sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
af0088295f368eb59219a019d4ba729a04139b20d19bd85ad57dcaa3c5e6e278 | MATLAB | 17,350 | 485 | %% This code is used to generate the figure related to miniscope imaging in Masuda et al., 2023
%% Calculate and store information score in batch by comboSession
load ('neuronIndividualsf.mat');
load ('behavIndividualsf.mat');
load ('thresh.mat');
meanFRindividuals = cell(1,length(neuronIndividualsf));
infoPerSecIndivi... |
4a152210968bdabb1b9657fe75256a70ec27070ca5630a76f1ca1dd9ef1698e8 | MATLAB | 17,376 | 414 | function SBJ08e_HFA_plot_grp_GLM_reg_cnfmat_ROI(SBJ_id, proc_id, stat_regs, hemi, roi_id,...
plot_out, plt_id, save_fig, varargin)
%% Plot confusion matrix of overlap in mass GLM effects per ROI
% COMMENTS NOT ADJSUTED SINCE STROOP COPY
% INPUTS:
% SBJ_id [str] - ID of subject... |
ee0a15bb0c5b709670ae0b4fba6c817f46b276aa193bb247a1d6e4af2c7a9cb6 | MATLAB | 17,377 | 479 | %absolutes_for16_rms05.m mlp 4/22/2009
%rm332_development_rms05.m mlp 3/22/2004
%this is similar to rm332_corrected and RM316_analysis however, will extract first trial, will extract second trial,will extract 1-5
%trials, and then avg of 2-6 trials
%will also plot cdfs aand sawtooth mlp 8/2011
%an... |
d2db2d0eab2af5764fac6914238456770bd901441f20128bed1f6864e7a10231 | MATLAB | 17,411 | 388 | function ps2pdf(varargin)
%PS2PDF Function to convert a PostScript(R) file to PDF using GhostScript(R)
%
% Converts a PostScript file into PDF. The resulting PDF file will contain
% one page for each page defined in the PostScript files, so a multi-page
% PostScript file, like those generated by using the '-app... |
6df98dfc497f8b1898ee97d4b1f30d29d2af17e76abd7be2e6d0a2c5dd636a42 | MATLAB | 17,422 | 656 | function R = Rmem(kappa, varargin)
%function R = Rmem(kappa, [options])
%
% Build roughness penalty regularization "object" based on C = Cdiff() object,
% for regularized solutions to inverse problems.
% This version is somewhat less flexible than Robject() but uses less memory.
%
% General form of nonquadratic penalty... |
7a62f2501ea2e6c246fee653bf4236b7edc96f0ce4cad84fea10de293f079276 | MATLAB | 17,448 | 489 | classdef SuhTree < handle
% AUTHORSHIP
% Primary Developer: Stephen Meehan <swmeehan@stanford.edu>
% Math Lead & Secondary Developer: Connor Meehan <connor.gw.meehan@gmail.com>
% Bioinformatics Lead: Wayne Moore <wmoore@stanford.edu>
% Copyright (c) 2022 The Board of Trustees of the Leland Stanford Junior ... |
c8b07881634171fc3d95522e57101b8ef0ec097b950deeed878e6432345b4e1a | MATLAB | 17,455 | 493 | %% Analyzing Neural Time Series Data
% Matlab code for Chapter 18
% 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... |
41a29c9583720721c704305fb024be291eb377dc5590717806d3800d68c73031 | MATLAB | 17,459 | 488 | function [M,scal] = spm_affreg(VG,VF,flags,M,scal)
% Affine registration using least squares
% FORMAT [M,scal] = spm_affreg(VG,VF,flags,M0,scal0)
%
% VG - Vector of template volumes.
% VF - Source volume.
% flags - a structure containing various options. The fields are:
% WG - Weigh... |
90533d76779b4728ab83af910f10a5c867dff6f3d311c00d8a017743a8e9d0a6 | MATLAB | 17,485 | 312 | function [vessel_graph, record] = fun_graph_refine_by_classifier(vessel_graph, vessel_image, vessel_mask_dt, opt)
%% Initialize parameters
% Record refinement process
if ~isfield(opt, 'record') || ~isfield(opt.record, 'num_self_loop')
record = struct;
[record.num_self_loop, record.num_bilink_loop, record.n... |
45bbc629e36d5124b5cde6e1aa87a87e964c78dd3b8233d049c99b42980d817d | MATLAB | 17,504 | 444 | % pop_loadbv() - load Brain Vision Data Exchange format dataset and
% return EEGLAB EEG structure
%
% Usage:
% >> [EEG, com] = pop_loadbv; % pop-up window mode
% >> [EEG, com] = pop_loadbv(path, hdrfile);
% >> [EEG, com] = pop_loadbv(path, hdrfile, srange);
% >> [EEG, com] = pop_loadbv(path, hdrf... |
18449d1b5c2409455e5ec58c513ed766eb384efd6c4ea640c918b1508a395731 | MATLAB | 17,511 | 369 | % As the reviewer requested, I analysed the sliding windowed FCs
% This script is for doing stats
%
% Xiao Chen 230706
% chenxiaophd@gmail.com
%% initialization
clear; clc;
work_dir = 'DynamicFC';
data_dir = 'DPABISurf Preprocessing Files';
template_dir = 'DPABISurf surf template directory';
hemisp_name_set = {'lh',... |
d4aa2d0bf23f34744b24859dd4bfe98cdf4b10b74f77177cd0991b960f1a3528 | MATLAB | 17,527 | 485 | function [SPM] = spm_spm_Bayes(SPM)
% Conditional parameter estimation of a General Linear Model
% FORMAT [SPM] = spm_spm_Bayes(SPM)
%__________________________________________________________________________
%
% spm_spm_Bayes returns to voxels identified by spm_spm (ML parameter
% estimation) to get conditional parame... |
b4fe18e42d09791fcfe734df1d28e66cc0d3da2e448c34c347b06fb236349944 | MATLAB | 17,553 | 498 | function [pox,poy,pot,wmap] = getWALK2(varargin)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% ################################################################# %% DESCRIPTION
%FUNCTION short descr.
% long descr.
%
% USAGE:
% [out] = template(in,in2)
%
% INPUT:
% in - input... |
93cc26aa6177ec00c1ef4f8a9eb308826a71e6555385083a2d5e5f316e5274b0 | MATLAB | 17,556 | 774 | function fit = de_ftab_fit(sl, fm, varargin)
%function fit = de_ftab_fit(sl, fm, [options])
%|
%| Fit to DE table fm(), suitable for subsequent interpolation / extrapolation.
%| Uses either classic polynomial basis functions,
%| or an exponential model: -log(sum_k p_k exp(-m_k . s))
%|
%| in
%| sl {L} sample location... |
5986aefdad96b8d55303c859f68125e5b5e59e07156249ad8a2b89ff777642d9 | MATLAB | 17,669 | 396 | function [f_orco,exitTrajectory,timeOfState,kinOfState,biasOfState,dFSmooth,FSmooth] = ...
getDistInhibition(f_orco,crossingtype,timeInterval,plotFig)
% getDistInhibition calculates the turn optimality, and kinematics as a
% function of time since the start of the inhibition period
%
% Inputs: f_orco = fl... |
74b6a0f63e7b9336850a0b864473043d16f6625016d60fd5ff4f237a8002c34e | MATLAB | 17,677 | 396 | %PlotPlaceCellAna.m
% This function is to plot the result of PlaceCellAna using output from PlotBinFirRate.m
% 1) Find Place field centers
% 2) plot Spatial info of cells from PF analysis
% 3) plot averaged rate map.
% place cell input data (pData.mat file made by PlaceCellAna.m)
% pData{ian, iday}.spi(icells... |
cc6e3131e4fe81727abc6b8d6d099b649f7e6fc2037347614e749af2d3e8ad3b | MATLAB | 17,690 | 387 | % ParforProgressbar Progress monitor for `parfor` loops
% ppm = ParforProgressbar(numIterations) constructs a ParforProgressbar object.
% 'numIterations' is an integer with the total number of
% iterations in the parfor loop.
%
% ppm = ParforProgressbar(___, 'showWorkerProgress', true) will display
% t... |
413c6050d86f7a9f46d528da82d3d431dde54c879dcc3d2e4916da57915e28a6 | MATLAB | 17,697 | 483 | % GBM Numerical Simulations
clear
load('ABCminfornumsim.mat') %ABCfigures.m output (obtained after using minimum data for ABCrejection.m)
minminP = minP;
errormin = min(error(1:n));
load('ABCmaxfornumsim.mat') %ABCfigures.m output (obtained after using maximum data for ABCrejection.m)
maxminP = minP;
errormax = min... |
78883e00bff6c3fba795e8933cdf30fd8ed0732ba7fea6fdc4780b68ce580127 | MATLAB | 17,715 | 553 |
% NOTE: this script is meant to be run with the matlab GUI to make figures
clear all
% Add package to read NPY files into matlab
addpath('/Users/askeller/Documents/npy-matlab/')
% Load in prediction results
folder_name='/Users/askeller/Documents/Kellernet_PrelimAnalysis/RidgeResults/results_matchedsamples_081822/al... |
bb73751c5e0ffac0ff54ddf256516336c883c99db0daf0ac7ab1647a447c88e0 | MATLAB | 17,734 | 523 | function varargout = playfmf(varargin)
% PLAYFMF M-file for playfmf.fig
% PLAYFMF, by itself, creates a new PLAYFMF or raises the existing
% singleton*.
%
% H = PLAYFMF returns the handle to a new PLAYFMF or the handle to
% the existing singleton*.
%
% PLAYFMF('CALLBACK',hObject,eventData,handl... |
6347f75e3bda2016a590ae6ef9c7ffd915cac973594734c8df77be41d82096de | MATLAB | 17,753 | 551 | function spm_dcm_review(DCM,action)
% Review an estimated DCM
% FORMAT spm_dcm_review(DCM,action)
%
% DCM - DCM structure or its filename
% action - one of:
% 'fixed connections'
% [' effects of ' DCM.U.name{i}];
% 'contrast of connections'
% 'location of regions'
% 'i... |
84b6df9ff6c1cfd4831f7487a168a72b91754eed0d7bf5158d03441aff007fd7 | MATLAB | 17,766 | 314 | % COMPARES SIGNIFICANT EVENTS BETWEEN PRE AND POST
% INPUT:
% State: 'sleep' for only sleep replay, or 'ALL' for both sleep and awake replay
function pre_post_comparison(state,method)
% Load replay information
if strcmp(method,'wcorr')
cd('X:\BendorLab\Drobo\Lab Members\Marta\Analysis\HIPP\Chapter 2\Raw_repl... |
9bc080b82e3a3a12ebe280842f687cd6a9c24d29fd604c3aae0027e4098e08b1 | MATLAB | 17,823 | 395 | %% Script to create output for Fig. S8 - parameter comparisons between Young, healthy Elderly, MCI unknown, MCI negative, MCI positive
% Andrea Castegnaro, UCL, 2023 uceeaca@ucl.ac.uk
% Fits the model on all of the groups and run a two-way Anova
% (group*condition) on Young Controls, Elderly Controls and MCI Unk/Neg/Po... |
06d29b675ef1326843cb3972799d6e90992a84b8886bd46d09ceaba4c265262d | MATLAB | 17,848 | 398 | % Script to generate the average score vector and the Group
% Consensus vector for each user subtype :
% "experts (exp), researchers (re), Non-experts (ne)"
% and each phase (phase 1 and phase 2) of MODA.
%
% Generates the score averaged across annotators as a "scoreAvg_usr_px.mat"
% (without 2.5 s overlap between... |
17bf130c6a19b3e18c89b4b1980b9d90efa20039bf973d0664580b13c6978cf0 | MATLAB | 17,862 | 466 | function varargout = retrack_settings(varargin)
% RETRACK_SETTINGS M-file for retrack_settings.fig
% RETRACK_SETTINGS, by itself, creates a new RETRACK_SETTINGS or raises the existing
% singleton*.
%
% H = RETRACK_SETTINGS returns the handle to a new RETRACK_SETTINGS or the handle to
% the existing ... |
e23fee6e47a72a84bbbe8daafd86dcc590162f750d60d71d4254884f558282aa | MATLAB | 17,888 | 404 | function [qrs_amp_raw,qrs_i_raw,delay]=pan_tompkin(ecg,fs,gr)
%% function [qrs_amp_raw,qrs_i_raw,delay]=pan_tompkin(ecg,fs)
% Complete implementation of Pan-Tompkins algorithm
%% Inputs
% ecg : raw ecg vector signal 1d signal
% fs : sampling frequency e.g. 200Hz, 400Hz and etc
% gr : flag to plot or not plot ... |
a1de28a8610aea5625ed9881cff42a96131e60a9260987e0b4f6fbccc7f61516 | MATLAB | 17,916 | 452 | %aim to calculate the latency of the response, find the time and CI, and
%the bootstrap
% close all;
clearvars;
addpath ('/data/local/u0114568/ephysio/test');
listname={'sp_color', 'con-vs-att', 'allSP', 'Att-Inte', 'Att-Congruency', ...
'cueRight', 'allCon', 'all', 'allIncon', 'targetLeft',.... |
4404af49a6681186a864787a6a0d1d4fff75028d0f196ca3a99465347d1c4e04 | MATLAB | 17,936 | 559 | %% Figure 5a
% this script rungs additional analysis required for the plots of figure
% 5a.
%% - HOUSE KEEPING - %%
clear
close all;
ft_defaults;
%% -SUBJECTS - %%
% ID 18 missing because of LP at 50Hz
subjects = {'OSL13', 'OSL14', 'OSL17', 'OSL21', 'OSL24', 'OSL26', 'OSL27', 'OSL28', ...
'OSL29', 'OSL30', 'OS... |
efbce48f63a8eaf8bc40f354beb011652d5ced18723e09e20f26f895e3f1b0d5 | MATLAB | 17,950 | 407 | %% load_XML.m
%
% Purpose:
% Load the accessory data from xml format PSG files.
%
%
% Usage:
% [events, stages, epochLength] = readXML.m(myFile.xml)
%
%
% Notes:
% This currently only supports loading of compumedics-format xml.
% Need to modify to load physioMimi format xml
%
% 6 Sections of the compumedics.xml:
% <... |
433d430dc35c837d5d66582d465620b674c5512fa69451493b8a5ae6a9e4f125 | MATLAB | 17,953 | 311 | function plot_proportion_cum_replay_periods(epoch)
% Plots distribution of replay events over sleep periods for each track and protocol.
% Histogram is calculated as the sum of cummulative replay event times across tracks & each experiment (always within a protcol),
% and averaged (divided) by number of active sessio... |
821ed3cc624b2d210754fd40c11198be11ee9adbcfabe120f076c7d7006a7797 | MATLAB | 17,953 | 405 | function out = loadVideo(filename, varargin)
% Open a video file for playback and return the handle to it.
%
% The file is opened for the stimulus window, so initWindow has to run
% before this function. Nothing is shown yet: startVideo starts the
% playback engine, showVideo fetches and draws the single frames.
%
% Th... |
fb1127e8a0044e9d5d06f6e86deac517ff75088414637c423afe4bb8fad1d2df | MATLAB | 17,958 | 279 | function plot_SWR_temporal_log2(bayesian_control,rest_option,time_chunk_size,time_window)
if ~isempty(bayesian_control)
load('X:\BendorLab\Drobo\Lab Members\Marta\Analysis\HIPP\Chapter 2\Raw_replay_analysis\extracted_time_periods_replay_excl.mat')
path = 'X:\BendorLab\Drobo\Lab Members\Marta\Analysis\HIPP\Chap... |
a6b97aa67c0ae0625048a8259b3c3b65eff2f409bddea2784c8175b5299f14ba | MATLAB | 17,987 | 204 | function [eeg_data, Sorig, Sclean, f, amps, freqs, g] = cleanline2(eeg_data, s_rate, varargin)
% CWH: This modifided version from KLA hopefully runs without EEGLab and taking just [trials,time] matrix
% Mandatory Information
% ------------------------------------------------------------------------------... |
5112eccddd72d315100d869b7c5d2a6d2342c19e5758be85dfb50d9b07b15e71 | MATLAB | 18,026 | 356 | % plot bar plot
stim_onset = 301;
%% CA1-3-dg
rng = 4
dur = 750
subj_length = 5
bar_vector_mn1 = [nanmean(nanmean(squeeze(nanmean(CA13_1(freq<rng,stim_onset:stim_onset+dur,: ),1))', 2))...
nanmean(nanmean(squeeze(nanmean(CA13_2(freq<rng,stim_onset:stim_onset+dur,: ),1))', 2))...
nanmean(nanmean(squeeze(nanme... |
c78bcd2c26fcff88a797893d767b2f1b92452ccb39d6d4f42727bf2f4fd1d655 | MATLAB | 18,046 | 668 | function st = newfft(om_in, Nd_in, varargin)
%function st = newfft(om, Nd, [options])
%|
%| New version of NUFFT (pun intended) that uses real interpolation kernels.
%| (The original NUFFT code used complex interpolation needlessly.)
%|
%| This returns a "strum" object with methods for both forward and adjoint
%| d-di... |
c7950855ddd2e029f836fce171154d213535ec5b56cd14bfd80f76883af58ab0 | MATLAB | 18,070 | 433 | function norm_theta_seq = plot_num_theta_seq_lap(data_type)
% MH 2021
% Plots both number of theta sequences per laps and normalised number of sequences in lap by the time spent moving in each lap
% Also calculates the difference in the number of theta sequences lap by lap
if strcmp(data_type,'main')
load('X:\Bend... |
2f069a0999139d1f73734d869c9016051bde4c4f8cb3496600b038a612be8ad7 | MATLAB | 18,113 | 495 |
close all;
clear;
load('U:\rg-yoshida\Yoshida_lab_shared_folder\Shared Data\Antonio\In vivo data\Babak\preliminary analysis\Space_bin\Space_bin_filt30Kalman.mat')
load('U:\rg-yoshida\Yoshida_lab_shared_folder\Shared Data\Antonio\In vivo data\Babak\preliminary analysis\Space_bin\Spatial_Vel_ROI4.mat')
load('U:... |
abe9501de96a9578220ed4b41a4203bec06e3bf27db127c6a0abb0d3fec666fe | MATLAB | 18,157 | 390 | classdef LeG_vox2atlaslabel < handle
properties
NMMDir; %default atlas (required)
AtlasDir; AtlasNames;
AtlasLabels; AtlasVals;
AtlasData; AtlasMat; AtlasCnt; AtlasImg;
DefFile; DefMat; Def; %ptvox2mnimm deformation (iy_MR.nii)
VoxDef; %ptvox2atlasvox d... |
7dded95186dff6670f64aa2784b76a47262df853594a7738e7de3f124995a124 | MATLAB | 18,160 | 546 |
%% add paths
%addpath(genpath('/Users/maria/Documents/Matlab/cbrewer'))
%addpath('/Users/maria/Documents/MATLAB/raacampbell-shadedErrorBar-9b19a7b')
options = continuous_RDK_set_options('LTHiMac');
EEGpreproc = options.path.preproc.behaviour; % path to behav data all subjs
condition = {'Tr frequent TR short', 'Tr ... |
2722b5011b0bb0d2bad69e578a950d17cf06287d36f19bcd4593c7eb2c66cf85 | MATLAB | 18,182 | 473 | classdef AdaptiveBins<handle
% Class for probability binning described at
% https://www.ncbi.nlm.nih.gov/pubmed/11598946
% Bioinformatics inventors
% Original: Roederer M1, Moore W, Treister A, Hardy RR, Herzenberg LA.
% Incorporation into QF matching:
% Darya Orlova <dyorlova@gmail.com>
% ... |
99672116c6032b770f159c13f7147e0e7d6e482028d7ae765a592329b9384111 | MATLAB | 18,203 | 570 | %% Analyzing Neural Time Series Data
% Matlab code for Chapter 29
% 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 ... |
7af075ec6e91ff1853d949be45419c19e0581f9c9599b6c029faf81be1209211 | MATLAB | 18,232 | 315 | function plot_track_replay_periods(epoch,bayesian_control)
% Plots distribution of replay events over sleep periods for each track and protocol.
% Histogram is calculated as the sum of cummulative replay event times across tracks & each experiment (always within a protcol),
% and averaged (divided) by number of activ... |
9b3e7601a5c6d3904156031353e25f713aaefe11326e851c0d743759fdee27b2 | MATLAB | 18,235 | 494 |
close all;
clear;
load('U:\rg-yoshida\Yoshida_lab_shared_folder\Shared Data\Antonio\In vivo data\Babak\preliminary analysis\Space_bin\Space_bin_filt30Kalman.mat')
load('U:\rg-yoshida\Yoshida_lab_shared_folder\Shared Data\Antonio\In vivo data\Babak\preliminary analysis\Space_bin\Spatial_Vel_ROI4.mat')
load('U:... |
7280a87e21fe852277e23e03afb71d5486f08186afc4fd93474236c1a7143286 | MATLAB | 18,253 | 371 | function plot_directional_place_cells
% Load name of data folders
if strcmp(computer,'GPU')
sessions = data_folders_GPU;
session_names = fieldnames(sessions);
else
sessions = data_folders;
session_names = fieldnames(sessions);
end
c = 1;
[track_lap_cells(1:4).unidirect] = deal(nan(20,16));
[track_lap_... |
e5771762ed16c1eaec86aa289da242bdea58640336427a30d426108282f5dc7c | MATLAB | 18,269 | 790 | %% Autocorrelations
clear all
close all
rec_data_path = 'C:\Users\xscogno\MATLAB\Flavio2\Preprocessed_data\';
dpath='C:\Users\xscogno\MATLAB\Flavio2\Preprocessed_data\';
save_data_path = 'C:\Users\xscogno\MATLAB\Flavio2\Preprocessed_data\';
clusters=10;
mice_number=14;
mice=['L08M1';'L08M2';'L08M3';'L08M4';'L09M1';'L... |
5b98bce815d49284317a0c41ea93bdf19a57a03ef568ca40646edba9e9b44715 | MATLAB | 18,284 | 481 | function [stats,stats1,stats2,t] = mTRFattncrossval(stim1,stim2,resp,fs,Dir,tmin,tmax,lambda,varargin)
%MTRFATTNCROSSVAL Cross-validation for attention decoder optimization.
% STATS = MTRFATTNCROSSVAL(STIM1,STIM2,RESP,FS,DIR,TMIN,TMAX,LAMBDA)
% cross validates a forward encoding model (stimulus to neural response)... |
6db8b186a62887e508ddbf7c06796a2f2fdfea57726b13df497ce9ad95ee63ae | MATLAB | 18,310 | 346 | function mergedata_newFeatures_082421( strain_name )
%mergedata_newFeatures_tocsvs_021621.m
% This function loops over _FINAL.mat files in a directory and processes
% them to derive new fields using NewFeaturesPostProcessing_021321.m
% Generates .mat files
timenow = datestr(now,'mm_dd_yy');
outpath = uigetdir(pw... |
7a3875e0a8e0d54e6a68a86bf0ac61c9ce524a220b6e3c76219532f968159b47 | MATLAB | 18,344 | 515 | %% Figure 2a & b (lower left)
% this script rungs additional analysis required for the plots of figure
% 2a & b.
%%
clear
ft_defaults
%% - SUBJECTS - %%
% ID 18 missing because of LP at 50Hz
subjects = {'OSL13', 'OSL14', 'OSL17', 'OSL21', 'OSL24', 'OSL26', 'OSL27', 'OSL28', ...
'OSL29', 'OSL30', 'OSL31', 'OSL32... |
c6cea178be85ec4def77ff5a78960f99b137ecdd08503789f6867bbf02d4347b | MATLAB | 18,353 | 462 | function out = readcfs(cfsn,plotdata)
% This function reads the CED's signal file (.CFS) and gives a structure as
% an output.
%
% cfsn: full file name of the CFS file (including directory, if not in the
% same folder)
%
% out: structure with the following sub-structures:
% out.GH: General Header
% out.FVI: File Variab... |
213d9cf4a2b3a0ab38f4fab8dd16efd95881988fe0fd5b2d5130f198dde5d4c8 | MATLAB | 18,354 | 508 | function [skeleton cWidths] = linearSkeleton(headI, tailI, minP, minI, ...
maxP, maxI, contour, wormSegSize, varargin)
%LINEARSKELETON Skeletonize a linear (non-looped) worm. The worm is
%skeletonized by splitting its contour, from head to tail, into short
%segments. These short segments are bounded by matching pai... |
b8e109b4505634a7058214f0376052f2ea0e34c010802251703ae92dcae89b73 | MATLAB | 18,405 | 602 | %% run each visium genes: find axial patterned genes
clear all
close all
clc
direct='/data/Technion_analysis/goldfish/visium/spatial_fs';
cd(direct)
%% update names and colors
load('/data/Technion_analysis/goldfish/scRNAseq_gf/gf_sc_new/non_normalize_all_data_15_8_2022.mat','g_geneid','g_cluster_name','g_data','g_ca') ... |
35677458318c56e2e53e71e5e4f0ece8f0a6fcbf987a837659dc90f99db34092 | MATLAB | 18,407 | 451 | function varargout = spm_diffeo(varargin)
% Mex function called for image registration stuff
%
%_______________________________________________________________________
%
% FORMAT u = spm_diffeo('vel2mom', v, param)
% v - velocity (flow) field n1*n2*n3*3.
% param - 8 parameters (settings)
% - [1][2][3] Voxel... |
44436088969af971a7efd72ba3f4fee2d18fee55d45e1f675fb5a850df546d42 | MATLAB | 18,422 | 503 | classdef SuhGater < handle
% AUTHORSHIP
% Primary Developer: Stephen Meehan <swmeehan@stanford.edu>
% Copyright (c) 2022 The Board of Trustees of the Leland Stanford Junior University; Herzenberg Lab
% License: BSD 3 clause
properties(SetAccess=private)
gml;
fcs;
gates;
gate... |
67d47cb62d6d0b10a27f300811f6d33353efc1d49f08c7d85d738948746ecca3 | MATLAB | 18,443 | 604 | function output = inspects_multi_img(img,gradechoinv,mask,options)
%inspect quatitative mapping version
%assumes that the signal from each voxel is a weighted sum of contributions
%from different spectra
if ~iscell(img) %single image as input - just put into a unit cell
tempimg = cell(1);
tempmask = cell... |
b74a32b7e7cc60a5b491df1404c794eca4356ea97b6c3392b5452d64d200e908 | MATLAB | 18,465 | 359 | function mmap = initMRIChatPart1
% Prepare the trigger channel of an MRI experiment. Call this BEFORE
% initWindow; initMRIChatPart2 has to be called after it, and that one waits
% for the first triggers.
%
% The scanner triggers are not read by the toolbox itself. A separate
% application -- the trigger counter -- cou... |
b05e74aca873d33e7c43bd62b10f7487a5eb5930abc3cec0d8de6aa184ff6ca4 | MATLAB | 18,486 | 822 | function fit = de_ftab_fit(sl, fm, varargin)
%function fit = de_ftab_fit(sl, fm, [options])
%|
%| Fit to DE table fm(), suitable for subsequent interpolation / extrapolation.
%| Uses either classic polynomial basis functions,
%| or an experimental exponential model -log(sum_k p_k exp(-m_k . s))
%|
%| in
%| sl {L} sam... |
41a3fce8121917aa2a38f587b8167900591deb2bba96884df4837e159cc89004 | MATLAB | 18,557 | 434 | function SBJ08e_HFA_plot_grp_GLM_reg_pie_ROI(SBJ_id, proc_id, stat_regs, hemi, roi_id,...
plot_out, plt_id, save_fig, varargin)
%% Plot pie chart of overlap in mass GLM effects per ROI
% COMMENTS NOT ADJSUTED SINCE STROOP COPY
% INPUTS:
% SBJ_id [str] - ID of subject list to l... |
147124ade8d711be9f754295b641c5a8f0a7bd31c6c64a6849a3d95359a2f565 | MATLAB | 18,657 | 430 | % function MAP_fig_1_multi(mname,config)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% ################################################################# %% DESCRIPTION
% Script to analyse data for:
% Grieves et al. (2020)
% Quantification of firing rate map procedures
%
% This script
% NO... |
83532ca8dec5db940c3b5cd11504e6163f2d99909040cc5b132a18ac805f6c11 | MATLAB | 18,673 | 594 | function [Ep,Cp,Eh,F,L,dFdp,dFdpp] = spm_nlsi_GN(M,U,Y)
% Bayesian inversion of nonlinear models - Gauss-Newton/Variational Laplace
% FORMAT [Ep,Cp,Eh,F] = spm_nlsi_GN(M,U,Y)
%
% [Dynamic] MIMO models
%__________________________________________________________________________
%
% M.IS - function name f(P,M,U) - generat... |
3b4939628633ce76614cc0439ed1712bcf45fa1b5057692a5067dfc92c5cf0c0 | MATLAB | 18,801 | 480 | function varargout = FCSVCompare(varargin)
% FCSVCOMPARE MATLAB code for FCSVCompare.fig
% FCSVCOMPARE, by itself, creates a new FCSVCOMPARE or raises the existing
% singleton*.
%
% H = FCSVCOMPARE returns the handle to a new FCSVCOMPARE or the handle to
% the existing singleton*.
%
% FCSVCOMPA... |
98593a90802a29ffe65887001b5cd4d46521defaea5f58237c0ab5e73f4aa7dc | MATLAB | 18,801 | 625 | freq_ranges = [2.9 6] % low theta
freq_name =[];
freq_ranges = [40 200];freq_name = 'g'
addpath('/mnt/yassamri/iEEG/sandra/analysis_pipeline_final')
% elecs
if strcmp('57', subj)
fro_chan_idx = [1 4:8 13 16:17 25:27 35 37 44 46:47 70:83 89 91 98 99 101 102 108 109 110 112];
MTL_chan_idx = [4... |
f3e1dd9059efb7d677c77539fbcb3267ba3315167e60f4af2fe2691f3c34ee94 | MATLAB | 18,805 | 600 | %% One session - PI
N_sh_dist=500;
w=8;
row_w=waves(w);
count=count+1;
mouse=['L',num2str(big_table(row_w,1)),'M',num2str(big_table(row_w,2))];
day=big_table(row_w,3);
s=big_table(row_w,4);
munit=big_table(row_w,5);
clus=10;
disc_phase=10;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Load files
load([rec_data_path,strcat('re... |
98cac1bd1486d8a819af211085274ce3ebe96533b466efd0e6f0ad55cfc37c2e | MATLAB | 18,888 | 404 | % function MAP_fig_1_multi(mname,config)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% ################################################################# %% DESCRIPTION
% Script to analyse data for:
% Grieves et al. (2020)
% Quantification of firing rate map procedures
%
% This script
% NO... |
051c1f7ce6cd456601238bd2b6de3e44c3f7a0441cb024c35ba652d36c716854 | MATLAB | 18,918 | 278 | % EXTRACT THETA SEQUENCES FOR EACH LAP
% MH 2020
% For each track, extracts theta sequences for each lap. Uses place fields
% from individual laps. Meaning each lap needs to be decoded individually
% Then averages all theta sequences within a lap and scores it using quantification methods,
function extract_lap_thetase... |
b684d3cbe6175af03d796af3ce1f2c7d6087b2e82e9922967c800a13db148916 | MATLAB | 18,934 | 277 | % EXTRACT THETA SEQUENCES FOR EACH LAP
% MH 2020
% For each track, extracts theta sequences for each lap. Uses place fields
% from individual laps. Meaning each lap needs to be decoded individually
% Then averages all theta sequences within a lap and scores it using quantification methods,
function extract_lap_thetase... |
0ee305669626f4e8769d9cbd17274478f5e4640b60c04849e6b4c7110d2f20b9 | MATLAB | 18,953 | 262 | % CALULATES AVERAGE THETA CYCLE PER TRACK AND DIRECTION
% MH 2020
% For each direction and track, gets decoded theta windows and centres to the actual position of the animal at the trough. Then creates a smaller
% window of 100ms around the trough and centres the decoded position around it. Finally average of all cente... |
16fa4b30378ec6a4bddc723dc8d9f9622fd0d54f253fb0f222d9549bacc06f32 | MATLAB | 18,960 | 278 | % EXTRACT THETA SEQUENCES FOR EACH LAP
% MH 2020
% For each track, extracts theta sequences for each lap. Uses place fields
% from individual laps. Meaning each lap needs to be decoded individually
% Then averages all theta sequences within a lap and scores it using quantification methods,
function extract_lap_thetase... |
8a5aa362aec68b13b0cd0df30b651d175929d87fd77b87891d64a969a7399333 | MATLAB | 18,968 | 418 | function [epp, subset_ids]=run_epp(csv_file_or_data, varargin)
%RUN_EPP runs exhaustive projection pursuit to discover/learn significant
% subsets of a given data set.
%
% EPP is an unsupervised data subset discovery method intended for any
% N-dimensional dataset containing continuous numeric measurements. See
%... |
a1c6e115cec4f7c1a01731cd337edc28dc80b787b0f6de85877da3a27695eb2b | MATLAB | 18,977 | 279 | % EXTRACT THETA SEQUENCES FOR EACH LAP
% MH 2020
% For each track, extracts theta sequences for each lap. Uses place fields
% from individual laps. Meaning each lap needs to be decoded individually
% Then averages all theta sequences within a lap and scores it using quantification methods,
function extract_lap_thetase... |
70c6617d17eb67ff8e5c25e8f31144d3ad60731e571c810e87ae98c0c5bbed3b | MATLAB | 18,990 | 825 | function ob = Cdiff1(isize, varargin)
%function C1 = Cdiff1(isize, [options])
%|
%| Construct Cdiff1 object that can compute C1 * x and the adjoint C1' * d
%| for a "finite differencing" matrix C1 for roughness penalty regularization.
%| This object provides the finite difference in only a *single* direction,
%| so it... |
b566090f0ca5b85d0447a80e968320ccedb3d800e71d0804ed7157d8da2a3dc2 | MATLAB | 19,060 | 417 | % This script does stats based on FC analysis from one cluster
% Xiao Chen 220429
% chenxiaophd@gmail.com
%% initialization
clear;clc;
data_dir = 'DPABISurf preprocessing directory';
work_dir = 'FC_analysis';
TemplateDir = 'DpabiSurf SurfTemplates directory';
if ~exist(work_dir, 'dir'); mkdir(work_dir); end
% load ... |
002aac06a9313b5822bf9ae4c0002a3e04db3f3ad9ee383b1421be451156db1a | MATLAB | 19,083 | 391 | function d = datastrm(a,varargin)
% DATASTRM - data object constructor for opening data recorded with MC Rack
%
% d = datastrm(a,varargin) is a datastrm constructor for MC Rack data files of all MC Rack versions
% (MC Rack version 1 and above uses the OLE Server Object contained in MCSt... |
4b78576f523f155c1460672c95fe8afb7725c5618dd43a251ad5a34786831d03 | MATLAB | 19,108 | 443 | % This script loads the tracked data, adds together data from multiple flights, and rotates the pattern to align with the cardinal coordinataes
clear all
close all
%load data
%select all data from the same animal same pattern (=multiple flights)
filenames=uigetfile('data*.mat','multiselect','on');
%if only on... |
c9cb4be3053228b12b66674282886e4524617c9f775f816e752f60c9f2958c4f | MATLAB | 19,143 | 375 | function linear_Model(exname, varargin)
% Bharath Talluri & Jacob Yates, Dec 2021
% adapted from linear encoding model used in the study "Single-trial neural
% dynamics are dominated by richly varied movements" by Musall et al., 2019
% Inputs:
% datadir <char>: path to experiment director
% exname <char>: ... |
940c67cdbc14a935c4257f2613c57aa728b4be9612e43cebaa6b0cee7c0f9c32 | MATLAB | 19,156 | 451 | function [I_shar, I_syn, I_unx, I_uny, q_opt] = partial_info_dec(p)
% partial_info_dec(p) Partial Information Decomposition of a trivariate probability distribution.
%
% [I_shar, I_syn, I_unx, I_uny] = partial_info_dec(p) gives the four atoms of the
% partial information decomposition following the pro... |
8f3d312411ae23e4b823d2d6c9c0e41c8ec5e7a3f28b54e77adbe6631c8e481b | MATLAB | 19,168 | 399 | % PLOT DECODING ERROR ACROSS LAPS
% Uses information extracted with 'bayesian_decoding_error_stability.mat' to plot median decoding error across laps (each lap compared to the last
% lap(s). Plots for each rat, for each protocol, and a figure with all protocols overlapped (showing both first and second exposure). Also ... |
f4c92c3d5c53aacbde92e9fd612b7ae40057976d6d070b2411703cdf2d9df3be | MATLAB | 19,183 | 533 | classdef SortTable < handle
% AUTHORSHIP
% Primary Developer: Stephen Meehan <swmeehan@stanford.edu>
% Math Lead & Secondary Developer: Connor Meehan <connor.gw.meehan@gmail.com>
% Bioinformatics Lead: Wayne Moore <wmoore@stanford.edu>
% Copyright (c) 2022 The Board of Trustees of the Leland Stanford Junio... |
ba12b14410cad6bce4637d305b660f0266d56f8d2962b180e9b2b710487d13ad | MATLAB | 19,331 | 594 | clear all
close all
dbeh_path='C:\Users\xscogno\MATLAB\Flavio2\Waves\Tracking data\';
rec_data_path = 'C:\Users\xscogno\MATLAB\Flavio2\Preprocessed_data\';
dpath_spikes='C:\Users\xscogno\MATLAB\Flavio2\Preprocessed_data\';
dpath_sorting='C:\Users\xscogno\MATLAB\Flavio2\Waves\Sorting\';
dpath='C:\Users\xscogno\MATLAB\F... |
139889f12bbce5b0131caf9375810952704e8f57025e383eac05bf07312bc242 | MATLAB | 19,367 | 541 | function P = spm_realign(P,flags)
% Estimation of within modality rigid body movement parameters
% FORMAT P = spm_realign(P,flags)
%
% P - char array of filenames
% All operations are performed relative to the first image.
% ie. Coregistration is to the first image, and resampling
% of image... |
edff10d8ede87428483b46b70fa38529335842683cd52b25edd03e8f1b04aa33 | MATLAB | 19,390 | 563 | function spm_dcm_fmri_csd_results(DCM,action,fig)
% Review an estimated DCM for BOLD CSD
% FORMAT spm_dcm_fmri_csd_results(DCM,action,fig)
%
% Action:
% 'Spectral data'
% 'Coupling (A)'
% 'Coupling (C)'
% 'Inputs'
% 'Outputs'
% 'Transfer functions'
% 'Cross-spectra (BOLD)'
% 'Cross-spect... |
fea641bdd953e062a4211265dd614b7fd3aa2c93fe5f22fcd0ecc6ec4e995ece | MATLAB | 19,416 | 638 | clear all
close all
dbeh_path='C:\Users\xscogno\MATLAB\Flavio2\Waves\Tracking data\';
rec_data_path = 'C:\Users\xscogno\MATLAB\Flavio2\Preprocessed_data\';
dpath_spikes='C:\Users\xscogno\MATLAB\Flavio2\Preprocessed_data\';
dpath_sorting='C:\Users\xscogno\MATLAB\Flavio2\Waves\Sorting\';
dpath='C:\Users\xscogno\MATLAB\F... |
ca19235dfef63ee3a925c17bce17cec369f25ba0d03c18cd02c87f88da9ffca5 | MATLAB | 19,497 | 584 | clear all
close all
dbeh_path='C:\Users\xscogno\MATLAB\Flavio2\Waves\Tracking data\';
rec_data_path = 'C:\Users\xscogno\MATLAB\Flavio2\Preprocessed_data\';
dpath_spikes='C:\Users\xscogno\MATLAB\Flavio2\Preprocessed_data\';
dpath_sorting='C:\Users\xscogno\MATLAB\Flavio2\Waves\Sorting\';
dpath='C:\Users\xscogno\MATLAB\F... |
248dbab9a893d7342354efc49e488035d7e920ca139ffbb52a7015a481c5dd25 | MATLAB | 19,505 | 498 | function [details,paths] = conrdk_subjects(id,options,reference,csdFlag)
%DPRST_SUBJECTS Function that sets all filenames and paths
% IN: EITHER (for quering both general and subject-specific paths:
% id - the subject number as a string, e.g. '0001'
% options (optional) - th... |
8e2c7fd378d8ce52be316a6c651852f56dd1f0eec9741f7dda77f816c39a3530 | MATLAB | 19,550 | 487 | classdef SuhCompensator < handle
% AUTHORSHIP
% Primary Developer: Stephen Meehan <swmeehan@stanford.edu>
% Copyright (c) 2022 The Board of Trustees of the Leland Stanford Junior University; Herzenberg Lab
% License: BSD 3 clause
properties(Constant)
DONE='Already compensated';
CHOI... |
0b495c94633a574bd7bccb465719a9dd0917153c37bebc2dda10f5ff48e938a8 | MATLAB | 19,566 | 249 | % Marie_FP_IndivData_quantification
% Marie Labouesse, marie.labouesse@gmail.com - Feb 2021
% INDIVIDAL TRIALS AND MEAN, PEAK/DIP QUANTIFICATION
% load matlab spaces generated in: Marie_FP_IndivData_extraction and PooledData
% uses individual trials aligned to specific event to calculate basic quantifications to ... |
56b138ef290a6539b5a30efe6c53d903b256f1256f604cb392180991e3311019 | MATLAB | 19,569 | 453 |
clear
close all
clc
MainFolder = uigetdir([], 'choose main directory with all subjects'); %Main directory with all subjects
subjects = {''};
for subj=1:length(subjects)
currSubj = subjects{subj};
disp(currSubj)
%cd to subj dir
cd(fullfile(MainFolder,currSubj))
tdcs_lf = sim_struct('TDCSLEADFI... |
2cb53a88f9d1a158ea5c849a451b796ecc0459df73d44a21881d31894a76ed72 | MATLAB | 19,574 | 383 | function [outGroup] = CalculateTrackingPath(Group, config)
%% CalculateTrackingPath
% Andrea Castegnaro, UCL, 2022 uceeaca@ucl.ac.uk
% Calculate trial information based on tracking data.
% Despite having a separate output in our dataset (i.e. cones locations,
% cones triggered position, cones spawning times, triggered ... |
3e065d37371e8122a9507a8eded7cf20e38c2f0558aecb7522927cb0dd0abeb4 | MATLAB | 19,611 | 341 | % Plot correlation across laps at a population level
% Marta Huelin
% INPUT:
% 'parameter' is a string and defines the information you are analysing and that is a field in singleCell_LAPScorr structure.
% Can be centremass_r, peak_r, normcentremass_r
% 'comparison_type' is a string that defi... |
b71c8a9168d9008a0bed076e92c5cf343478e386af69b6cb62c9c3413adc58ce | MATLAB | 19,635 | 466 | function A = NUMERIC_SIM(Sim)
% comments here
% last change: 2015/06/11 by PS
% NUMERIC_SIM solves the Bloch-McConnell equations stepwise for trains of
% arbitrary pulses.
% it requires:
% Sim.xZspec
% Sim.B1, Sim.n,Sim.tp,Sim.DC
% Sim.shape
% Sim.MT_lineshape
% pool concentrations
% y0 = [0 0 0 0 0 0 0 0 0 0 0 0 Sim.f... |
d0b4a68eecab5477923fd57b71f9c223b599b4e000b0626655dc395e7ae91784 | MATLAB | 19,656 | 516 | function nfmi = using_FMI(ima, imb, imf, feature, w)
% FMI calculates the Feature Mutual Information (FMI), the non-reference
% performance metric for fusion algorithms, proposed in:
%
% M.B.A. Haghighat, A. Aghagolzadeh, H. Seyedarabi, "A Non-Reference Image
% Fusion Metric Based on Mutual Information of Ima... |
fb658a2904a1ba78969d529a9f4817d23c522fb206b21a5e00405258b7f19149 | MATLAB | 19,659 | 648 | function [output,outputsummary] = inspect_seg(img,gradechoinv,mask,kernel,options)
%inspect segmentation version
%assumes that each voxel is associated with a single cluster, and each
%cluster has an associated spectrum
%
% Reference: ?Slator, P. J. et al. ?InSpect: INtegrated SPECTral
% Component Estimation and Mapp... |
537cb9f73e14acd05db786035f7a92ebce3600b24760fc8948edc1b0d25dd601 | MATLAB | 19,684 | 351 | function plot_track_replay_periods(epoch,bayesian_control)
% Plots distribution of replay events over sleep periods for each track and protocol.
% Histogram is calculated as the sum of cummulative replay event times across tracks & each experiment (always within a protcol),
% and averaged (divided) by number of activ... |
57530514ad23559bcde119c4e0041ef3ac892e4a39056e2341e130341335f78c | MATLAB | 19,732 | 557 | function [table_u_r,N,T]=identify_waves_latestversion_6_f(mouse_num,day,num_clus_discr,dt,make_fig,spikes)
min_amp=5; %used to be 2
[N,T]=size(spikes);
[~,sorting,~] = get_sorting(spikes);
for i=1:N
FRp(i,:)=full(fire_rate(spikes(i,:),1*dt,'g')); %smooth in the dt chosen for each session
end
[~,sorting_FRp,~] =... |
051a2b19ae53a0f13b3b7a0b65761b05b4796157382b95a22ca1309610875369 | MATLAB | 19,759 | 532 | % Script to plot Figure 4, calculate statistics reported, and save data to
% Excel spreadsheets that are then imported into the Source Data file.
% Kit Longden 2023.
addpath ../../src/Shared_functions/
% Data parameters
frate = 6.83;
N_frames = 52;
dt = 1/frate;
t = (0:dt:dt*(N_frames-1))-3;
t1 = ceil((3+1)*frate);
t2... |
cda83ca42b84ca265dbabe4e18b2e72c1a4c656edbadacb1f20026457cff4366 | MATLAB | 19,769 | 555 | function [table_u_r,N,T]=identify_waves_latestversion_6_f(mouse_num,day,num_clus_discr,dt,make_fig,spikes)
sf=7.73;
min_amp=5; %used to be 2
[N,T]=size(spikes);
[~,sorting,~] = get_sorting(spikes);
for i=1:N
% FRp(i,:)=full(fire_rate(spikes(i,:),1*dt,'g')); %smooth in the dt chosen for each session
FRp(i,:... |
9f3e567c1957ee8b915a2d8d519a97e4e2f5564a81ae607e6e9853151ace06c4 | MATLAB | 19,786 | 638 | function varargout = spikesort_iofxn(varargin);
%
% Function spikesort_iofxn()
%
% This function is the file input interpolator for the spike sorting package.
% It contains the file format specifications and interpreters for a variety of
% file formats. Any additional file formats should be specified here.
%
%... |
2eee3c5b5102c91d11f17613741d42fbe0885ebae74362886b46e50bac5b2541 | MATLAB | 19,813 | 558 |
close all;
clear;
load('U:\rg-yoshida\Yoshida_lab_shared_folder\Shared Data\Antonio\In vivo data\Babak\preliminary analysis\Space_bin\Space_bin_filt30Kalman.mat')
load('U:\rg-yoshida\Yoshida_lab_shared_folder\Shared Data\Antonio\In vivo data\Babak\preliminary analysis\Space_bin\Spatial_Vel_ROI4.mat')
load('U:\r... |
2733cc455d9db94d4ac0bf7108acec77a367e75b913fcc4144110e3a6f7e9ccb | MATLAB | 19,879 | 402 | % PLOT DECODING ERROR ACROSS LAPS
% Uses information extracted with 'bayesian_decoding_error_stability.mat' to plot median decoding error across laps (each lap compared to the last
% lap(s). Plots for each rat, for each protocol, and a figure with all protocols overlapped (showing both first and second exposure). Also ... |
ebd736068b9762386a5ca113060983422fbac4ac143e5b5e576cf5c5d4f808a9 | MATLAB | 19,907 | 610 | function ress = plot_figures(model, params, opt)
% Show or not vars
plotNeuronVoltage = opt.plotNeuronVoltage; %1% Single Neuron Voltage and Glutamate
plotSpikeRaster = opt.plotSpikeRaster; %3% Voltage Map
plotIappMAP = opt.plotIappMAP; %5% I applied M... |
fb4309f006519432bbc21eb4d6681ec106985e63f8f09d53524eeccc4b929fac | MATLAB | 20,003 | 556 | %LOAD_ACQ load BIOPAC's AcqKnowledge file format for Windows PC
%
% Usage: acq = load_acq(filename, [force_chan_by_chan_load])
%
% acq - AcqKnowledge file structure containing ACQ header field,
% and data matrix.
%
% filename - BIOPAC's AcqKnowledge file
%
% force_chan_by_chan_load - Optional. By default, this op... |
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