sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
6711fa1c8dd4586a537f97aa5659633f22b0deea8cdbdb891bb48193d6688a96 | MATLAB | 3,128 | 96 | function logB = mkSoftEvidence(localCPD, X)
%% Make a soft evidence matrix logB(j, t) = log p(X(:, t), S(t) = j)
% (unnormalized), where S(t) denotes the state of node t.
%
%% Inputs:
%
% localCPD - a struct as created by e.g. gaussCpdCreate, representing a
% local conditional probability distribution in a ... |
43715a8429738e0e37bcaf90496368a1c14a67e0f8129d2451dffa6037e987ec | MATLAB | 3,129 | 122 | function save_data(data,fn)
% save_data(data,fn)
%
% where data is the structured variable containing
% amplitude and phase (*.paa)
% Saves calculated boundary data. Automatically determines type of data
% check if the user reversed the inputs
if ischar(data)
temp = data;
data = fn;
fn = temp;
end
% SPE... |
fe8a44f92d907255396eb530cdebc524e0cd6c22595c5f0124d79b541fe37199 | MATLAB | 3,131 | 81 | %% Converting a tensor to a matrix and vice versa
% We show how to convert a tensor to a matrix stored with extra information
% so that it can be converted back to a tensor. Converting to a matrix
% requies an ordered mapping of the tensor indices to the rows and the
% columns of the matrix.
%% Creating a tenmat (tens... |
1d878f8c3e907873d5528e0da8daac46281d8b0fa07cc835875979cd6cb6566e | MATLAB | 3,132 | 97 | function varargout = mat2xml(matfile, xmlfile)
%MAT2XML Convert a MAT-file into an XML file
% MAT2XML(MATFILE, XMLFILE) loads MAT-file MATFILE and converts it
% into an XML file saved in XMLFILE.
% T = MAT2XML(MATFILE) also converts the MAT-file MATFILE to XML
% but returns it instead as an XMLTree object T.
%
% ... |
4fd0a9326774e5281236562529b2d32b77d7c71109d208f11b0ac50768e21eef | MATLAB | 3,132 | 72 |
[Patterns] = extract_variables ('ICA_results',[],resultsPath, 'Patterns',[],conditionType,conditionValue);
[int_id,pyr_id] = extract_variables ('nCells',[],resultsPath, 'int','pyr',conditionType,conditionValue);
int_weights = zeros(1,size(Patterns,2));
pyr_weights = zeros(1,size(Patterns,2));
int_weights_clust_rec =... |
ed181a6c1d81ee2d07cabc6134b137dbf34934ce20e5f86d6b8881b6fb95ce67 | MATLAB | 3,132 | 103 | function H=w_PlotEdge(Matrix, ElementLabel, ...
HigherOrderNetworkIndex, HigherOrderNetworkLabel,...
ColorMap)
LineWidth=2;
LineColor=[0, 0, 0];
NumNode=size(Matrix, 1);
if ~isempty(HigherOrderNetworkIndex) && NumNode~=size(HigherOrderNetworkIndex, 1)
errordlg('Invalid Length of HigherOrderNetworkIndex')... |
96ef6af9ca1f5c6a3ba7770d8595172e946fd161dcfac6f7daee4254c8b5487b | MATLAB | 3,133 | 110 | function boundaries=compute_boundaries(faces)
% compute boundaries of a mesh
%
% boundaries=compute_boundary(faces);
%
% faces: triangulation
% boundaries: cell array containing ordered list of vertices for each
% boundary
%
% based on function by Gabriel Peyre, modified to deal with mu... |
a6d0aa92f0e2e69b514688271ee701672c8bce8f494d57dc2dde0b42cfcd5457 | MATLAB | 3,134 | 97 | function error_DG = measure_round_alignment_mExR(params,fov)
% Calculate's registration error (in nm, in post-expansion units) of
% registered image volumes using the method for ExSeqProcessing by Dan Goodwin
% from Alon et al., Science 2021
%extract parameters
parentfolder = params.parentfolder;
rounds = params.round... |
06c2b93b8f20a7aec7bbea39bd1be83108aaa21078545ef0af38ae4412b6f714 | MATLAB | 3,136 | 100 | %% Analysis of binomial differences on lefthand/righthand data
% PMTKneedsStatsToolbox ksdensity
%%
% This file is from pmtk3.googlecode.com
clear all
requireStatsToolbox
setSeed(0);
y1 = 9; n1 = 52; y2 = 4; n2 = 48;
%sf=1;
%y1 = 1*sf; n1 = 50*sf; y2 = 25*sf; n2 = 50*sf;
[deltas, post, thetas] = contingencyTableUnpair... |
27a1024b277bfdf2f2036e8af4585ed8cbab69f040544e838b274b9dc63dfafe | MATLAB | 3,136 | 115 | function [source] = estimate_fwhm2(source, maxdist)
% ESTIMATE_FWHM2(SOURCE, MAXDIST)
%
% This function computes the Gaussian fwhm of the spatial filters, according to
% least-squares Gaussian fit including data points up until MAXDIST from the
% locations of interest.
%
% This function can only deal with scalar filt... |
9d01f4b95656da2b57396854a1063e7e29adb9f4c03b150f69e1c8fd998a963a | MATLAB | 3,136 | 106 | %% initialize
% When in each video do you want to begin the mean velocity calculations?
% (in seconds)
start_delay = 0;
yAxisLabel = 'Average velocity (mm/s) ';
%% select files
[inputFiles] = uipickfiles();
if (start_delay == 0)
start_delay = start_delay + 1;
end
%% read each file and calculate mean veloc... |
ab37a8841cc7d4073237fe4e867fd011bfdbbc4a10da7cd240b1ffc7cb9c3a74 | MATLAB | 3,136 | 98 | %% Plot a Mixture of Gaussians
%
%%
% This file is from pmtk3.googlecode.com
function mixGaussPlotDemo()
%{
mu = [0.22 0.45; 0.5 0.5; 0.77 0.55]';
Sigma1 = [0.018 0.01 ; 0.01 0.011];
Sigma2 = [0.011 -0.01 ; -0.01 0.018];
Sigma3 = Sigma1;
Sigma = cat(3, Sigma1, Sigma2, Sigma3);
mixmat = [0.... |
c8a72d82cf934499ca4cc902d8bae38b1ce6adc9638b3b42f3fb526d2f718517 | MATLAB | 3,136 | 84 | % This is an example of how to load and display the functional (fMRI) and
% structural (sMRI) ICA maps, provided for the 2014 MLSP Competition, using
% MATLAB R2011a.
%
% It also includes an example of how to compute the correlation between the
% fMRI and sMRI maps, like was done in reference [2] below.
%
% This script... |
9d675d3af9e54bc1af471017a49281207fff3855a3df1f7a9d34244706e58beb | MATLAB | 3,137 | 63 | classdef CBIG_ASDf_unit_test < matlab.unittest.TestCase
% Written by Nanbo Sun and CBIG under MIT license: https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
methods (Test)
function test_short(testCase)
% create output folder
CBIG_CODE_DIR = getenv('CBIG_CODE_DIR');
... |
b456e519d85be9bad6e79537449f3068729bc091ff31f0d3adb84a1b5abda73b | MATLAB | 3,137 | 85 | function [mappedX, mapping] = charting(X, no_dims, no_analyzers, max_iterations, eig_impl)
%CHARTING Performs manifold charting on dataset X
%
% [mappedX, mapping] = charting(X, no_dims, no_analyzers, max_iterations, eig_impl)
%
% Performs manifold charting on dataset X to reduce its dimensionality to
% no_dims dime... |
becb3afebd0d09d13650d9392e511a0ffbacb4ded90903962e0f7f845c479406 | MATLAB | 3,137 | 101 | function [tracedtensor] = diagsum(tensor1, d1, d2)
% C = DIAGSUM(A, d1, d2) Performs the trace
% C(i[1],...,i[d1-1],i[d1+1],...,i[d2-1],i[d2+1],...i[n]) =
% A(i[1],...,i[d1-1],k,i[d1+1],...,i[d2-1],k,i[d2+1],...,i[n])
% (Sum on k).
%
% C = DIAGSUM(A, d1, d2) traces A along the diagonal formed by dime... |
fcd18ca32e99ed2af5d5787f12e8fb960855743773c708e637750e1afbfbfce3 | MATLAB | 3,137 | 81 | function [dfdx, fx] = VBA_numericDiff(fName, idxArg2Diff, varargin)
% // VBA toolbox //////////////////////////////////////////////////////////
%
% [dfdx, fx] = numericDiff(fname, idxArg2Diff, fArg1, fArg2, ...)
% numerical evaluation of derivatives
%
% This function evaluates numerically the derivatives of the functio... |
3a41abec0eb1b9dbc68bb89dfde5eaec9bb192633ae97da23f58be366dfd1053 | MATLAB | 3,138 | 106 | function [cond_info] = get_conditionInfo_byname(cond_name, srmr_nr)
%% return info of the different conditions
if srmr_nr == 1
% conditions:
% 1 = rest,
% 2 = median stimulation,
% 3 = tibial stimulation,
% 4 = alternating median and tibial stimulation
if strcmp('rest', cond_name)
... |
ab612bdfe9f553411b6241ba245d6fd2d2aeeb945ec7f6996c9f1c0761d4ea22 | MATLAB | 3,138 | 93 | function [varargout] = endsWith(varargin)
% ENDSWITH True if text starts with pattern.
% TF = endsWith(S,PATTERN) returns true if any element of string array S
% starts with PATTERN. TF is the same size as S.
%
% This is a compatibility function that should only be added to the path on
% MATLAB versions prior to R... |
bca0f7cedcac597f0258788b3c0f301f445b3e7e0fd8d5139bb86b05269bc34b | MATLAB | 3,139 | 133 | function show_isa(vi,diags,locs_2D,para);
[nchan,nf]=size(diags);
kmax=12;
kjump=0;
reso=50;
xf=0:nf-1;
nplot=12;
compjump=2;
myaxis=[];
tf=[];
myxlabel=[];
myylabel=[];
if nargin>3
if isfield(para,'resolution');
reso=para.resolution;
end
if isfield(para,'kmax');
kmax=para.kmax;
end
i... |
97d73c81134980abc6951726cb66645dc6f533c707d6f78219d5e5764d763b32 | MATLAB | 3,140 | 85 | function Xr = pcaRandVec( U, mu, vars, k, n, hypershpere, show )
% Generate random vectors in PCA subspace.
%
% Used to generate random vectors from the subspace spanned by the first k
% principal components. The points generated come from the gaussian
% distribution from within the subspace. Can optionally generate p... |
d45245a8e48c0ac149dc88eeb4f6e74fe18fd1388c50ecd339c141e28e3d08f4 | MATLAB | 3,141 | 122 | function newmap = bluewhitered_drdt(m)
%BLUEWHITERED Blue, white, and red color map.
% BLUEWHITERED(M) returns an M-by-3 matrix containing a blue to white
% to red colormap, with white corresponding to the CAXIS value closest
% to 1. This colormap is most useful for images and surface plots
% for computing d... |
3447da0ca20557ff287bbf65ab66156175c5ab7625b22451dc9f417e45bcb5e0 | MATLAB | 3,142 | 104 | function nsz = get_network_size(net_type,im_sz,batch_size,is_ma)
% Estimates GPU memory requirement.
% net_type is the network type. Eg: 'mdn', 'deeplabcut' etc.
% imsz is the image size in pixels. eg [1024,1024].
% batch_size is the batch size. eg. 8
% return the memory size in MB.
if is_ma
dat_file = fullfile(APT.R... |
ef75746c99e60fc7b5304651639c7bbdf3ebda7dd085580b8fee0950ade3860e | MATLAB | 3,142 | 132 | clear;
clc;
% close all;
%%
hist_data = [];
n_subjects = 8;
load ach_data_final.mat
load x_ecog_rt_data.mat;
srate = 400;
%%
all_rt_data = [];
for isub = 1:n_subjects
load(['s' num2str(isub) '_cho_method_allperiod_v1.mat']);
rt_data = subj_rt_data{isub};
start... |
a55e5cdaf0d5c1429779f00eb7d524d2409834cd082dd2ad7967d2a5e24c9bbd | MATLAB | 3,143 | 107 | addpath('../matlab_data/Simple-Brain-Plot-main/');
% %location to save the data. Note subject IDs will be appended to the end
% saveLoc='../figures/';
%
% %create folder if it doesn't exist
% if ~isfolder(saveLoc)
%
% mkdir(saveLoc);
%
% end
%load in the plot specifications
load('../matlab_data/plotSpe... |
b56ed873a1770e3d149f6d0af0c9dad226077c18ba4ee2a914e71bf294701e7f | MATLAB | 3,145 | 102 | function model = mlpFit(X, y, varargin)
% Multi-layer perceptron for classification and regression
%% INPUTS: (specified as name value pairs)
% nHidden ... Number of hidden units. Can be a vector.
% regType ... 'L1' or 'L2' or 'none'
% nclasses ... the number of output classes
% lambda ... regul... |
b70922a323a3c4ae1b23ff441009236708c651b29707dcde32ac10b8ce04d4af | MATLAB | 3,146 | 107 | addpath('../matlab_data/Simple-Brain-Plot-main/');
% %location to save the data. Note subject IDs will be appended to the end
% saveLoc='../figures/';
%
% %create folder if it doesn't exist
% if ~isfolder(saveLoc)
%
% mkdir(saveLoc);
%
% end
%load in the plot specifications
load('../matlab_data/plotSpe... |
fa4930b03d151470f7283cf352f9ce0caa0276cad2dcbd8a8b9da7d36af5ff58 | MATLAB | 3,148 | 89 | % Calculate cosine similarity between random projections in state space
load(fullfile(saveLoc, 'mediation_randProj_gauss'), 'randProj')
numPerm = size(randProj{1},1);
cosine_sim_rand = nan(8,numPerm);
for sess = 1 : 8
for n = 1 : numPerm - 1
w1 = randProj{sess}(n, :);
w2 = randProj{sess}(... |
4240be24ae14f5b43ed690ba04a64195f844ce14080756239ba2a512a432c827 | MATLAB | 3,150 | 91 | % trim_dat_all allows the user to remove intervals from the binary files
% (amplifier, auxiliary, digitalin, time) from Intan recordings.
% Useful in case of unplugs, bad wires, or other mishaps that happen during
% recordings.
% Pipeline: 1) Copy origial files 2) Set intervals to remove 3) save new
% files with int... |
8844473fdac85c43f4c92e4eaced1f56e4d0dda700bb0068afc2e82205bd1167 | MATLAB | 3,150 | 96 | % 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 Software Foundation; either version 2 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,... |
7715c8a0b1f4fc0f02754dbb669ca5b8d1086b0d4e96a1d5e97c8cdd9f150662 | MATLAB | 3,151 | 125 | function out = NB_Run(S,vid)
%% Unload variables
[ t,t_lim,f,div,uprd,uprt,s, ...
NEUR,N2M,K,PART,ALPHA,updm,RES,MOV ] = structvar(S);
[X,Y] = meshgrid(1:div);
N_neur = size(NEUR,1);
% UPR tracking
UPR = zeros(1,1+N_neur);
WB = waitbar(0);
clear S;
%% Body
while t<t_lim && sum(PART,'all')
for n=... |
e15cd41c97d26bc45f74f5a10911c332487154490d7e10480527fa747c5a5892 | MATLAB | 3,151 | 109 | function hhh=hline(y,in1,in2)
% HLINE add horizontal line on current axes
%
% function h=hline(y, linetype, label)
%
% Draws a horizontal line on the current axes at the location specified by 'y'. Optional arguments are
% 'linetype' (default is 'r:') and 'label', which applies a text label to the graph near the l... |
1fc298af3aca9be377cd4055ac0cb9eeac7a0769054c22bcc9605453791ed6ea | MATLAB | 3,152 | 99 | function [hdr, dat] = read_mpi_ds(dirname)
% READ_MPI_DS reads all DAP files from a directory containing files or
% alternatively a single DAP file and returns it in a simplified FieldTrip
% format. The analog channels and spike channels are both returned in a
% continuous format.
%
% Use as
% [hdr, dat] = read_mpi_... |
57a15e7c850f0e44379953112cc66a1a46f0b2c532fe06599e46609e22adddc0 | MATLAB | 3,152 | 104 | %% pxTools_matrixRandomWalk()
%
% Simulate a specified number of transtions, given an input transition
% matrix (e.g. Markov), under the assumption of a random-walk
%
% Generate N_STEPS transitions for N_SIM sets of data;
%
% NOTE: 'Steps' will be given at the resolution of the transition matrix
% (e.g. a mark... |
dd0b7c146de1653feae40d48e7d47760267b028a5e7b2c63550707290c141d29 | MATLAB | 3,152 | 86 | function [e,out] = VBA_designEfficiency(f_fname,g_fname,dim,options,u,flag)
% computes the design efficiency for param estimation or model comparison
% function [e] = VBA_designEfficiency(f_fname,g_fname,dim,options,u,flag)
% This function expects a state-space model of the form:
% y_t = g(x_t,P,u_t)
% x_t = f(x_t-... |
31d409e876d705ad2780495567a513b4c0142cbb379ada31a223f773d9c66b0a | MATLAB | 3,153 | 101 | function [bels, logZ] = mrfInferQuery(mrf, queries, varargin)
%% Compute sum_H p(Q, H | V) for each Q in queries
%
%% Inputs
%
% mrf - a struct as created by mrfCreate
% queries - a query, (i.e. a list of variables) or a cell array of queries
%
%% Optional named inputs
%
%
% sliceCliques [true] if true, factors... |
91547acce5b803fd67fb2eff5949b834a2d7c0cc22185d7c666c5249e5c7b1e7 | MATLAB | 3,153 | 101 | function [tfr,t,f] = tfrsp(x,t,N,h,trace);
%TFRSP Spectrogram time-frequency distribution.
% [TFR,T,F]=TFRSP(X,T,N,H,TRACE) computes the Spectrogram
% distribution of a discrete-time signal X.
%
% X : signal.
% T : time instant(s) (default : 1:length(X)).
% N : number of frequency bins (default ... |
140ff2fd3152addbbfe8d02ea7eb47c5a4432994d1cdec5b3f1df3cf93677e49 | MATLAB | 3,154 | 90 | clear;clc;close all;
%% 1D data
X = -0.02*9.8+0.04*9.8*rand(100,1);
y = [-0.00*X,0.016*X,0.004*X,0.016*X];
% add noise
% y = y + 1e-4*randn(size(y));
split_ratio = 0.7;
num_train_examples = round(size(X, 1)*split_ratio);
rand_indices = randperm(size(X, 1));
X_train = X(rand_indices(1:num_train_examples), :);
y_train =... |
b6d17ff73f1bd7bcd3ddfdb270b8e725d86068d0bcd507f2097db6abe5339fca | MATLAB | 3,154 | 103 | function Perm_1tp_Figs2(dataOut,IM,B,params)
%% Set parameters
if ~exist('B','var'),B=1;end
Pth=0.05./B;
[NNidx,Tidx,TNidx]=IM2idx(IM);
Nnets=max(IM.key(:,2));
NetKey=[[1:Nnets]',[1:Nnets]'];
cMap=[1,1,1;0,0,0];
%% Enrichment
a=reshape(dataOut.Chi_pval0,[],1);b=reshape(dataOut.Chi_EWpval,[],1);
c=reshape(dataOut.H... |
5ee000dff0682ead31b3dbd889d56851b6a2e06884716a25013c489b1f0ac2e1 | MATLAB | 3,155 | 108 | % demo for mixed-effects analysis (2-levels hierachical model)
% This script simulates and analyses data under two different hierarchical
% models, namely (i) H0: group-mean=0, and (ii) H1: group-mean~=0.
% The effect of H1 and H0 onto both the group-mean parameters estimates
% (given H1) and the log-Bayes factors (com... |
6969793ec390d551e5d9a56884cfbcaaf304c553fa4e93fc5a05d0cae65313e0 | MATLAB | 3,155 | 107 | function plotsid(t,iflaws,k);
%PLOTSID Schematic interference diagram of FM signals.
% PLOTSID(T,IFLAWS,K) plots the schematic interference diagram of
% (analytic) FM signals.
%
% T : time instants,
% IFLAWS : matrix of instantaneous frequencies,
% with as may columns as signal components.
% K : dis... |
b5c8c479d41ead65b974d52b12fa34fba19f57b6ac992fafaf5191d933dc3ec0 | MATLAB | 3,155 | 53 |
ind = 1;
data = dir('/DATAPOOL/ERRORMONITORING/SEM_mri_data/P*');
%%
for i = 1:size(data,1)
data_func = dir([data(i).folder, '/', data(i).name, '/mri/SEM_', data(i).name, '/nifti/func/run*']);
fprintf(data(i).name);
for j = 1:size(data_func,1)
dir_folder = [data_func(j).fol... |
afb7260ee4b469130b567965e34065d0513d0474098a5efb9cb3cc4622af16bf | MATLAB | 3,156 | 106 | % MF_SPDIAG Extract diagonal using multifrontal factorization via sparse
% apply/solves.
%
% This algorithm computes each diagonal entry by multiplying from the left
% and right by unit coordinate vectors, taking advantage of the sparsity of
% each such operation.
%
% Typical complexity for N = ... |
038faec1f11e805b475e0a2de71d608a8d8f3f21f270eda63b5ab02a776fd917 | MATLAB | 3,157 | 86 | function renumberfile(f,pat,newnum,nz,opt)
%RENUMBERFILE Re-number the indices of a series of files.
% RENUMBERFILE(NAME, P) renumbers the files matching NAME having an index
% following the substring P, using consecutive indices starting from 1.
% Wildcards (*) and brackets (see RDIR) may be used in NAME. If P ... |
18ddb2c26ebfe8ea0f01f33656f3f518f968bd012cc61a0a8d5713f4fd129f72 | MATLAB | 3,157 | 97 | function plotall_lan(data,chanlocs,ltime,y_lim)
% v.0.0.1
% <*LAN)<]
%
% Pablo Billeke
%
LAN_DEF
color =[{'blue'},{'red'},{'yellow'},{'green'},{'cyan'},...
{'magenta'},{'yellow'},{'black'}];...
nbchan = length(chanlocs);
if isfield( chanlocs(1), 'electrodemat')
electrodemat=chanlo... |
e96d4d6f7d295d3a9f099cfed236717fd7b06716cebd7a95a3cea5d8666d6e22 | MATLAB | 3,158 | 117 |
if ~exist('n_ima'),
fprintf(1,'No calibration data available.\n');
return;
end;
check_active_images;
for kk = 1:n_ima,
if ~exist(['dX_' num2str(kk)]), eval(['dX_' num2str(kk) '= dX;']); end;
if ~exist(['dY_' num2str(kk)]), eval(['dY_' num2str(kk) '= dY;']); end;
end;
if ~exist('wintx'),
wintx = [];
... |
e5884ca30c3b922c19f600b5feac045ee3c451e9e0399bcf4e3fb0e68142be4e | MATLAB | 3,159 | 119 | %% plot_px
%
% A simple plotter for visually inpecting distributions.
% Given an arbitrary number of px values, plot these as overlapping
% distributions on the same figure
%
% INPUTS
% - pxData: [N x K] doubles array, summing to ~1 across a dimension,
% corresponding to a probability distribution.
% - OPTI... |
182c2922e2d168da8f87377cb4f91d293705e6589c5e76a6d46326af3e679a5d | MATLAB | 3,161 | 96 | function [tfr,t,f] = tfrmmce(x,h,t,N,trace);
%TFRMMCE Minimum mean cross-entropy combination of spectrograms.
% [TFR,T,F]=TFRMMCE(X,H,T,N,TRACE) computes the minimum mean
% cross-entropy combination of spectrograms using as
% windows the columns of the matrix H.
%
% X : signal.
% H : frequency smoothing wind... |
21cdbe1b724489cd79ab8e5a52baf2a770c559414209f356c83d436b1d650d9d | MATLAB | 3,161 | 32 | subjs={
'/m/cs/scratch/networks-pm/pm_denoise/sub-01/sub-01_task-movie_SGdetrend_24HMP-8Phys-4GSR-Spike_HPF.nii'
'/m/cs/scratch/networks-pm/pm_denoise/sub-02/sub-02_task-movie_SGdetrend_24HMP-8Phys-4GSR-Spike_HPF.nii'
'/m/cs/scratch/networks-pm/pm_denoise/sub-03/sub-03_task-movie_SGdetrend_24HMP-8Phys-4GSR-Spike_HPF.ni... |
513e51e1b132f6a673affeb9154d9b3ab9e851077150ebe0e058c96088a43e05 | MATLAB | 3,161 | 82 | %% sdoAnalysis_ppDataCell_new
% Generates a data structure for point-process data ('ppDataCell'),
% pre-populated with fields used in the SDO Analysis toolkit.
%
% PREREQUSITE: None
%
% INPUT PARAMETERS:
% N_TRIALS : A postive integer, corresponding to the number of
% independent recording trials/sessions use... |
86e40d4ebfaa7f064c953ca6802afed8acaaea7d6dea56ef0baf63c2e1d71f4f | MATLAB | 3,161 | 98 | function [dat] = read_wdq_data(filename, hdr, begsample, endsample, chanindx)
% READ_WDQ_DATA reads data from wdq files
%
% Use as
% [dat] = read_wdq_data(filename, hdr, begsample, endsample, chanindx)
% or
% [dat] = read_wdq_data(filename, hdr, 'lowbits')
% Copyright (C) 2011, Jan-Mathijs Schoffelen
%
% This file ... |
92bb959db5a9f1c000baf629f47ea0a33f77f37ce7139a3de359c3172ed0a48e | MATLAB | 3,161 | 82 | function CBIG_KRDNN_KRR_UKBB(setup_file, num_val_tes, lambda_set)
% CBIG_KRDNN_KRR_UKBB(setup_file, num_val_tes, lambda_set)
%
% This function runs the kernel ridge regression algorithm to predict
% behavioral measures for UK Biobank dataset.
%
% Inputs:
% - setup_file
% Path of the setup file (.txt file).
% ... |
ee31aaa2578689f1653272318cae5404d28ae766ff9fda7775487d955662fe1a | MATLAB | 3,162 | 117 | function K = fixsigns(K,K0)
%FIXSIGNS Fix sign ambiguity of a ktensor.
%
% K = FIXSIGNS(K) makes it so that the largest magnitude entries for
% each vector in each factor of K are positive, provided that the
% sign on *pairs* of vectors in a rank-1 component can be flipped.
%
% K = FIXSIGNS(K,K0) returns a vers... |
f9945b1318891453cc097895312fa0068bc2629b68b71d1da0a4625188c69701 | MATLAB | 3,162 | 79 | function mni2sub(out_path)
%MNI to subject's space coregistraion using FSL FLIRT. it requires WSL to work in Windows Systems
%
%out_path: folder which contains the Pseudo_STATIC PET image. It will be used to store the coregistered images and the transformation matrix
%
%template_path: folder which contains the MNI temp... |
fde3c7ac87b5bfd359265d8021c72042ae6e02c66adeee1c8c8ae2997dff9821 | MATLAB | 3,162 | 119 | function datadisp(T, dimlabels, opts)
%DATADISP Special display of a ktensor.
%
% DATADISP(T,LABELS) displays the largest positive entries of each rank-1
% factor of T using the corresponding labels. LABELS is a cell array of
% size ndims(T) such that LABELS{n} is a string cell array of length
% size(T,n).
%
%... |
d0c207e37945f4efc5824de9970586bbb10d72aa74bd5f48893cc3156530fd93 | MATLAB | 3,163 | 102 | function [muPost, SigmaPost, logZ, lambda] = varInferLogisticGaussCanonical(...
y, W, b, muPrior, SigmaPriorInv, varargin)
% Use a variational approximation to infer a Gaussian posterior
% given a Gaussian prior and a logistic likelihood
%
% p(x(1:q) | y(1:p)) propto N(x(1:q)|muPrior, SigmaPrior) * p(y|x)
% where p... |
157d2160357821eb11469843d1c595be60d200a8d2d6ce19431f29fa476637af | MATLAB | 3,164 | 117 | function catFAdemo()
% Factor analysis with categorical and continuous data
% We reproduce the demo from
% http://www.cs.ubc.ca/~emtiyaz/software/mixedDataFA.html
% This just check the code runs, it is not intrinsically interesting.
clear all
setSeed(16)
% generate data - data is stored along columns not rows!
[trainD... |
6c258c4f5a727db4a6a432217c357b16f0b8ec9a50fe924a48bd78cb1552f1c5 | MATLAB | 3,164 | 70 | clear
close all
clc
%% Load atlas
% path to MRI template - must include hdr and img files
atlas = niftiread('/Users/jstrohl/Documents/20230908 Pipelines for neural data analysis/Dependencies/ATLAS-MICE 2/2011-10-30_MRI-template3.hdr', ...
'/Users/jstrohl/Documents/20230908 Pipelines for neural data analysis/D... |
2d409b1aa25daafb9c54c45487bc518630aa18963d98179bc7678fd3783bbbd6 | MATLAB | 3,166 | 100 | function [file] = read_ctf_ascii(filename)
% READ_CTF_ASCII reads general data from an CTF configuration file
%
% The file should be formatted like
% Group
% {
% item1 : value1a value1b value1c
% item2 : value2a value2b value2c
% item3 : value3a value3b value3c
% item4 : value4a value4b va... |
84833ce108c5a95c9b2f25a0507e6ad2cd33635194e66eb9d34daa0d5d7327d9 | MATLAB | 3,166 | 106 | clear;
basefolder = fileparts(fileparts(fileparts(mfilename('fullpath'))));
codefolder = [basefolder,'/code/utils'];
toolsfolder = [basefolder,'/tests/tools'];
addpath(genpath(codefolder));
addpath(genpath(toolsfolder));
% [1] setup small test example
plotall = false;
dataset = [
0.2, 0.6;
0.233, 0.581;
... |
6a894d950363d636bbec37d934f312a0379432980989f4d23b9de52836ba484b | MATLAB | 3,167 | 63 | function [spmat] = helm_neu_near_corr(S,zpars,eps)
npts = S.npts;
npatches = S.npatches;
ipatch_id = zeros(npts,1);
uvs_targ = zeros(2,npts);
norders = S.norders;
ixyzs = S.ixyzs;
iptype = S.iptype;
srcvals = S.srcvals;
srccoefs = S.srccoefs;
npp1 = npatches+1;
n3 = 3;
n... |
94b0522769b19a49423744d4fdf286e6bbbe792ccdeeb9bd095ff2ea676b9609 | MATLAB | 3,168 | 105 | %% Generates the figure 6a
%% Requires set:
% - fn_timing
% - res_path
% - mapper_name
% - CHANGE_POINTS
workspace = getenv('WORKSPACE');
fn_timing = [workspace, '/data/cme/timing.csv'];
res_path = [workspace, '/results/cme/ch10_mappers_cmev3_disp.json'];
mapper_name='BDLMapper_12_30_60';
CHANGE_POINTS = 10;
% Get th... |
a40d52b03beffbd3140e3a3e9720b0bc5dd9febd36615f384353b967e626edee | MATLAB | 3,172 | 102 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% wrapper_full_calibration.m -- A wrapper for the calibration runtime. %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function []=wrapper_full_calibration(pat_ix, model)
pat_cell = {'110411','117122','120... |
3cb83b641c4cdf1d72e8a3619ee3fc44b4ce86e1e4e153ca404da9c9ba64cad1 | MATLAB | 3,173 | 79 | function reprocessRoundFromColorCorr(rounds)
loadParameters;
if params.DO_DOWNSAMPLE
filename_root = sprintf('%s-downsample',params.FILE_BASENAME);
else
filename_root = sprintf('%s',params.FILE_BASENAME);
end
%Color Correct from File
%fprintf('COLOR CORRECTION:\n');
for rnd = rounds
colorcorrection_3D_from... |
5ec2a91fd74f0668a43fb87b07c61d73e3d1db5b647a60112fe80e1a732b3cf9 | MATLAB | 3,173 | 101 | function [EI,Pth,Qthth] = lme_EI(X,Zcols,W,CBhat,SIGMA,L,phi,ni)
% [EI,Pth,Qthth] = lme_EI(X,Zcols,W,CBhat,SIGMA,L,phi,ni)
%
% Expected information matrix for the restricted log-likelihood.
%
% Input
% X: Ordered design Matrix (according to time for each subject).
% Zcols: Vector with the indices of the colums of X th... |
0d091d8389be957de223d05086e8e942b6bf42e5f0369b77a29e456b61547b72 | MATLAB | 3,174 | 82 | function N = tangentspherefactory(M, x)
% Returns a manifold struct. for the sphere on the tangent space to M at x.
%
% N = tangentspherefactory(M, x)
%
% N defines a manifold that is the unit sphere on the tangent space to M
% at x. Points are represented as tangent vectors of unit norm. Tangent
% vectors are r... |
ccf9c6c1757c5195c8ecd59279c7a82bd3bff22e99f027426dac9a0f5b76a6a8 | MATLAB | 3,175 | 86 | function [Success,ExperimentID,SubID,Phase,Box,MatlabStartDate,...
Duration,tsdata,Notes,Weight,Program,FileReportedUnits] = ...
TSloadBalsamRaw(filename)
% reads Balsam's raw data files, which have text headers followed by a
% single column of raw data in which the last 4 digits are event codes, and
% the time... |
d39601ddbea9c99975eee8d54acf3d51c6223d8f480e7af1adb708f18bf40f86 | MATLAB | 3,175 | 163 | %% Demonstrate cutset conditioning in cyclic undirected markov chain
% Based on http://www.cs.ubc.ca/~schmidtm/Software/UGM/cutset.html
% This file is from pmtk3.googlecode.com
%% Setup model with a single loop
nNodes = 13;
nStates = 25;
adj = zeros(nNodes);
for i = 1:nNodes-1
adj(i,i+1) = 1;
end
adj(nNodes,1)... |
778a3121b5747599ca2214dbb81f77849c3695134c98e9ade9bc3f68d0a323f0 | MATLAB | 3,176 | 81 | function [actual_phase,fullCycle] = ETP_AutoCorrect_edge(data)
% This function adjusts for the bias in the algorithm and returns the best
% fullCycle. Bias is a constant variable that is added to the typical cycle
% length.
bias = 0;
bestBias = 0;
minError = 100; % A large number to begin with
while 1
[actu... |
7c6334c91f8041a281b0c260a180efb9eb0c67b294ad156bd21da612203493f8 | MATLAB | 3,176 | 78 | function h_zoomInByClick3(handles, zoomFactor, syncFlag)
global h_img3;
if ~exist('syncFlag','var')||isempty(syncFlag)
syncFlag = 1;
end
[currentInd, handles, currentStruct, currentStructName] = h_getCurrendInd3(handles);
[x_lim,y_lim,z_lim] = h_getLimits3(handles);
currentXLim = get(handles.imageAxe... |
a232b0d0d603959ee612567c174748b8cc95f43e3c5ab790456cd71206d6bf01 | MATLAB | 3,176 | 94 | %% RFR prepare pipeline
%This main file will call all the neccessary routines to generate the RFR
%results of the publication: "Stimulus-Induced Rotary Saturation imaging of
%visually evoked neuroelectric response: preliminary results and data
%analysis" (currently under review)
% Author: Milena Capiglioni, University... |
fc8249c44edb9300fc1420b9204c7bdb85923aaf151618fb34d749c897ec0f68 | MATLAB | 3,176 | 90 | function data = edf2fieldtrip(filename)
% EDF2FIELDTRIP reads data from a EDF file with channels that have a different
% sampling rates. It upsamples all data to the highest sampling rate and
% concatenates all channels into a raw data structure that is compatible with the
% output of FT_PREPROCESSING.
%
% Use as
% ... |
7eadff0311877b62292ed293e54397094ce1824b741f830e343bc21764151130 | MATLAB | 3,177 | 155 | % load movie information
[File0, Path0] = uigetfile('*.tif');
info=imfinfo([Path0,File0]);
Nz=length(info);
Ny=info(1).Width;
Nx=info(1).Height;
%A=zeros(Nx,Ny,Nz*NTif);
%% Load movie and first average
movie = zeros (Nx, Ny, Nz);
for i = 1: Nz
movie (:, : , i) = imread (File0, 'TIFF', 'INDEX', i);
end
Ref= mean ... |
e33bbff98cd425cf497a99bb3ad0ef3272e5f428647125eb562f9e5c887e2b40 | MATLAB | 3,178 | 112 | function data = PlotIntanData(datDir, varargin)
% hard-coded parameters
vScale = 0.000195;
bytesPerSample = 2;
% default values of optional arguments
trodeList = -1;
chanList = [];
timeRange = [0 20];
yRange = [-2 2];
returnData = false;
makePlot = true;
for i = 1 : 2 : length(varargin)
switch lower(varargin{i}... |
fbfe97527716b28cc5e3126ee75f27157dc1e0dc0c4f9b633b1e4d659c20ed09 | MATLAB | 3,178 | 105 | % GetProbMap: create probability map from ImageArray_Max
% By running over all pixels in AllBoutonsRegion.
% and counting how many times responses occured in each pixel
function OneImage_ProbMap = GetProbMapZN_parallel2(ImageArray_Max_Input, AllBoutonsRegion, NumOfIndices,se,StackLimits);
... |
64c7aa37b837d86debf6fec202b4625ee242df6f6491ac39cfacba45dab35f55 | MATLAB | 3,179 | 84 | function ft_preamble(cmd, varargin)
% FT_PREAMBLE is a helper function that is included in many of the FieldTrip
% functions and which takes care of some general settings and operations at the
% begin of the function.
%
% This ft_preamble m-file is a function, but internally it executes a
% number of private scripts i... |
6e4ff8bc504fd7bd366c827b5020192494d69d9cdd05608a6d5a1dc38bcf113d | MATLAB | 3,180 | 110 | %% Look at relationship between euclidian distance and wire distance
mkdir([DIRresults, '/wires/']);
clear total_distance all_wires_dist
counter = 1;
big_counter = 1;
every_wires = [];
all_distances = [];
for z=1:num_groups
the_indexes = big_list_indexes{z};
for elect1 = 1:length(the_indexes)
... |
94f4362272d7fdcaacc232d8c94391038da5b14aaa7c173f83fedd87a7d54343 | MATLAB | 3,180 | 78 | %-----------------------------------------------------------------------------------------
% Decoding perfromance as a function of decoding time window lenght
%
% Kehl et al. 2024 (DOI:XX)
% Single-Neuron Representations of Odors in the Human Brain
% Figure 2d
% Author: Marcel Kehl
% License: MIT License
%----------... |
319ebb976635a0b137ca71fd83822edd531c2ea0238417d6c4e218822aa28a3f | MATLAB | 3,181 | 104 | function [A,B,out,posterior] = VBA_LinDecomp(Y,k,options)
% linear decomposition of a matrix Y
% function [A,B,out] = VBA_LinDecomp(Y,k)
% This function operates a probabilistic linear decomposition of matrix Y,
% as follows: Y = A*B + cst + e, where A and B are unknow matrices, cst is
% a constant term and e is assume... |
68771d2e7c34444ad8c1c7843f12e7ae0d9bd63a76f7f7e0d723b2f0e44735a1 | MATLAB | 3,181 | 121 | function [fdiff,fnot1,fnot2] = structdiff(s1,s2,varargin)
% [fdiff,fnot1,fnot2] = structdiff(s1,s2,varargin)
%
% fdiff: cellstr, fields present in s1 and s2 that differ
% fnot1: cellstr, fields not present in s1 but present in s2
% fnot2: etc
%
% Output args apply only to top-level fields.
%
% Optional PVs:
... |
b7241f431a2b799cfcd846f6256795a8abf7b347fd6cddcdbc6df1ab26bdaf57 | MATLAB | 3,182 | 82 | function [y,iflaw]=fmsin(N,fnormin,fnormax,period,t0,fnorm0,pm1);
%FMSIN Signal with sinusoidal frequency modulation.
% [Y,IFLAW]=FMSIN(N,FNORMIN,FNORMAX,PERIOD,T0,FNORM0,PM1)
% generates a frequency modulation with a sinusoidal frequency.
% This sinusoidal modulation is designed such that the instantaneous
% frequency... |
495d2a027d63de19c615ec8424fd0cf6782f5463b29a3b367440e0527a11a61c | MATLAB | 3,183 | 71 | % CREATE NEW CELL ID FOR REMAPPING STRUCTURE
% Function to convert each session original cell IDs to a new ID in the
% remapping structure.Created to avoid ID repetitions.
% New ID structure: YXXX - Y = session ID (1 to 5), XXX - orginal cell ID (e.g. 004, 027, 112)
% INPUT:
% - method: 'wcorr' (default) or 'spea... |
5a99d6ae178b09c0b649f5ed9f814dadb10e5cb8dd492593d8113ba25ba02272 | MATLAB | 3,183 | 83 | function segmentation = fixsegmentation(segmentation, fn, style)
% FIXSEGMENTATION is a helper function that ensures the segmentation to be internally
% consistent. It is used by ft_datatype_segmentation and ft_datatype_parcellation.
%
% % See also CONVERT_SEGMENTATIONSTYLE, DETERMINE_SEGMENTATIONSTYLE
switch style
... |
c9a6cef643765005938a0ad647b37960bf366791b97d5e57a5d40c1eefc8fd8c | MATLAB | 3,183 | 110 | function [posterior,out] = VBA_PRESS(y,u,f_fname,g_fname,dim,options)
% Train/test predictive power evaluation for VBA model inversion.
% function [posterior,out] = VBA_PRESS(y,u,f_fname,g_fname,dim,options)
% This function evaluates the predictive power of any nonlinear state-space
% model of the form:
% y_t = g( x... |
4dccd772392c5e169b394c1868225fdfe086ced2b0c5ab24c3bfdc75d5ba2c43 | MATLAB | 3,184 | 99 | %% (predictSDO_plot) px_v_x
%
% A method to visually compare predictions by looking at how the
% distributions of the predicted post-spike state line up with the assigned
% post-spike state.
% Can reveal state-wise prediction behavior
%
% INPUTS:
% 'pdPx1' - Predicted Probability Distribution (Organized as a Mx1 c... |
9d7e23c97955c6613ffd5caf6b89b6ff9e9289cf1c6ac23b16044d9fe3f2a36c | MATLAB | 3,184 | 93 | %_______________________________________________________________________
%
% newTargets sets up targets for erasing and displaying
%_______________________________________________________________________
%
function targets = newTargets(screenInfo,targets,targetIndex,x_position,y_position,diameter,tcolor)
%
% targets =... |
4c9bef924690b4cb5e8319ea503edd25898f222e70019300ffb0995b169b5db8 | MATLAB | 3,185 | 107 | %% Look at correlation as a function of distance (GAMMA)
corr_data = Low60GammaData(good_chan,(task_data(1,3)-500):(task_data(end,3)+500));
close all
clear eucl_distance_all;
for z = 1:num_groups
z
actual_index = big_list_indexes{z};
geographic_mean = round(mean(actual_index));
for q=... |
65dc2e381ba7ba471b80220400cab122ba7922e644869af4d75853ae3350398f | MATLAB | 3,188 | 125 | function out = AS_Run(S,vid)
%% Unload variables
[ t,t_lim,f,div,uprd,uprt,s, ...
NEUR,N2M,K,PART,ALPHA,updm,RES,MOV ] = structvar(S);
[X,Y] = meshgrid(1:div);
N_neur = size(NEUR,1);
% UPR tracking
UPR = zeros(1,1+N_neur);
WB = waitbar(0);
clear S;
%% Body
while t<t_lim && sum(PART,'all')... |
66ae5e7964ad2c21651819d28bb652897d35ea895b00ef84add0e69c09f26292 | MATLAB | 3,190 | 105 | function [p,S,mu] = polyfit(x,y,n,w)
%POLYFIT Fit polynomial to data.
% P = POLYFIT(X,Y,N) finds the coefficients of a polynomial P(X) of
% degree N that fits the data Y best in a least-squares sense. P is a
% row vector of length N+1 containing the polynomial coefficients in
% descending powers, P(1)*X^N + P(2... |
9907f0c3cafac7689343ad39a76fc14ddc0f2d5db8115cbcf1e9aa696bcad08c | MATLAB | 3,190 | 118 | function indx = iimg_xyz2spheres(xyz, mask_xyzlist, r)
% :Usage:
% ::
%
% indx = iimg_xyz2spheres(xyz, mask_xyzlist, r)
%
% :Inputs:
%
% **mask_xyzlist:**
% is a list of voxel coords of all in-mask voxels
%
% **xyz:**
% is a list of voxels to be convolved with spheres
%
% **r:**
% is sphe... |
be8657e6352e2dd2822a44b863ac643d88deea77b450e00147b48d1f246898eb | MATLAB | 3,191 | 168 | function LAN = del_epo(LAN, epocas)
% Only for simple LAN structur
% Delete epoch number [epocas]
%
% v.0.2
% Pablo Billeke
% 24.05.2021 fix compatibility
% 23.01.2013
% 16.11.2010
% 16.6.2009
if iscell(LAN)
error(' Only for simple LAN structur')
end
%%%%-------
%%%% data
epocas = sort(epocas); %,'descend');
... |
96b00f9c8c40f8db1101581f43be797a3da751ab80b42ea9d32802e05fa804af | MATLAB | 3,192 | 132 | function [ CONDXY ] = ConditionalHist( X,Y,varargin)
%[CONDXY] = ConditionalHist(X,Y) Calculates the conditional probabilty
%of Y given X
%
%INPUT
% X
% Y
% (options)
% 'numXbins' (default: 50)
% 'numYbins' (default: 50)
% 'Xbounds'
% 'Ybounds'
% 'Xbinoverlap' (default: 1)
% 'minX' (default: 25)... |
9ef3d8217fbda3b4dc17ecdb8e5e8cc0b481c602709133ea3d1287ffb64f018a | MATLAB | 3,192 | 91 | function ydata = tsne(X, labels, no_dims, initial_dims, perplexity)
%TSNE Performs symmetric t-SNE on dataset X
%
% mappedX = tsne(X, labels, no_dims, initial_dims, perplexity)
% mappedX = tsne(X, labels, initial_solution, perplexity)
%
% The function performs symmetric t-SNE on the NxD dataset X to reduce its
% d... |
525b4d34a2134b23c8625eaa21e0ee0b51f0701e6b26d90c35c4ade762ba2ff2 | MATLAB | 3,193 | 102 | function M = skewsymmetricfactory(n, k)
% Returns a manifold struct to optimize over k skew-symmetric matrices of size n
%
% function M = skewsymmetricfactory(n)
% function M = skewsymmetricfactory(n, k)
%
% Returns M, a structure describing the Euclidean space of n-by-n
% skew-symmetric matrices equipped with t... |
8b704cb6c10500ea627245d38041e9e648d6a9d4b287932fa85edf75795d0d9c | MATLAB | 3,193 | 127 | function DCM = VBA_spm_dcm_test(DCM)
% tests whether DCM parameters are zero using Savage-Dickey ratios
% function DCM = spm_dcm_test(DCM)
% IN:
% - DCM: the DCM structure
% OUT:
% - DCM: the DCM structure, augmented with field .sdr, which contains the
% Savage-Dickey ratios in the same format as DCM.Ep
n = size... |
0cf6674b61f28e8cc8d8f5c1974f8b3add8878fb6d5ba3f21a1797989ecf9dfe | MATLAB | 3,194 | 130 | function vc = get_vizConventions()
% get_vizConventions()
% this function give the parameters are needed only in visualization (visualization conventions)
%% general settings
vc.ecnSize_all = [50 76 100 126 150 176 200 300 400];
vc.f2.kecn_id = 3; % network of 100
% vc.f3.sel_ecn_ids = [1 2 3 5 6 7 8];
% this include ... |
5e6fdc05367009fbf9a53876959371574222a5de71517159ecdee5a94848309a | MATLAB | 3,194 | 88 | function [timelock, cfg] = freq2timelock(cfg, freq)
% FREQ2TIMELOCK transform the frequency data into something
% on which the timelocked source reconstruction methods can
% perform their trick.
%
% This function also performs frequency and channel selection, using
% cfg.frequency
% cfg.channel
%
% After source r... |
6862d42474820a7d4efc79cd2c8774183b7b4dc9d9f1039f64c7e14dad9b7c6b | MATLAB | 3,194 | 104 | function filteredNoiseIm = makeFilteredNoise2(imSize, contrast, ...
orientation, orientBandwidth, ...
sfBandLow, sfBandHigh, ...
pixelsPerDegree, maskWithAperture, filterSpecial)
% function filteredNoiseIm = makeFilteredNoise(imSize, contrast, ...
% orientation, orientBandwidth, ...
% sfBandLow, sf... |
973993786ad05ee057f42367854e88d220540711c2da1306c82266e63a88a368 | MATLAB | 3,194 | 102 | clear srcinfo
ns = 4000;
srcinfo.sources = rand(3,ns);
srcinfo.e_charge = rand(1,ns)+1i*rand(1,ns);
srcinfo.e_current = rand(3,ns)+1i*rand(3,ns);
srcinfo.h_current = rand(3,ns)+1i*rand(3,ns);
nt = 3999;
targ = rand(3,nt);
eps = 1e-6;
ntests = 6;
ipass = zeros(ntests,1);
errs = zeros(ntests,1);
zk = complex(1.1);
nte... |
e612aaa128e18b4b009951805b524930b42b27518d10a4456ec0b4dd008c6472 | MATLAB | 3,194 | 81 | % Evaluate estimated sources based on saved resource after performing otsu
% threshold.
clear
dataset_name = '_sample_source2';
fname = '_sample_source2';
model_id = '75';
gt = load(['../source/Simulation/test' dataset_name '.mat']); % ground truth
num_source = size(gt.selected_region, 2);
load(['../model_r... |
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