sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
d1a61030f0e52525a73c024903ec2e8298b02d343e3239e5eda28033bb5ee139 | MATLAB | 19,259 | 445 | %{
1.Put all the files in the same folder
2. run this script
This script is designed to work with binary files generated by Bonsai`s
MatrixWriter node(sink/dsp)
!!!!! IMPORTANT!!!!!
filename has a certain structure to it, and the code is using the filename
to assign conditions to different channels
for exampl... |
0ad63098c699569774d078ecc8c4282aa3ef3915856c3883af738a58ea184c05 | MATLAB | 19,266 | 536 | function varargout = DPABI_QC_Surf(varargin)
% DPABI_QC_Surf MATLAB code for DPABI_QC_Surf.fig
% DPABI_QC_Surf, by itself, creates a new DPABI_QC_Surf or raises the existing
% singleton*.
%
% H = DPABI_QC_Surf returns the handle to a new DPABI_QC_Surf or the handle to
% the existing singleton*... |
6ce6de7f77a002d8ba09c9486b792cbd2960ad52aabf9b8f670533aad3e12c08 | MATLAB | 19,266 | 577 | function [parcel] = ft_sourceparcellate(cfg, source, parcellation)
% FT_SOURCEPARCELLATE combines the source-reconstruction parameters over the parcels, for
% example by averaging all the values in the anatomically or functionally labeled parcel.
%
% Use as
% output = ft_sourceparcellate(cfg, source, parcellation)
... |
af5d6b6a00034cace53a94304d8d6733d44b3e34aeda8fa85a4916873c448e0d | MATLAB | 19,275 | 496 | function [type] = ft_senstype(input, desired)
% FT_SENSTYPE determines the type of acquisition device by looking at the channel
% names and comparing them with predefined lists.
%
% Use as
% [type] = ft_senstype(sens)
% or
% [flag] = ft_senstype(sens, desired)
%
% The output type can be any of the following
% 'c... |
02dd2105265a25a68d12bf3fb8aead116197fdf3597a8e6f45ca23fd8168446e | MATLAB | 19,289 | 509 | function CrossDecodeAnalysisImproved(AllTrialData,Labels,DecodeLabels,TimeWindow,OutDir,Tag,varargin)
% Train on dim 1 to classify dim 2, test on how well it generalizes.
% e.g. train on nancy to diff bp, test how well bp diff'd in Tyson.
[~,PThresh] = Utilities.ProcVarargin(varargin,'PThresh','fdr');
% [~,NIter... |
252ee4e9629d21caee1b68e50de2e22b5ea472f71a279b7b19864dfbfec15b8e | MATLAB | 19,311 | 516 | % Address the basic question, when x is the salient category, what
% percentage of units are tuned to x.
function SlidingTimeDPrimeMotorVersionStatic(config,varargin)
[varargin,DateName] = Utilities.ProcVarargin(varargin,'DateName',[]);
[varargin,Pmethod] = Utilities.ProcVarargin(varargin,'Pmethod','fdr')... |
9bb9f419526444e98a6207f41bacc40a94d21e2b78014a98fd2a97cb045caba3 | MATLAB | 19,313 | 653 | % clear
% clc
MuscleMomentArmOW_time = [];
MuscleMomentArmWI_time = [];
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluste... |
a9c27248ca056996c2474042443e35538388ce3297e406e4d2f2585ccec3ed4f | MATLAB | 19,322 | 467 | function [data] = ft_megrealign(cfg, data)
% FT_MEGREALIGN interpolates MEG data towards standard gradiometer locations by
% projecting the individual timelocked data towards a coarse source reconstructed
% representation and computing the magnetic field on the standard gradiometer
% locations.
%
% Use as
% [interp]... |
8e38bcab8b310309a5f1b41adaafa8ac957aa7ec189f8a3eddcee90af2680ff0 | MATLAB | 19,323 | 652 | % clear
% clc
MuscleMomentArmOW_time = [];
MuscleMomentArmWI_time = [];
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluste... |
21c35b5081418da1b173f1bf5f8346a20712756c5685870a7d9e844670cb0602 | MATLAB | 19,334 | 414 | function [optimalDV, optimalFD, optimalPCT, minMSE] = mCotWrapper(workingDir, varargin)
%UNTITLED Summary of this function goes here
% Detailed explanation goes here
%mseParameterSweep needs:
% numRunsDataNeededPerSubject,minSecDataNeededPerSubject,minNumContiguousDataSeconds
numPreprocWorkers = 4... |
209a8e91c1eca77df3e9b55f97f1e099f151d2b5ce0b952f7aadf66c300e9de9 | MATLAB | 19,349 | 652 | % clear
% clc
MuscleMomentArmOW_time = [];
MuscleMomentArmWI_time = [];
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluste... |
8d889d0b97c81421434a1ff2f25ee097fc474787020e2955952dace39f228cfd | MATLAB | 19,395 | 558 | % 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>
% Provided by the Herzenberg Lab at Stanford University
% License: BSD 3 clause
%
classdef SortG... |
d502f6ceeaae7825dcb91f0241a4325da4dce672b88c60cb974530a9a836567f | MATLAB | 19,399 | 391 | function perform_encoding_analysis(p)
%
% - This code is written for performing whole brain encoding analysis from specified features.
% - Whole brain data was parsed into "nparse" groups for parallel computations.
% - A whole brain analysis for one subject and for one feature type takes about 2-3 hours with single cpu... |
8edc61215d154d97367257d37279a2032a0875a557fecafbdcf6687b6e6089f8 | MATLAB | 19,402 | 651 | function [ss,avr,moes]=FscatJit2_mergeGroups(identifiers, data, varargin)
% Plots data as both jittered points and an error bar, in the
% order the identifiers are passed to it. It preserves whatever order
% was in the fbmm structure. Also includes the option to define the circle
% size or use a default. Also allows ... |
e3cd773e48e201e4cf144e31fc1c07a61c60e97b28ccfec504491548aee8d34a | MATLAB | 19,411 | 258 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
62b60c86d84c699a34e80b0bd3f3923e3382164260e07dfc41998af2c8d19913 | MATLAB | 19,414 | 531 | % Author: Birgit Nierula
% nierula@cbs.mpg.de
function prepro_extractSepParameters(subject, nerve, srmr_nr)
%% set variables
subject_id = sprintf('sub-%03i', subject);
savepath_eeg = [getenv('EEGDIR') subject_id '/'];
savepath_bs = [getenv('ANADIR') subject_id '/bs/prepro/'];
savepath_bs2 = [getenv('BSDIR') subject_... |
b0856cac749fa5cdc2a272e315d67468c1cefd961e90a842618097894af4d9c8 | MATLAB | 19,414 | 654 | % clear
% clc
MuscleMomentArmOW_time = [];
MuscleMomentArmWI_time = [];
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluste... |
623f3d184bcbac6644bc2104bc0e8f93a82c2679834193978e05f9a7c9ceb8e1 | MATLAB | 19,448 | 597 | function tfrview(tfr,sig,t,method,param,map,p1,p2,p3,p4,p5);
%TFRVIEW Visualization of time-frequency representations.
% TFRVIEW(TFR,SIG,T,METHOD,PARAM,MAP,P1,P2,P3,P4,P5)
% allows to visualize a time-frequency representation.
% TFRVIEW is called through TFRQVIEW from any TFR* function.
%
% TFR : time-frequency rep... |
27c7904ed3810900fc19a8de9b8452eb1ecd38ab9b4c0b29388c7ef115081ca3 | MATLAB | 19,465 | 474 | classdef FeaturesAnnotator < FeaturesReporter
properties
featureSet
end
properties (Transient)
rangeFeatureBeingAdded
settingsEdited
settingsTable
selectedSet
featureSetsPopup
lastChosenFeatureSetName
reporterName
... |
11a72b70db636fadd8b929ced6bbf1d9d7f142ce25c72ebf2982b3bfeea8e735 | MATLAB | 19,469 | 534 | function [Out1, Out2, Out3, NonConvergence] = fastica_nwb(mixedsig, varargin) % NWB adjusted to output "NonConvergence=1" if fastica_symm does not converge (which causes issues with ICA cleaning)
%FASTICA - Fast Independent Component Analysis
%
% FastICA for Matlab 7.x and 6.x
% Version 2.5, October 19 2005
% Copyright... |
94c70572c8e3d7167f64285f1548a33be72d5546f20a52d28352d42325374ef0 | MATLAB | 19,480 | 545 | function [outputs] = CHO_v21(signal, srate, param)
% CHO_v16() - Return detected bounding boxes of oscillations, 1/f fitting output,
% and the std of the input signal
%
% * Each bounding box has following attributes:
% center_fp: center frequency
% center_tp: middle ... |
d58d5f829108d97d2e3e5c0ab0fcc6274baa4bd870f5c6e27918bc874a00a5c7 | MATLAB | 19,551 | 456 | function [data] = femdata_spec_TR(mesh,t,dt,varargin)
% FEMDATA_SPEC_TR Returns time-resolved photon fluence rate for the
% 'spectral' FEM mesh in NIRFAST format. This function generates sparse
% matrices representing the FEM problem (the time-resolved 'mass'
% mastrices and sparse sources vectors). Then, it call... |
84c4b072ae64c2a354fd54a0ae412366be7c0f6a126ca312607d4388e76a1a9f | MATLAB | 19,558 | 635 | %go_calib_optim_iter
%
%Main calibration function. Computes the intrinsic andextrinsic parameters.
%Runs as a script.
%
%INPUT: x_1,x_2,x_3,...: Feature locations on the images
% X_1,X_2,X_3,...: Corresponding grid coordinates
%
%OUTPUT: fc: Camera focal length
% cc: Principal point coordinates
% al... |
d2955a8986f668586a350efdbe1ce29b2afc18b57a4734de369bfc6f27eb0c56 | MATLAB | 19,570 | 513 | function varargout = DPABI_RESLICE_TOOL(varargin)
% DPABI_RESLICE_TOOL MATLAB code for DPABI_RESLICE_TOOL.fig
% DPABI_RESLICE_TOOL, by itself, creates a new DPABI_RESLICE_TOOL or raises the existing
% singleton*.
%
% H = DPABI_RESLICE_TOOL returns the handle to a new DPABI_RESLICE_TOOL or the handle to
%... |
79e116f12540584251b0c77b3278b051ae6d06221885d1b12d0282babc30988e | MATLAB | 19,581 | 493 | function mri = ft_defacevolume(cfg, mri)
% FT_DEFACEVOLUME allows you to de-identify an anatomical MRI by erasing specific
% regions, such as the face and ears. The graphical user interface allows you to
% position a box over the anatomical data inside which all anatomical voxel values will
% be replaced by zero. You ... |
a3461a01e7a126cf18a968afd160cf158106c42a162cbded4bc0bf2e71c85f85 | MATLAB | 19,581 | 493 | function mri = ft_defacevolume(cfg, mri)
% FT_DEFACEVOLUME allows you to de-identify an anatomical MRI by erasing specific
% regions, such as the face and ears. The graphical user interface allows you to
% position a box over the anatomical data inside which all anatomical voxel values will
% be replaced by zero. You ... |
35602a2cbaa940d6ee4beb0aae1ea49be37282db8a0507515bfa74e865cab0a0 | MATLAB | 19,594 | 532 | classdef Kld<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>
% Provided by the Herzenberg Lab at Stanford University
% License: BSD 3 cla... |
988c93cf8938c9914fe0dc5e2c0d8d5d638b9871254111a1f8509cc9388718bb | MATLAB | 19,601 | 648 | function lan_setref_micromed(LAN)
% <*LAN)<] toolbox
% v.0.0.8
%
% Pablo Billeke
% Francisco Zamorano
% 12.12.2014 compatibility with lantoolbox.m GUI
% 10.01.2014 add "add coord" button (PB)
% 16.11.2013 add delete channles and aunto ref and buttons, and add warning
% when there is no ... |
fc5a9d2bde960e5335432781846f6362aaa87930b8d7703d77e94c2958bfedcf | MATLAB | 19,630 | 408 | function [p_sr_IpsiContra,p_sr_BilaSum,W_sr_IpsiContra,W_sr_BilaSum] = plotUniWingPitch(uStimLeft,uStimRight,meta,fNum)
close all
ipsi = [uStimLeft.uLE;uStimRight.uRE];
contra = [uStimLeft.uRE;uStimRight.uLE];
bi = [uStimLeft.bE;uStimRight.bE];
ipsi_avg = nanmean(ipsi);
contra_avg = nanmean(contra);
bi_avg =... |
9adf4fa131a39c6052dc44f3963453e400a032f970559e1a9823eba8543c4aa0 | MATLAB | 19,676 | 297 | function event_related_results_FIR(stat_path,exp_folder,q_level,ground_truth)
% ========================================================================
% Ruslan Masharipov, July, 2024
% email: ruslan.s.masharipov@gmail.com
% ========================================================================
load([stat_path fil... |
84310ad773e9034860e2dbefe13db85908ecd472996bfe203a26e5b81d83904e | MATLAB | 19,694 | 487 | %% for baclofen injections through the grids, analyzes the baseline, saline, baclofen, and recovery data
%
clear; close all; clc
%paths to the relevant data sets baclofen
%Mouse 1 paths to baseline data, saline injection, baclofen, and recovery
dataPaths{1,1} = 'D:\InVivo\S1-Baclofen\2022-02-10_Baclofen\3-basel... |
0a3da4a892553864e13ec088e2e21d008bec6cec99fe3153a3bdd632ac52d628 | MATLAB | 19,695 | 475 | function [backgroundColor, API, windowClipPanelWidth] = ...
createWindowClipPanel(hFlow, imgModel)
%createWindowClipPanel Create windowClipPanel in imcontrast tool
% outputs =
% createWindowClipPanel(hFig,imageRange,imgHasLessThan5Levels,imgModel)
% creates the WindowClipPanel (top panel in contrast tool) in ... |
4b0aaf3656407e4c62167a5713cc0f6595ef711ee17b1ce6a8e5674dac332bde | MATLAB | 19,696 | 548 | function [outputs] = CHO_v22(signal, srate, param)
% CHO_v16() - Return detected bounding boxes of oscillations, 1/f fitting output,
% and the std of the input signal
%
% * Each bounding box has following attributes:
% center_fp: center frequency
% center_tp: middle ... |
69108a61fb46ad6066a606350b73908ee906b3ef7541e4847fcc9417f815fa97 | MATLAB | 19,725 | 656 | function Answer=inputdlgWithBrowse(Prompt, Title, NumLines, DefAns, Resize, BrowseInfo)
%INPUTDLGWITHBROWSE Input dialog box.
% ANSWER = INPUTDLG(PROMPT) creates a modal dialog box that returns user
% input for multiple prompts in the cell array ANSWER. PROMPT is a cell
% array containing the PROMPT strings.
%
% IN... |
a2f6f689324999004472a131ef1b27852611ee5cd577d61fa69bbca3dd24fab2 | MATLAB | 19,779 | 369 | % TrioEEGPipelineCall.m
%
% call trioEEGPipeline with the epochInfo and flagInfo saved here as a
% record.
clc; clear all; close all;
%% ITI
sectionsToRun = struct( 'convert', 0,... % convert ov2mat
'load', 0,... % trioEEGLoad (incl trigger stuff)
'merge', 0,.... |
330fac541b0587cbace82a511917f8d82e3630fe0d3ac3dc59446b86542b3d98 | MATLAB | 19,788 | 591 | function compute3Dfrom2D(savefile,frontviewvideofile,sideviewvideofile,...
frontviewmatfile,sideviewmatfile,kinematfile,dosave,experiment_name)
% outdated. moving to maintaining only _KB.
compute3Dfrom2D_KB(savefile,frontviewmatfile,sideviewmatfile,kinematfile,...
'/tmp/front.trk','/tmp/side.trk');
return;
%%
... |
5dd301b7174721666ee79d82a52c9bff6c800697415a55fc0cb6e9f1424a04b2 | MATLAB | 19,790 | 481 | function varargout = DPABI_BIDS_Converter(varargin)
% DPABI_BIDS_CONVERTER MATLAB code for DPABI_BIDS_Converter.fig
% DPABI_BIDS_CONVERTER, by itself, creates a new DPABI_BIDS_CONVERTER or raises the existing
% singleton*.
%
% H = DPABI_BIDS_CONVERTER returns the handle to a new DPABI_BIDS_CONVERTER or t... |
73e3f14353dbe303fbaded56aafaeb2c1d00ea151255ca40bfe917d50dec9c90 | MATLAB | 19,794 | 539 | function hs = ft_plot_sens(sens, varargin)
% FT_PLOT_SENS visualizes the EEG, MEG or NIRS sensor array.
%
% Use as
% ft_plot_sens(sens, ...)
% where the first argument is the sensor array as returned by FT_READ_SENS or
% by FT_PREPARE_VOL_SENS.
%
% Optional input arguments should come in key-value pairs and can incl... |
f1df02b07c0acd1416b5667e869feb1c7024180a8cd64c5c225cae08ce8c920e | MATLAB | 19,826 | 485 | function linescanVelocityAnalysis(fname, prefs)
%
% line scan analysis - use radon transform on linescan stripes (from RBC
% shadow) to detect RBCV
% Patrick Drew code from github, adapted by Dori, Kira & Orla, July 2017
% updated May 2018 by Kira
% NB this code now removes bad frames, so no need to manually de... |
2ed6be4465394038652b662c51a264f1e2ad652d386ca32145979029d0991688 | MATLAB | 19,837 | 507 | % Address the basic question, when x is the salient category, what
% percentage of units are tuned to x.
function SlidingTimeDPrime(config,varargin)
[varargin,DateName] = Utilities.ProcVarargin(varargin,'DateName',[]);
[varargin,Pmethod] = Utilities.ProcVarargin(varargin,'Pmethod','fdr');
[varargin,saveF... |
d3b9ded05834aa8d3b030c08aab6352ded3e4155dad54907e441d58f07220379 | MATLAB | 19,887 | 407 | %% Get statistics from data
function [ xhatStats, xhat_Stats, ...
xhatTable, xhat_Table] = GetDataStats(xhat, xhat_, dataType)
% Extract mean, within individual standard error and between individual
% standard error for xhat and xhat_ and store in structs. Also format
% tables to display the relevant sta... |
1d32062f99da7658a842cd88db263d973024c27fa9f8edf9ef311bf6bb41db44 | MATLAB | 19,947 | 406 | % LIP choice decoder including/excluding Tin, Tout, Min populations -
% time-dependent <--
% Renames from calc_whatDecoder_acrossTPTs
load(fullfile(saveLoc, 'par_sess'))
winSize = 0.05 ;
winCenter = 0.001 : 0.005 : 0.6;
winStart = winCenter - winSize/2;
winEnd = winCenter + winSize/2;
winCenter = mean([win... |
8e906ff27f05c0325b08c989b406a1479cc613a6c54fc824bbde4dafba4d4868 | MATLAB | 20,000 | 520 | function test_Path()
for platform = enumeration('apt.Platform')'
if platform == apt.Platform.windows
nativePathAsString = 'C:\foo\bar\baz';
expectedPathAsString = '''C:\foo\bar\baz'''; % Expect quoted version
correctNativePathAsList = {'C:', 'foo', 'bar', 'baz'} ;
else
nativePathAsString = '/foo/b... |
3dda47faab1ef4be6c16c7620a80e4508232984b3c7066d102e1fa9fb2cc746e | MATLAB | 20,019 | 596 | //========================================================================
// GLFW 3.4 macOS - www.glfw.org
//------------------------------------------------------------------------
// Copyright (c) 2009-2019 Camilla Löwy <elmindreda@glfw.org>
//
// This software is provided 'as-is', without any express or implied
// ... |
45366dc3e614deb0e9c2ef7436bd358d619d8b69996bc247fc89edca0e43a465 | MATLAB | 20,031 | 478 | function [cfg] = ft_clusterplot(cfg, stat)
% FT_CLUSTERPLOT plots a series of topographies with highlighted clusters.
%
% Use as
% ft_clusterplot(cfg, stat)
% where the input data is obtained from FT_TIMELOCKSTATISTICS or FT_FREQSTATISTICS.
%
% The configuration options can be
% cfg.alpha = num... |
c2f1f8f28e3d8f7f2543321daa49daf0c38f41e4b04af0a8287931a7a5588a38 | MATLAB | 20,033 | 540 | %% Load the trained network.
load('./data/hfopt_.mat')
net.layers(1).transfun = @(x) x;
net.layers(1).Doperator = @(y) ones(size(y));
net.layers(2).transfun = @ tanh;
net.layers(2).Doperator = @(y) 1.0-y.^2;
net.layers(3).transfun = @(x) x;
net.layers(3).Doperator = @(y) ones(size(y));
inv_trans_fun = net.layers(2).inv... |
d3ac0bf44c38e3c986386c68277dc89ed6052e3f0220927b8c3191179977d4c4 | MATLAB | 20,053 | 422 | function [mappedA, mapping] = compute_mapping(A, type, no_dims, varargin)
%COMPUTE_MAPPING Performs dimensionality reduction on a dataset A
%
% mappedA = compute_mapping(A, type)
% mappedA = compute_mapping(A, type, no_dims)
% mappedA = compute_mapping(A, type, no_dims, ...)
%
% Performs a technique for dimension... |
0b6d14186272957ec0acb71cfa1070847d72a47fb7e3ad1ba42765ac38e2b8e1 | MATLAB | 20,054 | 540 | function [theta_opt, objfun] = hfoptimizer(net, init_conditions_ntrials, inputs_ntrials, targets_ntrials, varargin)
%% This code is created based on David Sussillo's implementation (https://github.com/sussillo/hfopt-matlab).
% Initial settings.
do_parallel = false;
save_path = './'; % Save snapshots for hf iterations.... |
19483547f8e60bde67cb93aebd69d170bee66fed0dc9361881d3f98239430eba | MATLAB | 20,054 | 448 | function [data, result]= readtext(text, delimiter, comment, quotes, options)
% Usage: [data, result]= readtext(source, delimiter, comment, quotes, options)
%
% Whatever text (file) you give it, readtext returns an array of the contents (or send me a
% bug report). Matlab can't read variable length l... |
41c330fa16d3b922f3ba5c353237f30b3a0397458df15682a9570b12efa31bf3 | MATLAB | 20,054 | 694 | %% Minfunc demo
% PMTKinteractive
% PMTKslow
%%
% This file is from pmtk3.googlecode.com
f = 1;
options.Display = 'none';
%% Huber robust regression
% Generate linear regression data set with outliers
nInstances = 400;
nVars = 1;
[X,y] = makeData('regressionOutliers',nInstances,nVars);
% Least squares solution
wL... |
29653128ce6405778bfb6f2f2f972c092aafd9fa7bd02c74985eb5b8203beade | MATLAB | 20,103 | 472 | function [headmodel, cfg] = ft_prepare_headmodel(cfg, data)
% FT_PREPARE_HEADMODEL constructs a volume conduction model from the geometry
% of the head. The volume conduction model specifies how currents that are
% generated by sources in the brain, e.g. dipoles, are propagated through the
% tissue and how these resul... |
e3a0cf2e5b37d292b0f1ec99953f0891d86ee2c6d299acf448dc3abbda7946dd | MATLAB | 20,145 | 544 | function varargout = SMA_settings(varargin)
% SMA_settings MATLAB code for SMA_settings.fig
% SMA_settings, by itself, creates a new SMA_settings or raises the existing
% singleton*.
%
% H = SMA_settings returns the handle to a new SMA_settings or the handle to
% the existing singleton*.
%
% ... |
a798750246d6b5eeb51e6ba383cf3f8626918f22a7129583f558c9d7f341747a | MATLAB | 20,200 | 375 | function model = runMonteCarlo(model,varargin)
% Requires
% MCmatlab.mex (architecture specific)
%
% See also plot, runMonteCarloFluorescence, simulateHeatDistribution
%%%%%
% Copyright 2017, 2018 by Dominik Marti and Anders K. Hansen
%
% This file is part of MCmatlab.
%
% MCmatlab ... |
49c5b801213b29fb838c424af3ab4035f77f5fb32f7a0f6b583554dd8077641f | MATLAB | 20,212 | 587 | function [outputs] = CHO_v24(signal, srate, param)
% CHO_v24() - Return detected bounding boxes of oscillations, 1/f fitting output,
% and the std of the input signal
%
% * Each bounding box has following attributes:
% center_fp: center frequency
% center_tp: middle ... |
3e8a94dcd024ee022f1a2b625b2b6dfa14130cdd5df5a1ad2fb7f10787a81e1a | MATLAB | 20,261 | 269 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
807ebaa970b7f3bf9b975031d6e2c8cf0c8acdad1f92a4af2cea296113fc97a0 | MATLAB | 20,279 | 514 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
disp(['Making Figure: ',FigName])
figure('name',FigName)
set(g... |
d2328a691e9513c7ed1722b45774458e99f3181d189e1d1059da7a42a9586784 | MATLAB | 20,280 | 673 | %%% WARNING!!! This code is not in working condition yet.
%%% AS A RESULT, IT IS NOT SUPPORTED!!!
%%% -Jean-Yves Bouguet
%go_calib_optim_iter_weak
%
%Main calibration function. Computes the intrinsic andextrinsic parameters
%for the weak perspective camera model
%Runs as a script.
%
%INPUT: x_1,x_2,x_3,...: Feature lo... |
71c0d41d5b75c12502d8e34782a1df819aa923f502198ecc33a89f8835b436ba | MATLAB | 20,293 | 544 | % Function where all the parameters are set up for the simulation, which is
% coded in the file 'EIF1DRFfastslowSyn.c' (C code for computation time).
% Code based on Chengcheng Huang's code:
% https://github.com/hcc11/SpatialNeuronNet
% https://github.com/hcc11/FI_SpatialNet
% I modified and drastically simplified the... |
093b60f75e596e2a8de97c2c0f60a0bd8178fd682cce4d20d39a6c031f942c0e | MATLAB | 20,298 | 403 | function [varargout] = read_plexon_plx(filename, varargin)
% READ_PLEXON_PLX reads header or data from a Plexon *.plx file, which
% is a file containing action-potential (spike) timestamps and waveforms
% (spike channels), event timestamps (event channels), and continuous
% variable data (continuous A/D channels).
%
%... |
82ab216548c05c65bcc195b52e75cde2c184858a87e8f6450acf2519de5facc2 | MATLAB | 20,305 | 538 | function [S,NlogL,optimInfo]...
=gmcluster(X,k,start,reps, CovType,SharedCov, RegV, options)
%GMCLUSTER Gaussian mixture fit.
% S = GMCLUSTER(X,K) fits a K-component Gaussian mixture model to the
% data. The fit maximizes the log-likelihood of the data using
% Expectation Maximization (EM). Rows of X corresp... |
d7dabfe1e01e5e6de716552912f98db2bd3d6e609f6604af38affa6d01341100 | MATLAB | 20,309 | 531 | classdef DataCollection < handle
% A DataCollection is a collection of several Datasources. Data-collections
% and datasources are explained in broad terms here:
% <a href="matlab:dspace.help('dspace.resources.docs.Introduction_to_Dataspace')">Introduction to Dataspace (Dataspace Help)</a>
%
%... |
aa5a503bbeb1badbe67a323da8a527c7be3edf1983497059269bb20432c0cbff | MATLAB | 20,329 | 493 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
disp(['Making Figure: ',FigName])
figure('name',FigName)
set(g... |
462357daafb863762c486fb6590daf0d51a5493464cc1668900559ea87b51e9c | MATLAB | 20,407 | 533 |
% Set up format of simulation data to match mediation analysis
medA = load(fullfile(saveLoc, 'for_plots_mediation_norm_to_se'));
sim_dat = load(fullfile(saveLoc, 'race_model_simulation'));
load(fullfile(saveLoc, 'par_sess'))
% Add time point 0.2s (0)
sim_dat.diffusionPaths = [zeros(60000, 1) sim_dat.diffus... |
4510bb3ac0079c9a95aaac438c30005624cfd9268fb0eabcfc40df029d7a4dd3 | MATLAB | 20,489 | 431 | function EpisodicImageChecker(StatusFig,LastIndexNumber,Temp_ImageArray_FirstImages,Peak_TempDeltaF,End_TempDeltaF,MeanStartingFluorescenceVector,Peak_TempDeltaFMaxVector,End_TempDeltaFMaxVector)
warning on all
warning off verbose
warning off backtrace
OS=computer;
if strcmp(OS,'MACI64')
... |
214f8ebd5efd34b696e85007e46917f94cdc3fad45da71b0c1c2c8ff990c7972 | MATLAB | 20,504 | 677 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Musc... |
89f7300a85be745d03bad87e9def448782819d35affe0b11c70e20ee05b3bcfd | MATLAB | 20,583 | 498 | function [d,si]=abfload(fn,varargin)
% ** function [d,si]=abfload(fn,varargin)
% loads and returns data in the Axon abf format.
% Data may have been acquired in the following modes:
% (1) event-driven variable-length
% (2) event-driven fixed-length
% (3) gap-free
% Information about scaling, the time base and t... |
62944fe0da84b9e8ecd3cc137c9c7c31f62624741a5f49c0f08f39703116c575 | MATLAB | 20,601 | 504 | function res = mapper(X, options)
% Main function to run the Mapper Algorithm on (time x features) data
%
% Mapper is running multiple steps, named and defined below as
% transformations on the data:
%
% input data X
%
% -> [1] (optional) preprocessing
%
% -> [2] distance: pair-wise distance calculation
%
% ->... |
87ce0130653723d7a4bb33f1f02509e75e322043ce5b6fbddf1067111a658722 | MATLAB | 20,616 | 666 | function varargout = scnlab_outlier_id(varargin)
% Methods (modes of operation)
%
% :Setup:
%
% Run this method first to generate an options structure OPT
% that can be passed in along with any data vector for speedy
% processing
%
% :Usage:
% ::
%
% OPT = scnlab_outlier_id('setup', 'tr', 2, 'sperses... |
f48e592f53155b57f54cba44c8a8e4196d12b23999f14426bf342544f9d8f87b | MATLAB | 20,617 | 731 | function [A] = meg_nonparametricTest(data1,data2,nPerm,stat,variableName,condName,t)
% MEG_NONPARAMETRICTEST()
%
% performs nonparametric testing on time series data with two conditions (Maris & Oostenveld 2007)
% - ttest data1, data2
% - randomly permutes data into 2 conds nPerm times
% - ttest on random permu... |
5bb076f87dc1054e8c2bf56ce4c47af268226b995542d838d6ff494b59fcf2c3 | MATLAB | 20,622 | 549 | % Function where all the parameters are set up for the simulation, which is
% coded in the file 'EIF1DRFfastslowSyn.c' (C code for computation time).
% Code based on Chengcheng Huang's code:
% https://github.com/hcc11/SpatialNeuronNet
% https://github.com/hcc11/FI_SpatialNet
% I modified and drastically simplified the... |
6763c624c758e907dcb5d356317d64b0b8c06bf8616707b165cc42b00fc1b0e8 | MATLAB | 20,653 | 427 | function [PO_to_PrefO_count,...
bin_centers,...
PO_bin_frac_sig_ofTuned,...
comp_type_label,...
ratio_conTOdiv,...
ratio_conTOdiv_CI,...
dPOsig_perint,...
dPOsig_perint_all,...
output]...
= StripeRearing_stability(data,timepoint_labels,varargin)
p = inputParser();
p.KeepUn... |
ad7fdab900c4a3ed0d50790be9362068e642f8096824afb4c3704b26dfbe56fe | MATLAB | 20,654 | 443 | function [] = CreateCompilationVideo_new(meta,Data,body,wings)
close all
flyID = meta.flyID;
nTrials = numel(flyID);
DLC_model = meta.DLC.model;
DLC_dataSubFold = meta.DLC.subFold;
cc = distinguishable_colors(9);
tracked_ptLabel = meta.trackedptLabel;
rotMatX = @(theta) [1 0 0; 0 cosd(theta) -sind(theta); 0... |
1db7c677af4d96f314c3f8beb07a512230180d8817b4e80ad77910849861dadd | MATLAB | 20,655 | 585 | function tfrview(tfr,sig,t,method,param,p1,p2,p3,p4,p5);
%TFRVIEW Visualization of time-frequency representations.
% TFRVIEW(TFR,SIG,T,METHOD,PARAM,P1,P2,P3,P4,P5)
% allows to visualize a time-frequency representation.
% TFRVIEW is called through TFRQVIEW from any TFR* function.
%
% TFR ... |
38eabd29928c431d1914ca7037491d9892f88e4ca2b94f7332a005e24e2393ef | MATLAB | 20,672 | 663 | function [comparison,means,h,gnames] = multcompare_ZN(stats,varargin)
%MULTCOMPARE Perform a multiple comparison of means or other estimates
% MULTCOMPARE performs a multiple comparison using one-way anova or
% anocova results to determine which estimates (such as means,
% slopes, or intercepts) are significantly... |
0fa14da4ae057b32153b4786f11f32546b31c3163d4057019ce75361beb2eb61 | MATLAB | 20,673 | 406 | function [log_odd] = extract_ground_truth_info(folders,option,method,shuffles,ripple_zscore_threshold,p_val_threshold)
% Input:
% Folders: Each Folder is data for a session
% method = 'linear' or 'wcorr' or 'spearman' or
% p_val_threshold = If you want to extract events at specific p value,
% set the p value threshold... |
d9ee3fa97bb87b948acb968b047a9c45024e7657a91ec394bf7a1fc381ff52b9 | MATLAB | 20,673 | 574 | function [output] = ft_singletrialanalysis_aseo(cfg, data, erp_fft)
% FT_SINGLETRIALANALYSIS_ASEO executes single-trial analysis, using the
% ASEO algorithm (Xu et al, 2009)
%
% Use as:
% [output] = ft_singletrialanalysis_aseo(cfg, data_fft, erp_fft)
% where data_fft is the observed data in the frequency domain, erp_... |
92586074bfe52bcec7bd5d76675d0485c128843586f9d956e723378bf258fea9 | MATLAB | 20,674 | 594 | function varargout = DPABI_QC_TOOL(varargin)
% DPABI_QC_TOOL MATLAB code for DPABI_QC_TOOL.fig
% DPABI_QC_TOOL, by itself, creates a new DPABI_QC_TOOL or raises the existing
% singleton*.
%
% H = DPABI_QC_TOOL returns the handle to a new DPABI_QC_TOOL or the handle to
% the existing singleton*... |
4ddc2f9646b4b1ce8c0d64f76c404944fa720f7d3766e6535498413d12e83c04 | MATLAB | 20,678 | 293 | %% Analysis of the functional data using RSA
% Defining parent directories (each containing a separate directory for each subject)
all_subjects_GLM_files_dir = 'C:\Users\michaelpeer1\Google Drive\bar_mord_shared_files\Experiment_data\GLM_files'; % The per-run GLM results
all_subjects_RDMs_dir = 'C:\Users\... |
c968346cbf4d1d142d5d6a103a6e81e32b757aaaf8b1f229035a46ebe8cd6447 | MATLAB | 20,693 | 436 | function DataStructure=PowerFit_Custom_ZN1(Xdata,Ydata,Power,FitOption)
%Newer version includes different fit methods
if size(Xdata,1)<=1&&size(Xdata,2)<=1&&size(Ydata,1)<=1&&size(Ydata,2)<=1
warning('Need to provide more than one data point to fit!')
DataStructure=[];
elseif size(Xdata... |
098686d2ef110523b3abbca9af9952023d603c7af487522e13bff00865f7ebf5 | MATLAB | 20,700 | 513 | %% SDO Multi-Mat
% Data Class for holding and manipulating multiple sdoMat classes. Designed
% for use within the SDO Analysis Toolkit.
%
% Used to rapidly compute multiple SDOs at once.
% Can be exported to 'sdoMat' class for analysis.
%_______________________________________
% Copyright (C) 2023 Trevor S. Smith
% ... |
63800d0ff87e38975a89b319e070ffb64f7d679515e30d046aa03d98030d9957 | MATLAB | 20,726 | 510 | function [cfg] = ft_clusterplot(cfg, stat)
% FT_CLUSTERPLOT plots a series of topographies with highlighted clusters.
%
% Use as
% ft_clusterplot(cfg, stat)
% where the input data is obtained from FT_TIMELOCKSTATISTICS or FT_FREQSTATISTICS.
%
% The configuration options can be
% cfg.alpha = num... |
126d45366d084a2887b293cae9f53619fc14da84bcaa16e9c43628fda7c87d93 | MATLAB | 20,727 | 670 | function invert = spm_cfg_eeg_inv_invertiter
% Configuration file for configuring imaging source inversion reconstruction
%__________________________________________________________________________
% Copyright (C) 2010 Wellcome Trust Centre for Neuroimaging
% Vladimir Litvak
% $Id: spm_cfg_eeg_inv_invertiter.m 7660 20... |
c9e2107f4c1b2b1c531192094d5a5fb270ccd2a36bf43224391d13547cb11400 | MATLAB | 20,733 | 580 | function [ FIFF ] = fiff_define_constants()
%
% [ FIFF ] = fiff_define_constants()
%
% Defines structure containing the FIFF constants
%
%
% Author : Matti Hamalainen, MGH Martinos Center
% License : BSD 3-clause
%
% Revision 1.23 2009/03/30 11:37:37 msh
% Added copying of measurement info blocks fro... |
408c48017de62e99be10cdaa21416fd4e538c0c58d1da928b69d886f3d3fd531 | MATLAB | 20,736 | 464 | %%
function bodyData_openBody = TOP(videoName,opMatName,frames2use,ptCloudDir,mkvSourceDir,alignDepth,verbose)
if ~exist("frames2use","var")
frames2use = 0;
end
if ~exist("verbose","var")
verbose = 1;
end
if ~exist("mkvSourceDir","var")
mkvSourceDir = [];... |
48461bd0599345a61d01d28583f06d9585f22f976df7e9139d76b126616b18bc | MATLAB | 20,744 | 685 | %% Load Unit Data
datRoot = '/Volumes/LaCie/Farries recordings/';
load([datRoot 'unitDat_final_culled.mat'])
uName = arrayfun(@(u) strsplit(u.name), unit, 'UniformOutput', false);
uRat = cellfun(@(u) u{1}, uName, 'UniformOutput', false);
uSes = cellfun(@(u) u{2}, uName, 'UniformOutput', false);
uTrode = cellfun(@(u) ... |
25780c7940c14cb158ae69db7ad4d192dc4d3629000fba51c04022795c24f2ea | MATLAB | 20,765 | 753 | function fit = barsP(data,dataRange,trials, bp)
% Usage: fit = barsP(data,dataRange,trials, bp)
% data: a vector of count data. Data must be equally spaced along x
% dataRange: the x values for the first and last point of the data
% trials: number of repeats that gave rise to these count data
% bp (optional): ... |
04b48a9afedf9a70645ed2d47855cc98103f5a7d018492c94c60005b6763c2c4 | MATLAB | 20,766 | 631 | %% BROAD categories of consonants: spatiotemporal map
[~, stim_names ]= xlsread([DIRscript, '/event_file_name_sound_both_v1.2eh_resorted.xlsx'],'E3:E590');
categories = xlsread([DIRscript, '/event_file_name_sound_both_v1.2eh_resorted.xlsx'],'G2:AB590');
[~, labels ] = xlsread([DIRscript, '/event_file_name_sound_both_v1... |
fc53a2e3950d4b2517e7d3bae7c75e5a6166fe2c255ebf066a39cf521613a628 | MATLAB | 20,767 | 688 | % clear
% clc
% f = gcf;
% exportgraphics(f,'Fig1_Original.png','Resolution',600)
MotionData = load('MotionData.mat');
MusculoskeletalData = load('MusculoskeletalData.mat');
MuscleCluster = load('MuscleCluster.mat');
MuscleCluster = MuscleCluster.MuscleCluster;
C1 = MuscleCluster{1};
C2 = MuscleCluster{2};
C3 = Mus... |
8d86386acfadfc62fbb4d8cd15c19becf382c3aa26384b94ee8c24749cd3445d | MATLAB | 20,774 | 491 | function [headmodel, cfg] = ft_prepare_headmodel(cfg, data)
% FT_PREPARE_HEADMODEL constructs a volume conduction model from the geometry
% of the head. The volume conduction model specifies how currents that are
% generated by sources in the brain, e.g. dipoles, are propagated through the
% tissue and how these resul... |
8f7a3c8979d2742a48b7cf372ff4cb7a26b454ba4d049bf1cc8f37d1cf2897aa | MATLAB | 20,790 | 585 | function [h, T2] = ft_plot_slice(dat, varargin)
% FT_PLOT_SLICE plots a 2-D cut through a 3-D volume and interpolates if needed
%
% Use as
% ft_plot_slice(dat, ...)
% or
% ft_plot_slice(dat, mask, ...)
% where dat and mask are equal-sized 3-D arrays.
%
% Additional options should be specified in key-value pairs an... |
6fe555b70f006637576e295a21344d0507f0020311a43ed6f4aa76304801d71e | MATLAB | 20,807 | 666 | %% =====================================================================
% This code produces the simulation results and figures in the manuscript
% "A Computational Framework for Understanding the Impact of Prior
% Experiences on Pain Perception and Neuropathic Pain" by authors Malin
% Ramne (Chalmers University of ... |
26bf66488f727f8b480ed03253d0c48badc3a369ffb6457fd4e865f8631b60b0 | MATLAB | 20,830 | 417 | % AUTHORSHIP
% Math Lead & Primary Developer: Connor Meehan <connor.gw.meehan@gmail.com>
% Secondary Developer: Stephen Meehan <swmeehan@stanford.edu>
% Bioinformatics Lead: Wayne Moore <wmoore@stanford.edu>
% Provided by the Herzenberg Lab at Stanford University
% License: BSD 3 clause
%
function [red... |
dd4ce9ede5d8caa1d30b350c9129c8d99507d9b53af10654a36721366b25ae1e | MATLAB | 20,847 | 486 | % This object is used to represent physiological data. The object uses the
% design_matrix() class to for the data set and has additional fields for the
% sampling frequency.
%
% :Usage:
% ::
%
% physio_data: data class for creating a physiological data object
%
% :Inputs:
%
% **dat:**
% M x N numeric mat... |
113ae9aeeb90f57bd3d134b8e3bc1de1abe13acc3fd28cfd94c149400e2c8a08 | MATLAB | 20,870 | 629 | classdef LabelCoreController < handle
% Labeling controller base class
%
% Owns all graphics handles for labeling: point markers, text labels,
% skeleton edges, occluded box points. Receives GUI callbacks (axis clicks,
% key presses, mouse motion) and delegates data logic to LabelCoreModel.
% Listens to model events an... |
5f1f32debc736d894cf002cac4bcaa15af499d44dedf2f42c19b9ef7db360d45 | MATLAB | 20,884 | 477 | function [ seg8t, tis, vx_vol, trans] = boney_segment_get_segmat(out,job_ouput_writevol,normCT,verb)
%boney_segment_get_segmat. Get and evaluate SPM preprocessing structure.
%
% [seg8t, tis, vx_vol] = get_spm8(P)
%
% P .. one image
% seg8t .. spm8 structure without larger fields
% tis .. measures ... |
c2ad3fd7ac78b4ad316a062a09ed03ed623639df2a2f95cb36db04a46d3bb3a2 | MATLAB | 20,885 | 560 | function varargout = manually_warp_images(varargin)
% MANUALLY_WARP_IMAGES, is a GUI which shows the controlpoint grid used
% during registration of two images, allowing the user to drag control
% points to get a better initial alignement before pressing the start
% registration button.
%
% Example,
% Istatic=im2do... |
42c82491d80687f03b86a19b5919f12cd207fe91f44fd70cad3cd55753017b9c | MATLAB | 20,917 | 757 | function summary = barsP(data,dataRange,trials, bp)
% Usage: fit = barsP(data,dataRange,trials, bp)
% data: a vector of count data. Data must be equally spaced along x
% dataRange: the x values for the first and last point of the data
% trials: number of repeats that gave rise to these count data
% bp (optiona... |
42b7fe4a2bc18735be9511c566bab1fdee17a986a3ea2ef1c7211ca959ffb5e0 | MATLAB | 20,942 | 655 | function [hh,hii]=showmri_test(mri,para,varargin);
% shows mri-slices eventually plus sources
% usage: [hh,hii]=showmri(mri,para,source1,source2,...);
%
% input:
% mri in general complicated structure containing information about
% the mri. The necessary fields are mri.data, a variable with
% 3 indices c... |
965783c69d2146e893f80939df88098ae9284a37879e13658c07e607fc45b36f | MATLAB | 20,959 | 618 |
% load data
% contact Lead Author for data
%% CELL SEQUENCE
% 3B-C
%%%% COMMON CELLS BETWEEN AO1 - VS1
df = cat(2, allDfofs_1{1,1,:});
fields = cat(2, allFields_1{1,1,:});
seq1 = df.*fields;
df = cat(2, allDfofs_1{2,1,:});
fields = cat(2, allFields_1{2,1,:});
seq2 = df.*fields;
iidx = find(... |
427a2651381b0c751310de778ac074b9c92b5077e0152f385f2598d03d9e8e6e | MATLAB | 20,964 | 361 | %% Script for analysis of FlyTracker output of free-walking experiments (Sapkal et al 2024 Fig. 1)
% This only works with speicific folder,subfolder arrangement as in
% superfolder/genotype subfolders/fly folders/tracking data
% add path of edmond.de folder
% run each section in order from start to end, one section... |
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