sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
d4e4eefa01b6a5716bb8120718554ab517df0025ce77a0c3f0a228225f906a1c | MATLAB | 164 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'map', 'pipeline_map');
end
obj = schemaObject;
end
|
e1f895e21b55814df3a5e52f6946b14f8ef37ee4eb582364c4f68612cd0b99f2 | MATLAB | 164 | 9 | function vl_test_print(varargin)
addpath(fullfile(vl_rootnn(), 'examples', 'mnist'));
net = cnn_mnist_init('networkType', 'dagnn');
net.print(varargin{:});
end
|
4945ece978c60dee74dee8fdeec62978813f03ab0eb93b001c16a9b92e978d47 | MATLAB | 165 | 12 | %Creado por Luisca Mayo09.
function [SSRR]=SortSRR(A)
N=size(A,2);
j=1;
for i=1:(N-1)
SSRR=sortrows(A,i);
end;
pcolor(SSRR)
%save Ras_Ren.dat Ras_Ren -ascii |
dc7ba0ad84e319a4a1b5ca0dfa558a5de01253ffe68c15ec948eae9b86cc97c0 | MATLAB | 165 | 9 | function lg = logGamma(a, p)
if nargin < 2
p = 1;
end
lg = (p*(p-1)/4)*log(pi);
for i=1:p
lg = lg + gammaln(a + (1-p)/2);
end
end |
077784d3cf34bae4c5655a61de98199bf0ff7d7e3545ec3b26c3b2209979b213 | MATLAB | 166 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'fuse', 'pipeline_fuse');
end
obj = schemaObject;
end
|
4c6a9b43950bd33954886839b4a23a79c976ee2ecebac0c6a013a323c8e84922 | MATLAB | 166 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'meso', 'pipeline_meso');
end
obj = schemaObject;
end
|
795ba0aba0f6558068be4efaa514f2da1a4279968c42f6c9ac0f232a9a7a715a | MATLAB | 166 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'reso', 'pipeline_reso');
end
obj = schemaObject;
end
|
7b0151a5b051c4382d7ac22d313d0a5b7384a98eeabf021d573cd7dc6cde0d13 | MATLAB | 166 | 5 | function fn_par = sero_srr_fn_nii2par(fn_nii)
% function fn_seq = sero_srr_fn_data2nii(fn_data)
[a,b] = msf_fileparts(fn_nii);
fn_par = [a filesep b '_par.nii.gz'];
|
8302e7512b32ef075fa20f4b6c85eaa81cf7542a714920416748cb2745d73514 | MATLAB | 166 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'tune', 'pipeline_tune');
end
obj = schemaObject;
end
|
e94b701ce2dd8efd9776083d36ef895a035afd8347700e63cedb0ac520ce077f | MATLAB | 166 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'sync', 'pipeline_sync');
end
obj = schemaObject;
end
|
32e77f2196f5005d6b5d18c2176ef41af3ada61327fc24c2d272b65ad5d03a50 | MATLAB | 167 | 4 | % return boolean value to check if the input name contains certain string
function bool = ContainName(name,string)
bool= ~isempty(strfind(lower(name),string));
end |
c3f404ea4a716907e4c59cd93d0fd31e95608db05a75832c4c8eaeb7b1ec3b4e | MATLAB | 167 | 11 | function [states] = tapas_vlinear_init_states(data, model, inference)
%%
%
% aponteeduardo@gmail.com
% copyright (C) 2016
%
states = cell(inference.niter, 1);
end
|
04d7e9a5e192d2d13acdbc35664b7163521e8eb4a931ff546de76434a6c2efb3 | MATLAB | 168 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'stack', 'pipeline_stack');
end
obj = schemaObject;
end
|
0e4c4e9e33ee60fec2e534f504be642d43bf6698393bbd4d8c2f59082e5ce7ec | MATLAB | 168 | 9 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'vis2p', 'vis2p_manolis');
end
obj = schemaObject;
end |
5a2fefd5e6659745e2b1fa39f78b859f79d7bdf60f711e4762f3f97fb79fb1aa | MATLAB | 168 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'monet', 'pipeline_monet');
end
obj = schemaObject;
end
|
7706dcd0b15ec14ce3188149d76daacc227cb646fb75e88e383352a6aacc5972 | MATLAB | 168 | 8 |
function anomalies = detect_anomalies(outputs, factor)
errors = abs(outputs - mean(outputs));
threshold = factor * mean(errors);
anomalies = errors > threshold;
end
|
9f03a1922ae1725c417d465f63f8c9b496f15744e8e479f2729eb33f54ac142d | MATLAB | 168 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'vreso', 'pipeline_vreso');
end
obj = schemaObject;
end
|
a78afd5a0e28a0b5f712bed9d1a616eb96484bafb2e33bce52de3431505bb755 | MATLAB | 169 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'stack', 'pipeline_stacks');
end
obj = schemaObject;
end
|
0d7d44b30e6562229f3d493159ed0b40b72aa3998fb4ef2380e04a94748b6a46 | MATLAB | 170 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'shared', 'pipeline_shared');
end
obj = schemaObject;
end
|
10ace9a0087222a16fc7c2259d6903ec29b09940b9023be8267d06c4cb286f4d | MATLAB | 170 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'movies', 'pipeline_movies');
end
obj = schemaObject;
end
|
dec76a5c2913c6e8d6c4b437c537a0f17a3e2b546d389f0ffaccdf8746609f9c | MATLAB | 170 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'trippy', 'pipeline_trippy');
end
obj = schemaObject;
end
|
ea8538215cf1e57cfca405aa7bc9de66fe1bc7387a8d3e6b1e47d53ae727b209 | MATLAB | 170 | 3 | function x = is_search(x)
% Determine whether some object is a search() expression.
x = all(isfield(x,{'head','parts'})) && numel(x)==1 && strcmp(char(x.head),'search');
|
f3ab15defce7fc9bbbf705dc7793485585b4686d14a32f21a0f8961233338e0c | MATLAB | 170 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'tuning', 'pipeline_tuning');
end
obj = schemaObject;
end
|
0f212721d0d92e158463f1695cd065f89f9dc171a1c62c6fa767c95496ec8deb | MATLAB | 171 | 8 | function fn_xps = sero_pulseq_fn_seq2xps(fn_seq)
% function fn_xps = sero_pulseq_fn_seq2xps(fn_seq)
[a,b,c] = fileparts(fn_seq);
fn_xps = fullfile(a, [b '_xps.mat']);
|
f42fa0d00a8a9d50749b9b6c8ae3d5f525c3da02a0fbed3ef6347e0e6fe02411 | MATLAB | 171 | 4 | function x=gather(x)
% GATHER Compatibility stub for the GATHER() function
% GATHER() is a function in the Parallel MATLAB toolbox. MATCONVNET
% can work without it.
|
1574127ada3b01fbe9b417da5df58013ace229d94a39a0b6b6ff0684b0ad23ae | MATLAB | 172 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'anatomy', 'pipeline_anatomy');
end
obj = schemaObject;
end
|
3b8b029658f495857afb5eacc58e47b6f99b7cf0f7a80d4fc3530f22dae3fe77 | MATLAB | 172 | 12 | function [flg] = filexist (filename)
%
% [flg] = filexist (filename)
%
% if filename exists then flg = 1, else flg = 0
%
flg=0;
if exist(filename,'file')
flg=1;
end
|
9418685050dde0ca70b9a0a09da05c2317017b935bb2f670ab53b54f07624bb4 | MATLAB | 172 | 11 | function p = sero_srr_tr2position(TR, w)
% function p = sero_srr_tr2position(TR, w)
if nargin < 2
w = sum(TR>0, 2);
end
ind = 1:size(TR,2);
p = (TR>0) * ind' ./ w;
|
a1e1dc7aaf676a54212fc223c066ab5786ffb342c0fc3fc612e509bc6e47cb7e | MATLAB | 172 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'quality', 'pipeline_quality');
end
obj = schemaObject;
end
|
e2db9fac475c152340de0bcfa9fb1884d70a09ed2457e441f3a36ec2acdefdb8 | MATLAB | 172 | 8 | function close_all_ports
load tone
PsychPortAudio('Stop', tone.pahandle);
% Close the audio device
PsychPortAudio('Close', tone.pahandle);
% Clear the screen
sca;
|
2214900cf14cd836b42f321a53434dffdb18485c562ade810c603435db10057b | MATLAB | 173 | 5 | function fn_seq = sero_srr_fn_data2nii_aco(fn_data)
% function fn_seq = sero_srr_fn_data2nii_aco(fn_data)
[a,b] = fileparts(fn_data);
fn_seq = [a filesep b '_aco.nii.gz'];
|
8544d57e92dba5193c9a30d0e6a1741ee6df1e162b0eb2e49ad986689c1c5990 | MATLAB | 174 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'trk', 'pipeline_pupiltracking');
end
obj = schemaObject;
end
|
91d41eb5cc546d4cf3b1d9fefb670a872705cbee3e41fd6a83c011d684a69081 | MATLAB | 174 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'pre', 'pipeline_preprocessing');
end
obj = schemaObject;
end
|
d4dc91f81d45fb4c76e7359107bfa3d8bc8bc338a9459a71f15fca963636012b | MATLAB | 174 | 10 | %{
# field/channels that should not be segmented (used for web interface only)
-> experiment.Scan
-> shared.Field
-> shared.Channel
%}
classdef DoNotSegment < dj.Manual
end |
4422877d8ec63fbddb646e8e90302bde33a03285e86a6397192fd9e15450cd5e | MATLAB | 175 | 10 | %{
experiment.SessionPMTFilterSet (manual) # Filters used in the session
-> experiment.Session
---
-> experiment.PMTFilterSet
%}
classdef SessionPMTFilterSet < dj.Relvar
end |
bca67585fcfd3fbf73fb259cc15eac5abd240977d6c1e7817391d67df38fc109 | MATLAB | 175 | 12 | function [ny] = tapas_ti_init_y(y, ptheta, pars)
%% Default initilization.
%
% aponteeduardo@gmail.com
% copyright (C) 2016
%
ny = cell(1, numel(pars.T));
ny(:) = {y};
end
|
d16308152be32ffb39302414ada41039e565fd39309102887ca93c4bea72471b | MATLAB | 175 | 9 | function Z = multiprod_ez(X, Y)
Z = zeros(size(X, 1), size(Y, 2), size(X, 3));
for k = 1 : size(Z, 3)
Z(:, :, k) = X(:, :, k) * Y(:, :, k);
end
end
|
3cdd5a82a438f9fd9a62ec6763b348d608e95ac69cd9ee8c88bccaf2fd56f094 | MATLAB | 176 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'aod_monet', 'pipeline_aod_monet');
end
obj = schemaObject;
end
|
66a17daf2ed9c69ee131948e898b4223fd97087229ca5fb260dc5a20f0549297 | MATLAB | 176 | 13 | function f = flatten(m)
% function f = flatten(m)
%
% <m> is a matrix
%
% return as a row vector.
%
% example:
% a = [1 2; 3 4];
% isequal(flatten(a),[1 3 2 4])
f = m(:).';
|
729073b6df60743d4bbf3e43189af92a49fa693f6d06f95436178e8984ea9644 | MATLAB | 176 | 7 | function output = oddRatio(lposP, lnegP)
%% see Mladenic, D. & Grobelnik, M. (1999)
output = log(...
exp(lposP).*(1-exp(lnegP)) ./ exp(lnegP).*(1-exp(lposP))...
);
end |
78c89869ebb20ef1fa9386d47113a169a7627e154baaaf13f4a04d11b45958ec | MATLAB | 176 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'treadmill', 'pipeline_treadmill');
end
obj = schemaObject;
end
|
c81bbf857b129ffcd10790e2027d488ea9c1775b5d25cd63534c6a82d056e319 | MATLAB | 176 | 4 | % MATLAB runs the test
[result,rt] = UnitTest('MEGA',1,1);
assert(sum(rt{1}.Failed) == 0 && sum(rt{1}.Incomplete) == 0 &&sum(rt{2}.Failed) == 0 && sum(rt{2}.Incomplete) == 0)
|
f2f0f10fbe8bc923b93eb9cc86d5f4dcb1ecc72aaf1ed3d6da7296d1f7742c35 | MATLAB | 176 | 8 | %{
# recording channel, directly related to experiment.PMTFilterSet.Channel
channel : tinyint #
%}
classdef Channel < dj.Lookup
end |
fb43c6ed730d3ff6f5c2637e57414e652e099187a0ab154c7d4272a07148cc74 | MATLAB | 176 | 5 | function fn_par = sero_srr_fn_nii2par_avg(fn_nii)
% function fn_par = sero_srr_fn_nii2par_avg(fn_nii)
[a,b] = msf_fileparts(fn_nii);
fn_par = [a filesep b '_par_avg.nii.gz'];
|
0f7c9ea6b648ad85480001aa92f8ad7a99bf73fc2794184695ec124094b3043d | MATLAB | 177 | 4 | % MATLAB runs the test
[result,rt] = UnitTest('PRESS',1,1);
assert(sum(rt{1}.Failed) == 0 && sum(rt{1}.Incomplete) == 0 &&sum(rt{2}.Failed) == 0 && sum(rt{2}.Incomplete) == 0)
|
72fe9b0ac4099856c59e88a9eb5c4bae98e9818c1770c34c20434bccb011d4f7 | MATLAB | 177 | 6 | function gzip_if_needed(niiPath)
if endsWith(lower(niiPath), '.nii')
if isfile([niiPath '.gz']), delete([niiPath '.gz']); end
gzip(niiPath);
end
end |
e3558d89ed05a7861c6595471f998051fa6b72004c8ad74c256f2452f6a6e8bb | MATLAB | 177 | 8 | function matchIdx1=greedyPairs(C,THD)
% C: cost matrix
% THD
[minCost,idx] = min(C,[],2);
matchIdx1 = find(minCost<=THD);
matchIdx1 = [matchIdx1, idx(matchIdx1)];
end |
ebe89026463f5b48517beaa8b39726147f948af23c7a084a41b01f60ff7086cb | MATLAB | 177 | 11 | function obj = paramopt(obj)
% optimize parameters for an NBC classifier
% parse parameter vector into params
% [s_param w_act_noact w_prior_vs_like]
acc = 1 - nbc.b_err;
|
1263d5781696f32b657a1678b584eabb816a5f1b26daa7e89dceeb7c3ad6d3e0 | MATLAB | 178 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'preprocess', 'pipeline_preprocess');
end
obj = schemaObject;
end
|
9cba713be3fcdd9f8471ebb49622b8b191ec2487e009184089c47be555c0497f | MATLAB | 178 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'experiment', 'pipeline_experiment');
end
obj = schemaObject;
end
|
90ac23669438128a30a0f0ab0e4434e4642bf19e40c683ed729bc768e4e2d07f | MATLAB | 179 | 7 | function refPath=getRefPath(voltagePath)
% 2020-06-27 04:22:40 RC
refPath=strrep(voltagePath,'cG','cR');
if ~isfile(refPath)
error('Reference file has not been found')
end
end |
91cb885263a5a83aa7dcd63644f44ac0db9ad3dc86c6b1da471cb54f0019e7f6 | MATLAB | 179 | 5 | function rotatematlab(varargin)
% This function calls the Matlab rotate function
% This prevents the issue with the function in the private folder of Dipfit
rotate(varargin{:});
|
1759346d02ad9e8a854ab9f6ad18c0438c756d6d698695b20b3871197258bce0 | MATLAB | 180 | 5 | ecfunction p = mixpdf(x, mu, K, Pm)
%weighted mixture of von Mises pdf and uniform pdf
%p = vonmisespdf(x,mu,K)*Pm + 1/(2*pi)*(1-Pm);
p = circ_vmpdf(x,mu,K)*Pm + 1/(2*pi)*(1-Pm); |
1925f8f6d64a4067bfa0352638b614f05847a08026a04295bdfd481d07a0ac2f | MATLAB | 181 | 6 | clear all, close all
mex -O -outdir ../compiled/ lbfgsAddC.c
mex -O -outdir ../compiled/ lbfgsC.c
mex -O -outdir ../compiled/ lbfgsProdC.c
mex -O -outdir ../compiled/ mcholC.c |
a68b2fdee3c9a0ce7638e377259912af02d3b86457fb585f1bd739e0d0b598e6 | MATLAB | 181 | 7 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
schemaObject = dj.Schema(dj.conn, 'aodpre', 'pipeline_aod_preprocessing');
end
obj = schemaObject;
end
|
15ab74ba40eb49ff4b09a15be2b57075d5a47960aa93f0dd1792d61c41a979ab | MATLAB | 182 | 9 | %{
preprocess.Method (lookup) # methods for extraction from raw data for either AOD or Galvo data
extract_method : tinyint #
---
%}
classdef Method < dj.Relvar
end |
c0968f9a52795e061d74556f2ecf177c0b85d2e912153e29cd0df866b8568560 | MATLAB | 182 | 3 | function res = is_impure_expression(x)
% an impure expression is a MATLAB structure with a .tracking.expression field
res = isfield(x,'tracking') && isfield(x.tracking,'expression'); |
07add6ad4d9f0bb5fd19964edc328c5e5a4e6e1df821373c10b83f241a81a6a6 | MATLAB | 184 | 3 | function res = has_canonical_representation(x)
% determine whether an expression is represented as a struct with the fields 'head' and 'parts'.
res = all(isfield(x,{'head','parts'}));
|
e6c6c60b716a5d987ac2cba883ee1d4f573cd491d1a947f2884820f7f573a99b | MATLAB | 184 | 3 | % determine whether some object is a raw EEGLAB data set with no BCILAB constituents
function res = is_raw_dataset(x)
res = all(isfield(x,{'data','srate'})) && ~isfield(x,'tracking');
|
19af4d8fb6e170e62f30abbff5764bf46fae75db9559f46d3ac713c2eb1053e2 | MATLAB | 185 | 7 | function effectsize = effsize_ind(x, y)
meandiff = mean(x, 'omitnan') - mean(y, 'omitnan');
std_pooled = std(x, 'omitnan'); %independent groups
effectsize = meandiff./std_pooled;
end |
36555ff60ea13b8269d9c9f1978e5a46cc18c230ea857dc46a30f8c020896cf6 | MATLAB | 185 | 10 | function profile_start(profile_on)
if ~exist('profile_on','var'), profile_on = 0; end;
if profile_on == 0,
tic
elseif profile_on > 0,
profile on -detail builtin
tic
end;
|
5c840b793366348816207520fa3554e389acc3fa8ae3bf1ebd9137e7050f8845 | MATLAB | 185 | 3 | function result = hlp_memfree
% Get the amount of free physical memory, in bytes
result = java.lang.management.ManagementFactory.getOperatingSystemMXBean().getFreePhysicalMemorySize();
|
f2ba0b13752c359d6994d09a13f92e7fda7cec68e876eb42c4f260838b006a33 | MATLAB | 185 | 6 |
function duline(start, stop, varargin)
% Line annotation in data units.
annotation(gcf, "line", du2nfu([start(1) stop(1)],'x'), du2nfu([start(2) stop(2)],'y'), varargin{:})
|
17fc0090c1bfdf276233b17af8848b178d6452b44e587f27d61e98fa11c1ddca | MATLAB | 186 | 10 | function outp(address,byte)
global cogent;
%test for correct number of input arguments
if(nargin ~= 2)
error('usage: outp(address,data)');
end
io64(cogent.io.ioObj,address,byte);
|
61463a77ca015a89e81c08ae12af2f80178f6d93496f8145495a88b57be344cb | MATLAB | 186 | 7 | function callLCModel(controlFile, pathLCModelBinary)
% Wrapper function for LCModel binary
callLCMCommand = ['"' pathLCModelBinary '" < "' controlFile '"'];
system(callLCMCommand);
end |
a9da64b879b93e77aea5a399c49b33f06cff790aace12bb41da9e46459eb2526 | MATLAB | 186 | 9 | function pial=mapPial(region)
VG=spm_vol(region(1,:));
pial=zeros(VG.dim(1:3));
for i=1:VG.dim(3),
pial(:,:,i) = spm_slice_vol(VG,spm_matrix([0 0 i]),VG.dim(1:2),1);
end
end |
ae6a5698766961d7ba597fd8cb25405b6372f7ee76e02ec7d090cd146d06a151 | MATLAB | 186 | 10 | %{
# key to original visualization
-> vis.MostExciting
---
-> `cortex_ex_machina_madness_analysis`.`__visualization__most_exciting_image`
%}
classdef MostExcitingCNNKey < dj.Manual
end |
e33862969c2df832cde55fde4c19d659521c8bbfb603a8c3393ecd6a76e73cf5 | MATLAB | 186 | 4 | function p = mixpdfGauss2(x, mu, sigma, Pm, sigma2, Pm2)
%weighted mixture of von Mises pdf and uniform pdf
p = normpdf(x,mu,sigma)*Pm + normpdf(x,mu,sigma2)*Pm2 + 1/(2*pi)*(1-Pm-Pm2); |
e51da381431457b97c582f1040829be7df36a1a767bd2cbd70b72256d1ed86df | MATLAB | 186 | 10 | %{
experiment.Person (lookup) # Users
username : char(12) # lab member
---
full_name : varchar(2048) #
%}
classdef Person < dj.Relvar
end |
e090f0c44eb608abe1ca98593f44db1ebc3322a37b332cec02e0df47ed76a142 | MATLAB | 187 | 9 | function tr = sero_srr_time2tr(t)
% function tr = sero_srr_time2tr(t)
tr = nan(size(t));
for i = 1:size(t,2)
ind = find(~isnan(t(:,i)));
tr(ind, i) = diff([-inf; t(ind,i)]);
end |
0cfee5d5593f4770bb8bacb2e9e0f756a07f86c12cb1ead44bd823c1a5f9eb26 | MATLAB | 188 | 5 | data = readtable("file.csv");
test_data = data(data.freq_band == "high_gamma", :);
[p, tbl, stats] = anova1(test_data.dur, test_data.period);
[results, ~, ~, gnames] = multcompare(stats); |
bdbfe2b851c33a4e1b5de3b94ce70ebabf2443287714cf4f12645c834b36debd | MATLAB | 188 | 6 |
function duarrow(start, stop, varargin)
% Arrow annotation in data units.
annotation(gcf, "arrow", du2nfu([start(1) stop(1)],'x'), du2nfu([start(2) stop(2)],'y'), varargin{:})
|
e88fc31441c7142bc1b8fd4d5d51911041b49f263528d42f514e8f406bb392f5 | MATLAB | 188 | 7 | % this function is called when the file is being saved
function obj = changefile(obj, newfile)
movefile(obj.dataFile, newfile);
obj.dataFile = newfile;
obj.writable = false;
|
1a947434eec1ecafe12b070b95250881104ca86a67c7ec6a25cc64cab5a2881d | MATLAB | 189 | 5 | function ptime(T)
ehour = floor(T/3600);
emin = floor((T-ehour*3600)/60);
esec = floor( T - ehour*3600 - emin*60);
fprintf('ELAPSED : %d hours %d min. %d sec.\n', ehour,emin,esec );
|
1f74e879fce42750a68597b86f5e5832ab95b3758a8bb0a8fa58eb2d44ca2831 | MATLAB | 189 | 11 | function [states] = tapas_linear_init_states(data, model, inference)
%%
%
% aponteeduardo@gmail.com
% copyright (C) 2016
%
state = tapas_linear_init_states(data, model, inference);
end
|
95e2c9cb1649f80635088d6a098320ef47764468801874e984ebb86dea159956 | MATLAB | 189 | 9 | %{
preprocess.Channel (lookup) # recording channel, directly related to experiment.PMTFilterSet.Channel
channel : tinyint #
---
%}
classdef Channel < dj.Relvar
end |
e1c1a354eccd38e5cd1da326cae24a10d1bb42290d1d61af56ee2f851c4736b6 | MATLAB | 189 | 15 | function [ant] = iseven (x);
% [ans] = ISEVEN (x)
% returns 1 if x is even and 0 if x is odd
%
% Ziad Saad May 8 95.
d = x ./ 2;
if fix(d) == d,
ant = 1;
else
ant = 0;
end;
|
8766a24cef389ed6eadefca43eb2aa19472b56c7e38f8fec02bdb8db73705921 | MATLAB | 191 | 17 | function S = getPES(data)
nPars =numel(data);
nCons = 2;
for pars = 1:nPars
parData = data(pars).outcome;
parData = data(pars).rt;
end
end
|
aa282f249008c9d52248eb628c6c749bdc12ab84525e7e83c1917ce7526068c8 | MATLAB | 191 | 3 | function res = is_builtin_function(x)
% check if the given object is a valid handle to a builtin function
res = isa(x,'function_handle') && exist(char(x),'builtin') && ~exist(char(x),'file'); |
f0d8d1d2f5b345fb946b27b0bb05721d7dd310bc9a01a1d58fc1b28b2aeb4a65 | MATLAB | 192 | 10 | %{
# key to original visualization
-> vis.MostExciting
---
-> `cortex_ex_machina_madness_analysis`.`__linear_visualization__most_exciting_image`
%}
classdef MostExcitingLNKey < dj.Manual
end |
f3f0851655d1aeebc8ba07b7c1e1829673bf2293535aba5ff8cddea7c269403a | MATLAB | 192 | 11 | clc,clear
imgs = {'t1.nii'
'meanfundata.nii'};
TMSSloverSPM(imgs,'correct')
close all
clc,clear
imgs = {'t1.nii'
'mfun (copy).nii,1'};
wgr_slover_spm(imgs,'false')
close all
|
804d9b8443c1a6325aa3e727fb073b54a91a9944de389dd2a028bc0ca34a1a9c | MATLAB | 193 | 8 | function plot(acq, cfg, varargin)
% Placeholder plot function for a McsCmosLinkedDataSource object
%
% function plot(acq, cfg, varargin)
%
% This function is empty at the moment
end |
1561618b34972fcefcad16712c368850216418d564ff557880abbb3362b0526a | MATLAB | 194 | 10 | function Models = modelRDMs_SL_sim()
%
Models.main_clusters = kron([
0 0 1 1
0 0 1 1
1 1 0 0
1 1 0 0], ones(10,10));
% Models.random = squareform(pdist(rand(40,40)));
end%function
|
7ecfe6bb69f2a0a1d27a6c2c45c762758d0a2988e67424a5d41c9517507d7eb8 | MATLAB | 194 | 10 | function unwarpfun = unwarp(warpfun)
for i = 1:warpfun(end)
n = find(warpfun ==i);
if isempty(n)
unwarpfun(i) = unwarpfun(i-1);
else
unwarpfun(i) = n(1);
end
end |
41a3de3a670d1214fa1150a304866400f9f70bcbef9ba643d99b53057c380dc8 | MATLAB | 195 | 10 | %{
preprocess.MethodGalvo (lookup) # extraction methods for galvo
-> preprocess.Method
---
segmentation : varchar(16) #
%}
classdef MethodGalvo < dj.Relvar
end |
71b95fcfd8ed79d761017589c6ead5abefdfdb2b5dcdd74935fcb640e42c51f1 | MATLAB | 195 | 5 |
% MATLAB runs the test
[result,rt] = UnitTest('SinglePRESS',2,1);
assert(sum(rt{1}.Failed) == 0 && sum(rt{1}.Incomplete) == 0 &&sum(rt{2}.Failed) == 0 && sum(rt{2}.Incomplete) == 0) |
69ab95945afd18dabfaec358f558eb6bfede8c53527902a1d2fc14c11f548044 | MATLAB | 196 | 6 | function ID = coordinates2ID(coordinates, numRows, numCols)
ID = numRows*(coordinates(1)-1) + coordinates(2);
if ID > (numRows * numCols)
error('ID out of range!')
end
end |
74e6695fbb661b0680293ae0351bcfe283a9272ec7ab9b2d7072c0b55e7428ef | MATLAB | 196 | 7 | function effectsize = effsize(x, y)
meandiff = mean(x, 'omitnan') - mean(y, 'omitnan');
std_pooled = (std(x, 'omitnan').^2+std(y, 'omitnan').^2)./2;
effectsize = meandiff./sqrt(std_pooled);
end |
0d85ae6cdbe63220aa1e2e4319091688c4310469b9521a425128c369f7110bdb | MATLAB | 197 | 10 |
list = ls('*.m');
for k = 1 : size(list, 1)
s = strtrim(list(k, :));
s = s(1:end-2); % remove '.m'
if ~strcmp(s, mfilename()) % don't run yourself
eval([s, ';']);
end
end
|
d241ebbe5665bc154c04adb7eb40cb1d4786b5fab3276619d83838705021c9a0 | MATLAB | 198 | 10 | %{
preprocess.CircleRadii (lookup) # my newest table
# add primary key here
radius : smallint # radius of the cluster
-----
# add additional attributes
%}
classdef CircleRadii < dj.Relvar
end |
f5781be97f245ed5ff6ed96d776471f6214182d86d2302de1a4cb5f8bfcd3046 | MATLAB | 198 | 7 | function [ output_args i_shuffle ] = shuffle_vector( input_args )
%shuffle Shuffles the elements of a vector
i_shuffle = randperm( numel( input_args ) );
output_args = input_args( i_shuffle );
end |
3c7bee8d9c0a3d281c14b74efbed251e809d26839fe6aa8d32ea9f5bfee4b2eb | MATLAB | 200 | 11 | function hsi_nr = remove_hylid_noise_bands(hsi)
hsi_nr = hsi;
[nb,nb_inds] = hylid_noise_bands();
hsi_nr.info.wavelength = hsi.info.wavelength(~nb_inds);
hsi_nr.Data = hsi.Data(:,:,~nb_inds);
end |
7a82ad59e16c2c8e303d62068c55e1edd17acc46634103762275ad9d54b747d4 | MATLAB | 200 | 9 | function res = afni_isdigit(s)
% res = afni_isdigit(s);
% res(i) = 1 is s(1) is a digit between 0 and 9 (inclusive)
%
digit_msk=s >= '0' & s <= '9';
res = zeros(size(s));
res(digit_msk) = 1;
return;
|
03c09864b451853764c90abe48fa94ff932adc5b33d5179abcea5057aa024ffb | MATLAB | 201 | 12 | function dg = diGamma(a, p, k)
if nargin < 3
k = 0;
if nargin < 2
p = 1;
end
end
dg = 0;
for i=1:p
dg = dg + psi(k, a + (1-i)/2);
end
end |
d0daa4019cd7cf4f6af049acbf01fa7adccf2c8d76c3c7b3d2feba38ea8a27f8 | MATLAB | 201 | 8 | while true
parpopulate(pre.ExtractSpikes)
parpopulate(monet.DriftTrialSet)
parpopulate(monet.DriftResponseSet)
parpopulate(monet.VonMises)
parpopulate(monet.RF)
pause(1000)
end
|
b06b0f7cc786a09f7227f897272cb549c66b2de2cb4d1eafcef93251c79e7a27 | MATLAB | 202 | 8 | function retval = startsWith(s, prefix)
n = length(prefix);
if n == 0 # Empty prefix
retval = 1; # Every string starts with empty prefix
return
endif
retval = strncmp(s, prefix, n);
end
|
e8b7e1f73d450a80891b7fc46abeee7372910a8916b95f2cdc940ffa79e8944e | MATLAB | 202 | 12 | function y=Afunc(x)
% y=Afunc(x)
% Testfunction returning a linear operator applied to x.
% Used for testing lansvd.
%
% y = A'*x
% Rasmus Munk Larsen, DAIMI, 1998
global A MxV
y = A*x;
MxV = MxV + 1; |
546a7ff527ce052a05f1ef284a69fa9bd792ec8b8ba217dd52edd4b195bad559 | MATLAB | 203 | 9 | function obj = getSchema
persistent schemaObject
if isempty(schemaObject)
psy.getSchema;
common.getSchema;
schemaObject = dj.Schema(dj.conn, 'rf', 'pipeline_rf');
end
obj = schemaObject;
end
|
dc6ed8b8d0a9eb90d821afc1c18018d0350fd610c73d79bf3f7a694e39512466 | MATLAB | 203 | 16 | function [data] = tapas_linear_data(y, u, pars)
%%
%
% aponteeduardo@gmail.com
% copyright (C) 2016
%
tapas_linear_validate_data(y, u);
data = struct('y', [], 'u', []);
data.y = y;
data.u = u;
end
|
16b76780d783278cf6dbccca9467622416f52b909ef4c01c7eb5d0fe170d3356 | MATLAB | 204 | 14 | function generate_SPD_matrix(n, density)
A = sprandsym(n,density); % generate a random n x n matrix
A = A + n*speye(n);
X = rand(n, 1);
B = A*X;
save A4.txt A
save B4.txt B
save X4.txt X
endfunction
|
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