sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
85e4510cd0888e04317b7c4ecfd6b05e5408a22dcd18bf88fcf3205b4206d2d6 | MATLAB | 1,306 | 42 | function [data_sound_filt, W]=FilterOutGivenPatternsSound_BF(A, data_sound, Msound)
% This function uses beamforming to create a spatial cleaning filter which takes
% into account the smoothing step by SOUND to remove an artifact topography
% (or multiple) estimated prior to SOUND
%
% Input:
% A - artifact topo... |
92b4efa6c0f2ebd71f78465d6aab74480fec1eee0473d15ece4f9af19543bc4f | MATLAB | 1,307 | 42 | function [subj] = set_object(subj,objtype,objname,obj)
% Replaces an entire object, i.e. a cell from one of the main 4 cell arrays
%
% [SUBJ] = SET_OBJECT(SUBJ,OBJTYPE,OBJNAME,OBJ)
%
% For internal use. Use set_mat, set_objfield and set_objsubfield
% License:
%=========================================================... |
dbe736d3b177b66c3382db32308da5b9f7d5f5fe0b51d1df961cc64b0f1b1c23 | MATLAB | 1,307 | 50 |
%% Add path
addpath(genpath('../Ionnic Channel'),genpath('../Tools'), genpath('../Synapses'), genpath('../Datas'));
%% Import data from neuron (FS)
dt = 0.01e-3;
Vmem = -90e-3:dt:60e-3;
l_V = length(Vmem);
%% Anonymous function
Minf_simp = @(v) 0.4499*tanh(20.01 + 50... |
cf10a8796f2258d2e9700fc0d5fea958889c52cef26a3dc345541747a009823e | MATLAB | 1,308 | 39 | % This is single-channel classification using kfolds
% timePt needs to be in sample points
% folds are partitioned outside of this function.
% Classify all channels including pruned channels.
% for preprocessFNIRS06_CV_GLM_ssBeta_SingleChn_MultiOnly
function [validationScore] = trainLDA_SingleChannelHbX_TrTst(...
... |
fa95fbb45038e7d72a94ccf57f1fe6456483520db3a2ee7c65e96242dc333b00 | MATLAB | 1,308 | 33 | % Uses an eigenmode-based analysis to study how neuronal tracing connectome eigenmodes constrain cortical activity.
clc
clear all
% set code folder path
mypath='E:\Marmoset_CFD-main\';
addpath(genpath(strcat(mypath,'functions_matlab')))
% set the number of null model
n_null = 1000;
%% Step1: compute and ... |
3230d4d089d7f08a3e4f112195615715cc8ef7f9b51f5960b570d25fb54d79a2 | MATLAB | 1,309 | 44 | clear all
[task_info,supPath] = loadDBAndSpecifyDataPaths('Vermis');
req_params.grade = 7;
req_params.cell_type = 'SNR|BG|PC|CRB';
req_params.task = 'speed_2_dir_0,50,100';
req_params.ID = 4000:5000;
req_params.num_trials = 50;
req_params.remove_question_marks = 1;
raster_params.align_to = 'cue';
raster_params.time_... |
2379922a05c1d53a93634fd5b0033254fef35483470613c8a3f360bb0a80320d | MATLAB | 1,310 | 30 | function f = crossCorrModel_Intensity(preTrain,postTrain,params)
% Computes the cross-validated intensity of a spike train given a (predictor) spike train in a
% gaussian temporal window.
%
% USAGE
%
% f = crossCorrModel_Likelihood(preTrain,postTrain,preTest,postTest,binT,params)
%
% preTrain pre-synapti... |
6c84e9cf1433abce550a70cb6047b7ca14eb05bb8800267807bf24884b0b56fb | MATLAB | 1,310 | 55 | function [starttime timeleft] = eta(cur,total,starttime, interval)
% Display an estimate of time remaining in a calculation.
%
% function [starttime timeleft] = eta(cur,total,starttime, interval)
%
% Usage:
%
% t0 = clock;
% t0 = eta(cur,total,t0,0.05);
% License:
%=====================================================... |
aece6ebc9cd3dfd305b2b8c4a54e14571e211c995601227f04b41e62daaba8a3 | MATLAB | 1,311 | 31 | % Matlab Toolbox for Dimensionality Reduction
% Version 0.8 18-APR-2012
%
%
%Main functions
%----------------------
% compute_mapping Performs dimension reduction using the specified technique
% out_of_sample Performs out-of-sample extension using trained DR technique
% out_of_sample_est Performs out-o... |
fb2281ed5ea9dbe1045361ad128172c096a74e452a87d8569013b3d02f60977e | MATLAB | 1,311 | 35 | function [ExpansionorShrinkagePercent,rotation_deg,xTranslation_um,zTranslation_um] = transform2Knobes (histologyToOCTT,histologyScale_umperpix,octScale_umperpix)
% histologyScale_umperpix - size of histology image pixels,
% octScale_umperpix - size of oct image pixels
% ExpansionorShrinkagePercent - expansion, - sh... |
93b87a2e55c993eaa59c6246422d79ff9d6d289a3b61b74ee00142bb21c38443 | MATLAB | 1,314 | 31 | clear clc close all
%%
Folder = '\\storage1.ris.wustl.edu\kerschensteinerd\Active\Emily\NaturalSceneVideo\Analysis\Results\SpatialCorrection_110123';
LM = load([Folder '\SpatialCorrelation_luminance_Intercept.mat']);
SR = load([Folder '\SpatialCorrelation_Surround_Intercept.mat']);
TS = load([Folder '\SpatialCorre... |
cd87725549b7ec6a8bcdbdc9fe52de076312d0c0bd112801e10cf75ba3d2af2c | MATLAB | 1,314 | 49 |
ndip = 20;
snr = [0.6 0.5];
snr2 = [0.5 0.8];
amp = [0 0.2 0.3 0.4 0.5 0.6 0.7 0.8];%[0.2 0.4 0.6];
phs = 2.*pi.*[0 1/17 2/17 4/17 8/17];
patchsigma = 0;
N = [50 150];%100;
state = rng(1,'twister');
%state = output.state;
list = reshape(1:100,[10 10])';
for i0 = 1:size(list,1)
for i1 = 1:numel(ndip)
for i2... |
fe123b1da1f1b73a6e6659c217f618594ab8fedc8e4c73fe31aba5e880c82c00 | MATLAB | 1,315 | 49 | function [objcell] = get_type(subj,objtype)
% For internal use. Returns the entire cell array of a given type
%
% [OBJCELL] = GET_TYPE(SUBJ,OBJTYPE)
% License:
%=====================================================================
%
% This is part of the Princeton MVPA toolbox, released under
% the GPL. See http://ww... |
5f441ad88bf962bbbb3ee4f5050f4d0dbd49dd470a5f5ab1324c7117e1c56b9a | MATLAB | 1,319 | 57 | %% 12-6-17: SSFP steady state, BM equations
function [Mss] = ssSSFP_BM(alpha, TR, dw, T1x, T2x, f,ka,delta)
if nargin==7
delta=0;
end
M0b = f;
M0a = (1-f);
kb = ka * M0a/M0b;
R1a = 1/T1x(1);
R1b = 1/T1x(2);
R2a = 1/T2x(1);
R2b = 1/T2x(2);
%%% Full system matrix including off resonance in "+" basis
Ea = diag([-R... |
39d3560a9083c5ec1a9155856ec2b3315d33e827ca050729fe7fc84309744881 | MATLAB | 1,320 | 41 | function [unit_match_mat] = match_units_to_find_axons_211019(nmi_mat,nmi_thresh)
%This function finds unit pairs which optimizing based maximum NMI
% The algorithm finds the max NMI value in the matrix, stores the row and
% col unit numbers, and then looks for the same in the remaining
% rows,col.
% Note: the ... |
407972705833e3ba373040c8371fe2f7a99de81c1e60ce3ba42557b5a9e10ccf | MATLAB | 1,320 | 55 | function [burstBool burstIsiPk] = BurstIndex(S,varargin)
% [burstBool burstIsiPk] = BurstIndex(S,ep)
%
% Computes burst index
% Input:
% S: a ts object of spike timing.
% ep (optionnal): intervalSet object of the epoch of interest
%
% Output:
% burstIdx: burstiness index of the cell (1 for a Poisson... |
f451aa5711a9faade7a0f5a86de71e6dbea2e999a93ad9e799bac6e7e48d0e93 | MATLAB | 1,320 | 70 | function transients = get_transients(t, dff, debug)
lowPassTh_Hz= 0.5;
highPassTh_Hz= 2;
% filterType = 'bandpass';
fc = [lowPassTh_Hz highPassTh_Hz];
filterType = 'high';
fc = lowPassTh_Hz;
fs = 1/median(diff(t));
[b,c] = butter(4,fc/(fs/2),filterType);
nan_idx = isnan(dff)
dff(nan_idx)=0;
filtered_sig = filtf... |
847f02e82d10e1cc881c89f2362d1ddcb2552f920a27134fb2c21b114415eb61 | MATLAB | 1,321 | 34 | % function output = transres_accuracy_matrix_minus_chance(decoding_out, chancelevel, varargin)
%
% Get a matrix of the accuracies of all pairwise comparisons (i.e. chance =
% 50 %) from a multiclass classification. Libsvm doesn't provide this
% directly. The diagonal is not defined and set to NaN.
% IMPORTANT:
% This f... |
e51f38841f82609383819458f9bfeac927f779736b55656079f2ac14bc15e1cf | MATLAB | 1,321 | 45 |
s = size(Lm,2);
p = size(Y,1);
Ps = sum(Qns,1)/n;
Fmatrix = zeros(6,s);
tempdig_a = digamma(a);
for t = 1:s
kt = size(Lm{t},2);
temp_alt = Xm{t}.*repmat(Qns(:,t)',kt,1);
Xcor_t_Qns_weighted = Xcov{t}*sum(Qns(:,t),1) + Xm{t}*temp_alt';
Fmatrix(2,t) = eps;
Fmatrix(3,t) = +.5*n*(kt-1)*Ps(t) ...
... |
464a1dfbfdd6198aa3978e42298abe7886996a308f1708c220dba06b3c44ea34 | MATLAB | 1,322 | 49 | function [val] = get_objfield(subj,objtype,objname,fieldname)
% Retrieves a field from an object
%
% [VAL] = GET_FIELD(SUBJ,OBJTYPE,OBJNAME,FIELDNAME)
%
% e.g. nvox = get_objfield(subj,'mask','wholebrain','nvox');
%
% Use OBJTYPE = 'subj' and OBJNAME = '' if you want to get a field
% in the root of the subj structur... |
8b7990363f42ea876bf446fbaa9cbecf33ea63b47f340b1b6fadc0696e6e0b89 | MATLAB | 1,322 | 47 | function vviq_distribution_plot = vviq_plot_distribution(p)
% Load VVIQ data
vviq_file = dir(fullfile(p.dirs.data, 'questionnaire', 'vviq.mat'));
if isempty(vviq_file)
warning('vviq.mat not found.');
return;
end
load(fullfile(vviq_file.folder, vviq_file.name), 'vviq')
% Get histogram counts
x = 16:2:80;
hc = ... |
9498e90441c71b0089c7e6e01489e224153d99d1db6d7bdb074ca5834b616c4c | MATLAB | 1,322 | 38 | function find_spiking_distro_sim()
fdr_prefix="gc_can_"; % folder name prefix
use_hopper_data=1; % access hopper data locally
local_run=5; % local run number
hopper_run=5; % hopper run number
save_gridscore_file=1; % save gridscore to file
run_on_hopper=0; % run from hopper's system
p1=0;
... |
9db44a1c0a7c365aad14cb4888d4980dd7af29e5adabc4f0986754db0fad8df8 | MATLAB | 1,325 | 78 | function [meanwav, stdwav] = MeanStdWaveforms(TTfilename, T)
chunk_length = 5000;
offset = 1;
meanwav = zeros(4,32);
stdwav = zeros(4,32);
nSpikes = 0;
while 1
TT = LoadTT(TTfilename, offset, chunk_length);
EndingTime = EndTime(TT);
StartingTime = StartTime(TT);
T1 = Restrict(T, StartingTime, EndingTime);
T... |
080a427d14253b319db67c8b8f583deeb8a7f0a6a8bb94905f89b565a99e7bd7 | MATLAB | 1,326 | 76 | function PhaseMap = PhaseMap(X,Y,ph)
%peyrache 2007, beta
times = Range(ph);
phase = Data(ph);
gw2D = gausswin(20)*gausswin(20)';
gw2D = gw2D/sum(sum(gw2D));
[occH, xbin, ybin] = hist2d(Data(X), Data(Y), 200, 200);
figure(1)
imagesc(convn(occH,gw2D,'same'))
PhaseMap = zeros(length(xbin),length(ybin));
phH = zeros... |
b61263fe4e96641669440847be9ac7ac8a43dc27c3f27061ecddba1b0fb80ce2 | MATLAB | 1,327 | 52 | function [errCode, txt] = awsCmd(cmd, knownErrorText, v)
% This function executes system command: system(cmd). It will parse the
% error codes and decide if to throw an error, or retry command.
% INPUTS:
% cmd - command to be executed
% knownErrorText - cell array containin known error texts to ignore, they
%... |
129c95011b2a51347e62779fddb2f3717cd693f4e09fc01e8f3df1d7aae502b6 | MATLAB | 1,330 | 48 | %% Plot V4 MI noise correlations d' and bias split at cued location
%% Code
load('V4_rsc_MI_pre_bl.mat')
bl_onset_win = [400 0]; epoch = num2str(bl_onset_win(1))+"_to_"+num2str(bl_onset_win(2));
load('MI_split_RFTrials.mat')
plot_flag = 1; % to plot or not to plot
fig_id = ["c","d"];
for bin_flag = 12 % > 5 spikes/... |
dc95c6874ffe909ee8b4c782cb5ac32cfd89a2ca1dc6ecd23903e2e3978dc46d | MATLAB | 1,330 | 35 | %function [EegSegAv EegSegStd EegSTrange] = TriggeredAvM(data,T,chanIx,win,sr,nChannels,SignalType)
% trigered averages subset of channels from file or matrix
% within a window of win (in msec)
% method : 1, to load all channels for each segment around trigger (if nChannels/nTriggers is large)
% 2, to loa... |
795cd4cd635a1d9af04082a152f5e954e083ada3555eb6481782fb50b7f4d380 | MATLAB | 1,332 | 37 | function [vol] = get_ref_vol(subj,patname)
% Returns the mat of the pattern's masked_by mask
%
% [VOL] = GET_REF_VOL(SUBJ,PATNAME)
%
% Think of this as getting the pattern's 'reference volume'.
%
% Every pattern needs to know which mask it's masked by, because the
% pattern throws away information about where its voxe... |
ecbe79ac02457fed7ef8fce76b81a1ba5d63f175dd785b806620e21ec2438621 | MATLAB | 1,332 | 25 | function decoding_out = newton_test(labels_test,data_test,cfg,model)
if isstruct(data_test), error('This method requires training vectors in data_train directly. Probably a kernel was passed method is use. This method does not support kernel methods'), end
switch lower(cfg.decoding.method)
case 'classifica... |
42a07544f4c8490c486ebbd52da212f670372a0f5aaf8b6e3a14f43224541db4 | MATLAB | 1,333 | 44 | % This script computs stats for the following libraries
libraryNames = {'LH','LG','LF','LE','LD','LC'};
isGoogleSheetUpdate = true; % Set to just update json and skip google sheet updates
%% Time dependence
if isGoogleSheetUpdate
% No need to run once every 7 days logic when no google sheet update.
... |
3dbb869438fb31909cc9a5dfeead73f5c8900e051a7b75797e8a2dc861d576cc | MATLAB | 1,334 | 36 |
function [data_X,data_y_player,pair_num, dataCh] = data_epo2DeepNet(epos_player,epos_observer,session_role)
% BBCI data format:
% epo.x - timesteps x channels x trials
% epo.y - classes x trials
%
% convert to data format:
% dataX - EEG data
% [pariticipant x tria... |
5d6749e8b813f308b8193de1e094f552c7c71504309cd0f2be7910f18dfcd430 | MATLAB | 1,334 | 41 | function gmmmixes = mdn2gmm(mdnmixes)
%MDN2GMM Converts an MDN mixture data structure to array of GMMs.
%
% Description
% GMMMIXES = MDN2GMM(MDNMIXES) takes an MDN mixture data structure
% MDNMIXES containing three matrices (for priors, centres and
% variances) where each row represents the corresponding parameter
% va... |
fc8206be66bb28bd883e3cc6dfbf0e22168039c51fcc91053ed201c8ff97e5e7 | MATLAB | 1,334 | 28 | % This function compare if two correlation coefficients are significantly
% different.
% The correlation coefficients were tansfered to z scores using fisher's r
% to z transformation.
% ref: http://core.ecu.edu/psyc/wuenschk/docs30/CompareCorrCoeff.pdf
%-----------------------------------------------------------... |
0bfa10fa1b3ff1f8e27ae68afbfbed8bc03330a4bb8948e7d338d2486cdeb5dc | MATLAB | 1,336 | 34 | function [z] = sandwich2x2(x, y)
% z = sandwich2x2(x, y)
% compute x*y*x' provided y = hermitian
% and dimensionatity is 2x2xN
%FIXME build in check for hermitianity
z = complex(zeros(size(x)));
xconj = conj(x);
xabs2 = abs(x).^2;
z(1,1,:,:) = xabs2(1,1,:,:) .* y(1,1,:,:) + ...
xabs2(1,2,:,:) .* y(2... |
be146df0ddbf48dba444e3fb94126b7220f5fed9dadd8c212f99628d88083c10 | MATLAB | 1,338 | 39 | function [ypred,dval] = test_libsvm(cf,X)
% Applies a LIBSVM classifier to test data and produces class labels and decision values.
%
% Usage:
% [labels,dval] = test_libsvm(cf,X)
%
%Parameters:
% cf - classifier. See train_libsvm
% X - [samples x features] data matrix -OR-
% ... |
31a17e61a58170327c8d0a850d5fdd980da54e90403dacc30f9b6cdf2ff07786 | MATLAB | 1,341 | 40 | function [ft,signIF] = instantaneous_frequency( xph, Fs )
% *WAVE*
%
% INSTANTANEOUS FREQUENCY returns the instantaneous frequency estimation
% from the formula:
%
% d(Psi)/dt = \Delta(Psi_n)/dt = Arg(X_{n+1}*X_{n})/dt (2.11)
%
% where X is the analytic si... |
89c21f463498f4b4932ad72eebb86bf0b032b443abb8077c57130d5fc1e2a9ed | MATLAB | 1,341 | 42 | function Xi = Xi_diff_BM(Xi_T,Xi_L,diff,kmax,N)
% Function to build Xi operator with diffusion effects for BM (6 states per
% k-value).
% This function efficiently uses spdiags to build the large band diagonal
% matrix that results from applying Xi_L and Xi_T to each order of states.
% Diffusion changes the matri... |
4809f422d1d0c658989997f7e6ce207444fccc1da4b811f2685ab1c641073a29 | MATLAB | 1,342 | 47 | function fid = platform_fopen(fname,perm)
% fid = platform_fopen(fname,perm)
%
% :description
%
% A utility function that opens a binary file across multiple
% platforms. On the Mac and SGI, BrainVoyager file bytes must be swapped.
%
% :inputs
%
% fname file to open, e.g. 'myvolstats.vmp'
% perm ... |
622ac37ddf432c4b9b6757c6672fdbf81bcbfec11bab97b71c37b409150d86d4 | MATLAB | 1,342 | 52 |
%% 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);
%% Matrix Initialization
sinf = zeros(1, l_V);
sinf_error = zer... |
a94598abc91aab7120fa31f3af91f24744ecb80100d975108444658d4cbf0772 | MATLAB | 1,342 | 50 | function [fields] = get_special_fields_list(objtype)
% Internal - gets a list of special fieldnames
%
% [SPECIAL_FIELDS_LIST] = GET_SPECIAL_FIELDS_LIST([OBJTYPE])
%
% Returns a cell array of field names that you can't remove or mess
% with.
%
% OBJTYPE (optional, default = ''). If this empty, returns the
% common requ... |
b06132aaacaf0a7381240e920b8871b1e66244570b3edaea9fe3864651480887 | MATLAB | 1,342 | 55 | function [evals, evec] = eigdec(x, N)
%EIGDEC Sorted eigendecomposition
%
% Description
% EVALS = EIGDEC(X, N computes the largest N eigenvalues of the
% matrix X in descending order. [EVALS, EVEC] = EIGDEC(X, N) also
% computes the corresponding eigenvectors.
%
% See also
% PCA, PPCA
%
% Copyright (c) Ian T Nabney ... |
07f6468daa674c890aefdcae6f046324ada9add9d6eb8b22339f780c11a51773 | MATLAB | 1,343 | 48 | function [run_dat, p] = eye_extract_runs(dat, event, p)
% Initialize output variables
run_dat = [];
% Define number of timepoints
n_tp = p.eye.seconds * p.eye.hz - 1;
% Identify run events
run_ind = [find(strcmp({event.type},'!MODE'))]';
run_events(:,1) = run_ind + 1;
run_events(:,2) = [run_ind(2:end); numel(event)]... |
4182842a6c3e1d3ded8551df943664dcc7f2be3617743342efded99610f3ed67 | MATLAB | 1,344 | 37 | function theImage = Matrix2Grayscale(matrix)
%Matrix2Grayscale Make a grayscale image from an input 2D matrix
% theImage = Matrix2Grayscale(matrix)
%
% Used by MakeGrayImageFigureForMatrix
%
%See also MakeGrayImageFigureForMatrix
%
%History
%
% 7/26/2002 bds wrote it for matwave package, D.R.Williams' lab
%... |
f98fe90ee98236824a31c783917031ea02b3fcb1539724bc4d23bf90b38811ac | MATLAB | 1,346 | 40 | function [W,U]= GSP_Laplacian(mypath)
%% load CC matrix
W=load(strcat(mypath,'Data/marmoset_CC_connectivity.mat'));
W=W.matrix_fle;
% only analysis 55 regions, source (55 regions) * target (55 regions)
n_ROI = size(W,2);
W=W(1:n_ROI,:);
[kden,N,K] = density_dir(W);
% % ==================================... |
16660a12b1cf37f0aca7f52f2d19b37cf60078e3c5a03ef7942fdc13551db5bb | MATLAB | 1,348 | 32 | function vidObj = msExtractdFFTraces(vidObj)
%MSEXTRACTDFF Summary of this function goes here
% Detailed explanation goes here
segmentMask = nan(vidObj.cellAreaLimits(2)*2,vidObj.numSegments);
for segNum=1:vidObj.numSegments
index = find(vidObj.segments(:,:,segNum)==1);
segmentMask(1:length(in... |
545edecb7a18a97560464edc0b43f63ac9553f3895844b0b226c0284f6a9649d | MATLAB | 1,348 | 71 | function [SynC,ConStr] = XcConnection(HO,Hm,HeI,HeS,Hstd,B,varargin)
% [H0,Hm,HeI,HeS,P] = XcConnection(t1,t2,binS,nbBins)
% Find Synpatic connectivity based on cross-correlograms
%
% INPUT:
% H0: raw CrossCorr
% HeI: lower bound of confidence Interval
% HeS: upper bound bound of confidence Interval
%
... |
ff493c1ecb35417e2880709181556851cff822a66a99798cbc292492e097cf9f | MATLAB | 1,348 | 40 | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of th... |
28ed989f70199419a0ae4b6add988f8d37ce8e2e9ab653639ad79d27ce912a08 | MATLAB | 1,349 | 56 | function [C,rate]=confmat(Y,T)
%CONFMAT Compute a confusion matrix.
%
% Description
% [C, RATE] = CONFMAT(Y, T) computes the confusion matrix C and
% classification performance RATE for the predictions mat{y} compared
% with the targets T. The data is assumed to be in a 1-of-N encoding,
% unless there is just one colu... |
605c54deb6a143856d5bc10736fb8c936df0fdff31b060b7c5de10ee15737fe5 | MATLAB | 1,349 | 41 | function uploadMarkLinesImageToLog(slidePath)
% This function uploads mark lines overview image to cloud
% Written at Mar 11, 2020.
%% Input checks
if ~exist('slidePath','var')
slidePath = [s3SubjectPath('04','LE') 'Slides/Slide03_Section02/'];
end
slidePath = awsModifyPathForCompetability([slidePath '/']... |
23393d46aab8348ae9fadce4dd715e9631be56d0e44f4d8b3751d24b0c869c52 | MATLAB | 1,350 | 43 | function LL = ME_vMMM_LL(y, r, mu, ka, Ik)
% _
% Log-Likelihood Function for a von Mises Mixture Model
% FORMAT LL = ME_vMMM_LL(y, r, mu, ka, Ik)
%
% y - an N x 1 vector of angles (domain: -pi <= y_i <= +pi)
% r - a 1 x K vector of mixture frequencies
% mu - a 1 x K vector of means
% ka - a 1 x K ... |
dfaead19394bff74e7126515efa9bd6d06d0a6c611aa1141b22c690d2078e88a | MATLAB | 1,351 | 55 | function b=lengthstandard(a,x)
% function b=lengthstandard(A,x)
% standardizes the length of each vector
% input:
% A:vector or matrix to be standardized
% if A is an matrix, it standardizes each column-vector in the matrix
% NaN are taken out of the matrix-coumns and not standardized
% x:if x is sca... |
15363d78b087c9fc1e312750ada9ba13d1f806aae2e75849a4fccdce7ccb2ded | MATLAB | 1,352 | 70 | function [errmsgs warnmsgs] = unit_separate_regressors(varargin)
% USAGE :[ERRMSGS WARNMSGS] = TEST_(type)
%
% This is a script that tests the cross correlation performance metric.
%
% INPUT ARGUMENTS:
%
% STDOUT (optional, default = true) If true, prints errors and
% warnings to screen as they occur.
%
% OUTPUT ARGU... |
a86f0733c1f216f76d6f1007ba475f03f660a411d0df46056446b2b75f2cf592 | MATLAB | 1,352 | 53 | REPEATS = 1000;
T = 100;
L = 2:10;
ettaEffectSize = nan(REPEATS,length(L));
omegaEffectSize = nan(REPEATS,length(L));
for j=1:length(L)
for ii = 1:REPEATS
response = randn(1,T);
group = mod(1:T,L(j));
[~,tbl,~,~] = anovan(response(:),{group},'model','full','display'... |
5ba3b2e7ed1dc7d895b01c19b98601ccb4f4882a279706cc48acd72a410d604f | MATLAB | 1,353 | 25 | function ss_str = selectSimulation(true_field, rep_field)
% sets the specified field to true in s_str. All other fields are false.
% These match field names in setLoadingFunctionNames and correspond to
% specific simulation runs.
% initialize ss_str
ss_str.run_morrell1field = false; % analyze the envfield_fixe... |
0cbb9d2167a3005720777b5fcf3990334b5ee80c0388e729c6fe1dee5bfe2f2d | MATLAB | 1,356 | 46 | %% Plots V4 MI noise correlations (raw and MI) in cue epoch at cued and cue-opposite locations for superficial and deep layers
%% Code
layer_id = 1:2; fig_id = ["e","f"];
layer_labels = {'superficial','deep'};
for layer_flag = layer_id
load(sprintf('V4_rsc_MI_%s.mat',layer_labels{layer_flag}))
cue_onset... |
354a06c97d88440f7de734c9164ae9cb315e86063a410489564816e7eda1ceff | MATLAB | 1,356 | 49 | function model = lvl2_estimate(p)
% function model = lvl2_estimate(p)
%
% Performs a SPM second-level analysis.
%
% Input:
% - p: Structure with analysis parameters.
%
% Output:
% - model: Structure with all SPM settings used for this analysis
% step (would be 'matlabbatch' in SPM).
%
% Simon Weber, sweber@b... |
73fee0c8270b9b5fa2ffe41ae4a68082c304b9f7b25a647b44b7118aee01aa75 | MATLAB | 1,356 | 39 | % This file is part of the Matlab Toolbox for Dimensionality Reduction v0.7.2b.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you
% maintain the name of th... |
16d9d6976d37171b4a54b28519b3b3eb27225663bbd0aee046cdf6df5cc74a67 | MATLAB | 1,358 | 50 |
ndip = 20;
snr = [0.6 0.5];
snr2 = 0.7;%[0.5 0.8];
amp = [0 0.2 0.3 0.4 0.5 0.6 0.7 0.8];%[0.2 0.4 0.6];
phs = 2.*pi.*[0 1/17 2/17 4/17 8/17];
patchsigma = 0;
N = [50 150];%100;
state = rng(1,'twister');
%state = output.state;
list = reshape(1:100,[10 10])';
for i0 = 1:size(list,1)
for i1 = 1:numel(ndip)
f... |
766f787dfdfdef2d34de8907865b88f16340f82ea7a57a4782bec4055a213bf5 | MATLAB | 1,359 | 42 | %{
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... |
94ace2dc44b7ef8dd5f73d8edce0bbcf41767e3ec58ec89d19656a11ceabfb98 | MATLAB | 1,359 | 32 | function [cfg, X, clabel] = mv_preprocess(cfg, X, clabel)
% Applies a preprocessing pipeline to the data (train or test).
%
% This function is usually called automatically from within the high-level
% functions such as mv_classify_across_time.
%
% Usage:
% [cfg, X, clabel] = mv_preprocess(cfg, X, clabel)
%
%Parameters:... |
2c8e6f177ea6e5aa52e884b1bde935a521f0c3c34d84248b9fe5c4056b2625fe | MATLAB | 1,361 | 52 | function [returnVar,msg] = Process_InvertDatSign(fbasename,nbChan,varargin)
% USAGE:
% Process_InvertDatSign(fname)
% This function inverts the sign of the recording (e.g. for Neuralynx)
%
% INPUTS:
% fname: dat file name (with or without '.dat' extension)
% Copyright (C) 2019 Adrien Peyrache
%
% ... |
00e45ad44f844d8931fd352e014a1e260a9f607667afb1c362bc15f478787a22 | MATLAB | 1,362 | 48 | function [data, c, prior, sd] = dem2ddat(ndata)
%DEM2DDAT Generates two dimensional data for demos.
%
% Description
% The data is drawn from three spherical Gaussian distributions with
% priors 0.3, 0.5 and 0.2; centres (2, 3.5), (0, 0) and (0,2); and
% standard deviations 0.2, 0.5 and 1.0. DATA = DEM2DDAT(NDATA)
% ge... |
87987ce4ce7c123694b9cd05d6a7725e294f26d70bd99da1ae29e6f5ccd86c97 | MATLAB | 1,362 | 53 | clear
[task_info,supPath] = loadDBAndSpecifyDataPaths('Vermis');
EPOCH = 'targetMovementOnset';
req_params.grade = 7;
req_params.ID = 4000:6000;
req_params.remove_question_marks = 1;
req_params.num_trials = 50;
req_params.remove_repeats = false;
req_params.task = 'saccade_8_dir_75and25|pursuit_8_dir_75and25';
req_... |
df0e58712a866245a7ada3e31ee12d2998518b7762a36ec51f51548b48907a86 | MATLAB | 1,362 | 56 | function CalibrateLEDorientation(varargin)
% USAGE
% CalibrateLEDorientation(angOffset)
%
% calibrate LED position relative to the expected position blue front / red Rear (to check...)
% inputs:
% angOffset: offset value (optional)
%Parameters
checkOldOffset = 0;
load('Analysis/BehavEpochs.mat','wakeEp')
if... |
802308d96517209fa6b01fa4d671bc856585299f438985cd27f63b3f211c0764 | MATLAB | 1,363 | 48 | % generate small size images
% last update: October 25 2022
% Amirhossein Farzmahdi
clear
close all
clc
% add path
addpath(genpath('functions'))
images_dir = 'mat_files/';
category_names = [{'car'};{'boat'};{'face'};{'chair'};{'airplane'};{'tool'};{'animal'};{'fruit'};{'flower'}];
load (images_dir+"names")
load(imag... |
4242b76260f0ca5c5d12cf7b21bf352cd3b369b3bcb61fca6fed0dc9b472cb0e | MATLAB | 1,365 | 49 | function [h, hdata] = hbayes(net, hdata)
%HBAYES Evaluate Hessian of Bayesian error function for network.
%
% Description
% H = HBAYES(NET, HDATA) takes a network data structure NET together
% the data contribution to the Hessian for a set of inputs and targets.
% It returns the regularised Hessian using any zero mean... |
b8dd7fed70574de6cdcdf7abcf898865d3dddc9dec4b8996e09c307768664e03 | MATLAB | 1,368 | 60 | clear
[task_info,supPath] = loadDBAndSpecifyDataPaths('Vermis');
EPOCH = 'targetMovementOnset';
req_params = reqParamsEffectSize("pursuit");
lines = findCspkSspkPairs(task_info,req_params);
significance = nan(1,length(lines));
for ii = 1:length(lines)
cells = findPathsToCells (supPath,task_info,[lines(1,ii... |
5e9c3a4bb206a5ad7de0c741b30de6e2840327b24d47da10698aa68392547786 | MATLAB | 1,369 | 60 | load('data/DataTest.mat')
load('data/nnets.mat')
computeStatsTest
y(y==-1)=0;
X_tst=squeeze(blobDataNan(:,:,:));
paramsRelevance=0;
X_tst(isnan(X_tst))=0;
X_tst=X_tst(:,:,paramsIdx);
Y_tst=categorical(y);
%%
clear acc accMeanT
clear xRocV
clear yRocV
clear aucV
clear xRoc yRoc
clear YPredV
... |
3b6194cc38688100cc4819e0d9baaa3a85b0826d11e1451015705b16a2df62b1 | MATLAB | 1,370 | 48 |
s = size(Lm,2);
p = size(Y,1);
Ps = sum(Qns,1)/n;
Fmatrix = zeros(6,s);
tempdig_a = digamma(a);
for t = 1:s
kt = size(Lm{t},2);
temp_alt = Xm{t}.*repmat(Qns(:,t)',kt,1);
Xcor_t_Qns_weighted = Xcov{t}*sum(Qns(:,t),1) + Xm{t}*temp_alt';
Fmatrix(2,t) = eps;
Fmatrix(3,t) = +.5*n*(kt-1)*Ps(t) ...
... |
def63401f76ebf32d805bf51f5bb730d9bb43cb613f00008af95a4abbc7a630e | MATLAB | 1,370 | 45 | %function h = PlotTraces(x,t,sr,scale,color,ifnorm, Axes2Fit,plotfun)
% x is a matrix of signals to plot: time x nchannels x ntraces
% t is time axis, sr - sampling, scale - coefficient to scale up the signals
% color - to use, norm - if you want ot normolize them all evenly
function h = PlotTraces(x,varargin)
x=squee... |
3eada19ad4a6a978c3cd0cd1f28e75b924dc73c98be8e2921de58f4f97f02449 | MATLAB | 1,372 | 58 | function [beta, R, r] = weighted_regression(y,x,w,wflag)
% Perform weighted_regression of y onto x using weights w
% Outputs: beta=beta weights, R=r-square value, r=residuals
% Use as [beta, R, r] = weighted_regression(y,x,w)
if nargin<4,
wflag = 0;
end
if ~wflag,
%compute output using voxel specific weights
[... |
6835cab453c41b424e7acc5c5c528141177e47f57010097602d7a8703a36de33 | MATLAB | 1,372 | 47 | function [phi, phi2] = evaluate_velocity(v, v0, mask, c, densities)
% [PHI, PHI2] = EVALUATE_VELOCITY(V, V0, MASK, C, DENSITIES)
%
% Evaluate velocity estimates, using the average spatiotemporal angular
% error.
%
% V - Estimated velocity field
% V0 - Correct velocity field
% MASK - Ignore values where mask is zer... |
b9b3a747e653a1145bcbafac99371c86d4facb844d9dd663007cceb4f4e911c6 | MATLAB | 1,373 | 34 | function EnhanceOpticalFlow(exportFolder, enhOptFlowParams, relMat, smoothDiffImage)
% Params
minR = enhOptFlowParams.minR;
maxR = enhOptFlowParams.maxR;
enhSig = enhOptFlowParams.enhSig;
enhNumSig = enhOptFlowParams.enhNumSig;
enhBgSubSig = enhOptFlowParams.enhBgSubSig;
rArray = minR:maxR;
% Enhance Optical Flow
filI... |
f72bd3d105a90950d671d886786a4ff4a1435f3d8bc7b36252f4b49fb3bcb760 | MATLAB | 1,374 | 47 | function [split,numpieces]=explode(string,delimiters)
%EXPLODE Splits string into pieces.
% EXPLODE(STRING,DELIMITERS) returns a cell array with the pieces
% of STRING found between any of the characters in DELIMITERS.
%
% [SPLIT,NUMPIECES] = EXPLODE(STRING,DELIMITERS) also returns the
% number of piec... |
4dbbafd81b15a33ff4448f157097e99bde56e234362da0bbc1e7ce7530c0c591 | MATLAB | 1,376 | 53 | function g = rbfderiv(net, x)
%RBFDERIV Evaluate derivatives of RBF network outputs with respect to weights.
%
% Description
% G = RBFDERIV(NET, X) takes a network data structure NET and a matrix
% of input vectors X and returns a three-index matrix G whose I, J, K
% element contains the derivative of network output K ... |
445604813a3358060adb83181407ba123064fa842ef90a548a02d8a0774ace9b | MATLAB | 1,377 | 59 | function [scanAbs,Pixel] = yOCTFromDicom (filepath, yI)
%This function loads a grayscale version of scanAbs from a Dicom stack file.
%Dimensions are (z,x,y)
%INPUTS
% filpath - filepath of output tif file (stack is z,x and each frame is y)
% yI - Optional, which y frames to load
if awsIsAWSPath(filepath)
%Load... |
e5f7c5afb299e9b402eee7f40f61cbf8239f952118c5cb4c50a6dfe318dce21d | MATLAB | 1,378 | 44 |
req_params.grade = 10;
req_params.cell_type = 'CRB|PC|BG|SNR';
req_params.task = 'speed_2_dir_0,50,100|pursuit_8_dir_75and25|saccade_8_dir_75and25';
req_params.remove_question_marks = 0;
req_params.ID = 4000:5000;
req_params.remove_question_marks = 0;
req_params.num_trials = 40;
sessions = uniqueRowsCA({task_info.ses... |
fe4c8e0e378e14381cb8c0a8da2de8052ec2f320043b0617cf9392014c2f1afa | MATLAB | 1,379 | 45 | function [sleep, res, fmt] = detSleep(actigraphy, epoch, varargin)
%DETSLEEP do sleep detection
%
% [SLEEP, RES, FMT] = DETSLEEP(ACTIGRAPHY, EPOCH, STARTTIME, QUALITY, WINDOW, PARAMETER)
% performs sleep detection on ACTIGRAPHY of epoch length EPOCH and
% return results to the caller.
% FM... |
966f1f3e2f2ffef4e3be7cea87cb111513cb27aaae18f5199964ff16a2e4267e | MATLAB | 1,380 | 44 | clear,clc
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% This script is used to generate figure for the mean map of grey matter volume on
% brainnectome 246 atlas across participants
%
% 7-22-2022
% Jin Liu
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%... |
a573f648eadd484f6fd8be9086d06fc2fe2f2b91d9a47c1f8d42627863604d00 | MATLAB | 1,380 | 49 | function [xf] = generate_decay_target_wave( image_size, dt, T, ...
frequency, lambda, sigma, varargin )
% *WAVE*
%
% GENERATE DECAY TARGET WAVE TEST DATA generates a simple test dataset for
% use with the functions in WAVE
%
% ... |
ab5a4e23782a69de756f291d32fde413f7cccb135eafbd89af4b472d11bf1c28 | MATLAB | 1,381 | 56 | function ic = intervalRate(tsa, is, TimeUnits, varargin)
% Returns rates of a TSD in each subset of an interval
%
% USAGE:
% ic = intervalCount(tsa, is, options)
%
% INPUTS:
% tsa - a tsd object
% is - an intervalSet
%
% OUTPUTS:
% ic - a tsd object, where the timestamps correspond to each ... |
62a33da715b7a52e7fb12d77b0f0a87d630320280d6d8548f5b7d6da5ede110e | MATLAB | 1,383 | 39 | % function varargout = get_filenames_spm5(varargin)
%
% Function to get file names using SPM5. Supports both wildcard and
% regular expressions for file names (not for paths).
%
% INPUT:
% file_path (1 x n string)
% optional: filenames with wildcards (e.g. *.img or rf*.img), or
% f... |
933719260d0627fe7598a4a1d75978eb0373bddf1774bc0fcfbfd9e799e42cfc | MATLAB | 1,383 | 39 | % function varargout = get_filenames_spm8(varargin)
%
% Function to get file names using SPM8. Supports both wildcard and
% regular expressions for file names (not for paths).
%
% INPUT:
% file_path (1 x n string)
% optional: filenames with wildcards (e.g. *.nii or rf*.nii), or
% f... |
3ff50bc55f69138cc269312654667f4c2a40f0c3c5d176ff67204c38b69e85f4 | MATLAB | 1,386 | 31 | function decoding_out = correlation_classifier_test(labels_test,data_test,cfg,model)
%
% History:
% 2016-08-09: Removed bug that true_labels were set to be labels_test
% rather than unique(labels_test) which is also how correlation_classifier
% deals with this
if isstruct(data_test), error('This method requ... |
983609d2a3da9baac1c9b487dc72e3b7c85da070c477d5ae0b3536e396e7a9bf | MATLAB | 1,386 | 45 | function net = netinit(net, prior)
%NETINIT Initialise the weights in a network.
%
% Description
%
% NET = NETINIT(NET, PRIOR) takes a network data structure NET and sets
% the weights and biases by sampling from a Gaussian distribution. If
% PRIOR is a scalar, then all of the parameters (weights and biases)
% are samp... |
ea0c9ce1066208cb563ddc199f2d5da0aec9150b67b2fe2b016a891d9fb50f77 | MATLAB | 1,387 | 36 | function copy_brain_mask(sub_id, p)
% function copy_brain_mask(sub_id, p)
%
% Copies the 'mask.nii' image (containing a binary mask of brain voxels)
% that was created during SPM first-level analysis into a subject-specific
% 'rois' folder, so that it can be used in later analysis steps.
%
% Input:
% - sub_id: ID of... |
2c9cc58a34b8478b44d15fa463c266f89a48400117a6137e2d9660e0ad0e384d | MATLAB | 1,389 | 58 | function [errs warns] = unit_confusion()
% [ERRS WARNS] = UNIT_CONFUSION()
%
% Tests the CONFUSION confusability matrix calculation.
errs = {};
warns = {};
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% BASIC TEST
[g d] = create_synth_guesses_desireds();
actual = confusion(g,d);
% N.B. this DESI... |
b85c983e32818ab797065c8ebcc0ccd5839633de6e49232346422c68ce621e05 | MATLAB | 1,389 | 39 | % function varargout = get_filenames_spm12(varargin)
%
% Function to get file names using SPM12. Supports both wildcard and
% regular expressions for file names (not for paths).
%
% INPUT:
% file_path (1 x n string)
% optional: filenames with wildcards (e.g. *.nii or rf*.nii), or
% ... |
2fe31a55adeb8e619141d2a879e8c6e89c2effb8f937ac93a1c05b3189ecb15c | MATLAB | 1,390 | 37 | function SubtractBackground(exportFolder, bgSubParams, origImage)
% Params
bgSubSig = bgSubParams.bgSubSig;
bgSubOneSidedNumSig = bgSubParams.bgSubOneSidedNumSig;
bgSubPadSize = bgSubParams.bgSubPadSize;
bgSubMeanFac = bgSubParams.bgSubMeanFac;
% Pad Image
bgSubImage = padarray(origImage, bgSubPadSize, bgSubMeanFac*me... |
956644df930f58d23326b04921325211c536edfe3a6601b77b0b9293bfd70ace | MATLAB | 1,391 | 49 | function result = mv_select_result(result, metric)
% For a result(s) with multiple metrics, selects a metric.
%
%Usage:
% result = mv_select_result(results, metric)
%
%Parameters:
% result - result struct or cell array of result structs
% metric - desired metric. All other metrics are removed.
%
%... |
caecc20086fb7646b09a595555666f111d8fdebba7434abb96b35f2d1fe03e9e | MATLAB | 1,391 | 52 | function [acts scratch] = test_lda(testpats,testtargs,scratch)
% Testing function of LDA, normally you don't need training and testing
% functions for LDA, but to keep cross_validation.m most flexible
% both functions exist
%
% function [acts scratch] = test_lda(testpats,testtargs,scratch)
%
% This is the testing fu... |
0e9992db7f06d4ccf2006fad8ddc0b7f3febe4fd8c98c759c16bdbf84a196f57 | MATLAB | 1,392 | 52 | clear all
nInst = 250;
nVars = 10;
X = randn(nInst,nVars);
w = randn(nVars,1);
y = sign(X*w + randn(nInst,1));
wTest = randn(nVars,1);
fprintf('Testing gradient using forward-differencing...\n');
order = 1;
derivativeCheck(@LogisticLoss,wTest,order,1,X,y);
fprintf('Testing gradient using central-differencing...\n')... |
679919a3a7292b3ae370aef095d10ea13954f3219827a0377b0f2ed32ecfd4ab | MATLAB | 1,392 | 65 | function [X,Y,GoodRanges,wstruct] = LoadPos(fbasename)
% loads position and speed from a position file file (ending in .whl)
%
% USAGE
% [X,Y,V,GoodRanges,wstruct] = HeadDirectionWhl(fbasename)
%
% INPUT:
% fbasename: session file basename
%
% OUTPUT:
% X: a tsd object of x position values
% Y: ... |
8e3301c17603ad419b3169a9bcf68fe8fa03d77eda6100e2375c6aa4f3853e5d | MATLAB | 1,392 | 49 | function [dt] = datetime(seconds)
% Spits out the date and time in yymmdd_HHMM format
%
% [dt] = datetime([seconds])
%
% The advantage of this is that alphabetic order is also
% chronological).
%
% If SECONDS = true, adds _SS at the end (defaults to false).
%
% UPDATE: it looks like there's a bug in DATESTR, so we're ... |
96693250cdbc69483295f63b169e1e30a8b99a95c30e54d32d8df94793c15507 | MATLAB | 1,393 | 36 | function [feat thresh conf] = decisionStump(wl,trainpats,weight);
% [FEAT THRESH CONF] = DECISIONSTUMP(WL,TRAINPATS,WEIGHT);
%
% Implements weak learner in the form of a decision stump as computed by
% AdaBoost.MH.
%
% This is part of the Princeton MVPA toolbox, released under the
% GPL. See http://www.csbmb.p... |
146b35cdb27b12cd600dbe07956e509c99082bf751be2c66618e9cfa141997e6 | MATLAB | 1,394 | 50 | function Dist = MaxDistCP(Qto,Qfrom,BB,MaxDist)
Dist=ones(1,size(Qfrom,2))*MaxDist;
Range=floor((BB(2,:)-BB(1,:))/MaxDist);
tic
Done=0;
LookAt=zeros(1,size(Qfrom,2));
for x=0:Range(1),
for y=0:Range(2),
for z=0:Range(3),
Low=BB(1,:)+[x y z]*MaxDist;
High=Low+MaxDist;
... |
3990c5c19b7bc87f9117abad7c79b98f55229ecb91088fe5018a281668732941 | MATLAB | 1,394 | 56 | function L = length(O, TimeUnits, varargin)
% Returns the length of each interval in the set
%
% USAGE:
% L = length(O, TimeUnits)
%
% INPUTS:
% O: an intervalSet object
% TimeUnits: a units object or the abbreviation string
%
% OUTPUT:
% L - a tsd object , where the timestamps correspond to ea... |
6334eafeee3dd79d41d99d364f12f2902a0153ece225f5656438149165b49405 | MATLAB | 1,395 | 45 | function [acts scratchpad] = test_template(testpats,testtargs,scratchpad)
% Template testing function
%
% [ACTS SCRATCHPAD] = TEST_TEMPLATE(TESTPATS,TESTTARGS,SCRATCHPAD)
%
% This is the template testing function to use when creating your
% own classifier testing function. Gets called after TRAIN_TEMPLATE
%
% See also... |
fdb7e8f3cd24c6cc195607a8c7c31d05d2acee0ac16deaabebb4ca8e40d7112b | MATLAB | 1,395 | 39 | % function varargout = get_filenames_spm12b(varargin)
%
% Function to get file names using SPM12b. Supports both wildcard and
% regular expressions for file names (not for paths).
%
% INPUT:
% file_path (1 x n string)
% optional: filenames with wildcards (e.g. *.nii or rf*.nii), or
% ... |
36968f2304a26e003c2e4e4e0e6ed3f5da5593390ab667e3fc3d478d957702b7 | MATLAB | 1,397 | 60 | function tsi = interp_missing(tsa, TimeUnits, varargin)
% Interpolate missing values in continuously sampled tsd
%
% USAGE:
% tsi = interp_missing(tsa, OptionName, OptionValue, ...)
%
% INPUTS:
% tsa - a tsd object
%
% OUTPUTS:
% tsi - a tsd object, with the missing values interpolated, assuming ... |
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