sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
b47027dd3394929c94f845e226ab0fc13bf535309b13b118649e42c98b6ca30e | MATLAB | 2,238 | 72 | function field = field_convert(inputField,dims)
% FIELD_CONVERT Converts cell field to matrix field and vice versa
%
% field = field_convert(inputField,dims)
%
% inputField - Field to convert
% dims - Number of dimensions
% field - Converted field
%{
Copyright © 2011 by Université catholique de Lo... |
6e94d7c92656a8cbe41b81ba402f6bdfb8daa1d84a186a67bfc0020331fe40c2 | MATLAB | 2,239 | 86 | % function [L weights] = CrossValidationPeerCheck(S,Q,ep)
function [L,weights] = CrossValidationAssemblyPrediction_Cells(Starget,Qtarget,Sref,Qref,angBin,ang,ep,lambda,bins)
nbEp = 10;
epV = regIntervals(ep,nbEp);
fprintf('Launching Cross-validated Peer info\n')
nbC = length(Starget);
nbCref = length(Sref);
dQ = Dat... |
bc7412c169bb546a5249aa55b2fd8bdfa7d93906416a39e8040d88a291ef72ad | MATLAB | 2,239 | 59 | function links=gap_closer_s(segmentlists_time,segmentlists,max_interframe_distance,max_gap_closing_distance,spots_s,voxel_size)
xy_ratio=voxel_size(1);
z_ratio=voxel_size(3);
% time matrix
segment_start_times=cellfun(@(cell_variable) cell_variable(1), segmentlists_time);
segment_end_times=cellfun(@(cell_variab... |
5e0802aea0a9db3dae5477d23d2d8a4d81e01a5c2a5f86d9130aa36fd2bb24a1 | MATLAB | 2,248 | 49 | function [anterdir,posterdir,dorsaldir,ventraldir,leftdir,rightdir]=apdvlr_axes_2(coords,guess,scale_factor,str, normal_vector,chosen_point)
midpoint=mean(coords,1,'omitnan')';
[coefs,~]=pca(coords);
dorsaldir_counter=0;
for i=guess(:)'
dorsaldir_counter=dorsaldir_counter+dot(midpoint,normal_vector)-dot(normal... |
7dd680b1a3666420ab3ea0cee6d9950e417295307dcf3e34ee10f2cb0234eaa2 | MATLAB | 2,248 | 66 | function [subj new_objnames group_mat] = duplicate_group(subj,objtype,old_groupname,new_groupname)
% Duplicate an entire group
%
% [SUBJ NEW_OBJNAMES GROUP_MAT] = DUPLICATE_GROUP(SUBJ,OBJTYPE,OLD_GROUPNAME,NEW_GROUPNAME)
%
% Calls DUPLICATE_OBJECT on each object in the group, renaming them
% based on the NEW_GROUPNAME... |
a81f540b9dba2ff2f7051caca8dc4f402e6d593e943f84581553b69a89dccdd9 | MATLAB | 2,248 | 90 | function ss = sumsOfSquares(y,groups)
ss.total = var(y)*(length(y)-1);
tot_mean = mean(y);
% Design Mat
levels_a = unique(groups{1});
levels_b = unique(groups{2});
DFa = length(unique(levels_a))-1;
DFb = length(unique(levels_b))-1;
N = length(groups{1});
X = nan(N,DFa+DFb+DFa*DFb);
first_ind_a = 1;
first_ind_b = ... |
ab6deab2e6bc0873f9c9b70d63986292c9f363c79249cc5c95f491a69858e809 | MATLAB | 2,249 | 79 | % Description:
% Computes the inverse of a symmetric matrix using the cholesky
% decomposition and compares the runtime to that of the standard inv
% function.
%
% Author:
% Eric Blake
% Black River Systems Company
% blake@brsc.com
% 05/14/2012
% Compile the MEX file.
clc;
disp('C... |
ed82fa30a91658d7e4395254c38ecca1e098a9bd331c34b57ba8b66055497c5f | MATLAB | 2,252 | 109 | function X = gaussFilt(X,N1,varargin)
% Xsm = gaussFilt(X,N1,N2)
%
% 1 or 2-d gaussian filtering with border correction
% Input:
% X: vector (or matrix) to smooth
% N1: sd of gausswin for the first dimension. If N1==0, no smoothing
% N2 (optionnal): in case of X is a matrix, sd of smoothing on the 2nd dim... |
1dbd2a5730b026c94dccb57d8343ca107db64996e685cb481007662e50556506 | MATLAB | 2,256 | 83 | clear
[task_info,supPath] = loadDBAndSpecifyDataPaths('Vermis');
SD =15;
raster_params.time_before = 350;
raster_params.time_after = 800;
raster_params.SD = SD;
raster_params.smoothing_margins = SD*5;
corr_window = raster_params.time_before + (50:250);
req_params.grade = 7;
req_params.ID = 4000:6000;
req_params.re... |
8a3f932908e0facc6ee6c5301e23f78ab9aa34532d369fc2fd174cd99181a16b | MATLAB | 2,259 | 83 | %
% X is #time points x [HbO/HbR/HbT] x #channels
% dc.dataTimeSeries is time x channels
% When dealing with DWT, almost never deal directly with wavelet and
% support functions, only need high and low-pass filters and input
% signal
function WX = calcDWTMatrix(X)
% X = ones(2^10,1);
% Let matlab takes ca... |
6093ba7bd5e6aaa51f82673d6a1a7cfb2be7e6719ed59d4b0e0011c6ab68ee7e | MATLAB | 2,260 | 78 | function [errmsgs warnmsgs] = unit_statmap_mitchell()
% USAGE :[ERRMSGS WARNMSGS] = TEST_STATMAP_MITCHELL()
%
% This is a script that tests the mitchell voxel selection method.
%
% ERRMSGS = cell array holding the error strings
% describing any tests that failed. If this is empty,
% that's a good thing
%
% WARNMSGS =... |
167daa53dc4ae6b33cad54e31339438bbe82593f582d3564972795a3fe774416 | MATLAB | 2,262 | 106 | function A = PhasePrecess(A)
% A = getResource(A,'PfcThetaPhase');
A = getResource(A,'HcThetaPhase');
A = getResource(A,'StartTrial')
startTrial = startTrial{1};
A = getResource(A,'TrialOutcome')
trialOutcome = trialOutcome{1};
% A = getResource(A,'PosYS');
% YS = YS{1};
% A = getResource(A,'PosPhiR');
% phiR... |
91d7955c0903b778d865ab071f02d88b59cf4ba5aaeedd90bb33119e7f944279 | MATLAB | 2,262 | 70 | classdef PopulationPsthDistanceClassifierModel < ClassifierModel
properties
psthMap
SD = 30
smoothingMargins = 300
end
methods
function mdl = train(mdl,X,y)
psthMap = containers.Map('KeyType','double','ValueType','any');
classes = un... |
eede9d92a405eb76911ab226e1c68ac6b00107aff2ebd7fdcff3700ac637df4a | MATLAB | 2,267 | 80 | %DEMGAUSS Demonstrate sampling from Gaussian distributions.
%
% Description
%
% DEMGAUSS provides a simple illustration of the generation of data
% from Gaussian distributions. It first samples from a one-dimensional
% distribution using RANDN, and then plots a normalized histogram
% estimate of the distribution using ... |
12533bf4a12383ab5342ceb888aa0e311c775094203a06c6d2d83f5ec020d537 | MATLAB | 2,270 | 79 | function K_out = updateK(B, I, S, Kb_eye, Kb_pc)
% Update all the relevant things in the K structure given a new set of eye
% and PC weights (concatenated)
K_out = [];
K_out.dt = B.dt;
K_out.p = I.p;
Kb_eye_pc_new = [Kb_eye(:); Kb_pc(:)];
% Default values from optional inputs
K_out.EH = [];
K_out.EP = [];
K_out.PH ... |
aeea4939bad10efd2ee973180c17732f562ed46ff49fba6ac14f413c3b3f7540 | MATLAB | 2,271 | 93 |
clear
[task_info,supPath,~,task_DB_path] = loadDBAndSpecifyDataPaths('Vermis');
EPOCH = 'cue';
PLOT_CELL = false;
ONLY_TIME_SIG = false;
req_params = reqParamsEffectSize("pursuit");
%req_params.cell_type = {'PC cs'};
%req_params.ID = 5666;
lines = findLinesInDB (task_info, req_params);
cells = findPathsToCells (s... |
946b4026a404c9c59fa5b4c5eaa29ad8229d142004f3a5d7410755b7c0321cf1 | MATLAB | 2,272 | 61 | function info_calc = computeMLEFieldInfo(eps_vals, eta_vals, J_i, n_obs)
% Computes the infromation between the MLE estiamte of the field value h
% and the true value h for conditionally independent spins coupled to h
% through the hamiltonian H = eta*J*s + eps*s.
% % Inputs:
% eps_vals: vector of epsilon values... |
1318c59a9ea8ae3b8a2c457916ef92c44803b588294d21c743951202c48db40d | MATLAB | 2,273 | 72 | clear all
supPath = 'C:\Users\Noga\Documents\Vermis Data';
load ('C:\Users\Noga\Documents\Vermis Data\task_info');
req_params.grade = 7;
req_params.cell_type = 'CRB|PC ss';
req_params.task = 'saccade_8_dir_75and25';
req_params.ID = 4000:5000;
req_params.num_trials = 100;
req_params.remove_question_marks = 1;
raster_p... |
6ae21b67751cda9a7d3b4fad74b03801125999a5ab6e1ccb896e3d07e1b27929 | MATLAB | 2,273 | 60 | %% Main Script walking through the steps for EEG analysis (Project: PT_EEG)
% belongs to manuscript "Neural correlates of perspective-taking in 4-year-olds"
%Run the Initializing Script to set variables and parameters
cd 'D:\PT_EEG_Analysis\' % specify location of EEG scritpts
Initializing_Script_PT_EEG();
%% ... |
728529e35a359393c8ca70213f5f0109d2cb826359cadff5a974ff4c8e6fa61c | MATLAB | 2,273 | 56 | function beh_file_generation(beh_input_path,N,output_path)
%% loading data
disp('==================================================================');
fprintf('covariate files preparation\n');
% load behavior data
beh_meas=readtable(beh_input_path,'sheet',['Stanford_cohort_' N]);
%% data preparation
numopt_mathres_v... |
a621d016a58d24c42bc296ff4fab7df4bdefa88f5fd493c8d377a841f80efbd4 | MATLAB | 2,278 | 77 | clear all;
addpath('/Volumes/LASA/TBI_OpenNeuro/TBI_openneuro/timeseries/inputs/');
addpath('/Volumes/LASA/TBI_OpenNeuro/Shared_code/Hopf_TBI_ON_personalized/');
load schaefer_MK.mat;
load tseries_openneuro_tbi_tp123_sch1000.mat; % Load your data
% Parameters of the data
TR=2; % Repetition Time (seconds)
NPARCELLS=10... |
2386065778c9dd7ace36cede7bee32ebc3ce0dbb86c648af7f586389aa9e94ba | MATLAB | 2,280 | 71 | function t_points = out_of_sample_est(points, X, mappedX)
%TRANSFORM_SAMPLE_EST Performs out-of-sample extension using estimation technique
%
% t_points = out_of_sample_est(points, X, mappedX)
%
% Performs out-of-sample extension using estimation technique on datapoints
% points. You also need to specify the or... |
559617e5f159f6f5c028bb2ca8158ae098d693c69e444c2eeabffe4bf3348e4f | MATLAB | 2,280 | 76 | % SURFACEOBJECT = FILLBULLSEYE(varargin) creates a
% wedge "patch" using xdata/ydata from a polar plot. The color of the patch
% is determined scaled by the value of the vector. To be used with
% createBullseye.
%
%
% varargin can take the following inputs:
% -cdata,rho1,rho2,theta1,theta2
% -cdata (assume... |
840c8d0e991e84e5d9ff7911d11e70ff949acc0ada4e1ebc8770b17426ef9129 | MATLAB | 2,281 | 69 | function test_suite = test_parse_filename_prefix %#ok<*STOUT>
try % assignment of 'localfunctions' is necessary in Matlab >= 2016
test_functions = localfunctions(); %#ok<*NASGU>
catch % no problem; early Matlab versions can use initTestSuite fine
end
initTestSuite;
end
function test_prefix()
filename = ... |
dc984557b013081ed038a6b1b092f46385ed33e708848dae7398b72ff56e9e23 | MATLAB | 2,281 | 79 | function [subj] = blur_pattern(subj, patname, fwhm, varargin)
% BLUR_PATTERN - Spatially blurs a pattern.
%
% Usage:
%
% [SUBJ] = BLUR_PATTERN(SUBJ, PATNAME, FWHM, ...)
%
% Spatially blurs a pattern by 3D convolution with a Gaussian
% kernel with Full-Width Half Max of FWHM voxels. FWHM is
% not restricted to integers... |
48bb0aad47cc5f1427786740634942dac7328d06540854eada6ea04ef8a17da5 | MATLAB | 2,282 | 73 | % IIRFILTFILT filter a signal using a double N pole butterworth.
%
% call Y = iirfiltfilt(X,Fs,flp,fhp,N,band)
%
% XF = IIRFILTFILT(X, FS, FLP)
% XF = IIRFILTFILT(X, FS, [], FHP)
% XF = IIRFILTFILT(X, FS, FLP, FHP)
% XF = IIRFILTFILT(...,N) deter... |
af1cb665b0d1410945268aae4b748e349fdd540f2356605c810cb2bdbb9f1ee9 | MATLAB | 2,283 | 40 | function [ peak_frames, peak_dFFMax, peak_latency ] = Peaks_3STD( dFF_trace, uncaging_frame, SecPerFrame, num_std, peakthresh, MinPeakWidth)
%Peaks_3STD finds peaks that are >= 3std above the baseline mean, and
%records the frames and the amplitude of these peaks, split into those
%peaks that occur before and after ... |
2730dea01269b409c5d97198198dfa94d5c159f5277d1b48f00439c90d12d0b0 | MATLAB | 2,284 | 81 | function [ annulus ] = createAnnulus( n, r, w )
%createAnnulus Create a binary annular mask
%
% INPUT (all optional)
% n - size of the annular mask as a scalar, or vector with coordinates
% r - radius of the annulus in pixels
% w - width of the annulus in pixels
%
% OUTPUT
% annulus - n x n matrix with the ann... |
56ccb2c7c8e16bd9a4aed908ced85d1d8b87db16ee4652eff2445f16ea4cfcab | MATLAB | 2,284 | 74 | % Run this script after scriptSetupML, it will connect to jupyter, copy
% files and can terminate the instance
%% Inputs
id = '';
dns = '';
origPath = ''; % Where this instance data started from
%% Jenkins
if exist('id_','var')
id = id_;
end
if exist('dns_','var')
dns = dns_;
end
if exist('or... |
aece41776cedc2fea5fd798ba7711041545f57e0500d670f3fe4ce5cd9a1175c | MATLAB | 2,285 | 67 | function PlotClusterTrackMovie(movieExportFolder, fileName, image, tracks, trackColors, pxPerMicron, secBtwFrames)
close all
%% Parse inputs
fullIdxList = unique(tracks(:, 4));
pointSize = 40;
lineWidth = 2;
numMicrons = 10;
%% Initialize
% Initialize figure
h = figure;
g1 = imshow(image(:, :, 1)); hold on;
set(h, ... |
19dbe2b2bb920b001f29e16b4b95bfdac84a0c286babb0ae71105b36a8ac351a | MATLAB | 2,287 | 67 | function layer = dropoutLayer(probability, NameValueArgs)
% dropoutLayer Dropout layer
%
% layer = dropoutLayer creates a dropout layer that randomly sets
% input elements to zero with a probability of 0.5 during training.
% During prediction, the layer has no effect. This can help prevent
% overfittin... |
cb8aa6d5557ab8d270e7136c80d03b6dcc142088212df1cd319e0983cdc73ce9 | MATLAB | 2,288 | 55 | function vidObj = msCalcSegmentRelations(vidObj,calcCorr, calcDist, calcOverlap)
%MSCALCSEGMENTRELATIONS Summary of this function goes here
% Detailed explanation goes here
if calcCorr
segCorr = zeros(vidObj.numSegments,vidObj.numSegments);
end
if calcDist
segDist = zeros(vidObj.numSegment... |
bb24e5409ae1cd203bf3516e167078e40f281b6ef2d16768de1e95fab5cc9048 | MATLAB | 2,292 | 47 | function [anterdir,posterdir,dorsaldir,ventraldir,leftdir,rightdir]=apdvlr_axes(coords,guess,scale_factor,str,ant_dx)
midpoint=mean(coords,1,'omitnan')';
[coefs,~]=pca(coords);
dorsaldir_counter=0;
for i=guess(:)'
dorsaldir_counter=dorsaldir_counter+dot(midpoint,coefs(:,2))-dot(coefs(:,2),coords(i,:));
end
d... |
9f384e0b5b6a7c2e5d11da854ae83065c80a70d75fb95243443c8e3de221ae9a | MATLAB | 2,295 | 84 | function stats = posthocVARfromData(data, net, estStates, sortFlag, nIter, p_ref)
if nargin<5
nIter =100;
p_ref = 0.01;
end
if nargin<6
p_ref = 0.01;
end
K = length( net.ARmodel);
M = size(data{1},1);
VBprior.ao = 10^-3; VBprior.bo = 10^-3;
VBprior.no = M + 2;
VBprior.Wo = 10^3*eye(M);
noSubjs = le... |
735dc254693630cbee72824db2c01dd556460ece1200f3074c182111167aa80b | MATLAB | 2,296 | 82 | % Simulate data to examine effect of noise correlations in populatin of neurons
%
% addNoiseCommon: add noise correlations
% NperGroup: number datapoints per group
% Ngroups: number of groups/classes. Default is =2, can also accomodate =3.
% addNoiseSignaldependent: 0 no, 1 add signal dependent noise
%
function [data, ... |
02804e3db1b99671b090dc3326e6379ab98edb56c629226bea0a64a258eb8bb9 | MATLAB | 2,298 | 81 | function d = estimate_disparity(leftim, rightim, kernelsize1, sigma1, ...
kernelsize2, sigma2, dmax)
% D = ESTIMATE_DISPARITY(LEFTIM, RIGHTIM, KERNELSIZE1, SIGMA1, ...
% KERNELSIZE2, SIGMA2, DMAX)
%
% Estimate disparity from a stereo pair, using the algorithm described
% in section 7.3 o... |
617c674cf16162b7f4105ba651264841ca321f74ab5d83e68992283c90a18166 | MATLAB | 2,298 | 71 | #import "UnityAds/UADSPurchasing.h"
#import "UnityAdsPurchasingWrapper.h"
#import "UnityAdsUtilities.h"
static UnityAdsPurchasingDidInitiatePurchasingCommandCallback iapCommandCallback = NULL;
static UnityAdsPurchasingGetProductCatalogCallback iapCatalogCallback = NULL;
static UnityAdsPurchasingGetPurchasingVersionCa... |
50232e242a8b23e485a18ddc978ef261b1652984f7ec01f369ab2f76fb06ba10 | MATLAB | 2,299 | 68 | function [X,y] = simulate_regression_data(typ, nsamples, nfeatures, scale)
% Creates univariate regression data.
%
% Usage: [X,y] = simulate_regression_data(typ, nsamples, nfeatures, scale)
%
% Parameters:
% typ - defines the relationship between predictors X and
% response variable y... |
80e0606efb2b4c8bea3e231d35d8d27c39e775af720af535c80a8ba9de09753b | MATLAB | 2,299 | 72 | function [scratchpad] = train_corr(trainpats,traintargs,in_args,cv_args)
% Correlation-based classifier
%
% [SCRATCHPAD] = TRAIN_CORR(TRAINPATS,TRAINTARGS,IN_ARGS,CV_ARGS)
%
% See the related TEST_CORR function that gets called
% afterwards to assess how well this generalizes to the test
% data.
%
% Doesn't calculate ... |
ee004d6e6e360cb588124ca98340ca027f6b6b23bfd8fb382ecf987befb4cba1 | MATLAB | 2,301 | 53 | % function make_design_alldata(cfg)
%
% Function to generate design matrix with all data as training and test
% data. This is useful e.g. for creating a weight map from all data. Please
% remember to set cfg.design.nonindependence = 'ok'
%
% IN
% cfg.files.chunk: a vector, one chunk number for each file in
% ... |
a8fe7b324692ccf2198e0c899f46a46a34a01af991fe5d8052901aad319c6950 | MATLAB | 2,302 | 78 | function [Ty,diff,CI,da,db,df,p]=yuen(a,b,percent,alpha)
% function [Ty,diff,CI,da,db,df,p]=yuen(a,b,percent,alpha)
%
% Computes Ty (Yuen's T statistic).
% Ty=(tma-tmb) / sqrt(da+db), where tma & tmb are trimmed means of a & b,
% and da & db are yuen's estimate of the standard errors of tma & tmb.
% Ty is distributed ... |
30bea7aa1eeab833d259f3383b832d225f455766ef02b5acb91f30c4d37eac34 | MATLAB | 2,303 | 73 | close all; clear all; clc;
Act = @(MaxAmp, Alpha, Tau_r, Tau_f, t) MaxAmp * Alpha * (exp(- t/Tau_f) - exp(- t/Tau_r)) /(Tau_f - Tau_r);
% Act = @(MaxAmp, Alpha, Tau_r, Tau_f, t) MaxAmp * Alpha * (exp(- t/Tau_f) - exp(- t/Tau_r));
SamplingRate = 1000; % times per second
% T = (0:TimeLength-1)/SamplingRate;
T = -1... |
ca0f7a2c84253f6aa02bb2152350b24d6e7d276a40f66e0a0554331c83543314 | MATLAB | 2,303 | 60 | % redefine stimulus classes into 6 diff. classes
% to be runned after generating stimuli marks from aux data in Homer3 GUI
% Combined multiple snirf files into one.
% strictly for ExperimentXXGUI folder
% Only 1 snirf file
function redefineStimulusClasses_MultiOnly(sbjNum,rawDataFN,respData)
dir = ['C:\Users\mn0mn\Do... |
fcfccd837be481e65fdce6ac01ecd6a9c374864b9f916f53758f324ffcf4da92 | MATLAB | 2,306 | 59 | for s=1:length(subject_list)
for trltype = 2
trodes=[];
G=[];
H=[];
looping_CSD_final=[];
subject = subject_list{s};
fprintf('\n\n\n*** Processing subject %d (%s) ***\n\n\n', s, subject);
% Load the componen... |
cf66e52383e491674928c4e4972daf17c6207b8df854fa927cadcdf58a7c0ace | MATLAB | 2,309 | 79 |
%% Add path
addpath(genpath('../../Ionnic Channel'),genpath('../../Tools'), genpath('../../Synapses'), genpath('../../Datas'));
%% Import data from neuron (FS)
dt = 0.01;
Vmem = -90:dt:60;
l_V = length(Vmem);
hinf = zeros(1, l_V);
tauh = zeros(1,... |
4fb13492cb260c8352c427f67458127cde0a3e0821804cdad32fe9c0781fcd99 | MATLAB | 2,312 | 51 | %% Supplemental CODE for CSFpulse calculation from EPI fMRI data
% Kim, J. H., Im, J. G., & Park, S. H. (2022). Measurement of CSF pulsation from EPI-based human fMRI. Neuroimage, 257, 119293.
%
% The data type can be either nii or Nifti.
% These codes are running based on Matlab 2020a.
% Manual processes are need... |
a34f89308323a60f48cf3ee4a2131ba1ee4c52f996daaeefbef566f6b2820a5f | MATLAB | 2,316 | 69 | function [data, mask] = load_raw_data_masked(p, sub_id, mask_only)
% function [data, mask] = load_raw_data_masked(p, i_sub, mask_only)
%
% Load (realigned) raw data, masked with ROI.
% Uses an adapted version of spmReadVolsMasked.m from the cvmanova toolbox
% by Carsten Allefeld, accessible at
% https://github.com/all... |
c3ef248d91407f6cb4fe488eb753388d13e5f8ad23f7a884f41b648faabf834f | MATLAB | 2,316 | 28 | %Fetches mni coordinates for all contacts across subjects that are within
%subregions of interest (contrast of 2 broader regions), with their flicker response amplitude and
%significance.
%Requires structure subject to have fields .anat, .flickerneuro_ssep_amp_val
%and .flickerneuro_ssep_amp_sig.
function [mat1,mat2,m... |
3169e10e00b5b8d0c4efa74519e26bd605e9478ba9d921413ac91c8d7c9cfc2e | MATLAB | 2,317 | 67 |
function plotFilterFreq(K, hf, cs)
if ~exist('hf','var')
hf = figure;
else
figure(hf); clf;
end
f = [0.5 1 2 3.33 4 5 8 10 12.5 16.6700 20. 25. 32.5000 40 50];
xlims = [0 0.05];
if ~exist('cs','var')
cs = {'b','k','r','c','m'};
end
sHandle = gobjects(4,3); for k = 1:12; sHandle(k) = subplot(... |
991002eea8a4bda98decc56065ebbce07e0205e6942d4b891c731b7034b6c65c | MATLAB | 2,317 | 80 | function [corrected_data, sigmas,dn, times, Mcorr] = SOUND_veryfast(data0, LFM, iter,lambda0,sigmas, estimate_just_noise)
%
% This function performs the SOUND algorithm for a given data based on beamforming filtering, which provides a very fast implementation
%
% input:
% data: channels x time points x trials (not... |
df01bc1f83b6288ab5bb24ba51e8bd2b7f6e97749d5cc3c9871f2f205853cce5 | MATLAB | 2,317 | 88 | function [no,xo1,xo2] = hist2(y1,y2,x1,x2)
%HIST2 2-D Histogram.
% [N, X] = HIST2(Y1,Y2) bins the elements of Y1 and Y2
% into 10x10 equally spaced containers
% and returns the number of elements in each container. If Y1 and Y2 are
% matrices, HIST works down the columns.
%
% N = HIST(Y1,Y2,M,N), wh... |
56b44621576082fe546bdf616f2ac59bf39f697e29d846415144ec31e48d2045 | MATLAB | 2,320 | 75 | function [err,Eq] = Plane_Equation (Triplets, verbose)
%
% [err,Eq] = Plane_Equation (Triplets, [verbose])
%
%Purpose:
% Determine the equation of the plane passing through three points
%
%
%Input Parameters:
% Triplets is an Nx1 vector of strucutres. Each structure defines a plane
% .XYZ : is a 3x3 matr... |
7dc72d664113cb700b001085919c0b3ca4d94d9b767c309e95cae821983bef41 | MATLAB | 2,320 | 65 | % Collect general section statistics
%% Inputs
datasetTag = ''; % Which dataset tag to load? Will load the latest unless specific date is writen in tag
isGroupBySubject = true; % Would you like to group results by subject
%% Load st structure
[datasetPath, datasetName] = s3GetPathToLatestDataset('10x',datase... |
91b628a3242a965c6c509eb91edfdd8bd3ff032d5d6414e0a45f1af236396593 | MATLAB | 2,320 | 63 | function [Vectors, Av_Vector, AngleDiff] = Initial_Vectors(Traj_in, radius,Target_Angles, Animal, TargetNum, merge)
% calculates the initial vector and average initial vector of Trajectories
% input should be trajectories truncated at specific radius
% inputs Target_Angles and TargetNum are needed to calculate th... |
92141e0319b3bff5a5a25112ba05d13912303ea14b884f54f160565d801e3489 | MATLAB | 2,321 | 86 | function [fh, rasterAx, histAx,matVal] = ImagePETH2(S, center, TStart, TEnd, varargin)
% Display a PETH-like plot for continuous variable such as EEG
% Uses RasterImagePlot2
% Adrien Peyrache 2007, adapted from Battglia
font_name = 'Arial';
font_size = 10;
font_weight = 'bold';
line_width = 2;
figure... |
54109658624c5b53b2f765c28e3ea449918aad97a8cce746a5ceafd29df40378 | MATLAB | 2,323 | 63 | % redefine stimulus classes into 6 diff. classes
% to be runned after generating stimuli marks from aux data in Homer3 GUI
% Combined multiple snirf files into one.
% strictly for ExperimentXXGUI folder
% Only 1 snirf file
function redefineStimulusClasses_AVCA()
dir = ['C:\Users\mn0mn\Documents\ResearchProjects\spata... |
5b5ebec3aba3e65f36b481cf7cf724c82498e4039fff366c1904136a21a3f1cf | MATLAB | 2,323 | 81 |
%% Add path
addpath(genpath('../../Ionnic Channel'),genpath('../../Tools'), genpath('../../Synapses'), genpath('../../Datas'));
%% Import data from neuron (FS)
dt = 0.01;
Vmem = -90:dt:60;
l_V = length(Vmem);
ninf = zeros(1, l_V);
taun = zeros(1,... |
60addadc6e601436b96fc20d57bab267b061b774c0aaced2cf433796526ebca9 | MATLAB | 2,330 | 61 | function DistributionSingleCellOverTime(histExportFolder, histDataFilename, relMat, diffIm, magMat, angMat, dataBinWidth, relThresh)
%% Calculate Histogram Data
% Set mainFlowMask
mainFlowMask = ...
double(relMat > 0).* ... % Where the relMat is > 0
double(diffIm > 0).* ... % Where the diffIm is > 0
do... |
6f03360aca907916b7197234475c8fb909171bbf4a0c75a9d5be2d0a8ac516a6 | MATLAB | 2,331 | 102 | factor=1
%%
load("neurons_by_hand.mat");
load('neurons_reconstructed_max_2_e.mat');
s1=size(hand_e,1);
s2=numel(neurons_reconstructed_max_2);
guessed=zeros(1,s1);
taken=zeros(1,s2);
sizes=12;
TP=0;
FN=0;
FP=0;
minimum_distance=sizes.*factor;
for i=1:s1
current_neuron=hand_e(i,[3 2 4]);
best=3... |
0e7e1d3d99d066fdb5d733d4c5e8629ab3627b30622506fe5b7afc9bde2dc22a | MATLAB | 2,332 | 77 | function a = gmmactiv(mix, x)
%GMMACTIV Computes the activations of a Gaussian mixture model.
%
% Description
% This function computes the activations A (i.e. the probability
% P(X|J) of the data conditioned on each component density) for a
% Gaussian mixture model. For the PPCA model, each activation is the
% condi... |
97e92fda59094f8ee9ee00cdb562531c497ddfddb956e9bedc234c759332e308 | MATLAB | 2,333 | 83 | %-Configfile for preprocessfmri.m
%- for spm 12
%__________________________________________________________________________
paralist.spmversion = 'spm12';
%-Please specify parallel or nonparallel
%-e.g. for preprocessing and individualstats, set to 1 (parallel)
%-for groupstats, set to 0 (nonparallel)
paralist.paral... |
eca47732b42f530525fe5c7cbf96aa3bfd7e8f45b11190bdd3f2f78620f4a89f | MATLAB | 2,333 | 81 | clear all
close all
format compact
clc
% script to calculate the statistics for each scan given this will currently only run if distances have been measured
% for all included scans (UsedSets)
[dataPath,resultsPath]=getPaths();
MaxDist=20; %outlier thresshold of 20 mm
time=clock;time(4:5), drawnow
method_string='T... |
0b9601213eadcaad9c73bd3f3598c51c67d5e9958b38faa1aeb1c23adfba76b3 | MATLAB | 2,334 | 75 | function mr_fmontage_vga(anat,func,mask,slices)
% functional montage superimposed on anatomical montage
% License:
%=====================================================================
%
% This is part of the Princeton MVPA toolbox, released under
% the GPL. See http://www.csbmb.princeton.edu/mvpa for more
% informat... |
9d0500ac15e60487f59357e323b7af13973be1baf90978bfe6a66c3a0ca2687b | MATLAB | 2,336 | 67 | function [r,T] = mvnrnd(mu,sigma,varargin)
%MVNRND Random vectors from the multivariate normal distribution. This is an
% open source version that emulates a subpart of the behavior of the same
% name function from the MATLAB stats-toolbox.
% It emulates the 3 input, 1 output argument MATLAB-version,
% where ... |
e1dc4988e5d491ce81f6497b6a022d68ba6f3bb718991241bb1e3407d6cec9ac | MATLAB | 2,337 | 43 | function protocolset = xMakeAnalogwaveformtabs(destinationpath)
% Make Analogwaveform objects using info from an excel file.
% See excel file specified below (path_in) for how to setup such a file
% -----------------------------------------------------------------------------------------------------------... |
6e99b2537fbfe81509e3a960fc73e73e590281c325dc7bbc36e639638d70293f | MATLAB | 2,339 | 49 | %Performs PSD analysis for all trials in a flicker session, and saves data.
%2024/02/25
function PSD_results=run_PSD_flicker(data,trials)
%initialize PSD_results variable:
PSD_results.label=data.label;
PSD_results.condition=repmat({''},1,length(trials.condition_code));
% Reformat data into chronux... |
bb37eff19d5ef3e92a5673ba929779c19c04c7e835408e4db3457d7ffb38c57d | MATLAB | 2,339 | 85 | %% Generates the initial ms data struct for data set contained in current folder
ms = msGenerateVideoObj(pwd,'msCam');
% Newer versions of the Miniscope system do not require Column Correction.
% If this is the case for you still run msColumn Correction() then run the two lines of code below it.
ms = msColumnCorrecti... |
9e983d39d79a5d6426012aa8273175f6e528c1a45f2e58fc10b07b26b51abbc5 | MATLAB | 2,342 | 62 | %{
Copyright (C) 2023 N. Suhas Jagannathan
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed i... |
efeb161c282c890308d68f5583969144b8a0acbea7077dba4fda037990974182 | MATLAB | 2,343 | 73 | %
% [Sin,SNin] = Sin_vsh_ctf(r_sphere,R,EX,EY,EZ,Lin)
%
% Calculate the internal SSS basis Sin for a CTF system
% using vector spherical harmonics
%
function [Sin,SNin] = Sin_vsh_ctf(r_sphere,R,EX,EY,EZ,Lin)
% Copyright (c) 2016, Elekta Oy
% ---------------------------------------
%
% Redistribution and use of the Sof... |
4bad813084084cd4a91fa497f3d8410a108d119be0a5daeaedb8bc587ca721e5 | MATLAB | 2,344 | 76 | function [bfca_cv, voxel_cv, fwhm_cv] = pSVR_nested_cv(all_results, p)
% function [bfca_cv, voxel_cv, fwhm_cv] = pSVR_nested_cv(all_results, p)
%
% Perform nested cross-validation to determine the optimal voxel-number and
% feature-space smoothing FWHM value for each participant, based on the
% results of all other pa... |
83047780d22c21737503b5ab3e3c97a0f97bae79c46b7cd170ac2621dc0ffc92 | MATLAB | 2,346 | 63 | function [fx] = f_Hampton(x,P,u,in)
% Hampton's "influence learning" model: evolution function (Hide and Seek)
% function [fx] = f_Hampton(x,P,u,in)
% Hereafter, the learner is referred to as the "agent", whereas her
% opponent is referred to as the "other".
% Let p=P(o=1) be the agent's prediction of the other's next ... |
5f0631330144ed8d0a30ec8495875daee760f5cc7e10108523708c674a82559b | MATLAB | 2,350 | 76 | function [perfmet] = perfmet_for_class_corr(acts,targs,scratchpad,args)
% This is the performance metric for the correlation classifier
%
% [PERFMET] = PERFMET_FOR_CLASS_CORR(ACTS,TARGS,SCRATCHPAD,ARGS)
%
% For each test brainstate, find the training brainstate that's
% most similar. The guess is the category of that ... |
4e30711067bea074adac3fb4660f92c4181b618e44e7d694721f1ba91f123525 | MATLAB | 2,351 | 100 | function hout=suptitle(str)
%SUPTITLE Puts a title above all subplots.
% SUPTITLE('text') adds text to the top of the figure
% above all subplots (a "super title"). Use this function
% after all subplot commands.
% Drea Thomas 6/15/95 drea@mathworks.com
% Warning: If the figure or axis units are non-default, this
% w... |
87da4afed412a536ca736350ed727b149c95ae8c4467a2f43204a8cb175b19a4 | MATLAB | 2,354 | 81 | % This initialisation is need to run this function in stand alone mode
clc;clear all;close all;
data_prefix = 'M89971_20210723_sess11_rec6_SWE';
rawdata_folder = 'E:\NAS_SD\SuiviClient\Triffilieff\AnnaRoman\DataAnna_Baseline\M89971';
analysis_folder = 'E:\NAS_SD\SuiviClient\Triffilieff\AnnaRoman\AnalysisAnna_Baseline\M... |
773d10ab7e69fefbbc3319e4c3eae84cedeb37d0ffafe045223bcfc57911b7da | MATLAB | 2,357 | 98 | function [tmd] = triggeredMeanData(tsa, events, tmax, varargin)
% Event-triggered mean of the Data component, triggered on events (TODO)
%
% TODO: Overlap wth the ETAverage function. Need cleaning
%
% USAGE:
% [tmd] = triggeredMeanData(tsa, events, tmax, options)
%
% INPUTS:
% tsa: a tsd object, assumed to... |
c4ad0320f831e8c1d731f062efbbeec0f269348b97bb0df9369f1f5932dccb2c | MATLAB | 2,357 | 50 | function [randomizedConditions, randomizedIndeces] = randomize_conditions(conditions, numberOfTrials)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Based on 'conditions', create output of length 'numberOfTrials', which is
% composed of the randomized elements of 'conditions' with the co... |
b31a65e557c2697ae4fd50b4a7df8fd65b0ea4f0fd884ff87d140c2ffd7c6cf4 | MATLAB | 2,360 | 95 | clear
[task_info,supPath,MaestroPath] = ...
loadDBAndSpecifyDataPaths('Albert behavior before recording');
PROBABILITIES = 0:25:100;
req_params.task = 'pursuit_8_dir_75and25';
req_params.num_trials = 50;
req_params.remove_repeats = false;
behavior_params.time_after = 1000;
behavior_params.time_before = 0;
behavi... |
9821f0a88e2fc026eb178b7af3863f42d81cbb2c2eb52dff42751c41c6be115e | MATLAB | 2,361 | 79 | % PCA OF BEHAVIORAL DATA
%
%
%
% Authors: Rafael Naime Ruggiero, 2021
%Load data matrix
load('Matriz_corr.mat')
%Zscore data matrix
Xz = nanzscore(X,2);
%Reordering the matrix.
Xz = Xz(:,[2,1,3,4,5,6]);
%PCA
[coeff, score, latent, tsquared, explained, mu] = pca(Xz);
coeff(:,1)=coeff(:,1)*-1;
... |
5d47d6866cb3ee0a15cfe1f9c5284a6099fc6574f39954a097dc91cb283e1f19 | MATLAB | 2,363 | 80 | function create_rois_from_prob_maps(p, roi_name)
% function create_rois_from_prob_maps(p, roi_name)
%
% Creates binary region of interest (ROI) masks from probability maps
% published by Wang et al (2015). The set of probability maps can be
% downloaded from:
% http://scholar.princeton.edu/sites/default/files/napl/fil... |
5c2f3c93b4e3693eb2318411cc71e13bcc2a0681508fce254e04f2b55c9fa664 | MATLAB | 2,364 | 49 | addpath(genpath('/dagher/dagher11/filip/Software/BrainSpace-0.1.2'));
addpath(genpath('/dagher/dagher11/filip/Software/gifti-main'));
addpath(genpath('/dagher/dagher11/filip/Software/netneurotools'));
addpath(genpath('/dagher/dagher11/filip/Software/neuromaps'));
addpath(genpath('/dagher/dagher11/filip/Software/nifti_t... |
a5d691111f62427fdbb12242b7b0584715f8e556f6689bd1b73a2a7a7d72a450 | MATLAB | 2,365 | 80 | function [Ty,CI,diff,se,df,p]=yuend(a,b,percent,alpha)
% function [Ty,CI,diff,se,df,p]=yuend(a,b,percent,alpha)
%
% Computes Ty (Yuen's T statistic) for two dependent groups
% Ty=(tma-tmb) / sqrt(da+db), where tma & tmb are trimmed means of a & b,
% and da & db are yuen's estimate of the standard errors of tma & tmb.
... |
821a5b0b523047b9a2a682142d6c22c00c4a1e8f06a9749e4f0f90931b486649 | MATLAB | 2,367 | 66 | function ellipse=fit_ellipse(XY)
% ellipse fitting of data points XZ = [:,2].
% Output of the function is a struct with the ellipse parameters
% [xc,yc,d1,d2,phi] and a list of xy values for plotting the result.
% This ellipse fit was proposed in article
% A. W. Fitzgibbon, M. Pilu, R. B. Fisher
% "Direct Le... |
2cab44d9d00cea78ef69bc2fc69c1e4e1fca3116fcc4f7707990e073f842a16b | MATLAB | 2,368 | 48 | % Check if variable Exclusion_Info exists already. If yes, load it.
if exist([output_dir output_specific 'Exclusion_Info.mat']) == 2
load([output_dir output_specific 'Exclusion_Info'],'Exclusion_Info');
end
for s=28:length(subject_list)
subject = subject_list{s};
fprintf('\n\n\n*** Processing subjec... |
cc2929e1dad2acecd26049ac6407e0b8f1dee33c160480e0f34cd6c551454c98 | MATLAB | 2,371 | 79 | clear all
nInst = 500;
nVars = 200;
X = randn(nInst,nVars);
w = randn(nVars,1);
y = sign(X*w + randn(nInst,1));
w_init = zeros(nVars,1);
funObj = @(w)LogisticLoss(w,X,y);
fprintf('\nRunning Steepest Descent\n');
options.Method = 'sd';
minFunc(@LogisticLoss,w_init,options,X,y);
pause;
fprintf('\nRunning Cyclic Steep... |
a0241b66cd83d2587132b9b3de6735762790e9be37608edec5cd96afa28be05f | MATLAB | 2,372 | 80 | % function [ranks,ind] = fget(labels_train,vectors_train,varargin)
%
% Feature selection subfunction using F-score
function [ranks,ind] = fget(labels_train,vectors_train,varargin)
labels = unique(labels_train);
for i = 1:size(labels,1)
train_index{i} = labels_train == labels(i); %#ok<AGROW>
end
if nar... |
c506b9fda94bded967a7082fb2e08183ba8ad5788c5d843492c110e0287c9e28 | MATLAB | 2,376 | 96 | function A = PhasePref(A)
%Parameters
b = fir1(96,[0.1 0.3]);
% cellId = 44;
A = getResource(A,'CellNames');
A = getResource(A,'SpikeData');
A = getResource(A,'HcTrace');
A = getResource(A,'Sleep1Epoch');
sleep1Epoch = sleep1Epoch{1};
A = getResource(A,'Sleep2Epoch');
sleep2Epoch = sleep2Epoch{1};
A = getResource(A... |
0dcf1a17bcfea2fcf408621f0e8526cf5e94531261e47f2da84de144ea6a40c6 | MATLAB | 2,377 | 55 | function [spike_burst_dyn_table]=well_compute_spike_burst_dynamics_220211(allregion_unit_matched)
%despite the name, this function computes spike dynamics for the wells
% This folder contains the contains the mea structs
%load(strcat(parent_dir,'\','allregion_unit_matched'))
% Parameters
fs = 25e3;
% Init... |
90b1fc8f3315a88f548e219bdb7b50c9b3083dc1326dee7f0b68109559072f47 | MATLAB | 2,381 | 76 | function create_readme(varargin)
%
% Create a README in a BIDS dataset.
%
% USAGE::
%
% bids.util.create_readme(layout_or_path, is_datalad_ds, ...
% 'tolerant', true, ...
% 'verbose', false)
%
% :param layout_or_path:
% :type layout_or_path: ... |
7d36940a4bdf6a46259eea6ebb514d9fe03456a17ba5ccca453da8e230319b6f | MATLAB | 2,382 | 104 | function tsa = tsd(t, Data, varargin)
% Constructor of TSD object
%
% USAGE:
% tsa = tsd(t,data, 'optionName1', optionValue1,... )
%
% tsd is a class of "timestamped arrays"
% composed of an array of timestamp (column vector), and an array of data
% associated to each timestamp (first index corresponding ... |
817df21a1e193800cd8350f5449737e9405c03ee050e6049aee6615691cc1113 | MATLAB | 2,382 | 83 |
%% Add path
addpath(genpath('../../Ionnic Channel'),genpath('../../Tools'), genpath('../../Synapses'), genpath('../../Datas'));
%% Import data from neuron (FS)
dt = 0.01;
dtSI = dt*10^-3;
Vmem = -90:dt:60;
VmemSI = Vmem .* 10^-3;
l_V = length(Vmem);
%% Matr... |
f53ac3d6573d053ee64171206217f524840a9f023d771591ee5d7a57d5480d26 | MATLAB | 2,383 | 66 | % @title Convert .mat of synaptic connections into .txt file
% @file mat_to_synmatrixf.m
% @author Romain Beaubois
% @date 06 Aug 2021
% @copyright
% SPDX-FileCopyrightText: © 2021 Romain Beaubois <refbeaubois@yahoo.com>
% SPDX-License-Identifier: GPL-3.0-or-later
%
% @details
% > **08 Mar 2021** ... |
9d75a22e5cad2951f080435298fe40d84d816353a9b4d9a1369b7ac6ee84949b | MATLAB | 2,384 | 79 | function spots=bd3d_t_local_s(wholestack_local,sizes_local,thresholds_local,sigmas_local,zsizes_local)
% volume LOG filtering
filtersize=[sizes_local sizes_local zsizes_local];
h=-fspecial3('log',filtersize,sigmas_local); % this can be taken out of the function
If=imfilter(wholestack_local,h,'symmetric');
% re... |
5f0d4b3230305e5856999258dd78628b5cc046efe85cbef09523e771dfeda72a | MATLAB | 2,386 | 62 | function [c] = savgolay( order, halfwidth, reverse )
% SAVGOLAY: Implements Savitsky-Golay smoothing. This smoothing technique fits a polynomial
% to a moving window of data using linear least-squares fitting. Derivatives, as
% well as the function value, can be determined at the center point. Since the
% coeff... |
3c0c3ff9514d0fdc063028fa7fd79104ea4bb72fc8fdd74d0dff8c347f5bcf59 | MATLAB | 2,388 | 71 | function parallel_pool(n_workers, restart, rng_shuffle)
% function parallel_pool(n_workers, restart, rng_shuffle)
%
% Initializes parallel worker pool, with the option to restart an already
% exististing pool and to shuffle the random number generator on each
% worker individually.
%
% Input:
% - n_workers: Number o... |
739705821d73e261a8d9403d1480ea1959e7c81367cfdac0aaea433277660cd0 | MATLAB | 2,389 | 77 | function model = lvl1_contrasts(sub_id, p)
% function model = lvl1_contrasts(sub_id, p)
%
% Defines and calculates constrasts for a SPM first-level analysis.
%
% Input:
% - sub_id: ID of the current subject.
% - p: Structure with analysis parameters.
%
% Output:
% - model: Structure with all SPM settings used fo... |
a7c8caaee42bfec8b24e0da34574236fdb24b3f032f355504800f056716a4dc6 | MATLAB | 2,390 | 72 | function EnableDisableGuiPlotBttns(handles)
global maingui
if isempty(handles)
return
end
if ~isempty(maingui.dataTree.currElem.GetRawData())
set(handles.radiobuttonPlotRaw, 'enable','on')
else
set(handles.radiobuttonPlotRaw, 'enable','off')
end
if ~isempty(maingui.dataTree.currElem.GetDod())
set(ha... |
cac1b2e457bd2078dd55cc48846f3803d1921c5a1f7df9d971443cc746819999 | MATLAB | 2,391 | 68 | function [fx,dfdx,dfdp] = f_Qlearning(x,P,u,in)
% // VBA toolbox //////////////////////////////////////////////////////////
%
% [fx,dfdx,dfdP] = f_Qlearning(x,P,u,in)
% Reinforcement-learning evolution function for a n-armed bandit task
%
% An RL agent learns by trial and error. A bandit task is such that, after
% eac... |
38e816f17b373b5ca3813def4e0f38f1588e598c7312d1c383d31d7c3800b314 | MATLAB | 2,392 | 93 | function [no,xo1,xo2] = histCont2d(z,y1,y2,x1,x2)
% HISTCONT2D 2-D histogram of continuous measure
% [N, X] = HIST2(z,Y1,Y2) bins the elements of Y1 and Y2
% into 10x10 equally spaced containers
% and returns the average value of z for each bins.
%
% N = HIST(z,Y1,Y2,M,N), where M and N are scalars, use... |
1beb2787915930dfbcbc7b0983584a8e81b1da7b5b373926f5e502771e52c99c | MATLAB | 2,393 | 61 | function pipeline_patdif_state_at_cue_decoding()
cfg = load('neurovar_cfg.mat').cfg;
for i_sub = 1:44
fprintf(strcat('Decoding Sub_', string(i_sub),'\n'));
pipeline_dir = fullfile(cfg.global.deriv_dir, 'pipeline_patdif_state_at_cue_1st', ...
strcat('sub-',sprintf('%02d... |
68be3cb8a9afd26a92f557007ef86d7368ad10a288d7f3a23d11b3d44a940f4b | MATLAB | 2,395 | 81 | function [errs warns] = spm_test_omnibus(varargin)
% [errs,warns] = UNIT_SPM_RUNALL(varargin)
% This function simply calls the entirety of the SPM unit tests creating a
% monolithic script that you can run to test everything in one shot
% instead of running them all seperately.
%
% FEXTENSION(*.nii/.img)
% If ... |
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