sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
18c081a5f601c0d5e556507b76f5fafb18b8bab5824a02587c06ef78d8c23033 | MATLAB | 189 | 9 | function make_nice_figure(name_plot,fontSize_4figures)
box off
set(gca,'TickDir','out');
set(gca,'FontSize',fontSize_4figures)
set(gca, 'Color', 'None');
save_plots(name_plot)
end |
54ea6a73ec11631415c7179ef5f2ef4208328aea1c8c5d869a0bf892fbeb5045 | MATLAB | 189 | 12 |
function tmp_str=cat_str(cellstrs)
tmp_str=[];
for c_idx=1:length(cellstrs)
if ~isempty(tmp_str)
tmp_str=[tmp_str '_'];
end
tmp_str=[tmp_str cellstrs{c_idx}];
end
end |
7b554d281f18b270e9ce076458caccb2b3dc05a3bbee919f67db3259b62a83bb | MATLAB | 189 | 9 |
function delaunay_one(A, U)
hold on;
gplot2(A | A', U, '-', 'LineWidth', 0.1, 'Color', 0.6 * [1 1 1]);
gplot2(A | A', U, '.', 'Color', [0.7 0.3 0], 'MarkerSize', 50);
axis off equal;
|
a101d90cca90e02e71f2a3e8a33e903b7b03d1358571212afec852cdc4b368b2 | MATLAB | 189 | 8 | function [c, lags] = mycorr(x,y,maxlag,Fs)
% plots corr(y->x) c(m)=(y(n)x(n+m)
% maxlag in frames
% plots in s, based on Fs
[c lags] = xcorr(x,y,maxlag,'coeff');
plot(lags/Fs,c);
|
134d0db2042745019634c64f9025b14cf0a4c6a062f480893051bb1cd0162d65 | MATLAB | 190 | 7 | function ml_motion_convert_nvt_to_h5( nvtFilename, outputFilename )
motionData = ml_motion_load_nvt( nvtFilename );
ml_motion_save_h5( motionData, outputFilename );
end % function |
3d0119b301342d15a9edc8ea2a1064d5b944b7fa39e523f7241faf5ead5a47f5 | MATLAB | 190 | 11 | function [PDF,lags] = isiPDF(delta_mat)
%isiPDF.m 5/28/22
ISI = getISI(delta_mat);
edges = [1:max(ISI)+1]';
cts = histcounts(ISI,edges)';
PDF = cts./sum(cts);
lags = edges(1:end-1);
end
|
9095c4d971d3f55a6402267bd25b1e0476d560bf2457aedb377e3adfa1ba2c79 | MATLAB | 191 | 8 | function [uz,cz] = ml_alg_cumdist(z)
uz = sort(unique(z));
cz = zeros(1,length(uz));
for i = 1:length(uz)
cz(i) = sum( z <= uz(i) );
end
cz = cz ./ length(z);
end
|
199b1a12e209472da7563e7d07f6740f7d2029a6887272ba2f7af5087e409df8 | MATLAB | 193 | 7 | function R2adj = adjustedR2forNumParams(R2,numDataPoints,numExplanatoryParams)
n = size(R2,1);
R2adj = 1- ( (1 - R2)*(numDataPoints-1) ) ./ (numDataPoints - numExplanatoryParams -1);
return |
534d68ff9c343ef71c54b1a1ac75726b991dcabf4ab7b0bde0f6dc6147e0c844 | MATLAB | 193 | 10 | function maskOut = masksmooth(maskIn, r)
% function newMask = masksmooth(oldMask)
if nargin < 2
r = 1;
end
% do some mask smoothing
se = strel('sphere', r);
maskOut = imclose(maskIn, se); |
dd51621ba486a4259c7b0ac4264584b85d5bbcaa07ec9a50865201cc3abd67e9 | MATLAB | 193 | 8 | function k = strfindi( text, pattern )
% k = strfindi( text, pattern )
%
% a case-insensitive version of the built-in strfind()
%
% JAB 3/23/10
k = strfind( lower( text ), lower( pattern ) );
|
65640787a8bf87dc3d209c4a9ec6361f18193f16e11edeb9e880151e9eeef8bf | MATLAB | 194 | 7 | function [similarity indSim] = SSSimFlowSize(a, b, blobStruct)
% Flow + Size
indSim(:,1) = SSSimFlow(a, b, blobStruct);
indSim(:,2) = SSSimSize(a, b, blobStruct);
similarity = mean(indSim, 2); |
691b6ce39e244e8d96adf65ea09607038991f529d67f4a903eebc6aacb389325 | MATLAB | 194 | 10 | function [] = printDescFile(fidDesc, ftest)
if issparse(ftest)
ftest = full(ftest);
end %if isssparse(ftest)
for t = 1 : numel(ftest)
fprintf(fidDesc, '%f\n', ftest(t));
end %for t
|
e6bbb827e8eb2c6233e36a8aef732fb2b2fc328baeb368a5062d11cb03969a99 | MATLAB | 194 | 13 |
% main_script
% Comment: Just run this script and it will do everything
%
bem0_load_model;
bem1_setup_coil;
bem2_charge_engine;
bem5_volume_XY_big;
bem5_volume_XY_zoom;
bem5_volume_YZ_zoom;
|
f83b4280ec80b3892b185451c92b5ef896e7547f316d16f4eac43b6843da692a | MATLAB | 194 | 10 | function X=SNR(s,n);
% Compute the signal-to-noise ratio in dB
% X=SNR(signal,noise);
% (it does not subtract the means).
es=sum(sum(abs(s).^2));
en=sum(sum(abs(n).^2));
X=10*log10(es/en);
|
233b1e83cf41bde693cde1ef85489150cb02c7fc73bef4cd67f4b8942dbc279a | MATLAB | 195 | 7 | function text_survivors=make_text_survivors(handles)
temp=sum(handles.mc.surv_ix,1);
text_survivors{1}=['std: ' num2str(temp(1),'%4.0f')];
text_survivors{2}=['mb: ' num2str(temp(2),'%4.0f')];
|
7da190b70b97b29de20421bce78f7abed35aabda9f455a4e52dda4d0839570a9 | MATLAB | 196 | 6 | % calcMFR.m
function mfr = calcMFR(peakTrn, acqTime, th)
nspikes = sum(peakTrn > 0);
mfr = nspikes/acqTime;
% all the channels whose mfr < 0.1 spikes/s are considered not active
mfr(mfr < th) = 0; |
6b82a9544d1af378008fed48f83d407429c5caf172ba22fbb861b8fc5fcf2aef | MATLAB | 197 | 8 |
function str = toSetField(str, fieldName, fieldVal)
% function str = toSetField(str, fieldName, fieldVal)
for k = 1 : length(fieldName)
str = setfield(str, fieldName{k}, fieldVal(k));
end
end |
b5b0fa2d73276191bd955739ac7bfd9908c25eb149c90b891038310fdc2c73bf | MATLAB | 198 | 13 | function dist = doPariwiseDistP(x,y,z)
xm = x*ones(1,numel(x));
ym = y*ones(1,numel(y));
zm = z*ones(1,numel(z));
dx = (xm-xm').^2;
dy = (ym-ym').^2;
dz = (zm-zm').^2;
dist = sqrt(dx+dy+dz);
end |
4d9f8f7187178944d87743096978d0353341177c904d190bfd024ef0810e453b | MATLAB | 199 | 10 | function top = RectTop
% yTop = rect(RectTop)
%
% RectTop returns the rect index of yTop.
% Also see PsychRects.
% 5/12/96 dgp wrote it.
% 2/25/97 dgp changed to agree with Apple's rects
top = 2;
|
d0d2ace4bbc9d96c471cfdb621457fac5b6f600cd292bc6f50d45b20367d087c | MATLAB | 199 | 11 | %
% Convert a complex number to a RGB value for visualization.
%
function [rgb] = complex2rgb(value)
h = angle(value) / (2*pi) + 0.5;
s = 1;
v = 1 - 1 / (abs(value) + 1);
rgb = hsv2rgb([h s v]);
|
4952b28774a3e35ffb6f4d25e61dee4f194b5ec384cba531a2ca16b4fb7098ae | MATLAB | 200 | 7 | function [similarity indSim] = SSSimColourSize(a, b, blobStruct)
% Colour + Size
indSim(:,1) = SSSimColour(a, b, blobStruct);
indSim(:,2) = SSSimSize(a, b, blobStruct);
similarity = mean(indSim, 2); |
99a051ffcc2b720fa1de7ce6bab589d3435ee6af15184c4898a26a81eed1e897 | MATLAB | 200 | 9 | function results = vl_test_slic(varargin)
% VL_TEST_SLIC
vl_test_init ;
function s = setup()
s.im = im2single(vl_impattern('roofs1')) ;
function test_slic(s)
segmentation = vl_slic(s.im, 10, 0.1) ;
|
b31e787066c20c1cb89d50354a4f1275fc96ee90995fcb94f28e21836bd3a1b2 | MATLAB | 200 | 9 |
function label_counts=my_label_count(label_values, label_data)
tmp_counts=accumarray(label_data, ones(size(label_data)), [max(label_values) 1]);
label_counts=tmp_counts(label_values);
end |
d002976e406cdcdcf1c7f11ea472745006166abf3f61e847a71f793acf5d115c | MATLAB | 200 | 12 |
% main_script
chamber_dim = 135; %15; %45; %135; %<---- SELECT CHAMBER DIMENSION HERE
bem0_load_model;
bem1_setup_coil;
bem2_charge_engine;
bem4_define_planes;
bem5_figure_scales;
bem5_volume_XY;
|
dc0a66b2f9adbc1b0429cdb3dfe0c09a33e64a2e1fc08da1cf15ac63d35e6ba7 | MATLAB | 202 | 5 | function ensure_parent_folder_exists(raw_file_path)
file_path = absolute_filename(raw_file_path) ;
parent_folder_path = fileparts(file_path) ;
ensure_folder_exists(parent_folder_path) ;
end
|
a71e2390b900ddcbb0763bd8d8310c0f188a51e079e01df588a525ce4174e43b | MATLAB | 203 | 6 | function [neuronDataset] = ml_cai_neuron_h5_read( filename )
neuronDataset = ml_cai_core_h5_read_header( filename );
neuronDataset.neuron = ml_cai_neuron_h5_read_timeseries( filename );
end % function
|
ad53c874874dee8bd480fb7eeea0e105612e68ca790da8bfcd81ccd024d1d88d | MATLAB | 203 | 11 | function [raster] = one_event_raster(spk_t,stevt,endevt)
%one_event_raster.m -
% bounding and start-subtracting
% 5/22/22
inbd = @(d,l,u) d(d>=l & d<u);
raster = inbd(spk_t,stevt,endevt)-stevt;
end
|
c23ef2bdfce586a65f65862003537be96c340e736133601ecae1449331930354 | MATLAB | 203 | 7 | function [similarity indSim] = SSSimTextureSize(a, b, blobStruct)
% Texture + Size
indSim(:,1) = SSSimTexture(a, b, blobStruct);
indSim(:,2) = SSSimSize(a, b, blobStruct);
similarity = mean(indSim, 2); |
05f77f8496e1051def1789fc4d8cfb32422da66e8d4faa8e2c8e0f3eb05d5a7c | MATLAB | 204 | 8 | function max_v = absolutevalue_maximum(v)
%calculates the maximums of the absolute value for A
[max_v, max_index] = max(abs(v),[],2);
signs = sign(v);
max_v = max_v .* signs(max_index);
end
|
676131ae39f38237279d1ae456cc2c50a8c6e326bf4eb117c998fef4cf973747 | MATLAB | 204 | 7 | function blobs = BlobAddSizes(blobs)
% This function add size fields to the blobs. Doing this once saves
% computation time.
for i=1:length(blobs)
blobs{i}.size = sum(sum(blobs{i}.mask));
end
|
dbc18b3536117b7756bbb6304918b453fc9772486c42de2e7b21d133ab38b0ce | MATLAB | 204 | 14 |
% main_script
% Comment: Just run this script and it will do everything
%
bem0_load_model;
bem1_setup_coil;
bem2_charge_engine;
bem4_define_planes;
bem5_figure_scales;
bem5_volume_XY;
bem5_volume_YZ;
|
fab6ea78fd2732b351de4a49806997e117cc9a6ac36359f01d1919d47efaa043 | MATLAB | 204 | 10 | function interrupt = keyboard_interrupt()
interrupt = 0;
if exist('testkeypress')==3 if testkeypress(' ')
interrupt = 1;
end; end
if exist('Keytest')==8 if Keytest.test(' ')
interrupt = 1;
end; end
|
ad87785f867189ed5513f95558a6e0a9f9ae33a9273adbc8453c4ddb4f626d25 | MATLAB | 205 | 7 | function [Z] = ml_bs_probxy_xy(bs, x, y)
% Compute the probability for the positions in (x,y)
Z = ml_bs_accumulate_xy(bs, x,y, 1);
s = nansum(Z, 'all');
Z = Z ./ s;
Z(isnan(Z)) = 0;
end |
bb50d5b21c4c509a09bd986384ed460b104dc1c1ae3eb3b9def6bed93bdc78e7 | MATLAB | 205 | 11 | % show vars
el=4; un=1; eee=1;
%Varr=nose.do2
Varr=Xin;
% squeeze(STA(el,un,eee,:,:))
% squeeze(STS(el,un).sts(eee,:,:))
figure(1)
h=hist(Varr,xBins);
xlim([min(xBins), max(xBins)]);
hist(X)
|
d515b715b32d75409fc434a1704d4c044f59d011c7e21ee4494a3b7420921d89 | MATLAB | 205 | 10 | function left = RectLeft
% xLeft = rect(RectLeft)
%
% RectLeft returns the rect index of xLeft.
% Also see PsychRects.
% 5/12/96 dgp wrote it.
% 2/25/97 dgp changed to agree with Apple's rects
left = 1;
|
bcc4b109157ac50be141e0b767b736e01e11ea1a40922483f8f7ac5988d7e6ba | MATLAB | 206 | 8 | function [ translatedxys ] = translateCoordinates(xys, translate_amount)
%TRANSLATESHAPE Summary of this function goes here
% Detailed explanation goes here
translatedxys = xys + translate_amount;
end
|
2689977814fc517af7dacf04e3298a54b6e391b65e8525ed02d6a28b1aed1c14 | MATLAB | 207 | 6 | function str = toSetField(str, fieldName, fieldVal)
% function str = toSetField(str, fieldName, fieldVal)
for k = 1 : length(fieldName)
str = setfield(str, fieldName{k}, fieldVal(k));
end
end |
a52208c0965866016c2280fef133c417a2e3561bdf1ee250f3fd22d7b0852bf1 | MATLAB | 207 | 13 | function result = linearSmooth1(data,smooth_res)
r = size(data,1);
c = size(data,2);
ci = 1:smooth_res:c;
result = zeros(r,length(ci));
for i = 1:r
result(i,:) = interp1(data(i,:),ci,'spline');
end
|
32a211a58da819543ca80d3a9c56e7b53d8037d45dd1ab5727de332d64102348 | MATLAB | 208 | 8 | function parsave1(filename, data, dataname)
if strcmp(filename(end-3:end), '.mat')
eval([dataname,'=data;']);
save(filename,dataname, '-v7.3');
else
error('filename must end with ".mat".')
end
end |
8d3b07d7b8dcef9766d11e0d44b0b1086eb2caa8902c7f22c42ad8a5b44cc613 | MATLAB | 208 | 16 | %
% Compute mean average precision.
%
% PARAMETERS
% p (e*1) Predictions
% T (e*3) To be predicted
%
% RESULT
% value MAP value
%
function [value] = measure_compute_map(p, T)
value = konect_map(p, T);
|
c4b1e797580db026d0aae1e6c1c801f4e16dadb3b30959006d480923674894e8 | MATLAB | 209 | 10 | function [PDF,lags] = isiPDF_vector(delta_vec)
%isiPDF_vector.m 5/26/22
ISI = getISI_vector(delta_vec);
edges = [1:max(ISI)+1]';
cts = histcounts(ISI,edges)';
PDF = cts./sum(cts);
lags = edges(1:end-1);
end
|
e369a80f9a4fc842d099f689cc3c669bfa49924f5c2d32a901d4ec78a9663ede | MATLAB | 209 | 14 | function iarr = unitn2i(narr)
globals;
if isempty(narr),
iarr=[];
return;
end
[I J] = size(narr);
for ii=1:I
i1(ii) = find(goodElectrodes==narr(ii,1));
end
iarr = [i1',narr(:,2)];
|
f961a76c763c65b19ef6df5279c1448bf25aa5c3a2dee86d23e8f812464f789d | MATLAB | 210 | 9 | function [lb_idx] = select_labeled_nodes(Y,num_lb)
lb_idx = [];
for c = 1:size(Y,2)
idx = find(Y(:,c) > 0);
rand_idx = randperm(length(idx));
lb_idx = [lb_idx; idx(rand_idx(1:num_lb))];
end
|
3701c303a1541b4786a07dbbeb533f50f9e1e0e352b18a61614c440898cf4109 | MATLAB | 211 | 9 | %% save analysis information
vars.mag1 = mag1;
vars.mag2 = mag2;
vars.vid = vid;
pathname = cd;
[~, filenameroot]= fileparts(pathname);
save(fullfile(cd, [filenameroot '_analysis.mat']),'mag1', 'mag2', 'vid'); |
7e7287a86563005aed85f7ab95f46332536b906fa4089e536deff8d80ea20cbc | MATLAB | 212 | 10 | function right = RectRight
% xRight = rect(RectRight)
%
% RectRight returns the rect index of xRight.
% Also see PsychRects.
% 5/12/96 dgp wrote it.
% 2/25/97 dgp changed to agree with Apple's rects
right = 3;
|
ef6aaec801157b89df31d1c5f786d0be203274cec8bb2b66a4503dd2934fca83 | MATLAB | 212 | 4 | % Marc Normandin, Muzzio Lab, Psychological & Brain Sciences, University of Iowa, 2024
function [fieldSets] = helper_load_bfo180_fieldsets_to_record()
fieldSets = helper_load_bfo90_fieldsets_to_record();
end
|
80c1718349dc14cb8d3be542b3c832ac9cff015c8a57dfd9795faa751535352f | MATLAB | 213 | 15 | function spikes = spikesCellToVec(s,dt,numt)
if(nargin<2)
dt = 0.01;
end
if(nargin<3)
numt = ceil(max([s{:}])/dt);
end
spikes = false(length(s),numt);
for i = 1:length(s)
spikes(i,ceil(s{i}/dt)) = 1;
end |
3fdc19dfa431798fa5aa824b7c57433b21dab9746d2140c2612fd0d8beeed93b | MATLAB | 214 | 5 | % Marc Normandin, Muzzio Lab, Psychological & Brain Sciences, University of Iowa, 2024
function [colours] = colour_matrix_yellow_green()
colours = [1, 1, 0; ...
0, 1, 0];
end % function
|
6155c0569b6769c3ff16159df9611db40d1190656897ed68fd86a884459083a9 | MATLAB | 214 | 14 | %
% Compute average precision.
%
% PARAMETERS
% p (e*1) Predictions
% T (e*3) to be predicted
%
% RESULT
% value Average precision
%
function [value] = measure_compute_ap(p, T)
value = konect_ap(p, T(:,3));
|
846b83230595709f6f6d3d34968958f6fe70e97048d99c92f4ff183511cb7c72 | MATLAB | 214 | 10 | function DVAR=dvars_from_cifti(X);
% This function calculates dvars based on grayordinates (WM and non brain excluded)
%% Check size
[g tr]=size(X);
if g<tr
X=X';
end
dx=diff(X,[],2);
DVAR=sqrt(mean(dx.^2));
|
a5e3a8b1750bf201d06ea9c524c6c16172261204644b3e8a71a1d267be4367c8 | MATLAB | 214 | 5 | function result = replace_extension(file_name, ext)
[folder_name, base_file_name] = fileparts(file_name) ;
result_name = horzcat(base_file_name, ext) ;
result = fullfile(folder_name, result_name) ;
end
|
fa7a28ae3c8fa3ef7bfd82a1c492573dba4d7ae33152015be9013ec8ccdf8664 | MATLAB | 214 | 10 | % This code runs the genTrialList function to generate param files
% Specify the list numbers and it will run them for you.
list = 10300:10399
for ii = 1:length(list)
num = list(ii)
genTrialList(num)
end |
e614f216b351de6b8a3432a2399fad94a254ab0ddcc6224ba120f32f6fe24f20 | MATLAB | 215 | 14 | %% plot simple spikes in range of xlims
xlimits = xlim();
spikeTimes = (recData(8).data);
spikeTimes(spikeTimes < xlimits(1)) = [];
spikeTimes(spikeTimes > xlimits(2)) = [];
vline(spikeTimes, 'k')
diff(xlim)
|
0073c188ca22f16fb5b0863c29199d65ac5f57943d2e216c64f577ac4ae393ae | MATLAB | 216 | 5 | function out = morletConjFT(w,omega0)
%morletConjFT is used by fastWavelet_morlet_convolution_parallel to find
%the Morlet wavelet transform resulting from a time series
out = pi^(-1/4).*exp(-.5.*(w-omega0).^2); |
c8bd611a8885deaf3f568aba7332c983f220f775f8815639c0c0d3948ea4bdd8 | MATLAB | 216 | 8 | function [xpred,ypred,thetapred] = cvpred(x2,y2,theta2,x1,y1,theta1)
dx = x1 - x2;
dy = y1 - y2;
dtheta = modrange(theta1-theta2,-pi,pi);
xpred = x1 + dx;
ypred = y1 + dy;
thetapred = modrange(theta1+dtheta,-pi,pi); |
afb4d1286e29aa190052f26b980fb674e8a669db1740cbf79b05bfbe261286e2 | MATLAB | 217 | 10 | function bottom = RectBottom
% yBottom = rect(RectBottom)
%
% RectBottom returns the rect index of yBottom.
% Also see PsychRects.
% 5/12/96 dgp wrote it.
% 2/25/97 dgp changed to agree with Apple's rects
bottom=4;
|
b89c27e551daf0113f66bd0e0fb4ddde8ed2219cb3666f8424ff05614b2ebfdb | MATLAB | 217 | 16 | %
% Compute mean area under the curve.
%
% PARAMETERS
% p (e*1) Predictions
% at (e*3) To be predicted
%
% RESULT
% value MAUC value
%
function [value] = measure_compute_mauc(p, at)
value = konect_mauc(p, at);
|
467e958945657314e9e6171ce8109ff6e399fdd7f1990ce45b44de607309bc62 | MATLAB | 219 | 9 | function SONCleanUp()
% SONCLEANUP File cleanup. Not used in Windows
%
% Author:Malcolm Lidierth
% Matlab SON library:
% Copyright © The Author & King's College London 2005-2006
calllib('son32','SONCleanUp');
return;
|
732861e55d2b05711c9ffc28fb3981437a6bc4cfb407336c88c3c85d2618cab6 | MATLAB | 219 | 10 | function [varargout] = size(ipa)
% INPLACE/SIZE Size of array
%
% [m,n] = size(ipa) Juse like SIZE for matrices and vectors.
%
% Example:
% ipa = inplace(ones(10,3));
% size(ipa)
varargout{:} = size(ipd.get_a()); |
6f8cf84422cd255deaaf825c054c6c66eaf6e57165438693777a25490e3f3ed7 | MATLAB | 220 | 6 | function gammaPower = getGammaPower(inputArg)
%GETBANDPOWER get gamma power of the signal
% Detailed explanation goes here
sampleFreq = 100000;
gammaPower = bandpower(inputArg, sampleFreq, [25 100]);
end
|
2f01580b079c6e70eddcf9f222577605148eedc30b8944c69b0e113f62c7d5c8 | MATLAB | 221 | 7 | function [edgesX] = ml_it_edges_x_uniformly_spaced(x)
N = length(x);
zi = (8+324*N+12*sqrt(36*N + 729*N^2))^(1/3);
nBinX = round( zi/6 + 2 / (3*zi) + 1/3 );
edgesX = linspace(min(x), max(x), nBinX+1);
end |
c89637b63f625b230b618aa89f302c00fa94267d974ffb6d109cb19278451a92 | MATLAB | 222 | 6 | function [nose, theta, irmv] = removeFrames(nose, theta, condtxt);
globals;
irmv = find(eval(condtxt)); % find frames to remove
[nose, theta] = removeFramesExplicit(nose, theta, irmv);
end % function removeFrames
|
1157d2133ed29358d976feccc940f14e14fdfd47b73fa62af646a540551311fb | MATLAB | 225 | 19 | %
% Format the value of a statistic.
%
function [text] = format_statistic(statistic, value)
[l i] = konect_data_statistic();
if i.(statistic)
text = format_number(value);
else
text = sprintf('%.3f', value);
end
|
1f6cd32b97ff6142470c4591b64b4199156b12cd35b92c7aca828abb52467ceb | MATLAB | 225 | 14 | %
% Compute area under the curve.
%
% PARAMETERS
% p (e*1) Predictions
% at (e*3) To be predicted
%
% RESULT
% value Area under the curve
%
function [value] = measure_compute_auc(p, at)
value = konect_auc(p, at(:,3));
|
bb462f978db8764564896c619eeea6c996266f948911e698a757cb252f070009 | MATLAB | 225 | 6 | function powerMapF = computePowerMaps(resF, gaborBank)
powerMapF(numel(gaborBank)).value = [];
for g = 1 : numel(gaborBank)
powerMapF(g).value = (resF(g).even .^2) + (resF(g).odd .^ 2);
end %for g = 1 : numel(gaborBank)
|
647ec9b5e452de4cda866cdc9a343bdeb9c0c66adfcf1a7116a2d68fcdbc802a | MATLAB | 226 | 6 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% stub for mglPrivateDigIO if it is not compiled %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function retval = mglPrivateDigIO(a,b,c)
retval = [];
|
7aedf5dc6649f5c608c8a2c282224aab885ece262ea8811b9161136de6005593 | MATLAB | 226 | 4 | function [climerICS] = ml_cai_climer_information_rate_smoothed(probXYS, traceXYS)
meanTraceS = nansum(probXYS .* traceXYS, 'all');
climerICS = nansum( traceXYS .* probXYS .* log2( traceXYS ./ meanTraceS ), 'all' );
end |
a956204941196879722a6912a5a895a480854420b122b218ee67ef1173529435 | MATLAB | 227 | 14 | function gids = ml_util_group_points_v2(x)
gids = zeros(1,length(x));
k = 1;
gids(1) = k;
for i = 2:length(x)
if x(i) ~= x(i-1)
k = k + 1;
end
gids(i) = k;
end
end
|
f1e34e446c7f2ab581c9606d5cd0921d0017b9dd97f9760a7165957732b32afb | MATLAB | 228 | 9 | function SONCleanUp()
% SONCLEANUP File cleanup. Not used in Windows
%
% Author:Malcolm Lidierth
% Matlab SON library:
% Copyright © The Author & King's College London 2005-2006
calllib('son32','SONCleanUp');
return;
|
01aff20aa749e771043d021afacdfd529e1d24e294dd22985655cdc491b201a6 | MATLAB | 229 | 5 | function displacement = find_displacement_between_two_centerlines(K, Old_K)
%finds the difference in euclidean distance between two centerlines
Kdis = (Old_K - K).^2;
displacement = sum(sqrt(Kdis(:,1)+Kdis(:,2)));
end |
a7b8838a6d13d0103beb97f21848d3cf2e48694f9a8c48df6bb5227c2d383452 | MATLAB | 230 | 6 | function [SC,OPT_S] = had_wind_size_change(SC,OPT_S)
OPT_S.mid_pix = OPT_S.mid_pixes(OPT_S.ind_change_windw);
OPT_S.mask_wind = had_set_mask(OPT_S.mid_pix,OPT_S.mask_wind_exp);
OPT_S.ind_change_windw = OPT_S.ind_change_windw+1;
|
b42ae2039eddd99c41b6275839829a8043b38c149c8442850ed69dc9f296621a | MATLAB | 230 | 10 | function n=end(ipa,a,n)
% INPLACE/END Allow inline "end" indices to work.
%
% n=end(ipa,a,n) Returns the ending index along dimension a.
%
% Example:
% ipa = inplace(cumsum(ones(5,1)));
% ipa(3:end)
n = size(ipa.get_a(),a); |
e0f138b9d8b8b3bd6c691ec7e078a982ef2161f22936e872373c164c38dc469a | MATLAB | 230 | 4 | % Marc Normandin, Muzzio Lab, Psychological & Brain Sciences, University of Iowa, 2024
function [colours] = colour_matrix_blue_red_yellow_purple()
colours = cat(1, colour_matrix_blue_red(), colour_matrix_yellow_purple());
end
|
10fe30f3b8e993645412582898da1ac1e4486710439f745e9467225e05553fcc | MATLAB | 231 | 14 | function VOCopts = get_voc_opts(path)
tmp = pwd;
cd(path);
try
addpath('VOCcode');
VOCinit;
catch
rmpath('VOCcode');
cd(tmp);
error(sprintf('VOCcode directory not found under %s', path));
end
rmpath('VOCcode');
cd(tmp);
|
9d8fd132c73859a5b2366c53ec14702bdeb8d0f56e6378c5e7ac1fc050b8dc84 | MATLAB | 232 | 4 | % Marc Normandin, Muzzio Lab, Psychological & Brain Sciences, University of Iowa, 2024
function [fieldNames] = helper_possibleScalar1FieldNames()
fieldNames = {'trialId', 'contextId', 'mfr', 'pfr', 'infoRate', 'infoSpike'};
end
|
e91694342423ab53fa9f2af492e3507f5dfdb7aa4b770a3e1a6b0b0949301ecf | MATLAB | 232 | 9 | function time=SONMaxTime(fh)
% SONMAXTIME returns the maximum time for data in a file
%
% TIME=SONMAXTIME(FH)
% where FH is the SON file handle. TIME is returned in clock ticks
%
% ML/05/05
time=calllib('son32','SONMaxTime', fh);
|
f1a0d5b55e11311926202e2c0332f7260f38b081375db2a046bc8bdd50493e22 | MATLAB | 232 | 11 |
xx=[1:10]; yy=[1:10];
xBins=0:2:10; yBins=0:2:10;
[N] = histcounts2(xx,yy,xBins,yBins);
figure(2)
surf(N)
view(0,-90);
colormap('jet');
colorbar('EastOutside');
set(h,'CDataMapping','scaled');
set(gca,'Ydir','reverse');
|
695d0a597dec1a6ef7286733240301d05ca8ef5eabcc0bf4ea1c029b389c2547 | MATLAB | 233 | 10 | function [S,lags] = spikeSurvivor_vector(delta_vec)
%spikeSurvivor_vector.m
ISI = getISI_vector(delta_vec);
edges = [1:max(ISI)+1]';
cts = histcounts(ISI,edges)';
S = cumsum(cts,'reverse')./sum(cts);
lags = edges(1:end-1);
end %fn
|
cf9baf636db4c1063591903525fcd2a02383547b093f29abd08ef94f9353212e | MATLAB | 233 | 10 | function [ind_data] = Indicator(data,NumInd,eps)
ind_data = zeros(size(data,1),NumInd);
data_prt = linspace(min(data),max(data)+eps,NumInd);
for i=1:NumInd-1
ind_data(:,i) = [data>=data_prt(i) & data<data_prt(i+1)];
end
end
|
e15f041b51d500aac87557f24b84f7e3fc896968ba01d1340bd337c6d1e225c5 | MATLAB | 233 | 14 | function [tDup, nDup] = men_timestamp_find_duplicates( t )
u = unique(t);
[n,bin] = histc(t, u);
ix = find(n > 1);
% The value that is duplicated
tDup = u(ix);
% The number of instances of the value
nDup = n(ix);
end % function
|
f4231217e22f798c71981a4121059d9da13b74c92370e51c6e59eda4ebf0640d | MATLAB | 233 | 8 | function [labels, numImagesAll] = computeLabels(files)
numImagesAll = numel(files);
labels = zeros(numImagesAll, 1);
for i = 1 : numImagesAll
filename = files(i).name;
labels(i) = str2double(getIndName(filename));
end %for i |
f6e7a18918ff8e2bc09bee94f077e33386d85e521e1424d7ffea96a39be0f17c | MATLAB | 233 | 13 | function d = qLength( Q )
% qLength: quaternion length (norm)
% d = qLength( Q )
% IN:
% Q - input quaternion
%
% OUT:
% d - quaternion's norm (length)
%
% VERSION: 03.03.2012
d = sqrt( sum( Q .* Q ));
|
213849bcd279efd95cf207a270145bc92bbac2cf665e87af603d8ba466e4541f | MATLAB | 234 | 7 |
function [groupExp, groupMaxExpVal,groupMaxExpIdx] = getGroupExp(x,y)
binsExp = 0.1:0.1:1;
[groupMaxExpVal,groupMaxExpIdx] = max( reshape(y(:,x,:), size(y,1),[]));
groupExp = binsExp(groupMaxExpIdx);
end |
34eac72c849229313d184eb0568e616b3bc58c664223e3ba9ea07a1abc8186fc | MATLAB | 235 | 5 | % Marc Normandin, Muzzio Lab, Psychological & Brain Sciences, University of Iowa, 2024
function [colours] = colour_matrix_blue_red()
colours = [0, 0.4470, 0.7410; ...
0.8500, 0.3250, 0.0980];
end % function
|
5ac591664e278dcdda5b89fbea046d77cb52de738726be074f2689671e87fe10 | MATLAB | 235 | 15 | function [xs] = runMvAvg(x,smooth_w)
%runMvAvg.m
% 5/22/22
flt = ones(1,smooth_w)/smooth_w;
if mod(smooth_w,2) == 0
xsraw = conv(x,flt);
xs = xsraw(1:end-1);
else % odd window
xs = conv(x,flt,'same');
end %if mod
end
|
8970ad3bb25f8dd40114ab79448a0182bdb9f75c3fdb735bd826af0695adafdd | MATLAB | 235 | 8 | function result = realpath(file_name)
if ispc() ,
result = GetFullPath(file_name) ;
else
stdout = system_from_list_with_error_handling({'realpath', file_name}) ;
result = strtrim(stdout) ;
end
end
|
95b52fc612a0dd21159af35837e52933c028eca0170005058d0f2f088cf189b8 | MATLAB | 235 | 11 | function FE=findFrameForEvent(frame,event)
%paried arduino event to 2p frame ttl
for t=1:length(event(:,1))
a=find(frame>event(t,1));
if ~isempty(a)
FE(t,:)=a(1);
else
FE(t,:)=nan;
end
end
end |
fe5fdfb29b456df55fe3fb3e7b613590ec7b5a019d338f2ae2404e85078ba21c | MATLAB | 236 | 10 | function ipa = assign(ipa,y,varargin)
% INPLACE/ASSIGN Assign an entire inplace array.
%
% assign(ipa,y) overwrites the contents of ipa with y.
%
% Example:
% ipa = inplace(ones(5,1));
% assign(ipa,pi*ones(5,1));
ipd.assign(y); |
9c47fe8516b91def82d29783eff309e88d791e3e3652114489fffafa083123fb | MATLAB | 237 | 11 | function fix_EnablePanel(h,v)
% set the enabled state for a handle and all its children
% splintered from fixerrorsgui 6/23/12 JAB
children = get(h,'children');
for hchild = children,
try
set(hchild,'enable',v);
catch
end
end
|
3e6bd66bb1cbeacda2ecdd8b7fbb5c497c560a79813cc32721a4f2cb629891b7 | MATLAB | 238 | 13 | function Q = qConj( Q1 )
% qConj: quaternion conjugation
% Q = qConj( Q1 )
% IN:
% Q1 - input quaternion
%
% OUT:
% Q - output quaternion
%
% VERSION: 03.03.2012
Q1 = reshape( Q1, 4, 1 );
Q = [Q1(1);-Q1(2:4)]; |
bed2a738d612d1c73708c51c4cbc91f6285c98d9d8451f08c67bd8996d11b575 | MATLAB | 238 | 15 | %
% The key used for sorting datasets.
%
% PARAMETERS
% network
%
function ret = network_key(metadata)
name = metadata.name;
name_no_space = regexprep(name, ' ', '-');
ret = sprintf('%s:%s', metadata.category, name_no_space);
|
86844827979b9ec7f989892ecc5a60501a4881bfc26ade101250d68e0b7086d3 | MATLAB | 239 | 10 |
function Av = updateAvalanche(Av, t, clusterSize)
if t > Av.endTime
Av.endTime = single(t);%Av.avtime(end);%X(2,end);
end
Av.duration = Av.endTime - Av.startTime +1;
Av.size = Av.size + clusterSize;%(X,2);%size(Av.event,2);
end |
3baab21349d5e1ee09da1683a3dbe29556cd47cb02d6ded319f48d5a67ac3c76 | MATLAB | 240 | 13 | function rval=rtest_5_henderson()
rval = 0;
try
rand('state',0);
A = erdos_reyni(100,.1);
if full(max(max(A))) > 1
error('erdos_reyni.m does not return an adjacency matrix.');
end
rval = 1;
catch
lasterr
end |
5877ab807969b8733dd4da70eb7e42d9b1bffc269fe6a10b1b9295dfa7c88b67 | MATLAB | 240 | 12 | function out = countprint(x)
% prints \b x, returns string for printing this as an output if nargout ==
% 1
if x == 1
out = num2str(x);
else
out = [repmat('\b',1,length(num2str(x-1))) num2str(x)];
end
if nargout == 0
fprintf(out)
end |
2368af8babd2a359a204fe1f0b48706386d6bf2f78512f33f62fb6b45486e3d0 | MATLAB | 241 | 9 | function time=SONMaxTime(fh)
% SONMAXTIME returns the maximum time for data in a file
%
% TIME=SONMAXTIME(FH)
% where FH is the SON file handle. TIME is returned in clock ticks
%
% ML/05/05
time=calllib('son32','SONMaxTime', fh);
|
90252fb021673d955870ca03ca5f30d3565f3bfc728b2a98cdac9142bf9d5b42 | MATLAB | 241 | 7 | function I=take_unsaturate_photo(SC,cam_str,mean_num)
%take photo with cam_str camera, make sure it is not saturated
%number of mean images is mean_num
%% delete this - just for the function to return something
I = rand(101); |
bc165e682c3aee9deb8e51c1ea0991512e30565a76a94712239fbac622b6bb99 | MATLAB | 241 | 6 | function [I] = ml_it_mutual_information(x,y, binMethod)
entropyX = ml_it_entropy(x, binMethod);
entropyY = ml_it_entropy(y, binMethod);
entropyXY = ml_it_entropy_joint(x,y, binMethod);
I = entropyX + entropyY - entropyXY;
end |
2191ad5529669ba9143e413703910fa44a6a178d0e9981fcac801bd59db2bc32 | MATLAB | 242 | 11 | function results = vl_test_mser(varargin)
% VL_TEST_MSER
vl_test_init ;
function s = setup()
s.im = im2uint8(rgb2gray(vl_impattern('roofs1'))) ;
function test_mser(s)
[regions,frames] = vl_mser(s.im) ;
mask = vl_erfill(s.im, regions(1)) ;
|
15578092351a2aec2a69171bbae2893309353cc68bd9f3cd31720ab01d94b3a4 | MATLAB | 243 | 11 | function [retVal, retValInd]= removeNanRows(Val)
NanRemovedVal = Val;
NanRemovedVal_index =~isnan(nanmean(Val,2));
NanRemovedVal = NanRemovedVal(NanRemovedVal_index,:);
retVal = NanRemovedVal;
retValInd = NanRemovedVal_index;
end |
a1ef49bb6b2d3bebe8700e2554b3393c5e9c531340a13250474eda23a8cb1ffa | MATLAB | 243 | 9 | function [userfolder]= selectfolder (string)
%
% by Michela Chiappalone (7 Febbraio 2006)
userfolder = uigetdir(pwd,string);
% if strcmp(num2str(userfolder),'0')
% errordlg('Selection Failed - End of Session', 'Error');
% return
% end |
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