sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
d612391399096edf2083d1daf95e9998cf0443e1f39335d780f48b8191b42bdc | MATLAB | 2,968 | 99 | function multCond_pe(cfg, i_sub, i_run, hbi_model_path, output_path)
%MULTCOND_PE Summary of this function goes here% Detailed explanation goes here
%% Load behav_data .mat
behav_data_path = fullfile('/projects/crunchie/moehring/neurovar_v2/log_files',...
strcat('sub-', sprintf('%02d',i_sub)),'be... |
667629faf85f68b847c37694a980f03ac014baecd74beeb825b2a72e157315ed | MATLAB | 2,976 | 87 | function MakeAverageSpkWaveF(fbasename)
% This function has to be launched from root data folder and will load the
% different dat files in each of the fbasename-xx folders
rootname = [fbasename filesep fbasename];
xmlFname = [rootname '.xml'];
analysisDir = [fbasename filesep 'Analysis'];
if ~exist(analysisDir,'di... |
3f29e793b61ca96215f2652eebd73853870edfafd8f98e2774904e462caf9eb1 | MATLAB | 2,978 | 85 | % Preprocessing unit test
%
% undersample
tol = 10e-10;
N = 120;
X = randn(N,1);
% Get default parameters
param = mv_get_preprocess_param('undersample');
%% if data is already balanced then undersampling should have no effect
clabel = ones(N,1);
clabel(N/3+1:2*N/3) = 2;
clabel(2*N/3+1:end) = 3;
[~, ~, clabel2] = m... |
5228333599700e75a5b3faa588098f115e0e6f2ae0822433859e47d70d8921af | MATLAB | 2,978 | 78 | function coord_w = minc_voxel2world(coord_v,mat,opt);
% Convert coordinates in the voxel space into coordinates in the world
% space.
%
% SYNTAX:
% COORD_W = MINC_VOXEL2WORLD(COORD_V,MAT,OPT)
%
% _________________________________________________________________________
% INPUTS:
%
% COORD_V
% (matrix N*3) each r... |
00ebe3f232be4cc90b581c3eafc45ddb7160cbaa6ef17ad4cca9578bbb804547 | MATLAB | 2,982 | 86 | function output_filename = create_participants_tsv(varargin)
%
% Creates a simple participants tsv for a BIDS dataset.
%
% USAGE::
%
% output_filename = bids.util.create_participants_tsv(layout_or_path, ...
% 'use_schema', true, ...
% ... |
aa91eca7e53d799d1be7557678313e6a4fb8eed01bb38460b146e4adc4f6c50a | MATLAB | 2,982 | 69 | % function [msg_length] = display_progress(cfg,cnt,n_decodings,start_time,msg_length)
%
% Function to display progress and the estimated time to go in The Decoding
% Toolbox (i.e. how far is the analysis?).
% MH 2015/06/18: introduced prev_time to get more accurate estimation of
% time to go if other processes s... |
8577842fe8e8e561860efe66fc355206c3aed8df6a8e97e998f407f7157c845e | MATLAB | 2,983 | 101 | %% Find beta bursts for each subject
% Import ROI time series. Filter and find bursts.
% The tool for finding bursts are found herE: https://github.com/mcvinding/beta_bursts
%
% Vinding, M. C., Eriksson, A., Low, C. M. T., Waldthaler, J., Ferreira, D., Ingvar, M., Svenningsson, P., & Lundqvist, D. (2021). Different fea... |
5aa53eb2ea20c112a49371d3d485130b1557ad463f25e5f90dd79ecdce287872 | MATLAB | 2,985 | 86 | %% Bar Plot of d' and LR Bias (Pooled + JK errorbars)
%% Code
monkey = ["G","B"];
global Trials
Trials = ["All","RF"];
sessions_list = {[4:15 18 20:24], [34:45]};
flag_scatter = 0;
for RF = 1
eval(sprintf("load('data/high_low_split/split_%sTrials.mat')",Trials(RF+1)));
par_all = cell(1,2);... |
1bd782a4e7d0484f0ea2ba65b6829274bfea011fd060c4baf638cd8d942067c7 | MATLAB | 2,987 | 75 | % this script changes the voxel size and keeps the bounding box of images
% (you can change the bounding box easily too).
%
% it creates all result files in a new directory
%
% this version is especially tailored if you like to reduce the file size
% of beta estimates from SPM and use it for TDT later.
%
% use ... |
99638e7e0795e31ceff5eae0c1fe1f9ee354455e5d1ea3431e11c96e0be9e472 | MATLAB | 2,988 | 97 | function multCond_svm_state_at_cue(cfg, i_sub, i_run, hbi_model_path, output_path)
%% Load behav_data .mat
behav_data_path = fullfile('/projects/crunchie/moehring/neurovar_v2/log_files',...
strcat('sub-', sprintf('%02d',i_sub)),'behav',...
strcat('sub-',string(i_sub),'_run-',string(i_run),'... |
ff38c343e91dd02856936c4c177de0bd4d14f325a062d6707c45921247e7db9c | MATLAB | 2,989 | 91 | function [optionsOUT]=DSC_mri_getOptions()
% OPZIONI DI VISUALIZZAZIONE
optionsOUT.display=2; % 0:off, 1:notify (text), 2:notify (images), 3:debug
optionsOUT.waitbar=1; % 0:off, 1:on
% OPZIONI PER LA PREPARAZIONE DEI DATI
optionsOUT.mask.npixel=300;
% rappresenta il numero di pixel minimi di una componente conness... |
2e9635340e4d8f4861f92ba7ecde85c237cbf9bba4f1743e7412def7dab56767 | MATLAB | 2,992 | 68 | % Example script - provides a simulated real-world use case for the code
% found packaged together with this example file.
%
% For more information, see:
% Z. Kaposzta, A. Czoch, O. Stylianou, K. Keumbi, P. Mukli, A. Eke,
% and F. Racz,
% Real-Time Algorithm for Detrended Cross-Correlatio... |
265f71d0945ba279d364e28a801da75fcb2a38a087e1e657f7d464a2e46841ca | MATLAB | 2,997 | 84 | function srf = bv_readsrf(fname,upto_neighbors)
% srf = bv_readsrf(fname,upto_neighbors)
%
% :description
%
% Reads in a version 3 (BrainVoyager 2000 4.4, QX) SRF file.
%
% :inputs
%
% fname file to read, e.g. 'mysurface.srf'
% upto_neighbors set to 1 if you only want to read in vertices, normals,
% ... |
5da87a451c5c39882d553e1729201a5b3f58af9b6c15a75819d5ffe207a7a27b | MATLAB | 2,998 | 102 | function [label_1, label_2, label_3] = gen_rp(data, SEGMENTATION, file, tw, fs, img_dim, delay, dE, rp)
% GEN_RP Generates recurrence plots (RPs) from segmented ECG data.
%
% [label_1, label_2, label_3] = gen_rp(data, SEGMENTATION, file, tw, fs,
% img_dim, delay, dE, rp) processes ECG data to generate RPs, saving t... |
88dba0fad7a475ac9f0a3158f2cc3e54cd7f5f6a38a6c6b91020a1f6619648f5 | MATLAB | 3,003 | 81 | function param = mv_tune_hyperparameters(param, X, y, train_fun, test_fun, eval_fun, tune_params, k, is_kernel_matrix)
% Generic hyperparameter tuning function using a search grid with
% cross-validation.
%
% Usage:
% ix = mv_tune_hyperparameters(param, X, y, train_fun, test_fun, tune_params)
%
%Parameters:
% param ... |
b1c40dff8abf72daa894b598838237fb258a218ceeb2c0f6b6cefcc97de7b55a | MATLAB | 3,003 | 107 | function [] = plot_stability(subj,raw_patname,runsname,varargin)
% Visualize the stability of your signal
%
% [] = PLOT_STABILITY(SUBJ,RAW_PATNAME,RUNSNAME, ...)
%
% Takes in an existing init'ed SUBJ structure, imports the
% raw (just motion-corrected) and the despiked data, and
% plots the mean activity (throughout t... |
3c9cccac84d59aa3fae9a3cebafaf48fe38689524affba34111c58af537680b1 | MATLAB | 3,009 | 91 | function [transformationMatrix] = ...
optical_flow_linear_registration2d(moving, fixed, varargin)
% OPTICAL_FLOW_LINEAR_REGISTRATION2D Estimates a transformation matrix using optical flow
%
% INPUT ARGUMENTS
% moving - Moving iamge
% fixed - Fixed image
%
% OPTIONAL INPUT ARGUMENTS
% ... |
a974f005e546ae7f160bb5bf34e16c3d29260cd86c52a2454b28776fa5f4befb | MATLAB | 3,009 | 70 | function univ_chosen_action_val_1st()
cfg = load('neurovar_cfg.mat').cfg;
pipeline_name = 'pipeline_chosen_action_val_';
for i_sub = 1:44
fprintf(strcat('1st Level PEs Sub_', string(i_sub),'\n'));
pipeline_dir = fullfile(cfg.global.deriv_dir, strcat(pipeline_name, '1st'), ... |
f190681ca3eaa9fb4616278eae54c3b235a03604411ec86470fa563762138e38 | MATLAB | 3,010 | 95 | clear,clc
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% This script is used to perform CCA for brain-behavior analysis
%
% Jin
% 11/20/2023
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% set the file path
current_path = pwd;
working_path =... |
d9682da9076cc853e769acd7065ddb47cd6dcdf7fe8821bca17e6400e2eaf3c9 | MATLAB | 3,013 | 107 | function [f,YTick,YTickLab] = plotraster(f, EventT,Var, Colour,yinc,ticklevel)
%RASTER creates a raster plot of aligned time-events (spikes, licks etc).
% Inputs:
% f = handle of axes for plotting raster plot
% EventT = 1-D cell array of NStim/NTrl x 1, each cell containing
% e... |
625d7deaa66475554db1b138d0c0ca23111e7972b8aa969e4a77c29afdce034a | MATLAB | 3,016 | 104 | function [thetaEst,thetaP,thetaMat] = BayesReconstruction_1D(pf,Q,thetaVec,tau,varargin)
% Bayesian reconstruction of a one dimensional signal (e.g. head-direction)
% based on neuron tuning curves and instantaneous firing.
%
% USAGE
%
% [thetaEst,p,thetaMat] = HeadDirectionReconstruction(allPF,binnedSpk,binVal,t... |
a14460a81ca111f0e29e64721f6803adac68c6e6d6d1c529c2196ae465385ab6 | MATLAB | 3,023 | 109 | clear; clc
[task_info,supPath] = loadDBAndSpecifyDataPaths('Vermis');
POPULATIONS = {'BG|SNR','BG|SNR';...
'BG|SNR','PC ss|CRB';...
'PC ss|CRB','PC ss|CRB'};
BIN_SIZE = 10;
DIRECTIONS = 0:45:315;
PROBABILIES = [25,75];
PLOT_PAIR = false;
raster_params.time_before = 199;
raster_params.time_after = 800;
raster_... |
96edc36d39da62bff0750039ade14fe8ec4441ecb01ed3eed7d531baa9a026e3 | MATLAB | 3,025 | 51 | %% Align EEG and video codes
% Check if variable EEG_timingtest_video_align exists already. If yes, load it.
if exist([output_dir output_general 'VideoBasedMarkers\EEG_timingtest_video_align.mat']) == 2
load([output_dir output_general 'VideoBasedMarkers\EEG_timingtest_video_align'],'EEG_timingtest_video_align'... |
1cf78ca6f2fc6e68a2861f04f7fb3820e5aa08029cc55850cfd63cc74c0e4020 | MATLAB | 3,026 | 78 | % Classifier unit test
%
% Classifier: logreg
rng(42)
tol = 10e-10;
mf = mfilename;
% Random data
N = 500;
nfeatures = 100;
X = randn(N, nfeatures);
X(2:2:end,:) = X(2:2:end,:) + 0.5;
clabel = ones(N, 1);
clabel(2:2:end) = 2;
%% check "prob" parameter: all returned values must be probabilities
% Get classifier par... |
7c77cefb0ad7e6761314ebacc7f42e0340c0c2309a9201a53559f701edff3081 | MATLAB | 3,027 | 110 | function RunAnalysis(fction,datasets,overwrite,varargin)
% USAGE:
% RunAnalysis(fction,datasets,overwrite,arguments)
% INPUTS:
% fction: name of the function to be called
% datasets: a cell array of directories
% overwrite: will overwrite, even if function already done.
%
% Adrien Peyrache 2011
% Par... |
c4d452fcdecb7116ca0f9f2ed244f92f43d728848778da68b653ec3b4d2ce243 | MATLAB | 3,028 | 100 | function plot_RT_over_Coh(performance, id, meth, save_flag)
% _
% This function is written to plot the reaction times
% for the different stimuli and coherence levels.
%
% IN: performance: structre, containing the recorded behavior for every
% subject
% id: structure, identi... |
5a25a55b876bfe214cb27c3d4d61d92ef6bf55d5b2d8c8bae07ec4010c41fcb2 | MATLAB | 3,032 | 106 | % Preprocessing unit test
%
% ssd
tol = 10e-10;
% Generate oscillatory data
cfg = [];
cfg.n_sample = 50;
cfg.n_channel = 16;
cfg.n_time_point = 512;
cfg.fs = 256;
cfg.n_narrow = 3;
cfg.freq = [1 2; 4 8; 8 12];
cfg.amplitude = [10 13 11];
cfg.narrow_class = [1 0; 0 1; 1 1];
[X, clabel] = simulate_oscillatory_data(cfg)... |
54ed054c9c3c8c444342bee86e3259a1789945a088ed8d85b2c572b56e2f06ba | MATLAB | 3,033 | 72 | function [ccabf, chs] = isccstep(abf)
%ISCCSTEP determine if abf file was created with a current step protocol
% Returns logical value ccabf
% Return chs which gives the channelnumbers for which it is true that a
% ccstep protocol was applied
chs=[abf.channel(:).number];
ccabf=logical(false);
%% Abf level
% Num... |
db661346e2d3e735311f551e8e8564e62234f03c084c05bdb6173593d2bf913c | MATLAB | 3,036 | 71 | function pipeline_svm_state_at_cue_1st()
cfg = load('/projects/crunchie/moehring/neurovar_v2/BIDS/code/neurovar_cfg.mat').cfg;
for i_sub = 1:44
fprintf(strcat('1st Level Sub_', string(i_sub),'\n'));
%% Prepare folder
pipeline_dir = fullfile(cfg.global.deriv_dir, ... |
df761f8a7d19a7c85d64796bbc4b86dc3f1c20f2be7753c5e783a59e07e50179 | MATLAB | 3,037 | 86 | function model = pp_smooth(sub_id, p)
% function model = pp_smooth(sub_id, p)
%
% Runs SPM smoothing on fMRI images that has been realigned, slice-time
% corrected and normalized (prefix 'war').
%
% Input:
% - i_sub: ID of the current subject.
% - p: Structure with analysis parameters.
%
% Output:
% - model: Str... |
95bf7f7a438c536830780279fe6ee7fa0892cb3a309b39088d1336ecd67046b1 | MATLAB | 3,038 | 104 | function hhh=vline(x,in1,in2)
% function h=vline(x, linetype, label)
%
% Draws a vertical line on the current axes at the location specified by 'x'. Optional arguments are
% 'linetype' (default is 'r:') and 'label', which applies a text label to the graph near the line. The
% label appears in the same color as ... |
80d2c16ffbbd10ee244404baeb8e59dba7dee45b2ab6ef81f558865c4f0d75e5 | MATLAB | 3,039 | 111 | %DEMKMEAN Demonstrate simple clustering model trained with K-means.
%
% Description
% The problem consists of data in a two-dimensional space. The data is
% drawn from three spherical Gaussian distributions with priors 0.3,
% 0.5 and 0.2; centres (2, 3.5), (0, 0) and (0,2); and standard
% deviations 0.2, 0.5 and 1.0. ... |
0bd7a1dc9a538f7679ca379b0633cd6f22a95237641cf18b3ebcda656df60de9 | MATLAB | 3,040 | 104 | function yOCTUnzipOCTFolder(OCTFolderZipFileIn,OCTFolderOut,isDeleteOCTZippedFile)
%This function unzips .oct file (OCTFolderZipFileIn) into OCTFolderOut, and
%deletes OCTFolderZipFileIn (default)
%% Make sure we have AWS Cridentials
if (strcmpi(OCTFolderZipFileIn(1:3),'s3:'))
awsSetCredentials (1); %Write c... |
cbcd4fa3c7bc84d042f2e368fb27c45458eda908e30936d419a27c51e7f66daa | MATLAB | 3,042 | 118 |
function [Ehat, Phat] = modelImpulse(F, I, head_vel, target_vel, light, sine, debug_on)
head_vel = head_vel(:);
target_vel = target_vel(:);
if ~exist('debug_on','var')
debug_on= 0;
end
nt = length(head_vel);
% Create target acc and pos in case needed for predictive target signal
target_vel(isnan(target_vel))... |
934346944c0d56473f09f038c902db581ca60caa618548d41680c09e1f783d71 | MATLAB | 3,045 | 95 | function status = compareDcAvg(group_h2, dataTree, datatype)
global DEBUG1
DEBUG1=0;
status = 0;
if ~exist('datatype','var')
datatype = 'dcAvg';
end
dataTreeFile = [dataTree.groups(1).path, dataTree.groups(1).outputDirname, dataTree.groups(1).outputFilename];
group_h3 = load(dataTreeFile);
if isempty(group_h3)
... |
3b736f91c1adc408fc0f131463c477f9cd34791e78855f6cba66fc6ac57f5914 | MATLAB | 3,047 | 80 | function model = pp_coregister(sub_id, p)
% function model = pp_coregister(sub_id, p)
%
% Runs SPM coregistration. Aligns a high-res anatomical image with a fMRI
% image (the 'meansub*' image from the realignment is used as reference).
%
% Input:
% - sub_id: ID of the current subject.
% - p: Structure with analysi... |
60d19894e85f2dcbc2e241fbca433e2f297158f64dad8dd882877c25ee2de0cb | MATLAB | 3,047 | 113 | clear
[task_info,supPath] = loadDBAndSpecifyDataPaths('Vermis');
req_params = reqParamsEffectSize("both");
%req_params.cell_type = {'SNR'};
lines = findLinesInDB (task_info, req_params);
cells = findPathsToCells (supPath,task_info,lines);
EPOCHS = {'cue','targetMovementOnset'};
for ii = 1:length(cells)
data = ... |
105eceacbb774df5cb19defdd6b6241a4815ce5d53c0a294335ee53d8a990ff0 | MATLAB | 3,052 | 83 | function [displacementField] = create_nonrigid_displacement_field2d(fieldSize,varargin)
% CREATE_NON_RIGID_DISPLACEMENT_FIELD2D Creates a 2D non-rigid displacement field
%
% [displacementField] = create_nonrigid_displacement_field3d(fieldSize)
%
% INPUT ARGUMENTS
% fieldSize - [rowSize colSize], assumed to ... |
1af2ac65cd09b896097f6448983c5636514493afef4b59207dd54a7a26330d52 | MATLAB | 3,052 | 83 | function Experiment = set_up_display(Experiment)
% Set Up Display
% Input: Struct 'Experiment' with fields 'mode'
% Output: Struct 'Experiment' with field 'Display'
% 'Display' contains fields:
% 'nScreen' - list of available output screens
% 'externalScreen' - screen where expeiment is displayed
% ... |
80610f10348ad093a09b3b68b3f8bdb96baa85edaba0aadcc27dcb64bc778592 | MATLAB | 3,055 | 82 | function [update] = polynomial_expansion_registration(...
moving, fixed, multiModal, transformationModel, varargin)
% POLYNOMIAL_EXPANSION_REGISTRATION Estimates a displacement field using polynomial expansion
%
% INPUT ARGUMENTS
% moving - Moving image
% fixed - Fixed image
%... |
e773fef7aa496803631eed9d1ec39ad17c95051a0aa078fad92a5d890c87a2d1 | MATLAB | 3,056 | 108 | function [proj] = elproj(pos, method);
% ELPROJ makes a azimuthal projection of a 3D electrode cloud
% on a plane tangent to the sphere fitted through the electrodes
% the projection is along the z-axis
%
% [proj] = elproj([x, y, z], 'method');
%
% Method should be one of these:
% 'gnomic'
% 'stereographic'
% 'o... |
2d1e9c728b40df36e0d068525aeaeb69892308937276f83923d1f3fa64e01dee | MATLAB | 3,059 | 108 | function RasterPlot(S, pos, varargin)
% RasterPlot(S, height, bar, tstart, tend)
%
% Plot spike trains as rasters.
% INPUT:
% S: tsdArray of ts objects, spike trains
% OPTIONS:
% Height: the height allocated to each cell (includes spacing), default 1
% BarFraction: the fraction of height occupied by a cell, default 0... |
d9289b153b8ee42c5ffa7bbe959d458f5bd3ad49b9108251956d8917e15f7763 | MATLAB | 3,066 | 103 | clear
[task_info,supPath,MaestroPath] = ...
loadDBAndSpecifyDataPaths('Vermis');
DIRECTIONS = 0:45:315;
req_params = reqParamsEffectSize("pursuit");
lines_pursuit = findLinesInDB (task_info, req_params);
req_params = reqParamsEffectSize("saccade");
lines_saccade = findLinesInDB (task_info, req_params);
raster_p... |
5ab7b9ff3dd356a7af92172a73221d10cd05adc8961c3e46769de8f80b3fdd6e | MATLAB | 3,068 | 86 | % output = transres_SVM_pattern_alldata(decoding_out, chancelevel, cfg, data)
%
% Calculates the pattern according to Haufe et al (2014), Neuroimage. This
% is done by first getting the weights in source space (primal problem), if
% a linear SVM was used (for non-linear methods no weights can be
% calculated for the pr... |
825e4918ffd8ffcfaafd28cff2a9e9a8e792535bdd68ba395b8bf6cccda791a8 | MATLAB | 3,076 | 90 | function [L,weights] = CrossValidationSpikePrediction_Ensembles(qPre,qPost,sPost,binT,ep)
% [L,params] = CrossValidationCrossCorrPrediction_Cells(qPre,qPost,sPost,ep)
%
% computes the loglikelihood prediction of an ensemble of target spike trains given another ensemble of (predictor) spike trains
% The prediction is ... |
d3ced8a03f73d229edd9c497c30471c868a3caf8457174400e792ec68a6d0560 | MATLAB | 3,078 | 91 |
%% Parameters and data
% parameters
nbins = 360;
smoothTh = 3;
velTh = 2; % velocity threshold
mergeTh = 0; % threshold for merging run epochs (0 = no merging)
lagVec = [-0.5:0.01:0.5];
%load data
load(fullfile('Data','BehavEpochs'));
load(fullfile('Data','SpikeData'),'S');
load(full... |
4b6a1e68e0f8cb1ed8db7ba6648a7c08596e89dab7c518cca58850a73fa9f371 | MATLAB | 3,083 | 71 | % plot correlation between time of trial and CA performance at 2-3s
% window
% do the same thing for var of beta coeff.
function plot_Corr_CAvsAvgTime(numClasses)
sbjList = {'08','12','13','14','15','16','19','21','22','23','24','25'};
figSaveDir = 'C:\Users\mn0mn\Documents\ResearchProjects\spatailAttention... |
bc940233bc5333de655f24efd1af3d3d7183dfb9b76cf6791f2944e80ff3a8c2 | MATLAB | 3,089 | 117 | function val = RasterImagePlot2(S, varargin)
% RasterImagePlot2(S, height, bar, tstart, tend)
%
% Plot a continuous function in a spike raster like form.
% RasterImagePlot2 plots each waveformin a 3D landscape style
% INPUT:
% S: tsdArray of ts objects, spike trains
% OPTIONS:
% Height: the height allocated to each... |
fc6c8719958aed4f4c94a03e4de441c90783f4b9719e87493b4c41619dd0db66 | MATLAB | 3,089 | 93 | function [ops] = setup_registration_phasecorr(refImg)
% Setup variables necessary for phase correlation registration
% Marius Pachitariu/Carsen Stringer Suite2P (2017)
% Modified by Henry Dalgleish for online image registration (2018)
[ops.ly,ops.lx] = size(refImg);
ops.yx_px = [1:ops.ly ; 1:ops.lx]';
% Paramet... |
a0ae87440b06ae1c42798a61bb184987e42958ff7d052f4f9b42f53534543c06 | MATLAB | 3,090 | 89 | function [labels] = AccuSleep_classify(EEG, EMG, net, SR, epochLen, calibrationData, minBoutLen)
% AccuSleep_classify Classify brain states
% Zeke Barger 081120
%
% Arguments: (note that these are data, not paths to files)
% EEG - a 1-D matrix of EEG data
% EMG - a 1-D matrix of EMG data
% net - a train... |
5d4974e424268ca4c2bbde00c5b0b90d2cb145c94c7935105db5fc5bf9cb90e9 | MATLAB | 3,095 | 72 | function plot_hist_scatter(performance, id, meth, save_flag)
% _
% This function produces the scatterplot of responses and true directions.
% The color represent the absolute deviation from the correct direction.
%
% IN: performance: a structure obtained by the function rdk_extract that
% must include the cells... |
0254ce47abb6fb22289a34b1e568cae18788310a7cdaa3db1ada75a55f0604c0 | MATLAB | 3,096 | 102 | function hhh=hline(y,in1,in2)
% function h=hline(y, linetype, label)
%
% Draws a horizontal line on the current axes at the location specified by 'y'. Optional arguments are
% 'linetype' (default is 'r:') and 'label', which applies a text label to the graph near the line. The
% label appears in the same color a... |
898674491273a6ea70e0cef03ae489210a353903a51f4e88aea33cd575bbba7a | MATLAB | 3,097 | 124 | % Preprocessing unit test
%
% pca
tol = 10e-10;
% Generate data
nsamples = 100;
nfeatures = 20;
nclasses = 2;
prop = [];
scale = 1;
do_plot = 0;
[X, clabel] = simulate_gaussian_data(nsamples, nfeatures, nclasses, prop, scale, do_plot);
%% 2D example - check whether sizes are correct
X = randn(30,40);
n = 5;
% Get d... |
38ece32bd97c681162397d5735f12bc4be1b2d5d3e15c4ea4a353f494bb19367 | MATLAB | 3,098 | 101 | function data = Scale(transformer, data)
%
% Scales the values of one or more columns.
%
% Semantics mimic scikit-learn, such that demeaning and
% rescaling are treated as independent arguments,
% with the default being to apply both
% (i.e., standardizing each value so that it has zero mean and unit SD).... |
45832305e52836caad2cdba0e64611336ab7d15ef22171b97aeceae8a5a350aa | MATLAB | 3,098 | 101 | function [name_vs_id,directions]=automatic_identity(neurons,chosen_point,voxel_size,fixed_time)
name_vs_id=[];
% variables initialization
y_scale_factor=voxel_size(2)/voxel_size(1);
z_scale_factor=voxel_size(3)/voxel_size(1);
% signal correlation matrix and coordinates matrix creation
C=correlation_matrix(neu... |
c8fe289de34941b30c7a575e03f6c5ad4541dc6cbf120a23518c0e8fc20d2544 | MATLAB | 3,100 | 64 | function [spike_burst_dyn_table]=compute_spike_burst_dynamics_axons_220822(allregion_unit_matched)
% This folder contains the contains the mea structs
%load(strcat(parent_dir,'\','allregion_unit_matched'))
% Parameters
fs = 25e3;
% Initializaing
ISI_cell={}; IBI_cell={}; IBSR_cell={}; spnb_cell={}; bd_cell={}; chan... |
2a3fc18e2858137a3f93435a9e1f0853b7389428bdbaf5be86dd0b4937e75656 | MATLAB | 3,103 | 84 | % Preprocessing unit test
%
% mnn
tol = 10e-10;
% Generate oscillatory data
cfg = [];
cfg.n_sample = 50;
cfg.n_channel = 16;
cfg.n_time_point = 80;
cfg.fs = 256;
cfg.n_narrow = 3;
cfg.freq = [1 2; 4 8; 8 12];
cfg.amplitude = [10 13 11];
cfg.narrow_class = [1 0; 0 1; 1 1];
cfg.narrow_weight = [];
[X, clabel] = simulat... |
f9e0e783102530a1b122742beaec510eb3b8b7539587ac19bc84796fb78fc035 | MATLAB | 3,104 | 99 |
%%
BCTypelabels = {'BC5o', 'BC5i', 'BC5t', 'XBC', 'BC6', 'BC7', 'BC89'};
%%
dazure = @(n) [linspace(0, 255, n); linspace(254, 0, n); linspace(255, 151, n)]'/255;
cqualthr = qualthr;
% cqualthr = 0;
close all
figure; hold on
ctyp = typ(any(Qual > cqualthr & Rept > 1, 2));
targetD = BSctr;
targetD(Qual < cqua... |
27c081795007cc33a0db34514b441a4b5e5f1cdb8fd2664553d5937bbf1b830c | MATLAB | 3,105 | 102 | function [X,clabel,Y] = simulate_radial_data(nsamples, prop, separation, do_plot)
% Creates two-class radial-shaped data in two dimensions.
% The first class is confined to the unit circle. The second class is
% confined to a ring around the first class. The ring has the same area as
% the unit circle.
%
% Useful for t... |
342880a0f2051d9c2c7ba6a2d88f21dc6c7191d1f659a9df4dbea2bca0a9e6bb | MATLAB | 3,108 | 72 | function [IncDec, fitresult, gof, SigFitTrace] = createFit_sigmoid(XVals, fluorescence_trace)
%CREATEFIT(XVals,FLUORESCENCE_TRACE)
% Create a fit to a sigmoidal curve. Equation is modified from Boltzmann
% charge-voltage equation
%
% Data for fit:
% X Input : XVals (1:length(fluorescence_trace))
% Y... |
0fe3c5dd02ed32f41909329e8a26784342180f731cce2733ded77d34e28b2b38 | MATLAB | 3,112 | 80 | %% Create intepolated SSD patterns and plot raw points (Figures 2B & S1)
repo_path = 'Path_to\Manuscript_repository';
addpath('Path_to\wjn_toolbox_tsbinns');
addpath(genpath('Path_to\spm12'));
addpath(genpath('Path_to\leaddbs'));
folderpath_analysis = 'Path_to\Project\Analysis\Results\BIDS_01_Berlin_Neurophys\sub-mul... |
bcc6c1ad4b63a9920f9b4dd938fa8ce9821015c94603a48fa915d0ce07477488 | MATLAB | 3,112 | 83 | clear all
load('../data/opt.mat')
for typeName = opt.epoch_type
players = {};
observers = {};
%% define epoch
if strcmp(typeName,'DecisionMaking')
event_name ='showCard';
time_interval = [-0.5, 3];
elseif strcmp(typeName,'Feedback')
event_name ='result_obsCor'... |
c2e65a143bc59e7b00731588ed9cb66a37d2f7afc57e30761a0bf0ffec432dcf | MATLAB | 3,113 | 80 | %% Create interpolated SSD patterns and plot raw points (Figures 2B & S1)
repo_path = 'Path_to\Manuscript_repository';
addpath('Path_to\wjn_toolbox_tsbinns');
addpath(genpath('Path_to\spm12'));
addpath(genpath('Path_to\leaddbs'));
folderpath_analysis = 'Path_to\Project\Analysis\Results\BIDS_01_Berlin_Neurophys\sub-mu... |
72ad38ac0ef89744902fa22d59684829da266d0289a0f9d65a5f3ce83707b9c7 | MATLAB | 3,114 | 80 | % @title Convert .mat of neuron types into .txt file
% @file mat_to_ntypematrixf.m
% @author Romain Beaubois
% @date 06 Aug 2021
% @copyright
% SPDX-FileCopyrightText: © 2021 Romain Beaubois <refbeaubois@yahoo.com>
% SPDX-License-Identifier: GPL-3.0-or-later
%
% @details
% > **08 Mar 2021** : file... |
d4d02fbf35b9df9fcead42f4e04f22e3dcb28ed16b1ada40487a9796efe72024 | MATLAB | 3,128 | 88 | function cube_integral_test
% This function creates a cube based in spherical co-ordinates and then
% plots in cartesian co-ordinates. The cube is divided into 4 pyramid.
% Each pyramid has a top, middle and bottom component as per the figure.
% The script also uses a quadrature based method to verify the volume (ele... |
c27a148a220b8613c865516681df67e86f845630b2501f76f05e6570b3e73275 | MATLAB | 3,129 | 98 | % Sub-sampling script: analysis of avalanche size distributions with fewer
% samples for a simulation with scaling at high sample count
% example parameters: N_F = 1, eps = -18, eta = 4
param_str = 'e-6.0et6.0';
uf_files = dir(['avalanches_code/data/fields1ultrafinesweep/sweep*' param_str '*tstat.mat']);
uffr_files = ... |
de6ed22c9d74bfabbeed4dda5e5a6dc58c34f774e18172879876f8136178de07 | MATLAB | 3,134 | 80 | % About
%
% Descriptions tba
%
% $Author: Peng Li, Ph.D.
% $Date: May 16, 2018
% $Modif.: Nov 15, 2022
%
% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
% (C) Peng Li 2016 -
% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
... |
737c0bb8f55087d14d457b67138feb36ef75fe70884310c9a10d85b7c070a111 | MATLAB | 3,135 | 80 | function TrackOFxMovie(origIm, flowTracks, vxMat, vyMat, clusterIm, fileName)
close all;
%% INITIALIZE OBJECTS
% Padding
padSize = 1;
vxMat = padarray(vxMat, [padSize, padSize, 0], 0);
vyMat = padarray(vyMat, [padSize, padSize, 0], 0);
clusterIm = padarray(clusterIm, [padSize, padSize, 0], 0);
% Save movie
vv = ... |
fbfe73bbefafee06d465f2b780e7847de47e54368b36a43c50834518cadd648b | MATLAB | 3,135 | 80 | % About
%
% Descriptions tba
%
% $Author: Peng Li, Ph.D.
% $Date: May 16, 2018
% $Modif.: Nov 15, 2022
%
% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
% (C) Peng Li 2016 -
% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
... |
562057ddb13ca3d5a2a52edfdce6375640d1604e9c4810c889fdfed142cbbda4 | MATLAB | 3,138 | 91 | function [h, hdata] = rbfhess(net, x, t, hdata)
%RBFHESS Evaluate the Hessian matrix for RBF network.
%
% Description
% H = RBFHESS(NET, X, T) takes an RBF network data structure NET, a
% matrix X of input values, and a matrix T of target values and returns
% the full Hessian matrix H corresponding to the second deriva... |
88fed66580cdb40644ef3b6d1762a442d9ec2bb644e6c61a5f8a822d5e55dc99 | MATLAB | 3,147 | 71 | function hf_canc = plotCancellationVisTuning(I, K1, K2, K0, K1_vis, K2_vis, K0_vis)
hf_canc = figure;
sine_on = 0.5; % (Hz)
light_on = 1;
dt = K1.dt;
tt = (1:2/dt)'*dt-dt;
head_curr = sin(2*pi*.5*tt);
target_curr = head_curr;
% if I.impulse_or_closedloop
% [E... |
90c81ae7a3e0bceba71290837abaf48f23ba6a6f9b326e117c97416332e7c8d7 | MATLAB | 3,147 | 93 | function [mixparams, y, z, a] = mdnfwd(net, x)
%MDNFWD Forward propagation through Mixture Density Network.
%
% Description
% MIXPARAMS = MDNFWD(NET, X) takes a mixture density network data
% structure NET and a matrix X of input vectors, and forward propagates
% the inputs through the network to generate a structure ... |
c742a9b98ed2b8c183e923067ccaab5eea5c8dd2295adae2e9bd0b53a7235131 | MATLAB | 3,147 | 136 | function [linSpd,angSpd, GoodRanges] = LoadSpeed_Wrapper(fbasename,varargin)
% LoadSpeed_Wrapper loads linead and angular speed from whl or pos files
% USAGE:
% [linSpd,angSpd] = LoadSpeed_Wrapper(fbasename)
% Adrien Peyrache 2015
if ~isempty(varargin)
w = varargin{1};
if ~isa(w,'struct')
error('arg... |
ccb4734890c345a1383e16c8c16fc9c2699234a80f6cd8efd1c065285cfabe58 | MATLAB | 3,147 | 99 | % This software converts moves into cpp and csv files.
% There are speed_limit and speed_mult options.
anim_angles = readmatrix('output/animal_angles.csv');
anim_speeds = readmatrix('output/animal_speeds.csv');
use_speed_limit = 0; % turn speed limit on or off
speed_limit = 25; % maximum speed allowed
alt_data = 0; % ... |
640fdd15e918d49c4f2e67d6f299117331b7982df5babb8596fad5cfabef04fd | MATLAB | 3,150 | 87 | function [results_data] = plot_split_histo(cueMI, oppMI, sessionDist, iMonkey, xl, yl, split_sessions, iSplit, bin_size, trial_type, loc_flag, plot_flag)
%% Plots V4 f-MI cued vs opp moving window plot (onset)
%% Code
cols = [0,0,1;0,0,1;1,0.5,0;1,0.5,0];
cols = [cols;cols];
cols(9:10,:) = [0,0,1;1,0.5,0];
cols_othe... |
8e04eeb760d651e7ee6558a5f32b545260422e149917f568902d3e135d40386a | MATLAB | 3,151 | 67 | function mutual_position_based_links=get_mpbl(distmatrix_s,coords_t,tmax_local,xy_pixel_size,z_pixel_size,max_gap_closing_mutual_distance)
distmatrix_s_mean=squeeze(mean(distmatrix_s,3,'omitnan'));
taken=[];
h = waitbar(0, 'Merging tracks based on mutual distance ...');
for i=1:size(coords_t,3)
waitbar(i./si... |
129ebadb0b8f48f671105a0f2f6a6127f91941f54f7b8f4383008d28f2b69995 | MATLAB | 3,156 | 79 | % This script generates dataset for ML models
%% Inputs - set defaults
magnificationName = '4x';
tagName = '';
octBScanToUseAroundCenter = [];
isAverageOCTBScans = [];
% Which libraries to take images from
libraryNames = {'LC','LD','LE','LF','LG'};
%% Jenkins
%This function updates all input varible n... |
8c3d9fc00d159c40af2307411ebdc89e46ca725bab321d83f4882a4910f88b9f | MATLAB | 3,160 | 83 | function transfer_data(p)
if isempty(p.dirs.source), return; end
%%% Transfer DICOM files %%%
fprintf('Trasferring DICOM ... \n')
for i_sub = 1:numel(p.subjects)
% Get subject ID
sub_id = p.subjects(i_sub);
% Get subject ID as string
sub_str = num2str(sub_id,'%02i');
for i_ses = 1:p.n_session
... |
122f089ecede357f6798876a56de8cdbdca832897aa151b9531eceb3a77152c4 | MATLAB | 3,162 | 125 | function fixticks(scale)
% FIXTICKS(scale)
% Put tick marks outside figure, sizes them according to full figure
if ~exist('scale','var')
scale = 1;
end
npoints = 2;
h = findall(gcf,'Type','axes');
% Get length of current axes
% pos = get(h(1),'Position');
% Based only on maximum VISIBLE axis, use special fun... |
c5ee47e35c8ed093a8611df80c7de7ca5a3daa7cef0e14b9f70f79742e815c42 | MATLAB | 3,164 | 90 | function PlotDistributions(histExportFolder, filePrefix, distOption, dataBinWidth, showBinWidth, HistogramData, plotType)
%% Assign values
rawCounts = HistogramData.RawCounts;
thrCounts = HistogramData.ThreshCounts;
relCounts = HistogramData.RelCountsWeighted;
magCounts = HistogramData.MagCountsWeighted;
%% Check vari... |
c6faf019f74472ea053b34227eb14c9b754a89b22cc75e4ffb761c7ac7c844c0 | MATLAB | 3,168 | 98 |
%% Load data
% input parameters
binsize = 0.1; % bin size for decoding in seconds
nBins = 360; % number of angular bins for tuning curves
sdSmoothTC = 3; % smoothing widnow for tuning curves
sdSmoothBD = 2; % smoothing window for spike trains
velTh = 2; % velocity threshold
% load session files
l... |
f6f5508e095dbd0c2977071bb1fbe150b29ebf335eade84bcaa62b3dfbb7d6ed | MATLAB | 3,168 | 93 | function net = gp(nin, covar_fn, prior)
%GP Create a Gaussian Process.
%
% Description
%
% NET = GP(NIN, COVARFN) takes the number of inputs NIN for a Gaussian
% Process model with a single output, together with a string COVARFN
% which specifies the type of the covariance function, and returns a
% data structure NET.... |
1a9bbeeb57d0843d08fe920b8132c25009fa29b7a70adb2c339fe72d3af7c889 | MATLAB | 3,181 | 105 | function Q = MakeQfromS(S, DT, varargin)
% Spike train binning on
%
% USAGE:
% Q = MakeQfromS(S, DT, parameters)
%
% INPUTS:
% S - a cell array of ts objects
% DT - timestep for tsd (measured in timestamps!)
%
% OUTPUTS:
% Q - a ctsd in which the main structure is a |t| x nCells histogram of firing... |
c361d65c212af9c9a419f2d87f42ade18eee9806d2999c6757c35098628bc333 | MATLAB | 3,184 | 85 | function [Y, pValue, Rsquare, Component, varargout] = CosAna(y, tsec, FitFreq)
%COSANA Cosinor fitting
%
% Inputs:
% y --- data to be fitted
% tsec --- time axis in seconds
% FitFreq --- target frequency (in hours), use vector for
% multipe compone... |
4634e3e008af51a92efd2d283bf7e39921db003a608691f0be97815d0cadc0aa | MATLAB | 3,186 | 118 | function create_quadrature_filters_for_phase_difference_registration2d(varargin)
% CREATE_QUADRATURE_FILTERS_FOR_PHASE_DIFFERENCE_REGISTRATION2D Creates 2D quadrature filters for usage in linear phase-difference registration
%
% create_quadrature_filters_for_phase_difference_registration2d()
%
% INPUT ARGUMENTS
% N/A
%... |
846bb64198d57d94737632f129fbb702ec038c40f7ae2d4eef090bc7749f539f | MATLAB | 3,187 | 118 | clc; close all; warning off all; clearvars;
% preparation
fileScript = matlab.desktop.editor.getActiveFilename;
[pathProject,name,ext] = fileparts(fileScript); % path to working directory/current folder
addpath (pathProject);
pathData = ([pathProject '/data']);
pathFT = ([pathProject '/fieldtrip-20220... |
5bdd8b3dd7dd942b688ff04cb33f4c8bb670d14df6b1536a9ab903b7aafa7cba | MATLAB | 3,188 | 122 | % [CleanWhl GoodRanges] = CleanWhl(Whl, StretchLen, JumpSize);
%
% "cleans up" a wheel file by interpolating missing stretches
% up to StretchLen long (default 20), for which the endpoints
% don't differ by more than JumpSize, (default 30).
%
% also returns the ranges where the whl file is valid (in .whl units)
% Good... |
d3368889fb612d2b2be20b9f28d7f9bedcf8e0ab17c2263fb9001e41750f671d | MATLAB | 3,188 | 101 | function [conds, tts, head, target, hevel, PC, sines, light, dt, cond_inds] =...
loadJR(data_path, t_step)
% [conds, tts, head, target, hevel, PC, sines, light, dt, cond_inds] = ...
% loadJR(data_path, t_step)
%
% t_step indicates the time at which step data is cropped
% ------------ JR DATA FOR MODEL FIT -------... |
2d77f66fafe097472320c5542f7ba1782a7e7e44e879e933654fb6df1c2e3124 | MATLAB | 3,189 | 80 | function [update] = phase_registration(...
moving, movingCertainty, fixed, fixedCertainty, ...
transformationModel, currentScale)
% PHASE_REGISTRATION Estimates a displacement field using phase-difference
%
% [update] = phase_registration(...
% moving, movingCertainty, fixed, fixedCertainty, ...
% trans... |
22cafc3327bc00ae3cf0187548ad43710dbca342e25125329ff79f5417d57175 | MATLAB | 3,190 | 85 | %% Whole-brain modelling in the homogeneous case
%This model is based on Stuard-Landau oscillators, the local dynamics of single brain
%regions using the normal form of a Hopf bifurcation.
%The dynamics of the N brain regions were coupled through the connectivity matrix,
%which was given by the connectome of healthy s... |
2b686175479cfcf94dba64a17b63febb6c4f7aa7cf1a430ce6bcb926bd26da5d | MATLAB | 3,190 | 85 | %% Whole-brain modelling in the homogeneous case
%This model is based on Stuard-Landau oscillators, the local dynamics of single brain
%regions using the normal form of a Hopf bifurcation.
%The dynamics of the N brain regions were coupled through the connectivity matrix,
%which was given by the connectome of healthy s... |
aeb7c3ba5b337240e2b261251932625d060239f4203a020ac2e40dea5bbd4a9f | MATLAB | 3,190 | 31 | % run from local pc and create gridscore, trajectory plot, and rate map plot
cd ..
initOAT
cd gridscore
preloaded_spk_reader=0; % save time of loading spike reader by using prior loaded one
if preloaded_spk_reader==0 && ~exist('keep_vars','var') clear all; preloaded_spk_reader=0; end; clear keep_vars;
use_supcomp_data=... |
9516fad67a0efb570804fa6d4317a8fbcbfc70dec04797062eeaf7e9cb8db7c0 | MATLAB | 3,193 | 117 | function [rgb cmap clim] = cdata2rgb(data, cmapname, clim)
% CDATA2RGB - Converts indexed image data to RGB with a specific colormap.
%
% This function converts a 1-D or 2-D indexed color image DATA
% (with colormap CMAPNAME) into a true color RGB image. It can also
% apply caxis limiting as specified by CLIM in the p... |
f5f87fad359b07a9e56041232486c304fdebf4f6115fe5b7a8b2246ef1bd53d5 | MATLAB | 3,196 | 118 | clear all
[task_info,supPath,MaestroPath] =...
loadDBAndSpecifyDataPaths('Vermis');
req_params.remove_repeats = 0;
req_params.grade = 7;
req_params.cell_type = {'PC ss','CRB','SNR','BG msn'};
req_params.task = 'choice';
req_params.ID = 5000:6000;
req_params.num_trials = 100;
req_params.remove_question_marks =0;
... |
62737c6c89d35ef3cccd7ca504933e5d8787d88efdb802a4afaf274769f6cf33 | MATLAB | 3,198 | 94 | function [modelBasePath, resultsPath] = s3GetPathToModelResults (modelName,executerName,otherIdentifier)
% This function will return a link to ml dataset
% Inputs (Option 1): s3GetPathToModelResults (modelName,executerName,otherIdentifier)
% modelName - name of the model trained (pix2pix, oct2hist etc). Can be
% ... |
fe79e59558952fe5a1690dd6975ec276170d0ec575c2f3ef16cece8699f5a874 | MATLAB | 3,203 | 98 | function res_str = consolidateAlphaTauFits(p_fit_arr, eps_val, eta_val, exp_field)
% res_str has fields [alpha_est, se_alpha_est, x_min_est, ks_min] for all
% simulations with specified eps_val and eta_val
% consolidation: looks at all k_min vs x_min, then picks a single x_min.
% Pulls the fitted exponents from each ru... |
bf4976be5c09573167e03b74f3c3d1e5f9886fad68692acb35e7be872555b16b | MATLAB | 3,204 | 95 | %ACTOGRAM
%
% Descriptions tba
%
% $Author: Peng Li, Ph.D.
% $Date: Jan 01, 2022
%
% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
% (C) Peng Li 2019 -
% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
%
classdef actogram < ha... |
c22ac57c79bc694820c1ee0fd5713270dd3614eb5a0ae261a5341f2e753980fd | MATLAB | 3,206 | 124 | function SID = parse_imetronic_data(data)
SID(1).name = 'LIGHT';
SID(1).num(1).name = 'HLED';
SID(1).num(2).name = 'LED1';
SID(1).num(3).name = 'LED2';
SID(1).num(4).name = 'LED3';
SID(1).num(5).name = 'LED4';
SID(1).num(6).name = 'LED5';
SID(1).num(7).name = 'LED6';
SID(2).name = ... |
dd64d015cb433e071c7b0bd4b6e5c77b7e40f424ea9c24aa48ca5f76654a664b | MATLAB | 3,207 | 105 | function glm = bv_readglm(fname)
% glm = bv_readglm(fname)
%
% :description
%
% Reads in a version 2 (BrainVoyager 2000 4.4, QX) GLM file.
%
% :inputs
%
% fname file to read, e.g. 'mymodel.glm'
%
% :outputs
%
% glm glm structure. List this function to see the field
% names.
%... |
325baddefb941fbbf6899c7bc1ba6dc078aade029c156827b9e39ed31004c327 | MATLAB | 3,211 | 83 | function model = pp_normalize_anat(sub_id, p)
% function model = pp_normalize_anat(sub_id, p)
%
% Runs SPM normalization on a high-res anatomical image that has been
% coregistered and bias-field corrected (prefix 'mr'). Uses the image and
% deformation fields that were calculated with pp_segment.m.
%
% Input:
% - i... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.