sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
21338b993f8faa32e7754093c94486df4792a713943c14f35358d1c8a42f9f3f | MATLAB | 287 | 13 | function [ XY_coords ] = centroids( CONTS )
%UNTITLED Summary of this function goes here
% Detailed explanation goes here
N=length(CONTS); %centros
for j=1:N
a{j}=mean(CONTS{j});
end
XY_coords=cell2mat(a');
%abrir y salvar las coordenas en un archivo de texto normal
|
808644f69c332610ffb219d3a791c360163c899dbc54771b01bdd09be75236ca | MATLAB | 287 | 14 | function nii_save(nii,niifile,vartype)
%author EML
if nargin > 2
nii.hdr.dime.datatype = vartype;
nii.hdr.dime.bitpix = vartype;
end
if isfield(nii, 'untouch')
disp('Untouch option found');
save_untouch_nii(nii,niifile);
else
save_nii(nii,niifile);
end |
4d24d155142c44c88d9892f99e0e4dee4cf5f0617b9cded1253d67f2be683641 | MATLAB | 288 | 12 | function matlab_version = icatb_get_matlab_version
% Get matlab version
matlab_version = version('-release');
[startInd, endInd] = regexpi(matlab_version, '\d+');
if ~isempty(startInd)
matlab_version = matlab_version(startInd:endInd);
end
matlab_version = str2num(matlab_version); |
7f08792c5e5acc5fe8f916624dc344ef45557de87865d04faac153642c05e2b0 | MATLAB | 288 | 10 | %{
# list of speakers in various setups
id : int # some number that identifies the speaker
rig : char # setup name
location : tinyint # 1 = Left, 2 = Right
%}
classdef Speakers < dj.Manual
end
|
b610b33d29c36c6624ce6c8bd241df85cab5b5e3993dc98293ca425879eb3333 | MATLAB | 288 | 11 | function reader = getGalvoReader(key)
[path, filename] = fetch1(experiment.Session*experiment.Scan & key, ...
'scan_path', 'filename');
fprintf('Loading from %s\n', path);
path = getLocalPath(fullfile(path, sprintf('%s*.tif', filename)));
reader = ne7.scanimage.Reader5(path);
end |
fb5304c4b82600857bec0dce247bae333a49b1339615650184c3adaea178269b | MATLAB | 288 | 19 | %{
#
-> tune.STAMap
---
snr : float # RF contrast measurement
%}
classdef STAQual < dj.Computed
methods(Access=protected)
function makeTuples(self, key)
%!!! compute missing fields for key here
self.insert(key)
end
end
end |
6c8d2001a8bea5ae7ae5e2482cfd297ed54d1960d3bf9307e242106000c04baa | MATLAB | 289 | 11 | classdef nnsigmoid < nntest
methods (Test)
function basic(test)
x = test.randn(5,5,1,1)/test.range ;
y = vl_nnsigmoid(x) ;
dzdy = test.randn(size(y)) ;
dzdx = vl_nnsigmoid(x,dzdy) ;
test.der(@(x) vl_nnsigmoid(x), x, dzdy, dzdx, 1e-3) ;
end
end
end
|
8ae12e9573b94fd9ed6e0cbf6ab5889d72cf65fec1e78ebb0d2ef3b4b3bab781 | MATLAB | 291 | 10 | %{
trk.VideoGroup (lookup) # table that groups videos into groups that can be tracked by the same SVM
videogroup_id : tinyint # id of the video group
---
group_name : char(20) # name of the group
%}
classdef VideoGroup < dj.Relvar
end |
df6cc31bc720f2e7baff00f3cd7eecdd633c7489f119c28601f05bd621bd4fbe | MATLAB | 291 | 14 | colors = wes_palettes('FantasticFox2');
close all
figure
set(gcf,'color','white')
nbars = size(colors,1);
hold on
for i = 1:nbars
bar(i,nbars+0.5,1,'FaceColor',colors(i,:),'EdgeColor',[1 1 1]*1);
end
set(gca,'xtick',[],'ytick',[])
xlim([0.5 nbars+0.5])
ylim([0.5 nbars+0.5])
axis square |
fd634ec30ca33fc69ffeca2753c054982c603655196c6a4db67a7389048ae3ed | MATLAB | 291 | 10 | %{
# Inspected movie clips that are not that bad
-> stimulus.MovieClip
---
judge : enum('human','machine') # was it a man or a machine?
notes : varchar(256) # a little explanation for the inclusion process
%}
classdef ClipInspected < dj.Manual
end |
3cd46bddd4c482d21169a813b391f9d0eec99ec69e44905abff2878586333dc1 | MATLAB | 292 | 10 | function arrFeatures = returnFeatures(param_arr)
measure_set = {'Mean','Median','StDev','Skewness','Avg_Signal'};
arrFeatures = zeros(1,5);
arrFeatures(1) = mean(param_arr);
arrFeatures(2) = median(param_arr);
arrFeatures(3) = std(param_arr);
arrFeatures(4) = skewness(param_arr); |
84fda6f11669044f5a15ba3328c165469216903d2325808d059c164ddfbea12b | MATLAB | 292 | 11 | % Mex version to compute C*A*C'
% Where C is the pairmatrix of K*(K-1)/2 x K size
% This consitutes the main time sink in RSA-based computations,
% for example for varianceLDC
% INPUT:
% A: KxK matrix: single or double
%
% OUTPUT:
% B : K*(K-1)/2 x K matrix
%
% (c) Joern Diedrichsen |
8bc5deb8d0a42beb7c8044136847bd90470bd0598f3bdb33e86390a2fbfc7c61 | MATLAB | 292 | 15 | function [nptheta] = init_ptheta(ptheta, htheta, pars)
% Precompute certain quantities
nptheta = ptheta;
if isfield(ptheta, 'prepare_ptheta')
nptheta = ptheta.prepare_ptheta(ptheta, htheta, pars);
end
if ~isfield(ptheta, 'sm')
nptheta.sm = tapas_ti_zeromat(nptheta.jm);
end
end
|
95c982c85442c0c40a60944616dedff6ce8b64d6618bce5c51a40e35c9b03d57 | MATLAB | 292 | 9 | function [] = remove_errorbarticks()
% remove errorbar ticks
xlims = get(gca, 'xlim'); ylims = get(gca, 'ylim');
hold on;
plot(xlim*100000, ylim*10000); % fool matlab into thinking we have much more data, which will automatically resize the errorbars
xlim(xlims); ylim(ylims); hold off;
end |
16a7a071a8557b38e1c21ae728825418f29e5ac9e7fd8784100eae20eec90b22 | MATLAB | 293 | 18 | %{
# summary images for each slice and channel after corrections
-> reso.MotionCorrection
-> shared.Channel
%}
classdef SummaryImages < dj.Computed
methods(Access=protected)
function makeTuples(self, key)
%!!! compute missing fields for key here
% self.insert(key)
end
end
end |
66bf83d9c87b97cf230a394a10f5302aac8de654dc735c79660ae1023db5f21e | MATLAB | 293 | 3 | function x = check_1d_distributed(x)
standard_distributions = {'bino','chi2','exp','ev','f','gam','gev','gp','geo','hyge','logn','nbin','ncf','nct','ncx2','norm', 'poiss','rayl','t','unif','unid','wbl'};
x = is_1d_distributed(x) && any(strcmp(x{1},standard_distributions)) && isnumeric(x{2});
|
6b0ea3b64b72c9b438f0742faf2245ddeb4647057d92429ba50fb5ee29d2e0be | MATLAB | 293 | 15 | function y = Cfunc(x)
% y=Cfunc(x)
% Testfunction defining a linear operator applied to x.
% Used for testing laneig.
%
% y = [ 0 A ] * x
% [ A' 0 ]
% Rasmus Munk Larsen, DAIMI, 1998
global A MxV
[m n] = size(A);
y = [A*x(m+1:end,:); A'*x(1:m,:)];
MxV = MxV + 2; |
9b275f07698d6925672ee5d7d43b89b7a17bbe09b6320201386a3e46234cf3d9 | MATLAB | 293 | 18 | %{
# summary images for each field and channel after corrections
-> meso.MotionCorrection
-> shared.Channel
%}
classdef SummaryImages < dj.Computed
methods(Access=protected)
function makeTuples(self, key)
%!!! compute missing fields for key here
% self.insert(key)
end
end
end |
abce3b47835bc1acec6eeab34e7229bb3d08073d41b18f3f27f5b467c5df6b1b | MATLAB | 293 | 22 | function r = uplus(p)
%UMINUS: this function applies a unary minus to the x, y, and z values of the structures that I use in
% the FVtool.
%
% SYNOPSIS:
%
%
% PARAMETERS:
%
%
% RETURNS:
%
%
% EXAMPLE:
%
% SEE ALSO:
%
% Copyright (c) 2012-2016 Ali Akbar Eftekhari
% See the license file
r=p;
|
03adcb15310cc031dac719cc1b0c8ef7e302c5d176f95dcdba1693ef1b4cd9ef | MATLAB | 294 | 1 | % CreateQuest.m
function q = CreateQuest(InitGuess)
tGuess = log10( InitGuess );
tGuessSD = 3;
pThreshold = 0.82;
beta = 3.5;
delta = 0.01;
gamma = 0.5;
grain = 0.01;
%grain = 0.0001;
range = log10( 1000 );
q = QuestCreate( tGuess, tGuessSD, pThreshold, beta, delta, gamma, grain, range);
|
6a900352680fd55e08718a8aa6f8706f9450358a9a3f957180e960dae50a9292 | MATLAB | 294 | 21 | %{
pre.ExtractTraces (imported) # my
-> pre.Segment
-----
%}
classdef ExtractTraces < dj.Relvar & dj.AutoPopulate
properties
popRel = pre.Segment
end
methods(Access=protected)
function makeTuples(self, key)
self.insert(key)
makeTuples(pre.Trace, key)
end
end
end |
a5265bcc038209450863fb7892e730a587b27d4fe3a3b70b1304a1bd45f7e782 | MATLAB | 295 | 8 | function ek = parse_echo_from_name(fp)
[~,name,ext] = fileparts(fp);
if strcmpi(ext, '.gz')
[~,name] = fileparts(name); % strip .nii
end
tok = regexp(name, '[_-]echo-([0-9]+)(?:[_-]|$)', 'tokens', 'once');
if isempty(tok), ek = ''; else, ek = tok{1}; end
end
|
c1d35bccaca13f550c0948e4c5a361853410316d8cc01d7d7faef0783e7fd6f7 | MATLAB | 295 | 13 | function tm = getFrameTimes(info, Nplanes)
Npts = round(numel(info)./Nplanes);
cnt = 0;
tm = zeros(Npts, Nplanes);
for i = 1:Npts
for j = 1:Nplanes
cnt = cnt+1;
tm(i,j) = getMetaVal(info(cnt).ImageDescription, 'frameTimestamps_sec');
end
end
tm = tm(:,1);
|
e94cf1244a70b6e04f96700cb5a75bbd659f48a4ad31d844b9fd817f06a489fc | MATLAB | 295 | 19 | %{
#
-> preprocess.ComputeTraces
(spike_method_1) -> preprocess.Spikes
(spike_method_2) -> preprocess.Spikes
%}
classdef SpikeMethods < dj.Computed
methods(Access=protected)
function makeTuples(self, key)
%!!! compute missing fields for key here
%self.insert(key)
end
end
end |
ed53680f01bb82818c008d19e8c0d0168b2edc89962ccb2ea405bf9cae1c2aa0 | MATLAB | 295 | 7 | % populate tables related to eye position clustering
cd /mnt/lab/home/atlab/pipeline/pipeline/matlab/scripts/Eye_Movements
startup
parpopulate(preprocess.CirclesLookup) ;
parpopulate(preprocess.CircleMap) ;
parpopulate(preprocess.EyePosClusterCenter) ;
%parpopulate(preprocess.ClusteredEyePos) ; |
4a023ff11a5f40d10c64d34b8025e792cbe10d15c0aa659124d586249636f1e2 | MATLAB | 296 | 17 | function y = extendswt(x, lf)
%EXTENDSWT extends the signal periodically at the boundaries
% Parameters
% -----------
% x :
% lf :
%
% Output
% ------
% y :
[r,c] = size(x);
y = zeros(r+lf,c);
y(1:lf/2,:) = x(end-lf/2+1:end,:);
y(lf/2+1:lf/2+r,:) = x;
y(end-lf/2+1:end,:) = x(1:lf/2,:);
end |
544bfa99ad9b771f3bed1509d5972b9db6e8d30eec53261fa6ae3635929fd623 | MATLAB | 296 | 15 | function [rez] = cleaning(rez,i,channel,Th)
del = [];
id_i = find(rez.st(:,end)==i);
i_chan= rez.Merge_cluster{i,5};
cha_id= find(i_chan==channel);
PDF = rez.PDF{i,cha_id};
del_id = find(PDF<Th);
id = id_i(del_id);
rez.st(id,:) = [];
end
|
d0af77a97a2e00a531e3557929b1220f9d5d300697e78682517cc7d5e970c33f | MATLAB | 296 | 20 | %{
#
-> preprocess.MaskClassification
-> preprocess.ExtractRawGalvoROI
---
-> preprocess.MaskType
%}
classdef MaskClassificationMaskType < dj.Computed
methods(Access=protected)
function makeTuples(self, key)
%!!! compute missing fields for key here
%self.insert(key)
end
end
end |
d75f69a0aea9d9d4a5302a93401fda4a7c0e5ab7c0fa91e7211e57897028f3db | MATLAB | 296 | 12 | % load HRF parameters
load('hrfparams.mat','params'); % 20 different HRFs x 7 parameters
% visualize
figure; hold on;
cmap0 = copper(20);
for p=1:size(params,1)
temp = spm_hrf(0.1,params(p,:));
temp = temp / max(temp);
plot(0:0.1:50,temp,'k-','Color',cmap0(p,:));
end
xlabel('Time (s)');
|
ef84e4f2caec915707db2d2ff6254f4b8da472dd446bf43ec4b934c7bccbf0fe | MATLAB | 296 | 10 | function output = image_center(input, window_size)
output = zeros([window_size window_size]);
size1 = size(input,1)-1;
size2 = size(input,2)-1;
offset1 = round((window_size-size1)/2);
offset2 = round((window_size-size2)/2);
output(offset1:(offset1+size1), offset2:(offset2+size2)) = input;
end
|
f1f705d8c28845c222480582e4b3d810e4adc35f80bfd8f80d0ed3584a824b6c | MATLAB | 296 | 12 | function combinedMagnitude = ComputeOptimalCombinedMagnitude(te,r2s,magn)
% Compute weights
wTE = zeros(size(magn));
for kt=1:length(te)
wTE(:,:,:,kt) = te(kt)*exp(-r2s.*te(kt));
end
% combine magnitude weighted by normalised weights
combinedMagnitude = sum(magn .* (wTE ./ sum(wTE,4)),4);
end |
0f2fe63f9c2a3867d2c783444c13b7f998191347de2e2fd8bb355314a4e78f20 | MATLAB | 297 | 14 | function [Xsm] = causal_smooth(X,win)
%simple code to do smoothing using data from future only
Xsm = zeros(size(X));
ns = numel(X);
for j = 1:ns
if j < win
Xsm(j) = 1/j*sum(X(1:j), 'omitnan');
else
Xsm(j) = 1/win*sum(X(j-(win-1):j), 'omitnan');
end
end
|
77a00af2bfb84797dd1a484a6afc84acbe8b4e83262a406f05ead7844ce8f9e8 | MATLAB | 298 | 17 | function [ndims] = tapas_sem_seria_ndims()
%% Return the number of dimensions of the seria family of models.
%
% Input
%
% Output
% ndims Number of dimensions of the parameters.
%
% aponteeduardo@gmail.com
% copyright (C) 2016
%
[ndims] = c_seria_ndims();
end % tapas_sem_seria_ndims
|
99833530d26c5901a24af1abc9c054f84a62e89177b5075ddf286da7469a941a | MATLAB | 298 | 11 | function createFolders(PATH)
%% createFolders
% check whether folders specified at all fiels of PATH structure exist, if
% not create them
FIELDNAMES = fieldnames(PATH);
for fi = 1:length(FIELDNAMES)
if ~ exist(PATH.(FIELDNAMES{fi}), 'dir')
mkdir(PATH.(FIELDNAMES{fi}));
end
end
end |
0045edfca4cafd4ca3ee4b13e0c35e3366f396c268f54a77fd0b49aad8827787 | MATLAB | 299 | 18 | for i=1:20
i
n1=num2str(900+i);
file=strcat('fort.',n1);
x=load(file);
Fs=100000;
inh=x(:,1)-mean(x(:,1));
amp=x(:,2)-mean(x(:,2));
nmd=x(:,3)-mean(x(:,3));
an=(amp+nmd)/2;
pin=rms(inh)^2;
pan=rms(an)^2;
ar(i)=pan/pin;
end
arm=mean(ar)
arstd=std(ar) |
927b61689dc95f5f31e4e9ca9286212cc8f92fd349c81007fd54b461268c85b0 | MATLAB | 299 | 13 | function subplot2(nrows, ncols, i, j)
sz = [nrows ncols];
k = sub2ind(sz(end:-1:1), j, i);
subplot(nrows, ncols, k);
if 0
ncols_plot = ceil(sqrt(Nplots));
nrows_plot = ceil(Nplots/ncols_plot);
Nplots = nrows_plot*ncols_plot;
for p=1:Nplots
subplot(nrows_plot, ncols_plot, p);
end
end
|
9faf88efccf6240cb4b79493325fcf810564230c16542bf3100dadcdf23d4526 | MATLAB | 299 | 7 | function [infGrad, genGrad] = modelGradients(encoderNet, decoderNet, x)
[z, zMean, zLogvar] = sampling(encoderNet, x);
xPred = sigmoid(forward(decoderNet, z));
loss = ELBOloss(x, xPred, zMean, zLogvar);
[genGrad, infGrad] = dlgradient(loss, decoderNet.Learnables, ...
encoderNet.Learnables);
end |
6134aefc423ccc8797671290abfadab3fad6e383d6f9d723a295410cb25e5e5f | MATLAB | 300 | 6 | px2cm = 0.169;
reg1 = mean(abs(WholeTestResult(:, 3) - WholeTestResult(:,4))) * px2cm
reg2 = mean(abs(WholeTestResult(:, 3) - WholeTestResult(:,5))) * px2cm
reg3 = mean(abs(WholeTestResult(:, 3) - WholeTestResult(:,6))) * px2cm
reg4 = mean(abs(WholeTestResult(:, 3) - WholeTestResult(:,7))) * px2cm
|
7800146941975dad48ee5be59adc5d440bcc3d03bbb4433088000e9429e1e729 | MATLAB | 300 | 13 | function A = loaddiag(A)
% FORMAT A = brca.utils.loaddiag(A)
% A - A square matrix
%
% Load A's diagonal until it is well conditioned for inversion.
factor = 1e-7;
while rcond(A) < 1e-5
A = A + factor * max([diag(A); eps]) * eye(size(A));
factor = 10 * factor;
end
end
|
12c0fc39940c239a1bba44d739f79f8f6c5865e1512b8027c4d81919959a9e8e | MATLAB | 301 | 11 | function x = z1(y)
% linear normalize between 0 and 1
x = (y-min(y(:)))/(max(y(:))-min(y(:)))+eps;
% for multidimensional stuff, this normalizes each column to between 0 and
% 1 independent of other columns
% T=length(y);
% y=y';
% miny=min(y);
% x = (y-repmat(miny,T,1))./repmat(max(y)-min(y),T,1); |
5e63a061ff8d86eee9803e186976f3454878c2c797ba585b5d90bae223301228 | MATLAB | 301 | 10 | %{
# current speaker configured in the setup
rig : char # name of setup
---
speaker_id : int # some number that identifies the speaker
location : tinyint # 1=Left, 2=Right
%}
classdef CurrentSpeakers < dj.Lookup
end |
8773f4e598798aab0fbd806b0e7c883bf5ff16255e9e7d5916be7a79e73dd84c | MATLAB | 301 | 10 | %{
trk.SelectionProtocol (lookup) # groups of filtering steps to reject bad frames
filter_protocol_id: int # id of the filtering protocol
---
protocol_name : char(50) # descriptive name of the protocol
%}
classdef SelectionProtocol < dj.Relvar
end |
af61f83139e15a2fc5b3f285d41d8c71471f3c1b5cbe538eb7f489a751a1e426 | MATLAB | 301 | 13 | function C=pairMatrix(K)
% function C=pairMatrix(K)
% function to make the pairs
% C= indicatorMatrix('allpairs',[1:K]);
%
% Joern Diedrichsen 2016
C=zeros(K*(K-1)/2,K);
where = 1;
for i=1:K
C(where:where+K-i-1,i)=1;
C(where:where+K-i-1,i+1:end)=-eye(K-i);
where = where+K-i;
end;
|
01df742fe27338e7e9f5050630e40ac55bac9a152b27dbd9ec61bd3ec3ea2fdc | MATLAB | 302 | 19 | %{
# brain area membership of cells
-> `pipeline_preprocess`.`_extract_raw__galvo_r_o_i`
---
-> experiment.BrainArea
%}
classdef AreaMembership < dj.Computed
methods(Access=protected)
function makeTuples(self, key)
%!!! compute missing fields for key here
%self.insert(key)
end
end
end |
2d67cba6817fcc8a1fdd870de50de27c4d904cdf8c552661fdf119899970bc56 | MATLAB | 302 | 20 | %{
#
-> tuning.MonetRF
-> preprocess.SpikesRateTrace
---
map : longblob #
%}
classdef MovieRFMap < dj.Computed
methods(Access=protected)
function makeTuples(self, key)
%!!! compute missing fields for key here
%self.insert(key)
end
end
end |
7173774dc8bcf76afe32fd0c511dce9aa23b9f7e6b7ef4090c0d94fb81f2a6c9 | MATLAB | 302 | 17 | function [ndims] = tapas_sem_prosa_ndims()
%% Return the number of dimension of the gamma family of models.
%
% Input
%
% Output
% ndims Number of dimensions of the gamma family models
%
% aponteeduardo@gmail.com
% copyright (C) 2016
%
ndims = c_prosa_ndims();
end % tapas_sem_prosa_ndims
|
89597c2d189a5b80a87cde9fb54f7bd07cc6b937c41fc967492d13680c9f0138 | MATLAB | 302 | 17 | function stars = get_stars_for_P (P)
if P<0.0001
stars = '****';
else if P<0.001
stars = '***';
else if(P<0.01)
stars = '**';
else if (P<0.05)
stars = '*';
else
stars = 'n.s.';
end
end
end
end
end
|
8ab665ca97fc5dd25284694fd3dfb1f871469a1feaebf7a311e153e4fe8f1a19 | MATLAB | 302 | 15 | function tick = SecToTick(sec)
% Converts seconds to tick in microseconds
%
% function tick = SecToTick(sec)
%
% Input:
% sec - Time in seconds
%
% Output:
% tick - seconds converted to microseconds.
%
% (c) 2016 by Multi Channel Systems MCS GmbH
tick = sec * 1e6;
end |
b4d64a66c9d59c0b7f2b0cf0edb35678e095afa57f5accb9c7d3838f0ea25e0b | MATLAB | 302 | 15 | function obj = init_data(obj, dataset)
obj.data = struct;
N = numel(dataset);
[obj.data(1:N).x] = deal([]);
[obj.data(1:N).z] = deal([]);
[obj.data(1:N).zz] = deal([]);
[obj.data(1:N).Az] = deal([]);
for n=1:N
obj.data(n).x = dataset(n);
end
end |
01b88bf85504c9a40654048cf150020ef789830e4875c87432f4159522d64bf8 | MATLAB | 303 | 9 | function reader = getReader(key)
[path, basename, scanIdx] = fetch1(rf.Session*rf.Scan & key, ...
'scan_path', 'file_base', 'scan_idx');
fprintf('Loading from %s\n', path);
path = getLocalPath(fullfile(path, sprintf('%s_*%03u_*.tif', basename, scanIdx)));
reader = ne7.scanimage.Reader5(path);
end |
2227dabd0f6bae4edccfd5721508f2e2c05f7c2439432dc4c23de337ef39126c | MATLAB | 303 | 8 |
function [elbo, rcLoss, klLoss] = FD_clVAE_ELBOloss(x, xPred, zMean, zLogvar, beta)
reconstructionLoss = sum((xPred - x).^2, 1);
KL = -0.5 * sum(1 + zLogvar - zMean.^2 - exp(zLogvar), 1);
rcLoss = mean(reconstructionLoss);
klLoss = mean(beta*KL);
elbo = rcLoss + klLoss;
end |
25c421ea596b95084c33c8b4a56ebc836e1e3f54772b22a58bec1e5dd43d63e4 | MATLAB | 303 | 9 | % List of open inputs
nrun = 1; % enter the number of runs here
jobfile = {'/Volumes/IqraMacFmri/visTac/fMRI_analysis/code/calcSearchlight/createMeanAnat_job.m'};
jobs = repmat(jobfile, 1, nrun);
inputs = cell(0, nrun);
for crun = 1:nrun
end
spm('defaults', 'FMRI');
spm_jobman('run', jobs, inputs{:});
|
319db4a94acd75b53fde01f60c45269d174933e6a69cb4abb701b83a242df512 | MATLAB | 303 | 17 | function [tr, t] = sero_srr_w_to_tr(w, t_shot)
% function [tr, t] = sero_srr_w_to_tr(w, t_shot)
t = w .* (1:size(w,1))' * t_shot;
tr = nan(size(t));
for i = 1:size(t,2)
ind = find(t(:,i));
ds = diff([-inf; ind]); % FIXME!!!
tr(ind, i) = ds * t_shot;
end
tr(isnan(tr)) = 0; |
67f17b1dea5268ed783ceaf75865d929e2dd9cb8017cf43072a88671fec209d1 | MATLAB | 303 | 20 | function [B,l,k ] = mat_extend( A,it )
m=size(A,1);
n=size(A,2);
kk=ceil(m/4);
ll=ceil(n/4);
k=abs(kk*4-m);
l=abs(ll*4-n);
if it
col_add=ones(m,l);
row_add=ones(k,n+l);
C=[A,col_add];
B=[C;row_add];
else
col_add=zeros(m,l);
row_add=zeros(k,n+l);
C=[A,col_add];
B=[C;row_add];
end
end |
a5b66ed91cb82417410f5a1277250c549ee347d6a1c3a082816d525e84691755 | MATLAB | 304 | 14 | function tapas_BLT_initCogent(store)
% tapas_BLT_initCogent starts cogent which is needed for the response box
%
% About:
% Coded by F.Petzschner 19. April 2017
% Last change: F.Petzschner 19. April 2017
if ~store.keyboard
config_serial(store.scanner.boxport, 19200, 0, 0, 8);
start_cogent;
end
end |
e8a75e27d812340e1d943b6572de3bd9b219daa10b679cc021dccb8842cdc374 | MATLAB | 304 | 14 | function this = eegobj(EEG);
if nargin == 1
if isa(EEG, 'eegobj')
this = EEG;
return;
end
for index = 1:length(EEG)
TMP(index).EEG = EEG(index);
end
else
TMP.EEG = eeg_emptyset;
end
this = class(TMP, 'eegobj');
|
7cda38f7231a42dbdf70e23583b9025b6ed250201455cbe17905a58300ecc08d | MATLAB | 305 | 25 | function Step1_getExpInfo()
clc; close all;
addpath('Wavesurfer-master');
pth = 'C:\Users\economom\Dropbox (Personal)\Analysis\VoltageImaging\2021-01-28';
tokens = {'_opto_0'};
nums = 1;
fn.si = getImFiles(pth, token, nums);
fn.ws = getWSFiles(pth, token, nums);
|
f7376bb65db461c88b01803784571e231452cb861b82650fa21a56aa462b6813 | MATLAB | 305 | 8 | function [pathname, filename, ext] = myfileparts(filename)
% FUNCTION [pathname, filename, ext] = myfileparts(filename)
%
% Robust against .nii.gz file extension
[pathname, filename, ext2] = fileparts(filename);
[~, filename, ext1] = fileparts(filename);
ext = [ext1 ext2];
|
55d00b94f3d4e622ab4ec049adb89b80d96dca6cacfcf1ff1f023feaeb3a6a07 | MATLAB | 306 | 9 | function roiMovedCallback(rectangle,evt)
% This runs when a box's rectangle is resized or moved
hObject = get(rectangle,'Parent');
handles = guidata(hObject);
tag = str2double(get(rectangle,'Tag'));
handles.data.calls{tag,'Box'} = rectangle.Position;
SortCalls(hObject, [], handles, 'time', 0, tag);
end
|
a2eafd0b83f981beee3bb9f88add4795abf6e792ee78f0c25c970cd5d95d1303 | MATLAB | 306 | 3 | function res = is_symbolic_lambda(x)
% internal: a symbolic lambda function is one which generates expressions when invoked with arguments (this is what exp_symbol generates)
res = isa(x,'function_handle') && ~isempty(regexp(char(x),'@\(varargin\)struct\(''head'',\{.*\},''parts'',\{varargin\}\)','once')); |
0e74a8d672916029d3dc04149745907c073d5209513070f4bb91c34a12db0335 | MATLAB | 307 | 17 | function [gt] = tapas_trans_mv2gt(mu, sigma2)
%% Transforms to the scaling of the gamma distribution
%
% Input
% mu -- Means
% sigma2 -- Variance
%
% Output
% gk -- K parameter of an gamma distribution
% aponteeduardo@gmail.com
% copyright (C) 2015
%
gt = mu ./ sigma2;
end
|
28c7f22b21b52152afc821ce8648d9faca3fc4e01b781603fac1d97b977378da | MATLAB | 307 | 14 | function isnum = afni_ni_is_numeric_type(t)
ni_def=afni_ni_defs();
if (isnumeric(t)),
isnum = ( (t) >= 0 & (t) <= ni_def.NI_RGBA );
elseif (isnumeric(afni_ni_rowtype_name_to_code(t))),
isnum = ( (t) >= 0 & (t) <= ni_def.NI_RGBA );
end
isnum = prod(single(isnum));
return;
|
30ecc237a7526e6825ca5ad9789d0b01e7ba7b3ff62933c10be8b0ff97b18856 | MATLAB | 307 | 13 | function colors = colorPalette(n)
% defines colors for n elements as rgb tripplets
% (c) Jiri, May12
if n < 4
colors = [0 0 1; % b
1 0 0; % r
0 1 0]; % g
else
func = @(x) colorspace('RGB->Lab',x);
colors = distinguishable_colors(n,'w',func);
end |
37ef93b209188696e60c90cac1f5abd6c86fdfdc98c44e9d131f1a572d4d499f | MATLAB | 307 | 14 | function ind = sero_pulseq_getAllRFBlockInd(seq)
% function ind = sero_pulseq_getAllRFBlockInd(seq)
%
% Function returns a list of indices that correspond to RF events.
nBlock = numel(seq.blockEvents);
ind = [];
for i = 1:nBlock
if all(seq.blockEvents{i}([2 5]))
ind(end+1) = i;
end
end
|
526edcf024a50112a185701f32333ab0595c9c9e99c22422f55ef2f66043d87e | MATLAB | 308 | 20 | %{
#
-> reso.Fluorescence
-> reso.SegmentationMask
---
trace : longblob #
%}
classdef FluorescenceTrace < dj.Computed
methods(Access=protected)
function makeTuples(self, key)
%!!! compute missing fields for key here
% self.insert(key)
end
end
end |
9b4893a4a16b069647ed41aeea928bb930f08b2a8d94206fd288b21da7aa556e | MATLAB | 308 | 20 | %{
#
-> meso.Fluorescence
-> meso.SegmentationMask
---
trace : longblob #
%}
classdef FluorescenceTrace < dj.Computed
methods(Access=protected)
function makeTuples(self, key)
%!!! compute missing fields for key here
% self.insert(key)
end
end
end |
006af446e674826f2b856c31bc271a4a5cf3b985f00d9b9aff0036be3acc673a | MATLAB | 309 | 10 | function b = ctx_dec2col(X, n)
if nargin < 2
n = max(X(:));
end
b = zeros(length(X),n); % Create an empty array w/ as many rows as X, and as many cols as the max val of X
for i=1:length(X)
b(i,X(i)) = 1; % In that array, for each row and the corresponding column where X=that row, put a 1
end
end
|
0c0e4a711e9d0c2608d2a88f0e734ef259f4bde5b1da69f2e0ab0507f2002493 | MATLAB | 309 | 16 | function y = getBoxVal(x)
quartile=prctile(x,[25,50,75]);
iqr=diff(quartile([1,3]));
minMax(1)=min(min(x(x>=quartile(1)-iqr*1.5)),quartile(1));
minMax(2)=max(max(x(x<=quartile(3)+iqr*1.5)),quartile(3));
y.lower=quartile(1);
y.median=quartile(2);
y.upper=quartile(3);
y.iqr=iqr;
y.minMax=minMax;
y.raw=x;
|
4e857f3a8c020f3a077c1fa5dc860ac72d8fb2037dd12177a522e908a08c0a40 | MATLAB | 309 | 5 | function nodal_stats2(Nodalmetric,Pathnewxsl,opthub,Pathnewplot,Scalev1,Nname,pthretype,restulsmat);
result_name=Nname;
load(restulsmat);
FourModelsigthre=arrange_nodal_Slope(Nodalmetric,indminNodalStrmdall,indminNodalStr,notenoughNodalStr,Pathnewxsl,opthub,Scalev1,Pathnewplot,pthretype,result_name);
end |
8550e1b6ee573bfb3c8cb04bab51605179918eb290a0424817a63be23157d289 | MATLAB | 309 | 15 | function hline(y, varargin)
% HLINE plot a horizontal line in the current graph
abc = axis;
y = [y y];
x = abc([1 2]);
if length(varargin) == 0,
varargin = {'color', [0.5 0.5 0.5]};
elseif length(varargin)==1
varargin = {'color', varargin{1}};
end
h = line(x, y);
set(h, varargin{:});
|
9f8ff1a165a010d2f68a2db6db1d3e145ba2108cbae41f80554d4cbfdd23c27f | MATLAB | 309 | 14 |
function feature_maps = spiking_conv(spike_trains)
feature_maps = spike_trains;
kernel = [1 0; 0 -1];
threshold = 1.5;
for layer = 1:2
conv_out = conv2(feature_maps, kernel, 'same');
[~, spikes] = spiking_neuron(conv_out(:), threshold);
feature_maps = reshape(spikes, size(conv_out));
end
end
|
dd59727f4e812fe0eb9d5db24946ea400a712b7eb60d2caf66c0e0a54fb89f76 | MATLAB | 310 | 14 | function vline(x, varargin)
% VLINE plot a vertical line in the current graph
abc = axis;
x = [x x];
y = abc([3 4]);
if length(varargin) == 0,
varargin = {'color', [0.5 0.5 0.5]};
elseif length(varargin) == 1,
varargin = {'color', varargin{1}};
end
h = line(x, y);
set(h, varargin{:});
|
f31ee421e4f0100c095f6be71a03ec4b2239bb9c441b07ddb352b386fd420352 | MATLAB | 310 | 19 | function ans = is_row (v);
% ans = is_row (v);
%returns 1 if v is a row vector
%returns 0 if v is a column vector
%returns -1 if v is a matrix
%
% Ziad Saad July 20 97
if (size (v,1) ~= 1 & size (v,2) ~= 1),
ans = -1;
end;
if (size (v,1) == 1),
ans = 1;
elseif (size (v,2) == 1),
ans = 0;
end;
return;
|
1e3a05d8fe61883d3b4a18d52d53c3f2a5e76cd10422004f7d617311543b5285 | MATLAB | 311 | 23 | function r = not(p)
%UMINUS: this function applies a unary minus to the x, y, and z values of the structures that I use in
% the FVtool.
%
% SYNOPSIS:
%
%
% PARAMETERS:
%
%
% RETURNS:
%
%
% EXAMPLE:
%
% SEE ALSO:
%
% Copyright (c) 2012-2016 Ali Akbar Eftekhari
% See the license file
r=p;
r.value = ~p.value;
|
ce5635d50935e66b6ea8d2e3786431765a7fdd6dc5035e6658f10ae269c4229c | MATLAB | 311 | 16 | function [nvCell] = struct2NV(s)
% Converts a struct s into a cell of name-value pairs.
fieldNames = fieldnames(s);
numFields = length(fieldNames);
nvCell = cell(1, 2*numFields);
i = 1;
for field = 1:numFields
nvCell{i} = fieldNames{field};
nvCell{i+1} = s.(fieldNames{field});
i = i+2;
end
end
|
0d3d4aa51bf53332b38edac1e410cbcae0fcc32f01bbe5cc23a5c60760a47f0a | MATLAB | 312 | 11 | function StateNumber = mfunc_GetStateNumber(binarizedData)
% Get State Number from binarized Data
% if binarizedData is N x M matrix, binarizedData(N,:) is MSB.
% Need to flipud.
%dat = (binarizedData > 0);
dat = flipud(binarizedData > 0);
StateNumber = bin2dec(num2str(dat'));
StateNumber = StateNumber + 1;
|
228a029f411148de20e7f48a9b399818d0d55073a1254b66655ff7be4b370430 | MATLAB | 312 | 10 | %{
# list of piezostimulators in various setups
id : int # some number that identifies the piezostimulator
rig : char # setup name
location : tinyint # 1 = Left, 2 = Right
%}
classdef Piezostimulators < dj.Manual
end
|
5525cbffeed631aa1f5ef14bc0aaace39bdbcdd19b96c519bebb6bdb6ac2605a | MATLAB | 312 | 8 | % Wrapper to apxGrid to remain backwards compatible.
%
% Note that covGrid is not a valid covariance function on its own right.
%
% Copyright (c) by Hannes Nickisch and Andrew Wilson 2016-08-25.
function varargout = covGrid(varargin)
varargout = cell(nargout, 1); [varargout{:}] = apxGrid(varargin{:});
|
7468d934e9a72ea33c34803d4b36f63f93feb716e04dd296053e6b32f1b4e36b | MATLAB | 313 | 10 | function xcov_values = xcov_signals(input_signal1, ...
input_signal2)
% assumes network signals are in each *column*
xcov_values = [];
for ii=1:size(input_signal2,2)
[c,lags] = xcov(input_signal1, input_signal2(:,ii), 10, 'coeff');
xcov_values(ii) = max(c);
end
|
756f037acd69b9e944e54ddb1fd87da033060ba1cf40dccc4084f453dbb406cc | MATLAB | 313 | 16 | function [gt] = tapas_trans_mv2igt(mu, sigma)
%% Transforms to the scaling of the inverse gamma distribution
%
% Input
% theta Matrix with parameters
%
% Output
% ntheta Transformation of the parameters
% aponteeduardo@gmail.com
% copyright (C) 2015
%
gt = 1./((mu .^ 3 ./ sigma ) + mu);
end
|
763e1bf1989bfc62cb73be2d64f42ef5dec5cb028608a7850448476ebc600e4c | MATLAB | 313 | 9 | cd('C:\Users\MysticSocialist\Documents\GitHub\CLOOSE_MS');
% Point to the main script or entry point of your project
[~, requiredToolboxes] = matlab.codetools.requiredFilesAndProducts('stream_send_data.m');
% Display required toolboxes
for k = 1:length(requiredToolboxes)
disp(requiredToolboxes(k).Name)
end |
a1eddefc158cf3b7484ffd7bca0f0205e53b739a65d0ac77222febf60012407c | MATLAB | 313 | 23 | function r = atan(p)
%atan: this function calculates the atan of a x, y, and z values of
% the structures that I use in the FVtool.
%
% SYNOPSIS:
%
%
% PARAMETERS:
%
%
% RETURNS:
%
%
% EXAMPLE:
%
% SEE ALSO:
%
% Copyright (c) 2012-2016 Ali Akbar Eftekhari
% See the license file
r = p;
r.value = atan(p.value);
|
c1b0526217dbe6b67fba79147902afd61a04d65e066528e165b9a2f78d631284 | MATLAB | 313 | 9 | function j = replace_ext(pathIn, newExt)
if endsWith(lower(pathIn), '.nii.gz')
j = regexprep(pathIn, '\.nii\.gz$', newExt, 'ignorecase');
elseif endsWith(lower(pathIn), '.nii')
j = regexprep(pathIn, '\.nii$', newExt, 'ignorecase');
else
j = [pathIn newExt];
end
end |
1d8542ff8a99b58e6d4c53029adfbc9cf2d3d9d10e7f4cf040fe12aae40812f9 | MATLAB | 314 | 5 | function nodal_stats3(Nodal_metrics,Pathnewxsl,opthub,Pathnewplot,Scalev1,Nname,pthretype,restulsmat);
result_name=Nname;
load(restulsmat);
FourModelsigthre=arrange_nodal_Slope2(Nodal_metrics,indminNodalStrmdall,indminNodalStr,notenoughNodalStr,Pathnewxsl,opthub,Scalev1,Pathnewplot,pthretype,result_name);
end |
5da68d4384d24d44599b33d9367d5a0e54ab52d8e1498eae69957fcae157131a | MATLAB | 314 | 14 | function [ rmse ] = rmse( in, true, use_abs )
%RMSE Summary of this function goes here
% Detailed explanation goes here
if nargin < 3
rmse = 100 * norm(in(:) - true(:)) / norm(true(:));
end
if (nargin == 3) && (use_abs == 1)
rmse = 100 * norm(abs(in(:)) - abs(true(:))) / norm(abs(true(:)));
end
end
|
d431308749ac872ee6432bc005ada9cbc947d7ac552d1575312b5488dcc708f7 | MATLAB | 314 | 14 | function [ranknum] = getRank(input)
% function to estimate the optimal rank for denoising
% Adapted from LESS denoising, Jizhou Li
[nx, ny, B] = size(input);
Y = reshape(input, nx*ny, B)';
[~,~,V]= svd(Y,'econ');
Vt=V';
rankindx = getRankIndex(Vt);
realindex = find(rankindx==1);
ranknum = numel(realindex);
end |
db3755c696068fa9a90dc7f5066dc573b0bff6df4e683f6c48faa41b8da7bc82 | MATLAB | 314 | 23 | function r = uminus(p)
%UMINUS: this function applies a unary minus to the x, y, and z values of the structures that I use in
% the FVtool.
%
% SYNOPSIS:
%
%
% PARAMETERS:
%
%
% RETURNS:
%
%
% EXAMPLE:
%
% SEE ALSO:
%
% Copyright (c) 2012-2016 Ali Akbar Eftekhari
% See the license file
r=p;
r.value = -p.value;
|
33f739576b9d8d11f9e53f7ef10a44cd501d9fb2f19b5e5073e8652ec97878a3 | MATLAB | 315 | 25 | function ans = eq_str (s1,s2);
%
% ans = eq_str (s1,s2);
%
% returns 1 if strings are equal
% 0 if they're not
%
% Ziad Saad July 20 97
ls1 = length (s1);
ls2 = length (s2);
if (ls1 ~= ls2),
ans = 0;
return;
else
ans = 1;
for i=1:1:ls1,
if (s1(i) ~= s2(i)),
ans=0;
end;
end;
return;
end;
|
ab41f1335792ec6aba56c4f2f6b27a712e6dc4d86d59fedb934eff42e53b3868 | MATLAB | 315 | 20 | %{
#
-> preprocess.Eye
-> preprocess.TrackingParameters
---
tracking_ts=CURRENT_TIMESTAMP: timestamp # automatic
%}
classdef EyeTracking < dj.Computed
methods(Access=protected)
function makeTuples(self, key)
%!!! compute missing fields for key here
%self.insert(key)
end
end
end |
cc30d6a7eda70a588ebbc77ec285c6864ec3de19ba5499ab798d4cfca3e5648d | MATLAB | 315 | 11 | %{
experiment.SessionFluorophore (manual) # Fluorophores expressed in prep for the imaging session
-> experiment.Session
-> experiment.Fluorophore
---
notes : varchar(255) # additional information about fluorophore in this scan
%}
classdef SessionFluorophore < dj.Relvar
end |
0ad765e686768237dea7f11addf5d19c085d206d855ac53b10d921506d9e5b13 | MATLAB | 316 | 15 | function sec = TickToSec(tick)
% Converts tick in microseconds to seconds
%
% function sec = TickToSec(tick)
%
% Input:
% tick - Time in microseconds
%
% Output:
% sec - microseconds converted to seconds.
%
% (c) 2016 by Multi Channel Systems MCS GmbH
sec = double(tick) * 1e-6;
end |
c5882d55845dcdde196757c6190137c4b784169f9672e3562fd5e29eea35305e | MATLAB | 316 | 16 | function v2 = prod_vf_sf(v1,s)
% prod_vf_sf - compute the product of a vector field by a scalar field.
%
% v2 = prod_vf_sf(v1,s)
%
% The result is the vector field defined by pointwise product.
%
% Copyright (c) 2004 Gabriel Peyré
v2 = zeros(size(v1));
for i=1:size(v1,3)
v2(:,:,i) = v1(:,:,i).*s;
end |
d4a1a83df55a0bb7d18d3d7fcdd90b773cf0d35051edafa8f94887f02d3aedb1 | MATLAB | 316 | 16 | function [gk] = tapas_trans_mv2gk(mu, sigma2)
%% Transforms to the scaling of the gamma distribution%
% Input
% mu -- Means
% sigma2 -- Variance
%
% Output
% gk -- K parameter of an inverse gamma distribution
% aponteeduardo@gmail.com
% copyright (C) 2015
%
gk = mu.^2 ./ sigma2;
end |
42b9e639b2397d0090aae637b25adfcd1d669beeb9a4693915b7cd78924684bf | MATLAB | 317 | 12 | %{
#
-> eye.ManuallyTrackedContours
frame_id : int # frame id with matlab based 1 indexing
---
contour=null : longblob # eye contour relative to ROI
%}
classdef ManuallyTrackedContoursFrame < dj.Manual
% Implemented in Python
end |
7b9dacfc7a0eabbebff54847c4d41408733cccb98d9b667ba13c7bbe05d3dfab | MATLAB | 317 | 12 | %{
# laser parameters for the stack
-> experiment.Stack
---
wavelength : int # (nm)
max_power : float # (mW) to brain
gdd : float # gdd setting
%}
classdef StackLaser < dj.Manual
end |
e0389cd15089e345bc11e7094d8bda0cc60502fa695051907056c32e94891b71 | MATLAB | 317 | 17 | function pred = pack_pred_struct(D, neighbors, w, dc, varargin)
pred = struct();
pred.D = D;
pred.neighbors = neighbors;
pred.w = w;
pred.dc = dc;
if nargin >= 5 && ~isempty(varargin{1})
pred.class_pred = varargin{1};
end
if nargin >= 6 && ~isempty(varargin{2})
pred.y_pred_weighted = varargin{2};
end
end |
46704597ec8bbff3031d166c7bb5fa8e78b24dc135a76db0480fc1c25a1efc21 | MATLAB | 318 | 12 | function val = getMetaVal(metadata, field)
ind1 = strfind(metadata, field);
if (~isempty(ind1))
ind2 = strfind(metadata(ind1:end), 10);
ind2 = ind2(1);
line = metadata(ind1:ind1+ind2-2);
val = sscanf(line,[field ' = %f']);
else
disp('Error - Invalid metadata field');
val = 100;
end |
7147b369dafc2b367c928adc2d633ed1468556af4fe7d5bdf04d05a17d04f7b5 | MATLAB | 318 | 15 | function [colorMapSort] = FindColorMap()
load(fullfile('..', 'Figure4','data', 'log10EC50.mat'));
log10EC50(isnan(log10EC50)) = 0;
[~, score, ~,~,~, ~] = pca(log10EC50);
[~, I] = sort(score(:, 1));
myColorMap = jet(34);
colorMapSort = zeros(34, 3);
for i =1:34
colorMapSort(I(i), :) = myColorMap(i, :);
end
end |
f69d0b6c687243aa7adb666886176ecdffab13e65c3b01d55029dd927f84c05a | MATLAB | 318 | 10 | %{
# current piezostimulators configured in the setup
rig : char # name of setup
---
speaker_id : int # some number that identifies the speaker
location : tinyint # 1=Left, 2=Right
%}
classdef CurrentPiezostimulators < dj.Lookup
end |
1569c4f5c9be2345629ff8d0fe8b19965d05a8f31dab328a4b3039fc196292e3 | MATLAB | 320 | 19 | %{
# l6-norm of each pixel across time
-> meso.SummaryImages
---
average_image : longblob #
%}
classdef SummaryImagesAverage < dj.Computed
methods(Access=protected)
function makeTuples(self, key)
%!!! compute missing fields for key here
% self.insert(key)
end
end
end |
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