (full path , 1 x n string)
% varargin: Any additional things that s... |
42dac05c0aac7fe79a5a4a5a5c9a4be159aef2e132b891828e5b6422893f44e3 | MATLAB | 1,438 | 49 | % plot average of coefficients across all subjects for each channel
% divide subplot into different conditions
sbjList = {'12','13','14','15','16','17'};
figSaveDir = ['C:\Users\mn0mn\Documents\ResearchProjects\spatailAttentionProject\ProcessedDatafNIRS\GroupResultAll\Figures'];
% hardcoded
ssBetaTotal = zeros(49,3... |
492dedda217bb277e451b37a5a80542e24b6d8f90c37ff41ac2ad0c0f147856b | MATLAB | 1,438 | 70 | function [c] = cellcov(x, y, dim, flag)
% CELLCOV computes the covariance, across all cells in x along
% the dimension dim. When there are three inputs, covariance is computed between
% all cells in x and y
%
% X (and Y) should be linear cell-array(s) of matrices for which the size in at
% least one of the dimensio... |
aa59da968d0418ee94a4682aabf453f89cac91f97b9127ebd0c2f80384c13be5 | MATLAB | 1,439 | 53 | function [pm_no] = get_perfmet_called(results,desired_name)
% [PM_NO] = GET_PERFMET_CALLED(RESULTS,DESIRED_NAME)
%
% If you have only one perfmet, this will return
% 1. DESIRED_NAME must be empty or the name of the single
% perfmet.
%
% If you have multiple perfmets, this will return the index
% of the one that matche... |
1126c16a5a8bd7abaca20b8dc60db0e5a86b58cd323a6684edbaadf02e12c6e8 | MATLAB | 1,440 | 69 | function [cleanAng,epCleaned] = CleanWhl_Ang(ang,ep)
st = Start(ep,'s');
en = End(ep,'s');
daSt = Data(Restrict(ang,st));
daEn = Data(Restrict(ang,en));
dt = median(diff(Range(ang)));
addTime = [];
addAng = [];
epCleaned = ep;
for ii = 1 : length(st) -1;
if st(ii+1) - en(ii)<5
da1 = daEn(i... |
21e71c4479ea70f1fe6ab63ed805865e9f8ad931423ac09d367de59989b0fb3b | MATLAB | 1,440 | 40 | % This function computes the second term of Eq. (6), which is an estimate of how eye movements and stimulus dynamics redistribute power in space-time.
%
% It relies on two fundamental assumptions which must be always taken into account, and must be used with caution:
% 1) eye movements do not depend on the ext... |
d0f9ea81b4816aa543c2ed6c58b2ba70744886ba8cb13eddfc3371796582c297 | MATLAB | 1,441 | 54 | function [xf] = generate_rotating_wave( image_size, dt, T, frequency, direction, varargin )
% *WAVE*
%
% GENERATE ROTATING WAVE TEST DATA generates a simple test dataset for
% use with the functions in WAVE
%
% data description: a 2d rotating wave
%
% INPUT
... |
1534554fc964d8e22cbba522af1edb487c46ad05409d3799e260c0716d70e202 | MATLAB | 1,443 | 47 | function trialStruct = buildTrialStruct(cards)
trialStruct = struct();
trialStruct.cards = cards;
trialStruct.isHigher = trialStruct.cards(1) < trialStruct.cards(2);
% choice left/right
trialStruct.guessL = NaN;
trialStruct.guessR = NaN;
% actual choice
trialStruct.respKey_Guess = NaN;
... |
6a9c5f232135692d8d2ae1a1377a60e8e579506b86337d1cd98676deebe6b20c | MATLAB | 1,445 | 38 | function FigureA_NoParams(fileExportFolder, imageA, vxMat, vyMat, relMat, thetaInDegrees, thetaInRadians)
h = figure;
dispImage = mean(double(imageA), 3);
dispImage = RenormalizeImage(dispImage, .1);
dispImage(dispImage(:) == 0) = mean(dispImage(dispImage(:) ~= 0));
imagesc(dispImage);
axis equal;
disp(' ');
numMasks =... |
f42f7a89fc12048d629df1043c561b426e1a339923a5601ebac92fc409c915e8 | MATLAB | 1,445 | 35 | function model = distance_train(labels_train,data_train,cfg)
% This function averages samples within the same class. It then passes the
% data for calculations of distance estimate carried out in similarity_test.
% TODO: place calculations in separate function even though they are
% simple.
switch lower(cfg.... |
0a84dbfb19aea19a268071bc8e58a6d68befb13b6bb7b1deecb2c7aea3080b33 | MATLAB | 1,446 | 43 | function stdshade_egNeurons(amatrix_all, amatrix_jk, alpha,acolor,F,smth,r)
% usage: stdshading(amatrix,alpha,acolor,F,smth)
% plot mean and sem/std coming from a matrix of data, at which each row is an
% observation. sem/std is shown as shading.
% - acolor defines the used color (default is red)
% - F assignes the us... |
12282d9780edd26cfc846671dc5cccdb2d560a98fde2971e3086e2c48fffd07b | MATLAB | 1,446 | 50 | function dispv(rule,str,varargin)
% function dispv(rule,str,varargin)
%
% Run fprintf with first the argument 'verbose argument level'.
% Input arguments:
% rule: minimal level of the current argument at which is printed
% (i.e. print only when this level or higher)
% level: verbosity (0:... |
1761e751a21211d594846660793fb7bd4fbcba34e1aad2633d44b0fe87a79822 | MATLAB | 1,447 | 53 | function [acts scratchpad] = test_ridge(testpats,testtargs,scratchpad)
% Generates predictions using a trained ridge regression model
%
% [ACTS SCRATCHPAD] = TEST_RIDGE(TESTPATS,TESTTARGS,SCRATCHPAD)
%
% License:
%=====================================================================
%
% This is part of the Princeton M... |
791f5ed18e2226dd8e88f297b77aa17b42afc329d7e82530f4a3b160c3b1409e | MATLAB | 1,447 | 43 | function [segmentlists_time,segmentlists]=build_links(links)
links{end+1}=NaN;
segmentlists=[];
segmentlists_time=[];
origin_id=1;
for i=2:numel(links)
origin_id=1;
traces_id=numel(segmentlists)+1;
while ~isempty(links{i}) && sum(~isnan(links{i}(:,1)))~=0
origin=links{i... |
759793488b424bf09535e5abdae79389d4af355e48d4b80039dd3a48a252feb4 | MATLAB | 1,448 | 32 | function [fx] = f_kToM(x,P,u,inF)
% wrapper around recursive k-ToM evolution function
% function [fx] = f_kToM(x,P,u,inF)
% A k-ToM agent with k>0 learns the unknown parameters (theta) of her
% opponent's learning and decision rules. Typically, theta includes her
% opponent's prior volatility about herself (which contr... |
dd76570ef04c64e072f65ac520a4e761dcd17a8fbb80e0a05af929c18c005d67 | MATLAB | 1,448 | 58 | %% Plotting L2 Norm results
load('data/L2_norm_RFTrials.mat')
theta_actual.G = best_thet_est.G{1}{1}(1,:);
theta_actual.B = best_thet_est.B{1}{1}(1,:);
for iLambda = 2:11
mb_sse.G(iLambda-1,:) = sum((theta_actual.G(end-15:end) - best_thet_est.G{iLambda}{1}(1,end-15:end)).^2);
mb_sse.B(iLa... |
33fc577f2a58cbbbd8027971e4c6da298a4b552d2e384c55f970d3249fc97014 | MATLAB | 1,449 | 42 | % function [sigma,shrinkage] = covshrink_lw(data)
%
% Function to calculate shrinkage according to Ledoit & Wolf (2004). See
% http://www.econ.uzh.ch/faculty/wolf/publications/wellCond.pdf
%
% Maximal shrinkage is done towards the mean variance.
%
% INPUT:
% data (n_samples x p_dimensions)
% OUTPUT:
% si... |
45ae0a047a05d791feb00a9f87126221cfa4359198828a185feabb2ad3a8a5a8 | MATLAB | 1,449 | 37 | function [gx] = g_VBvolatile0(x,P,u,in)
% computes the probability of the learner choosing 'a=1'.
% [gx] = g_VBvolatile0(x,P,u,in)
% This function derives the action emission law from the current belief of
% the learner. It is valid in an empirical context akin to a multiarmed
% bandit problem, whereby the learner is a... |
22b90a429173133fb6ee52d2632ee78369790aa019d6ae7b05133de49d940cdd | MATLAB | 1,452 | 36 | function ms = msMeanFrame(ms, downsample)
%MSMEANFRAME Summary of this function goes here
% Detailed explanation goes here
meanFrame = cell(size(ms.alignmentROI,2),1);
minFrame = cell(size(ms.alignmentROI,2),1);
for ROINum=1:size(ms.alignmentROI,2)
meanFrame{ROINum} = zeros(ms.alignedHeight(... |
9e78ea8b32ea6dbe924d37cc6289ad051b7aca19591bf1a91501741b455c705b | MATLAB | 1,453 | 45 | function Schaefervolumecombined = importfile(filename, dataLines)
%IMPORTFILE Import data from a text file
% SCHAEFERVOLUMECOMBINED = IMPORTFILE(FILENAME) reads data from text
% file FILENAME for the default selection. Returns the data as a table.
%
% SCHAEFERVOLUMECOMBINED = IMPORTFILE(FILE, DATALINES) reads data ... |
097879f2da74cadce4f198526b247e1d783e92c3094c8be33e9d44a0202ddee9 | MATLAB | 1,454 | 51 | % kernel unit test
%
% compute kernel matrix
tol = 10e-10;
% Random data
N = 30;
F = 10;
X = randn(N, F);
cfg = [];
cfg.kernel = 'rbf';
cfg.gamma = 1;
cfg.regularize_kernel = 1;
%% regularize_kernel should just be adding a diagonal
cfg.regularize_kernel = 0;
K = compute_kernel_matrix(c... |
9a4db8a4d3627c8261f90c8388ad58c448b1a8196bbfb04b999ec19fd8933230 | MATLAB | 1,454 | 69 | function [Y] = LaplacianScore(X, W)
% Usage:
% [Y] = LaplacianScore(X, W)
%
% X: Rows of vectors of data points
% W: The affinity matrix.
% Y: Vector of (1-LaplacianScore) for each feature.
% The features with larger y are more important.
%
% Examples:
%
% fea = rand(50,70);
% options = [];
% ... |
cc11d72b14fc19cf0978e3806db0414968d09bc26c9608d40b6eddd5b7afc6b1 | MATLAB | 1,454 | 54 | function [A, b] = prepare_displacement_matrices(A1, b1, A2, b2, displacement)
% [A, b] = PREPARE_DISPLACEMENT_MATRICES(A1, b1, A2, b2, displacement)
%
% Compute matrices used for displacement estimation as defined by equations
% (7.32) and (7.33) in Gunnar Farnebäck's thesis "Polynomial Expansion for
% Orientation and... |
b7f0440b97b5d2caac66d4a7291101dbfd8660e9bb105ac10ef7a57ca4c53b4f | MATLAB | 1,455 | 50 | function plotVisualOcclusion(I, K, c)
% Parameters
amp = 10; % (deg/s)
t_before = 0.2; % (s)
t_after = 1; % (s)
t_light_off = 0.5; % (s)
t_light_on = 1; % (s)
t_smooth = 0.02; % (s)
% Run simulation
tt = -t_before:K.dt:t_after;
head_curr = zeros(size(tt));
target_curr = smooth(amp*(tt>0... |
539997f98f0d3987a38df7ab3739be16a32c5f1bc2fdf9f01dfda7ccd6642d70 | MATLAB | 1,456 | 40 | function runPlotPerfBarChart(sbjNum,numClasses,feat)
processedDataDir = ['C:\Users\mn0mn\Documents\ResearchProjects\spatailAttentionProject\ProcessedDatafNIRS\Experiment' num2str(sbjNum)];
% pick one, duh. set feat = 0 if don't want to re-run.
switch feat
case 1
% cumulative sum from 0, ind channel
... |
183144716bb0c2d7b9154a6bdbd0b82a2ea79b3c05da3a254c90769104ffdefd | MATLAB | 1,457 | 67 | function r = rate(tsa, varargin)
% TODO: overlap with intervalRate
% r = rate(tsa, is, TimeUnits) returns average rate of tsd object, measures in
% its timeSpan period, or in an arbitrary interval
%
% INPUTS:
% tsa: a tsd object
% an intervalSet , if present rate will be calculated on that range only
% OUTPUTS:
%... |
856628b306862528b9d1955b1fe223d4751de0076b820cce860d2b5cc5ec3162 | MATLAB | 1,458 | 42 | function [imgs_C1_layer] = hmax_C1_layer(images)
% measuring C1res
READPATCHESFROMFILE = 0; %use patches that were already computed
%(e.g., from natural images)
patchSizes = [4 8 12 16]; %other sizes might be better, maybe not
%all sizes are required
numPatchSizes = le... |
029751b06cd5bf0d31cada7e64643187a4ee4a0de69ef47e1fce5421d402bd16 | MATLAB | 1,459 | 61 | %% 13-6-17: steady state with MT, balanced -- now matrix version, not analytic
function [Mss] = ssSSFP_MT(alpha,b1sqrdtau, TR,dw, T1x,T2,f,ka,G)
M0b = f;
M0a = (1-f);
kb = ka * M0a/M0b;
R1a = 1/T1x(1);
R1b = 1/T1x(2);
R2a = 1/T2;
G = G *1e-3; %<- convert from us to ms
gam = 267.5221 *1e-3; %< rad /ms /uT
WT = pi*gam^... |
598baad3b433720b862eb992a5c5f9f3ae95792b8acf080f010b4da2ac62aa9e | MATLAB | 1,461 | 51 | function [acts scratch] = test_bp_netlab(testpats,testtargs,scratch)
% Testing function for BP_Netlab
%
% [ACTS SCRATCHPAD] = TEST_BP_NETLAB(TESTPATS,TESTTARGS,SCRATCHPAD)
%
% This is the template testing function that fits with
% TRAIN_BP_NETLAB. See that for more info
%
% ACTS is an nOuts x nTestTimepoints matrix th... |
a286ba6a135c83b9634aed44530e0fde59fd17870fe3f49e594f593a86944d1e | MATLAB | 1,463 | 44 | function BaseEval2Obj_web(BaseEval,method_string,outputPath)
if(nargin<3)
outputPath='./';
end
% tresshold for coloring alpha channel in the range of 0-10 mm
dist_tresshold=10;
cSet=BaseEval.cSet;
Qdata=BaseEval.Qdata;
alpha=min(BaseEval.Ddata,dist_tresshold)/dist_tresshold;
fid=fopen([outputPath '/' method_st... |
f34a94603972bcbd62f8f445ceda132caff625bae334c9d99e837c7c844b4c23 | MATLAB | 1,463 | 47 | function [hf_basefit, rmse_eye, rmse_pc, nrmse_eye, nrmse_pc] = ...
plotBaselineResults(K, I, conds, tts, head, target, hevel, PC, light, sines, mask, hf_basefit)
% Plot JR data & model before learning & get error
% Use mask to select conditions if needed
if ~exist('mask','var') || isempty(mask)
mask = 1:min... |
3316b25126d44be3ee2de6a99aa1b14ebcefb1a5b3f01127cd8b28366481a67e | MATLAB | 1,464 | 34 | function [ SlimSkel ] = SlimSkel3D( CellToSkel,THR )
%SlimSkel3D Skeletonizes and slims output to reduce residual thick branches
%or spurs.
% Uses Skeleton3D and parts of Skel2Graph3D and Graph2Skel3D
%OUTPUT: 3D binary image of slim skeleton
%INPUT: CellToSkel is a 3D binary image containing the cell we want to
... |
d69ab6326d77fcacc3a5b821e19bdff75295a2efb92f14e2ac033e9255a4258b | MATLAB | 1,465 | 56 | function generateSimData(dirname, format)
if ~exist('dirname','var')
dirname = pwd;
end
if ~exist('format','var')
format = '.snirf';
end
dirname = filesepStandard(dirname);
files = DataFilesClass(dirname, format, 'standalone').files;
for ii = 1:length(files)
if ~isempty(findstr(files(ii).name, '_sim'))
... |
eaa61e4a4ca74320b9058a802eff2d7ccc65b57c5215d068d97983f10b15a036 | MATLAB | 1,465 | 31 | function [FirstFrame_AboveBaseline] = FirstFrame_AboveBaselineMean(FluorescenceTrace, BaselinePeriod, z, STDorSEM, SearchAfterFrameX)
%FirstFrame_AboveBaselineMean finds the first frame greater than or equal
%to Z STD or SEM above the baseline mean
% Michelle Cahill 20211111 adapted from Peak3STD written by MC 20211... |
1274a48210b5b519b4b9be733889650168642a6b6fba699bbc4a9203fd455602 | MATLAB | 1,466 | 57 | function h = hinton(w);
%HINTON Plot Hinton diagram for a weight matrix.
%
% Description
%
% HINTON(W) takes a matrix W and plots the Hinton diagram.
%
% H = HINTON(NET) also returns the figure handle H which can be used,
% for instance, to delete the figure when it is no longer needed.
%
% To print the figure correct... |
869be180db32f92a3b38ed4a335dcba6c82e3448cb4b092017fa687d9e62daff | MATLAB | 1,467 | 42 | %% Plot SSD patterns for DBS contacts (Figure 2C)
repo_path = 'Path_to\Manuscript_repository';
funcs_fpath = fullfile(repo_fpath, 'plotting\matlab_funcs');
addpath(funcs_fpath);
addpath('Path_to\wjn_toolbox_tsbinns');
addpath(genpath('Path_to\spm12'));
addpath(genpath('Path_to\leaddbs'));
folderpath_analysis = 'Path_... |
9c946a98f044c98868d0ffcee942f38a8891c08eb5059798572e7a9d117b157d | MATLAB | 1,467 | 31 | function p = betaScores(r)
%BETASCORES Compute Beta scores for rank vector
% p = BETASCORES(r)
% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
% INPUTS:
% r vector of normalized rank values on interval [0,1]
% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ... |
575f9df9ae384d0db07840f7b3228bffa39306e8a74d5eb91275c0b388c529bf | MATLAB | 1,468 | 68 | function L = CrossValidationSpatialCheck_Cells(S,lambda,X,Y,ep,varargin)
nbEp = 10;
dispBar = 0;
binSize = 0.5; %in cms
dispInfo = 1;
if ~isempty(varargin)
dispInfo = varargin{1};
end
epV = regIntervals(ep,nbEp);
L = NaN(nbEp,length(S),length(lambda));
if dispInfo
fprintf('Launching Cross-validated spatial... |
cc1c209f0e48f95337e44c7e6c27a23d3ea95a7157c30401415f83c9bca766b5 | MATLAB | 1,468 | 50 | function [e, edata, eprior] = rbferr(net, x, t)
%RBFERR Evaluate error function for RBF network.
%
% Description
% E = RBFERR(NET, X, T) takes a network data structure NET together
% with a matrix X of input vectors and a matrix T of target vectors,
% and evaluates the appropriate error function E depending on
% NET.OU... |
000e0f65398d9f636ace19efa55ad6463396d749526d73cee6e276d94081f903 | MATLAB | 1,469 | 61 | function [h,v,B,mu] = HeadDirectionField(tsa1,tsa2,ang,GoodRanges,varargin)
% USAGE
% [h,d,mu,p,kappa] = HeadDirectionField(tsa,ang,GoodRanges)
%
% Inputs:
% tsa: a ts object (typically a cell!)
% ang: a tsd object of head orientation
% GoodRanges: an intervalSet object defining the time of val... |
5c52837e783d031fe243c511de51842dcca1b2fc8a0b3d8398406eeb1ec0a9e0 | MATLAB | 1,469 | 65 | function A = PhasePref(A)
A = getResource(A,'SpikeData');
A = getResource(A,'HcChannels');
A = getResource(A,'MazeEpoch');
mazeEpoch = mazeEpoch{1};
A = registerResource(A, 'HcThetaPhase', 'cell', {[], []}, ...
'hcThetaPhase', ...
['spike phase with hc theta'],'mfile');
A = registerResource(A, 'HcThetaRhyt... |
db0fb1576734c1bc475b013b59fbf39581f7999c122a274601469e171c287ba7 | MATLAB | 1,469 | 46 | classdef PsthDistanceClassifierModel < ClassifierModel
properties
psthMap
SD = 20;
smoothingMargins = 300;
end
methods
function mdl = train(mdl,X,y)
psthMap = containers.Map('KeyType','double','ValueType','any');
classes = unique(y);... |
246f398ae741b299a19b0d5197e90c11aeb53a50f55dcc14eeb6ffd6568dc554 | MATLAB | 1,472 | 49 | function score = Cal_NMI(true_labels, cluster_labels)
true_labels = double(true_labels);
cluster_labels = double(cluster_labels);
true_labels = true_labels-min(true_labels(:))+1;
cluster_labels = cluster_labels-min(cluster_labels(:))+1;
if size(true_labels,2)>size(true_labels,1)
true_labels = true_label... |
c78dbce65c8edc2b40c2eada75738519b71f78daa25371f21fd842d50bf25315 | MATLAB | 1,473 | 62 | %% Behavior figure
clear
MAX_DELTA = 20;
[task_info,supPath] = ...
loadDBAndSpecifyDataPaths('Vermis');
behavior_params.time_after = 250;
behavior_params.time_before = -100;
behavior_params.smoothing_margins = 100; % ms
behavior_params.SD = 15; % ms
req_params.grade = 7;
req_params.cell_type = {'PC ss', 'CRB',... |
9db472d7242f0e5bb87a734e4bcbd19e1fb4ed8f7b57116ecb312ed83f97cea4 | MATLAB | 1,474 | 49 | function [e, edata, eprior, y, a] = glmerr(net, x, t)
%GLMERR Evaluate error function for generalized linear model.
%
% Description
% E = GLMERR(NET, X, T) takes a generalized linear model data
% structure NET together with a matrix X of input vectors and a matrix
% T of target vectors, and evaluates the error functio... |
d2265b77e99d321544d432556edc74c61a97e4ef815c2fc7bdb156b34c8f6818 | MATLAB | 1,474 | 49 | function [acts scratchpad] = test_smlr(testpats,testtargs,scratchpad)
% Generates predictions using a trained multinomial regression model.
%
% [ACTS SCRATCHPAD] = TEST_SMLR(TESTPATS,TESTTARGS,SCRATCHPAD)
%
% SEE ALSO SMLR, TRAIN_SMLR
% License:
% ======================================================================... |
faca43e3f67032aa6d9d94693a6b2f3e67d344b02ebc5aeb0c09e4593eb07efd | MATLAB | 1,474 | 42 |
function SimRes = simRespLatencies(CrossTime,SortedStim,uAM,thrs,offset,NDTime,maxRespDelay)
nStm = size(CrossTime,1);
nThr = size(CrossTime,2);
nUStm = size(uAM,1);
minRespDelay = 0;
respWin = 0.75; % for hit rate
RespDelaySim = min(CrossTime - offset + NDTime,maxRespDelay);... |
b1ed0dcb55bf94b5125a352e73fb10da8298756e17324194ac449037fc6e9978 | MATLAB | 1,475 | 66 | function [dmoco, beta, tstat, pval, sigma, CovB, dfe, w, P, f] = ar_glm_final( d,X,tune )
% preallocation
dmoco = zeros(size(d));
beta = zeros([size(X,2) size(d,2)]);
tstat = beta;
pval = beta;
w = zeros(size(d));
Sall=[];
for j = 1:size(d,2)
y = d(:,j);
B... |
fcf6cec45e34366910a5097db8977aed52731e9bc7c47b396cac338f9cc11814 | MATLAB | 1,475 | 68 | function iSD = GetSelectedSDPairIndex(iS, iD)
global maingui
SD = maingui.dataTree.currElem.GetSDG();
iSrcDet = maingui.axesSDG.iSrcDet;
nSDPairsSelected = size(iSrcDet,1);
nSrcs = length(SD.SrcPos);
iSDs = mod(iSrcDet(:,1) .* nSrcs + iSrcDet(:,2), nSDPairsSelected) + 1;
for ii = 1:length(iSDs)
iSD = mod(... |
37c7a7c1795afcd39273b222645bf126cb59c36b94741f4e537d1f4d65723209 | MATLAB | 1,477 | 49 | function [p_val] = permutationTest(X, Y, sum_metric, num_iter, test_dir)
% X has label 1, Y has label 2
% sum_metric: 'mean' or 'median'
% num_iter: number of iterations
% test_dir: 1 - left sided, 2 - right sided
% load the same set of (pseudo)random seed everytime
load('random_seed_permutation_test.mat','rand_seed'... |
674e840dd592d281f6a1887d0836faea52c87a4106ccbcd9c44793c463e5d858 | MATLAB | 1,477 | 31 | %
% Compare simulated spike times to real spike times
%
cd '/home/nmsutton/Dropbox/CompNeuro/gmu/research/sim_project/code/holger_data/nate_scripts/for_jeffrey/'
load_data
cd '/home/nmsutton/Dropbox/CompNeuro/gmu/research/sim_project/code/gc_can/scripts/'
animal_spikes = spikes(3,:)';
sim_spikes = readmatrix('/home/n... |
0202d04c46485b7e335e197c76f6e9f84b603fd54765aaeacdc87ec433a4a4b7 | MATLAB | 1,478 | 35 | %% Flow Cluster Control Script
intPower = 0.9;
clusterFilSig = 3;
clusterFil = fspecial('gaussian', 6*clusterFilSig + 1, clusterFilSig);
clusterFil(3*clusterFilSig + 1, 3*clusterFilSig + 1) = 0; clusterFil = clusterFil/sum(clusterFil(:));
cVec = (0.55:0.15:1)';
posThreshVec = 0.0001;%[0, 0.0001, 0.001, 0.005];
negThr... |
119d107e638ce7f3276a9dbb989df604c7b461f36ddba888f3e49b1d5397e470 | MATLAB | 1,478 | 59 | % DEMO_RPROP_3 Rprop with GPU acceleration
% Show the use of GPU acceleration for the Rprop function and compare its
% performances with the normal CPU-computed version
%
% Copyright (c) 2011 Roberto Calandra
% $Revision: 0.60 $
%% Init
numdim = [1000000 500000 100000 50000 10000 5000 1000];
p.verbosity ... |
f38fdb4d8e2499bdd0f215c2fb80c487b7b85a2b91964b782aed29b6db81aa69 | MATLAB | 1,478 | 55 | function [ matches ] = find_obj_regexp(subj, objtype, expr)
% FIND_OBJ_REGEXP - Searches for objects using regular expressions.
%
% Usage:
%
% [ MATCHES ] = FIND_OBJ_REGEXP(SUBJ, OBJTYPE, EXPR)
%
% Searches through the SUBJ structure to find objects of type
% OBJTYPE whose name matches the regular expression in strin... |
34ea52644c73e78d6dab89a315de9642c0c5b350f15967965a27854beee84b5b | MATLAB | 1,482 | 46 | function [theta_wedge,rho_wedge] = getWedgeBorder(theta1,theta2,rho1,rho2)
% GETWEDGEBORDER returns theta/rho vectors of the wedge border
% coordinates that can be plotted using "polar" where:
%
% -theta1/2 is the starting/final angle of the wedge in degrees.
% -rho1/2 is the inner/outer ra... |
bca985a398c94a8e89d5d690204e5e70aa3a9de364acb837c07c43fb451accb0 | MATLAB | 1,482 | 66 | function [output] = check_matlabVersion(varargin)
%
% This checks the supplied integers against the current version of matlab
% and returns a -1 if the current version is older, a 0 if they are the
% same and a 1 if they are newer. If you would like to only test the major
% and minor versions then you can call the fun... |
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