sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
40ff1955fc6a3b7ea72d2485e1a6ae62bbdbcaa414a77aac662cb7587561041b | MATLAB | 3,980 | 95 | %EPS2PDF Convert an eps file to pdf format using ghostscript
%
% Examples:
% eps2pdf source dest
% eps2pdf(source, dest, crop)
% eps2pdf(source, dest, crop, append)
% eps2pdf(source, dest, crop, append, gray)
% eps2pdf(source, dest, crop, append, gray, quality)
%
% This function converts an eps file to pdf f... |
082e5a7cadb167dd7aad561c68828debcc0887963a29be6ba22e674e5be4629e | MATLAB | 3,981 | 111 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Dependencies
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% GenericEEG.cnt: irrelevant data to strucutre EEGLAB properly
% Channel_Locations.xyz: locations of EEG channels
%%%%%%%%%... |
56f3486926362200236ae999839f58a3b444cc88292288b1013c77fc3f07046f | MATLAB | 3,982 | 101 | clear all; close all;clc
subj_list = {'39' '44' '57' '63' '66' '84' '85' '87'};%
addpath('/mnt/yassamri/iEEG/sandra/analysis_pipeline_final')
group_roi_mni = cell(1,8)
i=0
roi_name = 'OFC_FRO_TEMP_CING_INS_EC'%'OFC_FRO_TEMP_CING_INS_EC' %'OFC_FRO_TEMP'
for iSubj = 1:length(subj_list)
subj=subj_list{iSubj};
... |
f70457e5ff049c85a95bd8594f48a87eef3a85b38c89edffa07a252b1ab9cf79 | MATLAB | 3,982 | 111 | function [cp, p] = cross_validate(data, labels, varargin)
% cross_validate Perform cross-validated decoding using model of your
% choice.
%
% INPUTS:
% data: 2D double; [nImages x (xPixels*yPixels)];
% training data
% labels: (1 x nTrials) do... |
6539ad36dbd8dd3d9d2944393c289045809391b79925fe9b8a65522b8cbe9f57 | MATLAB | 3,985 | 75 | load('RevisionResults.mat');
subjectColors = [rgb(106, 27, 154); rgb(239, 108, 0); rgb(46, 125, 50)];
fig5.fig = figure('Name', 'Figure X: Correlation Within Arrays');
set(fig5.fig, 'Units', 'centimeters', 'Position', [40 20 8.8 4.6], 'NumberTitle', 'off');
fig5.panelA1 = axes('Position', [.08 .175 .25 .775]);
annota... |
deae67e451a2df651c603a7423e270f5c4cc67f6ece1b9d94bc3e9d0d6fc791b | MATLAB | 3,987 | 101 | function myParadigmSetup
% This function sets all basic parameters of the SOFTWARE in your setup.
% This function needs to be stored on each experimental computer. All
% required paradigm definitions are stored in the global variable SETUP
% created by this function. Edit this function when building a new setup.
% Desc... |
1f9327555933b2f7f400ef6e4f0046dfe8c7c9979dbebf260647dd831d1a3d7f | MATLAB | 3,990 | 97 | function [link_cc, varargout] = fun_skeleton_get_link_cc(voxel_list, voxel_neighbor_idx_list, l_link_start_voxel_idx, voxel_unvisited_Q)
% fun_skeleton_get_link_cc is a subfunction of fun_skeleton_to_graph, which
% finds the link connected components. Except for the beginning voxels,
% each voxels in the link has ex... |
f41ecfc8296fa987c3499e6d14a6a44016f38b84b064b90517e0807b2e43a09a | MATLAB | 3,990 | 103 | function plotRows=PlotROIResponses(numCell,traces,onsets,offsets,responses,responsesStd,mapData,im,cellper,roiHSVRescaled,prevFrame,eventDuration,params,plotRows)
mapParams=nan(length(mapData),1);
mapFrames=cell(length(mapData),1);
mapLabels=cell(length(mapData),1);
numStim=length(mapData);
for i=1:numStim
mapPara... |
71114060a0b157ddff519408a44bb125cbb9f8329f6f5cdeb64275fe6f2869d7 | MATLAB | 3,993 | 123 | function [y,t,cache] = mTRFresample(x,fsin,fsout,window,buff,varargin)
%MTRFRESAMPLE Resample uniform data by a non-integer factor via averaging.
% Y = MTRFRESAMPLE(X,FSIN,FSOUT) resamples the uniformly-sampled signal X
% from a sample rate of FSIN to FSOUT by averaging the nearest neighbours
% every FSIN/FSOUT ... |
e68855d1cc55d1c2b29d3c57ac8e66176532a217d9755e7e5a25fcd8fec74b40 | MATLAB | 3,993 | 77 | function Figure_5D_plot
%
% Written by Ayaka Matsumoto, October 2023
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Input: 'Fig_5D_SMBD_phase_data.mat'
% Raw data for neck bending angles and the activity of SMBD
% Related to... |
46ccef14c2ead0f77e9245a988780d0e4d386a61c3ce3b0851d928feae76d6c4 | MATLAB | 3,995 | 184 | % Oliver Hinds <ohinds@mit.edu>
% 2007-11-28
function [ps as Ts] = accum_cor(vol, onoff)
% hack to make scale same as dicom
vol = vol.*49;
hrf = spm_hrf(2);
ntp = size(vol,4);
imsiz = [size(vol,1) size(vol,2) size(vol,3)];
% build regressors
if(size(onoff,1) < size(onoff,2))
onoff = onoff';
... |
faaa9952c45f9a6fd7ccb204dc4c044c8721c3c798e915852cad0cade623b16f | MATLAB | 3,995 | 165 | function [C, wjk] = C2sparse(type, kappa, nbrs, chat, dist_power)
%function [C, wjk] = C2sparse(type, kappa, nbrs, chat, dist_power)
%
% create 2D penalty matrix C with 2nd-order pixel neighborhood
% that performs 1st-order finite differences.
% R = beta * C'*D(wjk)*C is the penalty Hessian (in quadratic case)
% in:
%... |
b1ef19176675586c14dfe766641699b7381864d4fe192beab81c7f462fb8ebac | MATLAB | 3,996 | 76 | % Define figure coordinates and axes
figX.fig = figure('');
set(figX.fig, 'Units', 'pixels', 'Position', OSScreenSize([12 10]), 'NumberTitle', 'off');
figX.modMC(1) = axes('Position', [.05 .535 .3 .415]);
figX.modMC(2) = axes('Position', [.37 .535 .3 .415]);
figX.modMC(3) = axes('Position', [.69 .535 .3 .415]);
figX.m... |
636bc8c68be40e503a9b83c4bbef828deb030a592ee402b47218b9826e1be99e | MATLAB | 3,997 | 104 | function rez = runTemplates(rez)
% this function will run Kilosort2 initializing at some previously found
% templates. These must be specified in rez.W, rez.U and rez.mu. The batch
% number at which the new template run is started is by default 1 (modify it by changing rez.istart).
% If you don't mind changes in tem... |
39e88b0c90b761784820fc96d246eb60f1e9f959b40a1529b16282c24b66fa1a | MATLAB | 3,998 | 126 | clc;clear;close all
root='..\';
addpath(genpath([root,'NeuralCorrelateEvidenceAcc']))
%%
rootpath='..\dataset\Mice\result\total_clusterArea_paper_results_kernel_20_half_gaussian\';
rocPath=[rootpath,'_auroc\'];
dpcaPath=[rootpath,'_clustering\_dpca\stim_dp_allUnits\'];
load([rootpath,'areaInfo']);
load([dpcaPath,'clus... |
59392a86ea378623802a4e0d5702ad1a6947e8c36e6741e78e21c662dc96f29a | MATLAB | 3,999 | 116 | function output = setKernelData(input,location,range,value,kernel_type,width)
% output = set_kernel_data(input,location,range,value,kernel_type,width)
% Author: Joe
% Affiliation: XMU
% Create: 2022-12
% Updated Joe: 2023-11
if nargin<5
kernel_type = 'boll'; % uniform
end
if nargin<6
width = [4,4... |
b58fff3ae9def09040ebcb75e52b8ed29e2e95a840f6c14bf6e758664a365660 | MATLAB | 3,999 | 126 | function [u, Ps, ue] = spm_uc_clusterFDR(q,df,STAT,R,n,Z,XYZ,V2R,ui,G)
% Cluster False Discovery critical extent threshold
% FORMAT [u, Ps, ue] = spm_uc_clusterFDR(q,df,STAT,R,n,Z,XYZ,ui[,G])
%
% q - prespecified upper bound on False Discovery Rate
% df - [df{interest} df{residuals}]
% STAT - statistical field
... |
dae6f7c900f4f7fe876ee000fd8b31bb1cc59f177c135ad30b6cf4deb4162970 | MATLAB | 3,999 | 75 | function plot_lap_thetaseq_shuffles(test)
if test == 1
load('Theta\lap_theta_sequence_quantification.mat')
load('Theta\lap_thetaseq_phase_shuffle.mat')
load('Theta\lap_thetaSeq_PREspikeTrain_circ_shuffle.mat')
load('Theta\lap_thetaseq_position_shuffle.mat')
elseif test == 2
load('Theta\lap_theta_s... |
127c717f117e194a88480c3c7c76cba66739326c2f235d74f9981bdfcc8e714b | MATLAB | 4,001 | 132 |
clear
cd D:\Exp_noInstruction\
resOut = [pwd '\results\'];
outdir = [pwd, '\data\']
load([resOut,'Exp6_mulogRT'],'Exp6_mulogRT');
%% create datatable
% templates
conditionTemplate = categorical(repmat({'periodic','aperiodic','cue'}',1));
% preallocate
resp = [];
condition ... |
29971667061a62512045a8cb745c1212a7ffc878c20242c8cbecfb207b06d610 | MATLAB | 4,001 | 94 | % Demo code for Population decoding and multiple linear regression models used used in the study
% Dynamics and maintenance of categorical responses in primary auditory cortex during task engagement
% by Chillale et al., 2023.
% Download the data from Zendo and run the following to reproduce the some
% of the figure... |
8680e66b96e4d64d6705f73fe25b714c21397cd3fb92a999f686c7f0f18c6614 | MATLAB | 4,001 | 146 | function fig = plotTerminationZoneSize(obj,sequence,percentile,titleText)
skipTables = true;
if(~exist('sequence') | isempty(sequence))
sequence = obj.loadIterSequence(skipTables);
end
if(~exist('percentile'))
percentile = 0.95;
end
if(numel(sequence) > 0 ...
& sum(sequence(1).numPres... |
2dbcdfcdb24ff3949a10c141146c5d472c01617ab2b0846c14d99d12c09a3cfd | MATLAB | 4,002 | 143 | function [Z,p1,U,p2] = b_rao(x)
%RAO Rao's Spacing Test.
% [Z,P1,U,P2] = RAO(X) calculates Rayleigh's Z-Statistic (Z) and Rao's
% U-Statistic (U) with the corresponding p-values (P1 and P2). Input
% argument X is the vector of phase angles in radians.
%
% See also WATSON and WATSONTWO.
% Balazs Ha... |
814bc86cfea350c3b796ccd9f7157ac2c8beec514ecd49ee1299294d1e769b80 | MATLAB | 4,002 | 142 | function [uht,PP] = DynamicMetaplex(A,Mass,mesh, sink, source,u0)
%DynamicMetaplex.m - Computes a diffusion process on the metaplex with
%subjacent network A. It can be used with the outputs of function
%DefaultSimplex.m
%
%INPUTS:
% -A: Graph of the simplicial complex, where the edges and tri... |
ffd0e6cc123329c347ece770226e38c167e13ee624a3dca5f3d42e42ca5e3915 | MATLAB | 4,002 | 123 | function stimulus_reconstruction
%STIMULUS_RECONSTRUCTION Stimulus reconstruction example.
% STIMULUS_RECONSTRUCTION loads an example dataset and trains a neural
% decoder that reconstructs stimulus features (speech envelope) from 2
% minutes of 128-channel EEG data as per Crosse et al. (2016).
%
% Example dat... |
26e5a4fbfea6e61cafa093c8bcc185d279fb0db042bf155113bfadc38c7ccad0 | MATLAB | 4,004 | 111 | function strOut = createGetRequestString(dataIDString,varargin)
%CREATEGETREQUESTSTRING creates the XML string to get data from the
% infoserver based on inputs. Required input is a dataIDString. If there
% are no other arguments, it will just create a string to request whatever
% is specified in the dataIDString. Subs... |
2d97f817649baa3463c856805c1ab11be4b52737a5a15d4d87aa47dac997a6c1 | MATLAB | 4,004 | 110 | function [lowbound,highbound,validpeaks,peakvals] = bounddetect_v2(cmap,stmstart,stmend)
load('norms.mat','normsig')
%Calculate dot product of raw voltammagrams with NE signal norm
rho = zeros(1,size(cmap,2));
for i = 1:size(cmap,2)
rho(i) = sum(cmap(:,i).*normsig);
end
rho(rho > prctile(rho,99.5)) = NaN;
rho(rh... |
6f4d6057c8f11c41b822f481b2c6d7d5c897dbbcbb7fdc37b3c02da11d8844b6 | MATLAB | 4,005 | 85 | function [input_graph, new_link_info] = fun_graph_pruning_convert_node_to_endpoint(input_graph, node_label)
% fun_graph_pruning_convert_node_to_endpoint convert a node of degree 1 to
% an endpoint. If the node contains more than 1 voxel, select one of these
% voxels that connected to the link.
% Input:
% input_grap... |
260c5737f1c320bcbd90fc4441d299cd8453a7209cd04d9990a2719d82e3fb56 | MATLAB | 4,007 | 100 | %% Plot Sup Figure 10b imaging and LFP traces
%% Define Paths of Loading Files and Save Files
currentFolder = mfilename('fullpath'); % determine the filepath based on where the script locates
scriptFN = mfilename;
filePath = erase(erase(currentFolder, scriptFN), strcat('scripts', filesep));
searchPath = strcat(filePat... |
ce50d22ce83249c37e5c47944bf031a07ae52a5721cacdb5d5c5ea75e264200b | MATLAB | 4,011 | 127 | function [Ct, Cp, Ca, ht] = ir_mri_dce_aif1(ti, Ktrans, kep, varargin)
%function [Ct, Cp, Ca, ht] = ir_mri_dce_aif1(ti, Ktrans, kep, varargin)
%|
%| Generate tissue contrast concentration curves according to Yang et al.
%| "Multiple reference tissue..." MRM Dec. 07 (yang:07:mrt)
%|
%| in
%| ti [Nt] [min] time sample... |
d5d35046e150f31a006589da77ba74c3e20a341b29fc78aca8aed108d66bc04f | MATLAB | 4,013 | 164 | % Gcone_twv_test.m
% test the Gcone object for "thread within view" version by Brian Gonzalez
% Copyright 2015-07-20, Jeff Fessler, University of Michigan
pn = jf_protected_names;
systypes = 'sf2,v:all';
systypes = {'sf2', systypes}
nsys = numel(systypes);
f.is_zxy = true;
if f.is_zxy
to_zxy = @(x) permute(x, [3 1 ... |
e886a617d2ac3bc51f72a64eba3a40b4206d5a5d4f9caf3388beb98027aab2c6 | MATLAB | 4,019 | 90 | function bootstrap_scene_vs_cat_decoding_peak_difference(subjects,task)
%BOOTSTRAP_DECODING_PEAK_DIFFERENCE Calculate the confidence intervals of the difference between peak
%latencies of categorization and distraction decoding.
%
%Input: subject IDs, task (1 for categorization, 2 for distraction)
%
%Output: confidence... |
24812aa7e816fd93ca23be165a24fdfdfe3007ea98663633b7f3196bc306eef0 | MATLAB | 4,021 | 106 | function [net,YTest,YPred,ClassRes] = trainFeatures(dataTable,ClassName,ClassID,SynthVar)
% dataTable is the input dataset for training. Default tables are tableOrig
% & tableSynth. "ClassName" is the class type to train the model on. "ClassID"
% defines the default IDs for each class.
% Change labels to model ... |
eb0d4af82a453f14bad1b63f80a67de458865b899910f3b3bb7e4523563108f1 | MATLAB | 4,026 | 79 | % This is the main script for performing the GPR reconstruction pipeline.
% The method is described in details on the manuscript.
% We have also provided a tutorial script which should help in
% understanding the basic functioning of the reconstruction procedure
% (See part 3 of the tutorial_gprML.m).
% The analysi... |
b6192fb4ea954c9ce7ca94434f5c57303a46d100eff6b68d0c22faecd83b733e | MATLAB | 4,028 | 115 | function analysis_01_lagged_coherence(study_info, varargin)
% Computes lagged coherence across a range of frequencies and lags
% Parse optional arguments
defaults=struct();
params=struct(varargin{:});
for f=fieldnames(defaults)',
if ~isfield(params, f{1})
params.(f{1})=defaults.(f{1});
end
end
pipelin... |
2e73829d8c5faed98dd54ddcb50133311e54296892b86ca01eb042b7ac1dcc49 | MATLAB | 4,029 | 148 | clearvars -except MCmean0
%input image variables
prompt = {'Pixel width (microns):','Day:','Position:','Enhancement factor:','image length:','Dilation factor:'};
dlgtitle = 'Input';
dims = [1 35];
definput = {'0.3405','1','1','20','500','1'};
answer = inputdlg(prompt,dlgtitle,dims,definput);
D=str2double(answe... |
569d321479951a3cda48f4b412efac669b2f4f32917b87774b168fbc26d5c1e9 | MATLAB | 4,029 | 95 | function [labels, names, colors, line_styles, markers] = fn_puns_category_label_styles(cat_id)
%% Converts the name of a category ID into category labels, plotting colors/styles
% Assumes model_id = 'EpnRPEs_DifFB' ~ EV + pRPE + nRPE
% RL Features: expected value (logistic regression), positive RPE, negative RP... |
81e7d7fb73ab417e164a553c43b6e9e2a9e5a21d65e14fda560c537292323a35 | MATLAB | 4,029 | 111 | function [taskEvents, taskEventType, taskLabels] = getTaskVariabels(D, varargin)
% Written by J. Yates, 2021
% Modified for sharing by Bharath Talluri, Dec 2022
% Get Task Variables will set up the task events for processing by the
% design matrix
% Inputs:
% D <struct>: The data struct (see io.preprocess_data)
% ... |
169671697e8f73937c1eaeb78eae4e3eb18ad8d1019de5571b0605502f62e749 | MATLAB | 4,031 | 128 | function Prepare_TestData_HR_LR()
clear all; close all; clc
path_original = './OriginalTestData';
% dataset = {'Set5', 'Set14', 'B100', 'Urban100', 'Manga109'};
dataset = {'Custom'};
% dataset = {'DIV2K', 'Flickr2K'};
%dataset = {'720P', '1080P', '4K'};
ext = {'*.jpg', '*.png', '*.bmp', '*.tif'};
degradation = 'BI'... |
5ad8e0e94ea66d872841fdff7faa28ae6403630f20f7f9f0924997d7468d5a56 | MATLAB | 4,031 | 89 | function [local_models] = getAllLocalModel(model_collection,coords,ifsave,savename)
savepath = '/vega/brain/users/sh3276/results/luis/fig/m21_sample_d2_d3_local_models/';
model_structs = cellfun(@(x) struct(...
's_lambda',find(model_collection.s_lambda_sequence==x.s_lambda),...
'p_lambda',find(model_collectio... |
9ad44723a64e7072abde1b0d535311a23df3cd6b6ad54e4fb3d4452c125fdba0 | MATLAB | 4,031 | 150 | function [Dice,pval]=wm_seg_adf(subject, th_pval)
% For the subject "subject": computes the Dice coefficient D=2Nab/Na+Nb
% where:
% Na is the volume of the WM obtrained trough the volume-based labeling
% Nb is the volume of the WM segmented in the surface-based stream
% Nab is the volume of the overlap
% Uses... |
5913afc49e229fdca308f435152c11636de93e37957faba8fbb485a26eb12795 | MATLAB | 4,033 | 100 | function outResult = IPEMWriteSoundFile (varargin)
% Usage:
% outResult = IPEMWriteSoundFile(inSignal,inSampleFreq,inFileName,...
% inFilePath,inSection)
%
% Description:
% Writes (part of) a sound signal to file.
%
% Input arguments:
% inSignal = sound signal (each row r... |
8d4df5b5b83d4d1579bbd60de904bee970dbd527e6289a4998fdb04f93f3ff9b | MATLAB | 4,033 | 129 | function [w0, phi] = adw_fan_new(sg, ig, wi)
%function [w0, phi] = adw_fan_new(sg, ig, wi)
%
% Compute the angular-dependent weighting for fan-beam geometry.
% w0(\Phi) = w(x0, y0, \Phi)
% For fully corrected penalty:
% w(s',\beta') * J(s') | \phi'=\Phi + ...
% w(s',\beta') * J(s') | \phi'=\Phi-pi
% See fessler chapte... |
2714d2112d539965fff5b19935237990f34155647b1e00958930131f9eec8427 | MATLAB | 4,039 | 93 | function [outSignalSegments,outSampleFreq] = IPEMSnipSoundFile (varargin)
% Usage:
% [outSignalSegments,outSampleFreq] = ...
% IPEMSnipSoundFile(inFileName,inFilePath,inTimeSegments,...
% inWriteSegments,inOutputPath)
%
% Description:
% Snips the sound file into segments.
%
% Inpu... |
887930f5e3bc441887bc7427a831acde7fae0caa2de37f4bc7a9edce6d9f90bb | MATLAB | 4,039 | 144 | function create_number_awake_replay_figure
% NUMBER OF AWAKE REPLAY EVENTS TRACK AND PROTOCOL
cd 'X:\BendorLab\Drobo\Lab Members\Marta\Analysis\HIPP\Chapter 2\Raw_replay_analysis\figures\Awake replay on track'
events = openfig('Norm Number of awake replay events per track__ALL_Protocol.fig');
% output figure
cd 'X:... |
087d739be4ec8a4f9ef710de1e8351d225dfcbff865c166d5c1da19e4750d371 | MATLAB | 4,041 | 139 | function [Acc, Y_hat, Score, Gini] = RandomForest_HRV_validation(data1, data2, data3, Y_tst)
% This function performs a Random Forest validation classification. This
% function uses Random Forest console as implemented in:
% https://code.google.com/archive/p/randomforest-matlab/
%
% Inputs:
% data1 data2: struct with t... |
81f556d143538765a93d0268266d37238862fdf27d74303111bd4ad8b83cc981 | MATLAB | 4,041 | 79 | classdef ieegStructMicro < ieegStructClass
% The ieegStructMicro is a subclass of ieegStructClass specialized for micro-scale iEEG data.
% It extends the functionality of ieegStructClass by adding additional methods for spatial smoothing
% and spatial averaging specific to micro-scale data.
propert... |
6b95aad043ce301299c6605300b75b8119a559fc4aeafef22607b0a41b31f096 | MATLAB | 4,042 | 132 | function [odata, sing, Vr] = ir_mri_coil_compress(idata, varargin)
%| MRI coil compression via PCA
%function [odata, sing, Vr] = ir_mri_coil_compress(idata, varargin)
%|
%| Given data (idata) from multiple MRI surface coils, use SVD/PCA
%| to find data (odata) for a smaller number of virtual coils.
%|
%| todo: current... |
82e23e9f22f6f2ece14b046e8eec812841d28a2826106f2af92478840d0261e3 | MATLAB | 4,049 | 123 | function comparison_method_to_groundtruth(xlsx_path,gt_xlsx_path)
% Compare output from a single method to groundtruth, used to examine data
% before and after curation.
% Detailed explanation goes here
% Gt xlsx
auto_tbl = readtable(gt_xlsx_path);
auto_tbl.Vessel_Length_Density = auto_tbl.Vessel_Length .* ...
... |
3eff88c6695b015896afc4b335aaf25eb51a5a85c9b0da7e98022064d3230793 | MATLAB | 4,057 | 99 | function dch = calcDecoherenceBandForSessions(fltrCells,dchFolderPath,paramsPath)
%filter for WT mec cells by session
seshes = unique(cellfun(@num2str,fltrCells.metadata(:,1),'uni',0));
numSesh = numel(seshes);
% load params tabel
params = readtable(paramsPath);
ds_factor = params.ds_factor;
%filter for WT mec cells... |
a105c0217f811c453cc96c795ae9f764c0a555d297722e563ca968cfdd7c77bd | MATLAB | 4,057 | 103 | function violins = violinplot(data, cats, varargin)
%Violinplots plots violin plots of some data and categories
% VIOLINPLOT(DATA) plots a violin of a double vector DATA
%
% VIOLINPLOT(DATAMATRIX) plots violins for each column in
% DATAMATRIX.
%
% VIOLINPLOT(TABLE), VIOLINPLOT(STRUCT), VIOLINPLOT(DATASET)
% p... |
45b15e7951167851d4721a4fc561ab0fddc4b484bc5035a6820c6e8e7705fa39 | MATLAB | 4,060 | 86 | %% Coding an MRI experiment
% In an MRI experiment the scanner sends a trigger for every volume, and
% the experiment has to know how many have arrived and when. The OTBR
% Toolbox does not read these triggers itself. A separate program, the
% trigger counter, counts them and writes the count and the time of the
% last... |
917b90e02d2d9f5a26a267f50fe7f836263bfa47eb8c493b44949e3cf868ddd6 | MATLAB | 4,061 | 99 | function [Data] = GetPopData(RootName,Animal,binsize,Sess)
Cat = [4,8,12,16,20,24];
if strcmp(Animal,'Timanoix')
load([RootName 'SessionInfo_RecSess.mat']);
load([RootName 'Channels_all.mat']);
Channels_all{26}.CorrectchId(end) = [];
Channels_all{26}.Correctch(end) = [];
params.pre = 0.5;
... |
4d1b00abf524a2140c336d4c5b2852655154404509c7065af5a7c5700f4d1e40 | MATLAB | 4,062 | 123 | function out = getButtonClick(stopTime, varargin)
% Wait for a mouse click on one of several buttons, or until a timeout.
%
% Get the click on a button that was created with createButton. The first
% argument is the waiting time in seconds, followed by one or more button
% handles as returned by createButton. The funct... |
d3d587af231b831dd0c81f319c0393a08dc3e7c72eed7a6a8ef0aede3b4c8ef5 | MATLAB | 4,067 | 162 | function inv2 = de_ftab_inv2(fit, sl, varargin)
%function inv2 = de_ftab_inv2(fit, sl, [options])
%|
%| Build object that does polynomial inverse of BH function.
%| (To map log data into corresponding to line-integrals of material density.)
%|
%| in
%| fit strum initialized by de_ftab_fit()
%| sl cell {s1, ..., sL} se... |
0cb539bbd17fdb9f1c8dabdf740b0f5bdf75e214deed0726e630e7be3a3d3eae | MATLAB | 4,070 | 107 | function k=LineCurvature2D(Vertices,Lines)
% This function calculates the curvature of a 2D line. It first fits
% polygons to the points. Then calculates the analytical curvature from
% the polygons;
%
% k = LineCurvature2D(Vertices,Lines)
%
% inputs,
% Vertices : A M x 2 list of line points.
% (optional)
% Li... |
ded9176818182acb27f53954af236a25bef4e9b97362644f0bead192d8c16841 | MATLAB | 4,070 | 180 | function ir_savefig(varargin)
%|function ir_savefig([rootdir,] base, ['c'])
%|
%| save current figure to eps file rootdir/base.eps with prompting.
%|
%| matlab's newer 'exportgraphics' is perhaps more useful
%|
%| options:
%| 'eps_c' save to color .eps file without inverting!
%| 'cw' color .eps file for use in white ... |
6862903630d138ea020956f57d97b4b14b38cf97f20118126f4c14f165424856 | MATLAB | 4,073 | 75 |
clc
clear
rats=getfolder
for i=1:length(rats)
cd(rats{i})
SD=getfolder;
for ii=1:length(SD)
cd(SD{ii})
trial=[repelem(7,1,300)]; %Create a vector for trial (300 sec of wake name with 7).
trial6=[repelem(7,1,600)];
files=dir;
... |
49014d1fcbd2a8594d814d8b91b087b91cde2a287aeb062ccaf830dc456b91af | MATLAB | 4,074 | 141 | function out = spm_dartel_warp(job)
% Register images to template data.
% format spm_dartel_warp(job)
%
% The outputs are flow fields.
%_______________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
% John Ashburner
% $Id: spm_dartel_warp.m 7182 2017... |
a140bee9d61b13020ead3e034f6cff00605bb425abd5caf6aec017683a00e549 | MATLAB | 4,074 | 109 | function outSignal = IPEMReshape(varargin)
% Usage:
% outSignal = IPEMReshape(inData,inShaper,inMethod,inSymmetry,inPlotFlag)
%
% Description:
% Reshapes the incoming data by substituting each original value by a
% new value using the given shaper matrix.
%
% Input arguments:
% inData = 1D data vector... |
52a379606f7066eb02af75918ba9cd559e9573f2329642a698e3c8d11e3f0d52 | MATLAB | 4,076 | 92 | function total = dotPresentationBirds(trackingAreaSize,trackingAreaSteps, stimulusSize, stimulus, resultsPresentationHuman, errorMargin)
% This function is used in conversionMatrixCreator_Birds to display stimuli
% in the desired tracking area and to track pecks on those stimuli.
%
% INPUTS
% * trackingAreaSize ... |
b8c93ba1597260d3393dca84e2b9ccce78c8f90dbb221b1cdfc1922d857688a6 | MATLAB | 4,079 | 125 | %Convert old Condition files
%Inputs: structure from old files
%Outputs: structure with data in new format
function New=convertOldConditionFile(Old)
%% ConditionsMatrix
%convert pages (Trial) to commam delimited
New=struct();
for i=1:length(Old.ConditionInfo(:,1))
Trials=Old.ConditionInfo{i,1};
TrialsSplit=st... |
61f95640d86cf92bd867a10b02d7b6cac6e2a761c9ae79e99d297d49b2bb301c | MATLAB | 4,080 | 108 | % The script is used to perform pairwise decoding on fMRI data
% add toolbox
addpath(genpath('spm12/'));
addpath(genpath('CoSMoMVPA-v.1.1.0/'));
addpath(genpath('libsvm-3.25/'));
% set input and output paths
beta_path = '../../mri_data/beta_map/';
mask_path = '../../mri_data/mask/individual_space/';
save_path = '../.... |
785a70fa53dec87688bbb19b638f6091d11022898df9c7ed3355e6ad66dda5f2 | MATLAB | 4,085 | 138 | function bmask = pm_brain_mask(P,flags)
% Calculate a brain mask
% FORMAT bmask = pm_brain_mask(P,flags)
%
% P - is a single pointer to a single image
%
% flags - structure containing various options
% template - which template for segmentation
% fwhm - fwhm of smoothing kernel for generating mask
%... |
df57581dd3b9378012513a8d2de089d305f433e6525d66a87a0527a9b94282ee | MATLAB | 4,085 | 108 | % THETA BATCH ANALYSIS PIPELINE
% MH 2020
function theta_phase_batch_pipeline(option,computer,plot_select,varargin)
% option: 1- phase precession ; 2 - extract directional clusters and place fields ; 3 - extract theta extrema ; 4 -bayesian decoding ;
% 5 - Theta sequence quantification ; 6 - shuffles ; 7 - finding the... |
75df43b21e8ac9eb91a24c6e0802bcf46a5ad9ef4a51d4342488f418488530b3 | MATLAB | 4,086 | 109 | function[]=ref_vol_stitch_after_BASIC(id,datapath, depth, thickness)
Xsize=1000;
Ysize=1000;
Xoverlap=0.15;
Yoverlap=0.15;
resize_factor=0.5;
% add path of functions
addpath('/projectnb/npbssmic/s/Matlab_code/PSOCT_code');
addpath('/projectnb/npbssmic/s/Matlab_code/ThorOCT_code');
addpath('/projectnb/npbssmic/s/Matlab_... |
9f98ce4c4c4709013460896f8bbe2d07af717384f3aabca4b9a0607073e0ea4d | MATLAB | 4,086 | 104 | % sense_recon_demo.m
% - demonstrates use of sense_recon.m, which implements the SENSE
% reconstruction
% - figure shows how Tikhonov regularization mitigates the increase in error
% as noise variance increases
% - applies complex AWGN to kspace
% - image must be even both directions
%
% 2012-06-08, Mai Le
% number... |
45916588ddb6caf4705895feb7a2ab2c9caf4520679a523b07c8f48de29ceb5d | MATLAB | 4,087 | 107 | function varargout = IPEMHandleInputArguments(inPassedArgs,inNumFirstOptionalArg,inDefaults)
% Usage:
% [...] = IPEMHandleInputArguments(inPassedArgs,inNumFirstOptionalArg,
% inDefaults)
%
% Description:
% This function is a simple but general input handling routine.
% Ju... |
2c05121dd6ab056037ad42b4eed751fd9309bfbcd879c38923410e78bbbb57f7 | MATLAB | 4,089 | 85 | function [input_graph, new_link_info] = fun_graph_pruning_convert_node_to_endpoint(input_graph, node_label)
% fun_graph_pruning_convert_node_to_endpoint convert a node of degree 1 to
% an endpoint. If the node contains more than 1 voxel, select one of these
% voxels that connected to the link.
% Input:
% input... |
dcbf6a8d7c938351003f7db71db8947cdbdb154b470955d81178374aad583704 | MATLAB | 4,089 | 110 | function [negloglikelihood] = Estimate_g2_g3_sigma_nu(g2, g3, sigma, nu, Input, config)
% Zilong Ji, UCL, 2022, zilong.ji@ucl.ac.uk
% Find the likelihood with parameters specified in the model function name
% Args (in common between all estimates functions):
% beta: decay factor for the mental distance (calculation err... |
aa465cb0c1598110f17359dce33481bcbf316d57c4a6258a7d1c9e8ef2d5ce0e | MATLAB | 4,091 | 87 | function connect_link_str = fun_graph_shortest_path_through_4pts(metric, ep1_sub, bp1_sub, bp2_sub, ep2_sub, connectivity)
% fun_graph_shortest_path_between_points finds the shortest path between
% two point, with distance measured by the metric array
% Input:
% metric: 3D numerical array, specify the distance betwe... |
7eeb006148f60e8b6b09896a1b7828795ba1537e4a6fc8285238559dcc2201bc | MATLAB | 4,093 | 146 | function [M_R] = vox2ras_rsolveAA(Vc_C, inPlaneRotation, varargin)
%%
%% NAME
%%
%% vox2ras_rsolveAA.m (vox2ras_r{otation}solveA{uto}A{lign})
%%
%% AUTHOR
%%
%% Rudolph Pienaar
%%
%% VERSION
%%
%% $Id: vox2ras_rsolveAA.m,v 1.4 2007/01/10 22:55:10 nicks Exp $
%%
%% SYNOPSIS
%%
%% [M_R] = vox2ras_rsolveAA(Vc_C,... |
92fe7d6c5c02a7d28b1c4c830bbffbfff567ba368b834547337317bc4f1c22b9 | MATLAB | 4,093 | 134 | function [nodes, edges, validatedNodes,validatedEdges] = regraphNodes_new(nodes, edges,validatedNodes,idx)
% Re-graph
nodePos = nodes;
nodeEdges = edges;
nNodes = size(nodePos,1);
nEdges = size(nodeEdges,1);
if ~exist('nodeDiam')
nodeDiam = zeros(nNodes,1);
end
hxy = 2;
hz = 2;
nNodesUnique = ... |
869ab8ac8b227a71e879e7b5f7be71e98345421eaceba24dfae548254be10982 | MATLAB | 4,094 | 95 | % specify OCT system name
sys = 'PSOCT';
% specify dataset directory
datapath = strcat('/projectnb2/npbssmic/ns/201028_PSOCT/dist_corrected/');
addpath('/projectnb/npbssmic/s/Matlab_code/fitting_code');
addpath('/projectnb/npbssmic/s/Matlab_code/PostProcessing');
addpath('/projectnb/npbssmic/s/Matlab_code/PSOCT_code... |
ab19917b54dbed7f380706e2352a5eea460851cfe12354d51b9d212bf6f40d6b | MATLAB | 4,094 | 148 | % header = ufmf_read_header(filename)
function header = ufmf_read_header(filename)
MAXNMEANSCACHED = 5;
header.filename = filename;
% open the file for reading, binary, little-endian
fp = fopen( filename, 'rb' , 'ieee-le');
header.fid = fp;
% ufmf: 4
s = fread(fp,[1,4],'*char');
if ~strcmp(s,'ufmf'),
error('Inval... |
ad4546aff9b607c98f7d29cc8954f97546854d279293d2bd51bb0782e0e2543d | MATLAB | 4,094 | 153 | master_startup;
example_rats = {'B052', 'T261', 'B104'};
%% plotting examples of different types of history effects that are observed in the dataset:
fprintf('\nPlotting example fits \n\n')
figure(2001); clf;
idx = find_rat(example_rats, ratdata);
bin_wd = 3;
range = 32;
markersize = 12;
ylabels = {'Fraction c... |
8df4ac712700a39a581e8190f75ef96dc75799b3dc3bab5406ba957a30adabbc | MATLAB | 4,096 | 111 | %Get data for Data Type Events
%Inputs: app properties
%Outputs: numTrials and Events for Data Type Events
function [numTrials, Events] = GetEvents(app)
%Determine which Detections Setting is being used, currently there is only one option
app.DetectionSetting = app.DetectionSettingsDropDown.Value;
switch a... |
de996803a93b8511cfd80982b64f24665aa0cd01646d58d0c208adcea4aac429 | MATLAB | 4,101 | 116 | function spm_vb_ppm_anova(SPM)
% Bayesian ANOVA using model comparison
% FORMAT spm_vb_ppm_anova(SPM)
%
% SPM - Data structure corresponding to a full model (ie. one
% containing all experimental conditions).
%
% This function creates images of differences in log evidence
% which characterise t... |
1cff694e592dd5229980de7d5788ab095b9b7189c261357b13e075645da7836b | MATLAB | 4,106 | 129 | function [ outputMap ] = generateColorMap(map, type)
%GENERATECOLORMAP
% This code generate a color map based in the results obtained from the
% HELICoiD classification result. It assigns colors depending on a class
% list and the label in each pixel of the classification map.
%
% INPUTS:
% - map: 2D ... |
287968703111b7a42aa5fd79284fdc862b0d611fe571a8ccf15db5300440d62d | MATLAB | 4,106 | 90 | function Figure_7C_plot
%
% Written by Ayaka Matsumoto, October 2023
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Output: Figures 7C
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
param_... |
47bd7239f8afd0056b4c7a6acdb34406a214368a2f2398797b676c5955d2f309 | MATLAB | 4,106 | 85 | % Denoise Poisson images using the Anscombe variance-stabilizing transformation, BM3D filter, and the exact unbiased inverse of the Anscombe transformation
%
% [y_hat, PSNR_y_hat, NMISE_y_hat] = Poisson_denoising_Anscombe_exact_unbiased_inverse(noisy, original)
%
% the input D is the filtered (e.g., denoised) signa... |
e4b4bdcb62deda0c27583f1604f8e897e184fb9daf8296485c5c20655201dd37 | MATLAB | 4,106 | 149 | % This is a wrapper function to the MEX function stepFast.c
function stepMex(obj, nStep)
% Select which step function to use
stepFunc = [];
if(obj.useLocalJumps)
if(obj.typeFlag == 4)
stepFunc = @obj.stepFastLocalChem;
else
stepFunc = @obj.stepFastLocal;
end
else
% This function... |
9af1e1dfe1f4657664d3aed6bb592876ddde089f3301aea64247b6666b447756 | MATLAB | 4,107 | 109 | function trigSDFandMI_M1(sesID,tsDir,trigDir,algTrigDir,rrmDir,analDir)
% function trigSDFandMI_M1(sesID,tsDir,trigDir,algTrigDir,rrmDir,analDir)
%
% input:
% sesID session ID
% tsDir path to dir of preprocessed data for RRM
% trigDir path to dir of triggerTimes for (no-)movement ons... |
06fd0d419bdb70a893fe0c142a4f70ff8bb63cc2d7646bbd95e0408f115773f2 | MATLAB | 4,113 | 123 | function plot_timewarped_lickBehavior(cells)
% Plots Timewarped Lick Accuracy: 30 min post-start; 30 min post-control;
% 120min post-ketamine
addpath(genpath('/Users/KeiMasuda/Documents/MATLAB/Add-Ons/Functions/gramm (complete data visualization toolbox, ggplot2_R-like)/code'));
seshes = unique(cellfun(@num2str,cells... |
73ca1c118aaa14c163e576f36bdc5a8b185b88e6504f3a7352b3459d1808fc38 | MATLAB | 4,113 | 152 | function [Fcompvec, RESNORM, Fcomp, dmean, Cmean] = inspect_map_F_update(allimg,FcompvecPrevStep,weights,K,Kalpha,gradechoinv,options)
ncomp = options.ncomp;
nvox = size(allimg,1);
Nmeas = size(allimg,2);
%Nk1 = options.ILT.Nk1;
%Nk2 = options.ILT.Nk2;
%Nk = Nk1*Nk2;
Nk = options.ILT.Nk;
%boolean denoting whethe... |
745b7a8ad6764395626fe2679d5867d5b2dded611890bc7852fc1d90b16b5b23 | MATLAB | 4,114 | 95 | folder = '/projectnb2/npbssmic/ns/Ann_Mckee_samples_20T/AD_10382/';
P2path = '/projectnb2/npbssmic/ns/Ann_Mckee_samples_20T/AD_10382_2P/'; % 2P file path
datapath=strcat(folder,'fitting/');
% add subfunctions for the script. Change directory if not running on BU SCC
addpath('/projectnb/npbssmic/s/Matlab_code/fitti... |
d118826a7f1f496bae1a9d2cd884ad78bb491644349f2f0ce8d63dbf9058bf72 | MATLAB | 4,121 | 119 | function [fNum] = plotTurnDensityRadialOccupencyCorr(radProbAllGen,turnDensAllGen,genAll,fNum)
posDiff = [];turnDiff = [];
for g = 1:numel(genAll)
posDiff(g,:) = radProbAllGen{1,g}.during.weightedMu-radProbAllGen{2,g}.during.weightedMu;
turnDiff(g,:) = turnDensAllGen{1,g}.Dur-turnDensAllGen{2,g}.Dur;
end... |
987a489c8a141b5cf20fc0d02d62596ccdc78ff23f75db17ed13a106f3e991dd | MATLAB | 4,125 | 123 | %% Comparison of volume headmodels
%
% Vinding, M. C., & Oostenveld, R. (2022). Sharing individualised template MRI data for MEG source reconstruction: A solution for open data while keeping subject confidentiality. NeuroImage, 119165. https://doi.org/10.1016/j.neuroimage.2022.119165
%
% Get summary statistics of the ... |
0e62055c8c74ac7b090f41ab3ae83b86af7e624c172c8a7dd727f644549eeb5a | MATLAB | 4,126 | 156 | function [mean_pos,ma_pos] = mean_posit_track(t,s,tau,train_len)
%| function [mean_pos,ma_pos] = mean_posit_track(t,s,tau,train_len)
%| INPUT
%| t: time indices
%| s: position sequence (should be same length as t)
%| tau: delay parameter,
%| set to be the equivalence index to about 0.5sec for breathing.
%| train_Le... |
69db5b6d86443dd8102b39e34d16e3e7c95d577d349c1ceb8908821f0ffcf221 | MATLAB | 4,134 | 98 | function outFrequency = IPEMCalcNoteFrequency (varargin)
% Usage:
% outFrequency =
% IPEMCalcNoteFrequency(inNoteNr,inOctaveNr,
% inRefNoteNr,inRefOctaveNr,
% inRefFreq,inNotesPerOctave,
% inOctaveRatio)
%
% Description:
% C... |
c50896648ee9c6ffa769413e048620d6f42b2ccfe041f329169be62af9c234c6 | MATLAB | 4,140 | 115 | function x = spm_invTcdf(F,v)
% Inverse Cumulative Distribution Function (CDF) of Students t distribution
% FORMAT x = spm_invTcdf(F,v)
%
% F - CDF (lower tail p-value)
% v - degrees of freedom (v>0, non-integer d.f. accepted)
% x - T-variate (Student's t has range (-Inf,Inf))
%________________________________________... |
934ad04a1bc2c378fb6e9a664d4164aa8ed2d8d74003d18c87ed2e1d33d5463b | MATLAB | 4,141 | 120 | function nodes = centerNodes_transversePlane( nodes, edges, I,verifiedNodes, idx )
nodes_len = size(nodes,1);
if nargin < 5
idx = 1:nodes_len;
end
len = length(idx);
new_nodes = nodes;
I = permute(I,[3 2 1]);
[ny,nx,nz] = size(I);
h = waitbar(0,'Please wait... Centering nodes(1 and 2 edges)');
for v = 1:len
i... |
f5921a90700ac49849b0a078022725e11e32dfd646079f504418abf63dcb15e0 | MATLAB | 4,141 | 125 | function [mx,my,mz] = blochsim2(Mi, bx, by, bz, T1, T2, dt)
% Evolve magnetization field using time-discretized Bloch equation
% Input
% Mi [3,dim] initial X,Y,Z magnetization
% Note: normalized to magnitude <= Mo=1
% bx,by,bz [ntime,dim] effective X,Y,Z applied magnetic field
% (Tesla), for rotating frame (... |
708a6529a34b4eeaa99c2fee7cd4d5e0bb6eb9af437abddee9c418aebc335147 | MATLAB | 4,143 | 71 | %%
clc; clear all; close all;
load('bgluSynchData_forRM.mat', 'bgluRawSynchData');
%%
cond0g_0b_div14 = [bgluRawSynchData.SF147.cond0g_0b.div14.SF14.rawCleaned;
bgluRawSynchData.SF147.cond0g_0b.div14.SF47.rawCleaned;
bgluRawSynchData.SF147.cond0g_0b.div14.SF71.rawCleaned];
cond0g_0b_div15 = [bgluRawSynchData.S... |
949c456e35c18cc908274ecdd3890d2ffe7d4b8b93a2b2e75fe9e2bbb7329dc2 | MATLAB | 4,145 | 119 | function main
%-- Settings
rotation_randomization = 0;
nRotations = 18;
voxelization_type = 'binary';%or 'density'
binaryMax = 1;
basic_dimension = 28; %The final size is this + 2*D
D = 4; %Padding
output_type = 'bin'; %or 'mat'
aligned = 'aligned'; %or 'nonaligned'
rotation_start_index = 0;
collate_singlerots = 0;
... |
8451df4c5d901e19d7942e2002fba3a88c7d365fb784945a53e26d9aa230a325 | MATLAB | 4,148 | 153 | clear all;
%% define grid pattern
% add subfunctions for the script
addpath('/projectnb/npbssmic/s/Matlab_code');
% displacement parameters
xx=200;
xy=0;
yy=200;
yx=0;
% mosaic parameters
numX=22;
numY=23;
Xoverlap=0.5;
Yoverlap=0.5;
% tile parameters
Xsize=400;
Ysize=400;
numTile=numX*numY;
grid=zeros(2,numTile)... |
5a315175af8f4eb2b53bdbf212fa348c8d12d5d54aad37f22da0a5bfa0cadbd1 | MATLAB | 4,152 | 98 | function [nwbAll, importLog] = NWB_importFromFolder(datasetPath, importRange, taskFlag)
%NWB_importFromFolder Returns a cell array containing all imported nwb
%files in the specified folder and import range.
% pathIn: Path to desired folder
% importRange: Range of SB/SC IDs to import.
%
% mkyzar 4/15/2023... |
3a52cc32785e6c3da63e567c597e14f4db5ae2f80423480498fe5def7b967316 | MATLAB | 4,155 | 104 | function [I_VE,I_seg] = vesSegment(I, sigma, thres)
% This function performs 3D vessel enhancemnt, filtering, and thesholding
% to segment the vessels from the surrounding tissue.
%
% INPUTS:
% I - inverted 3D angiogram (white = vessel, non-white = tissue).
% sigma - vector of standard deviation values of gaussian ... |
1da239d705816e6a97a710ff03b336324269d77b323072aeaf68ce290029d2fb | MATLAB | 4,156 | 125 | function [M_avg,d] = spm_compute_avg_mat(Mat0,dims)
% Compute an average voxel-to-world mapping and suitable dimensions
% FORMAT [M_avg,d] = spm_compute_avg_mat(Mat0,dims)
% Mat0 - array of matrices (4x4xN)
% dims - image dimensions (Nx3)
% M_avg - voxel-to-world mapping
% d - dimensions for average image
%
%____... |
60236a81954fb54c38ed6fabcb71e374bd831360c36b305e66b2716576b37856 | MATLAB | 4,156 | 79 | function [shuffled_track,all_shuffles] = parallel_shuffles_final_lap(shuffle_choice,analysis_type,num_shuffles,decoded_replay_events)
% runs run_shuffles on multiple cores in parallel shuffle_choice- determines what type of shuffles will be performed, entered as a string
%
% analysis type- vector of 0's and 1's for whi... |
de287d020dc209dc7aaeeb1a00e0d24cefc758f51b24dacdd1e20dcebed37443 | MATLAB | 4,157 | 152 | classdef c_GUI_uix_VBox < c_GUI_handle
% c_GUI_uix_VBox - wrapper around uix.VBox() that automates handling of children heights
properties
DefaultHeight;
DefaultFixedHeight;
DefaultMinHeight;
end
properties(SetAccess=protected)
hbs
hb
h_scroll;
numChildren = 0;
childHeights = [];
childMinHeights... |
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