sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
837e3b79a9ae278dd297adf76dd82a0ece2e003024fbea0d67c6c02dd436da27 | MATLAB | 256 | 15 | %% Function that makes Matrix: Cells x Frames
% Assumption:
% Frames>Cells
% Input
% R: matrix size: DxG
% Output
% R: maatrix size:
% if D>G
function Rout=makecellsxframes(Rin)
[D,G]=size(Rin);
if D>G
Rout=Rin'; % TRANPOSE
else
Rout=Rin;
end
|
9296b4c6a7665b2e9b61cbf1c8af1d5532a7ef5b0b70804bf8c259899d139dcb | MATLAB | 257 | 11 | %% Function of histidine decarboxylase in glia.
% Histamine synthesis in glia.
% UNITS in uM/h.
% b = gha.
function a = VHTDCg(b)
% b = cht
% c = G*
km = 270; %homosapiens BRENDA (other wild type =100, Komori2012)
v = 61.4250;
a = v.*(b./(km + b));
|
a949f94d1fd1e6af3d5f5cd3e300176df2afd4a2cffab49ade403f346756f5d3 | MATLAB | 257 | 9 | clc
clear
cd('I:\My Drive\ELYASHIV\PFC_paper_results_after_revision\effect size\f4\g')
T2 = importdata("eopn3_vs_scarlet.mat");
%%
stats = mes(T2.abs_IR(find(contains(T2.tag,'CONTROL')),:),T2.abs_IR(find(contains(T2.tag,'EOPN3')),:),'hedgesg')
|
e4056ece41e65e0e21a959be089fb41aafe1d045955e740220076dbe8e96fbab | MATLAB | 257 | 4 | function y = BPFilter6(x, fc, bw, factor)
% BPFilter6 has been deprecated. Use bp_filter_complex_equiripple instead.
warning('BPFilter6 has been deprecated. Use bp_filter_complex_equiripple instead.');
y = bp_filter_complex_equiripple(x, fc, bw, factor); |
4aa7f541980a435b50d0ae4d40dfc14c93c055b5eeb3204c462dc910f27b1fb3 | MATLAB | 258 | 15 | function A = Find_Angle360_2_Points(P1,P2)
dX = P2(1) - P1(1);
dY = P2(2) - P1(2);
A = abs(atand(dY/dX)); % [0,90].
if(dX >= 0 && dY < 0)
A = mod(-A,360);
elseif(dX < 0 && dY >= 0)
A = 180 - A;
elseif(dX < 0 && dY < 0)
A = A + 180;
end
end |
5d4e8f1462e75a025cca1bdeeab8286c69d8d57644655e370f1b9dd5591285de | MATLAB | 258 | 7 | %% Function of replenishment of histidine decarboxylase (HDC) during FMH inhibition.
% b represents ratio levels of HDC, while a represents rate of
% replenishment (h-1).
function a = HTDCin(b)
s = 0.55; %Strength of replenishment.
a = s*(1 - b);
end |
b4c8a8e92f32b816283629c2340bfd0137bd99e13f1e4b90503fbae29f9635bf | MATLAB | 258 | 4 | function [T0, T1] = CalculateTimeLags(peaks, phase)
% CalculateTimeLags has been deprecated. Use calculate_time_lags instead.
warning('CalculateTimeLags has been deprecated. Use calculate_time_lags instead.');
[T0, T1] = calculate_time_lags(peaks, phase); |
1406d99a6acdce023fbdb89d8b6d778fe2963cc970e05695bfb2a683671963eb | MATLAB | 259 | 4 | function [y, B, conv] = EASI(x, nsou, lambda, nlintype)
% EASI has been deprecated. Use easi_source_separation instead.
warning('EASI has been deprecated. Use easi_source_separation instead.');
[y, B, conv] = easi_source_separation(x, nsou, lambda, nlintype); |
4f91bb2fdb65fd6cf39dba33e4c82665940402a69e7979c02f6db274921aef1c | MATLAB | 259 | 4 | function [T0, T1] = SynchPhaseTimes2(peaks)
% SynchPhaseTimes2 has been deprecated. Use synchronous_phase_samples instead.
warning('SynchPhaseTimes2 has been deprecated. Use synchronous_phase_samples instead.');
[T0, T1] = synchronous_phase_samples(peaks); |
9dbda0d53106064a2994ae5d70662b7279f9a0e33cf207049c044a73c7cab79b | MATLAB | 259 | 17 | function fo=eddycorrection_fid(f,fw)
fo=f;
for nf=1:length(f)
fid = f(nf).fid;
fidw = mean(fw(nf).fid,2);
for k=1:size(fid,2)
fid(:,k) = fid(:,k) ./ exp(1*i*angle(fidw));
% fid(:,k) = fid(:,k) - angl
end
fo(nf).fid = fid;
end
|
265e067c7e8fba8d30614261cb6cae73e872381ca7a8f7079f8af4d28a976998 | MATLAB | 260 | 14 | function [res] = Ragu_ReadSPINV(infile)
fid = fopen(infile,'rb');
if fid == -1
res = [];
return;
end
f = fread(fid,'single');
fclose(fid);
res.nvox = f(1);
res.nchan = f(2);
res.spinv = reshape(f(3:(res.nvox*res.nchan*3+2)),[res.nchan,3,res.nvox]);
|
183f224bb60fdf7ade45b989edc778b6a50acb1dbb7c7efd5eac19770c36e228 | MATLAB | 262 | 4 | function [y, W, A] = SCA2(x, f0, bw, order)
% SCA2 has been deprecated. Use spectral_component_analysis_ma instead.
warning('SCA2 has been deprecated. Use spectral_component_analysis_ma instead.');
[y, W, A] = spectral_component_analysis_ma(x, f0, bw, order); |
5887a117b9d764b0779fa677bdd9c2a8a4bab9a0ee729926e6ab6271e0230423 | MATLAB | 262 | 4 | % HMM single file processing example by Yunxiang Zhang (yxzhang@gmail.com)
filename=['/Users/brunger/Dropbox/archive/programs/single_molecule/HMMbatchscript/examples/individual_traces/two_steps_photobleaching_event.txt'];
HMMint=Hmmtrace_atb(filename);
|
5a5612e986407ae4b6270c6e2e219a9a320a6f454a633c5a6181d0710c046328 | MATLAB | 262 | 16 | for i = 1:5
index(i) = sum(all_clusters_idx == i)
end
%%
all_idx = [];
for i = 1:3
all_idx = [all_idx; idx{i}]
end
%%
for i = 1:5
subplot(1, 5, i)
plot(mean(cell2mat(data(all_idx(all_clusters_idx == i), 5:end)), 1))
ylim([-2 6])
end
|
6fe37a1d620b925aa050d0f2c348bfa432d416eca67e6d7828f1356a27dc2d8a | MATLAB | 262 | 9 | function uInfo = exportUnitInfo(obj, iCluster)
%EXPORTUNITINFO Get all data pertinent to a cluster
uInfo = exportUnitInfo@jrclust.interfaces.Clustering(obj, iCluster);
if ~isempty(obj.unitSNR)
uInfo.SNR = obj.unitSNR(iCluster);
end
end
|
b02eea97636b6157be7e896834c04030f3a63040d65d09210e5240c1557ccc24 | MATLAB | 262 | 4 | function [y, W, A, B, C] = NSCA(x, I, J)
% NSCA has been deprecated. Use nonstationary_component_analysis instead.
warning('NSCA has been deprecated. Use nonstationary_component_analysis instead.');
[y, W, A, B, C] = nonstationary_component_analysis(x, I, J); |
ee71cb391c2d9ee7bc45c72950ba60685ecc7993d30a4ed0c722b954852ab633 | MATLAB | 262 | 8 | function [Matrix]=mnl_NormaliseVectors(Matrix)
% Input
% Matrix - row of points,columns represent dimensions
% Output
% Matrix - same as above but now normalised
matrix_Norm=sqrt(sum((Matrix.*Matrix),2));
Matrix=Matrix./repmat(matrix_Norm,1,size(Matrix,2));
end
|
4cc74c3167131e258a755fe0ca3b4259b57acb5932293b701d434180aa019adb | MATLAB | 263 | 4 | function y = BPFilterComplex(x, w0, N)
% BPFilterComplex has been deprecated. Use bp_filter_complex_ma_fixed_tap instead.
warning('BPFilterComplex has been deprecated. Use bp_filter_complex_ma_fixed_tap instead.');
y = bp_filter_complex_ma_fixed_tap(x, w0, N); |
e74844f6d90197a102ffd6b697b44632362d9293a417fc26befa8b426e22689f | MATLAB | 263 | 7 | % Remove unrelated directories from path
restoredefaultpath;
clear RESTOREDEFAULTPATH_EXECUTED;
% Add relevant directories to path (if they exist)
addpath(genpath(fullfile(pwd,'Toolbox')));
addpath(fullfile(pwd,'Structures'));
addpath(fullfile(pwd,'Data')); |
2bdc844594fd62108f4fec2486dcc17a5f71e8a1428f8fedb9e4dfeac4c30b56 | MATLAB | 264 | 4 | function [y, W, A] = PiCA(x, peaks1, varargin)
% PiCA has been deprecated. Use periodic_component_analysis instead.
warning('PiCA has been deprecated. Use periodic_component_analysis instead.');
[y, W, A] = periodic_component_analysis(x, peaks1, varargin{:});
|
5a61f5931143e22017e574af6af1b971a97374f2e1c216c440431482a338e716 | MATLAB | 264 | 4 | function [CosTheta, U, V] = SubspaceAngles(A, B)
% SubspaceAngles has been deprecated. Use subspace_principal_angles instead.
warning('SubspaceAngles has been deprecated. Use subspace_principal_angles instead.');
[CosTheta, U, V] = subspace_principal_angles(A, B); |
b04a1a5f853daaaee31b0db98098741472936baeefcafc9c742546f189550890 | MATLAB | 264 | 20 | N=100;W=0.05;
% for i=repmat(1:N,N,1);
for i=1:N
% A=2*W*sinc(2*W*(i-i'));
for j=1:N
A=2*W*sinc(2*W*(i-j));
end
end
[V,D]=eig(A);
figure(1)
for k=1:5
subplot(5,1,k)
plot(V(:,N-k+1))
end
figure(2)
plot(diag(D)) |
438e2c9ecdb6a6bc4fe38fbd1d077fa9d262c62e6076fa181b98d628bcb0be0f | MATLAB | 266 | 15 | function out = get_spm_affmats(P, refVol)
nV = length(P);
if nargin < 2
refVol = 1;
end
out.pars = zeros(12,nV);
out.mats = zeros(4,4,nV);
for iV = 1:nV
out.mats(:,:,iV) = P(iV).mat / P(refVol).mat;
out.pars(:,iV) = spm_imatrix(out.mats(:,:,iV));
end |
4857fbe89736fd06e99b0e56377cb444e1c1ddb5dffc623a5274f8187a7e537c | MATLAB | 266 | 17 | function out = flipAxes(dataToFlip,flip_xyz)
if isempty(flip_xyz)
flip_xyz = [0 0 1]; % current best guess :)
end
out = dataToFlip;
if flip_xyz(1)
out = flip(out,1);
end
if flip_xyz(2)
out = flip(out,2);
end
if flip_xyz(3)
out = flip(out,3);
end
|
b5b7427acb36c8ac2fa1e6de9c2c466579cc09a7b2da40740171dbe516b43a8f | MATLAB | 266 | 8 | function [d1] = bandpassfilter(highp,lowp,fs,rawdata)
%BANDPASSFILTER Summary of this function goes here
% Detailed explanation goes here
filter = [highp lowp]; % band-pass condition
[b,a] = butter(4,filter/(fs/2));
d1 = filtfilt(b, a, rawdata(:,:));
end
|
d5a50b2dc6ec86959a55f06a71c3a691782f51324b06148301a064fbc29b9eac | MATLAB | 266 | 10 | plot(F0000CH1.T,F0000CH1.V)
hold on
plot(F0005CH1.T,F0005CH1.V)
plot(F0009CH1.T,F0009CH1.V)
plot(F0013CH1.T,F0013CH1.V)
plot(F0017CH1.T,F0017CH1.V)
plot(F0022CH1.T,F0022CH1.V)
legend('10mW','20mW','30mW','40mW','50mw','60mW')
xlabel('Time (s)')
ylabel('Voltage (V)') |
064ddd9aae75a38565e74140ce41068f51f3e36e739975e4e0d1c48716dd0861 | MATLAB | 268 | 4 | function [T0,T1] = SynchPhaseTimes(peaks,phase)
% SynchPhaseTimes has been deprecated. Use synchronous_phase_samples instead.
warning('SynchPhaseTimes has been deprecated. Use synchronous_phase_samples instead.');
[T0, T1] = synchronous_phase_samples(peaks, phase); |
59b8c6fb2829b05b54baceb2e685427ec1a9db12df68a0d9ed7efdad51969b2c | MATLAB | 268 | 9 | %% 5HT catabolism in the cytosol
%Conventional Michaelis Menten rate.
% b = cytosolic 5ht in the terminal, sc is the scaling factor for time.
% UNITS IN uM and uM/h.
function a = TCcatab(b)
km = 95; %Gottowik93 and Fowler94
vmax = 4000;
a = (vmax.*b./(km + b));
end |
b733b4bd48c8dac212c572254bc3b3b33ecfc432a0551705d36293c9a0f81f02 | MATLAB | 268 | 7 | function SmoothData = smooth_spectrum(Power, Frequencies, SmoothSpan)
% function for smoothing data
% Data is a 1 x Freqs matrix.
FreqRes = Frequencies(2)-Frequencies(1);
SmoothPoints = round(SmoothSpan/FreqRes);
SmoothData = smooth(Power, SmoothPoints, 'lowess');
end |
57cad63db495be0e8361774c1f15ae52cfae5333ff5009a217fd78046ad5da2e | MATLAB | 269 | 4 | function [ECGmean, ECGsd] = ECGBeatVariance(x, peaks, wlen)
% ECGBeatVariance has been deprecated. Use avg_beat_calculator instead.
warning('ECGBeatVariance has been deprecated. Use avg_beat_calculator instead.');
[ECGmean, ECGsd] = avg_beat_calculator(x, peaks, wlen); |
d10d3caf326650443e8b9a7a9abbdf678d4ba657158b2d347016a272345086db | MATLAB | 269 | 11 | %% Function of histidine decarboxylase in mast cells.
% Histamine synthesis in mast cells.
% UNITS in uM/h.
% b = gha.
function a = VHTDCmc(b)
% b = cht
% c = G*
km = 270; %homosapiens BRENDA (other wild type =100, Komori2012)
v = 877.50;
a = v.*(b./(km + b));
|
39524a6f9fc533873bf3a704a78e951ac23955c9ef96549da76fd0e20b309370 | MATLAB | 271 | 12 | function h = SmoothStackPlot(x,y)
Colors = lines(size(y,2));
baseline = zeros(size(y,1),1);
px = repmat([x(:);flip(x(:))],1,size(y,2));
py = [cumsum([baseline y(:,1:end-1)],2); flip(cumsum(y,2))];
for i = 1:size(y,2)
h(i) = patch(px(:,i),py(:,i),Colors(i,:));
end |
4d0220cefb2d5181e5691589257dda54b40642f618edc28e582b57d7f670a3c8 | MATLAB | 271 | 8 | function changecolormap(obj,~,node,data)
% autocorrelatemap() and crosscorrelatemaps() depend on this function.
n = length(obj.Colormap(:,1))-1;
for i = 1:length(node)
node(i).FaceColor = repmat(obj.Colormap(floor(data.color(node(i).UserData)*n)+1,:),3);
end |
b69d9ea2e8d007ff05d79a9d1e140da583a83a81d472cc1e14e60815a83387b1 | MATLAB | 271 | 13 | function mnl_ExportEPSdense(h,fn)
% Code to switch back the renderer to a vector format from a bmp (image
% format)
% Input
% h = figure number (h=gcf)
% fn = filename
%
% Output
% .eps file
set(h,'renderer','Painters')
print(fn,'-depsc','-tiff','-r300','-painters')
end |
cd1267a208b66ec42fb35bafef91d19385500c2cd791d81a964ee2650893b013 | MATLAB | 271 | 4 | function [y, W, A] = SCA_NSCA(x, f1, f2, I, order)
% SCA_NSCA has been deprecated. Use sca_nsca_component_analysis instead.
warning('SCA_NSCA has been deprecated. Use sca_nsca_component_analysis instead.');
[y, W, A] = sca_nsca_component_analysis(x, f1, f2, I, order); |
30b478bd1459f93e38d226d3c91d5db1302ef0a71a636c104bc229b69f0c6b9c | MATLAB | 272 | 8 | function Folder = list_filenames(Folder)
% little function for getting whatever is inside a folder, ignoring the
% stupid dots and turning everything into a string
Folder = deblank(string(ls(Folder)));
Folder(strcmp(Folder, ".")) = [];
Folder(strcmp(Folder, "..")) = []; |
6a486c751494a92ac9f1c5ab5ea4a951515028ec6330d78b161ef5df758826f2 | MATLAB | 272 | 16 | function P=select_data()
rootdir = '/home/romain/data/spectro/hipo/dicom_files';
sujet_dir_wc = {'Sujet1[01]','Sujet09'};
fonc_dir_wc = 'TE64$';
s_dir = get_subdir_regex(rootdir,sujet_dir_wc);
s_sub_dir = get_subdir_regex(s_dir,fonc_dir_wc);
P = char(s_sub_dir);
|
9afa20e4bf554819faabf3d5cfd4b915411ee22681029c233b9a688cab7b5ee3 | MATLAB | 272 | 11 | function [ output_args ] = writeAvi( movie,frameRate,fpath )
%WRITEMOVIE Summary of this function goes here
% Detailed explanation goes here
v = VideoWriter(fpath,'Grayscale AVI');
v.FrameRate = frameRate;
open(v)
writeVideo(v,movie);
close(v)
end
|
2a19cab7480da0a2f45b042f734e06a8abec211100df3180198fdb8ab5682c91 | MATLAB | 273 | 9 | function tc = trueClass(vals)
%TRUECLASS Return class of vals, or, in the case of gpuArrays, the
% underlying class
if isempty(vals)
tc = class(jrclust.utils.tryGather(vals));
else
tc = class(jrclust.utils.tryGather(vals(1)));
end
end
|
c407befd8231afc5d00f75624fa5188846713fa26cd54de27e81446623cdd70e | MATLAB | 273 | 11 | %% Rate of reuptake from serotonin transporters in serotonin terminals.
% b = e5ht
%UNITS IN uM and uM/h.
function a = VSERT(b, sert_density, ssri, ki)
k = 0.060;
vmax = 250;
vmax_app = vmax * sert_density;
k_app = k * (1 + ssri/ki);
a = vmax_app.*b./(k_app + b);
end
|
0514d7811eacf1d827c18687dba2f0ddef0940e792f7e286fc0e2eab6cccbb0b | MATLAB | 274 | 10 | function Labels = indexes2labels(Indexes, Chanlocs)
% outputs channel indices based on chanlocs labels
AllLabels = string({Chanlocs.labels});
AllLabels(strcmpi(AllLabels, 'CZ')) = "129";
Labels = AllLabels(Indexes);
Labels = char(Labels(:));
Labels = str2double(Labels); |
d616f2008fc2da8bff6b9044edbba3fb29f20a0cac9bcc704af8a6857024c268 | MATLAB | 274 | 4 | function [index, rank] = ChannelIndex0(x, th1, th2, varargin)
% ChannelIndex0 has been deprecated. Use sqi_level_crossing instead.
warning('ChannelIndex0 has been deprecated. Use sqi_level_crossing instead.');
[index, rank] = sqi_level_crossing(x, th1, th2, varargin{:}); |
197d31d4165fea2f752ba0ff7ddf2a9a836a80c718b73dc5c0fcff51c10ab568 | MATLAB | 275 | 4 | function [index, rank, y] = ChannelIndex2(x, w, varargin)
% ChannelIndex2 has been deprecated. Use sqi_power_fluctuation instead.
warning('ChannelIndex2 has been deprecated. Use sqi_power_fluctuation instead.');
[index, rank, y] = sqi_power_fluctuation(x, w, varargin{:}); |
9ee9b0627ce4f2b576fce42378d033687628c7d17c2a4bbf22ff452a56a899be | MATLAB | 276 | 4 | function [A, AA, B] = PiCAMatrixes(x, peaks, peaks2)
% PiCAMatrixes has been deprecated. Use pica_matrices_double_peaks instead.
warning('PiCAMatrixes has been deprecated. Use pica_matrices_double_peaks instead.');
[A, AA, B] = pica_matrices_double_peaks(x, peaks, peaks2); |
68b8af3eb3b7cbdb6333f85b163d5d52df3348ef3bbc1738fdbc201322034fc9 | MATLAB | 278 | 4 | function [s, W, A, B, C] = MultichannelNotchFilter(x, fc, Q, fs)
% MultichannelNotchFilter has been deprecated. Use sca_notch_filter instead.
warning('MultichannelNotchFilter has been deprecated. Use sca_notch_filter instead.');
[s, W, A, B, C] = sca_notch_filter(x, fc, Q, fs); |
87a11f2356c9898f418ba58f4061d2026fb2da690cc545609a5a7e33cdc7f318 | MATLAB | 278 | 18 | function [y] = phi(x, param)
% scaled/biased logarithmic sigmoid: a * s(x - b)
%
% Parameters
% ----------
% - x: array
% Input
% - param: [scalar, scalar]
% Scale and bias parameters
%
% Returns
% -------
% - y: array
% Output
y = param(1) .* logsig(x - param(2));
end
|
5466067e89ad7f2984133700807a8f56ec45dc007a8997f58dfc443486e018ae | MATLAB | 279 | 8 | function out=madr(x)
% Computes the median absolute deviation to the median,
% adjusting by a factor for asymptotically normal consistency.
% This is the formula used in the R version of mad,
% which is different from the Matlab version.
out=1.4826.*median(abs(x-median(x))); |
2969593a8c3117f25bf69ffd0dfdcc41b665d34bf6e3e182c3d7c7a5fc6b176c | MATLAB | 280 | 4 | function [b2, b1] = BaseLine2(x, L1, L2, approach)
% BaseLine2 has been deprecated. Use baseline_sliding_window_twice instead.
warning('BaseLine2 has been deprecated. Use baseline_sliding_window_twice instead.');
[b2, b1] = baseline_sliding_window_twice(x, L1, L2, approach);
|
5a3afaa399d10bf9865f5aeb1a5764f7b8c0f68f0126790766ab9f290bf1c6b9 | MATLAB | 280 | 12 | function w=HanningWindow_(window_size)
w=zeros(1,window_size);
if mod(window_size,2)==0
for n=1:window_size,
w(n)=0.5-0.5*cos(2*pi*(n-1)/window_size);
end
else
for n=1:window_size,
w(n)=0.5-0.5*cos(2*pi*(n-0.5)/window_size);
end
end
|
6abcf401d4a4d850477ac56251b48941ef3806259ed94ec5d06b524a33372492 | MATLAB | 280 | 17 | function output = sumEveryN(input, N, dim)
if N<2
error('N must be > 1')
end
s = size(input);
% only works for 2d
id = find(~ismember([1 2], dim));
for ii = 1:s(id)
in_temp = input(:,ii);
output(:,ii) = reshape(sum(reshape(in_temp,N,[]),'omitnan'),[],1);
end
end |
a02df4d231e1781abf64871cab1ce7c93c4b2a9504d867b2487843c0cb0b6dba | MATLAB | 280 | 10 | function samplesIn = userFilter(samplesIn, filtKernel)
%USERFILTER Convolve samplesIn with user's custom kernel
if ~isempty(filtKernel)
for i = 1:size(samplesIn, 2)
samplesIn(:, i) = conv(samplesIn(:, i), filtKernel, 'same');
end
end
end
|
5772d63ef695f4316980bb9a42dd4b632e0ccf860aeca48ef6b3216ea6f22da4 | MATLAB | 281 | 10 | function [meanSize,stdSize,numOfCommunity] = community_size_mean_std(S)
uniqueS = unique(S);
for i = 1:numel(uniqueS)
sizeCommunity(i) = sum(S==uniqueS(i));
end
meanSize = mean(sizeCommunity);
stdSize = std(sizeCommunity);
numOfCommunity = numel(sizeCommunity);
end
|
6dcb1340feb6a78803fc03700b177c2e009a3b2d8448ed5b98c818fb644bbb22 | MATLAB | 281 | 17 | function bic = nhmm_bic(Y, Omega_est,map_states)
n = size(Y,1);
[p,~,k] = size(Omega_est);
mu = zeros(1,p);
nparams = k*p*(p+1)/2+k*(k+1);
for i = 1:n
llike = mvnloglike(Y(i,:),mu,Omega_est(:,:,map_states(i)));
end
llike = sum(sum(llike));
bic = log(n)*nparams-2*llike;
|
a3a31175740d9b4c80152c3193a335f96ff1d483db22d5a4a07e9dfb5c0c9d7a | MATLAB | 282 | 10 | function outstr = c_str(instr)
% eja function to read C-style null-terminated strings
% (strips everything past the first null)
%rrr added because fread use with type UINT8 instead of char
if isnumeric(instr)
instr = char(instr);
end
outstr = strtok(instr, char(0));
|
b91985909aad7e8a9f611cce4d621ae9af9c884f04abde21416b46cc0fd1a0af | MATLAB | 282 | 8 | function samplesIn = fir1Filter(samplesIn, order, passband)
%FIR1FILTER Convolve samples with a FIR bandpass filter of given order
ff = single(fir1(order, passband));
for i = 1:size(samplesIn, 2)
samplesIn(:, i) = conv(samplesIn(:, i), ff, 'same');
end
end
|
d4753133936797dd3677041e12186d7df2ce30ee6050fc70254a60b1a132ce9e | MATLAB | 282 | 9 | %% Function of histamine inhibition of histamine firing.
% Dependent on Serotonin-activated g-protein (b) and the equilibrium value
% of the activated G-protein (c).
% Units in uM.
function a = inhibRHAtoHA(b, c)
min_a = 0;
a = 1 - (2).*(b - c);
a(a<min_a) = min_a;
end
|
01c6234b73973bb4c8a3eb764ba7500fcc499ab1f88ed979aba027c8ba98b5f6 | MATLAB | 283 | 4 | function [y, W, A] = SCAmodified(x, fl, fu)
% SCAmodified has been deprecated. Use spectral_component_analysis_bp instead.
warning('SCAmodified has been deprecated. Use spectral_component_analysis_bp instead.');
[y, W, A] = spectral_component_analysis_bp(x, fl, fu, 'DFT_FILTER'); |
3a054af9f642a8e31b864cd963c7917c4cf6766516df9d3c872b3a9688c2bfbe | MATLAB | 283 | 15 | function centreOfMass = calcCOM(cellArray,xvals, method)
if nargin == 3
if strcmp(method, 'mean')
xSums = cellfun(@mean, cellArray);
end
else
xSums = cellfun(@sum, cellArray); % default
end
M = sum(xSums(:));
centreOfMass = sum((xvals.*xSums))/M; |
3ca1811ec675895c98accf2798e5b6e0ddb7a2411903e8642cb142896c244d11 | MATLAB | 284 | 15 | %% Global Variables:
global SIGNALS;
global DETSIGNALS;
global preDRIVE;
global preLAMBDAS;
global RASTER;
global Responses;
global Names_Conditions;
global fs;
global SIGNALSclean;
global SNRlambda;
global Experiment;
global RasterAlgorithm;
% global isSIGNALS;
% global notSIGNALS;
|
592a4fada41f74f3fd20e1ccef11f4d3314563e032d16b873ccb78e13472bfd5 | MATLAB | 284 | 8 | function cells = blockify_data(ob, data, varargin)
%function cells = blockify_data(ob, data, varargin)
if isempty(ob.handle_blockify_data)
fail('The %s of type %s has no handle_blockify_data', ...
class(ob), ob.caller)
end
cells = ob.handle_blockify_data(ob, data, varargin{:});
|
7895996362624607f6dadbd789304635cc534451f45fb0e94f7173577faff4f4 | MATLAB | 285 | 4 | function [peaks, mn, r] = PeakDetection3(x, fs, h, th, fmax)
% PeakDetection3 has been deprecated. Use peak_det_matched_filter instead.
warning('PeakDetection3 has been deprecated. Use peak_det_matched_filter instead.');
[peaks, mn, r] = peak_det_matched_filter(x, fs, h, th, fmax); |
4f56c29b843f9570d764dfa08b71d98c6f639940ae33a986af0c44793465b5a7 | MATLAB | 286 | 15 | function [map_states, sure] = mapstates(st_post)
N = size(st_post,2);
map_states = zeros(1,N);
sure = map_states;
for i = 1:N
tbl = tabulate(st_post(:,i));
sure(i) = max(tbl(:,3))/100;
counts = tbl(:,2);
[m,ind] = max(counts);
map_states(i) = tbl(ind,1);
end
end
|
ce1d5c8a85849a6a33222c41c5bcad0110ccd6e92dd6311a41646ab4e6e5ac7b | MATLAB | 286 | 14 | function [ tform] = getAffReflect( plane_normal )
%GETAFFREFLECT Summary of this function goes here
% Detailed explanation goes here
plane_normal = plane_normal(:);
reflmat = eye(4);
reflmat(1:3,1:3) = eye(3) - (2 * plane_normal * plane_normal');
tform = affine3d(reflmat);
end
|
1dd1300af534a2f64a9a9ddb92a97d961db25b1180b60821f223b932264d2a4f | MATLAB | 287 | 11 | function mmil_mkdir(outdir)
%function mmil_mkdir(outdir)
%
% NOTE: gives error if unable to create outdir
%
% Created: 04/04/11 by Don Hagler
% Last Mod: 04/04/11 by Don Hagler
%
[succ,msg] = mkdir(outdir);
if ~succ, error('failed to create output directory %s:\n%s',outdir,msg); end;
|
cd962bb571d8aab6a542a23a8ec28014ed8d575005fa1dadf91bef9a0f3cc15c | MATLAB | 287 | 9 | %% Function of serotonin activation of histamine release.
% Dependent on Serotonin-activated g-protein (b) and the equilibrium value
% of the activated G-protein (c).
% Units in uM.
function a = activR5HTtoHA(b, c)
min_a = 0;
a = 1 + (3).*(b - c);
a(a<min_a) = min_a;
end
|
d399fbdf7206b1e107a322c08c9b64c976318d2d192b4420e77412ac4e16757b | MATLAB | 287 | 17 | function Vout = apply_spm_affmats(V,Mats)
nV = length(V);
if (nV)~=size(Mats,3)
disp('')
disp('Error: there should be a 4x4 matrix for the n volumes from V')
disp('')
return
end
Vout = V;
Mstart = Vout(1).mat;
for iV = 1:nV
Vout(iV).mat = Mats(:,:,iV)*Mstart;
end |
a6a45f6455c379f884f3b7ad73294b12886a6bb26d0ff409a722efddd2f56b3e | MATLAB | 288 | 9 | function [x fs] = LoadSeizureEEGFull(path, subject, mode, number)
load([path subject '_' mode '_segment_' num2str(number,'%.4d') '.mat']);
varname = [mode '_segment_' num2str(number,'%d')];
eval(['data = ' varname '; clear ' varname ';']);
x = data.data;
fs = data.sampling_frequency;
|
be5bbfd7191be06ff90452e8f62a70d8a1d856293df99ce48e9f9f9052f3ca48 | MATLAB | 288 | 15 | function varargout = eig(ob, varargin)
% compute eig of object by converting it to full matrix.
% this will be very slow for big objects
ob = full(ob);
[v d] = eig(ob, varargin{:});
switch nargout
case 1
varargout = {diag(d)};
case 2
varargout = {v, d};
otherwise
fail 'eig bug'
end
|
05a6069b8924d997e9c638247687a906d95c47ba7a809fe770ba332c17d59687 | MATLAB | 289 | 4 | function [peaks, peak_indexes] = PeakDetection(x, ff, varargin)
% PeakDetection has been deprecated. Use peak_det_local_search instead.
warning('PeakDetection has been deprecated. Use peak_det_local_search instead.');
[peaks, peak_indexes] = peak_det_local_search(x, ff, varargin{:});
|
4f9ec6a451b73eeac3f188a320583336dbda5fb9d545600995ce5036350b6636 | MATLAB | 289 | 11 | function output = rgetfield(S, field)
% Split the fieldname on "."
parts = regexp(field, '\.', 'once', 'split');
output = [S.(parts{1})];
if numel(parts) > 1
% If there are more parts, recursively get them
output = rgetfield(output, parts{2});
end
end |
bd8e6664113b651b633bf827dfda86883347228e8069fe459822f0f9a249c4c4 | MATLAB | 289 | 7 | %% Function that models slow down in escit dissociation from SERTs due to allosteric binding
%Units in uM.
function ki = allo_ssri_ki(ssri)
min_ki = 0.001;
b = 4; %Strength.
ki = 0.05.*exp(-b.*ssri) + min_ki; %constant of inhibition of ESCIT in uM. doi:10.1017/S1461145706007486.
end |
ecf58c1c55c68989a8e6824774ab9eb98c687f2c2bdca7d644a19fd6488c3f93 | MATLAB | 289 | 11 | function out = NormDimL2(in,dim)
% Copyright 2009-2011 Thomas Koenig
% distributed under the terms of the GNU AFFERO General Public License
rep = ones(numel(size(in)),1);
rep(dim) = size(in,dim);
d = sqrt(mean(in.*in,dim));
%d = std(in,1,dim);
d(d == 0) = 1;
out = in./repmat(d,rep(:)'); |
43857a410473e41b0d782a55d940b6db312a7e2486b1a201230931cf9041d563 | MATLAB | 290 | 9 | %% Inihibition of serotonin release from 5-HT1B due to serotonin.
% Dependent on Serotonin-activated g-protein (b) and the equilibrium value
% of the activated G-protein (c).
% UNITS IN uM.
function a = inhibR5HTto5HT(b, c)
min_a = 0;
a = 1 - (1.5).*(b - c);
a(a<min_a) = min_a;
end
|
2e1740e72a80c2ce6e1ba54691aa1d2bdfd000c895ac9494d045bf536fc3f543 | MATLAB | 291 | 9 | function zScored = z_score(vals, mu, sigma)
%% function to z-score values according to a known normal distribution.
% example usage for analysing PSTHs:
% Generate normal dist by z-scoring baseline period and then use this to
% z-score the rest of the PSTH.
zScored = (vals-mu)/sigma;
end |
024e764a8f4b0d28dfc154d3a6e466a49bfe213209325e7c4d01d24b2a24426d | MATLAB | 292 | 4 | function [status, count] = MatToBinaryFileConverter(x, fs, ofname)
% MatToBinaryFileConverter has been deprecated. Use write_binary_signal instead.
warning('MatToBinaryFileConverter has been deprecated. Use write_binary_signal instead.');
[status, count] = write_binary_signal(x, fs, ofname); |
0e61ef6b4aa3ec1bbcc7a13d6449a8659d986fc0196355ef62ca15b234eb7806 | MATLAB | 295 | 7 | function [WhitenedPower, FooofFrequencies] = whiten_spectrum(Power, Frequencies, FooofFittingRange)
% uses FOOOF
FooofModel = fooof(Frequencies, Power, FooofFittingRange, struct(), true);
FooofFrequencies = FooofModel.freqs;
WhitenedPower = 10.^(FooofModel.power_spectrum-FooofModel.ap_fit)-1; |
4d6ab8087bb6bb17be9806fce0b51f9e8cd4af6596664a1876484a3ff6fddfcd | MATLAB | 295 | 4 | function [peaks, peak_indexes] = PeakDetection6(x,ff,th,varargin)
% PeakDetection6 has been deprecated. Use peak_det_amp_threshold instead.
warning('PeakDetection6 has been deprecated. Use peak_det_amp_threshold instead.');
[peaks, peak_indexes] = peak_det_amp_threshold(x, ff, th, varargin{:}); |
70a07ad103f6dc9cfb599643c72dfe9be8a4d49a0ffd375af587bba2bf0abf07 | MATLAB | 295 | 4 | function ratios = RelativeSpectralPower(x, fs, freqs, Q_factor)
% RelativeSpectralPower has been deprecated. Use relative_spectral_power instead.
warning('RelativeSpectralPower has been deprecated. Use relative_spectral_power instead.');
ratios = relative_spectral_power(x, fs, freqs, Q_factor); |
fcd12fb402bf4c780c256a38ee02809cbe6f8d0b6892132513db44c173eea4c3 | MATLAB | 295 | 4 | function baseline = BaselineEstimator(x_raw, baseline_removal_method, params)
% BaselineEstimator has been deprecated. Use baseline_filter instead.
warning('BaselineEstimator has been deprecated. Use baseline_filter instead.');
baseline = baseline_filter(x_raw, baseline_removal_method, params); |
14df62ba493143af96810b313821d5a4b902781b8e9862973f34fbe0980d2150 | MATLAB | 296 | 13 | function outstr = c_str(instr)
% eja function to read C-style null-terminated strings
% (strips everything past the first null)
if (isempty(instr))
outstr = '';
else
if (instr(1) == char(0))
outstr = '';
else
outstr = strtok(instr, char(0));
end
end
|
30a5a81af363ff16728ff35a190c25b0120b2b8d2458cdc42545c99fd1e6a689 | MATLAB | 296 | 4 | function [index, rank, yy] = ChannelIndex7(x, w, th, varargin)
% ChannelIndex7 has been deprecated. Use sqi_power_level_crossing instead.
warning('ChannelIndex7 has been deprecated. Use sqi_power_level_crossing instead.');
[index, rank, yy] = sqi_power_level_crossing(x, w, th, varargin{:});
|
98840732959e2726ddda6a1611d15fe36fc490e25ff63712c6288f86d8ce1931 | MATLAB | 296 | 11 | function varargout = tryGather(varargin)
%TRYGATHER Try to gather gpuArrays
for i = 1:nargin
if isa(varargin{i}, 'gpuArray')
varargout{i} = gather(varargin{i});
varargin{i} = [];
else
varargout{i} = varargin{i};
end
end
end
|
4cdc2e4d382551b7df0c3dddce6ed5c8a305c8a7877b8b54aab83594201df86c | MATLAB | 297 | 12 | function [ts_ecg_out, ts_bp_out, idx_bp_out] = realign_bp_ecg(ts_ecg_in, ts_bp_in, idx_bp_in)
ts_ecg = unique(ts_ecg_in);
ts_bp = unique(ts_bp_in);
idx_bp = unique(idx_bp_in);
tmp = find (diff(ts_bp)< 0.5 );
ts_bp(tmp) = [];
idx_bp(tmp) = [];
tmp = find(diff(ts_ecg) < 0.5);
ts_ecg(tmp) = [];
|
757a4b198929a6108dd3d7b80d18bc1dd5b1f847324b28ddd20cdee1d1cee4d0 | MATLAB | 297 | 4 | function [V, d, delta, similarity, epsilon] = LaplacianEigenmap(C, kappa)
% LaplacianEigenmap has been deprecated. Use laplacian_eigenmap instead.
warning('LaplacianEigenmap has been deprecated. Use laplacian_eigenmap instead.');
[V, d, delta, similarity, epsilon] = laplacian_eigenmap(C, kappa);
|
b3bb37617c89c959b70da632dee94dd747dfed5c78c4013c6135c1a667e368a2 | MATLAB | 297 | 14 | function [B, mu] = matrix_centering(A)
% Take a matrix A and return a centered version.
%
% Parameters:
% A: matrix
% A matrix of size (M x N)
assert(ismatrix(A), 'A has to be a matrix of size (M X N)')
assert(isreal(A), 'Please check A')
mu = mean(A, 1);
B = bsxfun(@minus, A, mu);
end |
0449c1d783a94db520f04123a19da56f1d6b958bd9294694d0e0fc00a4677724 | MATLAB | 298 | 10 | function c = correct_water_content_mean(c)
for npool=1:length(c)
c(npool).fgray = ones(1,length(c(npool).fgray)) .* mean(c(npool).fgray);
c(npool).fwhite = ones(1,length(c(npool).fwhite)) .* mean(c(npool).fwhite);
c(npool).fcsf = ones(1,length(c(npool).fcsf)) .* mean(c(npool).fcsf);
end
|
9b17c3673cb1a001231f6c5fae6e7a45ff69f8bb657890fffdf98c23657f95d8 | MATLAB | 298 | 9 | %% Serotonin synthesis inhibition from 5-HT1B autoreceptors.
% depends on levels of serotonin-activated g-protein (b)
% and the equilibrium value of the activated G-protein (c).
% UNITS IN ratio.
function a = inhibsyn5HTto5HT(b, c)
min_a = 0;
a = 1 - (0.1).*(b - c);
a(a<min_a) = min_a;
end
|
f37e7aebd79ac426967b954823a9120e9d6746e64867f350754fb0a4fe56d4d3 | MATLAB | 299 | 9 | function deleteAnnotated(obj)
%DELETEANNOTATED Delete clusters which are annotated to_delete
deleteMe = find(strcmp(obj.hClust.clusterNotes, 'to_delete'));
if ~isempty(deleteMe)
obj.deleteClusters(deleteMe);
else
jrclust.utils.qMsgBox('No units deleted');
end
end |
7e9d6b47cad77bc8b6c191c6b5483ee04e4d447915e6bbbf7406afd2f4fab383 | MATLAB | 300 | 4 | function [ECG, teta] = SingleChannelECGGenerator(teta, teta0, alphai, bi, tetai)
% SingleChannelECGGenerator has been deprecated. Use ecg_gen_gmm instead.
warning('SingleChannelECGGenerator has been deprecated. Use ecg_gen_gmm instead.');
[ECG, teta] = ecg_gen_gmm(teta, teta0, alphai, bi, tetai); |
0946bcfcdfae36104302814c4f10bb3badd72297d6376ae8c2c281df04157c60 | MATLAB | 301 | 13 | function sigma = XxGuassianFitting2D(psf)
addpath(genpath('../'));
[~, Nx] = size(psf);
x = double(1:Nx);
[~, I] = max(sum(psf,2));
psf_y = XxNorm(psf(I,:),0,100);
fun_gs = fittype('A*exp(-(x-mu)^2/(2*sigma^2))');
[cf_y, ~] = fit(x(:),psf_y(:),fun_gs,'Start',[1, Nx/2, 3]);
sigma = cf_y.sigma;
end |
3ab5f091b7884fdc623d9f0637125a28cbbd82395053ffa44f62c1bbb0a91332 | MATLAB | 301 | 6 | % Visual/Manual Proesssing Controler: (-+) & (--)
mf=msgbox({'For Visual Inspection of Detected Ca++ Transients';'Type: ';...
'>>Detected_Visual_Inspection';' ';'And for Undetected Ca++ Transients';...
'>>Undetected_Visual_Inspection'});
VisualInspector=[false,false];
waitfor(mf); delete(mf); |
5da415d77bb61af22bc9f43c7a4ec39d83f5365c378d1b62c7044b448ea70cdc | MATLAB | 301 | 10 | %% Histamine H3 inhibition of serotonin release.
%autoreceptors and depends on levels of g-coupled proteins activated (b) by
%histamine and basal levels of g-coupled histamine (c).
%UNITS IN ratio.
function a = inhibRHAto5HT(b, c)
min_a = 0;
%b = gha*
a = 1 - (3).*(b - c);
a(a<min_a) = min_a;
end
|
70ee9319048e4a4d9fffae083df84039238ed4e1330f1f340c6b6f2ce772402d | MATLAB | 301 | 23 | function fo = sum_fid_local2(fids,nz)
fo=fids;
for nbser = 1:length(fids)
fid = fids(nbser).fid;
num_fid = size(fid,2);
for kf = 1:nz:num_fid
fidn(:,(kf-1)/nz + 1) = sum(fid(:,kf:(kf+nz-1)),2) ./ nz;
end
fo(nbser).fid = fidn;
clear fidn;
% fo{nbser}.Nex = fidn;
end
|
4fd67ed635b951090c4d6e4e66d8f28aced34701e9132a6117ad7401c7908b82 | MATLAB | 302 | 4 | function [DIP, teta] = DipoleGenerator2(N, fs, f, alphai, bi, tetai, teta0)
% DipoleGenerator2 has been deprecated. Use vcg_gen_direct_sum instead.
warning('DipoleGenerator2 has been deprecated. Use vcg_gen_direct_sum instead.');
[DIP, teta] = vcg_gen_direct_sum(N, fs, f, alphai, bi, tetai, teta0); |
6cf2ebdd4abf6ef78f363fbd3a7b6d6712fb1f40ec68124a99aa05f483d86ea1 | MATLAB | 302 | 4 | function [DIP, teta] = DipoleGenerator(N, fs, f, alphai, bi, tetai, teta0)
% DipoleGenerator has been deprecated. Use vcg_gen_state_space instead.
warning('DipoleGenerator has been deprecated. Use vcg_gen_state_space instead.');
[DIP, teta] = vcg_gen_state_space(N, fs, f, alphai, bi, tetai, teta0); |
743681104da35add02843765b5862adf0eb41abf302eed83babe1e6ac33ec4d8 | MATLAB | 302 | 4 | function noise = NoiseGenerator(noisetype, SignalPower, SNR, N, varargin)
% NoiseGenerator has been deprecated. Use biosignal_noise_gen instead.
warning('NoiseGenerator has been deprecated. Use biosignal_noise_gen instead.');
noise = biosignal_noise_gen(noisetype, SignalPower, SNR, N, varargin{:}); |
de6c63ed3ea19b4de79dfa544be6c5ef3959be03563b07d5363d922a6872b3a2 | MATLAB | 302 | 14 | function update_cluster_popup(app)
%UNTITLED Summary of this function goes here
% Detailed explanation goes here
str_cell = {[]};
for i = 1: max(app.spike_res.cluster)
str_cell{i} = num2str(i);
end
app.popup_cluster.Items = str_cell;
app.popup_cluster.Value = app.popup_cluster.Items{1};
end
|
e5df61acaed425451c8cc3022bc84d44497d90daa3cfc6faa734db39c670de3d | MATLAB | 302 | 13 | function unpackStruct (structure)
fn = fieldnames(structure);
for i = 1:numel(fn)
fni = string(fn(i));
field = structure.(fni);
if (isstruct(field))
unpackStruct(field);
continue;
end
assignin('base', fni, field);
end
end |
8766fab1c7496b023a7a809d58987303cb0cd5c5c4ba6501cbb716d4715d7abe | MATLAB | 303 | 17 | function [y] = inv_phi(x, param)
% Inverse of scaled/biased logarithmic sigmoid: b - log((a / x) - 1)
%
% Parameters
% ----------
% - x: array
% Input
% - param: [scalar, scalar]
% Scale and bias parameters
%
% Returns
% -------
% - y: array
% Output
y = param(2) - log((param(1) ./ x) - 1);
end
|
cf68eda669f2b36d617a2b381e027c7d352f0028c0e978b1857a4deacd2d0656 | MATLAB | 304 | 13 | function IntervalSpike
clear all
global StartTime FinishTime
cd C:\Users\B133_2\Desktop\2601_170620
load 2601_170620
StartTime
FinishTime
dpath='C:\Users\B133_2\Desktop\CheetahData\2017-6-20_21-17-32 2601-11'
cd(dpath);
SpikeArray=GetSpikeAll(dpath,'Sc7_SS_01.t');
SpikeArray
RpegTouchall |
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