sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
aae58c8ab336dfa33a7bee537e622d6b2f8ec240b4f614d89b57c0798c72fd51 | MATLAB | 1,576 | 36 | function varargout = VBA_spm_sample_vol(varargin)
% returns voxel values from a memory mapped image - a compiled routine
% FORMAT X = spm_sample_vol(V,x,y,z,hold);
% V - is a memory mapped image volume
% x - matrix of x coordinates {pixels}
% y - matrix of y coordinates {pixels}
% z - matrix of ... |
06113565faee84c40084cdeba273d684e6e4f355ca8551aed117234f27db74cd | MATLAB | 1,577 | 44 | function Compute_Mask_OrigSpace(varargin)
invDeformMapFiles = ParseParams(varargin);
batchName = 'data/batch_Mask.mat';
Thresh = 0.1;
batchFile = batchName;
BATCH = load(batchFile);
spm_jobman('initcfg');
fprintf('Starting Time: %s\n',datestr(now));
for i=1:length(invDeformMapFiles)
[outpath outName] = fil... |
3fe94592b6f0d97cadbe5cb60bce3f8c6f63526e39afaa97af813a51d570b88d | MATLAB | 1,577 | 41 | function [maxBias] = maxBiasCalculation(totalNumFramesRemaining,totalNumFrames,targetedRs,randomRs)
%Calculation of maximum bias per Section 2.3.2.4
keepSubj = totalNumFramesRemaining(1,:) > 0;
subjTotalNumFrames = sum(totalNumFrames(:,keepSubj),2);
framesRemaining = sum(totalNumFramesRemainin... |
c45ed18f60091d88049dd593b785a5432fb6bf4fbeb73c6d34e8f70e266c22c5 | MATLAB | 1,577 | 47 | function resultindex = evaluatefilter(cellvar, filterString)
%
% resultindex = evaluatefilter(cellvar, filterString)
%
% Return the indices of the cell structure that have fields that satisfy
% the given constraints. Each row of the output is the index into one cell.
%
% CELLVAR - any cell structure, for example spikes... |
ca8266f769a5dc3b6e3d3b0dc16568ea451b2406c7a68fa6d68bc60d94fe4a58 | MATLAB | 1,577 | 61 | %% var_to_cpsd
%
% Calculate cross-power spectral density and transfer function from VAR
% parameters
%
% <matlab:open('var_to_cpsd.m') code>
%
%% Syntax
%
% [S,H] = var_to_cpsd(A,SIG,fres)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% A VAR co... |
0dc4eca2690f03978dc914f9361cded26639975aa64320fd04d6b5c1506fbe3a | MATLAB | 1,578 | 57 | function [epipole] = compute_epipole(xLp,R,T,fc_right,cc_right,kc_right,alpha_c_right,fc_left,cc_left,kc_left,alpha_c_left,D);
if ~exist('D'),
D = 400;
end;
uo = [ normalize_pixel(xLp,fc_left,cc_left,kc_left,alpha_c_left); 1 ];
S = [ 0 -T(3) T(2)
T(3) 0 -T(1)
-T(2) T(1) 0 ];
l_epipole = (S*R)... |
7f1215eeba7bd535ed50f385df801f77dff8c95c83f4520605a4a01fb9970b7f | MATLAB | 1,578 | 49 | function [data1,data2] = extract_variables (variable, subVariable , path, group1, group2, conditionType, conditionValue)
% if nargin < 6
% conditionType = [];
% conditionValue = [];
% end
%sizeThresh = 1;
files = dir(path);
fileNames = {files.name};
fileIsDir = {files.isdir};
fileNames = fileNames(cell2mat(f... |
91d6da7a3e8c2182c4e491807e2d3e55c547ba3afd3ffdba3fbebc9b4c861ab4 | MATLAB | 1,578 | 60 | function [DKL,DJS] = VBA_KL(m1,v1,m2,v2,distrib)
% computes the KL and JS divergences between two distributions
% [DKL] = VBA_KL(m1,v1,m2,v2,disp)
% Note that the KL-divergence is not symmetric w.r.t. to the two
% probability densities, i.e. KL(p1,p2) ~=KL(p2,p1). By convention, the
% KL-divergence is defined as follow... |
a9ea1927ad42b84fbbc436a7b3e03be1e87a9353f678d4077ad26efe5d1f0be4 | MATLAB | 1,578 | 97 | function [xd,dxddk] = apply_distortion(x,k)
% Complete the distortion vector if you are using the simple distortion model:
length_k = length(k);
if length_k <5 ,
k = [k ; zeros(5-length_k,1)];
end;
[m,n] = size(x);
% Add distortion:
r2 = x(1,:).^2 + x(2,:).^2;
r4 = r2.^2;
r6 = r2.^3;
% Radial distortion:
... |
fcb7f027fb23ce081840a375793e2a8732b8a4f54d9b7ae84c5fc2bc08571083 | MATLAB | 1,578 | 53 | % _ _
% ___ _ __ __ _| |__ ___ ___ _ __| |_
% / __| '__/ _` | '_ \/ __|/ _ \| '__| __|
% | (__| | | (_| | |_) \__ \ (_) | | | |_
% \___|_| \__,_|_.__/|___/\___/|_| \__|
%
% master dispatched when we want to reduce dimensions
function reduceDimensionsCallback(self,~,~)... |
206d1d760db606658db5877e18e13d7a8d581e9fc995fcf69641d6f1685f9388 | MATLAB | 1,579 | 52 | clear all;
load('/home/amax/YZY/code/retinotopysmall6.mat'); %视网膜刺激时间-模式文件
% stimulus is 200*200*300 200*200pix 300 means 300 seconds with TR=1
COpRF_options = [];
%run = 1
TR = 1;
% tseries ={};
data={};
%% Load in preprocessed data
data_dir= '/home/amax/YZY/20220523data/sub01/session01/preprocess/result_for_pr... |
a230c815b01c176b85f4fcbe3d8b51f1ee0ce78e96313a872f2e26f74b9400a2 | MATLAB | 1,579 | 46 | %% Computing When-decoders choice prediction
disp('Computing When decoder performance...')
load(fullfile(saveLoc, 'decoder_excl_SL'))
d = 6; % WhenD
clear pc_whenC_sign_dec se_whenC_sign_dec
for sess = 1 : 8
disp(['Session ' num2str(sess) ': Computing When decoder performance...'])
load(fullf... |
6d6d14e00babcb3eb4924a06a07bf55d527fae51528bf7abec6823552e3ddabe | MATLAB | 1,580 | 63 | %% Test PMTK mrf code against mrf2
%
%%
% This file is from pmtk3.googlecode.com
nNodes = 60;
adj = chainAdjMatrix(nNodes);
nStates = 7*ones(1,nNodes);
initial = [.3 .6 .1 0 0 0 0];
nodePot = zeros(nNodes, max(nStates));
nodePot(1,:) = initial;
nodePot(2: end,:) = 1;
edgePot = [.08 .9 .01 0 0 0 .01
.03 .... |
bfd30732dd4d2eb082ba73495c7c865e26b433c1a84b613de3a95c22a7e61eea | MATLAB | 1,580 | 74 | % [y,npadl,npadu] = padgrab(x,padv,l1,u1,l2,u2,...)
function [y,npadl,npadu] = padgrab(x,padv,varargin)
% parse arguments
usagestring = 'Usage: padgrab(x,padv,l1,u1,l2,u2,...)';
nd1 = ndims(x);
if mod(numel(varargin),2) ~= 0 || isempty(varargin),
error(usagestring);
end
isones = cellfun(@(x) x == 1, varargin(1:2:end... |
c5c5943773c2e27b3a1ea1919b1f7f2ddbe3f7cbed18cb80a5651ec7506d6724 | MATLAB | 1,580 | 79 | % conic_standard_form(A,B,C,D,E,F)
% A * x^2 + B * x * y + C * y^2 + D * x + E * y + F = 0
% returns the rotation theta and translation (x0,y0) required to put the
% equation in the form
% A * x^2 + B*x*y + C * y^2 + D*x + E*y + F = 0
% if not a parabola, then B = D = E = 0 and F = -1
% if a parabola then B = F = 0 and... |
df3777c24b9b70268aa5812e34683c601702d7e4a8292ccfbaa6a7a2e4b5543a | MATLAB | 1,580 | 57 | %% Infer the mean of a 1D Gaussian
%
%%
% This file is from pmtk3.googlecode.com
priorVar = [1 5];
Sigma = 1; % Assumed to be known
figstr = {'gaussStrongPrior', 'gaussWeakPrior'};
X = 3; % data
[styles, colors, symbols, plotstyle] = plotColors();
figure;
for i=1:numel(priorVar)
%%
prior.Sigma = priorVar(i)... |
9e335e90c8edee57a72fc3269ba8390a725797d495300b59167bacfa80c62c13 | MATLAB | 1,581 | 54 | % showSpikeInContext
%
% _ _
% ___ _ __ __ _| |__ ___ ___ _ __| |_
% / __| '__/ _` | '_ \/ __|/ _ \| '__| __|
% | (__| | | (_| | |_) \__ \ (_) | | | |_
% \___|_| \__,_|_.__/|___/\___/|_| \__|
%
% created by Srinivas Gorur-Shandilya
% https://srinivas.gs/
function sho... |
a95e7327b8d2ee76bafd3ca6a2c802444de284fe51f0f7d59e05ce4a474bd5b4 | MATLAB | 1,581 | 55 | % Spike detection
load('path to EEG mat file')
EEG2 = EEG.values;
%% set
time_range = [13000,10*3600];
Fs = 100;
% run
data=EEG2(time_range(1)*Fs:time_range(2)*Fs);
data = data(1:floor(length(data)/Fs)*Fs);
tvec = 1:length(data);
DC = 4;
[C,L] = wavedec(data,DC,'db4');
approx =... |
51961146066f8ad79f58659f1c856ae0e64dca27c6db3e02076bc77b13667349 | MATLAB | 1,582 | 38 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This script cuts the BIOPAC post-processed data according to the cuts %
% defined for the NII files. %
% The cut files are in the
% ... |
6c046bf6cf53508e93d007e933e71c855396847e594df72d123b38463db2ed58 | MATLAB | 1,582 | 51 | function [observed, hidden] = hmmSample(model, len, nsamples)
% hidden{i}(1:len(i)) ~ markov(model.pi, model.A)
% observed{i}(t) ~ discrete(model.emission(hidden{i}(t), :))
% or
% observed{i}(1:d, t) ~ gauss(model.emission{hidden{i}(t)}))
% This file is from pmtk3.googlecode.com
SetDefaultValue(3, 'ns... |
9f7aa8ac5af99fee5eaae7c6856846a344c6b3c04915853c672203edc1c04660 | MATLAB | 1,582 | 58 | function t = limo_ttest_light(type,data1,data2)
% t = limo_ttest(type,data1,data2)
% implement the one-sample t-test, paired t-test (type = 1)
% and the two samples t-test (type = 2). The ttest is performed on the last
% non-singleton dimension, which can be subjects or trials. If vectors are
% compared they sh... |
ba75c6353aea6bf56487bb3e55aa8b3d6587214487dca68736b4bb2959a29cb1 | MATLAB | 1,582 | 50 | %% Calculate Double Gaussian Curve
% Save cross-section intensity from Fiji. Normalize and center the curve.
% Save as CSV without column names. C1: Distance in nm. C2: Normalized intensity.
% Specify the file path and name
clear
file = 'P2P-2-b-norm.csv';
% Import the CSV file
data = readmatrix(file);
% Display the ... |
e4a1039fa3207e6a7d541baf20247b157f65f4efe2c37f30c8ca65155d606216 | MATLAB | 1,583 | 45 | function [True] = inside_tetrahedron(P,P1,P2,P3,P4)
% INSIDE_TETRAHEDRON checks if a 3D point belongs to a tetrahedron usign
% the 'volume' criterion.
%
% TRUE = INSIDE_TETRAHEDRON(P,P1,P2,P3,P4) Tests if point P belongs to the
% tetrahedron spanned on P1, P2, P3 and P4. All points are cartesian
% coordinates.
%... |
09d8d15d723ee4c7d91e3f6fc77c4ba87aa9619447be593437f0212d8659ba80 | MATLAB | 1,586 | 59 | %% Low level test of the TabularFactor code using the water sprinkeler BN
% C
% / \
% v v
% S R
% \/
% v
% W
%%
%testSprinklerDemo()
% Specify the conditional probability tables as cell arrays
% The left-most index toggles fastest, so entries are stored in this order:
% (1,1,1), (2,1,1), (1,2,1), (2,2,1), etc.
... |
55ac302091146f578abecabf77ec0cc77e14c2252ad0882ee9a96f274e355028 | MATLAB | 1,587 | 46 | function segments = CARL_auto_detect_on_offset(speechSignal, Fs, ind, segments, threshold)
% This function determines the on-and offset of a single speech sample as
% created by the main function. To determine the on- and offset, the
% short-time energy is used, downsampled and low-pass-filtered.
if nar... |
9de77e9bf1767787ebb6f6f9f1ce15b31f364ef2fd266ce8e1669b98414e35f5 | MATLAB | 1,587 | 31 | %%Plots and calculates the mean bin value of all laps for data in each given task
%Chad Heer; Sheffield Lab
function [mean_lap_pupil, h, j] = plot_binned_data(lap_pupil, tasks, x_units)
%lap_pupil = pupil or fluorescence data organized in this format {task}(laps, mean pupil area in each bin)
%tasks = string array lis... |
bd09f9f1b01aae5afcea531792f7771bab394ad00ee4bff364a73dc4857dcef8 | MATLAB | 1,587 | 60 | function logp = gaussLogprob(arg1, arg2, arg3)
% Log pdf of multivariate Gaussian
% L = gaussLogprob(model, X)
% L = gaussLogprob(mu, Sigma, X)
% L(i) = log prob X(i,:) is i'th case, can contain NaNs for missing values.
% Sigma can be a vector; this is interpreted as a diagonal matrix.
% *** In the univariate case, Si... |
11ccce75c4e83a2d9086af4e4ec1b3192badf29230c9d3fc4949822f2a90db82 | MATLAB | 1,588 | 61 | % Copyright Christopher Fricke, University Leipzig Medical Center,
% Neurology 2024
srcf1 = 'data\prep';
srcf2 = 'data\prep\coreg_indiv\datscan_normalized';
% klinische Daten laden
[~,~,r] = xlsread(fullfile(srcf1,'clinicaldata.xlsm'));
% Indices of interest
binds = [6 8 14:17 23:24 27:29];
% Datensatzl... |
d45a738a395570c75ce765fc693b478667161b59ce44a29b9639cbd37a0b2c30 | MATLAB | 1,589 | 38 | function varargout = avg_angle_from_cos(cos_theta,in_deg,return_only_value)
if (nargin < 2) || isempty(in_deg)
in_deg = false;
end
if (nargin < 3) || isempty(return_only_value)
return_only_value = 'all';
end
valid_return_values = {'avg','std','err','all','minmax'};
assert(any(str... |
3ce94a2e70607a6038b35f3430694550adad702f434d2d4d3972ad370a91f5b9 | MATLAB | 1,590 | 62 | function Patlak_wrapper(PET_dyn_path, t_PET_emi, t_PET_delta, BRAIN_path, Cp_in, Cp_time, anatomy,out_path, options)
if nargin<9
options = get_default_options();
end
[~, PET2D, ~]=load_PET(PET_dyn_path);
[PET_brain, PET2D_brain, ~]=load_PET(BRAIN_path);
voxs=find(PET2D_brain>0.1); % 0.5 if not normalized
nr_p... |
4d0db144067ef95b95604c7836e8a6406c995ac825649ddc474b5b696d0ca5cb | MATLAB | 1,590 | 63 | function LAN = lan_from_int(channels, srate_new, File, Path)
% IMPORTANT: number of channels required
%
% *************DEPENDENCIES*************
% - read_intan_data.m (Intan Technologies)
% (c) 2010, Intan Technologies, LLC
% For more information, see http://www.intantech.com
% For updates and latest version, see http:... |
75d38075a381f19123e937acc89994d388301808883a4e6bb8eae7d4cdb5dfe3 | MATLAB | 1,590 | 80 | function LAN = lan_rm_chan(LAN, ind,ica)
% v.0.3
%
% Pablo Billeke
% 18.04.2022
% 27 08 2021
% add save deleted comp
if nargin < 3
ica = 0;
end
if ischar(ica)
ica = strcmp(ica,'ica');
end
if abs(ica)
ind = sort(ind);
if iscell(LAN)
for lan=1:length(LAN)
LAN{lan} = lan_rm_c... |
235c7cbfe532e731bee281917df8488a2b41275b0030f25c1fef0212c476f797 | MATLAB | 1,591 | 66 | function writeSTL(filename,bel,nod)
% convert2STL(filename,bel,nod)
%
% Dartmouth College
% Written by Andrea Borsic using chunks of code from surf2stl of Bill McDonald
% Hanover, 06/10/06
% Modified by Hamid Ghadyani 03/14/2009 Worcester, WPI
% Added I/O checks
% This function converts a *.bel and a *.nod file to an... |
62ce8d5b3543af900159f2925f41359ae4d1897f6a3d99b8251adf4b07526158 | MATLAB | 1,591 | 51 | function s = calcMacroQuantities(a,N,tCorr)
% a -> activity struct vector (each element corresponding to an element in the vector of sizes N)
% fields:
% 'A' : activity raw timeseries (number of spikes for each time step)
% 'A2': number of spikes squared for each time step
% 'A4': ... |
2e471386f78b065d42a1843a5390f19e8d386606da7cb4098fda8c8b51d5809b | MATLAB | 1,592 | 51 | function [Zpca, U, mu, eigVecs] = PCA(Z,r)
%
% Syntax: Zpca = PCA(Z,r);
% [Zpca, U, mu] = PCA(Z,r);
% [Zpca, U, mu, eigVecs] = PCA(Z,r);
%
% Inputs: Z is an d x n matrix containing n samples of d-dimensional
% data
%
% r i... |
8c8c905ff595d70fa3133ad22aeea7f90ed3e629193b37979ac12c3910786a63 | MATLAB | 1,592 | 59 | function ns = count_squares_distorted(I,x1,y1,x2,y2,win, fg, cg, kg);
[ny,nx] = size(I);
if ((x1-win <= 0) || (x1+win >= nx) || (y1-win <= 0) || (y1+win >= ny) || ...
(x2-win <= 0) || (x2+win >= nx) || (y2-win <= 0) || (y2+win >= ny))
ns = -1;
return;
end;
if ((x1 - x2)^2+(y1-y2)^2) < win,
ns =... |
bb4aa3a79330aec02d592b11ee791f580f8a0576d1b74820f47d09492244c900 | MATLAB | 1,592 | 51 | function [elem,node] = abaqus2nodele_surface(files, outputdir)
% abaqus2nodele_surface(files)
% Reads list of files from cell array 'files' or from GUI and converts them
% to .node/.ele format (tetgen) format
% Get multiple abaqus inp files as input
if nargin==0
[files, pname, filterindex] = uigetfile({'*.inp','Se... |
d303412c452246a17a7ad3a533c92b65bc0d1e06ce2e604e7b31c33523bed394 | MATLAB | 1,592 | 54 | function [ci, sizes, label_id] = CBIG_ComputeConnectedComponentsFromSurface(avg_mesh, labels)
% Given average mesh and labels, compute connected components.
%
% [ci, sizes, label_id] = CBIG_ComputeConnectedComponentsFromSurface(avg_mesh, labels)
% Input:
% avg_mesh : average mesh read from CBIG_ReadNCAvg... |
0fa85621a2d57f94f98b455bbf1419129a2004b4e815face0a588690a6a6e2e7 | MATLAB | 1,593 | 38 | function DataStructure=LinearFitSimple_ZN(Xdata,Ydata)
if size(Xdata,1)<=1&&size(Xdata,2)<=1&&size(Ydata,1)<=1&&size(Ydata,2)<=1
warning('Need to provide more than one data point to fit!')
DataStructure=[];
elseif size(Xdata,1)~=size(Ydata,1)||size(Xdata,2)~=size(Ydata,2)
warning('Need ... |
392dc317adc94a1cf1defd9160a818ac1a73e2305cbac37c0c1fb0e56c63167c | MATLAB | 1,593 | 63 |
function [IX0,SigmaX0,deltaMuX0,suffStat] = VBA_IX0(X0,y,posterior,suffStat,dim,u,options)
% Gauss-Newton update of initial conditions
if options.DisplayWin % Display progress
set(options.display.hm(1),'string','VB Gauss-Newton on initial conditions... ');
set(options.display.hm(2),'string',' ');
drawno... |
cd58b549c9e7e3eddcde9621ed93270b99f7c0aeb67938f03328c1e6b6d4c465 | MATLAB | 1,593 | 47 | %% Bound optimization illustration
%
%%
% This file is from pmtk3.googlecode.com
f1 = @(x) log(gaussProb(x, 0, 0.25^2))' + 1;
f2 = @(x) log(gaussProb(x, 1, 0.2^2))' + 20;
f3 = @(x) 5*sin(2*(x-0.5)) + ...
f1(0.5*x) + ...
f2(0.5*x) + ...
... |
1cc9854bb1de0bacf83862f3ae7fd5b5ab31dcad31ff34241b7cc08a9839cbbf | MATLAB | 1,594 | 55 | function [celldata, eventindex] = getCellEventData(day, epoch, modelParam, riptimes, cellidxm, spikes)
% [celldata, eventindex] = decode.getCellEventData(day, epoch, modelParam, riptimes, cellidxm, spikes)
%
%
%% Set up
cellcountthresh = modelParam.cellcountthresh;
nCells = size(cellidxm, 1);
%% Return empty if no ... |
5c394ab4bc16c47e6509c4a8dfc5073d933e8131b1d9c6528b303623de0dca1c | MATLAB | 1,594 | 46 | % create a channel map file
Nchannels = 64;
connected = true(Nchannels, 1);
chanMap = 1:Nchannels;
chanMap0ind = chanMap - 1;
xcoords = ones(Nchannels,1);
ycoords = [1:Nchannels]';
kcoords = ones(Nchannels,1); % grouping of channels (i.e. tetrode groups)
fs = 25000; % sampling frequency
save('C:\DATA\Spikes\... |
814c828ca7f12c855e64b0d314867f3bf55807d2b47bfcf779107c424a24e02f | MATLAB | 1,594 | 46 | %% SDO Bootstrap
% Simple utility to iterate through the post-spike distributions using a
% bootstrapped combination of the existing distributions.
% Could be used to evaluate the sensitivity of particular spikes on the SDO
% matrix.
%
% INPUTS:
% - px0 -[N_STATES, N_SPIKES] column vectors of pre-spike probability... |
956163292e5efa0259ca9924deab2f187ab1cb2f5a96632269698deb996eac51 | MATLAB | 1,594 | 51 | function X = arrange(X,foo)
%ARRANGE Arranges the rank-1 components of a ktensor.
%
% ARRANGE(X) normalizes the columns of the factor matrices and then sorts
% the ktensor components by magnitude, greatest to least.
%
% ARRANGE(X,N) absorbs the weights into the Nth factor matrix instead of
% lambda.
%
% ARRA... |
d27dc45199cd5cabc7e9254a5e1b14f5de8520af5c2f9107411f9aa115cca642 | MATLAB | 1,595 | 65 | function [img,nr,nc] = getframe_invisible_nocheck(gfdata,sz,removeborder,doresize)
if nargin < 3,
removeborder = true;
end
if nargin < 4,
doresize = true;
end
if isfield(gfdata,'grayborder'),
GRAYBORDER = gfdata.grayborder;
else
GRAYBORDER = 204;
end
hfig = gfdata.hfig;
img = hardcopy(gfdata.hardcopy_args{:... |
15ad6a3a8755689db9b3d2ebf63e2797d333217defaad7851bf7593b95fe0171 | MATLAB | 1,596 | 57 | function [f, cxy] = lan_coherence(LAN, cfg, nomean)
%***************DEPENDENCIES***************
% - hann (Signal Processing Toolbox)
% - hamming (Signal Processing Toolbox)
% - lan_cspd (LAN toolbox)
% - lan_cspd_mt (LAN toolbox) # See Dependencies (Chronux)
if nargin < 3
nomean = false;
end
if ~isfield(cfg, 'twi... |
632e6276cc60812a7832d44577aa079bace411d7eeeb1f02ecfbcd4c1a6d94a7 | MATLAB | 1,596 | 54 | function som = integ(y,x)
%INTEG Approximate integral.
% SOM=INTEG(Y,X) approximates the integral of vector Y
% according to X.
%
% Y : N-row-vector (or MxN-matrix) to be integrated
% (along each row).
% X : N-row-vector containing the integration path of Y
% (default : 1:N)
% SOM : value (or Mx1 vecto... |
8d8219853c2ab79d413d157757d53d345f1495e2e165175a58eb86e64156dc94 | MATLAB | 1,596 | 54 | %% Parameters
dir_input = './';%'/om/user/dgoodwin/ExSeq/';
dir_rootoutput = './input/';
experiment_string = 'sa0916dncv';
if ~exist(dir_rootoutput, 'dir')
mkdir(dir_rootoutput);
end
files = dir([dir_input '*.ics']);
num_channels = 4;
for file_idx = 1:length(files)
datastack = bfopen([dir_input fi... |
4e3271832a59ef5d4173ca4384d89a171f37dcf1d46ed79b1fe2f5e3c7f4ad8f | MATLAB | 1,597 | 52 | function [nvar, names, types] = nex_info(filename)
% nex_info(filename) -- read and display .nex file info
%
% [nvar, names, types] = nex_info(filename)
%
% INPUT:
% filename - if empty string, will use File Open dialog
% OUTPUT:
% nvar - number of variables in the file
% names - [nvar 64] array of varia... |
555eda415939fd9aaf3cce1204626b3881c5de67a83bbd615f7e347b6db46b49 | MATLAB | 1,597 | 56 | function [s] = removefields(s, fields, varargin)
% REMOVEFIELDS makes a selection of the fields in a structure
%
% Use as
% s = removefields(s, fields);
%
% See also KEEPFIELDS, COPYFIELDS
% Copyright (C) 2014, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.fieldtriptoolbox.org
% for the docum... |
816fb6545b6a86955387f971249cec4252b761eaa5d4003cc3b92e2ecb80f78f | MATLAB | 1,597 | 60 | function [p, cc, np] = pcorr(x, y, Nit, Nstop)
%PCORR Significance of correlation coefficient using permutations
%
% [p, cc, np] = pcorr(x, y, Nit, [Nstop])
%
% Are data vectors x and y significantly correlated? The linear
% correlation coefficient cc is first computed. This function returns
% the probability that cc... |
22f8e22fe281c150c9a99eda7b8cff4b62a053e5594c38a6f273fa5c28996a33 | MATLAB | 1,599 | 48 | %% 非线性（动力学）特征：基于时域的熵 | nonlinear features: time-domain-based entropy
%% 排列熵 | Permutation Entropy
% 表征时间序列数据的复杂性（complexity）和规律性（regularity），通过计算序列中不同排列模式的概率分布来衡量序列的不确定性，值越大，时间序列越复杂、越不规律
% X: single channel EEG signal (either a row vector or a column vector)
% opts.tau: 延迟时间 delay time
% See also:
% feat_Shanno... |
63ea6daebdc095038cc50b25ad193830465bce68c5e8adeca635601aff09a1d1 | MATLAB | 1,599 | 52 | function AutoPlayback(ImageArray,PauseInterval,IntensityLimits)
figure();
set(gcf, 'color', 'white');set(gca,'XTick', []); set(gca,'YTick', []);
BringAllToFront();
Beeper(5,0.15)
message = sprintf('Ready to start playback of array, press OK to continue');
uiwait(msgbox(message));
pause(1);
pause on;
set(gcf,'Select... |
9cf4b7c1d8c161e0b7e2470a0361f920f5e16353c724d08079115d8849c1b532 | MATLAB | 1,599 | 62 | function Chen_correction_wrapper(venous, venous_time, IDIF_path, BG_path, qc_path, anatomy)
load(fullfile(IDIF_path,['IDIF_' anatomy '.mat']));
load(fullfile(BG_path,['BG_' anatomy '.mat']));
IDIF_uncorrected = IDIF.IDIF;
IDIF_uncorrected_time = IDIF.t_IDIF;
if IDIF.flag_Chen == 1
if IDIF.flag_Feng =... |
d61f6672fc6f586388b2af038fd24f5dfee8e8756b438d475933b5fbd2017e04 | MATLAB | 1,599 | 30 | % Create coordinates of intersection contours and intersection edges
tissues = length(tissue);
PofXY = cell(tissues, 1); % intersection nodes for a tissue
EofXY = cell(tissues, 1); % edges formed by intersection nodes for a tissue
TofXY = cell(tissues, 1); % intersected triangles
NofXY = cell(tissues, 1);... |
ee48b5b09023a1ab6976582b891d4bf7764b3efc08c29c91eef5d26c43c7ffd5 | MATLAB | 1,599 | 63 |
clear
% close all
Monkey = ['Monkey2']; % ['Monkey1' or 'Monkey2']
Base = ['PER_ON'];
Align = [ {'PER_ON_long'} ];
for al = 1:length(Align)
switch Monkey
case 'Monkey1'
load(['TEST_SpettroXCHANNELS_SD_base_' Base '_' Align{al} '_B2b.mat'])
start= 1;
case 'Monkey2'
load(['TEST_Sp... |
a8ae6abdd795db088c297673d173818cab29a520c5b6eeae0c79a16e08ce3242 | MATLAB | 1,600 | 33 | % Copyright (C) 2012-2013 Acoustics Research Institute - Austrian Academy of Sciences;
% Licensed under the EUPL, Version 1.1 or – as soon they will be approved by the European Commission - subsequent versions of the EUPL (the "License")
% You may not use this work except in compliance with the License.
% You may obtai... |
ae56f68e352b9864c090840c0ea693d8d37b409d8b527baca8ce2ad7d5a29538 | MATLAB | 1,600 | 60 | %% demean
%
% Temporal demean of time series data
%
% <matlab:open('demean.m') code>
%
%% Syntax
%
% Y = demean(X,normalise)
%
%% Arguments
%
% See also <mvgchelp.html#4 Common variable names and data structures>.
%
% _input_
%
% X multi-trial time series data
% normalise normalise (temporal) vari... |
b6905b08f0f21dadd5fb10307b4b9be45bafca20da45ff18e49411d1fbca13f1 | MATLAB | 1,601 | 50 | function [datfield, dimord] = getdatfield(data)
% GETDATFIELD
%
% Use as
% [datfield, dimord] = getdatfield(data)
% where the output arguments are cell-arrays.
%
% See also GETDIMORD, GETDIMSIZ
datfield = fieldnames(data);
datfield = datfield(:)'; % it should be a row-vector throughout the subsequent code
% thes... |
c85b77668a6c7428f49cd57336799e67b6ad3a419f5b52e45430891ce50d65e4 | MATLAB | 1,601 | 47 | function s = getDefaultFigureProperties()
s.pLines = {'--'};
s.pSymbols = [ 'o', '^', 'v', 's', '>', '<', 'p', 'h', 'x', '+', '*' ];
s.pColors = [[ 99,199,113;... % verde
210,45,45;... % vermelho
194,129,201;... % roxo
112,179,250;... % azul
... |
ee1c88b828bb26db0ba23cf9f65ce2a9135577b3e6998dcc1f51f5f5eb90dba2 | MATLAB | 1,601 | 49 | function y = amrect(N,t0,T);
%AMRECT Generate rectangular amplitude modulation.
% Y = AMRECT(N,T0,T) generates a rectangular amplitude modulation
% centered on a time T0, and with a spread proportional to T.
% This modulation is scaled such that Y(T0)=1.
%
% N : number of points.
% T0 : time center (default : N/2).... |
364ce0852426517054ce2bcea383e9a6145651bda3b6a6b115dc5e77bf41c23b | MATLAB | 1,603 | 53 | function [y, out] = VBA_removeOutliers (y, t)
% // VBA toolbox //////////////////////////////////////////////////////////
%
% [y, out] = VBA_removeOutliers (y, t)
% remove outliers based on robust z-scoring
%
% IN:
% - y: data to diagnostic
% - t: threshold for the outlier detection (default = 3.5)
%
% OUT:
% -... |
86ac56d10786ef53b1ff261640a57c8eb40ce82bddff576ffaf5fc681676c432 | MATLAB | 1,603 | 53 | % WARNING: this source is DEPRECATED, see SWITCH[...]InParallel.m instead
%
% Step 4: Keypoints and Descriptors
% This is the code that calculates the keypoints and descriptors at
% varying scale levels
%
% INPUTS:
% img is the image volume
% OUTPUTS:
% keys: a vector of structs that contain the keypoint and descript... |
f9c833f066681fd4db0ec8863603888452cd34aff958925204c2ed5386c63b60 | MATLAB | 1,603 | 37 | % SOFA API - demo script
% Copyright (C) 2012-2013 Acoustics Research Institute - Austrian Academy of Sciences
% Licensed under the EUPL, Version 1.1 or – as soon they will be approved by the European Commission - subsequent versions of the EUPL (the "License")
% You may not use this work except in compliance with the ... |
4f7b95be87f857fa99f6efce211dff44f09715391a02727ed06807f3dea2c85c | MATLAB | 1,604 | 52 | %% SDO Jack-knife
% Simple utility to iterate through the post-spike distributions using a
% 'leave-1-out' (Jackknife) operation to produce subsets of SDOs.
%
% Could be used to evaluate the sensitivity of particular spikes on the SDO
% matrix.
%
% INPUTS:
% - px0 -[N_STATES, N_SPIKES] column vectors of pre-spike ... |
5dcae2998b551c87330800847eb6ed41ce534a36a453eb7eca3ee1637ea3c011 | MATLAB | 1,604 | 49 | clear all
nInst = 500;
nVars = 100;
X = [ones(nInst,1) randn(nInst,nVars-1)];
w = randn(nVars,1);
y = sign(X*w);
flipInd = rand(nInst,1) > .9;
y(flipInd) = -y(flipInd);
w_init = zeros(nVars,1);
funObj = @(w)LogisticLoss(w,X,y);
fprintf('Running Hessian-Free Newton w/ numerical Hessian-Vector products\n'... |
64dc0de01681b7b754d0db539181f2d848e59710304193103de07f6db5ac19c1 | MATLAB | 1,604 | 76 | function showvc_prog_plain(loc,tri,view_dir,para);
cmap=1;
voxelkont=0;
if nargin>3
if isfield(para,'cmap')
cmap=para.cmap;
end
if isfield(para,'voxelfield')
voxelfield=para.voxelfield;
voxelkont=1;
end
end
colp=loc(:,3);
[np,ndum]=size(colp);
colp=loc*view_dir';
[cs,is]=sort(colp);
d=max(col... |
5f4676fde36f650243c4f1f0ec34b25a50b6b842c5fdab044e3645d1c770be6e | MATLAB | 1,605 | 36 | function [TTest2Cov_T,TTest2Cov_P]=y_TTest2Cov(Variable1,Variable2,CovariateVariable1,CovariateVariable2)
% [TTest2Cov_T,TTest2Cov_P]=y_TTest2Cov(DependentVariable,CovariateVariable)
% Perform two sample t test with or without covariates.
% Input:
% Variable1 - The Variable 1. Test if Variable 1 is greater than V... |
da243fcf4db906ac284840df4c4ea32f70e8587db1a8a36230942d3ec62fcb73 | MATLAB | 1,605 | 62 | function a = myatan(x,y,varargin)
%myatan Choose from various representations of getting the inverse tangent
%
% This function takes in two vectors of dx and dy and returns the vector of
% angles between dx and dy
% Inputs:
% Type: 'degrees' or 'radians'
% Options: 1 = -180:180 or 2 = 0:360
% Test Ca... |
2b5e79c775081c3f1801be9b0f8b4326d1d6566eec07f85d60c71374f184cc6e | MATLAB | 1,606 | 35 | %%% All subject PFs
load(fullfile(DataPath, 'SurveyMainData_Processed.mat'))
[palmar_mask, palmar_template, dorsal_mask, dorsal_template] = GetHandMasks();
[pbt_regions, dbt_regions] = GetHandSegments();
%% Generate temporary high-resolution frames
u_subjects = unique({SurveyMainData.Subject});
sub_labels = {'C1', 'P2... |
4ef0b03448cb741c3fc3c3b14ff14865aa8ea1a2c46d7de77225d018cf31b3af | MATLAB | 1,606 | 56 | %% ROC curves for two hypothetical classification systems
% A is better than B. Plots true positive rate, (tpr) vs false positive
% rate, (fpr).
%%
% This file is from pmtk3.googlecode.com
domain = 0:0.01:1;
fA = @(x)x.^(1/3);
fB = @(x)x.^(2/3);
linewidth = {'LineWidth', 3};
hold all;
plot(domain, fA(domain), '-r', ... |
6928211057a458b107c1bf8f771840fceee62d4291755ef22d948144cbb9cdb7 | MATLAB | 1,606 | 51 | % In the case of debugging a failed registration, it can be helpful to
% visually inspect the spatial distribution of the matching keypoints
% between two rounds
loadParameters;
%Choose a round that you want to visualize, make sure it's not the
%reference round!
ROUND_TO_EXAMINE = 3;
if params.REFERENCE_ROUND_WARP =... |
b70990d136b86b860d71c4a3086ad640b1fb91115575a306969590cd9e31b104 | MATLAB | 1,606 | 60 | function paramstr = wlAux_getStringFromParams(thisev)
% function paramstr = wlAux_getStringFromParams(thisev)
%
% This function builds a human-readable text string describing the fit
% parameters in the specified event record.
%
% "thisev" is the event structure to describe, per EVENTFORMAT.txt.
%
% "paramstr" is the ... |
b8724346a3ac0607c16dfbafb99ba59a56627aaae5826b1f09790778c54a3d11 | MATLAB | 1,606 | 54 | % HemoCorrection.m
% Sarah West
% 8/26/21
% Corrects the hemodynamics of a stack. Requires input of two matrices--
% the blue and violet data from a single stack.
% Inputs:
% bData -- the blue-channel only stack, in a 3D matrix (pixels, pixels,
% frames)
% vData -- the violet-channel only stack, in a 3D matrix (... |
c0a94c95d4547aa2dfb6e2ba976cd3e2c5f6c9bf8bb374fdd2be001b4c8fe06a | MATLAB | 1,606 | 53 | function y = afxSmoooth(y,FWHM,dim,mat)
% y = afxSmoooth(y,FWHM,dim,mat)
% convolve data in y (linearly packed in the spatial dimension with
% original dimensions dim and q-form matrix mat) with gausian smoothing
% kernel with full width at half maximum of FWHM
fprintf(' Smoothing ...')
... |
cdfaf78940c4a0d50760cc1dd6ef98066438cb8849e570ba3c6c316c9e3bd090 | MATLAB | 1,606 | 52 | function [newsubs, newsz] = renumber(subs, sz, range)
%RENUMBER indices for sptensor subsref
%
% [NEWSUBS,NEWSZ] = RENUMBER(SUBS,SZ,RANGE) takes a set of
% original subscripts SUBS with entries from a tensor of size
% SZ. All the entries in SUBS are assumed to be within the
% specified RANGE. These subscripts are t... |
f317c2cbedfc556bab95fec157110b69181d392a636a78a69202490f259df7e4 | MATLAB | 1,606 | 60 | function mrf2 = factorGraphToMrf2(fg, varargin)
%% Convert a factor graph to a pairwise Markov random field
% for use by Mark Schmidt's UGM library
% See mrf2Create for additional optional args
%%
% This file is from pmtk3.googlecode.com
fg = factorGraphMakePairwise(fg); % does nothing if fg is already pairwise
G = ... |
6d98326c7119c5fa2bb9f045ae2650d55a6eb7e6de53c28ee427ceeeecbd31dc | MATLAB | 1,607 | 45 | function CBIG_selectBETResults(BETOutFolderPattern)
% CBIG_selectBETResults(BETOutFolderPattern)
%
% This function selects the best (equivalently, the latest) BET result for
% each image. For example, if Image 6 is present in BET rounds 1, 2 and 3,
% then we should take the round 3 result as its final BET result.
%
% ... |
7a0463b6df205d7dead166508b75f2f22afc1d34ba791f683ad3163248ba284b | MATLAB | 1,607 | 61 | % AUTHORSHIP
% Primary Developer: Stephen Meehan <swmeehan@stanford.edu>
% Math Lead & Secondary Developer: Connor Meehan <connor.gw.meehan@gmail.com>
% Bioinformatics Lead: Wayne Moore <wmoore@stanford.edu>
% Provided by the Herzenberg Lab at Stanford University
% License: BSD 3 clause
%
function [fold... |
9bd54802803f10efaa75cdb7cda2aeceb3b9231a42853ab74e653eec9a5047c3 | MATLAB | 1,607 | 36 | l1 = load('/home/mayank/work/poseEstimation/RomainLeg/Apr28AndJun22_onlyBottom.lbl','-mat');
lfiles1 = {'/home/mayank/Dropbox/MultiViewFlyLegTracking/sep1616/sep1616-1531Romain.lbl'
'/home/mayank/Dropbox/MultiViewFlyLegTracking/sep1516/sep1516-1537Romain.lbl'
'/home/mayank/Dropbox/MultiViewFlyLegTracking/sep1316/se... |
c4938752d4462ff3e76f1d9b3a0df248be38a5827a34c8c7a0d93e8c39c30000 | MATLAB | 1,608 | 42 | clear all
clc
tmp=pwd;
cd ..
addpath(genpath(fullfile(pwd, 'utils/')));
addpath(genpath(fullfile(pwd, 'spmutils/')));
cd(tmp);
% set the value to the Pred_folder set in step 2
coreg_of_predMRA_dir='';
% set the value to the Pred_folder2 set in step 3
pred_of_coregMRA_dir='';
% coregister individual MRA v... |
5401d2054e934afe67385875a43e4683c5fbd02dcea19857aab4111a7bbf29bf | MATLAB | 1,609 | 58 | function [p,U2_obs,U2_H0]=watsons_U2_perm_test(A1,A2,N)
%
% [p,U2_obs,U2_H0]=watsons_U2_perm_test(A1,A2,N)
%
% Computes the probability that two samples of circular data (angles) come
% from the same distribution, or from distributions with the same direction
% (null hypothesis), using a permutation test and Watson's U... |
9e0ed54f8c8114369c4eb40c67b03659a5953c03d18b63bc2a5114f611b51b3d | MATLAB | 1,609 | 40 | % Load libraries
addpath C:\code\wjn_toolbox;
addpath C:\code\spm12;
addpath(genpath('C:\code\leaddbs'));
spm('defaults','eeg');
addpath C:\Code\wjn_toolbox-master;
addpath C:\Code\wjn_toolbox
% Parcellate ECoG master table
output_filename = 'ECoG_Atlas_MasterParc';
load ECoG_Atlas_Master_Table.mat; % Load the table
... |
33f58f685b52e928657fd931725ea02a6c69147fddf44d4eb701e6d044c1cce6 | MATLAB | 1,610 | 34 | function createPhysio_fromHCP_aging(HCP_physio_dir)
prate = 400; %% sampling rate of physio is 400 (every 0.025s);
cd(HCP_physio_dir)
%%% readin physio
tmp = dir('Physio_combined_*.csv');
tmp = importdata([tmp.folder,'/',tmp.name]); physio.RESP = tmp.data(:,4); physio.PULS = tmp.data(:,... |
68bf4e4b32d4ab8e604b2577dfc6bb1f9469ec8e95c937db1c86fc41b7e3e602 | MATLAB | 1,610 | 46 | function new_network = threshold_network(a,rho)
%The function thresholds the network using the approach of Kujala et al.
%(2016) https://doi.org/10.1111/ejn.13392
% a : undirected, weighted adjacency matrix
% rho : density of the network
% new_network : thresholded, binary network
no_links_percent = round(nnz(a(:))... |
bfa0d4ce6a1d7d5329c391eb61b4e3383897106a16cd0b88f4beafe76b349e53 | MATLAB | 1,610 | 73 | function w = iimg_smooth_3d(w, volInfo, sfwhm, varargin)
% Smooth 3-D images stored in columns with FWHM in mm of sfwhm
%
% :Usage:
% ::
%
% function w = smooth_3d(w, volInfo, sfwhm, [badvox])
%
% NOTE: Enter smoothing kernel in mm, as a scalar or [sx sy sz] triple
%
% :Inputs:
%
% **w:**
% Take v x n matr... |
89ec50bbb8d3e34f34e63853defc4a6e2c9334b8d280c4cfdbb8d47a63f3a965 | MATLAB | 1,611 | 44 | function y=lambdak(u,k);
%LAMBDAK Evaluate lambda function for Affine Wigner distribution.
% Y=LAMBDAK(U,K) evaluates the parametrization lambda function
% involved in the affine smoothed pseudo Bertrand distribution.
%
% LAMBDAK(U,0) = -U/(exp(-U)-1) for K = 0
% LAMBDAK(U,1) = exp(1+U exp(-U)/(exp(-U)-1)) for K = 1... |
afaeb5abc6079f6ec6f1def62b070209470d7b7fb4ba068edc9189a873f7658e | MATLAB | 1,612 | 59 | function [C] = VBA_spm_Ce(v,a)
% return error covariance constraints (for serially correlated data)
% FORMAT [C] = spm_Ce(v,a)
% v - (1 x n) v(i) = number of observations for i-th block
% a - AR coefficient expansion point (default a = [])
%
% a = [] (default) - block diagonal identity matrices specified by v:
%
%... |
bf84f74c9b85e3f2872ec11f392e4df2e7af8bc49cc9ed2f37f16af42e55146c | MATLAB | 1,613 | 73 | % Demo for Lotka-Volterra competitive system
% NB: this is a highly nonlinear system, which means that one may have to
% run the simulation many times before its returns non-NaN data...
clear variables
close all
% Choose basic settings for simulations
f_fname = @f_LV2D;
g_fname = @g_Id;
u = [];
n_t = 2e2;
delta... |
dc39c8d2815cb9a7008516c1416c6b0c4e61cd553fbefe12e26ea1db10516428 | MATLAB | 1,614 | 63 | function varargout = plot_avRainbow4(vcf, avsd, summaryInfoTable, avColors, selSmltIds, varargin)
%% handle optional inputs (varargin):
nOpVar=0; % counter for optional variable
nOpVar=nOpVar+1; opVars.avIndInterval = []; defaultValues{nOpVar} = 1;
opVars = handleVarargin(varargin, opVars, defaultValues);
%%
nsmlt =... |
ee1c67a0faeb89de923392a73fa7ad4ec840b928ff933e459dd4699503b08ed0 | MATLAB | 1,614 | 86 | function rankout = rankdata(m, varargin)
% ranks = rankdata(m) % default: midrank method
% ranks = rankdata(m, 'nomidrank') % do not use midrank
%
% ranks a vector, returns ranks in place of values
%
% Tor Wager
%
% Modified April 2007 to use the midrank method to handle... |
3a061b122e6c8da54220486bbcd64635c0228b7d2235d92c55eab06d62e19901 | MATLAB | 1,616 | 69 | function [sF,sDCM] = VBA_spm_split_dcm(DCM,nsplits)
% split DCM inversion using time series partition
% function [sF,sDCM] = spm_split_dcm(DCM,nsplits)
% IN:
% - DCM: the original DCM structure
% - nsplits: the number of splits in the partition
% OUT:
% - sF: log-evidences for each split in the partition
% - sD... |
e52b50699b2985805ee98d5156556e7dab856b3c3b01a90c730c67681c0640fe | MATLAB | 1,617 | 47 | % Writes/reads a large set of images into/from multiple directories.
%
% This is useful since certain OS handle very large directories (of say
% >20K images) rather poorly (I'm talking to you Bill). Thus, can take
% 100K images, and write into 5 separate directories, then read them back
% in.
%
% USAGE
% I = imwrite2... |
3f1a21197bb5aac598d381776c275881326b3d7c11e0d40c42b6e2ab0ccd8503 | MATLAB | 1,619 | 39 | datRoot = '/Volumes/LaCie/Farries recordings/';
rat = 'IM1153';
% list sessions
sesList = dir([datRoot rat '/*ic']);
sesList = arrayfun(@(x) x.name, sesList, 'UniformOutput', false);
for i = 1:length(sesList)
fprintf('%d) %s\n', i, sesList{i})
end
iSes = 1;
preprocessRange = [0 600];
timeRange = [0 Inf];
for iSes... |
11f65d788c477ba67395ceb052796727e144723735375cb55ac0b6159dfcc67c | MATLAB | 1,620 | 56 |
%% Binary Logistic Regression on SAT data
% Example from Johnson and Albert p87
%% Load data
% This file is from pmtk3.googlecode.com
stat = loadData('sat'); y = stat(:,1); X = stat(:,4);
%% Fit model
pp = preprocessorCreate('addOnes', true, 'standardizeX', false);
model = logregFit(X, y, 'preproc', pp);
%[X, ndx]... |
409628f550cdcf847415268157173ad42c2b26574e15781526f0002af4c98f9d | MATLAB | 1,620 | 53 | function Igrid=make_grid_image(Spacing,sizeI)
% This function creates a uniform 2d or 3D image of grid
% Igrid=make_grid_image(Spacing,sizeI)
%
% inputs,
% Spacing: Spacing of the b-spline grid knot vector
% sizeI: vector with the sizes of the image which will be transformed
%
% outputs,
% Igrid: Image of uni... |
8a02219f675163f55011ce5515b6273bf8011bd9d90b1780599d5085fafa81fe | MATLAB | 1,620 | 60 | function xc = CrossCorr(spkDat, refUnit, testUnit, varargin)
nSamp = 10000;
binSpec = [];
binEdges = -100.5:100.5;
noLaser = true;
for i = 1:2:length(varargin)
switch varargin{i}
case 'nSamp'
nSampTarget = varargin{i+1};
case 'binEdges'
binEdges = varargin{i+1};
cas... |
9aa2be9418a06a3e99a977b59626c0e9f2332b10755ba91d6a0f64f5018a340e | MATLAB | 1,620 | 51 | function [rho,pval]=MySpearman(x,y)
% This function is to be a dedicated super-fast Spearman correlation
% calculation for the input data X(col vector) and Y(matrix). Inputs are
% assumed to have the same number of rows and have no NaNs or Infs. Y is
% also assumed to have no duplicates within a column.
% Steps:
%... |
9b6c9c0601bb6bee96f34935c83d35407a45e7af1c702baba67132150ae38212 | MATLAB | 1,620 | 56 | function ns = count_squares_fisheye_distorted(I,x1,y1,x2,y2,win, fg, cg, kg);
[ny,nx] = size(I);
if ((x1-win <= 0) || (x1+win >= nx) || (y1-win <= 0) || (y1+win >= ny) || ...
(x2-win <= 0) || (x2+win >= nx) || (y2-win <= 0) || (y2+win >= ny))
ns = -1;
return;
end;
if ((x1 - x2)^2+(y1-y2)^2) < win,
... |
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