sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
f9e075004df031693d72f55fd4e1def94d0bb99acecada021b8643e2672226f2 | MATLAB | 276 | 9 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function tau_n = tau_n(v, VTraub)
tau_n = 1/(an(v, VTraub) + bn(v, VTraub));
end |
d61f7e62813fb453bae8037adb2d9c6ac5332aa468fc7d7549445e4a858ff2ec | MATLAB | 277 | 8 | function [vcondition]=load_condition_InfoCapON_tbi_mask2SD_weight(trial,cond)
for i=1:trial
load(sprintf('Wtrials_%03d_%d_err_hete_ON_tbi_mask2SD_weight.mat',i,cond));
vcondition.infocap_all(i,:)=infocapacity;
vcondition.suscep_all(i,:)=susceptibility;
end |
ee08ee8395edf2733d1c6b217904021deaeda35959ce67a416935225ada6adde | MATLAB | 277 | 9 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function h_exp = h_exp(v,Dt, VTraub)
h_exp = 1 - exp_t(-Dt/tau_h(v, VTraub));
end |
f1d1e3e71f50c6a2d932c12847b9cf0658b7e21f2462790bb8a3521e16091931 | MATLAB | 277 | 10 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function Minf = Minf(Vpre)
Minf = 0.1 + 0.9 / (1 + exp(-1000*(Vpre+0.04)));
end
|
48d7df3cfc93011c6764b4b63aea64b070116c044afb5cc1e77cc7012710dea7 | MATLAB | 278 | 9 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function m_exp = m_exp(v, Dt, VTraub)
m_exp = 1 - exp_t(-Dt/tau_m(v, VTraub));
end |
69e83843e02631b0de3d4f8f3a34a1ca035a1bcd197b848540229a3d084d6834 | MATLAB | 278 | 9 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function tau_m= tau_m(v, VTraub)
tau_m = 1/(am(v, VTraub) + betam(v, VTraub));
end |
a575b8fb2af81163396b76cbcc6dfe04ee6a4aca35c82bdaddc8f8560ed0335b | MATLAB | 278 | 14 | function p = t_cdf(t,df)
% A little slower and a little less precise (1e-12) than the statistics
% toolbox version.
% 2014/08/01 Martin (inspired by polyparci.m by Star Strider)
x = df./(t.^2 + df);
z = df/2;
w = 0.5;
p = 0.5*betainc(x,z,w);
p(t>0) = 1-p(t>0); |
0ea59652e563a8bb44ce967bfb28936139dc9be6581fc982d41647e9e348de02 | MATLAB | 279 | 11 | function cmap = Rainbow(n)
stdev = n/4;
m1 = n/4;
m2 = n/2;
m3 = 3*n/4;
x = 1:n;
y1 = exp(-(x - m1).^2/2/stdev/stdev); y1 = y1/max(y1(:));
y2 = exp(-(x - m2).^2/2/stdev/stdev); y2 = y2/max(y2(:));
y3 = exp(-(x - m3).^2/2/stdev/stdev); y3 = y3/max(y3(:));
cmap = [y1', y2', y3']; |
e72cd15ec3605b6e7ef79fc7f1f350753240f07c7eb03600db78708997177f1e | MATLAB | 279 | 10 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function s_inf = s_inf(v)
vx = 7;
s_inf = 1/(1+exp(-(v+vx+57)/6.2));
end |
23a39e22ba8d1bba6cfa46110c8ec5d1ff71bc1bd3e63ab69cf7290e15d9b4ff | MATLAB | 280 | 17 | function P = verify_stron_con(W)
% Verify the directed graph with strong connected
n_ROI = size(W,2);
P = zeros(n_ROI,n_ROI);
for k=1:n_ROI
C1 = W^k;
P = P + C1;
end
P = (P~=0);
if P ~=1
sprintf('This is a not strongly-connected graph')
end
end
|
454775ea0df1e3282effa0026b558fe95d3c1df717ddab212b5d901f078eb959 | MATLAB | 280 | 9 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function tauFunc = tauFunc(a, b, c, d, v)
tauFunc = c + d./ (1 + exp(a.*(v+b)));
end |
75e65c10efa02ac94331749566547bd061cf18d6ae29ae0529848ab523562120 | MATLAB | 280 | 19 | function SO = merge(S1,S2,offset)
% Merges tsd's in tsdArray
%
if length(S1)~=length(S2)
error('must the same length')
end
SO = cell(length(S1),1);
for ii=1:length(S1)
keyboard
SO{ii} = ts([Range(S1.C{ii});Range(S2.C{ii})+offset]);
end
SO = tsdArray(SO);
|
09f65da30eed1e014fedf67018c42d179df953276bbbdae0c5685553c532cf4d | MATLAB | 281 | 11 | % loading stack
load('.\..\wholestack.mat');
% loading cleaned tracks
load('.\neurons_cleaned.mat');
% specifying output folder
outputpath='.\..\output\';
% running the identification step
identification_step(wholestack, neurons_cleaned, outputpath,[0.267 0.267 2]);
|
ae07ee4ad78aaaa51d5d0d34cd1b48d0585719f4ee2279e2240c96989ccb31fb | MATLAB | 282 | 14 | function spd = angSpeed(ang,win)
if length(ang)>1
ang = ang(:);
da = diff(ang);
da(da>pi) = da(da>pi)-2*pi;
da(da<-pi) = da(da<-pi)+2*pi;
da = abs(gaussFilt(da,win));
spd = gaussFilt(da,win);
spd = [spd(1);spd];
else
spd = zeros(length(ang),1);
end |
f5a41a27ec002113a55b3a12d9bb7fd8fd6c944415f6d615b4189db4077fe483 | MATLAB | 282 | 9 | function niiFile = select_nii_wbepi(cfg, subject, prefix)
%SELECT_NII_ANAT Get the filename of the wholebrain EPI image
% Detailed explanation goes here
niiFile = fullfile(cfg.sub(subject).wbepi_dir, strcat(prefix, ...
cfg.sub(subject).wbepi_fn,',1'));
end
|
2f7a0a000b6ea5f0b341411272dd4cf4156d46f7c647e57b7e8ee0ca8b599a17 | MATLAB | 283 | 12 | function l = length(tsa)
% Returns number of elements in tsa (overoads length function)
%
% USAGE:
% l = length(tsa)
%
% copyright (c) 2004 Francesco P. Battaglia
% This software is released under the GNU GPL
% www.gnu.org/copyleft/gpl.html
l = length(Range(tsa));
|
4576df5eed525b9362e9e8593067f57d4eace90f076b8e73389f25190fad80c1 | MATLAB | 283 | 16 | % INDENT Indent text
% This function is used to indent a text that will be printed to the
% standard output, depending on the verbose level.
%
% Copyright (c) 2011 Roberto Calandra
% $Revision: 0.11 $
function indent(verbose)
for i=1:verbose
fprintf(' ')
end
end |
70bf65560cd6857770be38acd45cb73b36dc4a3dd28bdc0e1f6cc105dafddb79 | MATLAB | 283 | 8 | function dominant_states = getDominantStateIdsGroup(temporal_evolution_of_states, max_nstates)
K = max_nstates;
sc =zeros(1,K);
for j=1:K
sc(j) = sum(cell2mat(temporal_evolution_of_states)==j);
end
counts_post = sc/sum(sc);
[~, dominant_states] = sort(counts_post,'descend');
|
4b8f1c6801fb0729954a1173a9859458754b946271a9c58a7bbd2402731b51db | MATLAB | 284 | 8 | function boris_data = rename_roman_events(boris_data)
boris_events = boris_data.events;
n_events = max(size(boris_data.events));
% Loop on each event category
for i_event = 1:n_events
boris_data.events(i_event).name = boris_tools.key2name(boris_data.events(i_event).name);
end
end |
8c68bad0973d1283773859f524322f72144a5ef4d3ff0107cb5e51b7aa7d5045 | MATLAB | 284 | 14 | function sz = size(TSA)
% Return the size of the data component (overoad of the size function)
%
% USAGE:
% sz = size(tsa)
% copyright (c) 2004 Francesco P. Battaglia
% This software is released under the GNU GPL
% www.gnu.org/copyleft/gpl.html
sz = size(TSA.data);
|
1e117bb4ff3780b60babd7256fdf0899a888ebe9346fc7a8b647a8114d10147e | MATLAB | 285 | 14 | function s = minus(a, b)
% Returns the set difference of two intervalSet (overload of the - operator)
%
% USAGE:
% s = and(a, b)
% copyright (c) 2004 Francesco P. Battaglia
% This software is released under the GNU GPL
% www.gnu.org/copyleft/gpl.html
s = diff(a, b);
|
d9d5e3af4d44450ed73a08db44c6757273990c7d320f07db240d6f6745cb74c1 | MATLAB | 285 | 15 | function clear(O)
% remove all items from dictArray O
% copyright (c) 2004 Francesco P. Battaglia
% This software is released under the GNU GPL
% www.gnu.org/copyleft/gpl.html
if O.keysReadOnly
error('Keys are not modifiable');
end
O.keys = {};
O.values = {};
|
034147eaf120166390a3be17a8eed6caad6cfa6ca619274fbc22878812b965e2 | MATLAB | 286 | 10 | %
%produces a gaussian kernel, for use by conv(....)
%sig: sigma (in terms of # datapoints)
%n: nr datapoints (symmetric)
%
%urut/march05
function gaussKernel = getGaussianKernel(sig, n)
k=-n:n;
gaussKernel=1/sqrt(2*pi*sig^2)*exp(-k.^2/sig^2);
gaussKernel=gaussKernel/sum(gaussKernel);
|
25b46e61c503e8077ebf8396ef05f280c1f0e79f46f7f557276bdfa60e1176d6 | MATLAB | 286 | 8 | function y = nancellfun(cell_arr, field_name)
% extracts the specified field (must be a scalar) from the cell array
% cell_arr
y = nan(size(cell_arr));
isnotempty = cellfun(@(x) ~isempty(x), cell_arr);
y(isnotempty) = cellfun(@(x) x.(field_name), cell_arr(isnotempty));
end |
67e7bb082bf21a49436d02d497f16bbebfc2d63bacb3e6cd9efb9d301a16fdcb | MATLAB | 286 | 9 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function h_inf = h_inf(v, VTraub)
h_inf = ah(v, VTraub)/(ah(v, VTraub)+bh(v, VTraub));
end |
a7f4af04d4fbaf7c672357a64fb834d679a2363f553b5c12397fff561c470312 | MATLAB | 286 | 8 | function [vcondition]=load_condition_InfoCapON_tbi_mask1halfSD_bin(trial,cond)
for i=1:trial
load(sprintf(['Wtrials_%03d_%d_err_hete_ON_tbi_mask1halfSD_bin_corr.mat'],i,cond));
vcondition.infocap_all(i,:)=infocapacity;
vcondition.suscep_all(i,:)=susceptibility;
end |
22e6b50f2b77512fc7c7b7eb62cbb22acde95d73abb861b06b34d747220041c5 | MATLAB | 287 | 11 | function cmap = usa(N)
% American flag colors colormap
if nargin == 0
N = 100;
end
cmap = zeros(2*N,3);
cmap(:,1) = [linspace(0,1-1/N,N) linspace(1,0.8784,N)];
cmap(:,2) = [linspace(0.0032,1-1/N,N) linspace(1,0.0863,N)];
cmap(:,3) = [linspace(0.6471,1-1/N,N) linspace(1,0.1686,N)]; |
27a51721f09c6f5346e8cf7160352307791bbb8419d7ce90c040a785980634fe | MATLAB | 287 | 11 | function d = KullbackLeibler(c2,c1)
% D(P||Q) = KL(Cp,Cq) is the Kullback-Leibler divergence between distribution of covariance Cp and Cq
% mean are assumed to be 0
% Adrien Peyrache 2007
N = size(c1,1);
warning off
d = 0.5*(log(det(c2)/det(c1))+trace(inv(c2)*c1) - N);
warning on |
8f792c0525a5c5f537bcd05b68af3aa3dbae2a36f342339df346ba192864acdf | MATLAB | 287 | 8 | function [vcondition]=load_condition_InfoCapON_tbi_mask1halfSD_weight(trial,cond)
for i=1:trial
load(sprintf(['Wtrials_%03d_%d_err_hete_ON_tbi_mask1halfSD_weight.mat'],i,cond));
vcondition.infocap_all(i,:)=infocapacity;
vcondition.suscep_all(i,:)=susceptibility;
end |
a367688ab0206714bebfa151de0e423ac04ea2b576031da1214ee43c248edb95 | MATLAB | 287 | 14 | function s = and(a, b)
% Intersection between two intervalSet objects (overload of the & operator)
%
% USAGE:
% s = and(a, b)
% copyright (c) 2004 Francesco P. Battaglia
% This software is released under the GNU GPL
% www.gnu.org/copyleft/gpl.html
s = intersect(a, b);
|
bb2ce409350ac760fa0bfa0351ab3686d46a83439ad0252b6d72e6c52720843b | MATLAB | 288 | 9 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function m_inf= m_inf(v, VTraub)
m_inf = am(v, VTraub)/(am(v, VTraub)+betam(v, VTraub));
end |
fe0924a2899670ca20cdbcaa4646252341eb4b2e049143fe3e21d3cb90a75ecf | MATLAB | 288 | 9 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function fhinf = fhinf(v)
fhinf = 1/ (1 + 2*exp(180*(v+0.047)) + exp(500*(v+0.047)));
end |
b95ad4a1236bdff6825205928ae5baa5db9ff3d83d5a68938db9065969600d7b | MATLAB | 289 | 13 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function v = exp_t(x)
if (x < (-100))
v = 0;
else
v = exp(x);
end
end |
dd0794ddf24a1789b8f86e1e3a400f3d252579041c8455a07da3786ae0d80529 | MATLAB | 289 | 16 | function [dist] = euclidn(x,y)
% [DIST] = EUCLIDN(X,Y)
%
% Calculate the Euclidean distance between two n-dimensional
% vectors X and Y.
if ~compare_size(x,y) ~isvector(x)
error('X and Y must be vectors of the same length');
end
dist = sqrt( ...
sum( power(x-y, 2) ) ...
);
|
12fefd0f5d4a00cdbefd5d145db193860dbe691c5a5a395b3d01b8d287f942a4 | MATLAB | 291 | 9 | function varargout = read_voxels(software,varargin)
% read_voxels varargin:
% inputs: header, coordinates (n x 3 (XYZ))
% output: 1 x n vector of voxel values
check_software(software);
fname = [mfilename '_' lower(software)];
varargout{1} = feval(fname,varargin{:}); |
d7c10a94f6d0389a14579b743d6cf31c9ba3c530be3ca60401be42d336fa8204 | MATLAB | 292 | 13 | % Should cut down from 756 to 378
% mlList/mlActAuto isn't affected
function [hrf_GLM,mlList] = rmvSingleCond(hrf_GLM,numConds,mlList)
condList = [1 3 5];
[reshapeIdxToRmv] = reshapeIdxByCond(condList,numConds,mlList);
hrf_GLM(:,reshapeIdxToRmv) = [];
mlList(:,reshapeIdxToRmv) = [];
end |
3253c266494a6916d318d90de1b687bc29c655dfb817df76ece05da224c3faf4 | MATLAB | 293 | 13 | % function U = unity(A)
% A is n x m matrix, and each column is a signal.
% the function gives a unity signal for each column
% i.e. [signal-mean(signal)] / std(signal)
function U = unity(A)
meanA = mean(A);
stdA = std(A);
U = (A - repmat(meanA,size(A,1),1))./repmat(stdA,size(A,1),1);
|
a459e7e912407909027f7797e2b903b06894dae93a0e96654a824c6515873195 | MATLAB | 294 | 15 | pi=3.1415926535897932384626433832795028841971;
x=3;y=4;
t=1; % time
s=1*t; % speed * time
a=0; % angle of movement
a=a/360 * pi*2; % convert to radians
x=cos(a)*s+x;
y=sin(a)*s+y;
%fprintf("%f %f\n",x,y);
deltaX = 0 - 0;
deltaY = 0 + 4;
angle = atan2d(deltaY,deltaX);
fprintf("%f\n",angle); |
ef70b15784208bc679a7cdb0fdc6741cbe4fbc265082a09f64c50752bd8bb534 | MATLAB | 295 | 18 | function z = mdivide(x,y)
if iscell(x) && iscell(y)
z = cell(size(x));
for k = 1:numel(x)
z{k} = x{k}\y{k};
end
elseif iscell(x)
z = cell(size(x));
for k = 1:numel(x)
z{k} = x{k}\y;
end
elseif iscell(y)
z = cell(size(y));
for k = 1:numel(y)
z{k} = x\y{k};
end
end
|
82c91cbc8a9a76a2310b4f55d3d1224fed1c91bef91cc9e68f26b03a2c4bf1f8 | MATLAB | 296 | 9 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function taumCaFFunc = taumCaFFunc(v)
taumCaFFunc = 0.011 + (0.024/cosh(-330*(v + 0.0467)));
end |
a8d1f7e216e02314b245516e622ac1a2cc34ff5b2c5cc1f06f1c831b4220ad63 | MATLAB | 296 | 11 | function [f_max] = fmax(x,dt)
%FMAX x, dt
% Returns the estimation of maximal frequency [kHz] in the given signal x
% x signal
% dt time step size [ms]
[P_smoothed,f] = periodogram(detrend(x),[],2^18,1/dt);
[~,P_max_ind] = max(P_smoothed);
f_max = f(P_max_ind);
end
|
600e81e7cc846817b9c08c283a36ab01130570477f103b1705ec76a39721f428 | MATLAB | 297 | 15 | function s = cell2str_new(c)
s = c;
if iscell(c)
maxlen = 0;
for ii = 1:length(c)
if length(c{ii}) > maxlen
maxlen = length(c{ii});
end
end
s = char(zeros(length(c), maxlen));
for ii = 1:length(c)
s(ii,1:length(c{ii})) = c{ii};
end
end
|
744435ab3e283ee6f2debd4e94ea593b1c441013c945225ea075a5e480e16776 | MATLAB | 297 | 9 | resultPath='/home/dichang/code/related_work/u-mvsnet/dtu_eval/TotalStat_mvsnet.mat';
x=load(resultPath);
data=x.BaseStat;
varacc = mean(data.VarData)
varcomp = mean(data.VarStl)
varoverall = (varacc+varcomp)/2
medacc = mean(data.MedData)
medcomp = mean(data.MedStl)
medoverall = (medacc+medcomp)/2 |
8a60833801ad8480f010e8cd95fca2f2e87a8529295f1b5b0f8889e9a4605f54 | MATLAB | 297 | 9 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function simplified_sV2= simplified_sV2(v)
simplified_sV2 = (2^-1)*tanh(((2^-4)+(2^-6))*v+5)+0.5;
end |
d468c819e6ff18715ae6c35890966f2b36f962614ac72fab11bf7026f8f2354a | MATLAB | 297 | 10 | function im = awsimread (impath)
% This function is aws equivalent of imread. reads an image using datastore
% locally or from the cloud.
if awsIsAWSPath(impath)
awsSetCredentials(); % Read credentials are required
end
ds = fileDatastore(impath,'ReadFcn',@imread);
im = ds.read();
|
fd7a001ea4f55c48b27d14449ee318b9c3a102b28933de0d0fd163815bca44e7 | MATLAB | 297 | 10 | function C=correlation_matrix(n)
nen=numel(n);
for i=1:nen
for j=i+1:nen
valid_times=intersect(n(i).coords(:,4),n(j).coords(:,4));
crrltn=corrcoef(n(i).coords(valid_times,5),n(j).coords(valid_times,5));
C(i,j)=crrltn(2);
C(j,i)=crrltn(2);
end
end
|
581bf2f4062714817f8d59866bd0a24d3d42b2feeffebbed4fced94cd1ba9281 | MATLAB | 298 | 13 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function y = exptable(x)
if ((x> -25) && (x<25))
y = exp(x);
else
y = 0;
end
end |
9eaa78eeed55b7ba5e02ee1260e52e72c78cf5214bb76c18f07d037adfae666e | MATLAB | 298 | 11 | % loading stack
load('.\..\wholestack.mat');
% loading tracking results
load('.\..\output\neurons_reconstructed_max_2.mat');
% specifying output folder
outputpath='.\..\output\';
% running manual correction GUI
remove_and_add_neurons(wholestack,neurons_reconstructed_max_2,outputpath); |
1c82a74e46337ba2cf8ae243a6398088b14e23033196dd4e32720bf5b16877f3 | MATLAB | 301 | 3 | function [vxOut, vyOut] = RotateQuiver(vx, vy, thetaInDegrees, thetaInRadians)
vxOut = imrotate(vx, thetaInDegrees)*cos(thetaInRadians) - imrotate(-vy, thetaInDegrees)*sin(thetaInRadians);
vyOut = -(imrotate(vx, thetaInDegrees)*sin(thetaInRadians) + imrotate(-vy, thetaInDegrees)*cos(thetaInRadians)); |
792aa7d6b4bcf00a753886c220fce4c0fdcd705bba720b1a83aeb28acc6eefe3 | MATLAB | 301 | 12 | % H = coarse_grain(data, times, bincenters)
% builds an histogram of the data with centers specified by bincenters
%
% INPUTS:
% data: a vector with the data
% times: a vector with the times relative to the data
% bincenters: a vector of the bincenters
%
% OUTPUTS:
% H: the histogram
% batta 2000
|
eeb7b40ac9483aad0256dcbbded52b9505500058a412abe3e3546f2e0e9a67ee | MATLAB | 301 | 20 | function binOut=de2bi2(inp)
% de2bi only for 2 bin values
binOut=[];
for i=1:size(inp,1)
ix=inp(i);
if ix==0
bx=[0 0];
elseif ix==1
bx=[0 1];
elseif ix==2
bx=[1 0];
elseif ix==3
bx=[1 1];
end
binOut=[binOut;bx];
end
end |
268c710853d1a10742feea71eee141524015f97d9085aabdaa52fa29576ba03e | MATLAB | 302 | 13 | function [x] = demedian(x, dim)
% Helper function to subtract the median across dimension dim.
if nargin<2,
dim = find(size(x)>1, 1, 'first');
end
pvec = [dim setdiff(1:ndims(x), dim)];
x = permute(x, pvec);
siz = size(x);
mx = nanmedian(x, 1);
x = x - mx(ones(siz(1),1),:,:,:,:,:);
|
311449b9eacec51a216d02de62a387d9bfde4d955a88dfee37ea170eecaf0f23 | MATLAB | 302 | 12 | function combine_all_clusters_211026(folder_address)
cd(folder_address)
times_dir=dir('times_*.mat');
times_name=string({times_dir.name});
for j=1:length(times_name)
file=load(times_name(j));
file.cluster_class(file.cluster_class(:,1)~=0,1)=1;
save(times_name(j),'-struct','file')
end
end |
66e7ddf0fa80288cb213bc9b165b20cc3726851caa372fdefb81a84ab68248e7 | MATLAB | 302 | 10 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function pinf = p_inf(v)
pinf = 1 / (1 + exptable(-(v+35)/10));
%pinf = 1 / (1 + exp(-(v+35)/10));
end |
c18b32ecb807dc0f4e14eacfa54c370a8315dce1bf93dd0cde8f997d9d77db03 | MATLAB | 302 | 10 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function Mfunc = Mfunc(Vpre, Mpre, Dt)
dMval = dM(Vpre, Mpre);
Mfunc = Euler(dMval, Mpre, Dt);
end |
701db826552175915eafdd50d8ba4e27951297ca3a57d529b551666057eafa5f | MATLAB | 303 | 14 | function update_bar_colors(b, colors)
% updates the colors of a bar plot
% b : handle of the bar plot
% colors : colors to use, has to match data size
for ii = 1:length(b)
b(ii).FaceColor = colors(ii, :);
% b(ii).EdgeColor = "none";
% also change the width
b(ii).BarWidth = 1.0;
end
|
728c4edf976b8bc1cb02fa0cd4f941641ff7f74cd1cf7c42c3fdc1b19580ad3c | MATLAB | 303 | 10 | function SpiderPlotTracks(tracks)
idxs = unique(tracks(:, 4));
for idx = 1:length(idxs)
currIdx = idxs(idx);
currTrack = tracks(tracks(:, 4) == currIdx, 1:3);
plot(currTrack(:, 1) - currTrack(1, 1), currTrack(:, 2) - currTrack(1, 2)); hold on;
end
axis equal;
axis([-50 50 -50 50]);
drawnow; |
96a009ad78e5d4088787b2f7af0298ce981577964fcb21cdedb2895f776ac7a2 | MATLAB | 303 | 16 | function randomVec = randshuffle(origVec)
%Randomly permute the n*m vector along the row.
%Keeping the mean of each row unchanged
%
[n,m] = size(origVec);
newVec = zeros(n,m);
for i=1:n
temp = origVec(i,:);
idx = randperm(m);
newVec(i,:) = temp(idx);
end
randomVec = newVec;
end
|
1508fc20ad4510a9817fd8bf12ede726f3fd448c94e7e1086cb36fbbe50d0b2d | MATLAB | 304 | 12 | % function design = make_design_similarity(cfg)
%
% Function to create a design for a similarity analysis. The function calls
% make_design_rsa (representational similarity analysis).
%
% See also MAKE_DESIGN_RSA
function design = make_design_similarity(cfg)
design = make_design_rsa(cfg); |
38e494ad8154cb823e45a677b94044a021a8826540371c3baf68b2eb40f49fd0 | MATLAB | 305 | 8 | function grad = solveWithLinSolveBackProp(xRaw, gradIn, rowI, colI, rowII, colII, v, Ival)
N = size(xRaw,1);
deltaX = deltaActivation(xRaw);
DL = deltaX .* gradIn;
Amod = sparse(rowII, colII, [-v .* deltaX(rowI); Ival], N, N);
%grad = linsolve(full(Amod), DL);
grad = Amod\DL;
end
|
5682eddb8b85440ea5bed0a5280d6c6436ab14b5cee1fd2add3f1a7fda450155 | MATLAB | 306 | 10 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function Mfunc = MfuncV2(Vpre, Mpre, Dt)
dMval = dMV2(Vpre, Mpre);
Mfunc = Euler(dMval, Mpre, Dt);
end |
0d6f8cc25ff7040a5bff498d1ff1af5e0bd45bcbc0444874074c1829d45a4b95 | MATLAB | 307 | 9 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function hchan = hchan(v, hpre, Dt, VTraub)
hchan = hpre + h_exp(v,Dt, VTraub) * (h_inf(v, VTraub) - hpre);
end |
26082ec501eb3eb666089844622c172f6ea1566ffeaaf11239c95d465c945802 | MATLAB | 307 | 20 | function cout = lighten(cin, factor)
% function cout = lighten(cin, factor)
% Default 50%
% Hannah Payne
if ~exist('factor','var')
factor = .5;
end
if ischar(cin)
if strcmp(cin, 'none')
cout = 'none';
return
else
cin = colorspec(cin);
end
end
cout = (1-cin)*factor+cin;
|
26477154b08695e36b88309d193b5522b10430b6db2ae088f8eafa9c13a3a7bd | MATLAB | 307 | 16 | function p_silence = computeSilenceProb(eta_val, eps_val, J_i, h_vals)
%%
prob_s_fun = @(h, J1) 1./(1 + exp(eta_val*J1*h - eps_val));
% vectorize
J_i = J_i(:);
h_vals = h_vals(:)';
p_active = prob_s_fun(h_vals, J_i);
p_silence = exp(sum(log(1 - p_active), 1));
end |
87745d394c3e0de424b07b17548b36e13ea4379238a3f35cc538775e6b9ba17e | MATLAB | 307 | 13 | function y_out = interp1dec(x,y,x_out)
% Alternative version of interp1q that might be slower, but does not
% require the signal processing toolbox
y_out = zeros(size(x_out));
y_out(x) = y;
vals = diff(x);
for i = 1:length(x)-1
y_out(x(i):(x(i+1))) = linspace(y(i),y(i+1),vals(i)+1);
end |
ac191e9f5a878817c33f57b66815342769ac183f72ce30f0f2b7becb00430e91 | MATLAB | 307 | 10 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function uchan = uchan(v, upre, Dt)
du = (u_inf(v)- upre)/tau_u(v);
uchan = Euler(du, upre, Dt);
end |
dc1d5760d735c8ecb9b70fa5035f32036f24c904e150fb19cca5737355288a91 | MATLAB | 308 | 19 | function ans = isrow (v);
% ans = isrow (v);
%returns 1 if v is a row vector
%returns 0 if v is a column vector
%returns -1 if v is a matrix
%
% Ziad Saad July 20 97
if (size (v,1) ~= 1 & size (v,2) ~= 1),
ans = -1;
end;
if (size (v,1) == 1),
ans = 1;
elseif (size (v,2) == 1),
ans = 0;
end;
return;
|
e7d520c97b3fa934042442765837782e6b3f99cf72458fcb85fb79c91902b284 | MATLAB | 309 | 7 | function [dataPath,resultsPath]=getPaths()
% make sure the paths point to the data and where you want the results to be stored
dataPath='/ssddata/zmiaa/mvs-net/binary-mvs-exp/testing_20210221_2/collected_points';
resultsPath='/ssddata/zmiaa/mvs-net/binary-mvs-exp/testing_20210221_2/collected_points_eval'; |
61981dda828ca1b7b3cd3cfeb78cb5db5b0f5bb5f27e07f51ae4ef50cced3afc | MATLAB | 310 | 9 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function tauhNa = tauhNa(v)
tauhNa = 0.004 + (0.006/( 1 + exp(500*(v+0.028)))) + (0.01/(cosh(300*(v+0.027))));
end |
7961e89bfdc84610ba367f7cbb53d9728bad4ff75e99929d93a59c6364ef1511 | MATLAB | 311 | 9 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function pchan = pchan(Vprev, ppre, Dt, TauMax)
pchan = ppre + ((p_inf(Vprev)-ppre) / tau_p(Vprev, TauMax))*Dt;
end |
deecc720062b3b16cd6b08468f5eef1300e2867201dfa9e32c487f4995cdaf6b | MATLAB | 311 | 10 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function rchan = rchan(v, rpre, Dt)
dr = ar(v)*(1-rpre) - (br(v)*rpre);
rchan = Euler(dr, rpre, Dt);
end |
fb5de2a0524d314417243493debaf5dbc493f76f84fe6e4a09151a8b16847eb9 | MATLAB | 311 | 10 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function qchan = qchan(v, qpre, Dt)
dq = aq(v)*(1-qpre) - (bq(v)*qpre);
qchan = Euler(dq, qpre, Dt);
end |
0b0c2ff01102fac206610158bf9e1418114e8994738acb2ae67fb1d0c48da983 | MATLAB | 312 | 13 | function y = generateOUProcess(tau, dt, T)
% y = generateOUProcess(tau, dt, T) simulates a mean-zero OU process with
% variance 1 and autocorrelation timescale tau. Simulation resolution is
% dt.
N = ceil(T/dt);
y = zeros(N, 1);
for i_t = 2:N
y(i_t) = (1 - dt/tau)*y(i_t-1) + sqrt(2*dt/tau)*randn;
end
end |
a54af7047882a5c4f4f3fbd51c1b75db4fe9b3896957bdfbf54d4e97aacf3974 | MATLAB | 312 | 17 | function [dateNum, dateStr] = datestr2datenum(dateStr)
dateNum = 0;
c = str2cell(dateStr, ' ');
if length(c)<2
return
end
dateStr = c{1};
timeStr = c{2};
dateStr(dateStr=='-')='';
timeStr(timeStr==':')='';
timeNum = str2num(timeStr);
dateNum = str2num(dateStr) + timeNum/1e6;
dateStr = [c{1}, ' ', c{2}]; |
aa306a0a79ba4479987c6d78e872bcf4b5751024bac887812c267010343c9074 | MATLAB | 313 | 11 | function opt_vect = foptions()
% FOPTIONS Sets default parameters for optimisation routines
% For compatibility with MATLAB's foptions()
%
% Copyright (c) Dharmesh Maniyar, Ian T. Nabney (2004)
opt_vect = zeros(1, 18);
opt_vect(2:3) = 1e-4;
opt_vect(4) = 1e-6;
opt_vect(16) = 1e-8;
opt_vect(17) = 0.1; |
598d00ee132e1a7709e7cb4b1bffd0d99abe2b09041be48c296bd3dafecb5661 | MATLAB | 314 | 17 | function ac = TCcrosscorr(h1,h2)
if rem(h1,2) == 1 % if odd number of bins then make even
h1 = h1(1:end-1);
end
if rem(h2,2) == 1
h2 = h2(1:end-1);
end
nBins = length(h1);
ac = nan(nBins,1);
for nR = 0:nBins-1
hs = circshift(h2,nR);
ac(nR+1) = corr(h1,hs,'Type','Pearson');
end |
2e6f2b7d16aebd163defc0c375f711a9a8785b98af741c7a78c5cdf654cb7353 | MATLAB | 315 | 16 | function s = getSlice(ind_k, mask)
[N_row, N_col, N_slice] = size(mask);
slice_dim = zeros(N_slice, 1);
for k = 1: N_slice
slice_dim(k) = length(find(mask(:,:,1:k)));
end
s = zeros(size(ind_k));
for k = 1: length(ind_k)
s(k) = find(slice_dim>=ind_k(k), 1, 'first');
end
s = reshape(s, length(s), 1);
end |
08bd53443e20a23e32a09ea372f81c80e214ca72df6fe64f0a45d7b8970f37fe | MATLAB | 316 | 15 |
s = size(Lm,2);
if s>1
temp_mcl = cat(3,Lm{:});
temp_mcl = squeeze(temp_mcl(:,1,:));
temp_Lcov = cat(4,Lcov{:,:,:});
mean_mcl = mean(temp_mcl,2);
nu_mcl = s./( sum(squeeze(temp_Lcov(1,1,:,:)),2) ...
+ sum(temp_mcl.^2,2) ...
- 2*mean_mcl.*sum(temp_mcl,2) ...
+ s*mean_mcl.^2 );
end
|
1d4508aafd52cf749290699530de95be5089c5036d703000e49cc5fe29ff3204 | MATLAB | 316 | 12 | % function design = make_design_similarity_cv(cfg)
%
% Function to create a design for a similarity analysis. The function calls
% make_design_rsa (representational similarity analysis).
%
% See also MAKE_DESIGN_RSA_CV
function design = make_design_similarity_cv(cfg)
design = make_design_rsa_cv(cfg); |
38fd425f8a56ac87ca51d25e3261a702f51da54d050c2b552cf61d4aa6817244 | MATLAB | 316 | 16 | % [Clu, nClusters] = LoadClu(FileName)
%
% A simple matlab function to load a .clu file
function [Clu, nClusters] = LoadClu(FileName);
Fp = fopen(FileName, 'r');
if Fp==-1
error(['Could not open file ' FileName]);
end
nClusters = fscanf(Fp, '%d', 1);
Clu = fscanf(Fp, '%d');
nClusters = max(Clu);
fclose(Fp); |
4767047fda98ecc64cf80be0155d9fc90ad8b9e4145f92cb772c512675895fcb | MATLAB | 316 | 15 | function showStdErr = GetShowStdErrEnabled(handles)
showStdErr = [];
if nargin==0
return
end
if isempty(handles)
return
end
if strcmp(get(handles.menuItemViewHRFStdErr, 'checked'), 'on')
showStdErr = true;
elseif strcmp(get(handles.menuItemViewHRFStdErr, 'checked'), 'off')
showStdErr = false;
end
|
85a522d95a8e18b128930fac78b93c52d9489db7d11edd603140d7c09efb048b | MATLAB | 316 | 11 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function Pfunc = Pfunc(ICa, Pprev, Dt)
B = 10;
dP = ICa - (B * Pprev);
Pfunc = Pprev + dP * Dt;
end |
8b5a7cc00bee5e29d97b2632c39ca886b955e5f44287ed53e54bb8da1c7587d7 | MATLAB | 316 | 10 | function estimateVolumeStatisticsAdvance(subjectPath)
% This function advances estimateDepthOfPenetration. Writen for LG.
% Written at July 18, 2020.
if ~exist('subjectPath','var')
subjectPath = s3SubjectPath('04','LG');
end
OCTVolumesFolder_ = [subjectPath '\OCTVolumes\'];
estimateDepthOfPenetration |
24c8e516d275ed956d2d9220233440e74ee3409c4303ab3ae04bf7ce42335a29 | MATLAB | 318 | 14 | M = gifti(fullfile(spm('dir'), 'canonical', 'cortex_8196.surf.gii'));
ir = find(M.vertices(:,1)<0);
M.vertices(ir,:) = [];
A = spm_mesh_adjacency(M.vertices)
ii=[];
for i= 1:size(M.faces,1)
if any(intersect(M.faces(i,:),ir))
ii = [ii i];
end
end
M.faces(ii,:) = [];
figure;spm_mesh_render('Disp', A); |
b44073bc015093436fe757a64f10a31344dc59b14e4a92e0bb61b4ffa314553c | MATLAB | 319 | 12 | function awsRmFile(myFile)
%This function will remove file if it exists, in AWS or Windows
if ~awsIsAWSPath(myFile)
if exist(myFile,'file')
delete(myFile);
end
else
awsSetCredentials(1);
myFile = awsModifyPathForCompetability(myFile,true);
awsCmd(['aws s3 rm "' myFile '"']);
end |
78bf147897250c12905d14fa291de249f3ac6cc089c73b74c3030f2dbaa5c7f4 | MATLAB | 320 | 15 |
function box_out = threeclass_probability_Matrix_Initialize(box_in)
disp('Box settings are:')
for i=1:size(box_in.settings,2)
fprintf('\t%s : %s\n',box_in.settings(i).name, num2str(box_in.settings(i).value));
end
box_in.user_data.is_headerset = false;
box_out = box_in;
end
|
14256ad5eae393350791e6082b641b6dcd4592ddb2c7d26b26882bdf37b0e9a8 | MATLAB | 321 | 11 | function ms = msSegmentCentroids(ms)
%MSSEGMENTCENTROIDS Summary of this function goes here
% Detailed explanation goes here
ms.segCentroid = [];
for segNum=1:ms.numSegments
bwProps = regionprops(ms.segments(:,:,segNum),'Centroid');
ms.segCentroid(segNum,:) = bwProps.Centroid;
end
end
... |
ffbb3b5fd94dd33bacc338504ed6bdbb5ee2c8bf4ac4016838a0464d482807ac | MATLAB | 321 | 23 | function O = time_units(inp)
if isa(inp, 'units') % copy constructor
O = inp;
return
end
if ~isa(inp, 'char')
error('argument must be string');
end
l = get_lookup;
if ~has_key(l, inp)
error('unrecognized unit');
end
l.tag = inp
l = class(l, 'time_units');
... |
1366638f7818ac38835c4b1a4573617aafbf2afc5c7bdf3db9ac481ce2f786d9 | MATLAB | 322 | 12 | function ang = meanAngle(inputangles)
inputangles = inputangles(:);
inputangles = inputangles(~isnan(inputangles));
x = mean(cos(inputangles*pi/180));
y = mean(sin(inputangles*pi/180));
% keyboard;
if abs(x) < 1e-6
ang = 90;
else
ang = atan2(y, x)*180/pi;
end
e... |
431efe74eb0d59f3057714a750b75bbe86a4de08113cbb5a7e7f1680362e0834 | MATLAB | 322 | 15 | function [y] = cellvecadd(x, v)
% [Y]= CELLVECADD(X, V) - add vector to all rows or columns of each matrix
% in cell-array X
nx = size(x);
if ~iscell(x) || length(nx)>2 || all(nx>1)
error('incorrect input for cellvecadd');
end
if ~iscell(v)
v = repmat({v}, nx);
end
y = cellfun(@plus, x, v, 'UniformOutput', 0)... |
917deae7902974dd8f07cc3d93dbd498b001e9d55afe22ca509f318c405a32fd | MATLAB | 322 | 10 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function Afunc = Afunc(Vpresyn, Aprev, Dt)
dA = ( A_inf(Vpresyn) - Aprev ) / 0.2;
Afunc = Euler(dA, Aprev, Dt);
en... |
92a12524c6396fb756a4d692c771ab178fa34e61a5f94130ff23aa679baf82e4 | MATLAB | 322 | 10 | function order = get_homology_MMP(raw_ROI_label,homology_ROI_label)
%% find homology index
order =zeros(max(unique(homology_ROI_label)),1);
for i = 1:length(raw_ROI_label)
for j=1:max(unique(homology_ROI_label))
ind = find(homology_ROI_label ==j);
order(j) = raw_ROI_label(ind);
end
end... |
68e1e8cfc9bc8f7771b4701579ce5d8de43b9c86f6f871a6878a712a86cc128d | MATLAB | 323 | 14 | function [p]=bernoull(x,eta);
% [p] = bernoull(x,eta)
%
% Computes Bernoulli distribution of x for "natural parameter" eta.
% The mean m of a Bernoulli distributions relates to eta as,
% m = exp(eta)/(1+exp(eta));
e = exp(eta);
p = ones(size(e));
indx = find(~isinf(e));
p(indx) = exp(eta(indx).*x - log(1+e(indx))... |
7d636a2943cad6f945f8a9481320308077ff40aab205c94f5b459f400ea3751f | MATLAB | 323 | 10 | % Called by minimization functions to update weights
function K_out = updateKp(K_in, B, I, S, p_new, mask_tune)
Kb_eye_pc_new = K_in.Kb_eye_pc;
Kb_eye_pc_new(mask_tune) = p_new;
Kb_eye = Kb_eye_pc_new(1:length(K_in.Kb_eye));
Kb_pc = Kb_eye_pc_new(length(K_in.Kb_eye)+1:end);
K_out = updateK(B, I, S, Kb_eye, Kb_pc);
e... |
1acc3c158814bc9af172bc4e39ad851dfe3182184b1508a23233df41831640ac | MATLAB | 324 | 16 | function [mu] = winmean(x, percent, dim)
%Compute the %percent Winsorized mean across dimension dim.
%Implementation according to Wilcox; Robust Estimation
%and Hypothesis Testing
if nargin<3,
dim = find(size(x)>1, 1, 'first');
end
if nargin<2,
percent = 0.2;
end
x = winsorize(x, percent, dim);
mu = mean(x, di... |
e4783b687f6a596107297563b57be4bd651c9652c1e6fd5de6738c082e511f07 | MATLAB | 324 | 21 | % lin_expand linear expansion without any filtering
%
% y = lin_expand( x, f )
%
% see also: fft_upsample, interp1, linint
% 03-jan-13 ES
function y = lin_expand( x, f )
f = round( f( 1 ) );
sx = size( x );
sy = sx;
sy( 1 ) = sy( 1 ) * f;
x = x( : );
y = repmat( x, [ 1 f ] )';
y = reshape( y, sy );
return
%... |
f369f4f67e8fc83dda8e55efbb3c52d80ecd2efce24b5c2a30219ee88a015fae | MATLAB | 324 | 13 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function v = vtrap(x,y)
if (abs(x/y) < (1*10^-6))
v = y*(1- x/(y*2));
else
v = x/(exp_t(x/y)-1);
end
... |
df223cc79efc32975b5f7f052229993ef2879cd18d6d2eb59aa8510ab5b80a42 | MATLAB | 326 | 12 | function stat = intergroupVar(smpl,match)
groups = unique(match,'rows');
for i=1:length(groups)
if size(match,2)==1
ssw(i) = mean(var(smpl(match==groups(i),:)));
else
ind = find(sum(match==groups(i,:),2) == size(match,2));
ssw(i) = mean(var(smpl(ind,:)));
end
stat = mean(ssw);
... |
611bdd720efe167186bdddd7114a49c371ef0adb8c89ae782fe417bb4708133e | MATLAB | 327 | 14 | % loading stack
load('wholestack.mat');
% loading tracking results
load('neurons_reconstructed_max_2.mat');
% specifying output folder
outputpath='.\output\';
% specifying voxel size
voxel_size=[0.267 0.267 2];
% running manual correction GUI
parameter_selection_and_tracking(wholestack,outputpath,voxel... |
8ab6892519205321b23ce6a3d3c25abb8167c579d1f537920b0e8a9c4410a944 | MATLAB | 327 | 13 | % @author Farad Khoyratee
% @date 16 Apr 2019
% @copyright
% SPDX-FileCopyrightText: © 2021 Farad Khoyratee <farad.khoyratee@gmail.com>
% SPDX-License-Identifier: GPL-3.0-or-later
function [aq] = aq(v)
if v == -27
aq = 0.2090;
else
aq = 0.055 * (-27-v) / ((exp((-27-v)/3.8))- 1);
e... |
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