sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
6510d8f7a0aa8202c9a5bd85a67db612e56895995d3e30ae1458dad593bbb421 | MATLAB | 4,674 | 154 | %% LCMV virtual channels
%
% Vinding, M. C., & Oostenveld, R. (2022). Sharing individualised template MRI data for MEG source reconstruction: A solution for open data while keeping subject confidentiality. NeuroImage, 119165. https://doi.org/10.1016/j.neuroimage.2022.119165
%
% Calculate virtual channels in a set of pr... |
c97dcdc4295a2b93967f978015bc828fc4555ed32adfebb2f5c560caf0376115 | MATLAB | 4,674 | 161 |
%% compute average JND across all participants
cd D:\Exp_noInstruction\
resOut = [pwd '\data\'];
outdir = [pwd, '\results\']
load([resOut,'Exp_noInstruction_JNDblockXSub'],'JNDblockXSub')%% plot
GroupMean = mean(JNDblockXSub,2);
GroupSE = std(JNDblockXSub')/sqrt(length(JNDblockXSub));
figure
hold ... |
ca4b627ec5d499d965d6b92f104536ac9ac0bd1aa84f75b0ac8ed8ea9bb27425 | MATLAB | 4,675 | 152 | clc
clear
close all
data_name = 'Confocal_BPAE_B';
addpath('./code')
addpath('../utils')
dir_avg1 = ['../../dataset/', data_name, '/raw/19/']; % 50 images
dir_avg2 = ['../../dataset/', data_name, '/avg2/19/']; % 50 images
dir_avg4 = ['../../dataset/', data_name, '/avg4/19/']; % 50 images
dir_avg8 = ['../../dataset/',... |
a8d48daafd02aee832b583271b006054b7a8b8e8cdc20dcbdd7640cf83eaf833 | MATLAB | 4,678 | 125 | function [ens_nodes, results] = find_core_ext_IDs(best_model,data,UDF,num_controls)
% Input
% best_model: Trained CRF model to analyze.
% data: Expected to be a timeframes by neurons binary matrix.
% stimuli: Expected to be a timeframes by stimuli binary matrix.
% num_controls: Optional. Number of random ensemb... |
ec1e3efedae8dd90cae90bc02bc97c18c1a51b8685a7633de6ffdd8006ed9526 | MATLAB | 4,678 | 147 | function [g,tt,params,err,errF,errP] = getModelParams(val,XX,YY,lambda,plotFig)
badNdx = isnan(squeeze(nanmean(val,[1,2])));
val(:,:,badNdx) = [];
XX(:,:,badNdx) = [];
YY(:,:,badNdx) = [];
xlims = [min(XX(:)) max(XX(:))];
ylims = [min(YY(:)) max(YY(:))];
tt = (0:size(val,3)-1).*300./30;
zmin = floor(min(v... |
93d0f1b21c61daf15539463c45755700661cd2fa2eb6b519945e63bf754d814f | MATLAB | 4,679 | 117 | function Find_Grid_Distortion(folder, folder_curve)
%this is for finding grid distortion at different z depth
cd(folder);
files=dir('*.dat');
num_files=length(files);
if num_files>1
display('more than one files found, only need one file')
else
file_split=strsplit(files(1).name,'.');
file_split2=str... |
c78e61b4c59eaa2bab1251b811edcda2d16ba962b4f8bf8ffc4e5533cbb3c8e2 | MATLAB | 4,679 | 122 | function varargout = spm_shoot_greens(varargin)
% Build and apply FFT of Green's function (to map from momentum to velocity)
% FORMAT v = spm_shoot_greens(m,K,prm)
% m - Momentum field n1*n2*n3*3 (single prec. float)
% K - Fourier transform representation of Green's function
% - either size n1*n2*n3 or n1*... |
8efba174d33968b413eab9de3568d25f6fd0dc85ba83cafe2d5e9a019d209311 | MATLAB | 4,680 | 141 | function [mu, xi, twoMargs, energy, complexity, exact, logZ] = run_junction_tree(theta, W, varargin)
% [mu, xi, twoMargs, energy, complexity, exact, logZ] = run_junction_tree(theta, W, varargin)
%
% Same interface as solveBetheExact.
% solveDAI Find marginals of problem in Eq 1 by libDAI's methods
% [ logZ, joint,... |
c1c4f0e930ad60b41f0f7de29205451f2acab9c6722f3d9090a314c752536400 | MATLAB | 4,680 | 123 | function [cde] = getCode(funNme)
% Read the source of an *.m file into a numeric vector.
%
% Together with saveCode this is a way to store the code that produced a
% data set next to the data set itself, so a result can still be traced back
% to the exact version of the script that created it.
%
% Usage: cde = getCode(... |
2d52a028e9cc55e1f724f3863a2ee3b141bf315aff2d93315887183fc30d80dc | MATLAB | 4,684 | 172 | clear all
close all
rng(9)
%% initialization of parameters/matrices
run('param_init2.m')
%% creation of presynaptic inputs
run('input_init.m')
%% trial loop
for l = 1:num_trials
%choose a cue
cue_given = cues{l};
pos_map = PF_map;
temp = PF_map;
%induction protoc... |
27a107e78210b9c9a12a95eefa204d8828354be3b6a35d9376e7d931afc0a4b7 | MATLAB | 4,689 | 143 | % Here we calculate the optical flow change by eccentricity
%
% this script
% (1) reads in the stimulus image then calculate the optical flow
% from one frame to another using modules in matlab simulink.
% (2) The optical flow vector will be drawn on the original images.
% (3) We calcualte the overall magtitute of t... |
18710d5544fc730342d9d703df7bec6eb3deb4f48de3ea4a17740e952afd769a | MATLAB | 4,693 | 166 | function st = nufft2_init_kb(om, N1, N2, J1, J2, K1, K2, n_shift, kernel, useloop)
%function st = nufft2_init_kb(om, N1, N2, J1, J2, K1, K2, n_shift, kernel, useloop)
% Initialize structure for 2D NUFFT using KB interpolator,
% particularly the interpolation matrix in sparse format.
% in
% om [M,2] frequencies in rad... |
948ac0e9a14bc84f10b8a7dd56b48d74d6fa23cdeb842d0ec3efe66a2ff92741 | MATLAB | 4,693 | 111 | %computes the (normalised) flow field in a Drosophila embryo and
%plots the streamlines + heat map shown in Fig. 4d
N = 14; %number of modes kept in truncation of spheroidal harmonics series
%spheroid geometry
bx = 110;
bz = 270; ... |
f2bebce6f7eea785e6d23be567fb61600d915db92eb5f1f8dbc5033944e2f563 | MATLAB | 4,695 | 125 | function openNetworkIO
% Opens the network connection to the network IO device.
%
% Usage: openNetworkIO
%
%
% Copyright: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
% Contact: OTBR-Toolbox@ruhr-uni-bochum.de
% Source Code: https://gitlab.ruhr-uni-bochum.de/ikn/OTBR
%
% Please cite the OTBR Toolbox wh... |
2b6476c594098c6dcd11ee02246df69dab38e2cf20988636159d0d64d4f82002 | MATLAB | 4,697 | 111 | % compile
%
% Builds the trigger counter for the network IO box (triggerCounter3.m)
% into a standalone Windows program with the MATLAB Compiler.
%
% Run it in MATLAB, from any folder. Needs the MATLAB Compiler and
% PsychToolbox; from the toolbox it needs the network functions of
% IO\Interface7 only (openNetworkIO, b... |
b0a13d09533ba8a15433a04e963c0609bd9202a23f7d40d292db014a9a5e9931 | MATLAB | 4,697 | 104 | function bootstrap_decoding_peak_difference(subjects,analysis)
%BOOTSTRAP_DECODING_PEAK_DIFFERENCE Calculate the confidence intervals of the difference between peak
%latencies of categorization and distraction decoding.
%
%Input: subject IDs, analysis ('object_decoding' or 'category_decoding')
%
%Output: confidence int... |
e41316ee66381f0f414e3e35239898b7bf4efa349afd12d40069f5ae864d26c8 | MATLAB | 4,697 | 136 | %% Combined: load rerun (/data/Technion_analysis/zebrafish/zf_scratch_nocls_combined.m)
close all
clear all
clc
%%
disp('zf_sc')
direct='/data/Technion_analysis/zebrafish/sc_100410/comparative/COMBINED';% SLC17A6A % SLC32A1
cd(direct)
load([direct,'/rerun.mat'],'data_orig_all','sample','geneid_all','cellid','all_flags... |
2c4338b8c6e2807943b6b487be653f79f4927e8e1d7f4e959eb3868a59fded75 | MATLAB | 4,698 | 136 | function [] = find_crf_ensemble(param)
% parameters
expt_name = param.expt_name;
ee = param.ee;
ge_type = param.ge_type;
data_path = param.data_path;
result_path_base = param.result_path_base;
ccode_path = param.ccode_path;
rwbmap = param.rwbmap;
num_expt = length(expt_name);
qnoise = 0.7;
load(ccode_path);
load(rwb... |
cda280a492058076c8dddc915c1735714d03ec1bf2b61309909c73fc6b430730 | MATLAB | 4,705 | 124 | %% Thomas_Yang_et al, 2023 @Nuo Li lab
%% Fig. 7c selectivity of tagged glutamatergic neurons
clear all
close all
addpath('../func')
load Figure_7_c_data
%% plot contra selectivity
i_sel_unit = (sig_selective(:,1)==1 | sig_selective(:,2)==1);
i_t = find(t(1,:)<-.1);
figure; hold on
psth1 = PSTH_yes_... |
b499e3685c1a878b300a8979ee53719bd473630a96241db00e7a66965bfe18b5 | MATLAB | 4,706 | 154 | function make_figure5(plotVariables, options)
glmFlag = 'all_regressors';
% subject list
subjectList = [16 18:21, 24, 26, 27, 28, 29, 31, 33, 34, 35, 42, 43, 47, 50, 51, 52, 54, 55, 57, 58]; %32 taken out
%subjectList = [62:64,66,68,70]; % vertical motion only
csdFlag = 0; % 1 for csd transformed data
if csdFlag %... |
ed95ec150e12bfd8a90d913144fd472f3d9cf14ac7da041938bc7baf8116fc66 | MATLAB | 4,707 | 176 | function R = Rbuild(ctype, kappa, nbrs, beta, potential, delta, ...
match_aspire, dist_power)
%function R = Rbuild(ctype, kappa, nbrs, beta, potential, delta, ...
% match_aspire, dist_power)
%
% build a regularization "object" for penalized-likelihood reconstruction.
%
% general form of nonquadratic penalty functio... |
0cde754ba32ea676e81ed5ff1d3a8186c5f908a8dbb9f38421920f37bdf639fd | MATLAB | 4,709 | 108 | function cc = fun_cc_in_sparse_matrix(idx_list, mask_size) %#codegen
% This function collect the connected components in a mask
% The mask is specified by two inputs:
% idx_list: N-by-1 double precision array. List of mask voxel indices
% mask_size: size of the mask
% Output:
% cc: cell array, each contain... |
5f9081386b1c6f951583b6b92bbd4eaf55feed8a2f8221278e3ba2f743b07915 | MATLAB | 4,711 | 101 | function fn_plot_ts_errbr_sig(plot_info, data, var, sig, event, cond)
% Plot mean time series with shaded error bars on given plot handle
% INPUTS:
% plot_info [struct] - contains general plotting details
% .fig [figure handle] - handle of the figure where the plot should appear
% .ax [axis handle] - hand... |
81938317e5ff3b23c4a327219ab1be4747172b69959e7d63dfe62e1814af44d5 | MATLAB | 4,716 | 139 | function x = spm_invGcdf(F,h,l,tol)
% Inverse Cumulative Distribution Function (CDF) of Gamma distribution
% FORMAT x = spm_invGcdf(p,h,l,tol)
%
% F - CDF (lower tail p-value)
% h - Shape parameter (h>0)
% l - Scale parameter (l>0)
% x - Gamma ordinates at which CDF F(x)=F
% tol - tolerance [default 10^-6]
%___... |
dfad79c276c31027af8b0ef91934d4937eeabba3454e68b3acbfa787195bc071 | MATLAB | 4,716 | 148 | function [K,U,s] = splitclust(M)
% Given a matrix M, split its columns into two subsets
%
% See Section 3 in
%
% Gillis, Kuang, Park, `Hierarchical Clustering of Hyperspectral Images
% using Rank-Two Nonnegative Matrix Factorization', arXiv.
%
%
% ****** Input ******
% M : m-by-n data matrix (or a H-by-L... |
f840d826952332ae48c2fb0a5a137f9416379829c3612f5f064142cadbda2ef6 | MATLAB | 4,716 | 129 | function analysis_04_sort_tf(study_info, varargin)
% Parse optional arguments
defaults=struct();
params=struct(varargin{:});
for f=fieldnames(defaults)',
if ~isfield(params, f{1})
params.(f{1})=defaults.(f{1});
end
end
pipeline='NEARICA_behav';
exe_epoch_types={'EBM','LEXT','FTGE','EXGC','EXEND'};
obs... |
e0697a8b29c2202e803b05f01eac8a7458f84046d33c4134e6fdc1d432253bd4 | MATLAB | 4,718 | 136 | function [acc_rand] = RandomForest_HER_chance(set_avg, set_var, numrand, train1, train2, test1, test2, time_onset, time_offset)
% This function performs a Random Forest classification using arbitrary
% patients label. This function performs several classifications to obtain a
% chance accuracy distribution for unbalanc... |
07df1a2819b7c465297ebab0545fac71efdb7dfca72cb63221be5bb354c8c7c8 | MATLAB | 4,723 | 177 | function R = Rbuild(ctype, kappa, nbrs, beta, potential, delta, ...
match_aspire, dist_power)
%function R = Rbuild(ctype, kappa, nbrs, beta, potential, delta, ...
% match_aspire, dist_power)
%
% build a regularization "object" for penalized-likelihood reconstruction.
%
% general form of nonquadratic penalty functio... |
e9b5a5a50dd7fe01290073673c2ad6ef9fe677f777c0525cb52c8aa37783e71d | MATLAB | 4,724 | 137 | function load_excel_file_proboscis_heads(files)
% loads -csv files with the animal data in the following format: X, Y
% coordinate of the following points in this order:
% proboscis, head, thorax, proboscisbase, left antenna, right antenna, outline of the flower, pattern 1, pattern 2
% outline of the flower are 4 p... |
3f91f71fcc745336de5bb66c2e396d577bbffdd4044ec7e85b4508ca35348b91 | MATLAB | 4,727 | 142 | function [model] = spm_vb_models(SPM,factor)
% Specify models for ANOVAs
% FORMAT [model] = spm_vb_models(SPM,factor)
%
% SPM - SPM structure
% factor - Structure specifying factors and levels
% factor(i).name Name of ith factor
% factor(i).levels Number of levels
% ... |
ff8235ee8084384e1bb291ae0a50baeb5f0cf24ce11c0afca07210149089f21a | MATLAB | 4,730 | 169 | function [Dice, Pval, Isubj]=wm_seg_dir_adf(dirname, th_pval)
% For all the subjects in the directory "dirname":
% Computes the Dice coefficients D=2Nab/Na+Nb
% where:
% Na is the volume of the WM obtained trough the volume-based labeling
% Nb is the volume of the WM ... |
a18c85f4b1b61897be0f68e8f8259f1e1673355879308bcd4098d72eb671be7a | MATLAB | 4,735 | 116 | close all; clear; clc;
figX.fig = figure();
set(figX.fig, 'Units', 'pixels', 'Position', OSScreenSize([15 6]), 'NumberTitle', 'off');
figX.active(1) = axes('Position', [.04 .075 .175 .275]); figX.active(2) = axes('Position', [.04 .35 .175 .275]); figX.active(3) = axes('Position', [.04 .625 .175 .275]);
figX.grasp(1) =... |
23cc22d869ee5116b307461ff5d04d5eda79033408d356e044a1ffc037562c81 | MATLAB | 4,736 | 136 | %% Analyzing Neural Time Series Data
% Matlab code for Chapter 5
% Mike X Cohen
%
% This code accompanies the book, titled "Analyzing Neural Time Series Data"
% (MIT Press). Using the code without following the book may lead to confusion,
% incorrect data analyses, and misinterpretations of results.
% Mike X Cohen ... |
e722302d893d12743a51bbf6557294f4bcfff5c9347e253266f0c2c804b2b997 | MATLAB | 4,742 | 135 | function [] = plotDistributions(self,stateDistBefore,stateDistEarly,stateDistLate,earlyNdx,lateNdx,nMin,fNum)
% define basic paramters
lims = []; % y limits to set (example: [0 1])
isPaired = 'N'; % equivalent to paired t-test
circleSize = 60; % size of scatter pl... |
71fbd1b7fb37021cd923053e0e1478cb30b078d3eff24d9148ddc0cd6c2cfa2e | MATLAB | 4,743 | 143 | function outSet = simHawkesNetDemo(ratefwd, parsMdl)
% Simulate a linear Hawkes process with ring structure.
% ratefwd is the firing rate in a time bin of the feedfoward input
% This code is nearly all the same as simHawkesNet.m but outputs urec
% Wen-Hao Zhang,
% University of Pittsburgh
% Feb 3, 2020
% Release hea... |
17d97eb31c332aad062f65a308aaa928e54dfd98fc4fdb4f1c2cdd8e5798acdf | MATLAB | 4,744 | 99 | function link_info = fun_graph_get_free_link(vessel_graph, internal_offset, sortQ)
%
%
%
%
% Note:
% Link with two endpoints can appear in the list of link_info.ep1, because
% one of the endpoint can be out of the internal_offset and therefore the
% link effectively has only one internal endpoint
if nargin < 2
in... |
86022c49a2809b739ae76065b4f54cd6cbda5870a86c72fcab4712417571abcb | MATLAB | 4,747 | 117 | function cnd = cndReref(cnd,type)
%CNDREREF Re-referencing of neural data (e.g., EEG) in CND format.
% CND = CNDREREF(CND,TYPE) re-referencing of the neural data contained in
% the variable CND. The re-referencing can be to one particular set of
% external channels (e.g., 'Mastoids') or to the global average... |
da1c5627022632f2437361832ed2627647356684f302ec584b3a51d04195c34b | MATLAB | 4,750 | 159 | function [W Wgx Wgy Wgz] = makeW(Bcell, Acell)
%function [W Wgx Wgy Wgz] = makeW(Bcell, Acell)
%
% Create a Fatrix W operation of deformation based on rect basis
%
% in:
% 'Bcell' array of cells of fatrix operation B for warping {x}{y}{z}
% 'Acell' array of cells of warping coefficients Alpha {x}{y}{z}
%
% out:
%... |
3aab19f027ac802fb9de362afabc356514b84a0e046b9344dc4e6986cb14e3d3 | MATLAB | 4,751 | 121 |
function [Best_pos,Best_score,CHIO_curve]=CHIO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj) %¿ªÊ¼ÓÅ»¯
PopSize=SearchAgents_no;
MaxAge = 100;
C0 = 1; % number of solutions have corona virus
Max_iter=Max_iteration; %/*The number of cycles for foraging {a stopping criteria}*/
Spreading... |
4b8404ae960505105da88cbfafc6bb3284b47fc2e3d6c236f6c2a8b6cc246ff7 | MATLAB | 4,751 | 169 | function varargout=shadedErrorBar_anna(x,y,errBar,lineProps,transparent,logscale)
% slightly altered version of the original code https://de.mathworks.com/matlabcentral/fileexchange/26311-raacampbell-shadederrorbar
%
% Purpose
% Makes a 2-d line plot with a pretty shaded error bar made
% using patch. Error bar color i... |
746b58d95da893cab98427c788d6ee4220cc75db53047883c447c2a040ab35c9 | MATLAB | 4,751 | 98 | folder='/projectnb2/npbssmic/ns/Ann_7694/';
datapath=strcat(folder,'dist_corrected/');
% add subfunctions for the script. Change directory if not running on BU SCC
addpath('/projectnb/npbssmic/s/Matlab_code/fitting_code');
addpath('/projectnb/npbssmic/s/Matlab_code/PostProcessing');
addpath('/projectnb/npbssmic/s/Matla... |
82feac13265cf165e71135fb96c2c328127183023c8f8cc85d6969679610f152 | MATLAB | 4,753 | 117 | function doPCA(matPath)
addpath(genpath('/Volumes/groups/giocomo/export/data/Users/KMasuda/Neuropixels/MalcolmFxn/'));
addpath(genpath('/Users/KeiMasuda/Documents/MATLAB/Add-Ons/Functions/gramm (complete data visualization toolbox, ggplot2_R-like)/code'));
addpath(genpath('/Volumes/groups/giocomo/export/data/Projects/... |
fafaf50673d6dfa7511fcc35db147f1572330dab30ee8dee67f681f2e03f4f9b | MATLAB | 4,754 | 168 | function [block] = spm_vb_glmar (Y,block)
% Variational Bayes for GLM-AR modelling in a block of fMRI
% FORMAT [block] = spm_vb_glmar (Y,block)
%
% Y - [T x N] time series with T time points, N voxels
%
% block - data structure containing the following fields:
%
% .X [T x k] the design matri... |
05b0aee566be0018d885523dbd32c9fe30379df3a308f85ee6559620463f3043 | MATLAB | 4,757 | 133 | if ~exist('conId_orig', 'var')
clear conId patId
participant_list_module_repo % re-laod the sample info (conId, patId)
conId_orig = conId;
patId_orig = patId;
end
distances_1 = [];
distances_all = [];
for iss = 1:length(id_log)
if ismember(iss, [conId_orig patId_orig])
this_distance_mat... |
29c207ab91426ae661d2937dc2da146fe8f720439d3157cdb08db652f7854398 | MATLAB | 4,757 | 133 | optDesPath=genpath('/vxfsvol/home/research/dsnlab/matlab/CanlabCore/CanlabCore');
%scnPath=genpath('/Users/wem3/matlab/grimoire/SCN_Core_Support/');
addpath(optDesPath);
clear Models, clear MM
GA.conditions = [1 2 3 4 5 6]; %affect|neutral x equal|loss to share|loss to private
% ... |
ac534f64c611bf6467eeba40cbd4f89b19a226bea6cfa92eb1aeee917dc9eb94 | MATLAB | 4,757 | 145 |
%%% define variables for loading data, plotting & computing ITR values %%%
fileName = "C:\Users\Anna\surfdrive\Marble\publication\testAccuracies.xlsx"; % accuracy matrix: nsubj x ntrials
pathToViolinPlotFunc = "C:\Users\Anna\Downloads\brainAR-main";
chanceLev = 50; % chance-level value, in %
% variables for ITR com... |
3bba900d361ad793f6e7ac2cee11cd4740110adad8b0bff0347435b3777dafcf | MATLAB | 4,761 | 159 | function [y,w,S,Gu,Gn] = spm_csd_fmri_mtf(P,M,U)
% Spectral response of a DCM (transfer function x noise spectrum)
% FORMAT [y,w,S,Gu,Gn] = spm_csd_fmri_mtf(P,M,U)
%
% P - model parameters
% M - model structure
% U - model inputs (expects U.csd as complex cross spectra)
%
% y - y(nw,nn,nn} - cross-spectral density for ... |
68ecb3ca856cf407336c287be18c1596a2f6a2014792f9f8094a9bf847f6c3bc | MATLAB | 4,761 | 166 | function [Cxx,Cxy,Cxz,folds] = mlscovmat(x,y,z,lags,type,zeropad,verbose)
%MLSCOVMAT Covariance matrices for multisensory least squares estimation.
% [CXX,CXY,CXZ] = MLSCOVMAT(X,Y,Z,LAGS) returns the covariance matrices
% for multisensory least squares (MLS) estimation using time-lagged
% features of X. X, Y and... |
3df8e3ea29d3adc4fd2f5e8604ba506bd7f7fe058da5ab9feb81a43da1db5619 | MATLAB | 4,762 | 126 | function [outOnsetSignal, outOnsetFreq] = IPEMCalcOnsetsFromANI(varargin)
% Usage:
% [outOnsetSignal,outOnsetFreq] =
% IPEMCalcOnsetsFromANI(inANI,inANIFreq,inPlotFlag)
%
% Description:
% This function calculates the onsets for a given signal represented by its
% auditory nerve image, using an integra... |
c6ec2478bee6dfbde59a91852641b57a42d56e2cc33fe6f819786e6afe093cd3 | MATLAB | 4,762 | 124 | function launchJobs( exePathInFwHome, scriptPathOnFw, nodeMatlabName,...
batchId, nTask, varargin )
% Launch a job on the FwGrid cluster
%
% Each node function must be defined as:
% function fwNode( ind, batchId, savePath )
% and end with:
% save( [ savePath batchId sprintf('-%05d',ind) ], 'var1' );
% exit
%
... |
f873457fcf5f32d754c63f9fdb242fc72e5e6b093fe2e50c1ed7263158358df8 | MATLAB | 4,765 | 105 | % tSC_coupling_epprops_comp
%Compar differnt electrophysiological properties of tSC coupled and not
%coupled neurons from preprocessed porperty data.
% Properties: firing rate, theta coupling, intra-burst spike
% number, intra-burst frequency, burst-skip ratio, Inter spike interval histgorams,
% speed correlation,... |
82eb7b707b06682676a0efda4594e230809487a437568bc9f5aaeb76e8f86b96 | MATLAB | 4,768 | 123 | function IPEMExportFigures(varargin)
% Usage:
% IPEMExportFigures(inInputPath,inOutputPath,inFormat,inUseIPEMLayout)
%
% Description:
% Exports figures to specified graphics format.
%
% Input arguments:
% inInputPath = path to scan for .fig files
% if empty or not specified, the currently... |
6665409e60e327bd0402647edd0a9e229f4a85085b243976f8b12e6189e66846 | MATLAB | 4,770 | 129 | function Vi = spm_get_vc(I,factor)
% Generate error covariance components for factorial designs
% FORMAT Vi = spm_get_vc(I,factor)
% I - n x m matrix of factor level indicators
% I(n,i) is the level of factor i for observation n
% factor(i) - structure array of sphericity assumptions for each factor... |
f9dda6d02c44c5dff1cab08eabd5e8d7c4148fff996ff5249a4fe0dfd7a207df | MATLAB | 4,770 | 197 | function [pmf xs] = kde_pmf1(x, varargin)
%|function [pmf xs] = kde_pmf1(x, varargin)
%|
%| Compute an empirical probability mass function (PMF) using interpolation
%| that is similar to a kernel density estimate.
%| This routine is useful for making PMFs (interpolated / smoothed) histograms
%| for computing entropy ... |
f5607c48378ba1a9361f379bc03c90e7e073abb67bb1c932f9e8d7c07db6fc06 | MATLAB | 4,771 | 120 | %% Thomas_Yang_et al, 2023 @Nuo Li lab
%% Fig. 1f, lick behavior, unilateral SC photoactivation
clear all
close all
load Figure_1_f_data
%% licking temporal histogram, all mice
figure
n_plot = 0;
for i_aom = [0 0.1 0.3 0.4 0.5]
for i_SC_side = 1:2
n_plot = n_plot+1;
... |
e93d1ce907de7b9baeb32a77e5417a91e2cdf7cc43274553053f68504485b4e2 | MATLAB | 4,773 | 113 | function SVM_artificial_vs_natural_decoding_PDM_full_experiment(subject,task)
%SVM_ARTIFICIAL_VS_NATURAL_DECODING_PDM_FULL_EXPERIMENT Perform category decoding (artificial vs natural) using the SVM classifier.
%
%Input: subject ID, task (1=categorization, 2=fixation)
%
%Output: NxNxP vector of accuracies in %, where ... |
35c479ff9fffa2ef8ce70f70bea1d18db516f1555a73b6dc0a9d11a5a8598c80 | MATLAB | 4,774 | 156 | %% Curve fitting - estimate effective Reylaigh range and focus depth
clear all;
datapath = strcat('/projectnb/npbssmic/ns/190626_Thorlabs/2x/50um/');
% add subfunctions for the script
addpath('/projectnb/npbssmic/s/Matlab_code/fitting_code/');
% get the directory of all image tiles
cd(datapath);
opts = optimset('Di... |
338737ca6fd73714a4f7836c482a5875cd3a1040062979b2b6bd65ecd66e31b1 | MATLAB | 4,778 | 138 | function IPEMSetup
% Usage:
% IPEMSetup
%
% Description:
% This function sets up the Matlab environment for working with the
% IPEM Toolbox.
%
% Example:
% IPEMSetup;
%
% Authors:
% Koen Tanghe - 20011204
% ------------------------------------------------------------------------------
% ------... |
fc1b0045d06f74ccd4ff42ae56947ba1cbabfaca5b7bd778226a5d64adc382fa | MATLAB | 4,782 | 90 | % BAYESIAN DECODING ERROR WITHIN EXPOSURES
% Looks at place cell remapping between different and same track exposures by calculating bayesian decoding error.
% For each track exposure, calculates the decoding error using as template the other exposure to the same track. The template can be the whole session, first hal... |
71ccbdf53d1ccd7e1722213163ac9abcef5189f4986940ddf084e521e285a5d9 | MATLAB | 4,788 | 132 | function plot_RT_full_experiment(subjects,with_stats)
%PLOT_RT_FULL_EXPERIMENT Create a scatterplot of reaction times.
%
%Returns a scatterplot of reaction times in the categorization task vs
%reaction times in the distraction task, for each participant.
%Different colours represent different participants and differen... |
005eed076b53d01862528d7f2fb9112d083e4c2dd03b2d288dc108b68f8ba2e2 | MATLAB | 4,790 | 155 | % The script is used to perform 8-way frequency-resolved decoding
% on EEG data (theta and high-gamma bands)
% add toolbox
addpath('fieldtrip-20191213/');
addpath(genpath('CoSMoMVPA-v.1.1.0/'));
addpath(genpath('libsvm-3.25/'));
ft_defaults;
%% frequency analysis
% set input and output paths
data_path = '../../eeg_d... |
492770ed1e618312532597b3611675841ada1e2d631a342995287f09f64ad004 | MATLAB | 4,791 | 141 | %% Curve fitting - estimate effective Reylaigh range and focus depth
datapath = 'E:\Jiarui\Data\code\fitting_code\';
% add subfunctions for the script
addpath('E:\Jiarui\Data\code\fitting_code');
% load('/projectnb/npbssmic/s/Matlab_code/Matlab_batch/Zf.mat');
% get the directory of all image tiles
cd(datapath);
fi... |
4ae841fcf24aea533e1fd8f7737e81a5fb6913780872275a8151b60c0dfb6697 | MATLAB | 4,792 | 141 | function [DCM] = spm_dcm_mdp(DCM)
% MDP inversion using Variational Bayes
% FORMAT [DCM] = spm_dcm_mdp(DCM)
%
% Expects:
%--------------------------------------------------------------------------
% DCM.MDP % MDP structure specifying a generative model
% DCM.field % parameter (field) names to optimise
% DCM.U % c... |
916b8f8b06797523a77aa5ac4fc400aea505c473a7acc7f289f527866664e486 | MATLAB | 4,796 | 209 | % pwls_gca_test.m
% Test 2d penalized weighted least-squares with nonquadratic penalty
%
% Copyright Mar 1999, Jeff Fessler, University of Michigan
% generate data
if ~isvar('xtrue'), printm 'xtrue'
ig = image_geom('nx', 12, 'ny', 10, 'dx', 1);
ig.mask = ig.circ > 0;
xtrue = ig.circ(2);
im plc 3 2
im(1, xtrue, 'x... |
12cb95cb35b46cd3ac5a20bf1aca9f9cfe26b539767a6962d2a2bd29cfd2a733 | MATLAB | 4,797 | 167 | function [Dnew]=spm_eeg_simulate_frominv(D,prefix,val,whitenoise,SNRdB,trialind)
% Project a source inversion solution back out to the sensor level plus some noise
% D - original dataset
% prefix - prefix of new dataset
% val - use solution (and lead fields) corresponding to this index
% whitenoise ... |
e630da0ce51b791bd5a81dc3cadcc2a398f47cf3af540f61e7ef706e5f4c1359 | MATLAB | 4,798 | 130 | %% Thomas_Yang_et al, 2023 @Nuo Li lab
%% Fig. 7b selectivity of tagged GABAergic neurons
clear all
close all
addpath('../func')
load Figure_7_b_data
sig_selective = sig_selective_p<.01; %<------- user defined
%% plot contra selectivity
i_sel_unit = (sig_selective(:,1)==1 | sig_selective(:,2)==... |
0ab180ff6a0f49c9c4868ffe289c2203a196922a87c0831514d3a77049fb4281 | MATLAB | 4,800 | 199 | % xiayq @ 12/11/2022
% xiayq0121@zufe.edu.cn
% refered to Z. Yao and Y. Xia, Manifold Fitting under Unbounded Noise, arXiv:1909.10228
clear; %clc
% parameters for data
D = 3;
NumTrials = 20;
tau = 1; sigma = 0.04;%0.04
% method setup
algos = {'ours','cf18','km17'};
switch sigma
case 0.09
r1 = 2*sqrt(s... |
4aca734f51b9b74aa96a59450918950ed5bb76a7ce2a7830852309f7f6ea9157 | MATLAB | 4,800 | 102 | %generating macro scripts to unwarp XY plane
pathname='C:\Projects\serialsectioningOCT\Data\Griddistortion\test\macros\';
macropath=[pathname,'Macro.ijm'];
z_step_size=3;
macro_depth=20;
z=10;
fid_Macro = fopen(macropath, 'w');
file_path='C:\\Projects\\serialsectioningOCT\\Data\\Griddistortion\\test\\';
matrix_... |
fcc881cd0126cca2a5540c4c72170a48956f6f324e31d606614c76a4c509f628 | MATLAB | 4,800 | 142 | function [xCon,SPM] = spm_vb_logbf(SPM,XYZ,xCon,ic)
% Compute and write log Bayes factor image
% FORMAT [xCon,SPM] = spm_vb_logbf(SPM,XYZ,xCon,ic)
%
% SPM - SPM data structure
% XYZ - voxel list
% xCon - contrast info
% ic - contrast number
%__________________________________________________________________... |
ad4db8d952ef75038cbce9c4e94083592b39220eaf7c3e8830858f399d5cdb99 | MATLAB | 4,803 | 108 | %% Plot Figure 4a, EKG and imaging example traces
%% Define Paths of Loading Files and Save Files
currentFolder = mfilename('fullpath'); % determine the filepath based on where the script locates
scriptFN = mfilename;
filePath = erase(erase(currentFolder, scriptFN), strcat('scripts', filesep));
searchPath = strcat(fil... |
2cd5032a7057665e6c98c7494194e17c616a142619a0d033c7cce840ce859dcd | MATLAB | 4,804 | 168 | % The script is used to perform EEG data perprocessing
% To run this script, please first download the raw data from https://osf.io/57ryb/,
% and put the data in /eeg_data/raw_data/ folder.
% add fieldtrip
addpath('fieldtrip-20191213/');
ft_defaults;
% set input and output paths
data_path = '../../eeg_data/... |
5ce431a86132ce7acd15d6141780fb97c456e0a3be0014677723714f63964643 | MATLAB | 4,805 | 132 | %% Thomas_Yang_et al, 2023 @Nuo Li lab
%% Fig. 2e ALM selectivity map
clear all
close all
addpath('../func/');
load Figure_2_e_data
im_CCF = imread(['..\CCF_alignment\ARA_template\AllenRefVolCoronal_100275.tif']);
im_CCF_anatomy = imread(['.\ALM2x_mergedRGB_slice300_cltr1247_P55_NL09.tif']);
im_CCF_an... |
a9f932c2cc82e15ca4adce24830674d63358d94773ace22043020b85727b3742 | MATLAB | 4,806 | 176 | function st = nufft2_init(om, N1, N2, J1, J2, K1, K2, n_shift, useloop, alpha, beta)
%function st = nufft2_init(om, N1, N2, J1, J2, K1, K2, n_shift, useloop, alpha, beta)
% initialize structure for 2D NUFFT
% particularly the interpolation matrix in sparse format
% in:
% om [M,2] frequencies in radians
% N1,N2 imag... |
49bc01b0fb1281529c80c485bcadf0f3c52b1c810f726a0840ea78f27b14626d | MATLAB | 4,807 | 142 | function [reg_corner,rho,eta,reg_param] = l_curve(U,sm,b,method,L,V)
%L_CURVE Plot the L-curve and find its "corner".
%
% [reg_corner,rho,eta,reg_param] =
% l_curve(U,s,b,method)
% l_curve(U,sm,b,method) , sm = [sigma,mu]
% l_curve(U,s,b,method,L,V)
%
% Plots the L-s... |
c0a679130863d8305a8729463207a87acd8dfbbd5be667713e9cf9cd9bf38a36 | MATLAB | 4,807 | 161 | % cfsWriteRL64c.m
% an example of writing a cfs file for data as RL4, RL8
% must have CFS64c.dll in matlab path
% calls matcfs64c.mexw64
% note: this is by far the easiest way to write from Matlab
% stays in native format, not problems with Y scaling (or offset)
% 20.02.09: JG Colebatch initial version
% 24.0... |
61b348a6a3a797942036e372f9312868af1d338924241996300887773d84d5ed | MATLAB | 4,808 | 152 | %% Analyzing Neural Time Series Data
% Matlab code for Chapter 20
% Mike X Cohen
%
% This code accompanies the book, titled "Analyzing Neural Time Series Data"
% (MIT Press). Using the code without following the book may lead to confusion,
% incorrect data analyses, and misinterpretations of results.
% Mike ... |
6b3dc80656d91db0a09e1cff9ef360408e6661433db4124ae284bdd0a31b7101 | MATLAB | 4,808 | 199 | % ir_test_sf1_sf2.m
%
% Compare versions of separable footprint (sf1, sf2, sf3)
% for the case of large voxels and small rays.
%
% In this case, SF1 (ray-dependent scaling) is better than SF2,
% except in one region!?
%
% Copyright 2017-02-01, Jeff Fessler, University of Michigan
%% small system
if ~isvar('cg'), prin... |
57022f90fb51b885953926f90c34358bd0d597b33a2c622ccb05d2e5702780bf | MATLAB | 4,814 | 137 | function plot_RT_rnn_vs_human(model_type)
%PLOT_RT_RNN_VS_HUMAN Create a scatterplot of reaction times.
%
%Returns a scatterplot of human reaction times in the categorization task vs
%RNN reaction times.
%
%Input: model_type ('b', b_d' or 'bl')
%
%Author: Agnessa Karapetian, 2021
%
%% Add paths
addpath(genpath('/home... |
58dfae76ea6e45f9028aeccadfd66ee8dd84d97eb4de46e47d754b07b8b7edd5 | MATLAB | 4,821 | 105 | % Sets the default values for the FieldMap toolbox
%
% FORMAT pm_defaults
%
%__________________________________________________________________________
%
% This file is intended for site and/or scanner and/or
% sequence-specific customisations.
%__________________________________________________________________________... |
4d45b224a805a82b7c179bfd3afbca181be54e85307f82572a7982cb74d90474 | MATLAB | 4,826 | 160 | % This function simulates a virtual injection and calculates how
% large the resulting projection zone is
%
function [TZspreadInj,nRGCLabeled,nSCLabeled] = ...
calculateTerminationZoneSizeInjection(obj,nRep,percentile,plotFlag)
debugFigures = false; %true;
nRGCLabeled = zeros(nRep,1);
nSCLabeled = zeros(n... |
4827e20193b0b5a58e9cbad05c3bc04918ebe79878ec088599d266b4a97e3a93 | MATLAB | 4,832 | 107 | function pearson_object_decoding_full_experiment_sl(subject,task)
%PEARSON_OBJECT_DECODING_FULL_EXPERIMENT Perform object decoding (average of pairwise object decoding) using 1-Pearson's correlation.
%
%Input: subject ID, task (1=categorization, 2=fixation)
%
%Output: NxNxP vector of correlations, where N is the number... |
2a8c3ed5e55c5ad765865f822ff4e5a36bc4e3055e383d3d54d2310f6f0ce3ee | MATLAB | 4,833 | 168 | function img = easyhelix(cg, ig, proj, varargin)
%|
%| FBP reconstruction of cone-beam tomography data collected with
%| a circular source trajectory.
%| See feldkamp_example.m for example.
%|
%| in
%| cg ct_geom()
%| ig image_geom()
%| proj [ns nt na] cone-beam projection views (line integrals)
%|
%|
%| out
%| i... |
208e7bfaa735e0412cf24c2942eaa47aac6682ac4b15118bacb749ca87711496 | MATLAB | 4,840 | 103 | %% IR66 Processing Variables
[root_dir, app_dir] = fn_get_root_dir(); ft_dir = [app_dir 'fieldtrip/'];
if isempty(strfind(path,'fieldtrip')); addpath(ft_dir); ft_defaults; end
%--------------------------------------
% Basics
%--------------------------------------
SBJ_vars.SBJ = 'IR66';
SBJ_vars.raw_file = {'... |
eae8cae028bb8ca309bfda23f0212d425887a5b8b342c7e8d09aea1246d5a55c | MATLAB | 4,842 | 99 | function link_info = fun_graph_get_free_link(vessel_graph, internal_offset, sortQ)
%
%
%
%
% Note:
% Link with two endpoints can appear in the list of link_info.ep1, because
% one of the endpoint can be out of the internal_offset and therefore the
% link effectively has only one internal endpoint
if nargin <... |
1db0502319570b113024d7baf520421a254330b76c213b105e6888cfeaaef98d | MATLAB | 4,844 | 147 | function[]=ref_vol_stitch_high_res(id,datapath)
% mosaic parameters
Xsize=1000;
Ysize=1000;
Xoverlap=0.15;
Yoverlap=0.15;
% add path of functions
addpath('/projectnb/npbssmic/s/Matlab_code/PSOCT_code');
addpath('/projectnb/npbssmic/s/Matlab_code/ThorOCT_code');
addpath('/projectnb/npbssmic/s/Matlab_code/NIfTI_2014012... |
3bf48a2b2279bd8ca47330ef501871d861d7fd24e65a433a9f0f22859ff5a66e | MATLAB | 4,845 | 103 | function for_stats = all_subjects_for_stats(subjects,task,analysis)
%ALL_SUBJECTS_FOR_STATS Gather the subject x time matrices needed to perform cluster-based permutation tests.
%
%Input: subject IDs, task (1=categorization,2=distraction), analysis
%('object_decoding', 'category_decoding', 'time_object_decoding', 'time... |
cb43588883db39a851ea3076f59e7332e2db50388b78d31b2a494a6413f5d37d | MATLAB | 4,846 | 189 | %% mri_pixel_size_example.m
%|
%| Show the effects of small pixels on iterative reconstruction for MRI.
%| (Diminishing returns as number of pixels increase for quadratic reg.)
%|
%| Copyright 2003-7-23, Jeff Fessler, University of Michigan
%| create k-space samples, in units of 1/mm
if ~isvar('kspace'), printm 'setup... |
061130f7b5dd7f87cb43e0f2ad6731b044e16eb87ca0fb8e31c8275965358d80 | MATLAB | 4,849 | 140 | function img = feldkamp_helix(cg, ig, proj, varargin)
%|function img = feldkamp_helix(cg, ig, proj, varargin)
%|
%| FBP reconstruction of cone-beam tomography data collected with
%| a circular and ''helical'' source trajectory.
%| See ir_fdk_helix_example.m for example.
%|
%| in
%| cg ct_geom()
%| ig image_geom()... |
20eaaae89283db36f4829c46f66334d22dc399f285dabc53653033d9a50c806d | MATLAB | 4,856 | 173 | function out = cfg_justify(varargin)
% CFG_JUSTIFY Justifies a text string
% OUT = CFG_JUSTIFY(N,TXT) justifies text string TXT to
% the length specified by N.
%
% OUT = CFG_JUSTIFY(OBJ,TXT), where OBJ is a handle to a 'listbox' style
% uicontrol, justifies text string TXT to the width of the OBJ in
% ch... |
9cf07d11111eba239b44f56d6589a33d725661bec89e482d3ee5dbafea0d89b4 | MATLAB | 4,857 | 133 | clear; clc;
subjectID = 'P3';
sessionDate = 20230831;
stimElectrode = [2:30, 32:61, 63,64];
motorChannel = [1:64, 97:192, 225:256]; %[177, 236, 237, 253, 255];
freqId = 100;
sortedSpikes = 0;
saveData = 1;
plotFigures = 0;
% Permutation Params
alpha = 0.001;
numReps = 1000;
numDraws = 20;
% Baseline Folds
numFolds =... |
f62c2ab5b624b87f7cffdbf996dabedc6f7c898b8f35d9e0c34699850faafabe | MATLAB | 4,859 | 135 | function glm_eeg_rnn_rt(conditions)
%GLM_EEG_RNN_RT Perform the GLM analysis to determine how much variance in
%human RT is explained by EEG data and rCNN RTs.
%
%Input: conditions ('artificial','natural' or 'both')
%
%Output: results from the GLM analysis.
%
%Performs the GLM analysis on the selected scenes, using EE... |
0a28d06474ef5c5733d2ecf974cd8170bd1deb855011b7587a0d7e804bf22fd6 | MATLAB | 4,867 | 156 | function [Dsubj, nf, r, thdemean, M] = surf_registration_stats(dirname, arg2, arg3)
%
% surf_registration_stats.m
%
% Original Author: Laurence Wastiaux
% CVS Revision Info:
% $Author: nicks $
% $Date: 2007/01/10 22:55:10 $
% $Revision: 1.2 $
%
% Copyright (C) 2002-2007,
% The General Hospital Corporation (B... |
602fdf4c351fe69c4f199b1142406f74eca07020806a8b4dc4272b66ed19e921 | MATLAB | 4,867 | 205 | % pwls_sps_os_test.m
% Test 2d PWLS-SPS-OS with nonquadratic penalty
%
% Copyright 2002-2-13, Jeff Fessler, University of Michigan
% generate data
if ~isvar('yi'), printm 'generating data for pwls_sps_os_test'
f.dir = test_dir;
f.wtf = [f.dir 't.wtf'];
f.yi = [f.dir 'yi.fld'];
f.wi = [f.dir 'wi.fld'];
f.wtr = str... |
1dc0161633a8a0002487e2b6e3c5ad3de39160fdc92f183ea00076cbaaa1060a | MATLAB | 4,878 | 86 | %% Run this files to compute the network-human main effect and interactions for all for the VISION experiments
%% Must specify the paths to the required network data mat files.
function [] = run_anova_stats_matlab_network_human_results(EXP_NUM)
rng(517)
addpath('../matlab_statistics_functions')
NUM_BOOTSTRAPS = 1000... |
5155f6dd5bacc2e841449a40b7f57d64a8551851c46393ea3d4e8687eb950605 | MATLAB | 4,879 | 120 | function [outWidth,outWidthFreq] = IPEMCalcCentroidWidth (varargin)
% Usage:
% [outWidth,outWidthFreq] =
% IPEMCalcCentroidWidth(inWeights,inWeightFreq,
% inFrameWidth,inFrameInterval,
% inDistances,inCentroid,
% inPlotFlag)
%
%... |
a567c600ee3bc043c3b1d3a23d25da35646a0bb5bea139e08070264a1cb800da | MATLAB | 4,879 | 164 | function [y]=check_ROI(SubjectDir,th_pval)
%
% For one subject: check if the size of 20 ROIs is within the normal range
% The 20 following ROIs are checked: Left-Lateral-Ventricle Right-Lateral-Ventricle
% Left-Hippocampus Right-Hippocampus Left-Thalamus-Proper Right-Thalamus-Proper
% Left-Caudate Right-Cauda... |
9524fd329ca3ec1bbfb7740217f659bd87e08c4424f780e08d65e17d0ecbfd5f | MATLAB | 4,880 | 157 | function [edofimg, varargout] = fstack(img,varargin)
%FSTACK merging images of mutiple focal planes into one in-focus image.
%
% edofimg = fstack(img) merges img, an img array containing grayscale or
% color images acquired at mutiple focal distance, into one all-in-focus
% image.
%
% edofimg = fstack(img, option... |
fa02070df21ce4eee812ed800fcf120deda522fd44dc094af31124aaf1a5fd82 | MATLAB | 4,881 | 173 | function [peaks indices] = maxPeaksCircDist(x, dist, varargin)
%MAXPEAKSCIRCDIST Find the maximum peaks in a circular vector. The peaks
%are separated by, at least, the given distance.
%
% [PEAKS INDICES] = MAXPEAKSCIRCDIST(X, DIST)
%
% [PEAKS INDICES] = MAXPEAKSCIRCDIST(X, DIST, CHAINCODELENGTHS)
%
% Inputs:
% ... |
648cca7b72fcf7778e1e2044b3684a97c68cb6dc1ae01dac6ba1b74d05f4303c | MATLAB | 4,882 | 173 | function [peaks indices] = minPeaksCircDist(x, dist, varargin)
%MINPEAKSCIRCDIST Find the minimum peaks in a circular vector. The peaks
%are separated by, at least, the given distance.
%
% [PEAKS INDICES] = MINPEAKSCIRCDIST(X, DIST)
%
% [PEAKS INDICES] = MINPEAKSCIRCDIST(X, DIST, CHAINCODELENGTHS)
%
% Inputs:
% ... |
b04f2172a3da3cb0ff8b1edae490e21314bd499cd24d28f87afc31c35bf5a2b7 | MATLAB | 4,882 | 128 | %this script reads the tracking points from DeepLabCut, which are packaged
%in an .h5 file, and extracts the x/y positions of the points, as well as
%their labels, to feed them into a .csv file which we can further process
%with the Hedrick's Lab's dltv tracking software for post processing
clear all
close all
... |
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