sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
0737507f60ab01f9698d583cc4fa989a74054cc0e6f16cf381e7c34895e42bc9 | MATLAB | 9,452 | 311 | function fear_coactTrigWavelet_shuffle(basename,varargin)
% basename='~/data/Fear/triple/feuillien180830/feuillien180830';
% basename='~/data/Fear/triple/karmeliet190901/karmeliet190901';
load([basename '.basicMetaData.mat'])
fprintf('%s start %s with data of %s\n',datestr(now),mfilename,basicMetaData.SessionName);
l... |
33943d4abc0f8e801a5f261f7d5af343087eed42f6388a7f46713724fb650093 | MATLAB | 9,458 | 238 | %% Calcium Imaging Analysis Script
% This script analyzes processed calcium imaging data from CaImAn/ezcalcium
% It calculates burstiness, firing rates, spike probabilities, functional connectivity, and synchronization metrics
%% Clear workspace
clear;
clc;
%% User-defined parameters (modify these as needed)
... |
bcc0acfde81c133f526e2b353629648620c2962b43d61867ba51fde107b14a00 | MATLAB | 9,464 | 217 | % POP_RMBASE - remove channel baseline means from an epoched or
% continuous EEG dataset. Calls RMBASE.
% Usage:
% >> OUTEEG = pop_rmbase( EEG ); % pop up an interactive arg entry window
% >> OUTEEG = pop_rmbase( EEG, timerange, pointrange, chanlist); % call RMBASE
%
% Graphic interface:
% "Basel... |
665ed628abf9ee1ab613a8a8e8cf84dabe383c7dab8d7a50c245ea5ca665441f | MATLAB | 9,465 | 283 | function varargout = events_tsv(filename, hdr)
% EVENTS_TSV is called from FT_READ_EVENT to read the events from a BIDS _events.tsv
% file. Although this function also reads the header for the sampling rate, it cannot
% be used to read data. Please see BIDS_TSV for reading data.
%
% Use as
% hdr = events_tsv(filenam... |
de5db5d47b8f62debc4f50f4d913a471df34616d445edb56264eb8e4b5b7c012 | MATLAB | 9,465 | 257 | function varargout = spm_prf_fcn_numerosity(P,M,U,varargin)
% Template pRF response function. This example models neuronal
% activity as a scaled version of the inputs:
%
% z(t) = alpha * u(t)
%
% Where alpha is a parameter estimated from the data.
%
%
% Inputs:
%
% P - parameter structure
% M - model struct... |
034926a671c0b44420b1a08126b0ece48f65eef0fe4ce2c8c814b9b4a10f4825 | MATLAB | 9,467 | 272 | function [np_marker] = np_readmarker (filename, idx_begin, data_length, option)
%
% function [np_marker] = np_readmarker (filename, idx_begin, data_length, option)
%
% np_readmarker reads marker from a NEURO PRAX marker file (*.EE_).
%
% Syntax:
%
% [np_marker] = np_readdata(filename,idx_begin,data_length,'samples');... |
13209c481aebd26fbf9360178a7bb3b7d2db1f7a85c8a374bea508d7922f2ee9 | MATLAB | 9,469 | 309 | function [] = Figure4(datadir, codedir)
%Figure4 - Block sensitivity for wait times requires structure learning
% datadir = directory of dataset
% codedir = director of code
%% Set up paths
s = pathsep;
pathStr = [s, path, s];
onPath = contains(pathStr,...
codedir, 'IgnoreCase', ispc);
if ~onPath % only add ... |
931886c4399f52f990d38964e18f9b6dc6ebf6b7d9b50fa07d99fb15c5d3bdec | MATLAB | 9,469 | 251 | function [spikeTimes, spikeWaveforms, trace, threshold] = detectSpikesCWT(...
data, fs, Wid, wname, L, Ns, multiplier, nSpikes, ttx, ...
minPeakThrMultiplier, maxPeakThrMultiplier, posPeakThrMultiplier, ...
multiple_templates, multi_template_method, channelInfo, plot_folder, ...
run_detection_in_chunks,... |
92e13223569807c2154b691fd56cfe98b8c558f3a3a6d065d1e282bb588d4466 | MATLAB | 9,475 | 200 | function TOL_fig_wilson2018(config,params,dat)
% TOL_fig_wilson2018, figure of Wilson and Wilson's (1948) apparatus and design
% Figure S4 for: Duvelle and Grieves (2026) Tolman's Sunburst Maze 80
% years on: A meta-analysis reveals poor replicability and little evidence for
% shortcutting. European Journal of Neuros... |
1dcb22c6560855b5c5d6d15ffa4387c2fe7a89cf99e8644dc9d3d7935ae5a35a | MATLAB | 9,477 | 201 | %% =====================================================================
% EEG PRE-PROCESSING AND ERP EXTRACTION PIPELINE (EEGLAB)
% ---------------------------------------------------------------------
% Author : Suong Welp
% Date : 2025-07-24
% Purpose: pipeline for cleaning raw EEG,removing ocular / spe... |
24aaddead55caa9ba9e492b8fd9047c4c5476600bea8eaaabc6c2d6cf7a24f30 | MATLAB | 9,479 | 244 | function PlotMap(data, I, ui0, PathName, layer_names, markertype, markersize, bar_intervals, colourmap, x_lim, y_lim, varargin) % by Julia Becker, 14/10/2021
%PLOTMAP Plots heatmaps based on modulus values from data and specified
% scale on cropped image with optional scalebar
% OPTIONAL INPUTS
% - Answers t... |
1e34bbe1272e0826f72d27a3862ff78ac937b78816b66f5b6179b2665261a5f9 | MATLAB | 9,487 | 231 | function [B1temp, T1temp, MP2RAGEcorrected] = T1B1correctpackage(Sa2RAGEimg, B1img, Sa2RAGE, MP2RAGEimg, T1img, MP2RAGE, brain, invEFF)
% Usage:
%
% [B1corr, T1corr, MP2RAGEcorr] = T1B1correctpackage(Sa2RAGEimg, B1, Sa2RAGE, MP2RAGEimg, T1img, MP2RAGE, brain, invEFF)
%
% Sa2RAGEimg (and B1) and MP2RAGEimg (and T1i... |
c9e0f8f3dd807350e459c601d1697b06dd1bf38322efd60d762b61c8ce7c67fb | MATLAB | 9,490 | 258 | function ProcessData_Figure4(pstruct, bstruct, BestFitEarly, BestFitLate,...
codedir, savedir)
%ProcessData_Figure4 - function to process data to generate Figure 4. Saves
% data in savedir to be accessed by PlotFigure4
% INPUTS:
% pstruct - photometry data struct. Not public until all papers using this
% data a... |
1140c0abd0425fe3182011a838567706d48e8875d476bdf119ae04fc9f9592b9 | MATLAB | 9,498 | 264 | function run_github_examples
%RUN_GITHUB_EXAMPLES Run GitHub examples.
% Generates random multivariate data for 2 samples X and Y and runs the
% following examples for GitHub README:
% 1. Plots the permutation distribution and test statistic
% 2. Tests for differences in multivariate sample variances (... |
dcff7265e941b4087865cd2e06ca65eb0c105fd463ad75d473eef4303d55abc9 | MATLAB | 9,499 | 220 | function triggerAnalysis(params)
% determines trigger events and uses those samples to extract response in
% given time range
% triggers{clz}(sample,sess) are indicated by METHOD in params.triggerOnMETHOD
% (c) Jiri, Apr12 (in whitenoise), Mar16
if ~params.triggering.doTriggering
disp('Triggering on events not se... |
8dc1d7cd4a2253616e1e7e23ac8e378ea10cb3159c14e2e8da1e69d524b89358 | MATLAB | 9,503 | 304 | function [ obj ] = simulate( obj, clusters, sizes, varargin )
% Generate synthetic task-based fMRI time series data, using HUGE as a
% generative model.
%
% INPUTS:
% obj - A tapas_Huge object.
% clusters - A cell array containing DCM structs in SPM's DCM format,
% indicating the DCM network str... |
cefcc7d21d0656b35a1e39e25f7699f3676f0a57c5bde046d1dbd5bc2c270f8b | MATLAB | 9,505 | 209 | function MRSCont = osp_extract_minmax_fit(MRSCont, which_spec, conc)
%% out = osp_extract_minmax_fit(MRSCont, kk, which_spec, conc)
% Creates a figure showing data stored in an Osprey data container, as
% well as the fit to it, the baseline, the residual, and contributions
% from the individual metabolites.
%
% ... |
5966df961707ee669e888db0e681d23fad7558c407f471bb7203de9a85a17e0a | MATLAB | 9,510 | 327 | % Script for testing singular values computed using LANSVD, LANEIG and
% EIGS.
% Rasmus Munk Larsen, DAIMI, 1998.
global MxV MU MUTRUE NU NUTRUE
MU=[];
MUTRUE=[];
NU=[];
NUTRUE=[];
rand('state',0);
setup = 1; % set setup=1 to set up testproblem.
k=10; % Number of singular values to compute
HB_dir = 'Harwell-Boein... |
4dcbd8d6f0851031a0b5053640f8a6ae2aac1ee46bc46315a8de2d53bc64fe66 | MATLAB | 9,511 | 310 | function ft_tcontrast(cond_input, path_contrast, name_output, ...
name_sess, hrf_derivative, regressor_nointerest)
% Get t-contrast
%
% ft_tcontrast(cond_input, path_contrast, name_output, ...
% name_sess, hrf_derivative, regressor_nointerest)
%
% Inputs:
% cond_input - cell array containing filename... |
caebe3c16156d642b59c5d89aa061c37c042fe5816976670134247d35f059420 | MATLAB | 9,511 | 241 | %% BiExpFitModelT2Relax.m
% This function contains a T2 corrected biexponential diffusion model
% modelled as a 2D problem.
%
% lossFunction - calculates loss function for optimizer
% forwardJacobian - calculates the forward jacobian for optimizer
% forwardModel - converts parameter struct into model p... |
99ff5696b041db5f02d60a7c4df8bc21076eb4407e5a99a385d9f386a49a3ad1 | MATLAB | 9,518 | 249 | clear all;
clc;
load('vik.mat');
% Load class labels for proper labeling
class_label = load("meta.mat");
rdm_type = 'cosine'; % 'cosine' 'pearson'
wv = load('cifar100_ss20_wordvec.mat');
wv_data = struct2array(wv);
ct = load('cifar100_ss20_ni1e-1_ychen_trail1.mat');
ct_data = struct2array(ct);
% Clustering parame... |
5e3170366e55c16dcaa0b82859e1dec10728aea140229af77bbb145054cf1658 | MATLAB | 9,524 | 192 | function out = EX_ExtremeEventOrder(y, extremeThresh)
% EX_ExtremeEventOrder Temporal patterning of positive vs. negative extreme events.
%
% Labels each point in the top/bottom extremeThresh fraction of the
% distribution as a positive- or negative-direction 'extreme event', then
% asks not just how often each type ... |
561d45bf084b876159341ebf2d3bca230e0019c3a8005b32fc9a174b46d02f5c | MATLAB | 9,525 | 201 | function shuffleResults = stimShuffleTest(spikeData, allStimTimes, Params, Info)
% STIMSHUFFLETEST Circular-shift shuffle test for trial proportion significance.
%
% Builds a null distribution of trial proportion values by circularly
% shifting each electrode's spike times by a random offset (with
% wrap-around within... |
1666d4b51f0279fea2a294423b500f71c67ba25d7c5fdcb2ec74dafb2e23cd45 | MATLAB | 9,529 | 178 | %% ch2roi_wrapper: assignment of iEEG channel to ROI (region of interest)
% assignment based on:
% - brain atlas: isarg_atlas from RJ (brain_atlas_v07_standalone.m)
% - frequency band: in struct trialsData_freqBand (e.g. trialsData_hiGamma)
% - significance of activation: was*, not*, any*
% coded in structure 'gr... |
fde4ee4c3a4794922f75b37dbf36929b5baf9a508a3e08f5e247a4aaccf3d8ac | MATLAB | 9,533 | 257 | function SuppFigure_Tuning_vs_NormInd_MI(varargin)
%SUPPFIGURE_TUNING_VS_NORMIND_MI Generate supplementary figure for tuning vs normalization index mutual information
%
% Purpose:
% Creates a figure analyzing the relationship between preferred orientation
% and normalization index across model and experimental data... |
9c3101e6efed24a1d28411def622f4482d522d4f43d50efb6b3ae9a60c7328ac | MATLAB | 9,540 | 234 | function MSMregression(inputspatialmaps,inputdtseries,inputweights,outputspatialmaps,outputweights,wbcommand,Method,Params,VN,nTPsForSpectra,BC,VolParams)
%function [ output_args ] = MSMregression(inputspatialmaps,inputdtseries,inputweights,outputspatialmaps,outputweights,wbcommand,Method,Params,VN,nTPsForSpectra,BC,Vo... |
cf7d248b4b52452707240882bfd7256192eef4c11d485b2d85a953c8a88f1295 | MATLAB | 9,555 | 234 | % POP_SELECTCOMPS - Display components with button to visualize their
% properties and label them for rejection.
% Usage:
% >> OUTEEG = pop_selectcomps( INEEG, compnum );
%
% Inputs:
% INEEG - Input dataset
% compnum - vector of component numbers
%
% Output:
% OUTEEG - Output dataset wi... |
22134727469d1a172c5771750131d589b536f0f64e3788f61bbc136ab4465d03 | MATLAB | 9,556 | 311 | function ft_tcontrast(cond_input, path_contrast, name_output, ...
name_sess, hrf_derivative, regressor_nointerest)
% Get t-contrast
%
% ft_tcontrast(cond_input, path_contrast, name_output, ...
% name_sess, hrf_derivative, regressor_nointerest)
%
% Inputs:
% cond_input - cell array containing filename... |
9e4697281e8bc4f995c7d218a9e528cf1dd88d8087d7a01fbb40b753763cdc1e | MATLAB | 9,556 | 388 | function s=afni_niml_print(p, format)
% takes a NIML data structure and converts it to string representation
%
% S=AFNI_NIML_PRINT(P) takes an NIML datastructure P and returns a string
% representation S.
%
% This function is more or less the inverse of AFNI_NIML_PARSE.
%
% The current implementation will take any stru... |
e4f95e6db7ee9deac0508096651a22705d785b53c6f2271fc3866c15352376f7 | MATLAB | 9,562 | 275 | % parse up the beliefs into a 2d discrete distribution
function [output] = dSTR_decode_parsedata(usetorch, varargin)
%% general appraoch that should work for all cases
% for each neuron, get the following
% session_id
% neurons' average firing over time (2s after reward)
% p_mix and p_high for that trial
% which bi... |
89d95af24611f2d27ab510793cc3359944cde325da33f89073bee19d7084c614 | MATLAB | 9,563 | 120 | function se_hdr = GE_readHeaderSeries(fid, byte_align)
%
% se_hdr = read_series_header(fid, byte_align)
%
% Loads the series header from a file with filed id fid
% and returns it as a structure.
% if byte_align = 1 then 32-bit alignment (SGI, LX2 format)
% if byte_align = 0 then 16-bit alignment (Sun, 5.X format)
%
%
%... |
842bdbb90cdfe64db60dfa08ecb3725605856ae04959bf6543c28a494e25c017 | MATLAB | 9,564 | 237 | function [this, affineTransformation] = read_single_file(this, fileName, varargin)
% reads single file of MrImage from different file types
% allowing property-name/value pairs to be set for parameters
%
% Y = MrImage();
% Y.read_single_file(fileName,'PropertyName', PropertyValue, ...);
%
% This is a method of clas... |
27adc7f41e6a4feeff5a092acdf516142fee9f5b8b79de864149981d9b1d610c | MATLAB | 9,565 | 212 | function [offset,chai,knee_freq,log_spec,freqs,aperiodic,periodic,peak_params] = st_fit_LFP(frequencies, spectrum, fitting_range, peak_analysis)
%Function to fit 1/f to LFP data.
% Aperiodic component has 3 terms: offset, slope (chai), and knee
% output gives the absolute, aperiodic, and periodic spectra in log... |
3d072711b8ab6a9dda2c5fa80954de8a95a2bbf8142fe3f7348d864dbc5c9490 | MATLAB | 9,586 | 261 | function create_surface_meshes(input_img, numvertices, input_coordsys, ...
output_coordsys)
%
% Input: input_img <string> fullpath to folder, where the results of
% mysegment.m are stored
% numvertices <integer> number of desired mesh... |
a55d4e6f8ddda665ab2e55ae5351e18a2fdad35c7ca0711cedb5a6fa658dd2e6 | MATLAB | 9,587 | 204 | function [signal,removed_channels] = clean_channels(signal,corr_threshold,noise_threshold,window_len,max_broken_time,num_samples,subset_size)
% Remove channels with abnormal data from a continuous data set.
% Signal = clean_channels(Signal,CorrelationThreshold,LineNoiseThreshold,WindowLength,MaxBrokenTime,NumSamples,Su... |
8ea1201ee1d9f12116e31071b162bf47d32866e8175a123c93d4718101d18131 | MATLAB | 9,594 | 323 | function ft_tdm(img_input, cond_input, TR, stim_duration, sigma, mask_threshold, ...
fir_steps, basename, output_folder)
% TDM analysis
%
% ft_tdm(img_input, cond_input, TR, stim_duration, sigma, mask_threshold, ...
% fir_steps, basename, output_folder)
%
% Inputs:
% img_input - cell array of filenames of... |
29ed9c95cd221bc4fd3bd93d7f35e6823dd9109a1ac742008580d1400fe8aa77 | MATLAB | 9,596 | 252 | %% Loading data generated by the Multi Channel DataManager
% As an example, lets load a data set with at least 1 analog stream:
%
% data = McsHDF5.McsData('SOME_DATA_FILE.h5');
%%
% Up to this point, only the metadata has been read from the file. The
% actual data within the stream will be loaded as soon as y... |
5c360ce26851c515dba1ab5bedebd8c06273db5857acc2813c647994e159c20e | MATLAB | 9,599 | 236 | % POP_TIMEF - Returns estimates and plots of event-related (log) spectral
% perturbation (ERSP) and inter-trial coherence (ITC) changes
% timelocked to a set of input events in one data channel.
%
% Usage:
% >> pop_timef(EEG, typeplot); % pop_up window
% >> pop_timef(EEG, typeplot, lastcom); %... |
64a313175d4553b020597cce50e28ca78eca345c46efb96cf053ab762caeb6a6 | MATLAB | 9,599 | 265 | function SuppFigure_Superimposed(varargin)
%SUPPFIGURE_SUPERIMPOSED Generate supplementary figure for superimposed stimulus condition
%
% Purpose:
% Creates a figure showing model analysis under superimposed stimulus conditions,
% including normalization index distribution, correlation heatmap, and tuning
% simil... |
35e90dc3a8dcf156f64b053d04c148475afa7045f24e3815696bbd1e2b453b3a | MATLAB | 9,601 | 242 | function [mahal_dist,cfg] = mahal_dist_xgen(dat,theta,task,cfg)
% [mahal_dist,cfg] = mahal_dist_xgen(dat,theta,task,cfg)
if nargin < 3
help mahal_dist_xgen
error('missing input arguments!')
end
ntrl = size(dat,1); % number of timepoints
nsens = size(dat,2); % number of M/EEG sensors
nti... |
6f306cd2c64b5e5a45e10e865807d8cd86e54de3d8a249074ec5bd7f2d543845 | MATLAB | 9,602 | 316 | function [traj, infStates] = tapas_ehgf_jget(r, p, varargin)
% Calculates the trajectories of the agent's representations under the eHGF model of the jumping
% Gaussian estimation task (JGET)
%
% This function can be called in two ways:
%
% (1) tapas_ehgf_jget(r, p)
%
% where r is the structure generated by fit... |
18aeb2708ad79362ac9ea0f1acb66410ad654b7ff25d701ebe2c1898f2f0a580 | MATLAB | 9,605 | 233 | %% h = sepia_handle_panel_utility_mask_ventricle(hParent,hFig,h,position)
%
% Input
% --------------
% hParent : parent handle of this panel
% hFig : handle of the GUI
% h : global structure contains all handles
% position : position of this panel
%
% Output
% --------------
% h ... |
de6c876679aac8a68864752b7b5e0c9e28c93345017654962eafaa74d84fa0d2 | MATLAB | 9,620 | 321 | function plotFigureS9_naiveBehSupp(processedBehaviorPath)
%Plot figure S9 - raw wait time block transition dynamics for the first 15
% sessions and divisive normalization model predictions. Sigmoid fits to
% z-scored
% YOU MUST RUN processRawWaitTimes BEFORE RUNNING THIS FUNCTION
%INPUTS:
% processedBehaviorPat... |
376580c38df688d46bb1c594f7ee15f33943093e1c0ca0868e0957b481560465 | MATLAB | 9,623 | 289 | % EEG_PV - Compute EEG.data 'percent variance ' (pv) of the whole EEG data versus the projections
% of specified components.
% Can omit specified components and channels from the computation. Can draw a plot
% of the scalp distribution of pv, or progressively compute the pv fo... |
2bfb9f94d1fa9f5bf0e3d298ae058863a469212bd2d66afc9ad3760195c8abbf | MATLAB | 9,631 | 308 | function [exploration,sws,rem] = BrainStates(s,t,f,q,emg,varargin)
%BrainStates - Determine brain state using LFP, EMG and movement.
%
% USAGE
%
% [exploration,sws,rem] = BrainStates(s,t,f,q,emg,<options>)
%
% s spectrogram
% t time bins for spectrogram
% f frequency... |
41733a1187d03d1f9eed6a03e47ecfa230dd8b5c8a6d52f526a34928b1628d35 | MATLAB | 9,631 | 353 | function varargout=sigstar(groups,stats,nosort)
% SIGSTAR Add significance stars to bar charts, boxplots, line charts, etc,
%
% H = SIGSTAR(GROUPS,STATS,NSORT)
%
% Purpose
% Add stars and lines highlighting significant differences between pairs of groups.
% The user specifies the groups and associated p-values. The fu... |
ba52b33e1a5897cd294189b1e54ce3d860aff242c7af73e7620f1f8d5a7d02c5 | MATLAB | 9,631 | 232 | function [trl,dataselep] = iEEG_func_detectRipples_detector3(fsettings,data)
%% ########################################################################
% [trl,dataselep] = iEEG_func_detectRipples_detector3(fsettings,data)
% #########################################################################
%
% Ripple detection... |
65a1dbde0408ecbed1ce6e5d0119c16635fbbfafb0af6026c5af0859a1af8287 | MATLAB | 9,638 | 286 | % analysis_FeaturePrediction Run feature prediction
%
% Author: Tomoyasu Horikawa <horikawa-t@atr.jp>, Shuntaro C. Aoki <aoki@atr.jp>
%
clear all;
%% Initial settings %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Data settings
% subjectList : List of subject IDs [cell array]
% dataFileList : List of ... |
43a139445c2907be74a448bb629bc8b93ac3f2e11f6cc00c779561afea3374cd | MATLAB | 9,641 | 344 | %%
clear;
working_dir = 'G:\CEST-fMRI'; % the actual directory to put the codes
SPM_modified_path = [working_dir,'\scripts\SPM_modified'];
addpath(SPM_modified_path);
ds_dir = [working_dir,'\datasets\exp3']; % directory of datasets
saving_dir = [working_dir,'\scripts\saving\exp3']; % directory for saving resul... |
935b570abde1bac7759e35371ffb0b1e1e3f87035628cc4c1324031b1a60fcae | MATLAB | 9,641 | 171 | function analyze_communication_subspace(group)
close all
clearvars -except group
clc
% Analyze communication subspace.
% Select a folder containing data.
folder_name = uigetdir;
cd(folder_name)
switch group
case 'control'
load('behavior_control.mat')
load('activity_control.mat')
behavior ... |
1f103482c0e7abdae9ad0fdebcb85dcc8b0c62776282a0c05946df8f21a8120f | MATLAB | 9,644 | 75 | function map = magma(N)
% Colormap from MatPlotLib 2.0. Perceptually uniform and very attractive!
%
%%% Syntax:
% map = inferno
% map = inferno(N)
%
% Colormap designed by Nathaniel J. Smith and Stefan van der Walt.
%
% For MatPlotLib 2.0 improved colormaps were created in the perceptually
% uniform colorspace CAM02-... |
c8cf1ed3d0935180311815aad23eba7b834c1ac81771efb19cb6b221b522534d | MATLAB | 9,645 | 75 | function map = magma(N)
% Colormap from MatPlotLib 2.0. Perceptually uniform and very attractive!
%
%%% Syntax:
% map = inferno
% map = inferno(N)
%
% Colormap designed by Nathaniel J. Smith and Stefan van der Walt.
%
% For MatPlotLib 2.0 improved colormaps were created in the perceptually
% uniform colorspace CAM02-... |
1e7d384bd36067192ce869b53156b1f9b061bf008cb573ee792bbad643fa4c90 | MATLAB | 9,648 | 289 | % Cross-correlation analysis
ft_defaults
clearvars
%% Select data files and event types
%sbj1_session1
cd('Q:/MEG_data/20190724_b64d/190724/');
dataset{1,1} = 'B64D_S1P1.fif';
dataset{1,2} = 'B64D_S1P2.fif';
%sbj1_session2
cd('Q:/MEG_data/20190801_b64d/190801/');
% dataset{1,1} = 'B64D_S2P1.fif';
dataset{1,1} = 'B64... |
87b82524260b8791716d0ba5978753ed2adf355ea3c5ed675ff10fe73cebdf10 | MATLAB | 9,648 | 75 | function map = plasma(N)
% Colormap from MatPlotLib 2.0. Perceptually uniform and very attractive!
%
%%% Syntax:
% map = plasma
% map = plasma(N)
%
% Colormap designed by Nathaniel J. Smith and Stefan van der Walt.
%
% For MatPlotLib 2.0 improved colormaps were created in the perceptually
% uniform colorspace CAM02-U... |
3b0ced9798102994b591eec905d2e5a6b1d3ee8e8ffba30037457df07815d5b9 | MATLAB | 9,649 | 75 | function map = plasma(N)
% Colormap from MatPlotLib 2.0. Perceptually uniform and very attractive!
%
%%% Syntax:
% map = plasma
% map = plasma(N)
%
% Colormap designed by Nathaniel J. Smith and Stefan van der Walt.
%
% For MatPlotLib 2.0 improved colormaps were created in the perceptually
% uniform colorspace CAM02-U... |
aabf1a1afda5ceb3b0c4bdb1cff7bf11071c117001878b43d9bef88dcb78c0e1 | MATLAB | 9,656 | 75 | function map = inferno(N)
% Colormap from MatPlotLib 2.0. Perceptually uniform and very attractive!
%
%%% Syntax:
% map = inferno
% map = inferno(N)
%
% Colormap designed by Nathaniel J. Smith and Stefan van der Walt.
%
% For MatPlotLib 2.0 improved colormaps were created in the perceptually
% uniform colorspace CAM0... |
27165ed71bd8fe622d5ebbf43d927187d5cc0fe71c0dfaf5e74cdd96a34f5fe4 | MATLAB | 9,657 | 259 | % (C) Copyright 2018 Mohamed Rezk
% (C) Copyright 2020 CPP visual motion localizer developpers
%% Main Decoding Experiment 2
% Adapted by Iqra for tactile stimulation
% modifying trial_type data for log files
% check the order of blocks - randomised in two chunks
% 7 stimuli in 1 block, targets = 0,1,2
addpath(genpath... |
7950d7a9ec9072561b82227b5a23b045ef278a28a6b7bacd8a9fdcd4bca6104f | MATLAB | 9,657 | 75 | function map = inferno(N)
% Colormap from MatPlotLib 2.0. Perceptually uniform and very attractive!
%
%%% Syntax:
% map = inferno
% map = inferno(N)
%
% Colormap designed by Nathaniel J. Smith and Stefan van der Walt.
%
% For MatPlotLib 2.0 improved colormaps were created in the perceptually
% uniform colorspace CAM0... |
90401546ee0aa00b3a56522d288952344658a480c3134973db245e29fd754879 | MATLAB | 9,659 | 306 | %% generating script for the areas in each atlas
% -> Yeo7
% -> Yeo17
% -> Destrieux (incl. laterality)
% -> Mars (incl. laterality)
% (c) Jiri, Aug22
%% Yeo7 Networks
outDir = 'G:\shareData\visualization_normBrains\atlasAreas_MNI\yeo7';
assert(exist(outDir,'dir') == 7);
cd(outDir);
% load .nii file
pathBeg ... |
c67008cfd4c8ca48546cd87a6d24c438d0064c81e4c54da91e200b56786147be | MATLAB | 9,659 | 268 | function ellipse_t = fit_ellipse( x,y,axis_handle )
%
% fit_ellipse - finds the best fit to an ellipse for the given set of points.
%
% Format: ellipse_t = fit_ellipse( x,y,axis_handle )
%
% Input: x,y - a set of points in 2 column vectors. AT LEAST 5 points are needed !
% axis_handle - optional.... |
dc7490c325c865f0ad64e442d2562a6bf6a169c1e183756998bd5b3514dac497 | MATLAB | 9,659 | 157 | function collected = hlp_collect_datasets(directory,varargin)
% Find all loadable data sets in some directory tree and collect arbitary properties.
% Collected = hlp_collect_datasets(Directory, Options...)
%
% In:
% Directory : the directory to be searched for eeg data sets
%
% Options... : optional name-value... |
c3a20b4c8a00a9f4c9466b6b839347eafb74b596214d509431dffd721de7dfa3 | MATLAB | 9,661 | 228 | %% extract the beta parameters from ROIS
%from peak coordinates or centre of individual ROIs
% TACTILE MOTION LOCALIZER
% across two runs
clear all
%% from peak cooridinates
%get roi coordinates
r=readtable('/Volumes/IqraMacFmri/visTac/fMRI_analysis/code/roi/coordinates4.xlsx');
% check the roi label order
roiLabels =... |
3406551bc3413dfc3055b9ffac84e5706aa941ab2a251a4bf83f4827e3fdbc27 | MATLAB | 9,662 | 288 | function H= plot_scalpEvolutionPlusChannel(erp, mnt, clab, ival, varargin)
%PLOT_SCALPEVOLUTIONPLUSCHANNEL - Display evolution of scalp topographies
%
%Synposis:
% H= plot_scalpEvolutionPlusChannel(ERP, MNT, CLAB, IVAL, <OPTS>)
%
%Description:
% Makes an ERP plot in the upper panel with given interval marked,
% and dr... |
a1168418ecdae0c1cb29e01a4279c8f2f0fa00f50cfee28a3ca2f5c0386980ed | MATLAB | 9,663 | 204 | % STD_LOADALLEEG - constructs an ALLEEG structure, given the paths and file names
% of all the EEG datasets that will be loaded into the ALLEEG
% structure. The EEG datasets may be loaded without their EEG.data
% (see the POP_EDITOPTIONS function), so many dat... |
c067cc5ed3e8d715455adf4cec542d26d0ab439cecb853efe8630c78f5ff06a1 | MATLAB | 9,667 | 362 | function [ subject, data ] = nmri_autodetect_artifacts( subject, data, params )
%[ subject, data ] = nmri_autodetect_artifacts( subject, data, params )
% This function will do automated trials and channel artifact selection
% similar to the vft_rejectvisual
%
% subject = subject structure, see nmri_read_subject
... |
59f7da9b0537349ecbb3e527fe3d719417c4033c3c0b32bac9cc52603cc9dd45 | MATLAB | 9,668 | 288 | function out = perc_barplot(studycount,totalstudycount,code,varnamecode,regionnames)
% out = perc_barplot(studycount,totalstudycount,code,varnamecode,regionnames)
%
% given counts, does barplot and CHI2 analysis
% easy to use with output from dbcluster_contrast_table.m
%
% perc : counts or percent studies to plot; r... |
296b289a92dcfaaff05a85c39dd0336f74461d5504a322ae32254d3f9f8828d7 | MATLAB | 9,669 | 213 | function out = NL_SSA(y, L)
% NL_SSA Singular Spectrum Analysis of a time series.
%
% Constructs the trajectory (Hankel) matrix of the time series using a
% window length L (i.e., a time-delay embedding with delay tau = 1), and
% performs an uncentered singular value decomposition of the result.
%
% Unlike NL_EmbedPCA... |
8e9d55186ca41521d3a3c43c65db8cba27fd7018280522f2599c5f4f535dc3dc | MATLAB | 9,670 | 75 | function map = viridis(N)
% Colormap from MatPlotLib 2.0. Perceptually uniform and very attractive!
%
%%% Syntax:
% map = viridis
% map = viridis(N)
%
% Colormap designed by Eric Firing, Nathaniel J. Smith, and Stefan van der Walt.
%
% For MatPlotLib 2.0 improved colormaps were created in the perceptually
% uniform c... |
d9df701401218698bbe9b73597c5b7176800df40dd7420254f088f7f4489dd05 | MATLAB | 9,670 | 273 | function M = sympositivedefinitesimplexfactory(n, k)
% Manifold of k product of n-by-n symmetric positive definite matrices
% with the bi-invariant geometry such that the sum is the identity matrix.
%
% function M = sympositivedefinitesimplexfactory(n, k)
%
% Given X1, X2, ... Xk symmetric positive definite matrices, ... |
c9d2869fa7f8cdbfaa22c62043d168db8b292cb937430d1611ce1074d97ccf4e | MATLAB | 9,671 | 75 | function map = viridis(N)
% Colormap from MatPlotLib 2.0. Perceptually uniform and very attractive!
%
%%% Syntax:
% map = viridis
% map = viridis(N)
%
% Colormap designed by Eric Firing, Nathaniel J. Smith, and Stefan van der Walt.
%
% For MatPlotLib 2.0 improved colormaps were created in the perceptually
% uniform c... |
7f034cfae5c7ca6177049b27b4291eeb646bdd84c90d8ccaa164c4a302317e3b | MATLAB | 9,672 | 338 | %% %%%%%%%%%%%%%%%%%%%%%%% RUN ANALYZEPRF (VERY EXPENSIVE)
% Matlab (matlab2b) run some parpools on:
% $stone: 2 x 12
% atlas8: 16
% $surly: 16
% atlas9: 12
% $atlas10: 16
% $atlas11: 12
% surly: 12
% $atlas13: 32
% -atlas12: 2 x 32
% -atlas2: 16
% -atlas4: 16
% -atlas13: 32
% -atlas3: 12
% ~256 cores ... ... |
e684d71c8a78fcd8dc242fcfc67444125ac70e63f345fb18e7b9c11507a00b0d | MATLAB | 9,684 | 260 | function PlotFigure4(datadir, codedir)
%PlotFigure4 - Plots Figure 4.
% INPUTS:
% datadir - Local directory where ProcessData_Figure4.mat was saved after running ProcessData_Figure4
% codedir - Local directory where code (e.g., published/EstrousRPE/) was saved
%% Set up paths
s = pathsep;
pathStr = [s, path, s];
... |
400a7fc63831afef77acea01042b3296e0dfb8b1357db1ffbd7176e2ed5b9a6d | MATLAB | 9,689 | 182 | function [MRSCont] = osp_LoadSDAT(MRSCont)
%% [MRSCont] = osp_LoadSDAT(MRSCont)
% This function reads raw (but coil-combined) data from Philips SDAT files.
%
% USAGE:
% [MRSCont] = osp_LoadSDAT(MRSCont);
%
% INPUTS:
% MRSCont = Osprey MRS data container.
%
% OUTPUTS:
% MRSCont = Osprey... |
c5bfaa843e9dc79bc5bb7b0772aaff54f9a7bdd9dcf5ba5a7f25c4ef046ee9d4 | MATLAB | 9,695 | 374 | function [V,D,bnd,j,work] = laneig(A,nin,k,sigma,options)
%LANEIG Compute a few eigenvalues and eigenvectors.
% LANEIG solves the eigenvalue problem A*v=lambda*v, when A is
% real and symmetric using the Lanczos algorithm with partial
% reorthogonalization (PRO).
%
% [V,D] = LANEIG(A)
% [V,D] = LANEIG('... |
77c50610a1c4d55ed96c3458f8a58363903cf4c765fa3ffb1105e012d9652505 | MATLAB | 9,696 | 233 | %% %%%%%%%% Manually define parameters
nsdsetup;
%% %%%%%%%% Run FreeSurfer
% install the runtime into the FS60 folder:
% curl "https://surfer.nmr.mgh.harvard.edu/fswiki/MatlabRuntime?action=AttachFile&do=get&target=runtime2012bLinux.tar.gz" -o "runtime.tar.gz"
% tar xvf runtime.tar.gz
% prepare an expert options... |
d7d1bb3bffc43f408b4f9bfe70d490fb3b2013271ba3311fbaa2b828b4a66547 | MATLAB | 9,697 | 279 | function [t,p,ci,stats,dist] = permuttest2(x,y,varargin)
%PERMUTTEST2 Unpaired two-sample permutation-based t-test.
% T = PERMUTTEST2(X,Y) performs a two-sample permutation test based on
% the t-statistic of the hypothesis that the data in X and Y come from
% distributions with equal means, and returns the test ... |
5a54362e949df1b8721031071f1f20a2e5ae870b768ad84bea5531a31e0a71a1 | MATLAB | 9,698 | 240 | function out = SY_SlidingWindow(y, windowStat, acrossWinStat, numSeg, incMove)
% SY_SlidingWindow Sliding window measures of stationarity.
%
% This function is based on sliding a window along the time series, measuring
% some quantity in each window, and outputting some summary of this set of local
% estimates of that... |
9eecf67bd3ba13439a2eba7c7e22fd7cf8949b128016c9322e914b1108ff5a70 | MATLAB | 9,710 | 240 | function fix_3_clean(fixlist,aggressive,domot,hp,WBC,DOvol)
% fix_3_clean(fixlist,aggressive,domot,hp,WBC,[DOvol]) - CIFTI processing only (do not process volumetric data)
%
% fixlist is a vector of which ICA components to remove (starting at 1 not 0)
%
% aggressive = 0 or 1 - this controls whether cleanup is aggressiv... |
68fef7b91bf53bd276ef6e5db92d572c1376c18ecbb77b5c1ed375ed5791cbc0 | MATLAB | 9,711 | 266 | function [] = plotTaskComplexity(saveFigures)
% Plot results from task complexity analysis.
% save figures by default
if nargin==0
saveFigures = true;
end
% get file paths
paths = addPaths();
analysisName = 'task_complexity';
% load results
results = load(fullfile(paths.outputs, analysisName, "task_complexity_re... |
8a324bfd3cc3881eaaefb0fe4a5b954ce3257312c4dc4c3a08497afde84020ce | MATLAB | 9,714 | 215 | function c = tapas_hgf_ar1_binary_mab_config
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Contains the configuration for the Hierarchical Gaussian Filter (HGF) for AR(1) processes in a
% multi-armded bandit situation for binary inputs in the absence of percept... |
b7a2cdda3d238082a3f3ffb60a6c8ccb8d8e93d55bf19466d5a9fc1c6fb8d2bc | MATLAB | 9,715 | 338 | function plotFigure6_naiveBeh(behaviorPath, processedBehaviorPath)
%Plot figure 6 - behavior for the first 15 sessions of training
% YOU MUST RUN processBehaviorData BEFORE RUNNING THIS FUNCTION
%INPUTS:
% behaviorPath = local path to behavior data downloaded from zenodo
% savedBehaviorPath = path to where you hav... |
b9b0f0a72e88d6a7e150454c978b38937263d314aa8bc3bf1bbcff2fd82183ab | MATLAB | 9,718 | 268 | % script read_database
% reads a neuroimaging coordinate database with fields
% and transforms T88 coordinates to MNI coordinates
% When you set up your own database, several rules apply. Formatting the spreadsheet
% so it loads correctly can sometimes be the hardest part of running a
% meta-analysis.
%
% See https://c... |
91025a8aeef55bf486f2b7ea17cbb5db35bad71d19bdb13a23775758c0bdd6b6 | MATLAB | 9,723 | 207 | function [ output_args ] = ARTIFICIAL_CALCIUM_FLUORESCENCE_GENERATION( output_directory , id , N_units , T_durationRange , dT_durationRange , assembly_configuration_list , i_assembly_configuration , calcium_T1_2Range , eventFreqRange , eventMultRange , noiseSTDRange , saturationKRange )
%ARTIFICIAL_CALCIUM_FLUORESCENC... |
c0a34bd39e0c5ba185d74f1412073cd6da1aedfb008197810369cf069caa5c93 | MATLAB | 9,728 | 250 | clear all
close all
clc
exp_root = 'C:\Users\User\Documents\fMRI_EXP\';
more_code = fullfile(exp_root, 'cells_data_muller');
data_folder = fullfile(more_code, 'wetransfer_neil-s-data_2023-09-25_1057');
addpath(genpath(more_code));
addpath(genpath(data_folder));
grid_rats = [1, 3, 4];
num_grid_rats = length(grid_rats)... |
8d6d5b0f01bffb162bf6fbd59d9b5d1800e9395e59a14848e557401c8ce5bf0e | MATLAB | 9,739 | 221 | set_up_paths_and_data_directories;
bold_suffix = 'space-MNI_desc-ppres_bold.nii';
tr_length = 1.3;
stim_names = {
'After_The_Rain', 'Between_Viewings', 'Big_Buck_Bunny', 'Chatter', 'Damaged_Kung_Fu', ...
'First_Bite', 'Lesson_Learned', 'Payload', 'Riding_The_Rails', 'Sintel', 'Spaceman', ...
'Superhero', 'Tears_of_St... |
5d72a5bf887241e385b1fefe149704f304bbabd3f55e96d2b00909cfbd5b936d | MATLAB | 9,741 | 278 | function [xbest, fbest, info, options] = pso(problem, x, options)
% Particle swarm optimization (PSO) for derivative-free minimization.
%
% function [x, cost, info, options] = pso(problem)
% function [x, cost, info, options] = pso(problem, x0)
% function [x, cost, info, options] = pso(problem, x0, options)
% function [... |
20bdebce418dbe4ec5c43f2f860e0b32be71a590a0ef5ec9c0f39aded513677e | MATLAB | 9,745 | 217 | % Please cite this paper if you use any component of this software:
% D. Cunefare, L. Fang, R.F. Cooper, A. Dubra, J. Carroll, S. Farsiu, "Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks," Scientific Reports, 7, 6620, 2017.
% Re... |
827ce50eb2e0fccefbdeb89c159810a8e53c39a63d039c69aeaf39c2be36acdd | MATLAB | 9,746 | 225 | % std_getindvar - get independent variables of a STUDY
%
% Usage:
% [indvar indvarvals] = std_getindvar(STUDY);
% [indvar indvarvals] = std_getindvar(STUDY, mode, scandesign);
%
% Input:
% STUDY - EEGLAB STUDY structure
% mode - ['datinfo'|'trialinfo'|'both'] get independent variables
% linked to STU... |
9956352c7c0ef71d0ccf604e3fe88a881a292cfd4b624b9eb8bcc5ebfd5bb1b0 | MATLAB | 9,748 | 281 | clear all, close all
load fig5data
figure('Resize', 'off', 'Units', 'centimeters', 'Position', [0 0 svgData.figureWidth svgData.figureHeight], ...
'PaperSize', [svgData.figureWidth svgData.figureHeight], 'PaperPosition', [0 0 svgData.figureWidth svgData.figureHeight]);
population_type = 'patient';
% population... |
931fe711e28b83f2456260799225c026bc2b1d12c582fdf874a77e087cbb5352 | MATLAB | 9,755 | 316 | function [traj, infStates] = tapas_hgf_jget(r, p, varargin)
% Calculates the trajectories of the agent's representations under the HGF model of the jumping
% Gaussian estimation task (JGET)
%
% This function can be called in two ways:
%
% (1) tapas_hgf_jget(r, p)
%
% where r is the structure generated by fitMod... |
a144cfdf29389b700fb6b54125278df89701038a14c6abbd9c3e9d15a47014c3 | MATLAB | 9,759 | 294 | function [ subject ] = nmri_read_subject(subj_id,exam_id,analysis_dir,raw_dir,raw_dataset)
%subject=nmri_read_subject(subj_id,exam_id,analysis_dir,raw_dir,raw_dataset)
%
% This function will read a MEG or EEG dataset from the
% raw_dir/subj_id/exam_id directory and create the necessary directories,
% links and.m file... |
9d4d09c7f0eb451e78c4dac4d7a04b9c8d084ae3d5173008a3cd84cbf82cd66e | MATLAB | 9,774 | 249 | function [distMat_cl,cluster_Groupi,ord,handles] = BF_ClusterDown(distMat,varargin)
% BF_ClusterDown Reduce a pairwise distance matrix into smaller clusters.
%
% Yields a visualization of a pairwise distance matrix, including a set of
% clusters of objects showing highly correlated behavior.
%
%---INPUTS:
% distMat,... |
00b3741fb5ff74446688898e047c6a214710a1b88dcd85f080662f205e53060d | MATLAB | 9,780 | 255 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Setup
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Add paths
currloc=string(pwd);
homeDir=("C:\Users\dk818\OneDrive - Imperial College London\NCORE\miFCdFC");
% For inputs
str='\AtlasAndOtherInputs';
addpath(strcat(homeDir,st... |
ed2b30242cda953d9588f8153e0b2ea96590e778f5666de936f96ff4a49ecb0b | MATLAB | 9,782 | 209 | function [gain,shGain,predictions,er,sh,w] = GLMgain(source,target,varargin)
%GLMgain - Train a GLM on your "source" data and see how well it predicts
% the "target" data. This is measured as a gain improvement relative to
% shuffled versions of your data. To prevent overfitting, the data is
% divided into a number ... |
3a758f304c9a981fd77d7a08750d429b3292927071cb032cb85fee5013f6fb0f | MATLAB | 9,785 | 209 | classdef McsCmosSpikeStream < McsHDF5.McsStream
% Holds the contents of a Cmos spike stream object
%
% Important fields:
% SpikeData - (struct) Contains fields SensorID and Timestamp. If
% cutouts are present, it will also have a field
% Cutout. SensorID and T... |
dda937c20d5c95617466bd4dca0520588cfd4792237345e90154e770e55d0804 | MATLAB | 9,788 | 298 | % COMPMAP - Plot multiple TOPOPLOT maps of ICA component topographies
% Click on an individual map to view separately.
% Usage:
% >> compmap (winv,'eloc_file',compnos,'title',rowscols,labels,printflag)
%
% Inputs:
% winv - Inverse weight matrix = EEG scalp maps. Each column is a
% ... |
089f37f5f7556316d60ba8ffc675efc4c8b69609388848e6745b4f8ea0ad8314 | MATLAB | 9,792 | 282 | function [c, r, t, cpulse, acq_codes, verbose] = tapas_physio_read_physlogfiles_siemens_tics(...
log_files, cardiac_modality, verbose, varargin)
% reads out physiological time series of Siemens logfiles with Tics
% The latest implementation of physiological logging in Siemens uses Tics,
% i.e. time stamps of 2.5 ms... |
0767c976abd7f0b68607e7034d1b593a9835409b6144c97e5e891ba141b683b5 | MATLAB | 9,795 | 238 | function out = SP_PhaseFluctuationScaling(y, halfWidthFrac, numWindows, maxN)
% SP_PhaseFluctuationScaling Multi-scale fluctuation analysis of instantaneous phase
%
% Isolates the dominant oscillatory component of y (the frequency band
% carrying the most spectral power, excluding DC and Nyquist), takes its
% instan... |
25ce662d804ede95fae46180bb8c25bdc11af35590743590bb80b47c47d13911 | MATLAB | 9,795 | 236 | function [msg] = printLog(Module,kk,ll,nDatasets,progressText,GUI,MRSI,LCModel)
%% [MRSCont] = printLog(MRSCont)
% This function allows you to rebase your derviatives folder and files in
% case you have processed them on another machine
%
% USAGE:
% MRSCont = OspreyRebase(MRSCont);
%
% INPUTS:
% MRS... |
f71ada9c53e3ce211d3538bf1df696ff8bfbc5c2e290c97fafe13a0c30ab436e | MATLAB | 9,816 | 251 | function [Wrr, Wrf] = generate_weights_default(Ne,Ni,Nx,sigmaRX,sigmaRR,Prr,Prx,P_ts,TS_th,varargin)
% GENERATE_WEIGHTS_DEFAULT Recurrent & feedforward connectivity (spatial + tuning).
%
% Syntax
% [Wrr, Wrf] = generate_weights_default(Ne,Ni,Nx,sigmaRX,sigmaRR,Prr,Prx,P_ts,TS_th, Name,Value, ...)
%
% Description
% ... |
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