sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
06c422f1d65e682aff7a3e783a00c884d1b3ffa946b0baa12788e9c610c29188 | MATLAB | 272 | 8 | function [tspks] = poissonP(r,T)
%POISSONP Poisson process for generating stochastic spike trains
% Computes spike times for given rate(r) in Hz over total time(T) in s
dt = 0.0001; % .1 ms precision
spks=zeros(1,T*(1/dt));
R=rand(size(spks));
tspks=find(R<r*dt)/10;
end |
a0f9f878d5b4e9673b352ff5812e289dcf91948c103837865cff44def5248439 | MATLAB | 272 | 15 | function text = int2ordinal(num)
text = int2str(num);
switch text(end)
case '1'
text = [text 'st'];
case '2'
text = [text 'nd'];
case '3'
text = [text 'rd'];
otherwise
text = [text 'th'];
end
end
|
82954d81cb56e5f8c0c7d8e2c948eac4202bb5b9d17eb525be64c775eef59cda | MATLAB | 275 | 12 | function out = refreshWorkspaceBrowser()
saWhos = evalin( 'caller', 'whos' );
for ii = 1 : numel( saWhos ),
if strcmp( saWhos(ii).class, '(unassigned)' ),
evalin( 'caller', [ saWhos(ii).name, '=[];' ] ),
end
end
out = [];
end
|
f74fc20063a14f2191fdf016ba5756178f87c2fe970f772a33b9e9cec3dfcdfe | MATLAB | 275 | 18 | function str = capitalize(str, option)
if nargin < 2
option = 'first';
end
str = char(str);
switch option
case 'all'
idx = regexp([' ' str],'(?<=\s+)\S','start')-1;
otherwise
idx = 1;
end
if ~isempty(str)
str(idx) = upper(str(idx));
end
end |
18a2328e88a9e5e79019501318f1326bd2b0aa550a98e3daf18866d82a02d46a | MATLAB | 277 | 11 | function [color] = pastelize(color, gamma, beta)
if ~exist('gamma', 'var') || isempty(gamma)
gamma = [0.2,0.8];
end
if ~exist('beta', 'var') || isempty(beta)
beta = 0.6;
end
color = brighten( imadjust(color, [0;1], gamma), beta );
end
|
4da027bb19560d4bb0292e8a1d1aed4450dd72b1c95c50ce3bffc7e6cc5e1839 | MATLAB | 277 | 13 | %%
function [minimum] = emin(array, exceptTest)
% Exclusive minimum, over all elements except those passing the
% exceptTest.
if nargin < 2
exceptTest = @iszero;
end
array(exceptTest(array)) = nan;
minimum = rmin(array);
end
|
5e25e6feeae8f227f75baa828a4fb9f7a0c56c5a7aa2f35bcbefa5af31d51d8c | MATLAB | 279 | 13 | function index = getMonitorBySize(isLargest)
if nargin < 1
isLargest = true;
end
%%
fcnSel = {@min, @max};
monitors = get(0,'monitor');
screenArea = prod( monitors(:,3:end), 2 );
[~,index] = fcnSel{isLargest+1}(screenArea);
end
|
8aafa0cef70447e58e1b02595a45cbc0287bf6b21ada8e20658732e045238d43 | MATLAB | 279 | 14 | function [sz] = arraysize(x, noCellStr)
% ARRAYSIZE Like size(), but treats strings as a singleton.
if nargin < 2
noCellStr = false;
end
if ischar(x) || (noCellStr && iscellstr(x))
sz = [1 1];
else
sz = size(x);
end
end
|
d87574998354d325acb55632d448af0d1517587b5e4574094156617d7ad48cde | MATLAB | 280 | 9 | % Like ctranspose(), except that slices (3rd and higher dimensions) are supported.
function T = ctranspose3(A)
szA = size(A);
A = reshape(A, [szA(1:2), prod(szA(3:end))]);
T = permute(A, [2 1 3]);
T = reshape(T, [szA([2 1]), szA(3:end)]);
end
|
1c0edd585fada3e705692cb4d4e9ef8d2b5c750a88048daa14d8543a4b3a7f04 | MATLAB | 282 | 7 | if ispc()
mex -v mexlf.c liblfev.c liblocf.c libmut_win.c libtube.c mlfut.c
mex -v mexpp.c liblfev.c liblocf.c libmut_win.c libtube.c mlfut.c
else
mex -v mexlf.c liblfev.c liblocf.c libmut.c libtube.c mlfut.c
mex -v mexpp.c liblfev.c liblocf.c libmut.c libtube.c mlfut.c
end |
d16bbd3ac17b3379fd22d933b701dc9ecd10c02b1efa748935a87cc7a16c07ed | MATLAB | 285 | 13 | function p=my_t1cdf(x)
% cumulative distribution function of a t-dist. with 1 degree of freedom
%function p=my_t1cdf(x)
%input
% x = point
%output
% p = cumulative probability
%
%see also: tcdf
xsq=x.*x;
p = betainc(1 ./ (1 + xsq), 1/2, 1/2, 'lower') / 2;
p(x>0)=1-p(x>0); |
43ca8a9e3321b905abb62d785481e257aac48ebdf4554704fe8c76e9ccb6ee01 | MATLAB | 291 | 9 | function CI95 = ConfInt95(data, dim)
% calculates the 95% confidence interval of input data along dimension dim
% So, if observations are rows and variables are columns, then call
% ConfInf95(data, 1)
stdev = std(data, 0, dim);
N = size(data, dim);
CI95 = stdev/sqrt(N)*tinv(0.975,N-1);
end |
862ffaf914919be4e83c34d390d4edb615634f89829b616c432ba9c9acd65551 | MATLAB | 291 | 8 | function [ x ] = ctransform(x)
% CTRANSFORM Copula transformation (empirical CDF)
% cx = ctransform(x) returns the empirical CDF value along the first
% axis of x. Data is ranked and scaled within [0 1] (open interval).
[~,x] = sort(x, 1);
[~,x] = sort(x, 1);
x = x / (size(x, 1) + 1);
|
213b90ed07705ada228888fea65c7c6808dc46dc02f08fa17af5a5684e189fce | MATLAB | 293 | 14 | function cv2 = cv2(array)
%only for arrays with one raw
if size(array,1) > 1
error('input array dimension must be 1xN')
end
if size(array,2)>2
for i = 1:length(array)-1
cv2(i) = 2*abs(array(1,i)-array(1,i+1))/(array(1,i)+array(1,i+1));
end
else
cv2 = NaN;
end |
e97acab032422ad5283cb39ed6deb040bc80ad4b0a462d843ad559106cc1ccac | MATLAB | 296 | 8 | % Oversample the given tCenter vector so that it has points at all bin edges as well as centers
function tPoints = toBinPoints(tCenter)
tPoints = (tCenter(1:end-1) + tCenter(2:end)) / 2;
tPoints = [tCenter(1:end-1); tPoints];
tPoints = [tPoints(:); tCenter(end)];
end
|
52d44e66d22cfe3341eaac2deeecb48fbd5fb3162dc8d485284a7a13534729cd | MATLAB | 297 | 16 | %%
function [varargout] = getrange(items, dim)
if nargin < 2
range = [ rmin(items), rmax(items) ];
else
range = concat( min(items,[],dim), max(items,[],dim) );
end
if nargout < 2
varargout = {range};
else
varargout = num2cell(range);
end
end
|
2190c9db15292e33c6950cea272837c3537790d164b32a040bf6c8f328c0ee63 | MATLAB | 298 | 20 | function yes = makeDirectory(path)
if exist(path, 'dir')
yes = false;
return;
end
[dir,~,ext] = fileparts(path);
if ~isempty(ext)
path = dir;
end
if exist(path, 'dir')
yes = false;
else
yes = true;
mkdir(path);
end
end
|
669ad2fe76b787ddc2f646a784d9f078f263251dbfd693941309dba26b27a918 | MATLAB | 298 | 13 | %% function to bin goal-direction with from -pi to +pi
function [binned_GD, binCenter] = BinHDir(HDir, edges)
binned_GD = discretize(HDir, edges);
Nbins = length(edges)-1;
binCenter = zeros(Nbins,1);
bw = (edges(2)-edges(1))/2;
for i = 1:Nbins
binCenter(i) = bw+edges(i);
end
end |
16a8b8eb6ae50910fe1ff6e3c0dcd58e2ea99c08fcd0ce8ba2a7bc3e6af25b02 | MATLAB | 299 | 19 | function str = sigprint(p)
str = cell(size(p));
for iP = 1:length(p)
if p(iP) <= 0.001
str{iP} = '***';
elseif p(iP) <= 0.01
str{iP} = '**';
elseif p(iP) <= 0.05
str{iP} = '*';
else
str{iP} = '';
end
end
if length(p)==1
str = str{1};
end
end |
865c8c360ca6278c704a95b4f3bc21e60d2ab2f67eb70ba3858afd4ff88fb90b | MATLAB | 299 | 11 | function result = meanExclDiag(sqMatrix)
% Get the mean of a square matrix excluding the diagonal, and
% excluding NaN's
dim = size(sqMatrix);
if (length(dim)~=2)||(dim(1)~=dim(2))
disp(dim);
error('input is not a square matrix');
end
dim = dim(1);
result = nanmean(sqMatrix(~eye(dim)));
end |
a5ecfaa0615730a135338fa62a0826a94db2cbc168b5a9491acaafcb14e6c12d | MATLAB | 299 | 25 | classdef PLSData
properties
XS;
avgXS;
YS;
avgYS;
XL;
avgXL;
YL;
avgYL;
PCTVAR;
avgPCTVAR;
MSE;
avgMSE;
R2;
avgR2;
RMSE
avgRMSE;
y_test;
y_pred;
end
end |
bff65b743671f5583f6cdfad6129000571af61f6b2275f6928e652d07c819494 | MATLAB | 299 | 13 | function combineSurfVolGOrdfMRI(GOrdSurfFile,GOrdVolFile,GOrdFile)
load(GOrdSurfFile);
load(GOrdVolFile);
lsz=size(left_GO_fMRI,1);
rsz=size(right_GO_fMRI,1);
GO_fmri=Vol_GO_fmri;
GO_fmri(1:lsz,:)=left_GO_fMRI;
GO_fmri(1+lsz:lsz+rsz,:)=right_GO_fMRI;
dtseries=GO_fmri;
save(GOrdFile,'dtseries');
|
ece25a11096dc39595ab3353bc447b7b4810288c8de38bdd339d882c895ef2ce | MATLAB | 303 | 9 | %% Linearly interpolated (in CDF) quantiles.
function quantile = inverseCDF(x, probability)
[cdf,value] = ecdf(double(x));
value = value(2:end);
cdf = ( cdf(1:end-1) + cdf(2:end) ) / 2;
quantile = interp1(cdf, value, probability, 'linear', 'extrap');
end
|
737aa29abc7496f7cc917002856e63f1e9c6c3d2f472fd1b3121dbab0fed8a2d | MATLAB | 304 | 15 | %%
function [handles] = vcf(varargin)
if nargin > 0 && all(ishghandle(varargin{1}))
handles = varargin{1};
iOption = 2;
else
handles = 0;
iOption = 1;
end
handles = findobj(handles, 'Type', 'Figure', 'Visible', 'on', varargin{iOption:end});
end
|
cf9815463b495e728ee2604a5e42aacf0cfcab1b113e484ca39ef4c169696692 | MATLAB | 305 | 14 | %%
function [rMax] = rangemax(range1, range2)
if numel(range2) == 1
rMax = [ min([range1(1) range2]) ...
, max([range1(2) range2]) ...
];
else
rMax = [ min([range1(1) range2(1)]) ...
, max([range1(2) range2(2)]) ...
];
end
end
|
d9287c25cd3ff38b4737043ac7b9d81a01733b9e0495231d7847ebf8b7361f6b | MATLAB | 305 | 12 | function [target] = addstruct(target, varargin)
% ADDSTRUCT Adds all fields in subsequent structs to target.
for iArg = 1:numel(varargin)
input = varargin{iArg};
for field = rowvec(fieldnames(input))
target.(field{:}) = input.(field{:});
end
end
end
|
2c659532c42d9bc9f423cf814576c3e00942fe76366b5893b8b018932d52764f | MATLAB | 306 | 19 |
%%
function X=initialization(N,Dim,UB,LB)
B_no= size(UB,2); % numnber of boundaries
if B_no==1
X=rand(N,Dim).*(UB-LB)+LB;
end
% If each variable has a different lb and ub
if B_no>1
for i=1:Dim
Ub_i=UB(i);
Lb_i=LB(i);
X(:,i)=rand(N,1).*(Ub_i-Lb_i)+Lb_i;
end
end
|
b2b3f3292660d146669234dfe5d57c34174c0004b55c3f2e3fb022cb46d0c3dc | MATLAB | 308 | 15 | function [range, index] = maxrange(varargin)
% MAXRANGE Finds the range with the maximum span.
narginchk(1, inf);
index = 1;
for iArg = 2:nargin
if span(varargin{iArg}) > span(varargin{index})
index = iArg;
end
end
range = varargin{index};
end
|
ccfd8138c0223e1d2f4cd56d843ea0e604abeb59dff64da2232aaa50f7a60d0a | MATLAB | 308 | 9 | function quietwarning(varargin)
% QUIETWARNING(MESSAGE)
% Print a simple warning message with backtrace momentarily disabled.
% That's all this does; other functionality of warning() not supported.
S = warning('query','backtrace');
warning off backtrace
warning(varargin{:})
warning(S.state,'backtrace')
|
20912f8e2a65727e8a1de6d7c6294715cd2e88985bff3b5409a5e54d6d485d29 | MATLAB | 309 | 9 | %% function to get mean event rate of HDC cells
function MeanRate = GetMeanRateHDC(HD_list, HD_table, HDir_Rate)
MeanRate = nan(length(HD_list),1);
for i = 1:length(HD_list)
idx = find(HD_table.cellID == HD_list(i));
MeanRate(i) = mean(HDir_Rate(idx,:,:),"all");
end
end
|
229930b0729e412be31596d3f90c455ff1fc6ae8e38d9e36fe52e65146f1d75b | MATLAB | 309 | 8 | function string = csprintf(format, color, varargin)
string = sprintf ( '{\\color[rgb]{%.3g %.3g %.3g}%s}' ...
, color(1), color(2), color(3) ...
, sprintf(format, varargin{:}) ...
);
end
|
2a9c8bc0182b1a87896b04e86859643b3be92957caddcc42c75bb7eb7d5c292e | MATLAB | 310 | 13 | % Returns all current axes in the given figure, or all figures by default
function axs = allaxes(fig)
if nargin < 1
fig = findall(0, 'Type', 'Figure');
end
axs = gobjects(size(fig));
for iFig = 1:numel(fig)
axs(iFig) = get(fig(iFig), 'CurrentAxes');
end
end
|
1b0a336885cce2c3fb3c95389e9c57e41ac8cd9e044a48510ea9ee1aa09ca7c3 | MATLAB | 312 | 14 | % Replace all instances of old by new in a numeric array A; supports NaNs
function A = numrep(A, old, new)
%% Find all elements of A that matches old
if isnan(old)
sel = isnan(A);
else
sel = bsxfun(@eq, A, old);
end
%% Replace selected elements
A(sel) = new;
end
|
20104dbac1bc7cf7afedd5754a99081a46665a75e58ce3897c99f8a7dbb2c88a | MATLAB | 313 | 14 | % Returns the current axes in the given figure if there is one, otherwise returns an empty graphics array
function axs = curraxes(fig)
if nargin < 1
fig = get(0,'CurrentFigure');
end
if isempty(fig)
axs = gobjects(0);
else
axs = get(fig, 'CurrentAxes');
end
end
|
25fe839b9a1206f3a40718238cd0b1dcbd38cdd035d42fd7d3d600185e494bbd | MATLAB | 313 | 14 | %%
function [handles] = vca(varargin)
if nargin > 0 && all(ishghandle(varargin{1}))
handles = varargin{1};
iOption = 2;
else
handles = vcf;
iOption = 1;
end
handles = findobj(handles, 'Type', 'Axes', 'Visible', 'on', 'Tag', '', varargin{iOption:end});
end
|
66f5dc3495de5e8876b8924aabcb068fa154f13d7b22db38c19f56ac2d282451 | MATLAB | 313 | 10 | function out = mdphasescramble(md);
nframes = size(md,1);
nmarkers = size(md,2);
r = round(rand(1,nmarkers)*nframes);
for i=1:nmarkers
h = md(1:r(i),i,:);
md(1:nframes-r(i),i,:) = md(end-nframes+r(i)+1:end,i,:);
md(nframes-r(i)+1:end,i,:) = h;
end;
out=md; |
f9c925b4c35201b49718b09f8055ae4ce1d5bf3d9c6ae6340403125f02515c0d | MATLAB | 313 | 14 | %%
function [previousValue] = rset(handles, property, value)
previousValue = get(handles, property);
if iscell(value) && numel(value) == numel(handles)
for iH = 1:length(handles)
set(handles(iH), property, value{iH});
end
else
set(handles, property, value);
end
end
|
75af4df5bd4d2d23d143650c8d66c32c34b4959278dd7c8f5476a8dc49911283 | MATLAB | 315 | 13 | function datz = nanznorm(datin)
for rowi = 1 : size(datin,1)
dat = datin(rowi,:);
if any(isnan(dat(:)))
xmu=nanmean(dat);
xsigma=nanstd(dat);
datz(rowi,:) = (dat-repmat(xmu,size(dat,1),1))./repmat(xsigma,size(dat,1),1);
else
datz(rowi,:) = zscore(dat);
end
end
|
ee648acfa1358dcd6c7711ea073a3c69467bd68207c24ffa824ac95aaf8b9a12 | MATLAB | 315 | 14 | %
% Input
% I: binary image (black = true)
%
function plot_image_only(I)
assert(size(I,1)==105);
assert(size(I,2)==105);
newI = 1-I;
newI(isinf(I)) = 0.5;
image([1 105],[1 105],repmat(newI,[1 1 3]));
set(gca,'YDir','reverse','XTick',[],'YTick',[]);
xlim([1 105]);
ylim([1 105]);
end |
f47f2a7ca668691ca028b96e73633b2e5237c0c46e6f6aad3cdc0a7c089e9054 | MATLAB | 316 | 10 | function mask = diskMask(maskWidth, maskHeight, maskRadius, maskCenter)
if nargin < 4
maskCenter = ([maskWidth, maskHeight] + 1) / 2;
end
[col, row] = meshgrid(1:maskWidth, 1:maskHeight);
mask = ( (col - maskCenter(1)).^2 + (row - maskCenter(2)).^2 ) <= maskRadius^2;
end
|
3b514f2e8ed2fbd6f0e013a763f580b235a7b54efe51c86afee42eedbb118d5f | MATLAB | 317 | 9 | %% Function to get firing preference that are located in reward zone
function FiringPref = GetFiringPref(vec1,vec2,RewZone)
FiringPref = nan(length(vec1),1);
for i =1:2
idx = find(RewZone == i);
FiringPref(idx) = (vec1(idx)-vec2(idx))./(vec1(idx)+vec2(idx)) * (-1).^(i+1);
end
end |
5be13fa826fef1c5edd7b0ca18db7aa9d078b4aa69c7fd8d012492d7216dc3e5 | MATLAB | 317 | 13 |
function generateSurfGOrdfMRI(GOrdIndFile,fmri2surfFile,GOrdsurfFile)
% Tells the compiler that parallel processing is required
%#function gcp
load(GOrdIndFile);
load(fmri2surfFile);
left_GO_fMRI=datal_atlas(ind_left,:);
right_GO_fMRI=datar_atlas(ind_right,:);
save(GOrdsurfFile,'left_GO_fMRI','right_GO_fMRI');
|
f520a76ba306ccbc640ddde713c94d406f2efa0559c56ea38660685238efe517 | MATLAB | 317 | 11 | %%
% Converts the given structure x to an argument list
% {name1, value1, name2, value2, ...}
% Additional name/value pairs can be provided to be concatenated to this
% list.
function [y] = struct2arg(x, varargin)
y = rowvec([fieldnames(x), struct2cell(x)]');
y = [y, varargin];
end
|
0c9462a15142f5f66b883a7be48afda28106e83abbbc409ebd6bc55c9ab0ea08 | MATLAB | 318 | 15 | function [y, dydx] = softPlus(x, sharpness)
%% Default arguments
if nargin < 2 || isempty(sharpness)
sharpness = 1;
end
%% Smoothly rectified output
expkx = exp(sharpness * x);
y = log(1 + expkx) / sharpness;
if nargout > 1
dydx = expkx ./ (1 + expkx);
end
end
|
dd2547cf0b2b29d9b478be8f14dca0e6c1bbe7d0a124b117de76f6415bfb7c5d | MATLAB | 318 | 10 | function factors = getFactors(number, basis)
factors = nan(numel(number), numel(basis));
remainder = number;
for iBase = 1:numel(basis)
factors(:,iBase) = floor(remainder ./ basis(iBase));
remainder = remainder - factors(:,iBase) .* basis(iBase);
end
end
|
73dc1a56cd56fc64c17ee428a3381ced652dfcb32a8c392a6888630b22cc8a8a | MATLAB | 319 | 12 | function [dmeg, dspc] = block_delay(bmeg, bspc, d)
Nblock = length(bmeg);
dmeg = cell(Nblock,1);
dspc = cell(Nblock,1);
for bi=1:Nblock
common = min(size(bmeg{bi},1),length(bspc{bi}));
dmeg{bi} = bmeg{bi}((1+d):common,:);
dspc{bi} = bspc{bi}(1:(common-d));
end
dmeg = cell2mat(dmeg);
dspc = cell2mat(dspc); |
f029331f5bc63eea5073695593f5b02e49b84e8ba08d177dc7e9adc3737ad137 | MATLAB | 319 | 14 | %%
function [rMax] = rangemax(range1, range2)
if numel(range2) == 1
rMax = [ min([range1(1) range2]) ...
, max([range1(2) range2]) ...
];
else
rMax = [ min([range1(1) range2(1)]) ...
, max([range1(2) range2(2)]) ...
];
end
end
|
66f52f50903de1c4fac8cdfc21aac411fda5d92864fc6d62683b2da763a35d74 | MATLAB | 321 | 14 | function q = fdr_bh(p)
% Benjamini-Hochberg
[p_sorted, sort_idx] = sort(p);
n = length(p);
q_sorted = p_sorted .* n ./ (1:n)';
q_sorted = min(1, q_sorted);
for i = n-1:-1:1
q_sorted(i) = min(q_sorted(i), q_sorted(i+1));
end
q = zeros(size(p));
q(sort_idx) = q_sorted;
en... |
bfc918705f88161682964bc009cc2c9782f05ef3f7fe1123bf72549ec0ba56fc | MATLAB | 322 | 12 | function [yes] = isstrings(x)
% ISSTRINGS Returns true if the given object(s) are character arrays or
% cells of character arrays.
yes = false(arraysize(x, true));
for iX = 1:numobj(x, true)
obj = at(x, iX, true);
yes(iX) = ischar(obj) || iscellstr(obj);
end
end
... |
57921d6e16fe04a02e69bc70114e9a442e6b0cfe0aa47cf7359e28364aecd7dc | MATLAB | 325 | 12 | % Insert dimensions of length 1 at the specified dimension(s), like the opposite of squeeze
function B = fatten(A, dim)
Bsize = size(A);
% dim = sort(dim);
for iDim = 1:numel(dim)
Bsize = [ Bsize(1:dim(iDim)-1), 1, Bsize(dim(iDim):end) ];
end
B = reshape(A, Bsize);
... |
2d153ab4f4edacf6931426fd48484ed40990919a0f4cf2715e545c8e475b6095 | MATLAB | 327 | 7 | %TERRAIN green-brown colormap
function map = terrain(varargin)
% map = [0 40 42; 10 120 10; 80 150 0; 150 100 0; 220 150 0]/255;
% map = [0 0 50; 0 80 100; 50 160 0; 120 120 0; 160 80 0; 220 160 0]/255;
map = [0 0 120; 0 100 100; 50 160 0; 120 120 0; 160 80 0; 220 160 0]/255;
map = colormap_helper(map, varargin... |
240728fc893d7ef1fe048ff035755a18601caa40402ce259bec649c0f5822572 | MATLAB | 328 | 8 | % Converts the given array of elements into a vector along dimension dim, i.e. the output will be an
% array of size 1 in all dimensions other than dim, which will have all the input elements.
function v = vecD(elements, dim)
vSize = [ones(1, dim-1), numel(elements), 1];
v = reshape(elements, vSize);
... |
c14db8b0ea5e05957e49ab09db8b802baa9923ce1ad87b56a491863e104f182d | MATLAB | 328 | 19 | function x=nt_normrow(x)
% y=nt_normcol(x) - normalize each row so its msq is 1
%
% y: normalized data
%
% x: data to normalize
%
if ndims(x)>2
s=size(x);
x=nt_normrow(reshape(x,[x,prod(s(2:end))]));
x=reshape(x,s);
else
w=1./sqrt(mean(x.^2,2));
w(find(isnan(w)))=0;
x=bsxfun(@times,x,w);
e... |
c5e799e9917f7ae488a70f7fe0ad3841bb643dc6d85b75e8b429c37a62d63e90 | MATLAB | 329 | 12 | function str = camelCase(str)
if iscell(str)
for iStr = 1:numel(str)
str{iStr} = regexprep(str{iStr}, '[^a-zA-Z0-9]*([a-zA-Z0-9]+)[^a-zA-Z0-9]*', '${titleCase($1)}');
end
elseif ~isempty(str)
str = regexprep(str, '[^a-zA-Z0-9]*([a-zA-Z0-9]+)[^a-zA-Z0-9]*', '${titleCase($1)}');
end... |
e4f53df9fcd92c29a5c5940c2e64d06fef48ef2397b82cf1668a5edc092bba16 | MATLAB | 329 | 11 | % Convert spans [n1, n2, ...] to ranges {1:n1, n1+1:n1+n2, ...}
function index = span2index(span)
%% If a vector is provided, assume that it's the count of items in each span
if isvector(span)
endPoints = cumsum(span(:));
span = [1; endPoints(:)+1];
end
index = range2index(span);
... |
32a01a453e0d5d1dfbb4116886d4a9beef6ee1c820b591a23288f3c53527f467 | MATLAB | 330 | 14 | function pvalue = empiricalPvalue_abs(empiricalDist, observedValue)
arguments
empiricalDist (:,:) double
observedValue (1,1) double
end
N = numel(empiricalDist);
empiricalDist_abs = abs(empiricalDist);
observedValue_abs = abs(observedValue);
k = sum(empiricalDist_abs >= observedValue_abs);
pvalue = (k+1) / (N... |
98dd008c8e4865f16b583065fa483399c41beeffe6ca037b3e858e7a69ce0fc1 | MATLAB | 331 | 11 | function txt = sciFormat(number, precision)
if nargin < 2 || isempty(precision)
precision = '%.3g';
elseif isnumeric(precision)
precision = sprintf('%%.%dg',precision);
end
txt = sprintf(precision, number);
txt = regexprep(txt, 'e(-?)0*([0-9]+)', '\\times10^{$1$2}');... |
e815e7d06af069f63b95d71fb4c74a136ca7e3bb3062cf62179dcf07a3c0d3c5 | MATLAB | 331 | 12 | function ftDataOut = reselect_channels(ftData,channelInfo)
% Select only for channels that are connected
validChannels = strtrim(cellstr(num2str(channelInfo.Channel)));
validChannels = strcat('LFPx',validChannels);
% Now select subset
cfg = [];
cfg.channel = validChannels;
ftDataOut = ft_selectdata(cfg, ftDa... |
0d9d0e73e90537c2425e946ff91fb83b743c9b87b67df3743cd817c207f82361 | MATLAB | 332 | 10 | function x = copnorm(x)
% COPNORM Copula normalisation
% cx = copnorm(x) returns standard normal samples with the same empirical
% CDF value as the input. Operates along the first axis.
% Equivalent to cx = norminv(ctransform(x))
%
[~,x] = sort(x, 1);
[~,x] = sort(x, 1);
x = x / (size(x, 1) + 1);
x = -sqrt(2).*er... |
34da0bf37e9bc9a310540afe730025fe2fe2cbe2e540c6b40f65a96d4a46130e | MATLAB | 333 | 10 | function fmap = cpp_mex_default_function_map()
fmap = struct( ...
'copnorm_slice', 'copnorm_slice_cpp', ...
'info_cc_slice', 'info_cc_slice_cpp', ...
'info_cc_multi', '', ...
'info_cc_slice_indexed', '', ...
'info_c1d_slice', '', ...
'info_cd_slice', 'info_cd_slice_cpp', ...
'info_dc_slice_b... |
34dd73007dee573cd4b534ba6b62b2b077986474fc7e653d60b8149366a8ace7 | MATLAB | 333 | 13 | % Like figure(), but accepts a resizing factor.
function fig = sfigure(scale, varargin)
if nargin < 1
scale = 0.6;
end
fig = figure(varargin{:});
pos = get(fig, 'Position');
pos(3:end) = pos(3:end) .* scale(:)';
pos(3:end) = max(pos(3:end), 1);
set(fig, 'Position', pos... |
781a97e28dcb5bf87fc8d02749e4fe4b7336fc43d646b7f66f7abc348e6aca34 | MATLAB | 335 | 18 | function [minval maxval] = imrange(inpict)
% [min max] = IMRANGE(INPICT)
% returns the global min and max pixel values in INPICT
%
% INPICT can be a vector or array of any dimension or class
x = inpict(:);
minval = double(min(x));
maxval = double(max(x));
if nargout < 2
minval = cat(2,minval,maxval... |
63c5df7ad4bc03589ec0bc209455583224f93e35122d13fab78b242db6411e17 | MATLAB | 336 | 13 | function [dataWithoutNaNs] = remove_nans(data)
%% remove nans from array
dataWithoutNaNs = [];
for col = 1:size(data, 2)
columnData = data(:, col);
columnData(isnan(columnData)) = [];
dataWithoutNaNs(1:size(columnData,1),col) = columnData;
end
dataWithoutNaNs(dataWithoutNaNs ==... |
81722a9f410e8102e4f8aff7ac87e367b85081eb3be5afa0f5a66eeb85001a19 | MATLAB | 336 | 14 | %% function to get bin where HDC tuned to
function TunedBin = GetTuningBin(HD_list, HD_table)
TunedBin = zeros(length(HD_list),2);
for i = 1:length(HD_list)
idx = find(HD_table.cellID == HD_list(i));
TunedBin(i,:) = HD_table.IsPC_HDC(idx,2:3).*HD_table.RVMeanBin(idx,:);
end
TunedBin(TunedBin == ... |
b120bc272524f80139dd5d46debef05bf402f2ec8f15f51c395e65e6deae9512 | MATLAB | 336 | 13 | %%
% Computes a profile of a histogram over a given dimension.
% Todo: optionally also compute the spread
%
function [profile] = hprofile(histo, dimension)
profile = sum(histo, dimension);
counts = sum(histo > 0, dimension);
profile = profile ./ max(counts, 1);
profile = squeeze(profile)... |
0ddad0e8356fcfd4eb696c616aed5464fc976a0c148df62adda6413e26b9f8cc | MATLAB | 337 | 21 | function ExcelPrint(prefix,filename,T)
mydir=cd;
if isdir([mydir '/Data'])==0
mkdir('Data')
else
end
newname=strjoin([prefix,'_',filename,'.csv']);
if newname{1}(1)=="_"
newname=string(newname{1}(2:end));
end
a= [cd,'\Data'];
mydir=cd;
cd(a)
writetable(T,newname,'WriteVariableNames',false);
cd... |
1f996d084a770685c389cb9335511a6422ccad24a49dbf5bce06ac9811a89a4d | MATLAB | 338 | 8 | function [psth, centers] = return_histogram(spk_times, t_span, n_trials, smooth_win)
edges = 0:1:t_span;
centers = (edges(1:end-1)+edges(2:end))/2;
counts = histcounts(spk_times, edges);
counts = counts/n_trials;
sm_wind = hanning(smooth_win); % gausswin(smooth_win,smooth_win/10);
psth = conv(counts, sm_wind, 'same')... |
5e2fda245413d5f0dc5298f2fdd6de8ec61868888e0ef195ea2505d51c3f92b9 | MATLAB | 338 | 17 | function [s] = var2struct(varargin)
% VAR2STRUCT Returns a struct containing all input variables.
s = struct;
if nargin < 1
return;
end
s = repmat(s, size(varargin{1}));
for iArg = 1:nargin
values = num2cell(varargin{iArg});
[s.(inputname(iArg))] = values{:};... |
7b3857236c688a4d114b5926650009997ebb643873dd2381e4f1070deda4dd87 | MATLAB | 338 | 14 | % Pregenerate filter weights
function kernel = gaussianKernel1D(kernelWidth, dx, nSigmas)
if nargin < 2
dx = 1;
end
if nargin < 3
nSigmas = 4;
end
kernel = normpdf(-nSigmas*kernelWidth:dx:nSigmas*kernelWidth, 0, kernelWidth);
kernel = kernel / sum(ke... |
d96551e5f4757ab3285ccdd9f4a1883eb4907dbd91efdbafbf7bff7ef694f4c2 | MATLAB | 338 | 17 | %%
% Separates inputs into two lists based on the provided test.
%
function [pass, fail] = separate(test, varargin)
pass = varargin;
fail = varargin;
for iArg = length(varargin):-1:1
if test(varargin{iArg})
fail(iArg) = [];
else
pass(iArg) = [];
end
... |
fba14621f33d7c142c3123184b125de1ff35ebb57eadb55cc86a375d16e61369 | MATLAB | 338 | 16 | %%
% Recursively runs max() on an input tensor, returning the maximum over all
% element in all directions.
function [x, i] = rmax(tensor, dimensions)
if nargin < 2
[x, i] = max(tensor(:));
else
x = tensor;
for iD = 1:numel(dimensions)
x = max(x, [], dimensions(iD));
end... |
0b880f45e87a004cbbb0dcb303cabfdb9599aaacbdd223f24bc53f692dc1a161 | MATLAB | 339 | 17 | %%
% Returns entries that have a significant amplitude relative to the maximum.
%
function [x] = chopped(x, epsilon)
if nargin < 2
epsilon = 1e-10;
end
if numel(x) > 1
big = ( abs(x) >= epsilon * (rmax(x) - rmin(x)) );
else
big = ( abs(x) >= epsilon );
end
x =... |
148394fe38e45fcfb4eefd58e63f41ed0ff2aec53570ab109e9586a15a4a0fad | MATLAB | 339 | 17 | %%
function [maximum] = emax(array, exceptTest)
% Exclusive maximum, over all elements except those passing the
% exceptTest.
if nargin < 2
exceptTest = @iszero;
end
maximum = nan;
for iX = 1:numel(array)
if ~exceptTest(array(iX))
maximum = max(maximum, array(iX));
en... |
2478558716192b40f7f10bbc9d8d2bca3922bd8a004e9990c128f22541862e81 | MATLAB | 340 | 16 | function z = entropy(x)
% Compute entropy z=H(x) of a discrete variable x.
% Input:
% x: a integer vectors
% Output:
% z: entropy z=H(x)
% Written by Mo Chen (sth4nth@gmail.com).
if ~isempty(x)
n = numel(x);
[~,~,x] = unique(x);
Px = accumarray(x, 1)/n;
Hx = -dot(Px,log2(Px));
z = max(0,Hx);
e... |
ce2b0aa80023ae724aae1b7881709eb0e781289849856546cd0789b823159144 | MATLAB | 341 | 11 | % See also span2index
function index = range2index(range)
%% If a vector is provided, assume that it's the first element of all ranges
if isvector(range)
range = range(:);
range = [range(1:end-1), range(2:end)-1];
end
index = arrayfun(@(x,y) x:y, range(:,1), range(:,2), 'UniformOutput... |
314cb1e1e7852ad82ab6ea23ad9159290326efda417f83ad9ce1b2b9e76a8d97 | MATLAB | 342 | 15 | classdef UtilImage
% Image utilities
methods (Static)
function out = check_black_is_true(I)
% check whether the black pixels are labeled as
% "true" in the image format, since there should
% be fewer black pixels
out = sum(I(:)==true) < sum(I(:)==false);
end
... |
2302ff8606b630aafe5eb616b6c71b22e32a109db1ab6f471e5a5fbe3fc2a10e | MATLAB | 343 | 15 | %%
% Recursively runs sum() on an input tensor, returning the sum over all given directions.
function S = rsum(tensor, dim, varargin)
if nargin < 2 || isempty(dim)
S = sum(tensor(:));
else
S = sum(tensor, dim(1), varargin{:});
for d = dim(2:end)
S = sum(S, d, varargin{:});
... |
d1c8c02c3ca0d97888063d3ba07c37b01ab74a0a2b4550d4a89f4924e463faad | MATLAB | 343 | 23 | function listing = dir2(varargin)
if nargin == 0
name = '.';
elseif nargin == 1
name = varargin{1};
else
error('Too many input arguments.')
end
listing = dir(name);
inds = [];
k = 1;
while k <= length(listing)
if strcmp(listing(k).name(1), '.')
inds(end + 1) = k;
end
k = k + 1;
e... |
67090bdbc7a4344faf3046b4beec1f67ce9f1d27291cc7a7c60ded5e1120d4c9 | MATLAB | 345 | 14 | function draw_fixation(windowPtr, center, color)
% Draws round fixation marker in the center of the window by superimposing
% vertical and horizontal bars.
% Written by KGS Lab
% Edited by AS 8/2014
% find center of window
center_x = center(1);
center_y = center(2);
Screen('DrawDots', windowPtr, [center_x center_y], ... |
b45794424e8cfdd03f51a1c3a3ceab62ab4c5efe99c77ca07884abac1fa9c3a1 | MATLAB | 346 | 12 | function [x] = create_design_matrix_model(EEG1)
num_params = 4;
% initialize with size (num events, number of parameters)
x = zeros(length(EEG1),num_params);
% categorical variables
x(:,1) = [EEG1.AMs_i];
x(:,2) = [EEG1.NAMs_i];
% continous variables
x(:,3) = nanzscore(log10(abs([EEG1.pks])));
% Intercept
x(:,end) = ... |
21c9c5d0f4a43bb40b916d358efb9c891a277d3880a0d1e84d72b345cc6d62f0 | MATLAB | 350 | 10 | function [] = setup_miji()
% @author: pdzialecka
%% Add FIJI / MIJ paths
% ADJUST these depending on local directories
javaaddpath 'C:\Program Files\MATLAB\R2021a\java\jar\mij.jar'
javaaddpath 'C:\Users\Pat\Desktop\Fiji.app\jars\ij-1.53q.jar'
addpath(genpath('C:\Users\Pat\Desktop\Fij... |
4545f2243618153dfb0be5cf936710787c5e4d94bcbceb6da1e63b0af7ff3ea6 | MATLAB | 351 | 13 | %% Function to calculate Pearson's correlation and Fisher's z-score between two maps
function [Pearsons, Fishers] = GetPearsons(mapI,mapII)
[~,~,N_cell] = size(mapI);
Pearsons= zeros(N_cell,1);
Fishers = zeros(N_cell,1);
for i = 1:N_cell
Pearsons(i) = nancorr(mapI(:,:,i), mapII(:,:,i));
Fishers(i) = ... |
d6d4fe07f1c2e2f8dd0c221fdc048f033875a03282f2d65887a79ea9d9cd40cf | MATLAB | 352 | 7 | function spacing = infer_electrode_spacing(positions)
% INFER_ELECTRODE_SPACING Use the position coordinates to infer the average distance between electrodes
%
% 2010-06 phli - Abstracted out of plot_ei_.m
%
interpoint_distance_matrix = ipdm(positions, 'subset', 'nearest', 'result', 'struct');
spacing = median(inter... |
35601103486b0f26760f23a27af5ff5b9f464edd339d99a02fb202390bd2cb3d | MATLAB | 353 | 17 | %% Writes lines into a text file
function fid = writeFile(filePath, contents)
fid = fopen(filePath, 'w');
if iscellstr(contents)
for iLine = 1:numel(contents)
fprintf(fid, '%s\n', contents{iLine});
end
else
fprintf(fid, '%s', contents);
end
if nargout < 1
... |
52adc119d1d0ac9b6e79ff60b84c5b9077807054724ea79c7cabee523916d0f4 | MATLAB | 354 | 21 | %%
% Even safer version of division, whereby x/0 is taken to be 0 no matter
% what value x has.
%
function [z] = saferdiv(x, y, nil)
z = zeros(size(y));
sel = (y ~= 0);
if numel(x) > 1
z(sel) = x(sel) ./ y(sel);
else
z(sel) = x ./ y(sel);
end
if nargin > 2
... |
cb3a74ac1c8674a69322738fde9929b041bae122f2047f6c22b072842c1dd588 | MATLAB | 354 | 13 | function C = convnPadded(A, B)
% Applies replicate padding before convolution. Output is same size as A.
padsize = (size(B)-1)/2;
padsize_pre = floor(padsize);
padsize_post = ceil(padsize);
A_padded = padarray(A, padsize_pre, 'replicate', 'pre');
A_padded = padarray(A_padded, padsize_post, 'replicate', 'post');
C = ... |
dd7902ae9c87ed96194a9da84effbf03938a4749a28e98d0e47010bb5ca0a7f0 | MATLAB | 354 | 14 | function addaxis_zoom_pre(obj,evd)
% update the added axes with new zoom
% disp('zoom pre');
%========================================================
% Part of modification for ADDAXIS commands
ystart = get(gca,'ylim');
%========================================================
setaddaxisdat... |
c32c8d703a3ab3351713cb9ea31ee9594d534e4a1973a935aa7aca240faffedb | MATLAB | 355 | 17 | %%
function [name] = range2str(minimum, maximum, separator, precision)
if nargin < 3
separator = 'to';
end
if nargin < 4
precision = 3;
end
if approx(minimum, maximum)
name = num2str(minimum, precision);
else
name = [ num2str(minimum, precision) separator num2str(max... |
58cb7187b776e8c32abb9d54822e79808098a9b8f58c3e50887ea5a6fcc6e883 | MATLAB | 356 | 15 | function pfun=getSigmoidValue(X,sigmoid,m,width)
% this gets the value of a given sigmoid with parameters:
% 1: alpha
% 2: beta
%
if isa(sigmoid,'function_handle')
sigmoidHandle = sigmoid;
elseif ischar(sigmoid)
sigmoidHandle = getSigmoidHandle(sigmoid);
else
error('sigmoid of invalid type specified')
en... |
c4242cef3d44b8cb74ec136bddd3a210421bb497fd84799c868db4f20e5b08b4 | MATLAB | 356 | 17 | %%
function [output, status] = explorer(varargin)
args = [varargin{:}];
if ~exist(args, 'file')
try
args = eval(args);
end
end
if exist(args, 'dir')
[status, output] = system(['explorer "' args '"']);
else
[status, output] = system(['explorer /s... |
d1bebdfccc3f85af60d879228bd80a4a7044b848063ac887a1b14fad703cfd00 | MATLAB | 357 | 14 | function str = titleCase(str)
if iscell(str)
for iStr = 1:numel(str)
% str{iStr}(1) = upper(str{iStr}(1));
str{iStr} = regexprep(str{iStr}, '(^|\s+)(.)', '$1${upper($2)}');
end
elseif ~isempty(str)
% str(1) = upper(str(1));
str = regexprep(str, '(^|\s+)(.)... |
e767eebcc91b6aa081ceac1995ef797cd3965e1aea87ff17c02b6b005991ccdf | MATLAB | 359 | 15 | % NANOUT Replace with NaN the entries of X corresponding to veto = true.
%
% Created : 25-Jun-2018 21:15:03
% Author : Sue Ann Koay (koay@princeton.edu)
%
function X = nanout(X, veto, flagValue)
if nargin < 3 || isempty(flagValue)
flagValue = nan;
end
assert(isequal(size(X), size(veto)));... |
05bae163ce40a32353683a5810cf144cc7694a9f0597c5dffb6608e3a7d1eb9c | MATLAB | 363 | 13 | function [yes] = ishandletype(x, type)
% ISHANDLETYPE Returns true if the given item(s) are graphics handles of
% the desired type.
yes = false(size(x));
for iX = 1:numel(x)
yes(iX) = ishghandle(x(iX)) ...
&& any(strcmpi(get(x(iX), 'type'), typ... |
51a2d8fa54da4a2efd38bffae8930d10674517186e26d656ce73eea78246c3a6 | MATLAB | 363 | 15 | function config = read_config_file(socket)
filename = read(socket, 1024, 'int8');
filename = filename(filename ~= 0);
filename = char(filename');
warning( ...
"gadgetron:external:config_filename", ...
"[Unsupported] Client referenced config file by name: %s", ...
filename .... |
57b9de40d6e2152d80680b4a15047bf012e21209d38a879fe0fa885cbaa629ec | MATLAB | 363 | 14 | %% function to calculate events for splitted dataset
function EventTime = GetEventTime(CaMatrix,frames)
%% get splitted events
[N_cell, ~] = size(CaMatrix);
EventTime = cell(N_cell,1);
for i = 1:N_cell
QueryEvents = intersect(find(CaMatrix(i,:)==1),frames); % activity in query frames
EventTime{i} = Q... |
83dcafb4d44516ff90b93b540e8ed52212dd7fd1cac875d8e5346e62c40b6784 | MATLAB | 364 | 12 | %% Return a struct that has only fields of the input that match the given regular expression pattern
function K = keepfield(S, pattern, varargin)
K = repmat(struct(), size(S));
keep = fieldsmatching(S, pattern, varargin{:});
for what = keep'
for iObj = 1:numel(K)
K(iObj).(what{:}) = S... |
25783ff4cc2ca5b4a3b845269357fd9a944cf3986c251446e45c19b67dcad4dc | MATLAB | 365 | 17 | function [C,IA,IC,N] = nt_unique(A, varargin)
%[C,IA,IC,N] = nt_unique(A, varargin) - unique with counts
%
% N: number of occurrences
%
% See unique for definition of other variables.
%
% NoiseTools
[Asorted,iSort]=sortrows(A);
[~,iReverse]=sort(iSort);
[C,IA,IC]=unique(Asorted,varargin{:});
p=find([1;diff(IC);1]); ... |
0a2ad7985dcefb3f38343dc985b611429ee9cfb3dc31f1b0e39d8450ddcc06eb | MATLAB | 366 | 13 | %% BINOFITF Calls binofit() when provided a fraction and a total count.
function [phat, pci] = binofitf(fraction, total, varargin)
count = fraction .* total;
nearest = round(count);
if all(istiny(count - nearest))
count = nearest;
% else
% keyboard
end
[phat, pci] ... |
4b093f28ffd113fd593a96921b7749abc3c8bbd35331a92a1449183988248b82 | MATLAB | 368 | 20 | function [R,M,k,D] = circ(a1,a2,a3)
D = cross(a2-a1,a3-a1);
b = norm(a1-a3);
c = norm(a1-a2);
if nargout == 1
a = norm(a2-a3);
R = a*b*c/2/norm(D);
return
end
E = cross(D,a2-a1);
F = cross(D,a3-a1);
G = (b^2*E-c^2*F)/norm(D)^2/2;
M = a1 + G;
R = norm(G);
if R == 0
... |
c8a13552b3fc9beeafe302f3e5bc637e8658cd4dc66bb455748502813ea0e2b9 | MATLAB | 368 | 9 | function D2 = naneucdist(XI,XJ)
%NANEUCDIST Euclidean distance ignoring coordinates with NaNs
n = size(XI,2);
sqdx = (XI-XJ).^2;
nstar = sum(~isnan(sqdx),2); % Number of pairs that do not contain NaNs
nstar(nstar == 0) = NaN; % To return NaN if all pairs include NaNs
D2squared = nansum(sqdx,2).*n./nstar; % Correctio... |
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