sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
e396ab62e2eac95905eb8c2e97b2f977c6a3863055b3d02bdce0ae0f642ce7b6 | MATLAB | 162 | 5 | function checkSpatialFT(MRSIStruct)
if(~getFlags(MRSIStruct, 'spatialFT'))
error('Please fourier transform along the spatial dimension')
end
end
|
1a20e1a7e0f7cd34370f8fb7a296c9f693e52922d94f6e208548aec33cad67be | MATLAB | 164 | 10 | data = [];
line = plot(data);
for t = 1:1000
newdata = rand(2,1);
data = [data; newdata'];
plot(data);
xlim([max(0, t-10) t]);
pause(0.01);
end |
bdb1430f9140abb3453250db9af21b62ea3ebc3be63a6f0184aea5f0d0ef0cfb | MATLAB | 165 | 5 | function checkSpectralFT(MRSIStruct)
if(~getFlags(MRSIStruct, 'spectralFT'))
error('Please Fourier transform along the spectral dimension')
end
end
|
2d4e8d376ac6049c0e949de908bd1d2a4f14d5fc858383c202f22913a855cdbe | MATLAB | 168 | 7 | clear
clc
close all
subs=1:1:30;
parfor sub=1:length(subs)
[amplitude1,snr1,am_spectrum1,am_spectrum2,snr_spectrum1,snr_spectrum2]=savefor_Figure8(sub);
end |
7e86ce8472a7726b478e6348aab59508788e4b95b9a2a7022659df88742b1b31 | MATLAB | 168 | 5 | function [covarianceMatrix] = extractCovariance(eegData)
%% Trace normalised EEG data
covarianceMatrix=cov1para(eegData./(sqrt(trace(eegData'*eegData))));
end
|
a871219028dd327f191b180f6598c7510f530fbb6fdfb870e990a51bd28221e8 | MATLAB | 169 | 10 | function C = basisExpansion(slice,dmat,mask)
%BASISEXPANSION fit solid harmonic basis functions to field
%
% best-fit quadratic curve
C = dmat \ slice(mask);
end
|
e2d0cae362c395004b4704af939e910eb17b0ab55a864353c91bc206af9065dd | MATLAB | 169 | 9 | n = 3;
imshow('cameraman.tif');
r = 25;
for i = 1:n
[x,y] = ginput(1);
h = imellipse(gca(), [x - r/2, y - r/2, r, r]);
wait(h);
disp(getPosition(h));
end |
b32da075fef0d851080ba597e0434189ece41f134cc0768b68418848b5c360c2 | MATLAB | 171 | 7 | %% check_load_stack
%% test 1
stack = load_stack ('sample.stk', '-s');
tprint ('./panels/load_stack1', ...
'-jpg -HR', [10 10]);
|
1468d8ee9258d27e4a9b64239d167d0b03abf22556ceb16fa3eca11f3334ae8c | MATLAB | 174 | 8 | function tgt_ind = find_time(t, currInd, t_delta)
tgt_time = t(currInd)+t_delta;
if tgt_time > max(t)
tgt_ind = NaN;
else
[~, tgt_ind] = min(abs(tgt_time-t));
end
end |
41cb0660634825ae3c0a222af99270c940802c4d70936c88085ab3cf3c0f9ab9 | MATLAB | 176 | 8 | %% Image Metadata
%
% * <./id_md_Metadata.html |Metadata| quick start guide>
% * <./id_md_Calibration.html |CalibrationPixelSize| quick start guide>
%%
%
% <./index.html Home> |
a1d54825daee4c30709741650ead2250915cab06c389d7ddd756112a3cde8295 | MATLAB | 176 | 8 | %% Preprocessing
%
% * <./id_pp_motion_corr.html Motion correction quick start guide>
% * <./id_pp_utilities.html Other utilities quick start guide>
%%
%
% <./index.html Home> |
1714e997bea149d744805273a91520aa780963e3f2c27a8f0c14a15c529b168e | MATLAB | 177 | 13 | %% check_scheme
check_bezahlt
check_deg2rad
check_eucdist
check_gauss
check_gifmaker
check_rad2deg
check_rotation_matrix
check_roundshow
check_scalebar
check_shine
check_tprint |
230757590b56ba49f2fd0eafd84d02117a04f58f80938f98bc6985e1ce2151a8 | MATLAB | 179 | 9 | function col=mat2col(matrix)
c=0;
%convert half matrix (no diagno)
for i=1:size(matrix,1)-1
for j=i+1:size(matrix,2)
c=c+1;
col(c,1)=matrix(i,j);
end
end
|
e2b56dcce05babb1f3d027a353e7fdce4dbc3c6160700550032d9268fe83c157 | MATLAB | 182 | 8 | function runInfo = GetRunInfo(expt, dataDir)
for r = flip(1:expt.Nruns)
runInfo(r) = MakeInfoStruct( dataDir, expt.mouse, expt.date, expt.runs(r), expt.fov );
end
end
|
b59e6df87319b8036f5f82531c60f13bad994f2dde3cfd6d6df9e459ded64fde | MATLAB | 184 | 1 | function n=angle2pix(display,ang)
%ANGLE2PIX
%n=angle2pix(display,ang)
%returns width of square in pixels for visual angle ang
n=2*display.distance*tan(ang*pi/360)/display.pixelSize;
|
c49f017e2fa0f0f27f33873c59954c7d9ef01d750472cef08f1d6557d4294234 | MATLAB | 184 | 15 | %% check_electrotonics
check_elen_tree
check_gi_tree
check_gm_tree
check_lambda_tree
check_loop_tree
check_M_tree
check_sse_tree
check_ssecat_tree
check_syn_tree
check_syncat_tree
|
4f0a0b393c74c95ca76de62cb57c04d1df2e5d7fb9b3be872d5dd5d01389eae4 | MATLAB | 185 | 8 | %% Groups of Images (ImgGroup Classes)
%
% * <./ig_ImgGroup.html |ImgGroup| quick start guide>
% * <./ig_CompositeImg.html |CompositeImg| quick start guide>
%
%%
%
% <./index.html Home> |
b5c932ceb400f9c8db0cfe7ebb9a98d8c2ea3b4355c85c1e53e6994e99214f37 | MATLAB | 185 | 3 | function thresholdedCell = getCellArrayFromIndexThreshold(dataCell, indexCell, threshold)
thresholdedCell = cellfun(@(x, y) x(y > threshold), dataCell, indexCell, 'uni', 0);
|
ca9cf95049604581a1be6c60852446a4ce950f3f173e5376697b6249c0b4838b | MATLAB | 185 | 6 | trees = load_tree('wteene_aligned.mtr');
for i = 1:length(trees{1})
disp(trees{1}{i}.name)
swc_tree(trees{1}{i},['WT_EE_6mo_',trees{1}{i}.name(1:2),'_',num2str(i),'.swc'])
end |
4108d7b06d1dc7075a1b067e1bc021ce31e1e0b85d2daada2ade875283f857bb | MATLAB | 192 | 12 | classdef (Abstract, HandleCompatible) IMock
%UNTITLED Summary of this class goes here
% Detailed explanation goes here
properties
end
methods
end
end
|
8a5eb88cfee1e27dc64b9facaa92e0470e16fd1f36744541513cf350166348ba | MATLAB | 192 | 12 | %% check_IO
check_load_tree
check_neurolucida_tree
check_neuroml_tree
check_neuron_template_tree
check_neuron_tree
check_pov_tree
check_save_tree
check_swc_tree
check_ver_tree
check_x3d_tree
|
45cf74a65828993f8e0423687ed49fa770e8994f3ed34a5067edfb7e5d50ccd5 | MATLAB | 194 | 9 | %% Image Data
%
% * <./id_md.html Image metadata quick start guide>
% * <./id_ri.html Raw images quick start guide>
% * <./id_pp.html Preprocessing quick start guide>
%%
%
% <./index.html Home> |
9d5dc264ea2477502b0943e2f26cd58bfc8dfab6d6f1747ce4c9e672314df252 | MATLAB | 196 | 7 | function hlData = ReadHalflifeData(fn)
%fn = 'C:/Work/MatlabCode/projects/HMASandbox/HMA_Sandbox/Single-cell Modeling/NeuronModeling/data/pr101183k_si_002_HELA.txt'
hlData = readtable(fn);
end
|
f11c52aed011fc77ad30b37d08d10d6e5e88c50a015b243cd284b1be80cc7bc3 | MATLAB | 198 | 14 | % FileExists(FileName)
%
% returns 1 if the file exists, 0 otherwise
function exists = FileExists(FileName)
fp = fopen(FileName, 'r');
if (fp==-1)
exists = 0;
else
fclose(fp);
exists = 1;
end; |
365d8f467207bfe95e3485cf92a02d5dc59b1010b55423fd661f361414fe0482 | MATLAB | 199 | 17 | %%
clear; close all; clc
load('distsFOVFx_bad_global.mat')
%%
dDif = distsCom{2}./distsCom{1};
difMean = mean(dDif,1);
[difMx,difIdx] = max(dDif(1:end,:));
difIdx = difIdx;
|
1c2eb00133d4fbaab15f8720002fa09672469a6ccf240b12eeb7b58a8f41f720 | MATLAB | 200 | 14 | %% check edit folder
check_cat_tree
check_delete_tree%
check_elim0_tree
check_elimt_tree
check_insert_tree
check_insertp_tree
check_recon_tree
check_repair_tree
check_resample_tree
check_root_tree
|
1fa182e6e79968dd112cd55fe94a5b7118cdde7f8e45e4d96a65a92abc0cfae9 | MATLAB | 200 | 7 | function [transformed_data] = Affine_transformation(data,reference)
for i=1:size(data,3)
transformed_data(:,:,i)=inv(sqrtm(reference))*data(:,:,i)*inv(sqrtm(reference))';
end
end
|
d8e7ae5ca6349660f6fece8cf55b5d6d4594f7457d2b5f1716869529d95a06ea | MATLAB | 203 | 7 | function hlData = ReadHalflifeData(fn)
%fn = 'C:/Work/MatlabCode/projects/HMASandbox/HMA_Sandbox/Single-cell Modeling/NeuronModeling/data/pr101183k_si_002_HELA.txt'
hlData = readtable(fn);
end
|
21e06d9fb0b1698b51c0bacf000e38df27544ba3362ef86506b7117c16d95005 | MATLAB | 206 | 9 | % Figure01.m
% Figure 1 contains no data; everything was created in PowerPoint.
%
% EEG/fMRI STUDY, DONDERS INSTITUTE, NIJMEGEN.
% J. Algermissen, 2018-2023.
% Should work in Matlab 2018b.
%
% END OF FILE. |
d9922415fe591949c536c4c33f3c39d758c39b7a0c12afc9722225103583d0f3 | MATLAB | 206 | 11 | function Y=predictN(X,W,N,c,x_min,x_max)
% function for predicting
M=size(X,2);
x_N=Normalization(X,x_min,x_max);
Q=Kmatrix(x_N,M,size(X,1),N,c);
Y_N=Q*W;
Y=zeros(size(Y_N));
Y(Y_N>=-0.375)=1; |
e10c4411b5dd6b9065d97431a604b00a417e9549fd24c9ad6b5639558a6b2241 | MATLAB | 206 | 6 | function MRSIStruct = setDimsAllZero(MRSIStruct)
dimNames = fieldnames(MRSIStruct.dims);
for iName = 1:length(dimNames)
MRSIStruct = setDimension(MRSIStruct, dimNames{iName}, 0);
end
end |
4cf9c0ed09ff6edea6d03df9b35140cffee09d8121873e9ff5cd36342249e0ad | MATLAB | 207 | 6 | %% check_loadtifs_stack
%% test 1
stack = loadtifs_stack ('./data/stack_0002_MSO_P10_2-1_2.tif', '-s');
tprint ('./panels/loadtifs_stack1', ...
'-jpg -HR', [10 10]);
|
8dfdd8c273258d043399a2c21e30bbc745cb4f3aeb44e7e9bae9e582a266cf68 | MATLAB | 207 | 12 | function [ idx ] = chIdx(s, ch)
%CHIDX Get a channel's ID in a session
channels = s.Channels;
Nch = length(channels);
for idx = 1:Nch
if strcmp( channels(idx).ID, ch.ID)
return
end
end
end
|
ab8e438e4d2d3e53b50684260b8e1c715f4352a5c0c389e720311b96eeb66582 | MATLAB | 210 | 7 | function [] = setupfig(wid,ht,ft)
set(gcf ,'windowStyle','normal');
set(gcf,'color','w');
set(gcf, 'Units', 'inches');
set(gcf, 'Position', [1 1 wid ht]);
set(findall(gcf,'-property','FontSize'),'FontSize',ft) |
4a3a600ae4590ce2cc804df88273b42fa0b402ce03720324997f89c9a5239562 | MATLAB | 217 | 10 | function rms_out = rms_val(sig)
% function rms_out = rms_val(sig)
%
% Returns the root mean square value of the signal passed as 'sig'
%
% Author: Angela Onslow, May 2010
rms_out = norm(sig)/sqrt((length(sig)));
end |
e8fa08b84dbc2461ea086edf6c72dfef5f01064408b5fc1748c6edbc5dc2f668 | MATLAB | 219 | 7 | function B = ITR(N,p,s)
B = s.*(log2(N)+p.*log2(p)+(1-p).*log2((1-p)./(N-1)));
ix = find(p==0);
B(ix) = s.*(log2(N)+(1-p(ix)).*log2((1-p(ix))./(N-1)));
ix = find(p==1);
B(ix) = s.*(log2(N)+p(ix).*log2(p(ix)));
end |
f91594587e15a40173cabe70f71a40e7b3690ccb8d0ad89bd37d90c269e06712 | MATLAB | 221 | 6 | function shuffled_matrix = shuffle_matrix(original_matrix)
% Get the dimensions of the matrix
[num_rows, num_columns] = size(original_matrix);
shuffled_matrix = original_matrix(:,randperm(num_columns));
end
|
3a287febe16c74847fbfb827c4e9f808b6fdccd4d2c1ff962d973f1bf329b327 | MATLAB | 223 | 7 | %compare data:
clear all
dataFileName1 = 'C:\David\Phd2020\ElectordesClassification\res\2023-02-15_09-36-30\imputedDataAllBouts.xlsx';
[DataMatrix1, Labels1,MouseIDs1,Days1] = GetImputedDataMatrix(dataFileName1);
|
97a10461decc053b0b29e99e3daf9367cfbb9b44ed33196b5e6f82e0d7ff3cc9 | MATLAB | 225 | 14 | function [row, col, val] = get_triu_CC(mat)
N = size(mat,1);
t = 1;
for n=1:N-1
M = (n+1):N;
for m=M
row(t,1) = n;
col(t,1) = m;
val(t,1) = mat(n,m);
t = t+1;
end
end |
74c9dcd370b072e4ce693269e3352ba8ce850ed4a584a01acbdb912322ba8377 | MATLAB | 227 | 8 | % HSN_TREE load a sample HSN cell
%
% the TREES toolbox: edit, generate, visualise and analyse neuronal trees
% Copyright (C) 2009 - 2016 Hermann Cuntz
function hsn = hsn_tree
hsn = load_tree ('hsn.mtr', 'none'); |
eddadb90894624505b19ba3c00bffdc5d97399dd19a076b567377fd7c74eda64 | MATLAB | 227 | 8 | % HSS_TREE load a sample HSS cell
%
% the TREES toolbox: edit, generate, visualise and analyse neuronal trees
% Copyright (C) 2009 - 2016 Hermann Cuntz
function hss = hss_tree
hss = load_tree ('hss.mtr', 'none'); |
d70978601a145610a39adae72b337f269acecf8fb12afff475a76b7f3aaed25b | MATLAB | 228 | 12 | function [dataN,m,s] = standardizeData(Data,m,s)
l = size(Data,1);
if isempty(m)
m = mean(Data);
end
if isempty(s)
s = std(Data);
end
dataN = (Data-repmat(m,l,1))./repmat(s,l,1);
end |
c5cf9ad8bea627ed4eef957c60557aa7a36d82cebc7f786939529764e2ef6ffe | MATLAB | 237 | 9 | function fig = tch_fig(name, pos)
if nargin < 1; name = ''; end
if nargin < 2; pos = [.1 .1 .5 .5]; end
% pos = rectify(pos);
fig = figure('Name', name, 'Color', 'w', 'Units', 'centimeters', 'Position', pos);
tch_set_axes; hold on;
end |
634dbb4087d67f7527e94aeae6028c822d65d4fc20c101cdeee4a11bb0f4c382 | MATLAB | 238 | 13 | function [] = draw_1(Xq_R, Yq_R, vq_R,Xq_B, Yq_B, vq_B)
figure(4);
subplot(2,2,1)
imagesc(vq_R)
title('full')
subplot(2,2,2)
imagesc(vq_B)
title('small')
subplot(2,2,3)
mesh(Xq_R,Yq_R,vq_R)
subplot(2,2,4)
mesh(Xq_B,Yq_B,vq_B) |
365418694630e434c11d5a3bbd7b62b8b2d36c1090e74e949f1fc36b48a45fbc | MATLAB | 239 | 12 | function [dx_nxt, dy_nxt] = shrinkIso2DTV(dx, dy, tau)
% Perform isotropic soft thresholding
mag = sqrt(dx.^2+dy.^2);
magt = shrinkageOp(mag,tau);
mmult = magt./mag;
mmult(mag==0) = 0;
dx_nxt = dx.*mmult;
dy_nxt = dy.*mmult; |
57e046e7a5609a7a40ba97d926984296fb78ea7614f391702c222ea8ca6a37eb | MATLAB | 239 | 7 | function avgBoutKey = createUniqueAvgBoutID(mouseID,day,label6)
avgBoutKey = cell(1,length(mouseID));
for k=1:length(mouseID)
avgBoutKey{k} = ['M',num2str(mouseID(k)) ,'_D',num2str(day(k)), '_L',num2str(label6(k))];
end
|
c44b15b87b1f273243f57077416aa11c55fb8635f03950041670ff0d9b40aadb | MATLAB | 241 | 9 | %% Raw Images (RawImg Classes)
%
% * <./id_ri_SCIM_Tif.html |SCIM_Tif| quick start guide>
% * <./id_ri_BioFormats.html |BioFormats| quick start guide>
% * <./id_ri_RawImgDummy.html |RawImgDummy| quick start guide>
%%
%
% <./index.html Home> |
0dbc1b11c75f350c2e4e953b58b199ad90abbfedfd9da91843a175d5a760cdb8 | MATLAB | 243 | 14 | function saveFigure(h, fname);
set(gcf,'Units','points');
set(gcf,'PaperUnits','points')
size = get(gcf,'Position');
size = size(3:4);
set(gcf,'PaperSize',size)
set(gcf,'PaperPosition',[0,0,size(1),size(2)])
print(h,fname,'-dpng');
|
28549615c8aead00aa0d78c22d23fa75a0497f78ef79df830cd7a9f1adb9150a | MATLAB | 244 | 10 | function gamma = getGamma(argumentVariables)
arguments
argumentVariables.overTwoPi (1, 1) logical = 1;
end
if(argumentVariables.overTwoPi)
gamma = 42.577478518e6;
else
gamma = 267.52218744e6;
end
end |
e484e4ab3ea6ddf0279f3a1d610a8d23cbd5f4fe55e9569052f9b5b414a9219b | MATLAB | 244 | 14 | function str = getPvalStr(pval,baseStr)
str='';
if pval<0.00001
str = '#';
elseif pval<0.0001
str = '***';
elseif pval<0.001
str = '**';
elseif pval< 0.05
str = '*';
end
str = [str,baseStr,num2str(pval,'%.4f')];
|
8b519b09eebea19636ef027bdafcafe458738533eb2da4c01f3c6d0b46acca91 | MATLAB | 250 | 8 | % SAMPLE2_TREE load a simple sample tree
%
% the TREES toolbox: edit, generate, visualise and analyse neuronal trees
% Copyright (C) 2009 - 2016 Hermann Cuntz
function sample2 = sample2_tree
sample2 = load_tree ('sample2.mtr', 'none'); |
5bb95c63e52033537b6679c5138706b34cef20175c654f1f16a14072e59471af | MATLAB | 252 | 10 | function h = shadedLineMeanSte(dat,linewid,linez,colz)
% function h = shadedLineMeanSte(dat,linewid,linez,colz)
m=nanmean(dat);
hold on;
plot(m,'color',colz,'LineWidth',linewid,'linestyle',linez);
fillAroundLine(m,ste(dat),colz);
return |
0a06b91ed4338e4fcdb895f07fc78076284b058892b1a2c365f44beecf601d90 | MATLAB | 255 | 17 | clear
close all force
f1 = open('fig_Obj_a22_4_5.fig');
f2 = open('fig_6_a16_1.fig');
f3 = figure(3);
clf
ax3 = subplot(3,6,[1,2,7,8,13,14]);
ax1 = get(f1,'Children');
copyobj(ax1,f3);
ax3 = get(f3,'Children');
set(ax3(2),'position',[.1,.2,.3,.75]); |
9a48cba496ea5bf0bed8ee24fba4a85bb368d5f51d82f57bea7b519dc5087cd4 | MATLAB | 255 | 11 | function df = removeBetaOutlier(DT)
targetTmodel = {'1ch_glm','1ch_dcts', '3ch_stLN'};
for tm = 1:3
params.tmodel = targetTmodel{tm};
df{tm} = tableFilter(DT,params);
df{tm} = tableOutlier(df{tm},'beta');
end
df = [df{1}; df{2}; df{3}];
end |
6df66baae7c1ca5225aa0c022702be9913a345c284da3e1b6e40ab8bf94beaa4 | MATLAB | 256 | 10 | %% Getting Started
%
% * <./documentation_and_examples.html Documentation and examples>
% * <./system_requirements.html System requirements>
% * <./release_notes.html Release notes>
% * <./acknowledgements.html Acknowledgements>
%%
%
% <./index.html Home> |
a405749ceb6c41766c982e066122089e89fd185d3b842543c650c18bf023b262 | MATLAB | 256 | 17 | function me = ent(prob)
up = sort(prob,'descend');
me = 0;
dim = 0;
if size(up,1)==1
dim = 2;
else
if size(up,2)==1
dim = 1;
end
end
for i = 1:size(up,dim)
if up(i)>0
me = me - up(i) * log(up(i));
end
end
me = exp(me);
|
fac20a9d50edf02fb22fdd46d1a7403ca20f2bec179e095900f2583bbebce6cf | MATLAB | 256 | 9 | function log(cfg, message)
%LOG Print a timestamped pipeline message when cfg.verbose is true.
if isfield(cfg, 'verbose') && cfg.verbose
timestamp = string(datetime("now", "Format", "HH:mm:ss"));
fprintf('[%s] %s\n', timestamp, message);
end
end
|
96b32ba554e603e5cea1bf3430e4f2507a14c55e2574d6c18a19289f7923a9b6 | MATLAB | 257 | 8 | function tch_set_axes
set(gca, 'TickDir', 'out', 'FontSize', 20, 'FontName', 'Helvetica', ...
'XColor', 'k', 'YColor', 'k','LineWidth',2); hold on;
% ax=gca;ax.LineWidth=2; %axis square;
% set(gca,'TickDir','out','TickLength',[0.02 0.5]);
box off;
end
|
d88000395226f50af42f443c201c35df505ee77cb902381a9c03210bb6239dfe | MATLAB | 257 | 8 | % SAMPLE_TREE load a subtree of a sample HSN cell
%
% the TREES toolbox: edit, generate, visualise and analyse neuronal trees
% Copyright (C) 2009 - 2016 Hermann Cuntz
function sample = sample_tree
sample = load_tree ('sample.mtr', 'none'); |
ff275401b45ebb80552e37f94241a14d83e2aa8849b47beae02154b5366ea968 | MATLAB | 257 | 8 | % Set colormap to green purple.
function colormapG2P = setFavoriteColormap
%% Set the green purple divergent map from cpt-city maps.
cptFile = 'greenPurple'; %'PRGn_11'; %
colormapG2P = cptcmap(cptFile, 'ncol', 256);
colormap(gca, colormapG2P);
end |
32ffe5b34a7bdffeafccd3a0158d674e0fb09d16ab2ffc5060f7c90f8858c4df | MATLAB | 258 | 13 | function [Ev,Em] = eigMax_A(A,At,maxit,lmbd2,szX,useGPU)
X = 10*randn(szX);
if useGPU ==1
X = gpuArray(X);
end
for ii = 1:maxit
Xnew = At*(A*X) + lmbd2*X;
Em = (Xnew(:)'*X(:))/(X(:)'*X(:));
X = Xnew/norm(Xnew(:));
end
Ev = X;
end |
b1ba7040ba91732f69d256c4c8b69b1d03ea4df293d6f697128a05671ea34389 | MATLAB | 258 | 10 | function d = EuclideanDistance(X,Y,Z)
% X,Y,Z are matrices nPoints*2. calcola la distanza lungo la seconda
% dimensione
d = zeros(size(X, 1), 1);
for i=1:size(X,1)
d(i) = sqrt((X(i,1)-X(i,2)).^2 + (Y(i,1)-Y(i,2)).^2 + (Z(i,1)-Z(i,2)).^2);
end |
b5df26e8c24020e858995f67d86a6bea70703e7ba249e81de1dde4bd65eac620 | MATLAB | 259 | 8 | function startup_fmri_phase()
%STARTUP_FMRI_PHASE Add the v2 MATLAB toolbox to the MATLAB path.
rootDir = fileparts(fileparts(mfilename('fullpath')));
addpath(fullfile(rootDir, 'matlab'));
addpath(fullfile(rootDir, 'matlab', 'vendor', 'tapas_physio'));
end
|
6b00702028b6994df1fda0fdfaf67bfccbde40fa24c760ef81d34e3fb91781c6 | MATLAB | 260 | 15 | %%
load('dataVis.mat')
useDays = 1:10;
cats = {[1 3:5 8 10:14],[2 6 7 9]};
catNames = {'Good Learners','Bad Learners'};
data1 = dataVis(useDays,cats{1})';
data2 = dataVis(useDays,cats{2})';
[pA,pMC] = anovaRM2W(data1,data2);
% pMC = pMC';
|
bb4b40f3b086d89f4bbaa7cb042fd9677ea27cdce8b8130509176fbc554ea999 | MATLAB | 262 | 6 | function featureArray = getRoisFeaturePerChannel(featureCellArray)
for iChannel = 1: size(featureCellArray{1}, 1)
featureArrayTemp = cellfun(@(x) x(iChannel, :), featureCellArray, 'uni', 0);
featureArray(iChannel, :) = [featureArrayTemp{:}];
end
end |
eee9c1faff69f9e1aba419a2acf2164c91f5d45939805eaf2a2636bfb0e97f1d | MATLAB | 263 | 8 | %% check_fitD_stack
%% test 1
tree = load_tree ('./data/auto_MSO.mtr');
stack = loadtifs_stack ('./data/stack_0002_MSO_P10_2-1_2.tif');
D = fitD_stack (tree, stack, [], '-m');
tprint ('./panels/fitD_stack1', ...
'-jpg -HR', [10 10]); |
38ac5d8060eeefb50acc8015f256c9848e92913f261dff40a959a6ab71977586 | MATLAB | 264 | 9 | fid = fopen(fname,'w');
for i = 1:100
network = network_simulation_beluga(folder);
% Initialize post network
network = network.initialize_postsynaptic_network(2);
M = {network.neurons(:).mType};
fprintf(fid,'%s,%s\n',M{1},M{2});
end
fclose(fid) |
4d89bc3f59b86006a4543c3bfe6cfbdd9a3a6bbf831fe4076b17adf5e2a01583 | MATLAB | 265 | 10 |
function rotationDirection = getRotationDirection(signal, startIndex, endIndex)
signalPhase = phase(signal(startIndex:endIndex));
if(signalPhase(end) - signalPhase(1) > 0)
rotationDirection = 1;
else
rotationDirection = -1;
end
end |
8eb4167ff2b5f9decbed562339b2348a940163f2ae5b88359f4bc525bdb991ea | MATLAB | 265 | 9 | %% check_cap_tree
%% test 1
tree = resample_tree (sample_tree, 1, 'none');
stree = soma_tree (tree, 20, 30, 'none');
ctree = cap_tree (stree, '-i1 -s');
tprint ('./panels/cap_tree1', ...
'-jpg -HR', [10 10]);
|
17beded2cdca0bd120c24cb3d335551f9af0fe3a9a22dab3929abb894070ece3 | MATLAB | 266 | 18 | %% check_graphical
check_chull_tree
check_dA_tree
check_dendrogram_tree
check_gdens_tree
check_hull_tree
check_lego_tree
check_plot_tree
check_plotsect_tree
check_pointer_tree
check_spread_tree
check_vhull_tree
check_vtext_tree
check_xdend_tree
check_xplore_tree
|
b29d498523a719c25e94d0d62f2dd17a05d21291b0a1b678912663104b091e4a | MATLAB | 266 | 10 | % Yigang Peng, Arvind Ganesh, November 2009.
% Questions? abalasu2@illinois.edu
%
% Copyright: Perception and Decision Laboratory, University of Illinois, Urbana-Champaign
% Microsoft Research Asia, Beijing
function vecA = vec(A)
vecA = A(:); |
dfd89311fa9573c94d5eb6e9847ff339c098755f617234ae62690dcf898e48f1 | MATLAB | 266 | 9 | %% Recompose the image from the Nth component of the SVD
function [recomp, raw] = recomposeSVD(U,S,V,coeffVec,msize)
mask1 = zeros(size(S));
mask1(coeffVec,coeffVec) = 1;
S = S .* mask1;
raw = S*V';
recomp = reshape(U*raw,[msize,size(U,2)]);
end |
e17f02f539e52c386b17f05bf4d88e1b37e82471f92e73009f3f29ce02ed57cd | MATLAB | 267 | 10 | function MRSIStruct = setFov(MRSIStruct, dimension, value)
switch(lower(dimension))
case('x')
MRSIStruct.fov.x = value;
case('y')
MRSIStruct.fov.y = value;
case('z')
MRSIStruct.fov.z = value;
end
end |
e631235cf4e44758eaf3dc2d9d65ed0a480e36d76d7b755e77b5beea09d783f4 | MATLAB | 268 | 10 | % FigureS05.m
% For creating the masks displayed in FigS05,
% please execute rootDir/Analyses/CBM_Scripts/EEGfMRIPav_cbm_parameter_recovery.m.
%
% EEG/fMRI STUDY, DONDERS INSTITUTE, NIJMEGEN.
% J. Algermissen, 2018-2023.
% Should work in Matlab 2018b.
% END OF FILE. |
0fa5addcfb481eb3ec449a6429d0ffd194dadcb4d1ef2f6f466e5ca1671d538c | MATLAB | 270 | 11 | function r = randi(imax,n)
%fprintf(2,'legacy imax!\n');
assert(nargin > 0,'too few arguments');
if nargin == 1
r = 1+floor(imax*rand);
elseif nargin == 2
r = 1+floor(imax*rand(n));
else
error('please use array form for matrix dimension argument');
end
|
02bc03d7f2eb46cec20c29d659d0f486f1e03d21df2f50cffeea59ca0dc7cc5f | MATLAB | 274 | 8 | function [mle_output] = lyapExpLorenz(rho)
[t,a] = computelorenz(10, 2.667, rho ,[0,30], 0.01, 1,1,1);
fs = 100;
xvals = a(:,1);
[~,lag,dim] = phaseSpaceReconstruction(xvals);
mle_output = lyapunovExponent(xvals,fs,lag,dim,'ExpansionRange',200);
end
|
2a0298028895b5affacb8a936e7334f54418f1712b141b0b5ab478aa63ebfd19 | MATLAB | 274 | 20 |
function [output]=EOGchecker(EOGSignal,threshold)
hEOG=mean(EOGSignal(:,1:2)');
vEOG=EOGSignal(:,3)'-hEOG;
aEOG= mean(EOGSignal');
C=abs([hEOG;vEOG;aEOG]);
if(max(max(C))>threshold)
output=0;
else
output=1;
end
end |
3e3661f03d0e917343ef1c3ff0f1762e19e332df03f8e9f218dbebd046426d14 | MATLAB | 274 | 10 | %% check_plotsect_tree
%% test 1
clf; hold on;
axis off;
tree = sample_tree;
plot_tree (tree);
plotsect_tree (tree, [1 (size (tree.dA, 1))], [1 0 0], [0 0 10]);
tprint ('./panels/plotsect_tree1', ...
'-jpg -HR', [10 10]);
|
55aadc4358c4d9415ce38d9a972f336febabaf8c3ad595e1425a3e25d6dd14ac | MATLAB | 275 | 14 | function Z = pick_idx(cluster_size, N)
% given a vector of cluster sizes, return the corresponding indices the
% vectorized edge list.
temp = zeros(N);
temp(1:cluster_size,1:cluster_size) = 1;
for j = 1:N
temp(j,j) = 0;
end
Z= find(squareform(temp));
end |
8f7f945f9b86b26e7146d3277de69597def6c4f51d0d509cfab3f2308649e16d | MATLAB | 278 | 16 | function bsave(fname, matrix, arg2)
% saves a binary file
% bsave(fname, matrix, arg2)
% uses command fwrite(fp, matrix, arg2);
%
% arg2 specifies a precision - default 'short'
if nargin<3
arg2 = 'short';
end
fp = fopen(fname, 'w');
fwrite(fp, matrix, arg2);
fclose(fp); |
986563caebf155ba3dcd0e733896141467f870bae3efaca515dac63c7207b669 | MATLAB | 278 | 18 | function streamTiffData(frame)
out.data = frame;
out.EVENTTYPE = 'FRAME';
out.plane = 0;
pause(3)
jsonencode(out);
% p = parpool(1);
%
% r = rand(512,512,100);
% r = int32(r*100);
%
% for n=1:100
% tic
% f(n) = parfeval(@streamTiffData, 0, r(:,:,n));
% toc
% end |
528e4a50c276752f41f185a0f4b69c9188d6d246a4c39c9f09adb66000e69cb3 | MATLAB | 281 | 12 | function CVindex = GenerateCV(nsub, nlevel, seed)
rng(seed)
nfold = 10;
CVO = cvpartition(nsub, 'KFold', nfold);
CVindex = zeros(nsub, 1);
for i = 1:nfold
test = CVO.test(i);
CVindex(test) = i;
end
CVindex = repmat(CVindex, 1, nlevel)';
CVindex = CVindex(:);
end |
81f4b7c2b6773d6df79892ed4ea31103816f1a2b5d470212bf3d3dfd853fcc08 | MATLAB | 282 | 16 | function [RMP] = Analysis_RMP_pub(data, duration)
if nargin < 5
filter = 10000; %change the filter here
end
if nargin < 6
filename = 'Data file';
end
if nargin < 7
output_folder = 'Default';
end
RMP = mean(data);
end |
3fa3a5591696dbbeef3710b24801dc30023726133c92708f9531d8ab3c3626ed | MATLAB | 284 | 13 | function [x_N,x_min,x_max]=Normalization(x,x_min,x_max)
% function for normalization
if nargin==1
x_min=min(x);x_max=max(x);
else
[row,col]=find(isnan(x));
x(row,col)=0;
end
x_N=(x-x_min)./(4*x_max-4*x_min)-0.5;
[row,col]=find(isnan(x_N));
x_N(row,col)=-0.5; |
8e74379162fac2692beaa559238ec8c125fbc622b0e84357afe21b253ea2ad19 | MATLAB | 284 | 16 | %% check_cyl_tree
%% test 1
dLPTCs = load_tree ('dLPTCs.mtr');
for counterN = 1 : 1000
for counter = 1 : length (dLPTCs{1})
tree = dLPTCs{1}{counter};
cyl_tree (tree);
end
end
%% test 2
[X1, X2, Y1, Y2, Z1, Z2] = cyl_tree (sample_tree)
|
130fbf710f0f50799dcf983312ee65652fbcce15ece51a34cf75812953cf5102 | MATLAB | 285 | 6 | function predictedVals = GenerateRandPrediction(possibleLabels,nPredictions)
nLabels = length(possibleLabels);
randVals = rand(1,nPredictions)*nLabels;%now we have random distribution between 0..nVals
predictedVals = ceil(randVals);
predictedVals = possibleLabels(predictedVals); |
f56c0184c885818aecdc440cc00540972d65ec222c208c543eac96191727639c | MATLAB | 289 | 12 | function [fids, positions] = op_CSIExtractFids(MRSIStruct, kSpaceFile)
table = readTable(kSpaceFile);
TR = table.TR;
Kx = table.Kx;
Ky = table.Ky;
t = table.t;
[MRSIStruct, prevPermute, prevShape] = reshapeDimensions(MRSIStruct, {'t', 'ky', 'kx'});
end
|
f80d23ef43419f03b1f57f2692e434a29dfbeade1bd071ad12a029e26386b6ba | MATLAB | 291 | 10 | function MRSIStruct = setVoxelSize(MRSIStruct, dimension, value)
switch(lower(dimension))
case('x')
MRSIStruct.voxelSize.x = value;
case('y')
MRSIStruct.voxelSize.y = value;
case('z')
MRSIStruct.voxelSize.z = value;
end
end |
52f8ed6e44f69edf53ef808fa47c8cec303ba681595df818f40163270828534d | MATLAB | 292 | 8 | function [Ev,Ef]=fitness(X,Y,G,M,N,c,G1,G2)
% fitness function
r=find(N<0); N(r)=[]; c(r)=[];
K=Kmatrix(X,M,G,N,c);
W=pinv(K(1:G1,:))*Y(1:G1); % WDD method
Ef=100/G1*sum(abs(K(1:G1,:)*W-Y(1:G1))); % MAE: fitting set
Ev=100/G2*sum(abs(K(G1+1:G,:)*W-Y(G1+1:G))); % MAE: validation set |
61998486bb8a63961c85b48eb6ca8d9f53cacff33526bfd8764fa32ac09b9018 | MATLAB | 294 | 8 | function [outModel] = tagModel(inModel, tag)
outModel = inModel;
outModel.rxns = strcat(tag,inModel.rxns);
outModel.mets = strcat(tag,inModel.mets);
outModel.comps = strcat(tag,inModel.comps);
outModel.compNames = strcat(tag,inModel.compNames);
outModel.subSystems = strcat(tag,inModel.mets);
|
5d0c1aeb1d710398a7548b495e6e8e869bbd3cddb1cb5bad3ef505ad2d262d41 | MATLAB | 297 | 20 | function df= defaultThresh(DT,doBeta)
thresh=[];
if notDefined('doBeta')
doBeta = 1;
end
thresh.varexp = 0.1;
df = tableThreshold(DT,thresh,'lower');
thresh = [];
thresh.ecc = 12; % max ecc
df = tableThreshold(df,thresh,'upper');
if doBeta == 1
df = removeBetaOutlier(df);
end
end |
19d7dc1c5c3bf41339934468995b1ec4f347a24a81f6e9fb89b2df57cf1456b8 | MATLAB | 299 | 15 | function A = labelpanel(x,y,str,lowerCase)
if(nargin<4)
str = upper(str); % CellPress
else
if(lowerCase)
str = lower(str); % NPG
else
str = upper(str);
end
end
A = annotation('textbox', [x,y,0.05,0.05],'String',str, 'LineStyle', ...
'none', 'FontWeight','bold', 'FontSize',8,'Margin',0); |
fef81bc19411ecd29fa11aa46d491e42d888419bd0015d2240f023fbe0835a3b | MATLAB | 299 | 20 | %% check_metrics
check_angleB_tree
check_cvol_tree
check_cyl_tree
check_dstats_tree
check_eucl_tree
check_flatten_tree
check_flip_tree
check_len_tree
check_morph_tree
check_rot_tree
check_scale_tree
check_surf_tree
check_tran_tree
check_vol_tree
check_zcorr_tree
check_sholl_tree
check_stats_tree
|
ffc9c34c55b81d21b0f7d440f5f72c7e3a8065ec74ec4ef8edf5191369113f01 | MATLAB | 299 | 17 | classdef Mock_RawImgHelper < RawImgHelper & IMock
%MOCK_RAWIMGHELPER Summary of this class goes here
% Detailed explanation goes here
properties (Dependent)
name
rawdata
t0
end
methods
function to_long(~)
end
end
end
|
52c94db1a3c432fc1d2b7b32343ca52f01652aa5d26475bb4855424adb2ba52d | MATLAB | 300 | 11 | function colors = linearlyMapArrayToColors(xArray, colorMap)
% Normalize to range 0-1.
if ~range(xArray(:)) == 0
xArray = (xArray - min(xArray(:))) / range(xArray(:));
end
nColors = size(colorMap, 1);
colorInds = floor(xArray * (nColors - 1)) + 1;
colors = colorMap(colorInds, :);
end |
93913f916a5c883ed660e600ba769e4e3fa40dae307893ab560a8ea2bdc2efae | MATLAB | 301 | 21 | %%
clear all; close all; clc
d = dir('data_*');
nFile = length(d);
minGlob = NaN;
%%
for f = 1:nFile
load(d(f).name)
minCur = min(size(allCen.common,1),size(allCen.unique,1));
minGlob = min(minGlob,minCur,'omitnan');
end
save('minGlob.mat','minGlob')
|
015860eb21c10d235298e1a61bb1a94f96b232b3cbe6011aa9a15df44e392610 | MATLAB | 302 | 8 | function [outModel] = tagModel(inModel, tag)
outModel = inModel;
outModel.rxns = strcat(tag,inModel.rxns);
outModel.mets = strcat(tag,inModel.mets);
outModel.comps = strcat(tag,inModel.comps);
outModel.compNames = strcat(tag,inModel.compNames);
outModel.subSystems = strcat(tag,inModel.mets);
|
927760818293053573536c63e8916068f7ec9e917142a00ddb4ae89228d0b862 | MATLAB | 303 | 9 | function [volume, raw] = recomposeSvd(U, S, V, componentIdx, spatialSize)
%RECOMPOSESVD Recompose a volume from one SVD component.
componentMask = zeros(size(S));
componentMask(componentIdx, componentIdx) = 1;
raw = (S .* componentMask) * V';
volume = reshape(U * raw, [spatialSize, size(U, 2)]);
end
|
5ecacfb3c2ee72d396d540107d2f176be66dd1b7d8575ed1ea6669a3fec7f206 | MATLAB | 304 | 10 | function logElapsed(cfg, label, timerValue)
%LOGELAPSED Print elapsed time for a pipeline step.
if isfield(cfg, 'verbose') && cfg.verbose
timestamp = string(datetime("now", "Format", "HH:mm:ss"));
fprintf('[%s] %s finished in %.1f s.\n', ...
timestamp, label, toc(timerValue));
end
end
|
4b29147127aa049213c6a92bbfd44a857c841b8af5143e3b0249cead1854d0a3 | MATLAB | 309 | 10 | function RFparam = randomRFparam(StimSize,nVoxels)
rng('default') % For reproducibility
RFxy = normrnd(0,StimSize/4,[nVoxels,2]);
% if we use linlog (default) we can specify where the cutoff lies. This
RFsigma = boundUniformRand([0.2 StimSize/4],nVoxels);
RFparam = [ round(RFxy,2) round(RFsigma,2)];
end
|
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