sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
e42f2b886a32024b2b2808aa901e04a6bb0c9f2a499ab7558999f78704f40338 | MATLAB | 1,186 | 40 | function y = bp_filter_dft_varlen(x, fl, fu, N)
% bp_filter_dft_varlen - Bandpass filter using discrete Fourier transform (DFT) filtering with variable DFT points.
%
% Syntax: y = bp_filter_dft_varlen(x, fl, fu, N)
%
% Inputs:
% x: Vector or matrix of input data (channels x samples).
% fl: Normalized lower f... |
ec928b00dd22a8fb5724e21f61125d38042a931e0bb12fee53cecb14ae7b3c3d | MATLAB | 1,187 | 39 | function Answers = cleanup_LAT(AllAnswers)
% Take table from AllAnswers, save relevant information and make it in the
% correct format type.
Answers = AllAnswers(:, {'Participant', 'Session'});
Answers.Participant = string(Answers.Participant);
Answers.Session = string(Answers.Session);
Answers.Trial = cell2mat(AllAn... |
b1eea10e99337f73ab61b0fb5e0a6111f6ba2b91e660ff7742b3f03beff14ab0 | MATLAB | 1,188 | 56 | function off = change_file_path_to_preproc_dir(ff,par)
if isstr(ff),
ff={ff};
end
preproc_dir = par.preproc_subdir;
for nbser = 1:length(ff)
cff = ff{nbser};
[p f e] = fileparts(cff(1,:));
dest_dir = fullfile(p,preproc_dir);
if ~exist (dest_dir)
mkdir(p,preproc_dir)
if strcmp(spm_pl... |
0a0dc39683b58c3ba60bed8be96a523212eb9e85295749c9d844c5300c089c88 | MATLAB | 1,189 | 44 | function [valindSQI, indSQI] = SQI2(x, peaks)
%
% [valindSQI, indSQI] = SQI2(x, peaks)
% Channel selection based on R-peak amplitude constancy
%
% Fahimeh Jamshidian Tehrani,
% jamshidian.t@cse.shirazu.ac.ir,
% fahimeh.jt@gmail.com
% February 2020
%
% input:
% x: input JADE component of fetal ECG record
% peaks: R-... |
f3789f02c4a5054f6024b0f152571cc5c0a63b842140f3f37459e305165fc642 | MATLAB | 1,189 | 37 | clusters_mapping(m, p, shrinkage_factor, borders_table)
[file, path] = uigetfile('cluster_info.tsv');%When the user clicks the load data button, a window should open to enable the user to select a file.
datafile = fullfile(path, file); %save path
tdfread(datafile)
%find good clusters from group
good_clusters = fi... |
190fbd9090d43d5cbad6f4cc315f1b8cf5143c8229db46353d6a438c9bf4d148 | MATLAB | 1,190 | 40 | function Plot_all_features_ad(app)
% function Plot_all_features(handles)
data = app.user_data;
par = data{1};
inspk = data{7};
classes = data{6};
colors = [[0.0 0.0 1.0];[1.0 0.0 0.0];[0.0 0.5 0.0];[0.620690 0.0 0.0];[0.413793 0.0 0.758621];[0.965517 0.517241 0.034483];
[0.448276 0.379310 0.241379];[1.0 0.103448 ... |
43a0b08bb6c3742c990bd096c48c691c3d010a510afa15f702d08b220088de9e | MATLAB | 1,190 | 33 | function gr = GausGrad(t, p)
% Compute the gradient of a Gaussian function with respect to its parameters.
%
% Inputs:
% t: Time samples.
% p: Gaussian parameters [amplitude, width, center].
%
% Output:
% gr: Gradient of the Gaussian function with respect to its parameters.
%
% Reference:
% Fattahi, ... |
9ed4e004be4f31a7510b61405f5c0c2473fa438ad42ffdab43df7d3c8f6b364b | MATLAB | 1,190 | 31 | function [y,y2] = RaguClustSizeV2(hits)
% RaguClustSizeV2
% hits is a timepoint by randomization run matrix
% is the distribution of cluster sizes in the data
% y(1) is the count of clusters with size 1
% y(2) is the count of clusters with size 2
% y(n) is the count of clusters with size n
% Copyright 2009-2011 Thoma... |
87d1fa7670e499e60809caa62eb01f51e871005e99fbf4e564c9ba42bd2cac65 | MATLAB | 1,191 | 1 | function [wx,g]=rst_winsample(x,percent)
% function [wx,g]=winsample(x,percent)
% returns the winsorized sample of x as wx
% returns number of winsorized observations at each tail of sample as g
% x must be a vector
% percent must be between 0 and 100; the function converts the lower and
% upper extreme g values of x ... |
c2f5cf928fa17a942d596f932a0ae03d033cbb9d51d7c7582aa018ef1188b52e | MATLAB | 1,192 | 50 | % A naive implementation of Householder's 1958 algorithm for matrix
% triangularization.
% Ref: Householder, Alston S. "Unitary triangularization of a nonsymmetric
% matrix." Journal of the ACM (JACM) 5.4 (1958): 339-342.
%
% Reza Sameni 3/1/2019
% email: reza.sameni@gmail.com
clc;
clear;
close all;
% an arbitrary s... |
f337412e69ad73678e99fc5ef4d2f559aec72b469896ba5012504395b69915da | MATLAB | 1,192 | 1 | function [trimmedMean,g]=rst_tm(x,percent)
% function [trimmedMean,g]=tm(x,percent)
%
% Returns the trimmed mean of x as trimmedMean.
% returns number of winsorized observations at each tail of sample as g
% x must be a vector
% percent must be a number between 0 and 100
% The trimmed mean is calculated from x after d... |
3ecf8c4a6646d2d241cfda299d50f82939df4d4df0a9f07f62f85e1008f66276 | MATLAB | 1,194 | 48 |
function NewArray=MovWindow(Array,Mbin)
%moving average
%MbinÍФ̮©·windowARÌêA3,2,1,0,1,2,3Æ7windowÉÈéB
NewArray=zeros(1,length(Array));
for n=1:length(Array)
if n-Mbin<=0
NewArray(n)=(sum(Array(1:n+Mbin))+Array(1)*(abs(n-Mbin)+1))/(Mbin*2+1);
elseif n+Mbin>length(Ar... |
901d62e795529725f50d33de7093c4eb9abf07a323ef6705f271325e74ae6cce | MATLAB | 1,194 | 43 | %% plot example latent factors
speedcols = inferno(6);
for isesh = 1:5;
figure
allTraj = [];
for icond = 1:12
allTraj = vertcat(allTraj, s(isesh).session.cond(icond).meanTrajectory(:,:));
end
axisLimits = [min(allTraj,[],1); max(allTraj,[],1)];
for ifactor = 1:3
[~, idx] = max(abs(axisLimits(:,ifactor... |
7d59d70eecb4f042bd53360169b6d458c32eb5fd24ca625f6932418fca6424ba | MATLAB | 1,195 | 46 | expreiments = unique(CTP.experiment_id);
for i = 1:length(expreiments)
exp_idx = find(contains(upper(clusters.brain_render_table.experiment_name), expreiments{i}));
exp_CTP = CTP(contains(CTP.experiment_id, expreiments{i}), :);
for j = 1:length(exp_idx)
x = find(clusters.brain_render_table.id(exp_id... |
f1fba63a66d8f9668165884bf8ce444bc75ee00dfdc9b17e6e069aa16cea5327 | MATLAB | 1,195 | 44 | function MultimerConnProb20160527
%MakeMultimerProb20160517.m(Exp=5Ìêj
%MakeMultimerProb20160526.miExp=10Ìêj
%Éæèêv²ÆÉt@Cðì¬
%»êðp¢ÄMultimerConProbAllt@Cð쬷éBMatching10ñ̪۶³êéB
%MakeMultimerNet_Group20160520.mÅlbg[Nì¬
clear all;
close a... |
0f478c3846e1bfc8d2c7c68899ee43086024b7d3b2d50ea8920238cb75edcf3d | MATLAB | 1,196 | 40 | function [Data_hydrogel, Data_gel] = readimpdata(date,filename)
%READIMPDATA Summary of this function goes here
% Detailed explanation goes here
% Load the channel location file
load('chanloc_hydrogel.mat');
load('chanloc_gel.mat');
channel_num = 33; % 32 working + GND
addpath(date);
D = readlines(filename);... |
a8b03856f56fef017b8e9f558a4149adce289468f7675eba19a8a01b8493e5a5 | MATLAB | 1,196 | 35 | function [units, trials, runInfo, pupilInfo, sessionInfo] = preprocessAllenData(sessionFile, requiredStims, options)
%% TO DO:
% 'ITI' trials?
if ~iscell(requiredStims)
error('requiredStims must be a cell or cell array')
end
% load session file
load(sessionFile); % this is just the spike times dict atm
% generate... |
de5d40c39897881819bdbd3fb72bc37e88d377054dbbebad6fc1f0fab94ddb52 | MATLAB | 1,196 | 27 | cell_data = all_data.cell_4; %relevant_cell
y = cell_data.intensities(1:10).psth;
%% plot PSTH
nac_colores = {'c', 'm', 'r', 'g', 'y', 'k', 'm'};
% f4 = figure;
% set(f4,'color', [1 1 1]);
% set(f4,'position',[50 50 500 750]);
% for i = 1:36
figure
for j = 1:size(clusters.cluster_cells.cluster_2{i}.intensities... |
eea6d962a96adc2f99027e02a00207e425a07f39c063e353ff90d1000befd1a3 | MATLAB | 1,196 | 32 | function [ movie,lfi ] = filterMovie( p,movie,fType )
%FILTERMOVIE filters movie according to the paramneters set in p
% remove nans
movie(isnan(movie)) = 0;
%% create cutoffFilter and normalize to be between 0 and 1
padSize = size(movie,1);
filterSize = [size(movie,1) + 2*padSize , size(movie,2) ... |
ef80b52c3520da4838e2f47180d9657016d01008d78bb718f722ea48e2f00632 | MATLAB | 1,196 | 28 | function [fitresult, gof] = nakarushtonDB(xData,yData)
% Define the Naka-Rushton equation for curve fitting. The Naka-Rushton equation is
% typically used in psychophysics to describe the relationship between stimulus
% intensity and perception intensity.
% Equation form: r * (x^n / (x^n + k^n)) + b
... |
a02092a42b2454004e8f6bd4e14b3860ad37629ebb875234b018b81d54be476d | MATLAB | 1,199 | 44 | function plot_MM(f)
mean_line_broadening = 8;
for k=1:length(f)
fid = f(k).fid;
spec = f(k).spectrum;
resolution1 = spec.spectral_widht/(spec.n_data_points-1)/spec.synthesizer_frequency;
freqat0ppm1 = spec.FreqAt0/spec.synthesizer_frequency+4.7;
%Fppm = freqat0ppm1:resolution1:freqat0ppm1+(spec.n_... |
e7de17504dd8516f98ea6f26872069ff91ca294f031b54361b71e83e0eee80cb | MATLAB | 1,199 | 40 | %% batch process sessions for analysis
dataDir = 'C:\Users\edward.horrocks\Documents\Code\V1Dynamics\Data\basic_111022';
sessionTags = {'M22027', '20220517';...
'M22029', '20220607';...
'M22031', '20220524';
'M22032', '20220621';
'M22033', '20220706'};
%%
saveSuffix = 'PSTH_fastSlow_v2.mat';
inputSuf... |
7f8a8416a7a73161920b814f3267d87e2df0685dd456b5f2dd85b2e080c81bd3 | MATLAB | 1,201 | 21 | %This master script keeps track of the main processing steps for the project
%Preprocess data using Glasser2016 parcels, surface data, HCP100 data (msmsulc)
%Minimally preprocessed Human Connectome Project data are used as input. Publicly available at: https://www.humanconnectome.org/
%Glasser2016 parcels available a... |
fda5e29e33b89a298dbb48f8fae49ba1600714fc21b2b93d25c5f51afa596cf7 | MATLAB | 1,201 | 34 | % A test script for band_lim_sig_sat_recov(), which recovers saturated signals by iterative band-limited filtering
% Reza Sameni, 2024
% The Open-Source Electrophysiological Toolbox (OSET)
% https://github.com/alphanumericslab/OSET
close all
clear
clc
% Load signal from file (to be replaced with an arbitray signal)
... |
6eb7188592306efb5660c008343de0b7d39675f456bf8c083b8ad21df55abb78 | MATLAB | 1,203 | 27 | function [XYZ,ArcLength,Step_Length,Fs,Y1] = Distribute_Equidistantly(XYZ,Eval_Points_Num,Fit_Res)
% This function gets an ordered set of 3D points.
% First, it fits a cubic spline to the data.
% Then, it computes cumulative length along the fitted curve in order to find the step length needed to distribute
% th... |
c645eb53179f0f7821a51f99656fd3fb8b0417c152ac4f9c2db6be16fe5d732e | MATLAB | 1,203 | 30 | function fil_movie = get_lowpass_movie(p,movie)
% remove nans
movie(isnan(movie)) = 0;
fil_movie = movie;
%% create cutoffFilter and normalize to be between 0 and 1
padSize = size(movie,1);
filterSize = [size(movie,1) + 2*padSize , size(movie,2) + 2*padSize];
cutoffFilter = mat2gray(fspecia... |
b4ab6e597d49ce4079d2d8bc53e561d584350754429a55eaa7ee524ff2025103 | MATLAB | 1,204 | 31 | function rates = getFiringRates(obj, clusters, nSamples)
%GETFIRINGRATES Compute firing rates for specified clusters
if nargin < 2 || isempty(clusters)
clusters = 1:obj.nClusters;
end
if nargin < 3 || isempty(nSamples)
nSamples = round(obj.hCfg.recDurationSec()*obj.hCfg.frSampleRate);
... |
7b580d44d9b336d05f540738cb88658832604b42c65a11d36fb04c03075a98d0 | MATLAB | 1,205 | 40 | function [map_states] = mapstate_fb_draw(post_draws, Y, xDat)
%%
st_post = post_draws.states_post;
Omega_est = mean(post_draws.Omega_post,4);
sqrl = mean(post_draws.sqrl_post,4);
rho = mean(post_draws.rho_post,4);
N = size(st_post,1);
subs = size(Y,3);
S = size(Omega_est,3);
R = size(Y,2);
map_states = zeros(N,subs);
... |
aa54b09bfcacff5b288b79241f906e6c79f5290c4f0d6e8ddbb8ce312e35a894 | MATLAB | 1,205 | 27 | global resection_time
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% PT
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
time_PT_vect = xlsread('data.xlsx', 'C3:J3');
PT_mat = xlsread('data.xlsx', 'C4:J9');
N = size(PT_mat, 1);
PT ... |
f03d64adb611d78561a0f73edac842540d3d51f8b1270084647282bf1682d47c | MATLAB | 1,205 | 40 | function phase = PhaseCalculation2(x, y, xi)
% PhaseCalculation2 is incomplete and has been deprecated. Consider using phase_calculator instead.
warning('PhaseCalculation2 is incomplete and has been deprecated. Consider using phase_calculator instead.');
%
% [phase phasepos] = PhaseCalculation(x, y, xi)
% ECG phas... |
64c61f4f51b5f12e1bbd6036c906528d44301a95014774a14ac5698957baa38e | MATLAB | 1,206 | 31 | %% script for ONC analysis and figures
%% load data for UCL
load('alldata_EEG_control_post_UCL.mat')
load('alldata_EEG_control_pre_UCL.mat')
load('alldata_EEG_heellance_post_UCL.mat')
load('alldata_EEG_heellance_pre_UCL.mat')
%% load data for Exeter
load('alldata_EEG_control_post_exeter.mat')
load('alldata_EEG_contr... |
fba954b56125d2d0f162c54785c5388946c6faab858414d8cc5d6ac3b39fbdf4 | MATLAB | 1,207 | 38 | function [X_Fit,Y_Fit,Success_Flag] = Fit_And_Smooth(Vx,Vy,SmoothingParameter)
Success_Flag = 1;
Angle1 = atan2d(Vy(end)-Vy(1),Vx(end)-Vx(1)); % The angle of the new x-axis.
[Xr,Yr] = rotate_vector_origin(Vx,Vy,[Vx(1),Vy(1)],-Angle1); % Rotate vector to avoid repeating x-values.
dx = Xr(2:end) - Xr(1:end-1);
... |
6c78e6696b6cb09bb0cc92e7a0a2b0627a13128a734a39560ee21d010cc98b4f | MATLAB | 1,211 | 31 | %% Example 1: Extract Neural States from Raw Spike Trains using DimReduce
% Perform dimensionality reduction with DimReduce and automatically
% visualize data in DataHigh. The data has raw spike trains from 61
% neurons for 56 trials for seven reach directions. The bin size is 1ms,
% and each trial is 400ms (take... |
e307a71e1b9f1cb75c98bdd0a5ee39f1bfe48d70a8a0daa157cd94da9a661d6e | MATLAB | 1,217 | 49 | function [vals, vi] = subsample(vals, k, dim)
%SUBSAMPLE Sample k items from vals, optionally along dim
if nargin < 3
dim = 2;
end
if isvector(vals)
doReshape = isrow(vals);
[vals, vi] = subsampleVec(vals(:), k);
% reshape sampled values if necessary
if doReshap... |
eee0bdf8cb7ad49f4d77b8cdc5ce90b91946e606cdc82701a06c8e751a4f92e0 | MATLAB | 1,217 | 26 | function [ output_args ] = runSignalExtraction( p )
% runs extraction for every subject and session
% loop through subjects and sessions
for sub = 1:p.nSubjects
for ses = 1:p.nSessions
disp([char(datetime('now')) ' Starting signal extraction for Mouse ' num2str(sub) '/' num2str(p.nSubjec... |
22bd130b6d3268503609055de5442cea3c727a4461cdf7381453f6906a1fe832 | MATLAB | 1,219 | 37 | %Copyright (C) 2019, Adria Tauste Campo
%Contact details: adria.tauste@gmail.com
%Copyright (C) 2079, Jiantao Jiao
function [eta,beta_M,contT_M] = ctwupdate_M( eta, index, xt,alpha,beta_M,contT_M, j_index)
% countTree: countTree(a+1,:) is the tree for the count of symbol a a=0,...,M
% betaTree: betaTree(i(s) ) =... |
e35e409e7a832d0a9d77e22dfc6191d7b8c2afff58094e1c13a6251d1f286530 | MATLAB | 1,221 | 28 | clc
clear
Dis_statistics = cell2mat(Dis_statistics);
Max_Dis = max(Dis_statistics,[],2);
Min_Dis = min(Dis_statistics,[],2);
Dis_classify = zeros(8,1);
[x,~] = size(Max_Dis);
for i = 1:x
if (Max_Dis(i,1)/Min_Dis(i,1) < 3) && (Max_Dis(i,1)/Min_Dis(i,1) > 0)
if ((Max_Dis(i,1) > 10) && (Max_Dis(i,1) <... |
b87c4e3d699c072e9caed6ecace10a4d2c51f8b3e3154449367eb7b676bdc47c | MATLAB | 1,222 | 44 | % Plot Clean Calcium Transients
% After Manual Data Review
% Input
% SIGNALSclean,
% Indexes
% Onsets
% R_Condition: Durations
% Output
% Calcium Transients
function CaTransients=Plot_Calcium_Transients(SIGNALSclean,Onsets,Raster_Condition,Indexes)
% Setup
% New_Index_Active=New_Index(TotalActiveNeurons);
% A... |
dee668ea7a662db13749c9a1f37e5c9ffddbbbb8cae4a5b94efbf408cfb02e56 | MATLAB | 1,223 | 31 | % Authors : Simon Wadle 2019-2023
%function exports tif with the generic MATLAB function
function tiff_export(image_data, path)
output_file = Tiff(path, 'w');
[height, width, frames] = size(image_data);
export_data = uint16(image_data);
% export_data = reshape(export_data, height, width, 1, frames);
placeh... |
1eb5b339b57daf5f38bce1d8465541e2644b94fb5e033b591c4349502f51a8c5 | MATLAB | 1,224 | 63 | %Function to relabel simulation results for easier comparison
function map = relabel_draws_multi(Omega_prev,st_prev, Omega_next,st_next, alpha)
%%
[p,~,k] = size(Omega_prev);
[S,N] = size(st_prev);
parcor_prev = prec2parcor(Omega_prev);
parcor_next = prec2parcor(Omega_next);
%parcor = 1*(parcor~=0) ;
%%
%%
pr... |
d19dcd5ff7c0fc83d71b609478d296cee1f0550940aea152d06051b23170d380 | MATLAB | 1,226 | 44 | function [valindSQI, indSQI] = SQI5(x, winLen)
%
% [valindSQI, indSQI] = SQI5(x, winLen)
% Channel selection based on channel power in sliding window
%
% Fahimeh Jamshidian Tehrani,
% jamshidian.t@cse.shirazu.ac.ir,
% fahimeh.jt@gmail.com
% February 2020
%
% input:
% x: input JADE component of fetal ECG record
% wi... |
9d00b6f9ab5f7477c762f45f661c67dc8909352d93330e1001fcbdb25b3de090 | MATLAB | 1,227 | 38 | function seqTouch
cd('C:\Users\C238\Desktop\touchcellLtRt_matsuda');
load('seqTouch.mat');
Ltseq5TouchFRMeanArray=[];
Ltseq5TouchFRStdArray=[];
Rtseq5TouchFRMeanArray=[];
Rtseq5TouchFRStdArray=[];
for a=1:6
Ltseq5TouchFRMeanArray=[Ltseq5TouchFRMeanArray mean(Ltseq5TouchFRArray(:,a))];
Ltseq5TouchFRStdArr... |
cdb1521fdb6cd93abeea7b170b086e4aa8eb6751254f1ca14c2792645538804c | MATLAB | 1,228 | 58 | % str = mynum2str(data, sigfig, maxdec) - converts a number to a string
%
% mynum2str examples (sigfig=3 and maxdec=2)
% 123456.0 --> 123000
% 123.4560 --> 123
% 12.34560 --> 12.3
% 1.234560 --> 1.23
% 0.1234560 --> 0.12
% By Ran Klein 2012???
% Modified:
% 2013-12-31 RK Added NA result for empty data.
function str... |
e52b2a52691936e9ad17f12821aec8d3e9eb47c35844fbba8fddacb2b2ab40d6 | MATLAB | 1,228 | 42 | function [] = plotONOFFStim(COPE, savename, savepath, color)
% This program is intended to plot the contrast between transgenic and WT.
%
% input:
% COPE.roi: (n,1) roinames
% COPE.val: (n,1) valu
% COPE.groups: (n,1) groups
% datapath:
%
% output:
% .png figure in a savepath
%
% by Hiroaki Hamada, Dec 18th, Araya
... |
d97389d36d858dd8ed36f090a0e8e114957ab4ffcb5652a4d77c8d7878a6f555 | MATLAB | 1,229 | 29 | % y = Median(x,N,L1,L2),
%
% Single or Double stage median filter
% inputs:
% x: input vector. x should be in 'double' format.
% N: input signal length (length(x))
% L1: first stage median window length (in samples)
% L2: second stage median window length (in samples)
%
% output:
% y: output c... |
45580b7cb4f4bb4ee2c06cf85cd5b55b0d007403cf3d947462b986c20b0da63c | MATLAB | 1,230 | 58 | function y = reshapee(x, varargin)
%|function y = reshapee(x, varargin)
%|
%| reshape function that allows possibly one null argument, and all
%| other arguments can be vectors, unlike matlab that requires scalars.
%| example: reshape(rand(2*3*5,7), [2 3], [], 7) will become [2 3 5 7]
%|
%| in
%| x [(*dim)]
%| vararg... |
175b6401f1b491d2acaf109b2427fa6af8b9644932b4b16039177733590255e6 | MATLAB | 1,231 | 45 | function newlist = addsufixtofilenames(filelist,prefix)
% filelist must be a cell array where each element is a char-array
% containing a list of file names.
nocell=0;
if ~iscell(filelist)
filelist={filelist};
nocell=1;
end
newlist=cell(1,length(filelist));
for i=1:length(filelist)
wfiles=... |
f67bf818762e3850fe3773ebb47b0781fa06de8f7b9704c3715113de943ecd9c | MATLAB | 1,231 | 41 | function ind = ChannelIndex3(x,w,bin,N)
% Channel selection based on energy deviations followed by a bandpass
% filter using a DFT filter bank
%
% Reza Sameni
% December 2008
% L1 = size(x,1);
L2 = size(x,2);
% y = zeros(size(x));
% % % ind = zeros(L1,1);
x = (x - mean(x,2)*ones(1,L2)) ./ (std(x,[],2)*... |
004f5001054c40158c0bcfbdfbea8e3f2b1ede035a41b31b8eec460da0f2fbd9 | MATLAB | 1,232 | 36 | function [d,p] = getkpaths(map,K)
%% Calculate the K shortest paths between any pair of nodes
% This function calculates the K shortest paths from any node i to node j
% in the network. The length of a path is defined by the sum of the inverse
% projection of the nodes along the path. The shortest path (i,j) is the... |
c4c045a754ee07a15a9bb43b36b0a0066770e8dd38a293fb7de42ad0bc7489e9 | MATLAB | 1,236 | 45 | function c = get_water_content2()
marsbar('on');
%anat_dir = '/home/romain/data/spectro/hipo/OCC/anat';
%suj_dir = get_subdir_regex(anat_dir,'pilote')
anat_dir = '/home/romain/data/spectro/tourette/anat';
suj_dir = get_subdir_regex(anat_dir,'rett')
for ns = 1:length(suj_dir)
[p,f]=fileparts(suj_dir{ns}); [p,sujn... |
4c1f6b98f2b5cdee4f4432528c72d06794232fab8e58ba1f827dcf1e62a46992 | MATLAB | 1,237 | 41 | function b = baseline_sliding_window(x, L, approach)
% baseline_sliding_window - Baseline wander extraction from biomedical recordings using a single stage of median or moving average filtering.
%
% Syntax: b = baseline_sliding_window(x, L, approach)
%
% Inputs:
% x: Vector or matrix of noisy data (channels x s... |
bb2ddc71e7be36e17caa2a7c17dbbc9fb234c136b9d340d46c42ef8a0c5000d3 | MATLAB | 1,238 | 40 | function Plot_all_features(handles)
% function Plot_all_features(handles)
USER_DATA = get(handles.wave_clus_figure,'userdata');
par = USER_DATA{1};
inspk = USER_DATA{7};
classes = USER_DATA{6};
colors = [[0.0 0.0 1.0];[1.0 0.0 0.0];[0.0 0.5 0.0];[0.620690 0.0 0.0];[0.413793 0.0 0.758621];[0.965517 0.517241 0.034483];... |
610f9a434b3148241b96f814ca841db53862d132bdc1d98cde2c25483c7f05ac | MATLAB | 1,240 | 33 | function V = gompertz_exp_alpha0_beta(param, DT_invitro, temps, Vc, V0)
% param = [alpha_0, beta] parameters of Gompertz growth, according to
% dV/dt = (alpha_0-beta*log(V/Vc))*V
% hence alpha_ = relative growth rate at V = 1 cell (volume Vc)
% DTinvitro = doubling time in vitro in hours
DT_invitro = DT_invitr... |
6b26c9c90564d38c7b06f887d99235cedcf7186bb429508a274b3ef700397ee0 | MATLAB | 1,241 | 42 | function [] = plotONOFFStim2(COPE, savename, savepath)
% This program is intended to plot the contrast between transgenic and WT.
%
% input:
% COPE.roi: (n,1) roinames
% COPE.val: (n,1) valu
% COPE.groups: (n,1) groups
% datapath:
%
% output:
% .png figure in a savepath
%
% by Hiroaki Hamada, Dec 18th, Araya
% plo... |
031445933b4f60df390ef208fab6b45a70d1fa9e409960e3d23ab3af47ae0cdb | MATLAB | 1,242 | 46 | function [valindSQI, indSQI] = SQI3(x)
%
% [valindSQI, indSQI] = SQI3(x)
% Channel selection based on measure of non-gaussianity
%
% Fahimeh Jamshidian Tehrani,
% jamshidian.t@cse.shirazu.ac.ir,
% fahimeh.jt@gmail.com
% February 2020
%
% input:
% x: input JADE component of fetal ECG record
% output:
% valindSQI: va... |
79fa771ba77445e7230c8e2f2dd78218791ae9948936eaba50a203ed17c400d3 | MATLAB | 1,242 | 32 | % Function that gets the indexes to read variables from workspace
% with a specific sufix -Filterby-
% Input
% WorkSpaceVariables: list of variable names (who function)
% Filterby: Sufix of the Variable
% NC: N variables to read
% Output
% index_var index of the workspace variable
% index_CHECK check if it ... |
e0e802d0e4aa96949304388606d2eafbc76b4117febcb3f4d9e6cde254513e99 | MATLAB | 1,243 | 35 | function y = LPFilter(x,fc)
%
% y = LPFilter(x,fc),
% Second order zero-phase Lowpass filter
%
% inputs:
% x: vector or matrix of input data (channels x samples)
% fc: -3dB cut-off frequency normalized by the sampling frequency
%
% output:
% y: vector or matrix of filtered data (channels x samples)
%
%
% Open Source EC... |
6089f07d3fb64da65bcca425ee6ee6a40ab45eea3e7a5ba96013d61720682617 | MATLAB | 1,245 | 41 | function plot_results(session, channel, unit)
% Plot some results
%
% Parameters
% ----------
%
info = get_info();
fprintf('===== Plot some resutls =====\n\n');
main_timer = tic();
probe = [7, 6]; % probe
trials_type = 'test'; % 'train', 'test', 'validation' | scalar
perio... |
fccd011f737bdd522cb48f0e08329a4f4709368b6a0a30a32c55e7bdf12a25a6 | MATLAB | 1,246 | 45 | clear
clc
close all
%%% choose the file to look at
Filepath = 'E:\Data\Preprocessed\Clean\Waves\Music\P07_Music_Session2_Clean.mat';
% Filepath = 'E:\Data\Preprocessed\Clean\Waves\Game\P10_Game_Session2_Clean.mat';
%%
% Choose window to look at
Window = [80 95]; % seconds
Window = [0 400];
Window = [0 300];
WindowL... |
1a9a299cf110c377b21678e5992ca6d4514954d7e1d1ced15a44052547eea4f2 | MATLAB | 1,247 | 25 | cd('I:\My Drive\ELYASHIV\PFC_paper_results_after_revision\per_mouse\figure_1\1i')
T = importdata("side_per_session_data.mat")
%%
T.mouse = cellfun(@(x) x(1:6), T.sessions,UniformOutput=false);
T.res_n = T.GroupCount.*T.mean_is_responsive
T.IE_n = T.GroupCount.*T.mean_is_ir
Tm = grpstats(T,["mouse","side"],@sum,... |
df1a8d85c78b063b176593182106e96fbf5c01b7514a81c70c5b52ad438fbebf | MATLAB | 1,248 | 60 | % For a pixel vector, show how the map is
function [] = ShowField_Rec(Vec, Range, XPix, YPix, varargin)
if size(Vec,2) == 1
Map = reshape(Vec(Range),YPix,XPix);
imagesc(Map)
colormap jet
set(gca,'YDir','normal')
colorbar
elseif size(Vec,2) == 3
Map = zeros(YPix,XPix,3);
for LgnInd = 1:3
... |
bde593d001c4a24bf01736a9c2cf8d65a0cc41ce6eef3c4867fb852bc6bce5b9 | MATLAB | 1,250 | 35 | cd('I:\My Drive\ELYASHIV\PFC_paper_results_after_revision\eopn3\eopn3_vs_scarlet')
NDs = [10, 8, 6, 4, 3, 2, 1];
% save('eopn3_vs_scarlet', 'T2')
T2 = importdata('eopn3_vs_scarlet');
n_perm = 100000;
tail = -1;
t_stat = ['t'];
t_stat = ['w'];
% t_stat = ['td'];
[pval(:,1), t_orig(:,1), crit_t]=mult_comp_pe... |
b84b8e010fd1d2e6f18cdc50233c5af64c02c467090e4365fa4e804819fc252d | MATLAB | 1,251 | 36 | % The script calculates the Inter Spike Intervals (ISIs) and plots the
% results in a histogram. The proportion of ISIs violating a threshold
% is also calculated and indicated in the histogram.
% authors: Francesco E. Vaccari
% date: 10/2022
clear all; clc; close all;
addpath('supportFunctions')
currentFolder = pwd... |
0ea6e12d8248a9051d41145c3558602f7536f33b69d6d759ab382e05f15958cf | MATLAB | 1,252 | 37 | function download_data
%% Download Allen Mouse Brain Connectivity Atlas in JSON format.
base_url = 'http://connectivity.brain-map.org/api/v2/data/ProjectionStructureUnionize/query.json';
crit_opt = '?criteria=[section_data_set_id$eq%s],[hemisphere_id$ne3]&num_rows=all';
only_opt = '&only=section_data_set_id,struc... |
09d871759679655c4d28ed02038c78df31c8ce831132df2f2951979e9d038f8c | MATLAB | 1,254 | 36 | function polarity = ecg_polarity(ecg, fs)
% ECG_POLARITY - Calculate the polarity of multilead ECG data
%
% polarity = ecg_polarity(ecg, fs)
%
% This function calculates the polarity of multilead ECG data by estimating
% the skewness of the ECG signal after baseline removal and determining
% whether the skewness is p... |
4e2827bb230aeb8b4d99a7f1ac631191087becd7e877c232f3d9f7e3ab958c34 | MATLAB | 1,254 | 47 | function a_dist = spherical_dist(x,y)
%SPHERICAL_DIST Spherical distance between two points on the sphere
%
%Syntax
% a_dist = spherical_dist(x,y);
%
%Description
% A_DIST = SPHERICAL_DIST(X,Y) sets A_DIST to be the spherical distance
% between the two points X and Y.
%
% The arguments X and Y must be arrays of the sam... |
296e9b233227884d4a96ab740e195d7fcfbea630e4b38afad843401e2e093756 | MATLAB | 1,255 | 73 | function conversion_speed_testing
%
%
% conversion_speed_testing
%
%
% TODO: This should be moved to the testing package.
root_path = 'C:\Data\GSK\ChrisRaw';
root_path = 'C:\D\GSK_Data';
base_file = '140207 control cmg.';
file_path = fullfile(root_path,[base_file 'adicht']);
mat_fpath = fullfile(root_path,[base_f... |
8db1ad2a421016e568b4b04fe223a418f16bcbdc49d65851bf96977359dd5f1d | MATLAB | 1,255 | 37 | clear
clc
on_window = 40:50;
sus_window = 100:130;
%%
% Needs to be in D:\Data\IR data Shira_May_14_2022
[file, path] = uigetfile('*.mat','MultiSelect','on');%When the user clicks the load psetion button, a window should open to enable the user to select a file.
cd(path)
datafile = fullfile(path, file); %save... |
43bbc8768b3d1a63a8cf0e963f7e3421e98fc9abb652b5f36ef114fb759c9e5e | MATLAB | 1,258 | 57 | % Test program for fetal ECG ploting
%
% Dependencies: The baseline wander and ECG filtering toolboxes of the Open Source ECG Toolbox
%
% Sample code for testing naive maternal template subtraction technique
%
% Reza Sameni (C)
% Email: rsameni@shirazu.ac.ir
% Web: www.sameni.info
%
% Crated 2006
% Modified ... |
42e04dd3b8c515b1fe90e05e4b3fc5f6f7b60b980d416d5a1dbf439477f942b8 | MATLAB | 1,261 | 53 | function make_fwc_curve(input,par_input)
%Handler to call wave_features to draw feature stats curve
%Inputs:
% matrix of spikes
% name of file with spikes inside
if nargin<2
par_input = set_parameters();
end
fname = {};
if isnumeric(input) && min(size(input))>1
fname = {[]};
elseif ischar(input)
fname = {inp... |
5d460777b5cfc88806aef1b36c09db4af51793a3175b21b9069650ec8d15ceb3 | MATLAB | 1,262 | 34 | function [FiltData, Filter] = highpass_filter(Data, SampleRate, PassbandFrequency, ...
StopbandFrequency, DesignMethod, PassbandRipple, StopbandAttenuation)
arguments
Data
SampleRate (1, 1) {mustBePositive}
PassbandFrequency (1, 1) {mustBePositive}
StopbandFrequency (1, 1) {mustBePositive} = Passban... |
c3477e4f618e2ab1a1dba70b2ea0e2b33740114938c328986d5e332e4737acbb | MATLAB | 1,264 | 34 | %{
----------------------------------------------------------------------------
%Stimulus bias changes by block
%Just model analysis
%Based on Rao 2010 Front Comp Neurosci
%Value updating + Prior updating
----------------------------------------------------------------------------
%}
function Dual_RL_model_bl... |
e26ccdd44ee38bce7fab9fe02de77dd0b0d6f83c4bd2262d108aa170b7f36f47 | MATLAB | 1,264 | 34 | %{
----------------------------------------------------------------------------
%Stimulus bias changes by block
%Just model analysis
%Based on Rao 2010 Front Comp Neurosci
%Value updating + Prior updating
----------------------------------------------------------------------------
%}
function Dual_RL_model_bl... |
89e07b07bb88ab71ad997466bb846454a355ee8843feef7b4ced73f71e61cc67 | MATLAB | 1,265 | 44 | function [fo] = correct_baseline_mega(fids,nb_point)
% RETURNS: the integral and centroid of the peak (in ppm)
% nb_point number of points to estimate, from the end of the means spectra
fo = fids
for nbser = 1:length(fids)
fid = fids(nbser).fid;
info = fids(nbser);
ppm_center = info.spectrum.ppm_... |
aaf41757fbcefe4eb0e6aa04b61f49c45cb768ef75f185fcc36ffc2de7efe31a | MATLAB | 1,265 | 33 | function [] = process_single_segment(foldername, Fs)
% calculate connectivity
% Hanlin's CellExplorer hecking codes
% Fs : seconds
% Assumes that the working directory is where this matlab file is
addpath(genpath("./CellExplorer-master"))
results_tmp_matpath = [foldername '/mono_res.cellinfo.mat... |
2665877cab4ae2108388e85db305c716d15a95bacfcd9d19babb9e35ffa54343 | MATLAB | 1,266 | 48 | function [mData]=mnl_MedianFilterEachChannel_2D(Data,r)
%Function to perform a 2D median filter on a 3D stack of each channel (i.e.
%a 4D image)
%Re-arrange to split channels into individual Z stacks
if isempty(r)==1
r=3;
end
dim=size(Data);
DataSize_pts=dim(1)*dim(2)*dim(3)*dim(4);
if isa(Data,'uint16')==1
Da... |
c63ca8630992fb82896198d5ee45c3e5f728c6b753d93aff72981a9f99272f02 | MATLAB | 1,266 | 28 | function [pcc] = get_pcc_from_prediction_ranks(prediction,grnnet);
% GET_PCC_FROM_PREDICTION_RANKS Set p-values of interactions according to the ranks of evidence.
% PCC = GET_PCC_FROM_PREDICTION_RANKS(PREDICTION,GRNNET) for gene regulatory network GRNNET and interactions PREDICTION
% (#interactions-by-3) with ro... |
eb5994a181b87465a35e7c67aa93d5fcdf427ae2eb785cca64f08d2485cc600b | MATLAB | 1,266 | 40 | port = serial('COM9','BaudRate',115200);
fopen(port);
% Specify the conductance values (in nS) and diffusion constant values (in
% nS^2/ms).
g_shunt_CH1 = 0.0;
m_OU_exc_CH1 = 0.0;
D_OU_exc_CH1 = 0.0;
m_OU_inh_CH1 = 0.0;
D_OU_inh_CH1 = 0.0;
m_OU_exc_NMDA_CH1 = 0.0;
D_OU_exc_NMDA_CH1 = 0.0;
g_EPSC_CH1 = 0.0;
f_poisson... |
82cbf55b3433bab56d7ee5c337ce610541fc71031f590a7c83183cecb5deb2eb | MATLAB | 1,268 | 29 |
if (strcmp(recording_type,'bAP_incremental'))||(strcmp(recording_type,'noopto_somastim'))
stim_idx = find(diff(Im2(:,1))>300);
stim_idx(find(diff(stim_idx)<=15e-3/dt)+1) = [];
elseif strcmp(recording_type, 'paired_stim_Ca')
stim_idx = find(diff(Im2)>300);
stim_idx(find(diff(stim_idx)<=15e-3/dt)) = [];
... |
3716f52a29843764acf0232b33442d5bb976b7c0d054e2e9d9db31a744750155 | MATLAB | 1,269 | 41 | function diam_bound = eq_diam_bound(dim,N)
%EQ_DIAM_BOUND Maximum per-region diameter bound of EQ partition
%
%Syntax
% diam_bound = eq_diam_bound(dim,N);
%
%Description
% DIAM_BOUND = EQ_DIAM_BOUND(dim,N) does the following:
% 1) uses the recursive zonal equal area sphere partitioning algorithm to
% partition the ... |
44c70d39df49ce53a68100c2ff1eb2cfd895d7f4c63d96306e386e3c9fecc379 | MATLAB | 1,270 | 40 | % Function to plot pdf's of simmilarities
% between or among ensembles of different condition
function plot_pdf_simmatrix(SIM_MATRIX,Names_Conditions)
%% Setup
NC=numel(Names_Conditions);
auxcomb=1;
figSimmMAt=figure;
figSimmMAt.Name='PDFs for Simmilarities among ensembles';
ax1=subplot(1,1,1);
hold(ax1,'on');
%% Set C... |
7b2ff3c5900d6e68ff86e8979e22112b215265e82a884e59770f3752e243dcb7 | MATLAB | 1,270 | 37 | function flag = recover(obj, ask)
%RECOVER Inconsistent data recovery, with a hammer
if nargin < 2
ask = 0;
end
flag = recover@jrclust.interfaces.Clustering(obj, ask);
if flag < 1
return;
end
inconsistentFields = obj.inconsistentFields();
if flag == 2 || ismember('temp... |
379f594c28c60a1bebaeae179a05e8f59be1951270d850fc021ce02f23a0c9d3 | MATLAB | 1,272 | 34 | function [expKer,kerSize] = getExpKer(tau_decay,tau_rise,Fs)
%getExpKer - Get exponential kernel
%
% Usage:
% [expKer,kerSize] = getExpKer(tau_rise,tau_decay,Fs);
%
% Description: This function creates an exponential kernal that mimics
% the dynamics (rise time and decay time) of a fluorescent sensor... |
bdaefcc1854691d4b17c2f6777ca8d74fd391213c3e85cdbf677b37a77f1b3b3 | MATLAB | 1,275 | 34 | %This Script saves all the variables from the current workspace into a
%single structure array.
%Author: Andres Gonzalez
%Year:2012
%Version:0.2
%#########################################################################
%Summary:
%This function allows to save all the variables from the 'caller' workspace into a struc... |
af290c03913c36330028245d5bc7f23b878e86edcc6367483b9e443cfaced775 | MATLAB | 1,276 | 37 | % Funtion to get Positive Threshold of a Feature
% where p(signal)>p(noise)
% using indexes of detected signals and noise
function Th_pdf=get_threshold_pdf(Feature,signalindx,noiseindx)
% Check if there is Another Threshold ################
if ~isempty(noiseindx) % Detected Signal and Noise Records
% Support ... |
b5f987619c58bcb76e4acf4dbace43be2989475262d42363a00053fece5a6688 | MATLAB | 1,276 | 25 | function W = Adjust_Rect_Width(Workspace1,Step_Parameters)
xv = zeros(1,length(1:ceil(2*Step_Parameters.Rect_Width)));
yv = xv;
W = ceil(2*Step_Parameters.Rect_Width);
% for i=1:floor((ceil(2*Step_Parameters.Rect_Width)-1)/S) % For each width.
for w=1:ceil(2*Step_Parameters.Rect_Width) % Scan from 1-pixel widt... |
0ab9e73db1b1c8640b36d70d118a3b8e5ead9a792e695bc5c8e709ec18b80f29 | MATLAB | 1,277 | 37 | % load n-NRF magnitude data for both sites
both_sites_magnitudes = readtable('both_sites_magnitudes.csv');
both_sites_magnitudes_hyp1 = both_sites_magnitudes(both_sites_magnitudes.hypothesis1_include == 1, :);
%% UCL
UCL_magnitudes_hyp1 = both_sites_magnitudes_hyp1(both_sites_magnitudes_hyp1.Site == "UCL",:);
ucl_lanc... |
f1a75bfbc4b72cebd79ab544baa9eaa78fd56593b0015bad95ec9d898b0f400b | MATLAB | 1,277 | 42 |
clc, clear, close all
data = importfile('prediction_per_actual.csv');
N = length(data); M = N/2;
% show R2 curves
% X
x_act = data(2:2:end, 1);
x_est = data(1:2:end, 1);
figure; plot(x_act - 500, x_est - 500, 'ok', 'LineWidth', 2, 'MarkerSize', 5);
hold on; plot([-600, 600], [-600, 600], 'r', 'LineWidth', 2)
xla... |
1fbcb52dd6858ce6dceaf774acef9377f8fd90f5a430c9d6b1fd6358399e17d6 | MATLAB | 1,279 | 51 | function y = bp_filter_dft(x, fl, fu)
% bp_filter_dft - Bandpass filter using discrete Fourier transform (DFT) filtering.
%
% Syntax: y = bp_filter_dft(x, fl, fu)
%
% Inputs:
% x: Vector or matrix of input data (channels x samples).
% fl: Normalized lower frequency.
% fu: Normalized upper frequency.
%
%... |
c8fe9c71bd8b68cf748ac28bc504fcd439927f7519e3cbea08f0bde79631621c | MATLAB | 1,279 | 38 | % FastICA for Matlab 5.x
% Version 2.1, January 15 2001
% Copyright (c) Hugo Gävert, Jarmo Hurri, Jaakko Särelä, and Aapo Hyvärinen.
%
% Type fasticag to launch the graphical user interface
%
% Please refer to your Matlab documentation on how to add FastICA to your
% Matlab search path. (One place to start is th... |
ccd2457975309516dcaa340d9c40095f3b1440d6614053d64826079d45c5ce16 | MATLAB | 1,279 | 28 | %% Select_Raster Script and RASTER FEATURES for POSITIVE CELLS***************
% INPUT:
% MetaDataColocaliation.PositiveCells: Indexes of the POSITIVE SELECTED
% fs: Sampling Frequency
% R_merged: Cell off POSITIVE SELECTED cells x Frames
% XY_merged: POSITIVE SELECTED Coordina... |
99d46bb7dfb27d906a65a6af5c305cd4e8f7da62791641ac79db419957a88655 | MATLAB | 1,280 | 41 | function vertex_diam = eq_vertex_diam(dim,N)
%EQ_VERTEX_DIAM Maximum vertex diameter of EQ partition
%
%Syntax
% vertex_diam = eq_vertex_diam(dim,N);
%
%Description
% VERTEX_DIAM = EQ_VERTEX_DIAM(dim,N) does the following:
% 1) uses the recursive zonal equal area sphere partitioning algorithm to
% partition the uni... |
be481fe636ac14cd93b18ae4d244e721d0842d778fd114419b4e099342dc1ae5 | MATLAB | 1,280 | 60 | function o=get_subdir_regex_one(indir,reg_ex,varargin)
if length(varargin)>0
o = get_subdir_regex_one(indir,reg_ex);
for ka=1:length(varargin)
o = get_subdir_regex_one(o,varargin{ka});
end
return
end
if ~iscell(reg_ex), reg_ex={reg_ex};end
if ~iscell(indir), indir={indir};end
o={};
if lengt... |
ce6c9b76cf7ec3c1855825ccc07c48f8fccfd56d0c616aaca0b400711cc08af4 | MATLAB | 1,280 | 41 | function [ av_trials, err_trials, trans_data ] = project_trials_into_one_subspace(p,av_trials,err_trials,trans_data,trial_idx,q,cell_means,dr_dc)
%% project DC trials into subspace and pool over tasks
% project trials into joint subspace
t_pool_av = [];
t_pool_err = [];
t_pool_trans = [];
... |
e8099137e881ea0fb1aa20b2676e0e83c1d3f05fcec52468aaaa894ad50a4692 | MATLAB | 1,280 | 49 | classdef ControlFigure < dynamicprops & jrclust.views.Figure
%CONTROLFIGURE
properties
hControls;
hPanel;
end
%% LIFECYCLE
methods
function obj = ControlFigure(figTag, figPos, figName, figToolbar, figMenubar)
obj = obj@jrclust.views.Figure(figTag, figPos, fi... |
433aa5eb9cfe1501d9ba3929125f16c468b8c35f116f2519dc074cb60b5f69df | MATLAB | 1,281 | 34 | %% Iterations of LDE: Use precomputed function to determine output of a cell type
% Input: L4EmeshX, L4ImeshY Domain of precomputed functions
% LDEFrfunc_Subf Functions of all input type for certain cell
% population
% L4EUse,L4IUse All input L4E L4I
% LDEUse Just fo... |
47cb4c732c02fb3ee0891bcf8265386bad294d5fa6693e4e4871641ff70ec5be | MATLAB | 1,281 | 35 | function [] = imwriteTFSK(Volume,name)
%imwriteTFSK дÈë3D-tiffÎļþ£¨Ö§³Ösingle/double)
% imwriteTFSK(volume,name)
t = Tiff(name,'w'); % Filename by variable name
tagstruct.ImageLength = size(Volume,1);
tagstruct.ImageWidth = size(Volume,2);
tagstruct.Photometric = Tiff.Photometric.MinIsBlack;
if ... |
b2d6afa3444b75a175f90ba9af559d3485309ed07f9ed3f5e2b316877485c1dc | MATLAB | 1,281 | 48 | function Output = cache_function_output(Function, varargin)
% runs a function, saves the output on disk, and next time the function
% is run, if there's something on disk, it just uses that instead of
% running the function again. Use for slow functions like designfilt.
% Example: Output = cycy.utils.cache_function_out... |
9a9e8b21bb8c6f21a80d008e7fcaca30daa2b94b2277ca6cf27f7f0af0420941 | MATLAB | 1,282 | 38 | function [EEGAllSessions, BurstsAllSessions] = load_sessionblock_data( ...
SourceBursts, SourceEEG, Participant, Sessions, BurstVariable)
% load EEG and/or bursts data from multiple sessions together
% TODO extend also for pupil data?
% BurstVariable should be either 'Bursts' or 'BurstClusters'
EEGAllSessions = st... |
0de43b39ccc616abd978fe4e388c4eca69b1ef22d262a1246514e1bf52105f52 | MATLAB | 1,283 | 49 | function []=mnl_CumulativePlotMatrix(Matrix)
%mnl_CumulativePlotMatrix is designed to plot cumulative distribution plots of
%multiple groups to compare the differences in the spread of the data.
%Inputs
%Matrix - Rows =each value
% - Columns = each group
szM=size(Matrix);
cmap=colormap(jet(szM(2)));
for i=1:szM(... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.