sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
47628440d67edffdd8008c0cd6f56815681038ae5b39ecbf86b04e64df9c6c18 | MATLAB | 6,931 | 158 | function [inputMap, MBM, varargout] = mbm_read_inputs(MBM, varargin)
% Read inputs for analysis from file paths given in MBM.
%
%% Input:
% MBM - structure having the fields:
% MBM.maps.anatListFile - Character vector.
% - Path to either:
% ... |
2076247ebbf15062ba2732f8a7f18896d6fb256948ce69c6ce17e62d7315eba9 | MATLAB | 6,935 | 226 | function [A,Y,AtA,numIter,tElapsed,finalResidual]=vsmf(X,k,option)
% Versatile Sparse Matrix Factorization
% Definition:
% [A,Y,AtA,numIter,tElapsed,finalResidual]=vsmf(X,k)
% [A,Y,AtA,numIter,tElapsed,finalResidual]=vsmf(X,k,option)
% X: matrix, dataset to factorize, each column is a sample, and each row is a ... |
514b7a135b06e4faa8152accab96c2c6e579cd73ef2a3f59e74c37ed9c3cbd41 | MATLAB | 6,944 | 145 | %-----------------------------------------------------------------------
% Tapas function for BMPD dataset
% CL Septembre 2020, landelle.caroline@gmail.com // caroline.landelle@mcgill.ca
% Toolbox required: Matlab, SPM12, Tapas PhysiO Toolbox
% https://github.com/translationalneuromodeling/tapas/tree/master/PhysIO
% ... |
7d6d0722c8a496f6348eaa1fac504d31f1e20dcdf7ab2aac96871cc7c2e46f86 | MATLAB | 6,944 | 165 | % demo file for applying the NoRMCorre motion correction algorithm on
% 1-photon widefield imaging data
% Example files can be obtained through the miniscope project page
% www.miniscope.org
clear;
gcp;
%% read data and convert to double
name = '/Users/epnevmatikakis/Documents/Ca_datasets/Miniscope/msCam13.avi';
%add... |
6648004b059ac499ffc76ee433fa3610c6c33914de3dfa1f2ee5fdbef3ca43a2 | MATLAB | 6,948 | 148 | function [mappedA, mapping] = autoencoder_ea(A, no_dims)
%AUTOENCODER_EA Trains an autoencoder using an evolutionary algorithm
%
% [mappedX, mapping] = autoencoder_ea(X, no_dims)
%
% Trains an autoencoder using an evolutionary algorithm. The autoencoder is
% trained in such a way, that it reduces the dimensionality o... |
80fc84d06255892b1674bf185e56ad987c16fae95ed7d6ef6f29c41cacc53782 | MATLAB | 6,951 | 190 | function PD_ssrt_EEG_LFP_time_freq_MAIN(epoch_win,baseline_win, EventTypes,SubEventTypes)
% PD_SSRT_BEHAV_MAIN Main function for EEG and LFP time-frequency analysis
%
% Input parameters:
% EPOCH_WIN 1x2 vector, time window relative to event timestamp in sec, for data epoching (ex: [-2 2])
%
% BASE... |
979ee0b44e1e592d6f4ca659089b4ed159173481fb52850decf7267584c5784e | MATLAB | 6,958 | 76 | function labels_colors = defineLabelsColors_pd
%DEFINELABELSCOLORS_BALAZS Labels and colors for plotting.
% LABELS_COLORS = DEFINELABELSCOLORS_BALAZS returns preset labels and
% colors for plotting. LABELS_COLORS is an N x 5 cell with tag, label,
% color, second option for color, and line style in its colum... |
1fbd945bf906e6b3e98cf318b69fe7ddc69298c6fee8b46b615f96ed1df69955 | MATLAB | 6,960 | 166 | function [IO, mess] = ReadTabular(IO, fileflg, mess)
if ~exist('mess','var'), mess=[];end
switch fileflg
case 1
IO.Y = evalin('base',IO.M_edit);
IO.s_featnames = evalin('base',IO.col_edit);
IO.s_ID = evalin('base',IO.case_edit);
if ~isfield(IO,'oocvflag') || ~IO.oocvflag || IO.lab... |
da6451dd48fed73f0fbb32384ed7fc3d1ca0d1dce579f713a734918d56fbde80 | MATLAB | 6,977 | 267 | %% FUNCTION Least_Dirty
% Dirty Multi-Task Learning with Least Squares Loss.
%
%% INPUT
% X: {n * d} * t - input matrix
% Y: {n * 1} * t - output matrix
% rho1: group sparsity regularization parameter
% rho2: elementwise sparsity regularization parameter
%
%% OUTPUT
% W: model: d * t
% C: constant paramet... |
872ed9b686b8a9c8935200290e22debc606f4057035c52ef6fe1c683297290da | MATLAB | 6,981 | 222 | %> SEDITOR GUI to manually edit values from a stimulus class passed to it
%> from opticka, and optionally edit many stimuli at the same time
classdef seditor < handle
properties
handles
fn
stim
cprop
ckind
otherstimuli
mversion
optickahandle = 0
end
methods
function obj = seditor(stimin, ohandl... |
1e7e5f44d8983faa63e2acb9f0ad9b3d10030e447e6f18e2dec4298114f41811 | MATLAB | 6,991 | 167 | function [STACKING, act, mess] = nk_Stacking_config(STACKING, NMS, mess, parentstr)
global NM
act = 0;
%% Set-up configuration interface
% Check the status of previous analyses
if ~exist('NMS','var') || ...
isempty(NMS) || ...
~isfield(NMS,'completed_analyses') || ...
isempty(NMS.completed_a... |
927b20581c13d48c0577d5bee9c75af40e2b8434092efed076b3d89ec6cc077a | MATLAB | 6,999 | 156 | %% Pipeline for the proper concatenation of miniscope data across sessions
% Developed by Daniel Almeida Filho Mar/2020 (SilvaLab - UCLA)
% If you have any questions, please send an email to
% almeidafilhodg@ucla.edu
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Parameters
%%%***... |
727bfabe3c23c974a45acf2bc2c63a3754e68c264c56d05142fc6bb9c24f5fa3 | MATLAB | 7,005 | 210 | clear all;
close all;
setup_paths;
data=load('JupiterSPIKE distance all session_v2.mat');
winSize = 100;
xx=[];
yy=[];
xx2=[];
yy2=[];
xx3=[];
yy3=[];
xx4=[];
yy4=[];
for j=1:42
if j<4 |j>4
TOJ_begin_correct=data.TOJ_begin_early_all{j,1};
TOJ_begin_incorrect=data.TOJ_begin_late_all{j,1};
TOJ_end_co... |
d82756f2acbdac69bc2b425fa68106ff61fb53b1dd7f7df3a242776cf0b40b1d | MATLAB | 7,019 | 217 | function [JUSPACE, act ] = cv_JuSpace_config(JUSPACE, brainmask, parentstr, defaultsfl)
% from NM structure
% brainmask
% badcoords
% user input
Atlas = [];
Option1 = 4;
CorType = 1; % Spearman
Option3 = 0;
AutoCorCorrect = 1; % correct for spatial correlations
Option5 = 0;
PetList = [];
if ~exist('defaultsfl','var... |
9ec9672909d2ab897f44d2632530d604e13132bf9896674608df46ee0fab6ad0 | MATLAB | 7,029 | 191 | clear; clc;
load('...\LFPdata_Fig5E_S3.mat')
fs=LAT.fs;
t_start=LAT.t_start;
t_end=LAT.t_end;
saveFig=0;
% JA of Mk1/Mk2 (signals recorded simultaneously)
[allDiffLAT_P1Go_ja,allDiffLAT_N1Go_ja,allDiffLAT_P1MovOn_ja,allDiffLAT_N1MovOn_ja,allRT1_ja,allRT2_ja,allDiff_RT_ja,allBestLag_rVal_ja,allICD_lat]=f_Ca... |
cc293a9cea37f2845f4572011bb8db20604e131987c454022ad0c94e75ab3ccd | MATLAB | 7,033 | 183 | % ========================================================================
%> @class BarStimulusTest
%> @brief Class-based unit tests for barStimulus.
%>
%> Tests construction, property defaults, type list, bar dimensions,
%> contrast, interpMethod validation, colour handling. CI-safe tests
%> run without PTB; hardware... |
5ccf85009061dfbae3637f8c2553d5a1310606b1e70483b9b0c33bff393aa86a | MATLAB | 7,047 | 193 | function outclass = Mysvmclassify(svmStruct,sample, varargin)
%SVMCLASSIFY classifies data using a support vector machine
%
% GROUP = SVMCLASSIFY(SVMStruct,SAMPLE) classifies each row of the data
% in SAMPLE using the information in a support vector machine classifier
% structure SVMStruct created using SVMTRAIN.... |
8cfcc13063559ccb26c5803e3efa707870877d3cdd7dd7386e39e386720e67b9 | MATLAB | 7,055 | 214 | clear all;
close all;
setup_paths;
data=load('MercurySPIKE distance all session.mat');
winSize = 100;
xx=[];
yy=[];
xx2=[];
yy2=[];
xx3=[];
yy3=[];
xx4=[];
yy4=[];
for j=1:21
if j<22
TOJ_begin_correct=data.TOJ_begin_correct_all{j,1};
TOJ_begin_incorrect=data.TOJ_begin_incorrect_all{j,1};
TOJ_end_co... |
49e7890e2288d7b6226104dd58deee7dc6e0e3deceb2209eaa64f4b7c4531fa2 | MATLAB | 7,061 | 181 | function [featmat, emptfl, skiparr, missarr] = nk_GenCVdataMaster2(datid, cv, mastermat, tdir, basename, savefl, varind, varstr, concatfl, UIopt)
global SAV
featmat = []; skiparr=[]; missarr=[];
saveflx = false; if exist('savefl','var') && ~isempty(savefl), if savefl, saveflx = true; end; end
if ~exist('tdir','var') ... |
856109df98fa164c007b53179c736e655b376ee758d9df1445ae7128f6103724 | MATLAB | 7,069 | 213 | clear all;
close all;
setup_paths;
data=load('MercurySPIKE distance all session.mat');
winSize = 100;
xx=[];
yy=[];
xx2=[];
yy2=[];
xx3=[];
yy3=[];
xx4=[];
yy4=[];
for j=1:21
if j<22
TOJ_begin_correct=data.TOJ_begin_early_all{j,1};
TOJ_begin_incorrect=data.TOJ_begin_late_all{j,1};
TOJ_end_correct=d... |
db9c4a12c559bc0d38eebf3ff7daca339598e75a8ae1c5db4f7d6e8e2b3f7795 | MATLAB | 7,076 | 174 | function [bandwidth,density,xmesh,cdf]=kde(data,n,MIN,MAX)
% Reliable and extremely fast kernel density estimator for one-dimensional data;
% Gaussian kernel is assumed and the bandwidth is chosen automatically;
% Unlike many other implementations, this one is immune to problems
% caused by multimo... |
dfc1caef108caa6b7ea83bc15873fa4a483be8904b7f1c90eb7e54a850e562e6 | MATLAB | 7,088 | 166 | % =========================================================================
% = MODELPERF PLOT (BAGGED ENSEMBLES) =
% =========================================================================
function handles = display_modelperf_bagged(handles)
multfl = false;
predind = get(... |
60325642f936aae3ca767d78408512f86a09a85142db6fb93fcb886ffa3a6641 | MATLAB | 7,092 | 138 | %{
----------------------------------------------------------------------------
This file is part of the Sanworks Frame2TTL repository
Copyright (C) 2017 Sanworks LLC, Sound Beach, New York, USA
----------------------------------------------------------------------------
This program is free software: you ca... |
c653911fe3518cbd11e7f088d1cf465be3aeb02e7f8c865dc55df762721d7c8d | MATLAB | 7,108 | 205 | function [y, E] = sammon(x, n, opts)
%SAMMON Performs Sammon's MDS mapping on dataset X
%
% Y = SAMMON(X) applies Sammon's nonlinear mapping procedure on
% multivariate data X, where each row represents a pattern and each column
% represents a feature. On completion, Y contains the corresponding
% co-ordin... |
4de9e34cb5ea8c6d6b40a8531ab10bcd2e90d27e715d340f721d086e7beaa09b | MATLAB | 7,144 | 131 | function [varind, varstr] = nk_SelectVariateIndex(dat, unifl, varind, askfl, showvar)
% ----------------------------------------------------------------------------------
% FORMAT [varind, varstr] = nk_SelectVariateIndex(dat, unifl, varind, askfl, showvar)
% -------------------------------------------------------------... |
51779f32890e8017f2f2682c9becbb1c49e0be1bc1c1d86b1cf3ebf14a6c8b8f | MATLAB | 7,161 | 266 | function [u, v, C, success] = spls(X, Y, cu, cv, C, e, itr_lim)
%
% Sparse PLS algorithm, please check Monteiro et al. 2016 for details:
% doi:10.1016/j.jneumeth.2016.06.011
%
% Inputs: X, Y - data matrices in the form: samples x features. These
% should have each feature with mean = 0 and ... |
ebdfcefe72fa7a811e3ae7623e36a56e8caebd36bdedd1e7ec54554e8aaf16cb | MATLAB | 7,182 | 271 | %% FUNCTION Logistic_Trace
% Trace-Norm Regularized Learning with Logistic Loss.
%
%% OBJECTIVE
% argmin_{W,C} { sum_i^t (- sum(log (1./ (1+ exp(-X{i}*W(:, i) - Y{i} .* C(i)))))/length(Y{i}))
% + rho1 * \|W\|_*}
% where \|W\|_* = sum(svd(W, 0)) is the trace norm
%
%% INPUT
% X: {n * d} * t - input mat... |
0b583518d5d535faf3fdb52d62f29439aa894ea6187d133b298d1ba50746b81f | MATLAB | 7,183 | 193 | % =========================================================================
% function [P, Perf, d, crit] = nk_MultiDecideMaxWins(H, Y, Classes,
% maxfunc, weightflag)
% =========================================================================
%
% INPUTS:
% -------... |
4de82e5682f87b9639e012304e2c8f26e078669eae5aef4c40c967681ac21533 | MATLAB | 7,185 | 218 | function [P, newY, L, newV, idx]= sdecca2(Y, snn, regularizer, relative)
% doing semidefinitve embedding/MVU with output being parameterized by graph
% laplacian's eigenfunctions..
%
% the algorithm is same as conformal component analysis except that the scaling
% factor there is set as 1
%
%
% function [P, newY, Y] ... |
58d8ad0395b464b52053fc295564cf347a95f8734c87ee1cb83260dfae827a0f | MATLAB | 7,196 | 198 | function [P,apar,kpar,upar] = palm_pareto(G,Gdist,rev,Pthr,G1out)
% Compute the p-values for a set of statistics G, taking
% as reference a set of observed values for G, from which
% the empirical cumulative distribution function (cdf) is
% generated. If the p-values are below Pthr, these are
% refined further using a ... |
79f9d4549394da75a55863cc0c12568526774230158640a2d6fded69add2626a | MATLAB | 7,199 | 169 | function R = display_seqopt_perf(NM, analind, Cind, NodeN)
R=[];
if ~exist('analind','var') || isempty(analind), errordlg('Please specify a sequential prediction analysis'); return; end
if ~strcmp(NM.analysis{analind}.params.TrainParam.SVM.prog,'SEQOPT'), errordlg('The specified analysis is not a sequential predictor!... |
78cb123aa85fef4d310211fa3c5aded96251cd4ec51963d8ea8d88faa5017f00 | MATLAB | 7,203 | 207 | function [mat_data, dims, folder_analysis] = distribute_data(nam, patch_dims, ...
w_overlap, memory_size_per_patch, memory_size_to_use, dir_output, filter_kernel)
% divide the field of view into multiple small patches and save the data
% into these small patches individually. There are overlaps between these
% patc... |
45febe6ae689aab0c5cbf86b258ab6501073e89f394df987fdb64977007bc72a | MATLAB | 7,208 | 164 | %% mRVM1
% authors: Ioannis Psorakis psorakis@gmail.com, Theodoros Damoulas theo@dcs.gla.ac.uk
% Copyright NCR, 2009
%performs predictions given the results of the training phase, described by
%TRAIN_OBJECT (see train_mRVM1) and a collection of test samples Xtest. In
%case the test_labels are available, it also perf... |
3986e90b88ae93cbdd10fab5a8d1904c4e4002851bc379dfac21df1a4391e0a3 | MATLAB | 7,209 | 175 | % ========================================================================
classdef DMTSFunctions < userFunctions
%> @class myUserFunctions (child of userFunctions)
%> @brief For DMTS task
%>
% ========================================================================
%% ADD YOUR OWN VARIABLES HERE ↓
properties
comm... |
a86a72495e226cde91975bf54e6d94591b6e6794aa18b110995d3cc4ebc02c95 | MATLAB | 7,214 | 267 | % Test file for NMF base
%
% Created by H.Kasai on March 24, 2017
% Modified by H.Kasai on Oct. 18, 2017
% Modified by H.Kasai on May 21, 2019
clc;
clear;
close all;
anls_asgroup_flag = 1;
nmf_mu_flag = 1;
nmf_mod_mu_flag = 1;
nmf_acc_mu_flag = 1;
nmf_als_flag = 1;
nmf_hals_flag = 1;
nmf_acc_hals_flag = 1;
nmf_pgd_... |
97bab01cde10d1e695bad6545b8097a88e7ca3ecc397133905fb5ab32f82c449 | MATLAB | 7,221 | 229 | % SB2_INITIALISATION Initialise everything for the SPARSEBAYES algorithm
%
% [LIKELIHOOD, BASIS, BASISSCALES, ALPHA, BETA, MU, PHI, USED] = ...
% SB2_INITIALISATION(TYPE, BASIS, TARGETS, SETTINGS, OPTIONS)
%
% OUTPUT ARGUMENTS:
%
% LIKELIHOOD Likelihood structure (from SB2_LIKELIHOODS)
% BASIS Pre-processed full ... |
3136e69c48d61987cafd3ba94d07409608134348760fd9b21368526ab2fdfae7 | MATLAB | 7,240 | 221 | function result = agnes(x,method,vtype,stdize,metric,plots)
%AGNES is a agglomerative clustering algorithm. It returns a hierarchy of clusters.
%
%The algorithm is fully described in:
% Kaufman, L. and Rousseeuw, P.J. (1990),
% "Finding groups in data: An introduction to cluster analysis",
% Wiley-Interscience: ... |
18c3e3224092d83b063c3f405d154fa2266ccf4fb927c6d55370a3b628309ac2 | MATLAB | 7,281 | 236 | function [eigvector, eigvalue, elapse] = NPE(options, data)
% NPE: Neighborhood Preserving Embedding
%
% [eigvector, eigvalue, elapse] = NPE(options, data)
%
% Input:
% data - Data matrix. Each row vector of data is a data point.
%
% options - Struct value in Matlab. Th... |
afc01f87748f290e61233e523e8cf8ce75a502782eb7fcb462deaae600ca7c4d | MATLAB | 7,293 | 214 | % This class is part of Titta, a toolbox providing convenient access to
% eye tracking functionality using Tobii eye trackers
%
% Titta can be found at https://github.com/dcnieho/Titta. Check there for
% the latest version.
% When using Titta or this class, please cite the following paper:
%
% Niehorster, D.C., Anderss... |
faf87210fe2c979b42e7fa9fd464e11057306e73d5192eb0712aae721491fbb1 | MATLAB | 7,301 | 190 | % ========================================================================
%> @class DiscStimulusTest
%> @brief Class-based unit tests for discStimulus.
%>
%> Tests construction, property defaults, flash type, contrast/sigma
%> properties, colour handling. CI-safe tests run without PTB;
%> hardware-tagged tests exercis... |
96e284143c59359e6d1c37858b1092b15573b0b760ff334d91137ec0cd2ee03b | MATLAB | 7,314 | 285 | %% FUNCTION Logistic_Lasso
% Sparse Structure-Regularized Learning with Logistic Loss.
%
%% OBJECTIVE
% argmin_{W,C} { sum_i^t (- sum(log (1./ (1+ exp(-X{i}*W(:, i) - Y{i} .* C(i)))))/length(Y{i}))
% + rho1 * \|W\|_1 + opts.rho_L2 * \|W\|_F^2}
%
%% INPUT
% X: {n * d} * t - input matrix
% Y: {n * 1} *... |
7fa3ab193d814eda7fa9e398902cf88cd45bb9f55819433bceec81a553a6f819 | MATLAB | 7,411 | 256 | function gui_advc (action)
%
% This file is needed by FASTICAG
% This file holds the callbacks for advanced options -dialog
% @(#)$Id: gui_advc.m,v 1.3 2003/09/08 11:28:58 jarmo Exp $
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Global variables
% Handle to the window
global hf... |
549dfdb59ba2610a9089221f065b93c3cb026486b0806a7c9e1dadc4eed546a5 | MATLAB | 7,428 | 281 | %% FUNCTION Logistic_TGL
% L21 Joint Feature Learning with Logistic Loss.
%
%% OBJECTIVE
% argmin_{W,C} { sum_i^t (- sum(log (1./ (1+ exp(-X{i}*W(:, i) - Y{i} .* C(i)))))/length(Y{i}))
% + opts.rho_L2 * \|W\|_2^2 + rho1 * \|W\|_{2,1} }
%
%% INPUT
% X: {n * d} * t - input matrix
% Y: {n * 1} * t - outp... |
fea49488f49c098c30f769aaa3810fda4fbc6dad89f900427f65596ec372b5a0 | MATLAB | 7,429 | 166 | function handles = display_main(handles)
h_class = handles.popupmenu1.Value;
h_classlist = handles.popupmenu1.String;
h_list = handles.selModelMeasures.String;
h_val = handles.selModelMeasures.Value;
if h_val>numel(h_list), h_val=1; end
handles.curclass = h_class;
if isfield(handles,'S... |
0d79ce78a4775c069a0bf4fc6135a8fc2858f2c86a01fe0502acda90a6f0df8f | MATLAB | 7,436 | 218 | function PC_respcells_stacked_compare(pcevent,plot_win,fr_name,downsamp,PC_win,alpha,rectype,chanmean,subregion,PCdir)
% PC_RESPCELLS_STACKED_COMPARE
% PC_RESPCELLS_STACKED_COMPARE(EventTypes,pcevent,plot_win,fr_name,downsamp,PC_win,alpha,rectype,chanmean,subregion,PCdir)
% Plots stacked bar plots with count... |
3b72a0f7d864f11d5e226ecdb887564d3e93445edae990b72b149e0e3160817a | MATLAB | 7,453 | 198 | %% mRVM1
% authors: Ioannis Psorakis psorakis@gmail.com, Theodoros Damoulas
% theo@dcs.gla.ac.uk
% Copyright NCR, 2009
%
% Use of this code is subject to the Terms and Conditions of the Research License Agreement,
% agreed to when this code was downloaded, and a copy of which is available at
% http://www.dc... |
f4446f1c0ecd2795e889eb808281a0913199062a3b66747d7a93d14b135a873f | MATLAB | 7,458 | 214 | function param = nk_LIBLIN_config(res, param, defaultsfl, parentstr)
% Nikolaos Koutsouleris: Added bias term configuration option on the 19/02/2023
classifier = 0;
tolerance = 0.01;
b0 = 0;
weighting = 0;
bias = -1;
AutoFl = false; autoflag = false; autostr = 'no';
NonRegBias = false; NonRegBiasFlag = false; NonRegBi... |
c8947f4430706201828d2ba17786fb74d52b99c4c1ed8e96f377bc0182d0b8b9 | MATLAB | 7,494 | 215 | % =========================================================================
% = CLASSIFICATION PLOTS =
% =========================================================================
function handles = display_regrplot_oocv_labels_unknown(h, handles)
% Prepare axes
warning ... |
7b20030d09092f538d2cd2a3c3440ca75807f2006a1f5700232b97205d6edf51 | MATLAB | 7,498 | 283 | function [vol, M, mr_parms, volsz] = load_mgh(fname,slices,frames,headeronly)
% [vol, M, mr_parms, volsz] = load_mgh(fname,<slices>,<frames>,<headeronly>)
%
% fname - path of the mgh file
%
% slices - list of one-based slice numbers to load. All
% slices are loaded if slices is not specified, or
% if slices is emp... |
0708f548e8c9490b7429885b86b3cd5e7e02567aa435eb476b15adb69187b21c | MATLAB | 7,506 | 284 | % ======================================================================
%> @brief nirSmart fNIRS Sync manager
%>
%>
%>
%> Copyright ©2014-2025 Ian Max Andolina — released: LGPL3, see LICENCE.md
% ======================================================================
classdef nirSmartManager < optickaCore
propertie... |
fc6d284ac3501fff8f4d23dde3894c965e3f20fbe0019f1c7369141027888621 | MATLAB | 7,527 | 187 | function [CURACT, act ] = nk_PartialCorrelations_config(NM, CURACT, varind, parentstr, defaultsfl)
if ~exist('defaultsfl','var') || isempty(defaultsfl); defaultsfl = false; end
% Defaults
METHOD_DEF = 1;
COVAR_DEF = 1;
MCOVARUSE_DEF = 1;
MCOVAR_DEF = [];
MCOVARLABEL_DEF = 1;
COVDIR_DEF = 1;
INT... |
17df90987f7911707fa033f847851f7dcb092e41ca3df44fc32b4705548823cc | MATLAB | 7,529 | 284 | % ======================================================================
%> @brief Input Output manager, currently just a dummy class
%>
%>
%>
%> Copyright ©2014-2022 Ian Max Andolina — released: LGPL3, see LICENCE.md
% ======================================================================
classdef nirSmartManager < o... |
3cb2dfda101b495775e95b4747911e4328ac8e0f1f0266ca16934a58f71d1924 | MATLAB | 7,532 | 254 | function [eigvector, eigvalue, elapse] = IsoP(options, data)
% IsoP: Isometric Projection
%
% [eigvector, eigvalue, elapse] = IsoP(options, data)
%
% Input:
% data - Data matrix. Each row vector of data is a data point.
%
% options - Struct value in Matlab. The fields i... |
3a091b358249f0c42227ce8d1f1e46e5c87c35a1ae1447ce68ed7e08712a3912 | MATLAB | 7,536 | 189 | function [CURACT, act ] = nk_PartialCorrelations2_config(NM, CURACT, varind, parentstr, defaultsfl)
if ~exist('defaultsfl','var') || isempty(defaultsfl); defaultsfl = false; end
% Defaults
METHOD_DEF = 1;
COVAR_DEF = 1;
MCOVARUSE_DEF = 1;
MCOVAR_DEF = [];
MCOVARLABEL_DEF = 1;
COVDIR_DEF = 1;
IN... |
c086f1aa4f2483b7b0c618ba8e4c8774e77b9ca47b398e8f24eca8ba80e23620 | MATLAB | 7,580 | 282 | % ======================================================================
%> @brief mentaLab Sync Class
%>
%>
%>
%> Copyright ©2014-2025 Ian Max Andolina — released: LGPL3, see LICENCE.md
% ======================================================================
classdef mentalabManager < optickaCore
properties
devi... |
cea60b55404a28f0802d21de71260cc9af47515215075732f025f28a3b17807b | MATLAB | 7,587 | 221 | % Compute h-step Weisfeiler-Lehman shortest path delta kernel for a set of graphs
% Author: Nino Shervashidze - nino.shervashidze@tuebingen.mpg.de
% Copyright 2010 Nino Shervashidze
% Input: Graphs - a 1xN array of graphs
% Graphs(i).am is the adjacency matrix of the i'th graph,
% Graphs(i).al is the adjacency... |
6b2e4fc2fd0370c549b0993a7930555f20549f8a2ba6e58e7f3c771c1c535c4c | MATLAB | 7,589 | 195 | function [ GD, MD ] = nk_MLOptimizer_ParamCycler(GD, MD, DISP, Ps, Params_desc, mapY, algostr, f, d, npreml, nclass, ngroups, batchflag, PsSel, combcell)
% =========================================================================
% FORMAT [ GD, MD ] = nk_MLOptimizer_ParamCycler(GD, MD, DISP, Ps, ...
% ... |
cadcf6a100a45845e4d902616e1265636ec11cec21db066f11025d76a77b180d | MATLAB | 7,600 | 202 | %% mRVM1
% authors: Ioannis Psorakis psorakis@gmail.com, Theodoros Damoulas
% theo@dcs.gla.ac.uk
% Copyright NCR, 2009
%
% Use of this code is subject to the Terms and Conditions of the Research License Agreement,
% agreed to when this code was downloaded, and a copy of which is available at
% http://www.dc... |
eefd7fc3d64bfae6083ec3030807bbf835c7165bec8622405ad92e17741e2855 | MATLAB | 7,603 | 321 | classdef Cutter < handle
% Cutter superclass
properties (Constant)
CutterFuncKey = 'CutterOption_';
uicHeight = 0.05;
uicWidth = 0.175;
uicWidth0 = 0.075;
uicWidth1 = 0.10;
XLocs = [0.05 0.40:MClust.Cutter.uicWidth1:0.9];
dY = 0.05;
YLocs = 0.95:-MClust.Cutter.dY:0.05;
end
properties
% --- ... |
d956264a191d8c36fa19b4d570ff3b2e6da17e3dfb8247fde8d78023b2f1b6f9 | MATLAB | 7,628 | 173 | function writeMz3(filename, face, vertex,vertexColors,alpha, isCompress, isForceScalar)
%function writeNv(vertex,faces,filename)
% --- save Face/Vertex data as Surfice MZ3 format file
%inputs:
% vertex: Vx3 array with X,Y,Z coordinates of each vertex
% vertexColors: Vx0 (empty), Vx1 (scalar), Vx3 (RGB), Vx4 (RGBA), (Vx... |
df2f29ba3792b1ec7285ce813071e385f5b5e028a58ca2b00a3cac5ca3a02142 | MATLAB | 7,628 | 210 | clear all
close all
setup_paths;
for monkey=1
if monkey==1
monkey_name='Jupiter';
elseif monkey==2
monkey_name='Mercury';
end
[num,txt,session_name]=xlsread('Session_name.xlsx',monkey_name);
filename_marker=[monkey_name,'_stage2_nonprimate_marker_organized.xlsx'];
filename_spike... |
7b9651a178de951da05ce6a6a42fd632c52c578e44237c3243f3e0aaff1d3664 | MATLAB | 7,660 | 173 | function writeMz3(filename, face, vertex,vertexColors,alpha, isCompress, isForceScalar)
%function writeNv(vertex,faces,filename)
% --- save Face/Vertex data as Surfice MZ3 format file
%inputs:
% vertex: Vx3 array with X,Y,Z coordinates of each vertex
% vertexColors: Vx0 (empty), Vx1 (scalar), Vx3 (RGB), Vx4 (RGBA), (Vx... |
1307a8e0bf4fd811341741bd58d8baa655130fadfc52e144c18ef828c20e4d60 | MATLAB | 7,666 | 200 | function [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy,TY] = elm_kernel(TrainingData_File, TestingData_File, Elm_Type, Regularization_coefficient, Kernel_type, Kernel_para)
% Usage: elm(TrainingData_File, TestingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction)
% OR: [TrainingTime, Tes... |
740a2938edfa4c1ffe25d7ed5d1b4ff1632b2e5b119b86d047a424091c30b211 | MATLAB | 7,680 | 181 | %Sleep Analysis Widget
% THe purpose of this script is to input a specific window of data to be
% analyzed from the sleep_analysis_2020 code. This will ask for the window,
% which you must manually provide then spit out summary data for that
% window
% Requirement: Place a .mat file to analyze into Perl Ba... |
a73f7a87f7aa6600c6eecf80428250b525b547641c60ddb917d74ac17aee4db7 | MATLAB | 7,691 | 182 | function [ inp, contfl, analysis, mapY, GD, MD, Param, P, mapYocv ] = nk_ApplyTrainedPreproc(analysis, inp, paramfl, Param)
% =========================================================================
% [ contfl, analysis, mapY, GD, MD, Param, P, mapYocv ] = ...
% nk_ApplyTrainedPreproc(analysi... |
69e42f40f3adffd83f1e98b1cd74b06b1e388729627dcb304fee609d19ddec02 | MATLAB | 7,697 | 196 | function wcoh_onebyone(sess2analyse,event,subevent,isfig,freqs,downsamp,alpha,plot_win,baseline_win,chanmean)
% WCOH_ONEBYONE Mean-squared wavelet coherence (MSWC) map between intraop EEG and LFP.
% EEG_LFP_WCOH_PD calculates the MSWC map aligned to EVENT within FREQS frequency range
% patient-by-patient, incl... |
9f3550defe1f699195b50b0a12ac14c37a89b7f49c104191db89f5bf5831eefb | MATLAB | 7,706 | 196 | function varargout = ghostscript(cmd)
%GHOSTSCRIPT Calls a local GhostScript executable with the input command
%
% Example:
% [status result] = ghostscript(cmd)
%
% Attempts to locate a ghostscript executable, finally asking the user to
% specify the directory ghostcript was installed into. The resulting path
% is s... |
08adf8d21bd00d09d251fd2d39c37ed870759793a0fca35632737d6902907f4f | MATLAB | 7,709 | 191 | % script to combine multiple animalBehavioralAnalysis objects, output
% timeseries plots, reliability (z-score relative to WT) csv, and heatmap
%
% SurveyBott, 2019, info@surveybott.com
%#ok<*AGROW>
function data = combinedReliability(in,varargin)
wt = fullfile(fileparts(mfilename('fullpath')),'wt.mat');
% inputs
p = i... |
650b764747f82dda9f0cd565d2a94811187a6c75814321e9bba269aca74be51b | MATLAB | 7,723 | 185 | function ms = msGenerateVideoObjConcat(dirName, equipment, replaceRGBVideo, filePrefix)
%MSGENERATEMS Summary of this function goes here
% Detailed explanation goes here
ms.dirName = dirName; % Added by GE to keep track of where the files were initially
MAXFRAMESPERFILE = 1000; %This is set in the miniscope control... |
dae6ddaeb1a27c1d7ac428f138cdaff9192d1d7477b1487018e23b468eb00a14 | MATLAB | 7,734 | 231 | function palm_plot(Y,X,I,Z,res,F,opt)
% Take a vector of data, regressors from a design, then
% make an interaction plot.
%
% Usage:
%
% palm_plot(Y,X,I,Z,res,F,opt)
%
% - Y : Data.
% - X : Main effects (up to 3 colums, of which
% no more than 2 can be continuous.
% - I : The interacti... |
a84513b297ac933fa444b6d9b53663f13049a76f776df03253d4118a4166ce77 | MATLAB | 7,753 | 237 | classdef HEDTags
%HEDTags V8.3
properties
name
id
comment
end
enumeration
% Event/
Sensory_event ("Event/Sensory-event","","")
Agent_action ("Event/Agent-action","","")
Data_feature ("Event/Data-feature","","")
Experiment_control ("Event/Experiment-control","","")
Experiment_procedure ("Event/E... |
3f3a4c460e548fd2b6bca060200dd2ff02c38fe48722762e9360ea63c51727d8 | MATLAB | 7,784 | 224 | % =========================================================================
% function [P, Perf, d, crit] = nk_MultiDecideMaxWins(H, Y, Classes,
% maxfunc, weightflag)
% =========================================================================
%
% INPUTS:
% -------... |
3934f5c61d11afe756bf0ed78b600999c0b76e8d75e31d435fc6a17b9a0e1398 | MATLAB | 7,786 | 188 | function [ b sigma2_x x_est y_est stats] = deming(x,y,lambda,alpha)
% [ b sigma2_x x_est y_est stats] = deming(x,y,lambda,alpha)
%
% deming() performs a linear Deming regression to find the linear
% coefficients:
% y = b(1) + b(2)*x
% under the assumptions that x and y *both* contain measureme... |
9ce236a204796240955e67527c1bcf5481f352c66066db7c64d70abbe0231511 | MATLAB | 7,792 | 233 | % =========================================================================
% Coded by Youngmin Ha (y.ha.1@research.gla.ac.uk) on 21 Jan 2015
% by referring to Ha, Youngmin. "Fast multivariate relevance vector
% regression," submitted to Annals of Mathematics and Artificial
% Intelligence (2015).
% ============... |
b67e1b9f178a18d4e281b18e026564bcd5a1598e2c12f01c6532a1b3e9d279bf | MATLAB | 7,831 | 199 | function input_struct = brainframe_inputs_human(matpath,varargin)
%Loading the data necessary to create this default input_struct:
if mod(nargin,2) == 0 || isempty(matpath)
% matpath = [cd filesep 'Data'];
matpath = cd;
end
input_struct = struct;
load([matpath filesep 'brainframe_defaultHuman_datinput.mat'],.... |
290db18d98945e49334f2b206b0dbaf65bd7a96b7f9bb0e257481140d7d62a7f | MATLAB | 7,833 | 279 | %% FUNCTION Least_CASO
% Convex-relaxed Alternating Structure Optimization with Least Squares Loss.
%
%% OBJECTIVE
% argmin_{W, M} { sum_i^t (0.5 * norm (Y{i} - X{i}' * W(:, i))^2)
% + rho1 * eta (1+eta) trace(W' (eta I + M)^-1 W)
% subject to: trace (M) = k, M \preceq I, M \in S_+^d, eta= rho2/rho1
%
%%... |
c17ac10d84922786877fbd4e14b7eae0c98f6f03e90f1ee88f49acdf6c4e70fa | MATLAB | 7,843 | 403 | function ret=writesdpa(fname,A,b,c,K,pars)
%WRITESDPA Take a problem in SeDuMi format and writes it in SDPA format
%
% Usage:
%
% ret=writesdpa(fname,A,b,c,K,pars)
%
% fname Name of SDPpack file, in quotes
% A,b,c,K Problem in SeDuMi form
% pars Optional parameters.
% ... |
b84f1192a525fca1d5092350ca72a75830b90f3359b9e12e780d27e021ac3f9f | MATLAB | 7,854 | 231 | function [x, infos] = nmf_pgd(V, rank, in_options)
% Projected gradient descent for non-negative matrix factorization (NMF).
%
% The problem of interest is defined as
%
% min || V - WH ||_F^2,
% where
% {V, W, H} > 0.
%
% Given a non-negative matrix V, factorized non-negative matrices {W,... |
0c193c926bdc7a7b22445799438c14be7108ffc9c5ad8d04b613677bea83d66e | MATLAB | 7,872 | 216 | function CorrelationPlotter(outputs,savenclose)
if nargin < 2
savenclose = 0;
end
fldnames = fieldnames(outputs);
% Rvals across time points for optimal model
for k = 1:length(fldnames)
tempstruct = outputs.(fldnames{k});
data = tempstruct.Full.data;
predicted = tempstruct.Full.predicted;
notnanin... |
a94e5a1e93040622e70e8a1d1a5c4943286892d060dfb473fa4177ed933d325e | MATLAB | 7,875 | 179 | %% mRVM1
% authors: Ioannis Psorakis psorakis@gmail.com, Theodoros Damoulas theo@dcs.gla.ac.uk
% Copyright NCR, 2009
%performs predictions given the results of the training phase, described by
%TRAIN_OBJECT (see train_mRVM1) and a collection of test samples Xtest. In
%case the test_labels are available, it also perf... |
161f71723230895ddb23f95d78afd3170e5352f7e8ed29ce3150f455c2efa3ca | MATLAB | 7,910 | 174 | % ========================================================================
%> @class AudioManagerTest
%> @brief Class-based unit tests for audioManager.
%>
%> Tests construction, silent mode, property defaults, volume clamping,
%> and beep sound vector generation (via the private getBeepSoundVec
%> method, exercised th... |
4d4ac7364e36a718b1b00e8fbdd80d92fbecbc34edc424f7bb2f9d6e18308362 | MATLAB | 7,912 | 220 | function password = passwordUI(varargin)
% PASSWORDUI Dialog for entering and creating passwords
% PW = PASSWORDUI() brings up a dialog for entering a password.
% The dialog masks the characters with a "*".
% PW = PASSWORDUI(...) accepts up to 3 optional input arguments. The
% arguments can be:
% ... |
0c4fc7d21460c20ed57a9423ad267f6b2fb988c1455a43c3b7f57bb403584a74 | MATLAB | 7,920 | 186 | function [check_str, mess, t_label, IO] = CheckLabel(IO, act, t_Y, na_str, mess, Mx)
check_str = na_str;
t_label = [];
if ~isfield(IO,'label_edit') || any(strcmp(IO.label_edit,na_str)), return, end
if ~exist('mess','var'), mess=[]; end
if ~exist('Mx','var') || isempty(Mx), Mx = 'unknown matrix source'; end
if ~exist('... |
8051b7be635d1ba7069e102b0b14dde691bdb2533c6ac7fff4cb6697c2acbe46 | MATLAB | 7,967 | 182 | function CreateCutterWindow(self, varargin)
% MClustCutter.CreateCutterWindow
% varargin
CreateAxesControls = true;
CreateHideShow = true;
process_varargin(varargin);
MCS = MClust.GetSettings();
MCD = MClust.GetData();
%--------------------------------
% constants to make everything identical
uicHeight = self.uicHe... |
292a89c5f8ae8d4ce06a8f44324b18824f68f297bcb4d3485ff9d5d81e55b159 | MATLAB | 7,997 | 256 | %% plot_SAT_TTS_three_algs.m
% Plot SAT TTS scaling (median + IQR error bars) for three algorithms:
% 1) HO_color_full_precision
% 2) HO_single_spin_flip
% 3) QUBO
%
% Folder structure (relative to this script's location):
% HO_color_full_precision/coarse_summary.csv
% HO_color_full_precision/coarse_instance_... |
efe273b98f538b40e298aca8902722dfeb02069a63c3fce29b1540e8e9fde290 | MATLAB | 8,020 | 282 | %% FUNCTION Least_CMTL
% Convex-relaxed Clustered Multi-Task Learning with Least Squares Loss.
%
%% OBJECTIVE
% argmin_W { sum_i^t (0.5 * norm (Y{i} - X{i}' * W(:, i))^2)
% + rho1 * eta (1+eta) trace(W (eta I + M)^-1 W')
% subject to: trace (M) = k, M \preceq I, M \in S_+^t, eta= rho2/rho1
%
%% INPUT... |
01a46e7170094b8fb0a8bf5334d73cf423db205496ee8e93f956cc387132a22b | MATLAB | 8,039 | 201 | %% step1 确认保存位置
%change while needed
clear;
clc;
%
savepath = 'D:\930_paper_plotting\fig\yield'; % Create saving path
if ~exist(savepath, 'dir')
mkdir(savepath);
end
%% 手动load数据
% % includeHS
subject = {'P1', 'P2', 'P3', 'P4', 'P5', 'P6', 'P7', 'P8','P9','P10','P11','sum'};
single_unit_count = [78, 51, ... |
46e6d78e0fcddc55ea2985b31fb17736755506d8325d74a1ae746a2efa77fd51 | MATLAB | 8,041 | 235 | function [merged_ROIs, newIDs, obj_bk] = merge_close_neighbors(obj, show_merge, dmin, method_dist)
%% merge neighboring neurons if they are too close
% inputs:
% show_merge: boolean scale, manually verify the merging
% dmin: scalar, minimum distance (pixel)
% method_dist: str {'max', 'mean'}, method for computin... |
8ff0bcd6e194cdec88f6bfdbd4372bd4732e63647a2ac220483fff5eb01be935 | MATLAB | 8,057 | 198 | %This code is from the following two links
%https://www.mathworks.com/matlabcentral/fileexchange/45134-violin-plot.
%Bechtold, Bastian, 2016. Violin Plots for Matlab, Github Project
%https://github.com/bastibe/Violinplot-Matlab, DOI: 10.5281/zenodo.4559847, https://www.mathworks.com/matlabcentral/fileexchange/1701... |
4872943a379686313e47917a8e2a1e5a6f420d5923178741191c07ab00c5eadf | MATLAB | 8,076 | 205 | function input_struct = brainframe_inputs_mouse(matpath,varargin)
%Setting the path to load defaults; set matpath to [] if using default
if mod(nargin,2) == 0 || isempty(matpath)
% matpath = [cd filesep 'Data'];
matpath = cd;
end
%Loading the data necessary to create this default input_struct:
input_struct = ... |
36eef8fb1f9a1e7936af7b2faa63d4dfd461489f512af66ffded4f3e7396af17 | MATLAB | 8,079 | 266 | %% file example_Calibration.m
% a toy example on using different types of multi-task learning
% calibration algorithms.
%
%% OBJECTIVE
% see manual
%
%% LICENSE
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the... |
459e2dd2aa23ceaf21b4387fb5621c171724b31223b42359c24f1c50aedf8cb9 | MATLAB | 8,101 | 206 | function [varargout] = mbm_plot(MBM)
% plot MBM results
%
%% Inputs:
% MBM - structure having the fields:
%
% MBM.maps.mask - Vector of the binary mask.
%
% MBM.plot.saveFig - Option ('true' or 'false') to
% save the visualisation of the results.
%
% MBM.pl... |
408f6ebeac152b82684a9d53dbe0bf52069fba8cbbd2cdab51e9daace648afe6 | MATLAB | 8,120 | 220 | % =========================================================================
% FORMAT [opt_hE, opt_E, opt_F, opt_Fcat, opt_D, opt_Pred] = ...
% nk_MultiCVMax(E, L, EnsStrat, C)
% =========================================================================
%
% Inputs
% ------
% E ... |
7663ffe92dbd215f943409fcfbdce46e6c9aa54d9c984b0cf968ab6af6454a29 | MATLAB | 8,135 | 192 | function JH = nk_GridSearchEnsembleAdaBoost(Y, GD)
%
% function JH = nk_GridSearchEnsembleAdaBoost(Y, GD, C, G)
% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%
% Inputs:
% =======
% Y = Input Data structure (Y.Tr{x,y} = Training Data, Y.TrL{x,y} = Training Data
% Labels) p... |
244b1b0662111ada025d0b7d6c966cd2bb5fd07a9391fb8acb844a4220f4b34a | MATLAB | 8,144 | 220 | function [data_permuted] = generate_spatial_nullMaps(atlas,data,N,opt_symmetry)
% [data_rand_weighted] = generate_spatial_nullMaps(atlas,data,N,opt_symmetry))
% atlas: file path to the atlas
% data: extracted data from JuSpace
% N: number of nullMaps
% opt_symmetry: 1 (default) generate symmetric null maps, else 0 -->
... |
30e4ae9462a7e3768b38de20f4d129611cfde64ecb2ede897c9ce320a2ac09c7 | MATLAB | 8,170 | 239 | function [x, infos] = nmf_als(V, rank, in_options)
% Alternative least squares (ALS) for non-negative matrix factorization (NMF).
%
% The problem of interest is defined as
%
% min || V - WH ||_F^2,
% where
% {V, W, H} > 0.
%
% Given a non-negative matrix V, factorized non-negative matrice... |
ed82260079a28355bebec91c3fa0364f301566b94156cd3eccfc6b5904558b2c | MATLAB | 8,175 | 272 | function updrs_behav_corr(conditions)
%UPDRS_BEHAV_CORR calculates correlation between UPDRS scores and behav. parameters
% UPDRS_BEHAV_CORR calculates Pearson's correalation between preop and postop (DBS-on)
% UPDRS scores (tested both with and without medication) and reaction time (RT), stop signal delay at 50... |
9e5b5284e21e09705813eec1491c939ae4388f4318cefd2c4a9b9577b2038ae2 | MATLAB | 8,185 | 100 | function Correlograms = generateCorrelograms(ProcessedData,InjuryIndicies,AnalysisRegions,sw,plotProps)
%This function makes correlograms by comparing each raster against all others
% One raster acts as a reference, one is compared against the reference
% All reference events are set as t=0 (one by one),... |
d462b364bbaa200fecc526366cf8f1e50eaad04fb991dcec9bdd910db1a4596a | MATLAB | 8,185 | 306 | %% FUNCTION Logistic_SRMTL
% Sparse Structure-Regularized Learning with Logistic Loss.
%
%% OBJECTIVE
% argmin_{W,C} { sum_i^t (- sum(log (1./ (1+ exp(-X{i}*W(:, i) - Y{i} .* C(i)))))/length(Y{i}))
% + rho1 * norm(W*R, 'fro')^2 + rho2 * \|W\|_1}
%
%% R encodes structure relationship
% 1)Structure order... |
c1f48196806f9ec050f0aac1c58ca8eb4271c8b9c34d4beb9f67d6cb049eee4b | MATLAB | 8,187 | 239 | function clusplot(x,ncluv,span,xlabels)
%CLUSPLOT creates a bivariate plot visualizing a partition (clustering)
% of the data. All observations are represented by points in the plot,
% using principal components or multidimensional scaling. Around each
% cluster an ellipse is drawn.
%
%The algorithm is fully described... |
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