sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
0ad75f385d4e1561663e8e8463786240bc515827b9a80f14e9689be419c269c0 | MATLAB | 3,950 | 103 | function Both_RLt_Pattern_5Seq_20200304
path=['C:\Users\C238\Desktop\touchcellLtRt_terashita'];
cd(path);
LS=ls;
RLtouchPoint=zeros(1,6);
RLtouchFire=zeros(1,6);
X=[];
RLtouchUnit=[];
RLtouchPointUnit=[];
for i=1:length(LS(:,1))
TrimFolder=strtrim(LS(i,:));
if ~strcmp(TrimFolder,'.') &&... |
daf33b17a617508a1fe2e9b43141b84b2ac4a02fa244dbed79d4bd0b9edc6b44 | MATLAB | 3,950 | 130 |
%{
----------------------------------------------------------------------------
Determine the time window for task relevant neurons
%Start of task
%Sound on
%Sound off
%Before choice
%After choice (0sec)
%After choice (1sec)
%After choice (2sec)
%p = 0.001
%Each epoch, predict the prior, sensory and choice... |
27366ecd150af8c4b78b5b2cc446b5722497e5098908790d3b2925334d15bba0 | MATLAB | 3,952 | 65 |
%{
----------------------------------------------------------------------------
Determine the time window for task relevant neurons
%Start of task
%Sound on
%Sound off
%Before choice
%After choice (0sec)
%After choice (1sec)
%After choice (2sec)
%p = 0.001
%Each epoch, predict the prior, sensory and choice... |
bb7014f4c9836e7ac42d2fd2fde844c5e5a6c4b6b68f0edeec61eca27a33b76b | MATLAB | 3,952 | 102 | %% One-versus-all (OVA) Models ################
% Build model using : one-leave-out
% Input
% Xpca, Matrix of PCs
% ONEpca, PC i
% TWOpca, PC j
% Y, Vector of Labels of K unqiue labels
% BestKernel, Kernel for SVM
% x1Grid, Values t... |
00bcc6206189bc9a6fce7f66c2826cd3d26436703a28c9a186c99a268a428660 | MATLAB | 3,957 | 124 | % Options for Fixed Point Continuation (FPC)
%
%--------------------------------------------------------------------------
% DESCRIPTION
%--------------------------------------------------------------------------
%
% opts = fpc_opts(opts)
%
% If opts is empty upon input, opts will be returned containing the default
% o... |
f134cdf5b62bd641a52b98154d43140e394808d4abe0882e55143310415b7743 | MATLAB | 3,958 | 123 | function res = ComputeAnTopography(out,h, generalized)
% Copyright 2009-2011 Thomas Koenig
% distributed under the terms of the GNU AFFERO General Public License
if nargin < 3
generalized = 0;
end
UseWaitBar = 0;
if nargin < 2
h = waitbar(0,'Computing, please wait...');
UseWaitBar = 1;
end
if isempty(h)
... |
468d5961ed15da0ea97e9a521579a8cc259a420fb49abca110d88e7b2cc8ae1e | MATLAB | 3,959 | 158 | clear all
close all
clc
import_data
folder_fit = './fit_PT_met_SAEM';
fit_r = load([folder_fit '/fit.mat']);
beta = fit_r.beta;
psi = fit_r.psi;
[param_inds, param_ind_d] = compute_param_inds(...
folder_fit,...
subgroups,...
[2,7:11],...
subgroup_names,...
0);
pa... |
d09947c2c2023c634cbcce4e9d20afe3f857097493c56ffa31409cecb8ed3682 | MATLAB | 3,961 | 143 | function [a r phi zm] = circ_plot_ax(ax,alpha, format, formats, varargin)
%
% r = circ_plot(alpha, ...)
% Plotting routines for circular data.
%
% Input:
% alpha sample of angles in radians
% [format specifies style of plot
% pretty, histogram, density, []
% [formats standard matla... |
85d6babae97d43b26110d9b4a35ed55c94791d9cda46b78efa06143e2c24126e | MATLAB | 3,968 | 134 | function [clu, tree] = run_cluster_loc(par, multi_files)
dim = par.inputs;
fname = par.fnamespc;
fname_in = par.fname_in;
% DELETE PREVIOUS FILES
if exist([fname '.dg_01.lab'],'file')
delete([fname '.dg_01.lab']);
delete([fname '.dg_01']);
end
dat = load(fname_in);
n = length(dat);
fid = fopen(sprintf('%s.run... |
8e3aa889912c86c18a4042eca3730495a306e77816964b2e23f97b4d34ae27bb | MATLAB | 3,968 | 153 | function controlfilename = createLCmodel_control(metabname,l1,l2,l3,preproc_dir)
do_water_scaling=0;
single_NAA_basis=0;
gaba=1;
if single_NAA_basis
basis_set_file_path = '/home/romain/data/spectro/Basis_set/Gosia/NAA_090814.BASIS';
else
basis_set_file_path = '/home/romain/data/spectro/Basis_set/Gosia/basis_PRES... |
bb4db70f29f75cc8a12534b74a487f4d4c8d6d6e61e093062dd1fe784bbf2fc8 | MATLAB | 3,969 | 121 | function jobs = spmup_first_level_qa(varargin)
% routine calling spmup_basics and spmup_realign_qa
%
% INPUT jobs = spmup_first_level_qa
% jobs = spmup_first_level_qa(time_series,options)
%
% time_series is the fMRI set to analyze
% options define various choices to be used:
% 'Motion Paramete... |
03ba29e65e6e849b9957c30879d05ab0ab3ff4237c5080c8a1e80bae13ae27f4 | MATLAB | 3,971 | 126 | function [LFPsamples_Run TimestampArray filepath]=GetCSCall
global StartTime FinishTime fname
% StartTime
% FinishTime
DurationB=FinishTime-StartTime;
% cd C:\Users\B133\Desktop\WR_LVdata\6721_160218
% load 6721_160218.mat
cd C:\Users\kit\Desktop\CheetahData
%dpath='C:\Documents and Settings\kit\fXN... |
225d6f5447f45dcc2d1134edc8790fda967120a9b8c787c7fa9e442b26106177 | MATLAB | 3,971 | 173 | clear all
close all
sigma=0.5;
Veq=-76.72;
Vth=-39.51;
Vr=-41.70;
tau=11.85;
R=118.5;
C=0.098;
tau_Ref=0.1;
P=10;
taus=20;
epsilon=0.02;
Apostpre=1;
Aprepost=-1;
N_pres=150;
dt=0.1;
% Coeffs=1;
PLOTS=0;
wmax=0.2;
wmin=0;
nrewards=50;
nprepost=1;
Arewards=linspace(0.6,0.95,nrewards);
Apreposts=-1;%linspace(-1... |
5b18933de662c950878f5704056b132db3c0e509c454527fa154de481495ffa8 | MATLAB | 3,971 | 126 | function prediction20190809
%»ê¼ê̼àɨ¯és[N̸êÆïüÌJSDðÜÆßÄvè(rÇèɽµ½×E¾¯Å)
%CheetahWRLV20190703ÍCheetahWRLV20180729Ì©ç98or89ÆcomplexðÁ½ú¾¯ð²«oµ½Folder
path=['C:\Users\C238\CheetahWRLV20220213'];
cd(path)
Xerror=[];
Envedist=[];
... |
fd6517d1bde389716418c16b1f98a39b3a9dd4885731b9e7beecdb11cfb93f7d | MATLAB | 3,971 | 87 | clear all;
close all; clc;
%% Load all the file and call whichever you want to plot
load f_a_mat % benzaldehyde (satiated 5HT)
load f_B_mat %benzaldehyde (satiated control)
load s_B_mat % benzaldehyde (starved control)
load st_a_mat % benzaldehyde (starved 5HT)
%
load f_a_mat_lool %_linalool (satiated 5HT)
loa... |
9e54f374105c8f511b9e4f2ea1d6785901a1c9d4933cf809d3a55d5d3c656269 | MATLAB | 3,973 | 121 | % Copyright 2024 Eelke Spaak, Donders Institute.
% See https://github.com/Spaak/rift-phase-and-visibility for readme/license.
% Belongs with:
% Spaak, E., Bouwkamp, F. G., & de Lange, F. P. (2024). Perceptual foundation and extension
% to phase tagging for rapid invisible frequency tagging (RIFT). Imaging Neuroscience,... |
e564d0add36e22062e1f1fe954e57c042919d21265f2e8aad83d78a320e017f7 | MATLAB | 3,977 | 135 | function DoMyViz_node_Epilepsy(cortex, idx_atlas, Mean_Node_Diff_group, filename, vmin, vmax)
close all;
N = length(cortex.Atlas(idx_atlas).Scouts);
col = zeros(N,3);
figure(1)
subplot(2,2,1)
p=Mean_Node_Diff_group;
val_max=vmax;
val_min=vmin;
for i=1:N
p_scaledValue(i) = (... |
1484208c431083e4551b53848bf70f099d1d591eda0534bf79be1e2e2dbc4f6e | MATLAB | 3,980 | 95 | function extract_ecg_features_multileads(signal_name, data, fs, low_pass_fre, high_pass_fre, ...
median_window_coff, mean_window_coff, lead_list, ...
flag_po... |
4870ec2e9fba0e2e82b48987b97d9f7f0b361e25134df2ef07a75a22f1e18ac2 | MATLAB | 3,980 | 114 | classdef DetectTest < matlab.unittest.TestCase
%DETECTTEST Test spike detection
properties
hJRC;
nSpikes = 3736994;
end
properties (Dependent)
res;
hCfg;
end
%% SETUP
methods (TestClassSetup)
function setupProps(obj)
hCfg_ = jrclust.Confi... |
f2c4aa3de39aec1c5016b9ed22a474300d07b5d086621d23cbd595282fec551f | MATLAB | 3,983 | 80 |
%% run teh function and get all the wave forms
%wf = getWaveForms(gwfparams);
function wf = getbestWaveForms(gwfparams)
%
% Extracts individual spike waveforms from the raw datafile, for multiple
% clusters. Returns the waveforms and their means within clusters.
%
% Contributed by C. Schoonover and A. Fink
... |
c813624ef00b7ab67f58f1208c94716362088c91af5599afc2fbe529db483839 | MATLAB | 3,987 | 124 | % Function to accumulate Raster Distances
% Active Cells in each Conditions are neural vectors
% Then, it is calculated the distance among conditions
% Input
% Load experiment manually from different folder: one by one
% R_Condition: Selected rasters of the Experiment
% Output variables @ CSV File
% Raster_Dis... |
76eed212050b82de268a2f4464d65f2a6d2365c3da35971b73252c2007900df3 | MATLAB | 3,992 | 165 | %%% Automatic Image Processing %%%
function [im_binary, skel] = AutGUI (im_neur, fileFolder, fname, olddir)
if isempty(lasterror) == 0 %c'è errore
[fname, fileFolder] = uigetfile({'*.jpg;*.tif;*.bmp;*.png;*.gif','All Image Files'}, 'Pick an image');
str = strcat(fileFolder, fname); %compl... |
d4c32d58183ad990bf22df56ef0954f45ab3eb02a5a02e73879bddf509864d96 | MATLAB | 3,992 | 87 | function updateFigSim(obj)
%UPDATEFIGSIM Update similarity score figure
if ~obj.hasFig('FigSim')
return;
end
plotFigSim(obj.hFigs('FigSim'), obj.hClust, obj.hCfg, obj.selected, obj.showSubset);
end
%% LOCAL FUNCTIONS
function hFigSim = plotFigSim(hFigSim, hClust, hCfg, selected, showSubset)
... |
410c90eca9544ca13905f8e3ad00f6603b08021e89be107b245cb81d58216f0e | MATLAB | 3,993 | 97 | function SpikeForm20191122
%CheetahWRLV20180729Part1ÉÛ¶³êÄ¢é·×ÄÌtfileÌ×EÌf[^ÉεÄHalfPeakwidthÆPeaktoTroughðvZµÄvbg
% ScNumbersÍS×EH4×EÌêÍ3D
% CellNumbersÍ×EÔA1,2,3,4...A½¾µA0à éBÇÌ×EÉàYµÈ¢L^iHj
% DataPointsÌ1³ÚÍ32_ÌdÊA2³... |
713940af6f122895497fabbb896bfbb44b120dab4636856f583c5710c56e62a2 | MATLAB | 3,993 | 113 | %{
Max Interval Method to Burst Detection (Fixed threshold-based method)
Bursts are defined using five fixed threshold parameters (max ISI at start
of burst, max ISI in burst, min burst duration, min IBI, and min number of
spikes in burst). The values of these parameters are chosen a priori and
any series of spikes tha... |
52208481ac8cd9575af623c2e0d0be576c6631f4a76f7a2039d8a44b3792083f | MATLAB | 3,995 | 100 | function AG_MN = consensus_iterative_MSCD(MSPoI,idxNE,idxEX)
%CONSENSUS_ITERATIVE Construct a consensus (representative) partition
%using the iterative thresholding procedure
%
% [S2 Q2 X_new3 qpc] = CONSENSUS_ITERATIVE(C) identifies a single
% representative partition from a set of C partit... |
9ef7d9881af2fd0b4c52091aca45e6f282fda256e08d0a4b859da5d56360b72c | MATLAB | 3,995 | 125 | close all
clear
clc
% Convert all dat files in bash using this command: find ./*/ -type f -execdir wfdb2mat -r {} \;
datafilepath = '../../../../DataFiles/physionet.org/files/ptbdb/1.0.0/';
directory_list = dir([datafilepath 'patient*']);
filelist = dir(fullfile([datafilepath, '**/*lr*.mat'])); % get list of all ma... |
b8e4c8eb243d1230a8e661b46bce3612b99b42aa0f077cb1112735152f4ec7a1 | MATLAB | 3,997 | 171 | function varargout = write_fid_to_lcRAW(fids,par,fidwater);
if ~exist('par'), par='';end
if ~isfield(par,'root_dir'), par.root_dir = '' ; end
if ~isfield(par,'subdir'), par.subdir = 0; end
if ~isfield(par,'gessfile'), par.gessfile = 1; end
if ~isfield(par,'water_ref'), par.water_ref = 0; end
if ~isfield(par,'file... |
2b802b2c78c54134a3a3fc1c691ce2d5b5df7cda471135427fdbd43780553913 | MATLAB | 3,998 | 105 | function [onset_sampl, onset_time] = emg_onset(emg, fs, W, varargin)
%
% [onset_sampl, onset_time] = emg_onset(emg, fs, W)
% [onset_sampl, onset_time] = emg_onset(emg, fs, W, th_coeff, Trl)
%
% *************************************************************************
% * EMG Onset Detection Function ... |
7bcdebf9a7c841a1cafb7a5df6701e8553573e3e3e2fb3f5c9cf3d58e5ca343a | MATLAB | 3,998 | 38 |
%%% WMH interaction testing
clear SNP_P SNP_Cardio_WMH_P SNP_Cardio_WMH_T
for snp=1:length(snps.Properties.VariableNames)
Outcome=sqrt(AD_ordered.x25781_2_0); %Outcome=(AD_ordered.x25019_2_0+ AD_ordered.x25020_2_0)/2; Outcome=clean(Outcome, 4);
APOE=snps(:, snp);APOE=round(APOE{:,1});APOE(CT_SNPs.FID==0)=NaN;
ix= AD... |
8d4fc55016b362042a4f6b122c72fcde3bd55994697cb66843da0657aa44e1b3 | MATLAB | 4,002 | 164 | clear;
close all;
fs = 1000;
data = load('s0010_re.txt');
% data = load('s0014lre.txt');
time = data(:,1)';
data = data(:,2:end)';
data = data - LPFilter(data,1.5/fs);
data = LPFilter(data,150/fs);
I = 1:10*fs;
data = data(:,I);
N = length(data);
% % % mn = mean(data,2)*ones(1,N);
% % % data ... |
93a25ae595628f6109d0c12e6eedd4297356e4ca4a1c4a741a458a09707ae008 | MATLAB | 4,002 | 100 | function behav = msExtractBehavoir(behav, trackLength)
%MSEXTRACTBEHAVOIR Summary of this function goes here
% Detailed explanation goes here
ROI = uint16([behav.ROI(1) behav.ROI(1)+behav.ROI(3) behav.ROI(2) behav.ROI(2)+behav.ROI(4)]);
%% Removes background
% takes the median of each pixel from 100 frames spread... |
348f5c2d65cb3d12aa2950d17f841856a0e9d698e9fe66758c28700e9068b1cc | MATLAB | 4,008 | 144 | %compute T2 from c result structure
%select dir where lcmodel result are it assume a separate directory for
%each subject (and each TE acquisition in this dir)
d=get_subdir_regex('/servernas/images5/romain/spectro7T/LCmodel','T2_array','^[BCMO]','.*','no');
c=get_result(d)
SD_thresh = 50; %parameter to skip te measu... |
216b8ef4f308e31a1f320979c3a482aa10f45f85d4c0bc900b05b0a0762b1dbb | MATLAB | 4,010 | 89 | function [data] = JT_processDual(data,params)
%Process Dual Color Photometry
%
% [data] = processDual(data,params)
%
% Description: This function is designed to process dual-color or dual-fiber
% photometry experiments. These experiments require demodulation, and
% this code will find the acquired refere... |
8f77ef74492f4347c2b0a29a80a91b91e28eb8aa6c21c96e0d481cb014fc68c5 | MATLAB | 4,011 | 128 | function interval20190527
%XpCNÅACµ½^b`ÌqXgO©ç¶«ÌC^[oðvZ·é
global SpikeArray TurnMarkerTime RpegTouchallWon LpegTouchallWon PhaseIndex TotalTime frequencyPksLt pksA1Rt MeanpksA1Rt pksA2Lt MeanpksA2Lt DiffMaxMinR DiffMaxMinL...
IntervalIndex
cd('C:\Users\C238\Desk... |
30c38a874cbad5a8ae86bd1e8f4eed2504da4ab04baa46ed6e210a8d8357c04e | MATLAB | 4,013 | 81 | %%Prerequest
%spike data and behavioral data (make sure the part name (end-16:end-4) of
%spike data file is the same with behavioral data file)
%% 1. Retrieve data
disp('select brain region folder about spike data');
Folder_SpikDat = uigetdir('E:\Wang_SpikDat\');
FileList_SpikDat = dir(fullfile(Folder_SpikDat, '*... |
342852957488733c4084f48be01f88d9bd7da5d0bce6fb1dd002a4be85750ad4 | MATLAB | 4,013 | 135 | function MakeDistribution20200227
%%%%%%%%%ªzÌfigureðìéfunction
% global W3distanceArray LtWindowintervaLArray RtWindowintervaLArray
path=['C:\Users\C238\Desktop\touchcellLtRt_terashita'];
cd(path);
LS=ls;
IntervalPhaseUnit=[];
for i=1:length(LS(:,1))
TrimFolder=strtrim(LS(i,:));
... |
ced00aa021668368f303462b394a1af13a6ae2115bb6bc1f87a788ed3901b871 | MATLAB | 4,013 | 96 | function [ nexFile ] = nexAddWaveform( nexFile, WFreq, timestamps, waveforms, name, preThresholdTimeInSeconds, numberOfPointsInWaveform, wireNumber, unitNumber )
% [nexFile] = nexAddWaveform( nexFile, WFreq, timestamps, waveforms, name, preThresholdTimeInSeconds, numberOfPointsInWaveform )
% -- adds wavef... |
5fb040d41b328ac8d7728bb739b957abf6777349ba5419edc7d31f2520179383 | MATLAB | 4,017 | 61 |
%%
%[groomBins ] = behaviorExtractionFunct('openArm', 3,10,mouseData, true, false, 'quiescent'); %behaviorExtractionFunct(behavior, preBinTime, postBinTime, data, rawData, base, baseBehavior)
groomBins = bins;
clear bins
%normal (1) average to entire trace (2) Tobias average (3) normalize to
%quiescence (4) no... |
3f7ec6dd7684d1ad8985c87ae997e2ef0e3c1ff9d1d8ef55d886285e5cc5ef1d | MATLAB | 4,021 | 116 | function data = image_to_sequence(img, mode, method, varargin)
% IMAGE_TO_SEQUENCE Extracts a sequence/time-series from an image.
%
% This function processes an image to extract a time-series representation,
% for example an ECG images. The method to extract the sequence depends on
% the image's characteristics (e.g., ... |
626bce0d41973fc1c29a553a3fb7c0de047c36b229922c3a022eda1bc3f3f752 | MATLAB | 4,023 | 135 |
%{
----------------------------------------------------------------------------
Determine the time window for task relevant neurons
%Start of task
%Sound on
%Sound off
%Before choice
%After choice (0sec)
%After choice (1sec)
%After choice (2sec)
%p = 0.001
%Each epoch, predict the prior, sensory and choice... |
58dee18a79906c10b1c504b1524c1b3a97cf1dfc232c8ec9849d63c36ac934c3 | MATLAB | 4,025 | 165 | clear;
close all;
fs = 1000;
data = load('s0010_re.txt');
% data = load('s0014lre.txt');
time = data(:,1)';
data = data(:,2:end)';
data = data - LPFilter(data,1.5/fs);
data = LPFilter(data,150/fs);
I = 1:10*fs;
data = data(:,I);
N = length(data);
% % % mn = mean(data,2)*ones(1,N);
% % % data ... |
5f8212dacdc049a8184f7284b39b906beb9d553f8f54468eff4a37d6abbfcd63 | MATLAB | 4,027 | 137 | % test baseline wander removal using median filter
% Reza Sameni, 2005
clear;
close all;
% load LOUETTE_07-Oct-2005_40s.txt -mat;data = LOUETTE_40s;clear LOUETTE_40s
% load fortict_20-Jan-2006.datfortict_20-Jan-2006.dat_acq.mat;
% load fortict4_20-Jan-2006.datfortict4_20-Jan-2006.dat_acq.mat;
%load Bonhomme3_20-Jan-20... |
a01e3a6c0493cff5bf2eca08f51f84b4528e51940c969bb951b503d206c805fb | MATLAB | 4,029 | 134 | function [] = compile(regime, varargin)
clear mex;
kMainFolder = fileparts(mfilename('fullpath'));
cd(kMainFolder);
kMainFolder = pwd;
kCPUFolder = fullfile(kMainFolder, 'c++');
kCppSource = fullfile(kCPUFolder, 'sources');
kCppInclude = fullfile(kCPUFolder, 'include');
kCudaFolder = fullfile(kCPUFolder, 'cuda');
kC... |
87801ac17815f5f9f8abd7f3e3dac43a2431e0d16f7b8ac5e01a5d173ee391b2 | MATLAB | 4,035 | 104 | % Epoch each subject's preprocessed task data around each presentation of
% probability stimulus.
% Enter path to SPM 12 folder
SPM_folder = 'D:\spm12';
% Enter Path to the MEG_Decision_Study folder
study_folder = 'D:\MEG_Decision_Study';
%% Set folders to save things
mkdir(fullfile(study_folder, 'Epoched_Data'));
to... |
dfb0f6751d96e50f667bcdf081d3d5233517df7a8ddc95ca77f452c5dc965f78 | MATLAB | 4,039 | 103 | % recreate snapshots in rectengular format
clear
sub = 11;
thisSub = p.subjects(sub);
%% load data
% load traces, filters
fpath = fullfile(p.rootDir,'miniscope',['subject' num2str(thisSub)],'jointExtraction','extracted','norm_traces.mat');
load(fpath,'tr... |
b11c6f2e4d092b37b44aaf5d142e6c25504c304a33d30d8da511110deba3046d | MATLAB | 4,043 | 166 | clear;
close all;
fs = 1000;
data = load('s0010_re.txt');
% data = load('s0014lre.txt');
time = data(:,1)';
data = data(:,2:end)';
data = data - LPFilter(data,1.5/fs);
data = LPFilter(data,150/fs);
I = 1:10*fs;
data = data(:,I);
N = length(data);
% % % % mn = mean(data,2)*ones(1,N);
% % % % d... |
0914578c00b661ea5d58151eb588418d8ece074977686af4414842e1736bd53a | MATLAB | 4,048 | 155 | function controlfilename = createLCmodel_control_laser(metabname,l1,l2,l3,preproc_dir)
ind=findstr(metabname,'_TE');
sss=metabname(ind:end);
ind = findstr(sss,'N');
echotime = sss(4:ind-1)
basis_set_file_path = get_subdir_regex_files('/home/romain/data/spectro/Basis_set/Gosia/LASER',[echotime,'.*BASIS$'])
basis_s... |
1d52e5a726fce5e45f192dab9827698645c2f582d1c931fb2082d2038b95228f | MATLAB | 4,054 | 89 | % This script demonstrates how the burst detection works with just one set
% of critera, one channel, and one frequency band.
clear
clc
close all
%% load the EEG data
Path = fullfile(extractBefore(cd, 'examples'), 'example_data');
load(fullfile(Path, "EEGbroadband_fulltime.mat"), "EEGbroadband") % like this, it will ... |
c97ce71be157c80773c343edea8d452b1a23c8c1e5b4db84b58892e9dac62117 | MATLAB | 4,060 | 132 |
%{
----------------------------------------------------------------------------
Determine the time window for task relevant neurons
%Start of task
%Sound on
%Sound off
%Before choice
%After choice (0sec)
%After choice (1sec)
%After choice (2sec)
%p = 0.001
%Each epoch, predict the prior, sensory and choice... |
5da42dda7be5e817ca6c468cb81c9747a588342434e237445d099015d5fd674a | MATLAB | 4,063 | 97 | %% single cell simulation: to collect mean V (and firing rates)
% NeuronType: 'e' or 'i'
% ExpoType: 'Mean'or 'All'
function [V,fr] = MEanFieldEst_SingleCell(NeuronType, ExpoType, SimuTime, TimeGrid,...
f_EnI, ...
N_EE,N_EI,N_IE,N_II,.... |
162d2a2494afb21cd372815d4d904ab6b7851039f09dc0f72e5aa7f38b5d3ebc | MATLAB | 4,066 | 107 | clear
close all
clc
spectral_window = 5.0;
spectral_overlap = 4.75;
spectral_len = 1000;
spectral_overlap_len = round(spectral_len*0.5);
dataset1_path = '/Users/rsameni/Downloads/013000/';
dataset2_path = '/Users/rsameni/Downloads/g1/';
dataset1_fnames = dir(fullfile(dataset1_path, '*.mat'));
dataset2_fnames = dir(f... |
47274f70ebe19eb363cd4a0e59fd66c9652de4b4c866667950c223515cda9b45 | MATLAB | 4,066 | 113 | function y = band_lim_sig_sat_recov(x, th_bottom, th_top, n_itr, ff_low, ff_high, th_bottom_end, th_top_end, verbose)
% BAND_LIM_SIG_SAT_RECOV - Recovers a saturated signal by iterative band-limited filtering
%
% This function attempts to recover a saturated signal by applying
% iterative band-pass filtering while pres... |
d97b6db7f6ed86ae322125b6255cc73f5f5c7ce80eabbff7ac22b98b8fbd9c05 | MATLAB | 4,066 | 127 | function phase20190522
%XpCNÅACµ½^b`ÌqXgO©ç¶ÉηéE«ÌÊðvZ·é
global SpikeArray TurnMarkerTime RpegTouchallWon LpegTouchallWon PhaseIndex TotalTime frequencyPksLt pksA1Rt MeanpksA1Rt pksA2Lt MeanpksA2Lt DiffMaxMinR DiffMaxMinL
% cd('C:\Users\C238\Desktop\CheetahWRLV201807... |
729ee3d1a5e233517c6a0efc4eafa9227c3d41903a263e903f34b576d83a335d | MATLAB | 4,072 | 185 | function go_netviewer_perctl(C,thresh)
C(eye(size(C))==1) = 0;
if thresh < 1; thresh = thresh*100; end
limit = prctile(C(triu(ones(size(C)),1)==1),thresh);
mask = abs(C) >= limit;
cLims = [-max(abs(C(:))) max(abs(C(:)))];
C(~mask) = NaN;
edgeLims = sum(~isnan(C));
sphereCols = repmat([0 0 0]/255, 78, 1);
edgeLims = [... |
27920c23f5fc2ab32c8a4013ed1563d891b55f06afa108d37de453d8db14d526 | MATLAB | 4,073 | 108 | classdef (Hidden) h5_file_h < handle
%
% Class:
% adi.h5_file_h
properties
file_path
m %Structure containing the meta data
end
methods
function obj = h5_file_h(file_path)
%
% obj = adi.h5_file_h(file_path)
%
... |
3dd93a91d8906eaf7b55730144c484b2080bc8f649ef94ac9eaa4f2b5d425bd0 | MATLAB | 4,074 | 92 | function lat = latency_dist_PV(pvcells,sourcedir, stimuli)
%LATENCY_DIST_PV Load response latency statistics.
% LAT = LATENCY_DIST_PV(CELLIDS,DIR) loads latency statistics for cue,
% reward and punishment (STIMULI) responsive cells of CELLIDS from SOURCEDIR. The results
% are returned in a struct containing... |
188dbe5b79d97977dba27a71f78913dd3eb89d52f2fe6fb2bf89ddcc053e66e3 | MATLAB | 4,078 | 126 | % do basic decoding for 4 different settings:
% neural vs. speed
% a/e vs. iti
clear
p = params_2DAA;
fpath = fullfile(p.processed_data_dir,'allData.mat');
load(fpath,'evs','tis','bvs','traces')
[cols,alpha] = chooseColors;
%%
fpath = fullfile(p.data_dir,'basic_ae_with_spd_and_iti_control');
saveData = 1;
saveFigs = ... |
3a2beaafe29c89899f121dc1a4ed36626be4fe5b8795e80d987f08dcb9761c89 | MATLAB | 4,078 | 131 | close all
clear
clc
% Convert all dat files in bash using this command: find ./*/ -type f -execdir wfdb2mat -r {} \;
datafilepath = '../../../../DataFiles/PTBDataset/Physionet.org/files/ptbdb/1.0.0/';
directory_list = dir([datafilepath 'patient*']);
filelist = dir(fullfile([datafilepath, '**/*lr*.mat'])); % get lis... |
c510df16844e9b43c8042a830fe8e2854ffbef573593d6787e62daedb1d00610 | MATLAB | 4,078 | 125 | function [Dimension]=mnl_BootstrapSomas(Somas)
nTrials=200;
nCells=[10 25 50 100 150 200 250 300];
sz1=size(nCells,2);
EuThresh=0:0.025:0.5;
szTh=size(EuThresh,2);
nSomas=size(Somas,2);
maxDim=size(Somas(1).VecNormMean,2); %The maximum number of dimensions
Chans=1:1:maxDim;
for i=1:maxDim
[PermCombo]=mnl_PermsNoCol... |
6fb8cbb573cb68bbcb23b0b344e5f7794195502ebd11ae7e64d273b4b7394398 | MATLAB | 4,083 | 100 | function varargout = save_string(tree, formatting)
% XMLTREE/SAVE Save an XML tree in an XML file
% FORMAT varargout = save(tree,formatting)
%
% tree - XMLTree
% formatting - 0 for indenting of tags, 1 for no formatting
% varargout - XML string
%_______________________________________... |
8361c1124a9387d9633b543faf64a9e95a582f75b118fb416b73f4e176ea5d43 | MATLAB | 4,085 | 119 | function prediction20190729
%\ªlÆÀªlÌs[NÌX²ûü̸êÆïüÌJSDðvZ·é
global EnveRLPhaseTM EnveCCresultSpikePro tfile meanXaxiserror Envedist
meanXaxiserror=NaN;
Envedist=NaN;
nanEnveRLTM=isnan(EnveRLPhaseTM);
K=find(nanEnveRLTM==1);
if length(K)<100; ... |
631d8231c49507dd2f5e17016d04a4d32851a698b2f6c946646dc0441d26026f | MATLAB | 4,088 | 99 | function RLphase20190505
global ACresult ACresultW CCresultDrinkOn SpikeArray TurnMarkerTime RpegTouchallWon LpegTouchallWon...
RpNum LpNum CCresultSpike locsP1time locsP2time locsP1time2 locsP2time2 pksA1 pksA2 CCresultRtouchWon CCresultLtouchWon dpath3...
StartTime FinishTime DiffMaxMinR DiffMaxMinL fna... |
a1a9917c825ce8e52eecc8402a74b9b0eb1bf540685fa3fb2992821c2c8470d2 | MATLAB | 4,090 | 100 | function RLphase20190422
global ACresult ACresultW CCresultDrinkOn SpikeArray TurnMarkerTime RpegTouchallWon LpegTouchallWon...
RpNum LpNum CCresultSpike locsP1time locsP2time locsP1time2 locsP2time2 pksA1 pksA2 CCresultRtouchWon CCresultLtouchWon dpath3...
StartTime FinishTime DiffMaxMinR DiffMaxMinL fna... |
c967d2a4a18300c251a85e2ad940c00f225a52f454ec98aa4a3f23c5a74a50f8 | MATLAB | 4,091 | 125 | function [Dimension]=mnl_BootstrapPercentUniqueSomas(Somas)
nTrials=200;
nCells=[10 25 50 100 150 200 250 300];
sz1=size(nCells,2);
EuThresh=0:0.025:0.5;
szTh=size(EuThresh,2);
nSomas=size(Somas,2);
maxDim=size(Somas(1).VecNormMean,2); %The maximum number of dimensions
Chans=1:1:maxDim;
for i=1:maxDim
[PermCombo]=m... |
8d953874b4c44942aff0da57238c6830bd92d42d6bf2e3257719de05079b65f9 | MATLAB | 4,094 | 126 | function res = undeleteUnit(obj, spikeClusters, unitID, indices, metadata)
%UNDELETEUNITS Speculatively undelete a unit, returning a snapshot of
%the spike table and metadata fields.
if nargin < 5
metadata = struct();
end
res = struct('spikeClusters', [], ...
'metadata', [])... |
d4805830d0ae1ff1995047a557ba3355a6753ec8cd37aafa5bcf17f257d19095 | MATLAB | 4,094 | 138 | clear
close all
clc
load ECG2
s = data(14, 1:end);
% s = resample(s, 0.3*fs, fs);
% fs = 0.3*fs;
s = LPFilter(s - LPFilter(s, 0.3/fs), 40.0/fs);
s = s(:);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% warning('off', 'MATLAB:nearlySingularMatrix'); % THIS IS JUST FOR AVOIDING THE MATRIX... |
e3144e3db612b26201396a63f5b1ee2406ed63eb02f15eea228f038c7a27c794 | MATLAB | 4,095 | 62 | function Data = Add_Starting_Tracing_Steps(Data)
[Im_Rows,Im_Cols] = size(Data.Info.Files(1).Raw_Image);
Scale_Factor = Data.Info.Experiment(1).Scale_Factor;
Step_Length = Data.Parameters.Auto_Tracing_Parameters.Global_Step_Length;
Skel_Vertex_Overlap_Factor = Data.Parameters.Auto_Tracing_Parameters.Skel_Vertex... |
cdc216210c29cef67aed54680d36d8c29d27473c76f73f419175dcd5ce94d998 | MATLAB | 4,097 | 165 | clear;
close all;
fs = 1000;
data = load('s0010_re.txt');
% data = load('s0014lre.txt');
time = data(:,1)';
data = data(:,2:end)';
data = data - LPFilter(data,1.5/fs);
data = LPFilter(data,150/fs);
I = 1:10*fs;
data = data(:,I);
N = length(data);
% % % % mn = mean(data,2)*ones(1,N);
% % % % d... |
c7b8621eda4fa76b674974d116ae549a4fe340c646c2eee7334f11596d465a17 | MATLAB | 4,098 | 121 | function [index, rank] = sqi_pseudo_periodicity(x, ff, fs, varargin)
% Signal quality index based on pseudo-periodicity measures
%
% [index, rank] = sqi_pseudo_periodicity(x, ff, fs, method, num_peak_det_itr, ranking_mode, plot_results)
%
% This function calculates the signal quality index (SQI) based on
% pseudo-per... |
bb6f4ad64981a6c9c9c90807887123a17d660d2e28fc5203ceff5d5384bed8f6 | MATLAB | 4,101 | 142 | function [fid raw_data] = convert_raw_to_jmrui(filename)
if ~exist('filename')
filename = spm_select([1 inf],'any','select files','',pwd);
end
for k =1:size(filename,1)
[pathstr,fname,ext] = fileparts(filename(k,:));
fp = fopen(fullfile(pathstr,[fname,'.PLOTIN']));
fid(k).spectrum.cenfreq = find_float_aft... |
b165c105fa4d5d35f9a61f1532c496fbc870330eb2786fd0716a1e6c2ab46288 | MATLAB | 4,103 | 177 | %*******************************************************************************
% Copyright (C) 2013 Francois PETITJEAN, Ioannis PAPARRIZOS
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, v... |
d7564938bbd6ca0af589ed84b1dc1143757cb06b7736dc41bb831731946c8e39 | MATLAB | 4,103 | 122 | function import_data
%% Import experiment JSON data into MATLAB
% Determine injection hemisphere
% Sort projections into ipsi- vs contralateral
% Projections variable names
proj = {'density','energy','intensity','volume'};
proj1 = strcat('projection_',proj);
load('structures','structures');
load('experiments'... |
d301ebfc170e6f4b6cb950d532bf9a649b31ba508ff961dced0685aded889302 | MATLAB | 4,105 | 100 | % compare with this paper:
% S. Krishnan and C. Seelamantula, “On the selection of optimum savitzky-golay filters,” Signal Processing, IEEE Transactions on, vol. 61, no. 2, pp. 380–391, Jan 2013.
clear;
close all;
clc;
addpath 'G:\Sameni\Documents\Papers\Journal 40 (Spline ECG Filtering)\CODES'
randn('seed', 0);
rand... |
dd1370e74a069f7d22f3e7ab1b36abb13abd9d8b47e00de3c3e2733ebc78e9e1 | MATLAB | 4,111 | 121 | classdef (Hidden) process_varargin_result < handle
%
% Class:
% sl.in.process_varargin_result
%
% This is a result class from running sl.in.processVarargin
%
% See Also:
% sl.in.processVarargin
%Inputs ==============================================================
... |
ae965cccff884d29fea6ace7be09e86415e45bf4c2724b6005df326fff83d6aa | MATLAB | 4,112 | 95 | function autoDelete(obj)
%AUTODELETE Automatically delete clusters by SNR/spike count
if obj.isWorking
jrclust.utils.qMsgBox('An operation is in progress.');
return;
end
hFigDelete = jrclust.views.Figure('', [.5 .7 .35 .3], ['Delete Auto: ', obj.hCfg.sessionName], 0, 0);
if isfield(... |
2c923b4c7a328cb53a3f85d3827bfe3e3167d49f1be949c95f0d6586ae297a56 | MATLAB | 4,113 | 143 | % exclude : S6 - session 2, S4 - session 2 (BUTTON BROKEN)
clearvars -except Behav_path Path_figures Path_variables
SubjectID={'1' '2' '4' '5' '6' '7' '8' '10' '11' '12' '15' '17' '18' '19' '20' '22' '24','21','25','23'};
Drug=[3 1 1 2 3 2 3; 3 1 3 2 1 2 0; 1 0 2 1 3 2 0; 2 1 1 3 2 3 0; 2 0 3 1 2 3 0; 1 3 2 3 1 2 0; 3... |
39fc84e92bcd3ebfdd6f665d84488c944d4ec92d1dd7e3787c81d6ea70fde9b4 | MATLAB | 4,114 | 168 | % Test semsitibity of different ECG segments to ICA dim reduction
% Reza Sameni, Copyright 2008
clear;
close all;
fs = 1000;
data = load('s0010_re.txt');
% data = load('s0014lre.txt');
time = data(:,1)';
data = data(:,2:end)';
data = data - LPFilter(data,1.5/fs);
data = LPFilter(data,150/fs);
I =... |
cdcbc3548be9d05ed2a81628bb1743ed19b58a5ab8b38fd1a1386df506e2f340 | MATLAB | 4,115 | 81 | function h=subplot_tight(m,n,p,margins,varargin)
% http://www.mathworks.com/matlabcentral/fileexchange/30884-controllable-tight-subplot/content/subplot_tight/subplot_tight.m
% Copyright (c) 2011, Nikolay S.
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modificati... |
289758dc1a0a6ec311ecd540f682a98ad3c003e40c79481e4be2884258dd4bbe | MATLAB | 4,118 | 99 | function [ecg, phi] = ecg_gen_warping(N, fs, params, phase_bins, warping_order)
% ecg_gen_warping - Stochastic synthetic ECG generation using warping transformation from the phase domain.
%
% Method: Generates synthetic ECG signals by generating Gaussian mixture
% models with stochastic deviations in the phase do... |
b923fab6c770039126b1f4babc0f94e505690114cca720c8bbaa0888de2bf0c2 | MATLAB | 4,118 | 141 | function AllCombination_W5_20200306
path=['C:\Users\C238\Desktop\touchcellLtRt_terashita'];
cd(path);
Index5=[];
Index6=[];
Index56=[];
SeqIndex=[];
Seq56Index=[];
Array=linspace(1,10,10);
CombinationIndex = nchoosek(Array,5);
for i=1:length(CombinationIndex(:,1))
Target=CombinationIndex(i,:... |
56f2813487e578540639137bbbfae86e2700d3e385902db23d6177b3a512f73d | MATLAB | 4,120 | 136 | function [peaks, peak_indexes, width] = peak_det_modified_pan_tompkins(data, fs, varargin)
%
% peak_det_modified_pan_tompkins - R-peak detector based on modified
% Pan-Tompkins method. The filters and post-detection R-peak selection
% logic differ from the original algorithm
%
% [peaks, peak_indexes, width]... |
216acfd219369672f5e5c53d6b459babb598554b49cf4f67f3e684ef1b10392b | MATLAB | 4,122 | 117 | function parsed_chs = parse_data_NSx(filename,max_memo_GB,output_name,channels)
% Code only valid for recordings without pauses or data loss (Nsx.Data field can't be a cell)
% This code requires the file openNSx.m, from the NPMK, in the path. You can download the download NPMK from https://github.com/BlackrockMicrosyst... |
55934ed923fdeabc080df133a4f200b2c42da2a88de90f5d76b41388077bb614 | MATLAB | 4,123 | 87 |
%% run teh function and get all the wave forms
%wf = getWaveForms(gwfparams);
function wf = getWaveForms(gwfparams)
%
% Extracts individual spike waveforms from the raw datafile, for multiple
% clusters. Returns the waveforms and their means within clusters.
%
% Contributed by C. Schoonover and A. Fink
%
... |
943f096e9334d15e66b091b049738092249de19580da688c6eb32ac25e3a7794 | MATLAB | 4,123 | 89 | clear
dir_root = {...
% 'Z:/Yongsoo_Kim_Lab/STP_processed/2019_optical/20191212_UC_U504_C57J_FITC-fill_M_p67_optical';
% 'Z:/Yongsoo_Kim_Lab_2/STP_processed/2020_optical/20200220_UC_U547_C57J_FITC-fill_M_p559_optical';
% 'Z:/Yongsoo_Kim_Lab_2/STP_processed/2020_optical/20200227_UC_U548_C57J_FITC-fill_... |
afa1e9f872f983d0c1b64a860d1067e40e39de370ba9e69744a4ea9f3543b448 | MATLAB | 4,124 | 97 | function [vcg, phi]= vcg_gen_abnormal(N,fs,f,alpha,b,theta,theta0,STM,S0)
%
% vcg_gen_abnormal - Synthetic cardiac dipole/vectorcardiogram(VCG) generator using the
% state-space form of the sum of Gaussian dipole/VCG model. Refer to references
% for further details.
%
% Usage:
% [vcg, phi]= vcg_gen_abnorma... |
0c940039fc43d877e62879cfe919351783e960dcbaec9281292afe35d0ea44d2 | MATLAB | 4,128 | 188 | function go_netviewer_perctl(C,thresh,clim)
C(eye(size(C))==1) = 0;
if thresh < 1; thresh = thresh*100; end
limit = prctile(C(triu(ones(size(C)),1)==1),thresh);
mask = abs(C) >= limit;
if isempty(clim)
cLims = [-max(abs(C(:))) max(abs(C(:)))];
else
cLims = [-1,1].*clim;
end
C(~mask) = NaN;
edgeLims = sum(~isnan(C)... |
254cba18f95dd1a735732cb9ded977f75e51b88611af706d711b107e96e9f538 | MATLAB | 4,129 | 135 |
%{
----------------------------------------------------------------------------
Determine the time window for task relevant neurons
%Start of task
%Sound on
%Sound off
%Before choice
%After choice (0sec)
%After choice (1sec)
%After choice (2sec)
%p = 0.001
%Each epoch, predict the prior, sensory and choice... |
5184a790ae3ee5360cc05d38a870ff7591749d889803d68e98921badd8684090 | MATLAB | 4,130 | 144 | clear
close all
clc
% Fixed-Order variable Lambda
DiffOrder = 2; % smoothness constraint order greater than 1
% lambda = [1e-2 : 1e-2 : 100 500 1000 10000]; % smoothness factor
lambda = [1e-4 2.5e-4 5e-4 1e-3 2.5e-3 5e-3 7.5e-3 1e-2 2.5e-2 5e-2 7.5e-2 1e-1:1e-1:100 500 5000 1e4 2e4 1e5]; % smoothness factor
% lambda =... |
b0d71477602214d374e55b7ba6e41a9f075cf3aed8342d36fef559556eaa5378 | MATLAB | 4,135 | 127 | function [baseline,baseline1,baseline2] = BaseLineKF(y,fc,winlen1,winlen2,approach)
%
% UNDER DEVELOPMENT
%
% b = BaseLineKF(x,fc),
% Baseline wander extraction from biomedical recordings, using a first
% order Kalman Smoother
%
% inputs:
% x: vector or matrix of input data (channels x samples)
% fc: -3dB cut... |
b3bb6eb577c0a814740c753cfa2c1901800ec622a374932e103b7b23d96cbc66 | MATLAB | 4,137 | 169 | % Test semsitibity of different ECG segments to ICA dim reduction
% Reza Sameni, Copyright 2008
clear;
close all;
fs = 1000;
data = load('s0010_re.txt');
% data = load('s0014lre.txt');
time = data(:,1)';
data = data(:,2:end)';
data = data - LPFilter(data,1.5/fs);
data = LPFilter(data,150/fs);
I =... |
b399ed95a367f06aae01526bed6c65f8d813341f1f9b81c3c51ad08ca817fecb | MATLAB | 4,138 | 100 | function [mask, extinction_model]= makeMask(plane, start_mask, start_model)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Create a binary mask of an image (2D array) of a tissue section.
% The image should be from LSFM imaging with single-sided illumination.
% The function compensates for atten... |
9fa6d0ca63869c354f13a886bc7c729ea1d893cfe56da42e7ad1f0ae244193bd | MATLAB | 4,145 | 119 | clc
clear
%% load all files
[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 path
%%
%in case of selcting only one file
if ~iscell(datafile)
datafile = {datafi... |
c868fa3a7cce6e66dfcef80d616974095843ed27037f70d2d0b90bd5071020d9 | MATLAB | 4,145 | 108 |
%{
----------------------------------------------------------------------------
Determine the time window for task relevant neurons
%Start of task
%Sound on
%Sound off
%Before choice
%After choice (0sec)
%After choice (1sec)
%After choice (2sec)
%p = 0.001
%Each epoch, predict the prior, sensory and choice... |
7b22f0f9550c9f7f09c40eb405e7f8ad5d82fee52f54183490cd00a780e1b46a | MATLAB | 4,146 | 55 | function [MI, p_MI] = compute_MI_lme_interaction(data, demographics_tbl, contrast)
% data = MPC_interaction;
% demographics_tbl = demo_tbl_interaction;
% contrast = 'Resilience_vulnerable'
% % example input
% data = FC(:,:,logical(sum(group_indices(:,1:2),2)));
% demographics_tbl = demo_resilience_vs_vulnerab... |
fddeb0d7ccc72e21a65f542fc319b5898459a1c723249df4147246d25013ffb8 | MATLAB | 4,147 | 91 | function fsNative2fsLR(con, toCon, currSphereOut, midNewOut, midCurrOut, free_currSphere, fs_pial, fs_white, pths, hemi)
% Resamples data from FreeSurfer native space to fs_LR space.
%
% This function converts surface data from the FreeSurfer native space to the fs_LR surface
% space (used in the Human Connectome Proje... |
d0a8fd41c4e5ebf00dfe9f7cfc39a8a51457cddc58e06f59cf31c125a1355ae3 | MATLAB | 4,150 | 98 | function [A, f0, bw, fskew, skew, kurt, f_hilbert, A_hilbert] = instantaneous_signal_params(signal, fs, wlen, window, th, varargin)
% instantaneous_signal_params - Calculates instantaneous time/frequency domain features of an input signal in causal and non-causal modes
%
% Syntax: [A, f0, bw, fskew, skew, kurt, f_hi... |
4040f7e34cd02bee0c9527e31b125fa1f44a368e714df3e1f07c5337a626baa5 | MATLAB | 4,153 | 111 | %% make individual plots for fig for chosen cells
for j = 1:2
if j == 1
fpath = [p.data_dir '\annotation_examples_good'];
else
fpath = [p.data_dir '\annotation_examples_bad'];
end
load(fpath,'this_traces','this_filters','this_events','this_snapshotCollecti... |
9d6570a9c68059a57a9393877100bada7c23f607ceca3661aa07f88d413ff162 | MATLAB | 4,160 | 115 | function calculate_premutation_t1_for_3_ints_win(file, path)
%%
datafile = fullfile(path, file); %save path
if ~iscell(datafile)
datafile = {datafile};
file = {file};
end
windows = {40:50,65:125,140:150};
all_sugnificnt_cells = [];
all_p_val = [];
sugnificnt_cells = zeros(3,1);
cell_num = 0;
low_basline = 0;
p... |
eb76508d19a4105f840014feb9db7ea6826f2069adb15d039fe4cc060dfe953f | MATLAB | 4,161 | 148 | clear
close all
clc
% load ECG1
% s = data;
load ECG2
s = data(14, 1:end);
% s = randn(1, 10000); fs = 1000;
% load ECG3
% s = data(2, 1:end);
% s = resample(s, 0.3*fs, fs);
% fs = 0.3*fs;
s = LPFilter(s - LPFilter(s, 1.0/fs), 100.0/fs);
s = s(:)';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.