sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
2e0912d737d1e7ad64171618f4cca8a1d052647a32854702c996831a8bb200a7 | MATLAB | 5,147 | 174 |
%load IRdata.mat
load IRdata_example.mat
d=intensity; % intensity
for k=1:3
switch k
case 1
dataset=earlyres;
case 2
dataset=steadyres;
case 3
dataset=OFFres;
end
for i=1:size(dataset,1)
a=dataset(i,:);... |
3b4b407656e8fba02270df7a38f7e49f7a6668b45d3325796cfeaba85f12da1a | MATLAB | 5,152 | 148 | % Detects bursts in EEG data, saves them. Can take >6 h to run.
clear
clc
close all
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% load in and set parameters for analysis
% set parameters for how you want to run the script this time
RunParallelBurstDetection = true; % true for faster... |
73cd9a779087dd57845f534863112dc6926c58eb484bd6e40da7d48b5942b7ba | MATLAB | 5,153 | 100 | function [test_indices train_indices ind]=cv_stratified_multilabel_DBP(N,n_folds,bestmodel)
% N is the label matrix
%Stratifies by discretizing multiclass tracking data into one unique label
%after bining individual classes. The final discretized label makes up an
%combination of individual classes labels.
%best... |
2064e0ba03e1f8047ca6ba4f61320f39fbc1c35e22ba12ba86ca3a2ff76fbc0c | MATLAB | 5,154 | 141 | function Apply_And_Save()
p = 1;
% Input_Dir = uigetdir; % Let the user choose a directory.
Input_Dir = 'D:\Dropbox (Technion Dropbox)\Omer Yuval\Neuronalizer\PVD Images\Sharon\DEG_for_Menorah_analyser_Filtered_Format_Names';
Files_List = List_All_Files(Input_Dir,'mat');
% SaveDir = 'D:\Dropbox (Technion ... |
9bb71fcea7948cbb0bc525930ede1221f08926306e78f962737319ed195c2593 | MATLAB | 5,157 | 165 | function varargout = choice(varargin)
% CHOICE M-file for choice.fig
% CHOICE, by itself, creates a new CHOICE or raises the existing
% singleton*.
%
% H = CHOICE returns the handle to a new CHOICE or the handle to
% the existing singleton*.
%
% CHOICE('CALLBACK',hObject,eventData,handles,...) ... |
78451773f8c6f88cdd6f4fe905a7d47562b219616f2a6db50df748dd89f2ee9a | MATLAB | 5,158 | 183 | function [A,B,C,determinant] = IOT3Dnew(I0,I1)
%This function receives I0 and I1 as 3D input images and find an initial
%masspreserving mapping between them. The details for the method can be
%found in,
%
% Haker, Steven, et al. "Optimal mass transport for registration and warping."
% International Journal of Comput... |
211ad84c30026026349a9a180748af15ca7525aa53916e1327031b091311a6c0 | MATLAB | 5,165 | 180 | % caculate mean ramp per cluster
clear
clc
ploting = 1 ;
norm = 1;
ploting2 =1;
%%
[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
%%
counts = 0;
for i = 1... |
4cdfb247100f8814ce7c0a69962f77bb74d861f6fa8f91f84c538092fa775c76 | MATLAB | 5,165 | 148 | function [p1 n1 n2]=compute_spike_boundary(spike_ref,fs)
% COMPUTE_SPIKE_BOUNDARY - Function to detect spike endpoints
%
% Syntax:
% [p1, n1, n2] = compute_spike_boundary(spike_ref, fs)
%
% Inputs:
% spike_ref - 300 ms [10-60] Hz filtered spike segment with spike peak at
% 75 ms. The spike seg... |
b80d1804e4fd4374530690c7a167e75d0927b822f191ace3f022fe46fa155512 | MATLAB | 5,175 | 120 | %% Precomputing on Planes: Give input, compute firing rates as functions of inputs
% Output: save([SaveFolder 'MFpV_LDE_' num2str(InputCtgr) '.mat'],...
% 'f_EnIOut','meanVs','SteadyIndicate','FailureIndicate','L4ERcrd','L4IRcrd')
% Input: InputCtgr: Choices from 1-3. Orthogonal, suboptimal, optimal
%... |
c556a74f5e547cd5720324c1a2469e60f1e8d5a5f459c91dcd36650eb9ef96bf | MATLAB | 5,175 | 104 | %ica
eegdir1='G:\BORI\bedrest\RENDEZETT\A) Bedrest_EEG\Bedrest_PreEEG\EEGRest\';
eegdir2='G:\BORI\bedrest\RENDEZETT\A) Bedrest_EEG\Bedrest_PreEEG\EEGIca\';
%ActualSet = ['A_1'; 'A_2'; 'A_3'; 'A_4'; 'A_5'; 'A_7'; 'A_8'; 'A_9'];
%nrFD = [32, 32,29,31,31,31,30,32]; %A %chanel nr
%ActualSet = ['B_1'; 'B_2'; 'B_... |
dd0f9dc1318281de633a6ddae46e0ae5847cf8a2dd7c347d90b460f6380c0dbb | MATLAB | 5,175 | 120 | %% Precomputing on Planes: Give input, compute firing rates as functions of inputs
% Output: save([SaveFolder 'MFpV_LDE_' num2str(InputCtgr) '.mat'],...
% 'f_EnIOut','meanVs','SteadyIndicate','FailureIndicate','L4ERcrd','L4IRcrd')
% Input: InputCtgr: Choices from 1-3. Orthogonal, suboptimal, optimal
%... |
f797300d19a8db079251b9e1959ee6141c526c558a5abe254332d2820035e798 | MATLAB | 5,175 | 161 |
%{
----------------------------------------------------------------------------
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... |
09b21d84e2c021bf38aece4f8e8fa36b39aa0066229a4417ef8284710b71b7da | MATLAB | 5,180 | 82 | function Corrected_Angle = Correct_Projected_Angle(A,A0,At)
% This function...
% All angles are given in radians.
% A is the angle to be corrected (radians) of the rectangle, pointing from the vertex center outside.
% A0 is the reference angle (of the corresponding midline point).
% At is the local tilting angle... |
00233389b4ff111c9fbbef28b6751978d53dc376b5a8e3acf056cd09582b2a83 | MATLAB | 5,182 | 72 | function [taskFCvTaskFC_diffs, taskFCvTaskFC_diffs_tvals, taskFCvTaskFC_diffs_pvals, percentSigValuesFDR_total, percentSigValuesFDR_pos, percentSigValuesFDR_neg, FDRThreshP05] = calcTaskVSTaskFCDiffs(task1ConnMatrix, task2ConnMatrix)
taskFCvTaskFC_diffs=zeros(size(task1ConnMatrix,1),size(task1ConnMatrix,2),size(tas... |
9bebb0014bc7d2eae8af10dabc40167e4c38bdcd86630e5714fda5e00d31ea56 | MATLAB | 5,184 | 90 | function options = spmup_getoptions(BIDS_dir)
% routine to return all the spmup options with defaults set
%
% FORMAT options = spmup_getoptions(BIDS_dir)
% INPUT BIDS_dir is a folder in which analyses should be carried on
% OUTPUT options is a structure with spmup optional default parameters set
% 'outdi... |
952b0afc1508763e12fccbdb853242d690080e71adeefb85af40c7f7f1baef7c | MATLAB | 5,186 | 195 | function [fid_cor phase_cor Frequency_correction] = correct_freq_and_phase_by_correlation(fid,par,info)
global sw H1offset sfrq1H
sw = info.spectrum.SW_h;
sfrq1H = info.spectrum.synthesizer_frequency;
H1offset = info.spectrum.ppm_center;
% B0 correction using cross-correlation (based on Pat Bolan freqAlignXCorr.m)
m... |
4e00996b2af0813b24d9c626c126a5df977dac7d3477ab685893b71e5b881770 | MATLAB | 5,190 | 164 | %% CM_preprocessing
%% Enter mouse name, session date, recording site, data directory
clear
MouseName = 'EF0148';
seshDate = '190313';
recSite = 'left POm';
Genotype = 'Emx-Cre;Ai32';
Sex = 'male';
Manipulation = 'NaN';
inhSite= 'NaN';
seshType= 'NaN';
DoB = '180627';
MainDir = 'E:\';
IntanDir = [Mai... |
0887087c2cc9e19996a5ec4840ee6c0e1527c136e43757ebbd4d57e1d9a95e45 | MATLAB | 5,194 | 180 | function controlfilename = createLCmodel_control_press117T(metabname,l1,l2,l3,preproc_dir)
do_water_scaling=0;
doecc=0;
%triky do ecc only for suj46
if findstr(metabname,'Suj46_100803_MC_slaser_TE_35_NEX128')
doecc=1;
end
TEMET=1;
basis_set_file_path = '/home/romain/images5/spectro7T/basis/basis_sLASER_7T_TE... |
cc860323ca303b0b5a9c51da64b742f4278727cd337dc3666048b03759c9d5f6 | MATLAB | 5,198 | 147 | function [y1,y2,Pbar,Phat,PSmoothed,Kgain] = KFNotch(x,f0,fs,varargin)
%
% [y1,y2,Pbar,Phat,PSmoothed,Kgain] = KFNotch(x,f0,fs,Q,R,gamma),
% Removing Power-Line noise using a linear Kalman filter and smoother
%
% inputs:
% x: vector of noisy signals contaminated with power-line
% f0: the notch frequency
% fs: sampling ... |
8d9300ba2eb1d2bf7855159c1bacd3fb1ab35ce7e92f8915d35846d79963e036 | MATLAB | 5,203 | 93 | function update_cluster_plots(app)
%UNTITLED Summary of this function goes here
% Detailed explanation goes here
clu_idx = app.settings.cluster_display;
tmp = 'abcd';
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 ... |
507719302f84aa87c293ab4f0f549ff61dac5e58514ed7e639212e670aec20ed | MATLAB | 5,204 | 125 | % Detrending by Segments Linear & Smoothing Methods
% Input
% X: signals of Ns x Frames
% Output
% XD: Detrended Signals
% function XD=linsmooth_detrending(X)
[Ns,Frames]=size(X); % Ns-Signals of Frames
% Initialize Outputs:
XD=zeros(Ns,Frames);
%% MAIN LOOP
for i=1:Ns
x=X(i,:); % Read Single Fluorescence Sign... |
b3252af41a72abcb997a05f9ed993417f302fced2975e838fe6d81391e76003b | MATLAB | 5,207 | 126 | clear
close all;
clc
% parameters
% % % logfileame = 'SVMOptimizedParamters03.txt';
outputfileame1 = 'Submission55.csv';
outputfileame2 = 'Submission55_unbiased.csv';
% % % bins = [50 75 100 120 180 250 300 500 750];%75:6:81;%120;
% % % bins = 120;%50:1:550;%:13:250;%75:7:120;%75:6:81;%120;
% % % bins = 77;
% gmorder ... |
6c6f8351d5431030efd0b593b24f26c6762eeb610108a0c94dfdc8afe8f89a73 | MATLAB | 5,214 | 125 | %% Plot lick direction probability during rule trainsition
% Chang et al Fig 5a
% Get right and left lick probability during rule transition across mice
load('E:\Cross-Modal_Project\Revision\rule_transition\data_array.mat')
mainDir = 'E:\Cross-Modal_Project\Revision\rule_transition';
recSites = {'left S1', 'left S2',... |
fe86f4905b501ef88b983b8a78befb9ea5b5e9702d558b7db10d720305c4bdb9 | MATLAB | 5,223 | 96 | function [fid, hdr, cleanupObj] = write_nifti2_hdr(hdr, filename)
mydelete(filename); %avoid permission issues, and replace links with files instead of overwriting the pointed-to file
fid = fopen(filename, 'Wb'); %always write native-endian - capital W means fwrite() doesn't flush every call
if fid == -1
... |
4998accf3eee352bdb36f7f5cdb083eff24f05a584f6e62e757d793685e76cb0 | MATLAB | 5,226 | 209 | % simulate_periodic_eeg should create a vector of a specified length, of
% which a certain proportion should contain a sine wave of specified
% periodicity, with bursts a certain length.
clear
clc
close all
DefaultCenterFrequency = 10;
DefaultBurstAmplitude = 30;
DefaultBurstDensity = .4;
DefaultBurstDuration = 1;
De... |
859536e455a24c7fddf9c3e415b4fc9036fae9d25db79fade81bd630741379b7 | MATLAB | 5,227 | 188 | %% load centroids, tis, evs
clear
p = params_2DAA;
fpath = fullfile(p.processed_data_dir,'allData.mat');
load(fpath,'evs','tis','bvs')
[cols,alpha] = chooseColors;
pad_pre = 5;
pad_post = 10;
%% get traces
for sub = 1:p.nSubjects
for ses = 1:p.nSessions
pos_traces{sub,ses} = [];
pos_traces_aligne... |
156b179c3b97bd09098ae59e3a25f06b12ab2156934dc7148bf2730d61c31163 | MATLAB | 5,234 | 174 | clear
close all
clc
% load ECG1
% s = data;
load ECG2
s = data(1, 1250:2250);
% s = randn(1, 10000); fs = 1000;
% load ECG3
% s = data(2, 1:end);
% addpath 'G:\Sameni\Documents\Papers\Journal 40 (Spline ECG Filtering)\CODES'
% pth = 'J:\ECGData\Arrhythmia';
% fs = 360;
%
% wd = cd;
% cd(pth);
% fname = '233';
% s... |
ddf33f460808c6bfa525483e7d2b8cd44522ccc2f50e14f1bacee023ab87272d | MATLAB | 5,237 | 131 | % Epoch each subject's pre-processed localizer data. Also identify and save
% good channels for each subject.
% 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
% Folder where epoched trained data s... |
23fc4a69df6aac14ec5eaea78a8ad6288bee5d835902214fbdc7830ca8f31f29 | MATLAB | 5,238 | 149 | function varargout=wt_ax(ax,d,varargin)
%% Continous Wavelet Transform
% Creates a figure of wavelet power in units of
% normalized variance.
%
% USAGE: [wave,period,scale,coi,sig95]=wt(d[,params])
%
% d: a time series
% wave: the wavelet transform of d
% period: a vector of "Fourier" periods associated with wave
% sca... |
ac1cbb5fe83ff006cd650c527e1c13d4a0fc848a1cc8abb8bc14dc2ff340db34 | MATLAB | 5,239 | 180 | function gui_l (x, y)
%
% This file is needed by FASTICAG
% The load dialog for loading new data
% and new initial guess.
% 24.8.1998
% Hugo Gävert
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Global variables
% Handle to the window
global hf_FastICA_Load;
% Ha... |
aa31d4bca0a5076177a51b76b857cd5c4cbc0b473b6ab964d546b88b32d1e8e0 | MATLAB | 5,241 | 111 | function [par, confirm] = edit_parameter(par_in)
%EDIT_PARAMETER Summary of this function goes here
% Detailed explanation goes here
fields = { 'max_spikes_plot', 'to_plot_std', 'all_classes_ax',...
'mintemp', 'maxtemp', 'tempstep',...
'SWCycles', 'KNearNeighb', 'min_clus',...
'randoms... |
c94fd39b11b278b389b8ae769b426b1a1be254f8df03d5ebdecdfedec03cb741 | MATLAB | 5,247 | 166 | clear
close all;
clc;
pth = 'E:\Sameni\Projects\Seizure\SourceCodes\Probabilistic\SourcesWithResultsBackup\';
R1 = importdata([pth 'testFreqDomainEnergyIncreaseRate01.txt']);
R2 = importdata([pth 'testICASubspaceAngles01.txt']);
R3 = importdata([pth 'testWeightedHistogramAveraging02.txt']);
R4 = importdata([pth 'testP... |
9b0fae2e917f461b2e740d427c19c5ebc56c831c3e0003c70a2d2ff58f8ab205 | MATLAB | 5,248 | 197 | % Previous methods used either fixed thresholds or standard-deviation based
% thresholds to determine theta events. This is not ok when there's a
% highly variable amount of that oscillation in the signal.
clear
clc
close all
P = burstParameters();
XLim = [140 160];
Signals = [];
Titles = {};
% load high-theta sign... |
fe5ecfbee1097fa5b820154201dbeb506cc8e07f8c7b2a50b88f24b39c037864 | MATLAB | 5,251 | 105 | function vdm_files = spmup_compute_vdm(fmap_folder)
% create vdm maps for each fmri session - assumes data follow BIDS
%
% FORMAT: vdm_files = spmup_compute_vdm(fmap_folder)
%
% INPUT: fmap_folder is the folder where to find gradient field maps
%
% OUTPUT: vdm_files is a cell array with the names for the vdm maps fo... |
a0841d720b0aefcc89cd5c519d723dcae33ad85018f692a52147ddac6b12108b | MATLAB | 5,254 | 160 | function [spikes,thr,thrmax, noise_std_detect, index] = amp_detect(x,handles)
% Detect spikes with amplitude thresholding. Uses median estimation.
% Detection is done with filters set by fmin_detect and fmax_detect. Spikes
% are stored for sorting using fmin_sort and fmax_sort. This trick can
% eliminate noise in the d... |
c25aba0bbfbecfd6de82fcf36338f569be7999e4058122b76213ceebea90360a | MATLAB | 5,259 | 167 | function entiresessionPhotomPlot_LoneFigV2(x,y,eb,setupParam,TrialParam,allTrials,loneFigure)
allTrialsID = allTrials.ID;
if setupParam.CompareGroups == 1
% GroupParam = TrialParam.GroupParam;
groupnames1 = fieldnames(allTrialsID);
groupidx = zeros(1,numel(groupnames1));
for i = 1:numel(groupnames... |
cd8d77a7a496becd55c8557d52bb514ed40a9eb94632cc063a7db8f15c0c78ae | MATLAB | 5,260 | 124 | clear all
close all
clc
constants;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Load data
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
import_data;
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Settings
%%%%%%%%%%%%%%%%%%%%%%... |
526b713c159deca2ecef6790d337c1e234fe52c593dfa64ef1fc0c20149796c6 | MATLAB | 5,264 | 200 | function hdr = load_nifti(niftifile,hdronly)
% hdr = load_nifti(niftifile,hdronly)
%
% Loads nifti header and volume. The volume is stored
% in hdr.vol. Columns and rows are not swapped.
%
% Handles compressed nifti (nii.gz) by issuing a unix command to
% uncompress the file to a temporary file, which is then deleted.
... |
5801236f1ba14ebdb596d05012fee97cbe09febe598df503db8be5aca5fe03db | MATLAB | 5,265 | 133 | function [fid, dcmInfo] = spec_read(dn, debug)
% function to read spectroscopy data from siemens dicom .ima/.dcm files
% usage: [fid, dcmInfo] = spec_read(dn)
% arguments dn = filename
version = '2021.07.27';
if (nargin == 1), debug = false; end
dcmInfo = dicominfo(dn);
% R2006b dicominfo() now reads... |
5b31ab34b76986b2547ee158930b328afaa668878e9438ba47a2081b9f7e36fe | MATLAB | 5,265 | 167 | clearvars
close all
addpath(genpath('/storage/stella/MATLAB_toolboxes/')); % insert the path to NIFTI_studio toolbox
%% simple generating three tissue masks (CSF, GM, WM) integrating info from FIRST and FAST (using MNI priors for registration)
folder = pwd;
slice = 86; % just to visualize maps
% get structural mask ... |
27ebbe417157e12224e0a8bcde34eed920c4fe327f5c0e885149aabe95b6215c | MATLAB | 5,276 | 153 | function unitSims = unitWaveformSim(meanWfSet, clusterSites, hCfg, scoreData, iCluster)
%UNITWAVEFORMSIM Mean waveform similarity scores for one cluster against all others
updateMe = scoreData.updateMe;
cviShift1 = scoreData.cviShift1;
cviShift2 = scoreData.cviShift2;
simScoreOld = scoreData.simScor... |
a3a1b5ffde4d71a09cf0d1433e565fe533e92efca2757f707403a9385928bc66 | MATLAB | 5,276 | 121 | %% Precomputing on Planes: Give input, compute firing rates as functions of inputs
% Output: save([SaveFolder 'MFpV_LDE_' num2str(InputCtgr) '.mat'],...
% 'f_EnIOut','meanVs','SteadyIndicate','FailureIndicate','L4ERcrd','L4IRcrd')
% Input: InputCtgr: Choices from 1-3. Orthogonal, suboptimal, optimal
%... |
ad30a63e9af5be3f2a3aa4cb90e1307de6626f06194260964c0a2ff0522fc45c | MATLAB | 5,276 | 67 | function [taskFCvRestFC_diffs, taskFCvRestFC_diffs_tvals, taskFCvRestFC_diffs_pvals, percentSigValuesFDR_total, percentSigValuesFDR_pos, percentSigValuesFDR_neg, percentSigValuesFDR_total_bytask, percentSigValuesFDR_pos_bytask, percentSigValuesFDR_neg_bytask, FDRthreshByTask] = calcTaskVSRestFCDiffs(taskConnMatrix, res... |
b8829a97192feccadb9e9d7d562f7084ee953fa39e6ab1943e27d9d863cba886 | MATLAB | 5,276 | 126 | function PVD_CNN = PVD_CNN_Train(Training_Mode,S,net)
% Run Examples:
% PVD_CNN = PVD_CNN_Train(1,[]);
switch(nargin)
case 0
Training_Mode = 1;
S = PVD_CNN_Params();
net = [];
case 1
S = PVD_CNN_Params();
net = [];
case 2
net = [];
end
rng('default'); % Reset the random seed... |
9656b789562d1f297a7cdc5a60214d81ddef571dcdb39e39c4fe942848cffecb | MATLAB | 5,279 | 170 | function mnl_CompareDifferentThresholds_ClusterClassification
%% Load in each csv
c=1;
next=2;
Quant=struct('Threshold',[],'DataMatrix',[],'Single_n',[],'Single_pc',[],'Multiple_n',[],'Multiple_pc',[],...
'MultipleClose_n',[],'MultipleClose_pc',[],'Few_n',[],'Few_pc',[],'Scattered_n',[],'Scattered_pc',[]);
%Load in... |
82d70f55067951999aa2d09c5fabec10612da22c6a22889644f204797f90cdf0 | MATLAB | 5,281 | 127 |
%% loading data
tmp = load('dataFig45.mat','mono','IPD300','ILD35','FQs','PHs','ILDs');
FQs = tmp.FQs; % data for monaural AM tuning
monoPLk = mean(tmp.mono.RToutPLk); monoALk = mean(tmp.mono.RToutALk);
monoPSp = mean(tmp.mono.RToutPSp); monoASp = mean(tmp.mono.RToutASp);
monoPEx = mean(tmp.mono.RToutPEx); mo... |
a3c7d06421c3aac731f6e1f822cf2f28ada3a4f1911ba5812045ccde914942c1 | MATLAB | 5,283 | 140 | function this = gifti(varargin)
% GIfTI Geometry file format class
% Geometry format under the Neuroimaging Informatics Technology Initiative
% (NIfTI):
% http://www.nitrc.org/projects/gifti/
% http://nifti.nimh.nih.gov/
%_______________________________________________________... |
4e33b7136d41329c91d041b52f70f8385980346f9ab950870a3d4950fa11ec62 | MATLAB | 5,289 | 135 | function [d, tmpOut] = LR2fsaverage(f, hemi, d, pths)
% Resamples data from fs_LR to fsaverage space.
%
% This function converts surface data from the fs_LR surface space (used in
% the Human Connectome Project) to the fsaverage surface space (used in
% FreeSurfer). It supports resampling both metric and label data u... |
7f792f6164bb2ef4b76fa8dac362ca63ed1d439b4a72164a8f590241ce9d3816 | MATLAB | 5,290 | 116 | classdef UtilsTest < matlab.unittest.TestCase
%UTILSTEST Test that various utilities run as expected
properties
testDir;
testFile;
testText;
end
methods (TestClassSetup)
function createTextFile(testCase)
testCase.testDir = tempname();
mkdir(testC... |
088bc057b2496822f3a47b826c9f77eca53e3c42db8ca1ac4652daca8431d3a2 | MATLAB | 5,291 | 158 | function trials = load_trials(session, channel, unit)
% Make trials
info = get_info();
for fold = 1:info.num_folds
fullfilename = fullfile(...
info.folders.trials, ...
get_id(session, channel, unit), ...
sprintf('fold%02d.mat', fold));
file = load_f... |
b1efbbab52c4da6ba8889df654a59982fdf878fceffe39b3f056e65fbb8c48b4 | MATLAB | 5,291 | 178 |
%% Init
clear
path(pathdef);
addpath( genpath( 'Source' ) )
rng(42)
%% Load
load( 'Data\HRTF_Typical_Bat');
M = matfile('DataCalculated\Simulations/DataSonoNerf_IRs_UALogo_public.mat');
fnSave = 'DataCalculated/PreparedData/DataSonoNERF_Training_UALogo_test.mat';
%% Settings
... |
120cdee4bff4720f9abc5961be8223db8bb62fdfe9dea2f01ce1967097f1cf7b | MATLAB | 5,297 | 176 | function [ output_args ] = plot_summary_dim_projections( proj_a, proj_e, dim_ves, saveFigs,out_dir )
%% plot projections per dim
cols = chooseColors;
lims = [-2.5 7.5];
rep_mean_a = squeeze(mean(cat(3,proj_a{:}),3));
rep_mean_e = squeeze(mean(cat(3,proj_e{:}),3));
rep_mean_ve = squeeze(me... |
714928688b84cd1b56eb6a2837cf76ded0ec09d65b6ed9fbd2c0f438d713e5a8 | MATLAB | 5,297 | 167 | % Function to accumulate Rate of Activity (RoA)
% of each Cell For each Experiment of the same
% experimental setup, i.e., same experimental conditions
% --------------------------------------------------------------------
% Input
% Load experiment manually from different folder: one by one
% R_Condition: Selected... |
a3e45063c1af16a8e813347fc6da81b7b46643af519fae60f1615d2393e294cf | MATLAB | 5,299 | 116 | function [clusters] = loadKSClust(phyPath,varargin)
%loadKSClut - Load KiloSort Clusters
%
% Usage:
% clusters = loadKSClust(phyPath);
%
% Description: This function is designed to import data from silicone
% probe recordings after it's been sorted using KiloSort and manually
% curated using Ph... |
dedca462af9ab269388835e9ac74d5f12e511877c6e105c01101495fa590745f | MATLAB | 5,300 | 209 | function [spikeMatrix, peak_idxs, peakTimes_ms, classes, peak_vals, dip1, dip2 ]=comBIN(aaa,data,sr,spk_pos,NBINS_peak,NBINS_dip,NBINS_dip2,npointsbeforepeak,npointsafterpeak)
%comBIN(spks,data,sr,1,1,1)
if nargin<8
npointsbeforepeak=2... |
bb5648d7b3055674bbfae550212b579fa577418737e63f3217cbf0b7092286f1 | MATLAB | 5,301 | 149 | function [t,swc]=senpai_skeletonize(cIM,neuron,somas)
% senpai_skeletonize:
% produces a matlab tree structure and an swc-formatted matrix for
% the skeleton of a binary segmentation
%
% Execute the function in the command window:
% Syntax:
%
% [t,pred,swc,xn,yn,zn]... |
746d3be732f5b17cf607cdaf78a87506e8e301cde06d7dc75d43a7065161cdc2 | MATLAB | 5,303 | 152 | close all;
clear;
clc;
resultfname = 'testProcessSeizureEEGICAOnNormalizedHistogram.txt';
% parameters
subject = {'Dog_1', 'Dog_2', 'Dog_3', 'Dog_4', 'Dog_5', 'Patient_1', 'Patient_2'};
ffs = 120;
f0 = 60; % powerline frequency Hz
Q = 60; % notch filter Q-factor
% wlens = [1.0 3.0 10.0]; % energy window lengths in sec... |
1e7b97f5c79b4fc24225c76136064e10f368f262770c5704cbe24ff3fd45a753 | MATLAB | 5,305 | 149 | classdef ManualTest < matlab.unittest.TestCase
%MANUALTEST Test manual UI
properties
hJRC;
end
properties (Dependent)
hCfg;
hClust;
hCurate;
end
%% SETUP METHODS
methods (TestClassSetup)
function setupProps(obj)
hCfg_ = jrclust.Config(ful... |
64ad44766506b1a6a58c88da5d9481368def95c48ef1314458f17dc7b980d1dd | MATLAB | 5,309 | 183 | % A test of correlation dimension calculation
%
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 = 1:2*fs;
data = data(:,I);
% data = randn(si... |
c46c4682dff3374484867ade020481ceb436fff06da32857aae01e11ab769f13 | MATLAB | 5,309 | 161 |
%{
----------------------------------------------------------------------------
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... |
d9ba30bddecbc1c1303017da0883b7808f244ae2c1517acadc1c653200141bc7 | MATLAB | 5,310 | 151 | % Channel selection based on different methods (modified version 2.0)
% The channel selection method tries to rank the components according to
% their similarity to the maternal and fetal mixtures. Therefore, it
% should give the noisy channels the lowest rank.
% Notes:
% 1- The first objective of this routine is ... |
5913bb162b68a0cc682c649307b906787d6a2d9175403ecce0be48c4b32f0ee1 | MATLAB | 5,311 | 163 | function mnl_CombineManualClassificationGroups
%% Load in CSVs
disp('Please load in all the csv files')
[files]=uipickfiles;
%Pre-allocate groups for bars
Classes_Struct=struct('Thd',[],'DataMatrix',[], 'SingleCluster',[],'PartialCluster',[],'MultipleCluster',[], ...
'Single_n',[],'Single_pc',[],'Partial_n',[],'Par... |
123e05b376d8fcd3b7f4582c86e9f6b09317458f01f1685b005d9c6ff2888a52 | MATLAB | 5,314 | 207 | % Load NIFTI header extension after its header is loaded using load_nii_hdr.
%
% Usage: ext = load_nii_ext(filename)
%
% filename - NIFTI file name.
%
% Returned values:
%
% ext - Structure of NIFTI header extension, which includes num_ext,
% and all the extended header sections in the header extension.
% ... |
2aaf0de673ffed4e1db3ef35ecc3cbae9bb61f30d7c61bb200ebed51f8db45eb | MATLAB | 5,314 | 170 | %% analyze time
clear
clc
cd('I:\My Drive\ELYASHIV\PFC_paper_results_after_revision\time of experiment')
t = importdata("all_ints_data_with_time.mat");
%%
%%
t.Is_reponsive = cell2mat(t.Is_reponsive);
t.Is_reponsive_and_IR = cell2mat(t.Is_reponsive_and_IR);
% G1 = grpstats(t,"file_name","mean","DataVars",[... |
b20e6e5ff05d303605124a8c541f3ff63e191a36a99d6a7c5278ab9dc3a9de78 | MATLAB | 5,314 | 200 | % Statistical test for functional modularizations of a neural network.
%
% Reproduces the results shown in Fig. 5 discussed in the section "Inferring nested modularizations in neural networks" in
% "Correlations reveal the hierarchical organization of networks with latent binary variables" (2023) Stefan Häusler
% Runt... |
37f77b1ced4c9d57b682d442742649735dcfe8375c68cff053767309f3676cb4 | MATLAB | 5,316 | 162 |
%{
----------------------------------------------------------------------------
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... |
8a4bebafa3431653d7bf76367eeb12f9b1744411eb1ccd663f5c07169033492b | MATLAB | 5,317 | 67 | function Set_Plots_Menu(P)
% set(P.GUI_Handles.Plots_Menu_Handles(:));
P.GUI_Handles.Plots_Menu_Handles = gobjects(1,60);
H_Menu1_Length = uimenu(P.GUI_Handles.Menus(3),'Label','Length');
P.GUI_Handles.Plots_Menu_Handles(1) = uimenu(H_Menu1_Length,'Label','Neuronal Length per Menorah Order','Checked','on');
... |
5eda77005346d163c4363f7df218c80ce4aa97187f16f9e1cb68426b26c51f82 | MATLAB | 5,320 | 67 | function Set_Plots_Menu(P)
% set(P.GUI_Handles.Plots_Menu_Handles(:));
P.GUI_Handles.Plots_Menu_Handles = gobjects(1,60);
H_Menu1_Length = uimenu(P.GUI_Handles.Menus(3),'Label','Length');
P.GUI_Handles.Plots_Menu_Handles(1) = uimenu(H_Menu1_Length,'Label','Neuronal Length per Menorah Order','Checked','on');
... |
a988045e4e84fea564fe8cde2bef3ffd2242da485b9be510bccbb45bdca18ea1 | MATLAB | 5,320 | 177 | function controlfilename = createLCmodel_control_laser(metabname,l1,l2,l3,preproc_dir)
do_water_scaling=1;
doecc=0;
simul=0;
basis_set_file_path = '/home/romain/data/spectro/Basis_set/Gosia/First/basis_LASER_TE=65_CENIR.BASIS';
%basis_set_file_path = '/home/romain/data/spectro/Basis_set/Gosia/LASER/TE_65/basis_LASE... |
b3414b1ad7d4eaea9b0e1146fa417cc9c7ec167d65bb5d09d0b87de7b28e5644 | MATLAB | 5,324 | 113 | function [vcg, phi]= vcg_gen_stochastic(N, fs, f, f_deviations, alpha, delta_alpha, b, delta_b, theta, delta_theta, teta0)
% vcg_gen_stochastic - Synthetic cardiac dipole/vectorcardiogram (VCG) generator with beat-wise stochastic deviations
%
% [vcg, phi]= vcg_gen_stochastic(N, fs, f, f_deviations, alpha, delta_alph... |
688199afef8715cc86531086c24e9887649d8f4820d8165e59259f3eb5b74bb4 | MATLAB | 5,332 | 170 | function x = EOGRemoval(eeg, eog, power_env_wlen, th, num_ch_den_per_itr, num_itr, adapt_reference, flagplot)
% EOGRemoval has been deprecated. Use eog_canceller_nsca instead.
warning('EOGRemoval has been deprecated. Use eog_canceller_nsca instead. Compare input arguments for compatibility.');
%
% x = eog_canceller_ns... |
ea2ed7eee63ba6d3e735790a26a19132a37a364fc691831f85bbab4e48be7d37 | MATLAB | 5,338 | 158 |
%{
----------------------------------------------------------------------------
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... |
046cc8e2d19025e3a887deed231c9ed56e742c943e8c7c9d3f0b78298c53446c | MATLAB | 5,343 | 95 | function WindowApply20220316
global RtouchSpikeOnUnit LtouchSpikeOnUnit RtouchSpikeOffUnit LtouchSpikeOffUnit LtNegativeSpikeOffKaisuu RtNegativeSpikeOffKaisuu ...
RtPositiveSpikeOffKaisuu LtPositiveSpikeOffKaisuu pegpatternum pegpatternname...
LtPositiveFiringRate LtNegativeFiringRate RtPositiveFiringRat... |
c1c7cc4e821425cec22480c81d56f2b9e88cab4fd9a069d7482d9cefa2021423 | MATLAB | 5,345 | 135 | function RLphase190226
global ACresult ACresultW CCresultDrinkOn SpikeArray RpegTouchallWon LpegTouchallWon TurnMarkerTime ...
RpNum LpNum OneTurnTime CCresultSpike locsP1time locsP2time locsP1time2 locsP2time2 pksA1 pksA2 CCresultRtouchWon CCresultLtouchWon dpath3
%%^b`ÌlÌzñðgÁ½¶EÌÊð... |
07664db8be3131a68709bfe5d281bfb3ab43e370a4c272d3e6a33ea1ae70c230 | MATLAB | 5,351 | 207 | function [p, table] = anova_rm(X, displayopt)
% [p, table] = anova_rm(X, displayopt)
% Single factor, repeated measures ANOVA.
%
% [p, table] = anova_rm(X, displayopt) performs a repeated measures ANOVA
% for comparing the means of two or more columns (time) in one or more
% samples(groups). Unbalanced ... |
06eeb3dc113ad72a2233b422d28b434407007ca8da51fb9f804202a05f5c8146 | MATLAB | 5,352 | 93 | function [Step_Routes,Step_Normalization] = Normalize_Rects_Values(im,Step_Parameters,Parameters1)
if(Step_Parameters.Trial_Step_Index == 0) % If not in a trial or in the first step of a trial.
% Create a sub-matrix:
[XVb,YVb] = Get_Rect_Vector(Step_Parameters.Step_Coordinates,Step_Parameters.Previous_Angle,3... |
6e512ba7c6b741926e6188491a054107099b48bdf1565e7efeeed2c60fbf6188 | MATLAB | 5,352 | 220 | function par = set_parameters()
% LOAD PARAMS
par.segments_length = 5; % length (in minutes) of segments in which the data is cutted (default 5min).
par.sr = 10000; % sampling rate (in Hz). This parameter will be only used if the data file don't have a sr.
% PLOTTING PARAMETER... |
1639fd42349b0b9a52d9b860bce3593ca42b57ebfe0a559c5188f665169958cf | MATLAB | 5,357 | 170 | %Solve for the conc/copies of dynamin pre and post stimulation.
%Read in a vector of 8 parameters, that were solved for using the Python
%genetic algorithms:
%read in optimal parameters, assign to values
%INPUTS: (these are the first 8 values printed in optFitParms_prePost.txt
%files).
% kfDRC=parmVector8(1);
% Rdilute... |
512bad29f40f26cc64ece65334c3a24c7ce99ed8b0424f081c6217bfff108cfb | MATLAB | 5,360 | 329 | A1=rgb2gray(A);
I_filter = im2double (A);
sigma = 0.2; %default parameters
hsize_r = 3;
hsize_c = 3;
imageycbcr = rgb2ycbcr (I_filter);
lum = imageycbcr (:, :, 1);
im_gaus = filter2 (fspecial ('gaussian', [hsize_r hsize_c] , sigma), lum);
imageycbcr (:, :, 1) = im_gaus;
image_rgb = ycbcr2rgb (imageycbcr);
% ... |
fed8845474b31a8a12578af37de6add9de622e01b375e6b07c243c67c14bbfb2 | MATLAB | 5,361 | 209 | function [spikeMatrix, peak_idxs, peakTimes_ms, classes, peak_vals, dip1, dip2 ]=comBIN(aaa,data,sr,spk_pos,NBINS_peak,NBINS_dip,NBINS_dip2,npointsbeforepeak,npointsafterpeak)
%comBIN(spks,data,sr,1,1,1)
if nargin<8
npointsbeforepeak=2... |
903372e04fbd7702976fb262cf203fa050560ce52722593c34dca0825b1263d6 | MATLAB | 5,366 | 124 | function AnalyzeResults13_SpectralFeaturesFunction(outputfileame1, outputfileame2, per_subject, ntrees)
close all;
% % parameters
% % % % logfileame = 'SVMOptimizedParamters03.txt';
% outputfileame1 = 'Submission55.csv';
% outputfileame2 = 'Submission55_unbiased.csv';
% % % bins = [50 75 100 120 180 250 300 500 750];%7... |
14dfa236a60b180d9d1e12b07ef57f0c77f4fac8913862aa5b79158872d8ebcf | MATLAB | 5,374 | 105 | function [FinalIm,ConInc,FinalCompLevel,err1,datestamp] = IncompleteLetterFineFun(ImWidth,ImBorder,PropComp,CheckSize,WhichLetter);
% LetterIm = IncompleteLetterFineFun(ImWidth,ImBorder,PropComp,CheckSize,WhichLetter);
%
% needs inputs of ImWidth (image width in pixels), ImBorder (gap between letter and image boundarie... |
51c2cf388b968a3bf5fde258517c63e9bdaf7278a3a160e16bf418406ceccecb | MATLAB | 5,374 | 135 | % Analyze classifier activaitons on heldout localizer data - this is used
% to pick a l1 penalty and make some classifier performance plots.
% Enter path of MEG_Decision_Study folder
study_folder = '/Users/erussek/Dropbox/MEG_Decision_Study_WC';
% Path to localizer activations
group_struc_folder = fullfile(study_fold... |
4eff07b3e96d6701e5e7e5e23a9a652d6f9439c2704bace73d6dc556a0a31b59 | MATLAB | 5,376 | 153 | %% Read Data ##############################################################
% X & Y From Multiple Feature Tables
function [X,Y,NamesFeatures,folder_name,MatFileName,EXPIDs]=makedatatable()
X=[]; % Features: Observations x Features
Yall=[]; % Labels
Y=[]; % Labels
readOK = true;
Dirpwd=pwd;
slashesindx=find(Dirpwd=='\')... |
c883591b93cb4da15b0bb26439ec498fb212651622dc203db8a9487c4a330157 | MATLAB | 5,380 | 225 | function par = set_parameters()
% LOAD PARAMS
par.segments_length = 5; % length (in minutes) of segments in which the data is cutted (default 5min).
par.sr = 10000; % sampling rate (in Hz). This parameter will be only used if the data file don't have a sr.
% PLOTTING PARAMETER... |
b08e157ddcc65e8d0a6f31b1bfe2c107edd9df5cb22bfdd743017f46ec7c0f6a | MATLAB | 5,383 | 105 | function [FinalIm,ConInc,FinalCompLevel,err1,datestamp] = IncompleteLetterFineFitFun(ImWidth,ImBorder,PropComp,CheckSize,WhichLetter);
% LetterIm = IncompleteLetterFineFitFun(ImWidth,ImBorder,PropComp,CheckSize,WhichLetter);
%
% needs inputs of ImWidth (image width in pixels), ImBorder (gap between letter and image bou... |
acc8e71b01c1085145571d6b9c2c6a2bc8fec3f71f441b98f96866e1b7294a16 | MATLAB | 5,384 | 175 | function [err, sn, kernel] = nufft1_error(om, N1, J1, K1, kernel, sn)
%function [err, sn, kernel] = nufft1_error(om, N1, J1, K1, kernel, sn)
%|
%| Compute worst-case error for each input frequency for 1D NUFFT
%| using specified (inline) `ad hoc' kernel function (e.g., gaussian).
%| This is worst-case for a unit-norm ... |
cf1c43a4d503abe4c46701d1cef09e9d8092f09628447d4a00387dcbf5410a5e | MATLAB | 5,388 | 125 | %
% Test program for generating synthetic multichannel ECGs with beat-wise deviations and additive noise
%
% Open Source ECG Toolbox, version 2.0, April 2008
% Released under the GNU General Public License
% Copyright (C) 2008 Reza Sameni
% Sharif University of Technology, Tehran, Iran -- GIPSA-Lab, Grenoble, Fr... |
de83971f908cfc369e22c1d0367919e1e721bd1489157cea0b2e7c47206586cc | MATLAB | 5,388 | 165 | function [FinalClusters,FinalClusterIDs,NumTraceLim,MinDendriteLength,InitialClusterNum,Y]=mnl_EuclideanCrawler_Mag_Weighted_v3(Trace,EuThresh,dim,FigureDisplay,Y,MinPoints,MinLength)
%Function to create clusters based on the Euclidean Distances weighted by
%the norm mean magnitude of each trace. v2 has an improved plo... |
9dd504927e091f2c43df0f3d1a600a73e6fad7c4dd8a98f12b7b8cf2dba09a5b | MATLAB | 5,389 | 181 | function varargout = PostDimReduceSingleDim(varargin)
% SingleDim displays all trajectories versus time for single dimensions
% As shown in Yu et. al (2009) with 15 dimensions
%
% Copyright Benjamin Cowley, Matthew Kaufman, Zachary Butler, Byron Yu, 2012-2013
% ---GNU General Public License Copyright---
% This file i... |
9a5b96facefd8a848c6de12ae16fe57ef2cdcdd27cafc04d54f0890f2a774a6f | MATLAB | 5,393 | 233 | function par = set_parameters()
% LOAD PARAMS
par.segments_length = 5; % length (in minutes) of segments in which the data is cutted (default 5min).
par.sr = 10000; % sampling rate (in Hz). This parameter will be only used if the data file don't have a sr.
% PLOTTING PARAMETER... |
e6571e17d2558f8695dd83cd83d9afb71d532da442c87ce701bd686fbff3d359 | MATLAB | 5,393 | 166 | function [r_course,r_outliers] = spmup_volumecorr(varargin)
% compute the Pearson correlation between volumes of the time series
% and return possible outliers among correlated volumes (this assumes of
% course that r is not biased by outlying voxels between volumes)
%
% FORMAT [r_course, outliers] = spmup_volumecorr... |
c9fbe51b600467328e22faceb87f067884bf76fd6b55141c1c092918fd3777fb | MATLAB | 5,398 | 148 | function W4_Pattern_5_20201208
path=['C:\Users\C238\Desktop\touchcellLtRt_Hirokane'];
cd(path);
LS=ls;
LtWFRUnit=[];
RtWFRUnit=[];
LtW1234Point=0;
LtW1234Fire=0;
LtW2345Point=0;
LtW2345Fire=0;
LtW1235Point=0;
LtW1235Fire=0;
LtW1245Point=0;
LtW1245Fire=0;
LtW1345Point=0;
LtW1345Fire=0;
RtW1234Point=0;
... |
db4d8ea37845adb6e58dce9d6116551190003f6ab7670351258027e9e8e04f99 | MATLAB | 5,399 | 148 | function W4_Pattern_5_20200302
path=['C:\Users\C238\Desktop\touchcellLtRt_terashita'];
cd(path);
LS=ls;
LtWFRUnit=[];
RtWFRUnit=[];
LtW1234Point=0;
LtW1234Fire=0;
LtW2345Point=0;
LtW2345Fire=0;
LtW1235Point=0;
LtW1235Fire=0;
LtW1245Point=0;
LtW1245Fire=0;
LtW1345Point=0;
LtW1345Fire=0;
RtW1234Point=0;... |
ae36ecbcdd5dcdc457f5e0d09eab031b0a07e34c8d9a77856fe674455051e0f9 | MATLAB | 5,405 | 193 | % test fetal ECG morphology extraction by the deflation algorithm and double KF for fetal denoising
% Copyright Reza Sameni, 2018
% Created 2008
% Modified June 2018
%
% Modified Oct 2020:
% ECGBeatFitter replaced with new function call format
clear;
close all;
load('FOETAL_ECG.dat'); data = FOETAL_ECG(:... |
08d385813e242e4a11168d605e97b2d2edfd452dcd7874883eaae0f67cca285f | MATLAB | 5,406 | 145 | function corrData = plotAuxCorr(hClust, selected)
%PLOTAUXCORR Plot aux channel correlation with firing rates figure
if nargin < 2
selected = [];
end
hCfg = hClust.hCfg;
% load aux channel data
[auxSamples, auxTimes] = loadAuxChannel(hCfg);
if isempty(auxSamples)
corrData = ... |
27847c8247aa4233604ee85621a55978d10c1ef384bb486df0c0e9f377d9de0f | MATLAB | 5,408 | 192 | function [] = fmodel(session, channel, unit)
% Approximates the fitted S-kernels with Gaussian functions
%
% Parameters
% ----------
% - session: scalar
% Session number with format: yymmdd
% - channel: scalar
% Channel number
fprintf('===== F-Model =====\n\n');
main_timer = tic();
info = get_info();... |
559532f98c9934bb8b35046fb6bb0e672881e5156f1a441b28cf108d75915c81 | MATLAB | 5,412 | 187 | %
%
% 1. Extraction of 5HT receptors
% 2. Visualization of the expression
%
% by Hiro Taiyo Hamada, 2020/12/17
% 1. extraction of 5HT receptors
niftidirect = '/Volumes/HD-ADU3/tphskk/analysis_programs/optofMRIAnalysis/5HTR';
niftiList = makeList(niftidirect, '*.nii');
template = '/Volumes/HD-ADU3/tphskk/analysis_pro... |
88a6d5da670f84f06c0caa1b85e8afaf91bcc4f44e5a4a64630c86aee8e69725 | MATLAB | 5,412 | 121 | %% Calcium Imaging Analysis Operator (get active pocktes)
% CIAO_02e_activeROI allows you to identify active pockets based on a
% global image threshold using Otsu's method. We determine a threshold and
% create a corresponding mask for each stimulus.
% We assume that there is a 03_Data_Processed folder within the main... |
a08a32f297dada357a43009c7437e444dc5359229c02833478cc4ca7da0268f4 | MATLAB | 5,420 | 135 | function extract_ecg_features( input_path, output_path, opts)
% function extract_ecg_features(input_path, output_path, opts)
%
% Inputs:
% input_path (string or char): Path to the folder containing input ECG signals (WFDB format)
% output_path (string or char): Path to save the extracted feature output (.csv file... |
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