sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
5f0a5d8291d3f28508d43774dd477da562dec0a0896b4948a8e1e0d65b4eaba1 | MATLAB | 10,559 | 274 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% ################################################################# %% DESCRIPTION
% Script to analyse data for:
% Grieves et al. (2020)
% Quantification of firing rate map procedures
%
% This script
% NOTE: This script is dependent on graphtest... |
b4bde0885710ada0aca810cbf1d1212daa56e8fdd50f1338f93eca68ec9fa549 | MATLAB | 10,559 | 314 | function [pred,stats] = mTRFpredict(stim,resp,model,varargin)
%MTRFPREDICT Predict and evaluate the output of a model.
% PRED = MTRFPREDICT(STIM,RESP,MODEL) predicts the output of a forward
% encoding model (stimulus to neural response) or a backward decoding
% model (neural response to stimulus) using time-lagg... |
2c0b058e4d1eea5942e043660c11f7b5f80bdbd27a0d08422a29fc220a14e469 | MATLAB | 10,564 | 304 | function ALAN_Evaluate_PSNR_SSIM()
clear all; close all; clc
%% set path
degradation = 'BI'; % BI, BD
methods = {'alanv1_t', 'alanv1_s', 'alanv1_b'};
%methods = {'LatticeNet'};
dataset = {'Set5', 'Set14', 'B100', 'Urban100', 'Manga109'};
ext = {'*.jpg', '*.png', '*.bmp'};
num_method = length(methods);
num_set = lengt... |
b221e7b6aafbef4e1f8e2166cf40de0b10e8ee5d512afce6ec811cc8dcad734f | MATLAB | 10,572 | 324 | function [Y,xY] = spm_regions(xSPM,SPM,hReg,xY)
% VOI time-series extraction of adjusted data (& local eigenimage analysis)
% FORMAT [Y,xY] = spm_regions(xSPM,SPM,hReg,[xY])
%
% xSPM - structure containing specific SPM, distribution & filtering details
% SPM - structure containing generic analysis details
% hReg ... |
6f0c2cbab1797d86d40c19e4df8f733890af60cfdf48cc8ede401fd3adf83398 | MATLAB | 10,575 | 288 | %% Script to create output for Fig. 5 - receiver operating characteristic curve for each GLAMPI parameter
% Andrea Castegnaro, UCL, 2022 andrea.castegnaro@ucl.ac.uk
% Using support vector machine (SVM) to classify two groups based on their
% fitted parameters using an holdout strategy (60% training set 40% testing
% s... |
a5b2a3f2d9c82ba627ebbe9b9babb3a0dd1f9de9ba7d6a4b97364074970062db | MATLAB | 10,577 | 482 | function ob = block_fatrix(blocks, varargin)
%|function ob = block_fatrix(blocks, options)
%|
%| Construct block_fatrix object, a meta-Fatrix composed of Fatrix blocks,
%| such as block_diag(A_1, A_2, ..., A_M)
%| See block_fatrix_test.m for example usage.
%|
%| in
%| blocks {cell} cell array of the blocks
%|
%| opti... |
3b97cdf8fe9a5b92e0f5853d49bc1bbb57ce5f067a27fb2b57727a8ce9bf1278 | MATLAB | 10,578 | 429 | function [Dnew,meshsourceind]=spm_eeg_simulate(D,prefix,patchmni,simsignal,ormni,woi,whitenoise,SNRdB,trialind,mnimesh,dipfwhm,nAmdipmom);
%function [Dnew,meshsourceind]=spm_eeg_simulate(D,prefix,patchmni,simsignal,woi,whitenoise,SNRdB,trialind,mnimesh,dipfwhm);
%% Simulate a number of MSP patches at specified location... |
46d6c101994c44bbb0c1dc5aa2651e6d9e5469c78d939c5da4dada19414a0e70 | MATLAB | 10,578 | 305 | function Evaluate_PSNR_SSIM_SRACN()
clear all; close all; clc
%% set path
degradation = 'BI'; % BI, BD
methods = {'v5fs_plus', 'v5fs'};
%methods = {'LatticeNet'};
dataset = {'Set5', 'Set14', 'B100', 'Urban100', 'Manga109'};
ext = {'*.jpg', '*.png', '*.bmp'};
num_method = length(methods);
num_set = length(dataset);
%r... |
b012b192260296008afb3de06010b0d742cf4d7148f40df2c02b0a8ee6b5c8b5 | MATLAB | 10,589 | 305 | function Evaluate_PSNR_SSIM_RepECN()
clear all; close all; clc
%% set path
degradation = 'BI'; % BI, BD
methods = {'v9fs', 'v9fblt', 'v9fbxt'};
%methods = {'LatticeNet'};
dataset = {'Set5', 'Set14', 'B100', 'Urban100', 'Manga109'};
ext = {'*.jpg', '*.png', '*.bmp'};
num_method = length(methods);
num_set = length(data... |
b6d8e447a9cfd4a320d246a9c41b923e4fd9c9c8ee5e7a6e95a0ca1303d44a6b | MATLAB | 10,589 | 265 | function plot_dth_both_tasks(subjects,with_cross_task,with_stats,with_error_bars,varargin) %distance art, distance nat, RT
%PLOT_DTH_BOTH_TASKS Plot the fixed effects distance-to-hyperplane results for both
%tasks. Assumes that the correlations & stats have already been computed.
%
%Input: subjects' ID (e.g., 1:13), ad... |
d322a711d5e5104c55d623a12b09dfbf51cc5597a8be2079c677e562db71b083 | MATLAB | 10,607 | 221 | function gap_linking_str = fun_graph_get_linker_ep2skl_same_cc(vessel_image, vessel_mask, vessel_skl, ep_sub, ep_all_sub)
% fun_graph_get_linker_ep2skl use threshold relaxation and shortest path on
% designed metric to find a 3D line segment that connect the inut endpoint
% to a skeleton voxel in the nearby connecte... |
97bba3538987a9f892cfd5f47f0e8da1b2058211a6b6e3e6952052eff21f184e | MATLAB | 10,637 | 229 | %% Thomas_Yang_et al, 2023 @Nuo Li lab
%% Fig. 6a norm. FR, early delay SC photoinhibition, SC record
clear all
close all
addpath('../func')
load Figure_6_a_data
%% plot the selectivity in a user defined region
sig_selective_p(isnan(sig_selective_p))=1;
sig_selective = (sig_selective_p<0.01); %<--... |
85848376f16b657c6e86c0c115720e5779721ab596ca6c3f8e1586f98df8f8a8 | MATLAB | 10,668 | 340 | % Analyze the network results after scanning parameters.
% Before using this code, run simNet_ScanPars.m to collect the network outputs
% under different parameter settings.
% Note: Please assign correct fileName on line 17!!
% Wen-Hao Zhang,
% University of Pittsburgh
% Feb. 3, 2020
% Load data
setWorkPath;
addpat... |
e4aa5e450fed1e055eb3fa8841390c0a2cd8c3c9f248dcfbf737bb1ca53cdb55 | MATLAB | 10,704 | 289 | function [xVi, mask] = spm_est_non_sphericity(SPM)
% Non-sphericity estimation using ReML
% FORMAT [xVi, mask] = spm_est_non_sphericity(SPM)
%
% Required fields of SPM structure (see spm_spm):
% SPM.xY.VY - nScan x 1 struct array of file handles
% SPM.xX - structure containing design matrix information
% SPM.xX.W... |
e71b29821308fa08ff8fe34f2a1952277fca2caf10714ce6a1140000135a8496 | MATLAB | 10,718 | 257 | function epsilon = exclude_rep_by_epsilon(results_folder)
% 20230808 Calculate epsilon for repetitions
load([results_folder, filesep, 'varforrep.mat']);
%% Realign across repetitions
% repetition exclusion based on epsilon
Image_coil_ori = Image_coil; % save a copy of original images before registration
% use e... |
74c4a9832a69c8943f89cb54da420ac50babce52933f64640b518c3bd6461f7a | MATLAB | 10,724 | 267 | %% Basic analysis script that plots cosine tuning curves for each electrode channel
%% Set up directory system
% set direct path to data folder
dataPath = './Data';
addpath(genpath(dataPath));
% designate the data to be analyzed
dataName = 't5_06_02_2021';
%% Load data
dat = load([dataPath filesep dataName '.mat']);... |
55736eb25a82712685298ef39fa59b08abcdc1f3aab2de1be04aa372ea13f193 | MATLAB | 10,731 | 231 | folder = '/projectnb2/npbssmic/ns/Ann_Mckee_samples_20T/NC_8095/';
cd(folder)
% add subfunctions for the script. Change directory if not running on BU SCC
addpath('/projectnb/npbssmic/s/Matlab_code/fitting_code');
addpath('/projectnb/npbssmic/s/Matlab_code/PostProcessing');
addpath('/projectnb/npbssmic/s/Matlab_code/P... |
e365efd6e7bb753dd06e56a1cdc79f4a1890a963554b504729e0fea96977a857 | MATLAB | 10,731 | 300 | function SBJ08e_HFA_plot_recon_venn_ROI(SBJ_id, proc_id, stat_id, an_id, reg_type, show_labels,...
hemi, atlas_id, roi_id, plot_roi, mirror, varargin)
%% Plot a reconstruction with electrodes colored according to statistics
% INPUTS:
% SBJ [str] - subject ID to plot
% proc_id [str] - nam... |
cc659498bd908276394339bff230bc1a66af51975ec8935b59e13ac1ab11106b | MATLAB | 10,734 | 277 | function SBJ11b_HFA_conn_mLME(SBJs, proc_id, an_id, model_id, conn_id, lme_formula)
%% Set up paths
if exist('/home/knight/hoycw/','dir');root_dir='/home/knight/hoycw/';ft_dir=[root_dir 'Apps/fieldtrip/'];
else root_dir='/Volumes/hoycw_clust/';ft_dir='/Users/colinhoy/Code/Apps/fieldtrip/';end
addpath([root_dir 'PRJ_Err... |
4fd06dab5e6ccfc61b1ddc83d45aec5c42128bcdcaff4bb0f7341bf9cac1c2c9 | MATLAB | 10,735 | 300 | function fn_view_recon_atlas_grp_stat_ROI(SBJs, proc_id, stat_id, an_id, reg_type, show_labels,...
hemi, atlas_id, roi_id, plot_roi, mirror, varargin)
%% Plot a reconstruction with electrodes colored according to statistics
% INPUTS:
% SBJ [str] - subject ID to plot
% proc_id [str] - nam... |
dbe3c2e18e41a74e2a952b9423986f9f72507f12f4d33ddc2f27c5dc020af166 | MATLAB | 10,743 | 179 | function POKE_STRUCT = get_HP_during_interval2( POKE_STRUCT, NUMWORMS, FRAMES_IN_LAWN, stat_int )
%PLOT_HP_OVERTIME.m This function takes in a list of head poke events (track
%number, frame number pairs) and plots a head-poking rate per worm per
%minute for 40 minutes after transfer to the assay plate. Also generates ... |
bc347daaa735c00961ff854ef4739892757aebdb1e632a0313dba210febe1b81 | MATLAB | 10,745 | 205 | function [Objects] = f_imageAnalysis(Label, ch1, ch2, ch3, ch4, PreviewPath, MetaTable)
%vol(ch1, 0, 10000) Hoechst
%vol(ch2, 0, 8000) 647 TH
%vol(ch3, 0, 8000) 568 TUJ1
%vol(ch4, 0, 5000) 488 FOXA2
ObjectsThisOrganoid = table();
%% Max projections
%NucMax = max(ch1, [],3); imtool(NucMax,[]... |
4257317af1dfc1a44641af41eae3efa07bdd6d1e1c1e884b1b4410ebe538ac67 | MATLAB | 10,754 | 372 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Plotting representative data - RGC axons / SC somata
%
%
% This script generates Figures 2 and 3 and Supplemental Figures 2 and 3
% of the following paper:
%
% Molotkov D., Ferrarese L., Boissonnet T. and Asari H. (2023)
% Topographic axonal... |
c28a5c446e27283d80f5ef981956068eb2ee8818e47c0e68d16bd5421db067a0 | MATLAB | 10,757 | 175 | function [Objects] = f_imageAnalysis(Label, ch1, ch2, ch3, ch4, PreviewPath, MetaTable)
%vol(ch1, 0, 5000) Hoechst
%vol(ch2, 0, 4000) 647 HPIg
%vol(ch3, 0, 5000) 568 Sox2 (270 Mouse)
%vol(ch4, 0, 5000) 488 FOXA2
ObjectsThisOrganoid = table();
%% Max projections
%NucMax = max(ch1, [],3); imt... |
8474834362d7dfbf9972079689026d0828327de675b8fcf96572802c3f71ec83 | MATLAB | 10,759 | 283 | function Optical_fitting(co, cross, s_seg, z_seg, datapath, threshold, mus_depth, bfg_depth)
% Fitting scattering and retardance of brain tissue
% co: volume data for co pol
% cross: volume data for cross pol
% s_seg: slice number
% Z_seg: tile number
% datapath: datapath for original data
cd(datapath);
opts =... |
e3e716caaa56f5b6e8eeb280449634f3467bc1868e816e7dc185efd29e58d54e | MATLAB | 10,759 | 275 | function Ori_stitch_Ann(target, P2path, datapath,disp,mosaic,pxlsize,islice,pattern,sys,stitch)
%% stitch the retardance using the coordinates from AIP stitch
% add subfunctions for the script
addpath('/projectnb/npbssmic/s/Matlab_code');
id=islice;
filepath=strcat(datapath,'orientation/vol',num2str(islice),'/');
cd(fi... |
9a76972096a7ec50af3f2587f0d0c3a119cffbc1d906d5771d581b1b556bf321 | MATLAB | 10,765 | 258 | function fn_view_recon_atlas_grp_stat(SBJs, proc_id, stat_id, an_id, reg_type, show_labels,...
hemi, atlas_id, roi_id, plot_out, varargin)
%% Plot a reconstruction with electrodes
% INPUTS:
% SBJs [cell array str] - subject IDs to plot
% proc_id [str] - name of analysis pipeline, us... |
4058a6e40c2fe9470210b0144ac05c29abad60dfb6eb8c4666ccf49336b3e2b9 | MATLAB | 10,771 | 293 | %% Script to check the out-of-bound ratio in different groups of data
% Zilong Ji, UCL, 2022 zilong.ji@ucl.ac.uk
% Preparing the data
GLAMPI_PrepareBaseConfig
% Preprocessing the data
GLAMPI_PreprocessData
%%
%get the out-of-bound ratio for each participant under each condition
OoBratioYoung = getOoBRatio(YoungCont... |
23cb3e85c7a11308354aeb42842ad9b336dd618e2eb02414fc94a7849afdcf02 | MATLAB | 10,774 | 273 | function SBJ08c_HFA_grp_errbar_ROI_mGLM(SBJ_id,proc_id,an_id,model_id,stat_id,roi_id,...
plot_scat,save_fig,varargin)
%% Plot bar graph of proportion of significant electrodes for mass GLM
% Future: may also plot activation and RT correlations
% INPUTS:
% SBJ [str] - ID of ... |
709ddd16f7517c244a9d9eae1c4ff22691408199750fa5817240d1984435ded2 | MATLAB | 10,780 | 425 | function [phantom, params] = ellipse_im(ig, params, varargin)
%| Generate ellipse phantom image from parameters.
%function [phantom, params] = ellipse_im(ig, params, options)
%|
%| Parameters: [x_center y_center x_radius y_radius angle_degrees amplitude]
%|
%| in
%| ig strum image_geom() object
%| params [ne 6] elli... |
7bf9cb228abb818dd00d5309e81c724e1bae6846ffcb6e3ea810a71670d6be38 | MATLAB | 10,803 | 122 | function x0 = setModelInitialConditions(data,modelParameters,model,environment)
% function setModelInitialConditions(data,modelParameters,model,environment)
% Compute the glycemic profile obtained with the ReplayBG physiological
% model using the given inputs and model parameters.
%
% Inputs:
% - data: a timetable w... |
ffdbd42762ef80c89567a57e171ba1148d67684eb737a2071c5386905250b7e5 | MATLAB | 10,803 | 346 | % Script to plot Supplementary Figure 5, calculate statistics reported, and
% save data to Excel spreadsheets that are then imported into the Source
% Data file.
% Kit Longden 2023.
addpath ../Shared_functions/
% Thresholds used
% ---------------
L1.thr_used = 0.06;
L2.thr_used = 0.06;
L3.thr_used = 0.06; % 0.03 ... |
4c8f6a33b42b79e4c45ca7aad481a7621390aaae34d136440d7121e4008b80a3 | MATLAB | 10,836 | 270 | function N = spm_dicom_metadata(N,hdr)
% Export image metadata as side-car JSON file
% FORMAT N = spm_dicom_metadata(N,hdr)
% N(input) - nifti object
% hdr - a single header from spm_dicom_headers
% N(output) - unchanged nifti object (for potential future use)
%
% This function creates JSON-encoded metadata duri... |
28730cb7cb19fdfb2022378968e47f9fd327827991ddf2739280c0e1d63070ae | MATLAB | 10,846 | 219 | % Compare real to control data and quantify number of replay events in T1 and T2
function comparison_to_control_replay(control_type)
%control_type: 'Short_exposures' or 'Stability'
cd(['X:\BendorLab\Drobo\Lab Members\Marta\Analysis\HIPP\Chapter 2\Raw_replay_analysis\' control_type '_control_FIRST_laps\'])
load extrac... |
1c7027a813b1dff94ce3b0d259c78f341cf6bd6b05a6668cf0ca40d634e6d084 | MATLAB | 10,854 | 364 | function ob = Gtomo_nufft_3d_par(mask, dim_data, varargin)
%function ob = Gtomo_nufft_3d_par(mask, dim_data, options)
%
% Construct Gtomo_nufft_3d_par object. Currently only handle Fourier-based
% forward projection for 3D parallel beam case.
%
% See Gtomo_nufft_3d_par_test.m for example usage.
%
% Basically, you cr... |
f6169df6a5ce165330f9652a4b8671bf76969f58b3c676a109531ccde2cf6b90 | MATLAB | 10,863 | 309 | % use GLKF to estimate TV-MVAR model
clear all;close all;clc
% develop for 1 subj
subj = '39';
cd(['/tmp/yassamri/iEEG/sandra/subj_' subj])
load(['trial_data_subj_onset_' subj '_ref__select_chan_3.mat'])
trial_data = trial_data(:,:, 8:10);
%
% eamine data
% figure
% for trl = elec3trl%size(trial_data,1)
%
% plo... |
8e782bd163ba4598abd7617d7a2e84b1e437224ec5f75d3912dc9fa7af2682c1 | MATLAB | 10,865 | 293 | function [cleaned_trace, trend, var_params, v_elbos] = fit_ast_model(traces, n_sectors, varargin)
% FIT_AST_MODEL fit an Asymmetric Student-t model to remove neuropil data
%
% [cleaned_trace, trend, var_params, v_elbos] = fit_ast_model(traces, n_sectors, ...)
%
% INPUTS
% traces - signals from... |
51b41da36bcc530a6ac2fa48199f0ae4e1229dbd82494329543ef457c8ba6148 | MATLAB | 10,875 | 196 | %% Run this files to compute the network-human main effect and interactions for all for the VISION experiments
%% Must specify the paths to the required network data mat files.
function run_anova_stats_matlab_compare_human_responses(EXP_NUM, NUM_BOOTSTRAPS)
rng(517)
addpath('../matlab_statistics_functions')
%% NO AN... |
afa85a2a46d8cdb9d566aeedc90784c5cbb59e65cd6f71f7faf9ec90a44e3cc8 | MATLAB | 10,879 | 369 | function [out,cost,time] = fmap_est_pcg_ls(w, y, delta, smap, varargin)
%function [out,cost,time] = fmap_est_pcg_ls(w, y, delta, smap, varargin)
%|
%| Phase unwrapping of multiple data sets (w denotes frequency)
%| using a NCG with monotonic line search
%| 3D, multi-coil version
%|
%| cost(w) = sum(i=0 to n-1) sum(j=0... |
f518316b7d0064b18e59f25ea58638f5fce2fd752d33e10f987e8d1580066b4b | MATLAB | 10,879 | 168 | % UTILITIES
%
% Files
% arg_get - function [arg, ii] = arg_get(list, what, default)
% arg_pair - |function arg = arg_pair(varargin)
% beeper - make beeping noise to alert that a job is done.
% bspline_1d_coef - function coef = bspline_1d_coef(fn, varargin)
% ... |
3adca6a8b84fe8cfc164552a3ad8c70f57698aeefb5655e000812ad56e903ef2 | MATLAB | 10,906 | 261 | function bhv_cln = SBJ04_compile_clean_behavior(SBJ, proc_id, save_it)
% Reject all bad trials based on behavior and visual cleaning, then combine
% data from multiple runs
% Also checks prdm_vars are consistent across blocks and saves combined version
% Criteria:
% 1. Bob bad epoch
% 2. Bad trial (interruption... |
fae80ba60c1117089e370cc9e35c9ba9e5dcb3b01c8a077f6188b2cf372c0bc0 | MATLAB | 10,927 | 284 | %aim to calculate the latency of the response, find the time and CI, and
%the bootstrap
% close all;
clearvars;
addpath ('/data/local/u0114568/ephysio/test');
listname= 'targetLeft' ;
cn1={'sp','color', 'con', 'incon', 'att', 'congruency','fac','int'}; % condition names length=8
cn={ [cn1{1}],[cn1{2}]};
condnames=lis... |
c1b4b17de5c097d14b33dc966546e9c8741c4cda62c1fccaa7055569e812fdac | MATLAB | 10,933 | 156 | % The script is used to preprocess fMRI data
% To run this script, please first download the raw data from https://osf.io/57ryb/,
% and put the data in /mri_data/preproc_data/ folder.
% add spm toolbox
addpath(genpath('spm12/'));
% set data path
data_path = '../../mri_data/preproc_data/';
% set parameters
nSubjs = ... |
d3b4aee85cd1172735662ed854c62be7e8c29c3cdf013413d8eb316a6be03cdf | MATLAB | 10,938 | 205 | function [Objects] = f_imageAnalysis(Label, ch1, ch2, ch3, ch4, PreviewPath, MetaTable)
%vol(ch1, 0, 5000) Hoechst
%vol(ch2, 0, 4000) 647 MAP2
%vol(ch3, 0, 5000) 568 Sox2
%vol(ch4, 0, 5000) 488 TH
ObjectsThisOrganoid = table();
%% Max projections
%NucMax = max(ch1, [],3); imtool(NucMax,[])
... |
0547a9b7d9d306282f59fe7ec5ca9a0fcb1851359957b897e304b61d41f7afc0 | MATLAB | 10,954 | 287 | %% Script to create output for Fig. 5 - receiver operating characteristic curve for each GLAMPI parameter
% Andrea Castegnaro, UCL, 2022 andrea.castegnaro@ucl.ac.uk
% Using support vector machine (SVM) to classify two groups based on their
% fitted parameters using an holdout strategy (60% training set 40% testing
% s... |
ef96c9ffd3a8cebdeb90444daf21d994fa7ff150e1fd488a530b19683fb905f1 | MATLAB | 10,957 | 347 | function [M,ll,h] = spm_maff8(varargin)
% Affine registration to MNI space using mutual information
% FORMAT [M,ll,h] = spm_maff8(P,samp,fwhm,tpm,M0,regtyp)
% P - filename or structure handle of image
% samp - distance between sample points (mm). Small values are
% better, but things run more slowly... |
55a376ee9c37dfbd76274e651299fa14978316f7fd5046402f86d0a125ca3aba | MATLAB | 10,965 | 288 | classdef MlpGui < handle
% AUTHORSHIP
% Developer: Stephen Meehan <swmeehan@stanford.edu>
% Copyright (c) 2022 The Board of Trustees of the Leland Stanford Junior University; Herzenberg Lab
% License: BSD 3 clause
properties(Constant)
PROP_ROOT='Mlp.Root';
PROP_PARAMS='Mlp.fcsIdxs';
... |
91b27c1e01295b79ab4372158f76a43956906575316368dcacd928ab80f68393 | MATLAB | 10,971 | 284 | function SBJ07c_HFA_plot_stack_mean(SBJ, conditions, proc_id, an_id, actv_win,...
plt_id, save_fig, varargin)
%% Plots single trial stack and condition averaged HFA per electrode with activation vs. baseline
% sorts by condition, then by RT; scatter for events to show condition... |
cd0c15896e41e52c4eebc4c0c2786eb4faff1584b7688ab522b5931f0f1f3358 | MATLAB | 10,977 | 259 | function export_kinematic_data(study_info)
subject={};
condition={};
trial=[];
reach_dur=[];
grasp_dur=[];
end_dur=[];
% behavior
exe_df=readtable(fullfile(study_info.data_dir,'behavior_exe.csv'));
obs_df=readtable(fullfile(study_info.data_dir,'behavior_obs.csv'));
%% Loop over all data files
for s_idx=1:size(study_... |
47d5d88b12d181a4542895617952f29637fe8e3029d0e0e06753679b6a64f2f4 | MATLAB | 10,983 | 392 | function [dMapPair,dMapInd] = normalizationSingleTrialInference(spikes,parameters)
%% normalizationSingleTrialInference Finds the Maximum a Priori estimates for the normalization signal in a Ratio of Gaussians model
% INPUT
% spikes (2,number of stimuli, number of trials) - the spike responses
% parameter... |
bc73a64a49b7e36b45607ad99aae35323e846aae6e56df38a40db96140af98a0 | MATLAB | 10,986 | 434 | function fig = plotAxisProjection(obj,showAxis,useAll,reuseFigFlag,plotType,saveFigFlag,weightScale,markerAlpha,showExpFlag)
if(~exist('showAxis') | isempty(showAxis))
showAxis = 'NT';
end
if(~exist('useAll') | isempty(useAll))
useAll = true;
end
if(~exist('reuseFigFlag') | isempty(reuseFigFlag... |
f698f7e8f2480a0bb40701c60055bd81b58bef72e6a85a13d5d8eaa50b6eeb61 | MATLAB | 10,998 | 365 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%import the dataset all_infered_results.mat from folder 20211205_ai148_visual_stimulation_2
%brain_region 1*5540 each cell is the name of brain area
%valid_C a matrix of 5540cells*10012 time points
%download circularGraph function if required https://git... |
67ffec0210930c239eade2f43022ee24cfa12502d996db540f6d7e358a04e88f | MATLAB | 11,025 | 374 | function [out] = spm_eeg_render(m,options)
% Visualisation routine for the cortical surface
% FORMAT [out] = spm_eeg_render(m,options)
%
% INPUT:
% m - patch structure (with fields .faces et .vertices)
% or GIFTI format filename
% options - structure with optional fields:
% .texture - tex... |
7cf97e437eab37758075003cec7ad92b41b0acff8d0d62d94d233e494810584c | MATLAB | 11,026 | 355 | function out = spm_dartel_import(job)
% Import subjects' data for use with Dartel
% FORMAT spm_dartel_import(job)
% job.matnames - Names of *_seg_sn.mat files to use
% job.odir - Output directory
% job.bb - Bounding box
% job.vox - Voxel sizes
% job.GM/WM/CSF - Options fo different tissue classes
% j... |
b9dcd8f6ee7874c15d577f7ca9f0c50424f17e033844df009e88d08a6f51e213 | MATLAB | 11,038 | 270 | function rnn_dth_all_distances_median_RT(subjects,with_stats,with_error_bars,model_type,varargin) %distance art, distance nat, RT
%RNN_DTH_ALL_DISTANCES_MEDIAN_RT Performs the cross-modal distance-to-hyperplane analysis using
%the distances-to-hyperplane from each subject and the RTs from the RNN.
%
%Input: subjects' I... |
bfd0ec6bbf42eef1ee3bed8d6c701b7046a01f334eafe6584be62dea765ec658 | MATLAB | 11,082 | 399 | function plot_niceSingleCellFig(allCells, i)
addpath(genpath('./plottingFxns'))
%%
if isempty(i)
i = randi(size(allCells.spatialFR4,1));
end
% i = 1148 % G4, WT
% i = 300 % G2, WT
% i = 3213% HCNd1, WT
% i = 3484 % HCNd2, KO
i = 545; % G3, WT THIS IS WHAT IS USED IN FIG 3
% i = 285 % G2, WT
% i = 297 % G2, W... |
e7c20c3d8645c2f5728b46cbe97cf9a285830ead580020cf7c91eb933a8c1bb2 | MATLAB | 11,082 | 311 | function varargout = spm_select(varargin)
% File selector
% FORMAT [t,sts] = spm_select(n,typ,mesg,sel,wd,filt,frames)
% n - number of files [Default: Inf]
% A single value or a range. e.g.
% 1 - select one file
% Inf - select any number of files
% [1 Inf] - select 1 ... |
f637bd87f02894466d00cb3d1c0090b5c199a8507389b93ec4850662f0205cac | MATLAB | 11,095 | 249 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% ################################################################# %% DESCRIPTION
% Script to analyse data for:
% Grieves et al. (2020)
% Quantification of firing rate map procedures
%
% This script
% NOTE: This script is dependent on graphtest... |
e597f7229eae0d5327312c256a8215672107149936b6839722b6c47326c6a50f | MATLAB | 11,098 | 304 | %% read data from csv
clear all; close all; clc;
summary_file = 'D:\Anna\Myelin paper Spring 2022\origin_files\OCT_summary_del_NC.xlsx';
[mus_gm_AD, mus_gm_CTE, mus_gm_NC,...
mus_wm_AD, mus_wm_CTE, mus_wm_NC,...
ratio_gm_AD, ratio_gm_CTE, ratio_gm_NC,...
ratio_wm_AD, ratio_wm_C... |
8ed8df557df399446eaf2b081472f47b27b290cde1ffd3a76ace31121a9ae352 | MATLAB | 11,103 | 188 | function firing_rate_shuffle_population_vector_analysis(computer,bayesian_option,num_shuffles,save_option)
% Marta Huelin_February 2020
% Computes a variation of population vector analysis between shuffled matrices for the same type of comparisons used in the population_vector_analysis.m code (meaning,
% between same t... |
5f87449fbe1df7df1bf3443f3c45836a6c95cb6ec9a228b2d9e25d116d20ecb8 | MATLAB | 11,109 | 257 | function data_out = load_doppler_data(varargin)
% LOAD_DOPPLER_DATA Load specified dataset
%
% INPUTS:
% varargin:
% gui_title: string; Title for GUI
% session_run_list: (1 x 2) double; [session run]' If specified,
% skips selection GUI. Should match ses... |
384db5fb7e067d13fcbc06b18a462ab240d1b002b22cab145cf4d3c6dbe5cf9e | MATLAB | 11,124 | 373 | function c_EEG_plotEpochs(varargin)
p = inputParser();
p.addRequired('EEG',@isstruct);
p.addParameter('data','data',@(x) ischar(x) || isnumeric(x)); % name of field in EEG struct or raw data
p.addParameter('COI',[],@(x) iscell(x) || isnumeric(x)); % channel(s) of interest (channel labels if cell array), or
% indices i... |
2d296c20f9e21d50da0deeac3737472424433a74a190d8c7a9afae37da513f1b | MATLAB | 11,132 | 304 | %% Manually scoring the participants trajectory
% In order to the model to fit the data the participant had to demonstrate
% a clear intention of completing the triangle.
% This did not happen all of the time and therefore we had to exclude some
% data. Also we noticed by looking at the tracking data that participants
... |
7874176e174fa5cc1317e4a9f19b3fc84cebe29545d3c608bada627cf1ebf097 | MATLAB | 11,160 | 283 | function SBJ08a_crRT_nANOVA(SBJ,an_id,stat_id)
%% function SBJ08a_crRT_mANOVA(SBJ,an_id,stat_id)
% Run ANOVA with sliding windows for given time-frequency analysis
% Correlation with RT and regression of RT as confound supported
% INPUTS:
% SBJ [str] - subject ID
% an_id [str] - HFA analysis to run stats
% st... |
24c0a2b7df83f7b1b4db1fc04fbbb91e13a7c6dd6438b7f1c5b23640fcdad6be | MATLAB | 11,165 | 415 | function ftab = de_ftab_build(s_arg, varargin)
%function ftab = de_ftab_build(s_arg, [options])
%|
%| For multiple-kVp X-ray imaging, we must evaluate functions f_m(s_1, ..., s_L)
%| for m=1,..,M, where M is the number of kVp settings,
%| and L is the number of material components.
%| f_m(s1, s2) = -log( \int exp(- (m... |
a472402b108b29f6bd722079bd01279923848e3807e7c3e3eb292319c39eb9ce | MATLAB | 11,172 | 298 | function out = spm_shoot_norm(job)
% Spatially normalise and smooth fMRI/PET data to MNI space, using Shoot deformation fields
% FORMAT out = spm_shoot_norm(job)
% job - a structure generated by the configuration file
% job.template - Shoot template for aligning to MNI space. Aligns to population
% a... |
5cbee7be30fb509d1897aaa9d7fee7a0e84004d069cf98e52a9b49cba2c99895 | MATLAB | 11,186 | 269 | classdef FMeasureMerger < handle
% AUTHORSHIP
% Primary Developer: Stephen Meehan <swmeehan@stanford.edu>
% Copyright (c) 2022 The Board of Trustees of the Leland Stanford Junior University; Herzenberg Lab
% License: BSD 3 clause
properties(Constant)
USE_ORIGINAL_IDEA=false;
end
metho... |
15a66daccad33b44fd8c40dec7729817fb591ce2dfeec8ea5b7b02c697a9ddcb | MATLAB | 11,211 | 267 | function [fitFun,pHat,XX,YY,pHatBase,CorrMat1,CorrMat2,qBounds1,qBounds2,...
baselineDat,allDat,allSpk,alldSpk,allTime,allTime2,allFly,allFlyBaseline,turnOpt_during] = plotKNNMesh3D_Fit(f_orco, s, keyNdx, xGrid, yGrid, ...
zGrid, ratio, Thresh, K, state, kin, genotype, plotFig)%f_orco, condNdx
fe = f_orco.... |
63354a0cd2239f4901a684cd2fe57b0c4500df415cbac8a33358671be727c412 | MATLAB | 11,211 | 279 | %Version 1 with fixed window length
clear all
close all
dbeh_path='C:\Users\xscogno\MATLAB\Flavio2\Waves\Tracking data\';
rec_data_path = 'C:\Users\xscogno\MATLAB\Flavio2\Preprocessed_data\';
dpath_spikes='C:\Users\xscogno\MATLAB\Flavio2\Preprocessed_data\';
dpath_sorting='C:\Users\xscogno\MATLAB\Flavio2\Waves\Sortin... |
ce1289d3d7024594a33d4813cfdb2c3905bcfde51cd7da81e4a1ffaa5c66a20c | MATLAB | 11,235 | 262 | function [TRACKS, EXIT_STRUCT, POKE_STRUCT, SUMMARY_STRUCT] = tracks_postprocessing6( allTracks, ev_ho_dict, bg_struct, pixpermm, titlestr)
%TRACKS_POSTPROCESSING.m This function takes in the current tracks and the
%tracks that finished throughout the video and extracts a few more
%behavioral parameters from them.
%RO... |
cfd430813db18b386155187612c90efdf30cfecae3882cb4b0eb38d6b43748a6 | MATLAB | 11,235 | 401 | function back = cbct_back(proj, cg, ig, varargin)
%function back = cbct_back(proj, cg, ig, varargin)
%|
%| cone-beam backprojector for feldkamp.m
%|
%| in
%| proj [ns nt na] cone-beam projection views
%| cg strum ct_geom
%| ig strum image_geom
%|
%| option
%| 'use_mex' 0|1|2|3 1 mex with loop in mex (default)
%| ... |
02196e02ea0913c73f7d0659dba7bfa50bb12e07232cdef12d5eacb48ffd462b | MATLAB | 11,246 | 287 | function disconnectFromHost(h, varargin)
% Closes the connection from the control computer to experimental
% computers/Raspberrys, and reboots or shuts them down on request.
% connect2ControlComputer keeps running on the experimental computer and
% waits for the next control computer, so connect2Host works again withou... |
8c87e150a619496a289166cb1e87c7aa25aca60bd3ceca5788110b6c5ce6ffff | MATLAB | 11,255 | 350 | function [imvol_out, M_out] = unwarp_resample(imvol,M,imvol_out_size,M_out,Mdc,unwarpflag,Jacobianflag,plotflag,interp_method,inflag,thruflag,gradfilename)
%
% In this file:
%
% unwarp_resample - from AD's unwarp_and_resample_vol
% proj, jdproj - for projecting out e.g. throughplane component of
% displ... |
4659a2363f8258b609aa42ac2efb37880b9ba518773561c46c3eb5fd2a434bc9 | MATLAB | 11,257 | 253 | function num = connect2Host(singleHosts)
% Connects the control computer to one or more experimental computers, on
% which connect2ControlComputer runs and waits. It returns a handle for
% each of them; sendCommand2Host, getKeyBufferResult, disconnectFromHost
% and the other network functions address an experimental co... |
4847e547e250a47faf4b66afe5c2fec08b47ea20c0f95e1d22f7ebbdf97a92bc | MATLAB | 11,274 | 292 | function [seg_spktimes,seg_spikes,current,main_frontbuffer,main_residual,main_restime]= Spike_detector(bigdata,chans,thresh,filt_para,current,main_frontbuffer,main_residual,main_restime,sr,pre_seg,post_seg1)
%function [seg_spktimes,seg_spikes,current,main_frontbuffer,main_residual,main_restime]= Spike_detector(bigdat... |
40ec4d7683e9abb8cbb9fc4313610303d4f2b256edb019ebbda16e02c06d562c | MATLAB | 11,280 | 319 | function N = spm_parrec2nifti(parfile,opts)
% Import PAR/REC images from Philips scanners into NIfTI
% FORMAT N = spm_parrec2nifti(parfile,opts)
% parfile - name of PAR file
% opts - options structure
% .ext - NIfTI file extension {'img','nii'} [default: spm_file_ext]
% .outdir - output directory [def... |
577d645fe864d6e9734bddc61acf0241966772af03222378a6aa4ca955d3a1fc | MATLAB | 11,285 | 260 | function [tbl,rm] = simple_mixed_anova_partialeta(datamat, varargin)
% tbl = simple_mixed_anova(datamat, between_factors, within_factor_names,
% between_factor_names)
%
% Repeated-measures or mixed ANOVA with any number of factors.
%
% Function built on top of existing MATLAB functions in an attempt to
% simplify user... |
17d43c08059733bd0262d6dd9f5c2f0a9b9b3a311f1214c645dc8022fddc83a0 | MATLAB | 11,294 | 230 | % GET PLACE FIELDS OF A TRACK EXCLUDING 1 OR MORE LAPS
% loads list_of_parameters.m, extracted_clusters.mat,extracted_position.mat, extracted_waveform.mat
% uses function extract_laps.m, skaggs_information.m
% INPUTS:
% track_id: track being analysed
% chunk1: start lap ID and end lap ID in chunk before excluded lap. E... |
023da1cccf2cc9927603cd538029f9d29f2760f5b3c726101785256aac3d7c28 | MATLAB | 11,303 | 254 | clear all;close all; clc;
tic
disp('CFC')
subj = '39'
addpath('/mnt/yassamri/iEEG/sandra/analysis_pipeline_final')
[fro_chan_idx,MTL_chan_idx,temp_chan_idx,insula_chan_idx,cingulate_chan_idx,OFC_chan_idx,CA3_chan_idx,CA1_chan_idx,HC_chan_idx] = get_elecs(subj);
cd(['/mnt/yassamri/iEEG/sandra/subj_' subj])
load('cond_... |
efdffdb9d8a9141336437c4d0ca093a7ab5caede0363955aca7af9eec3702872 | MATLAB | 11,312 | 364 | clear all
close all
dbeh_path='C:\Users\xscogno\MATLAB\Flavio2\Waves\Tracking data\';
rec_data_path = 'C:\Users\xscogno\MATLAB\Flavio2\Preprocessed_data\';
dpath_spikes='C:\Users\xscogno\MATLAB\Flavio2\Preprocessed_data\';
dpath_sorting='C:\Users\xscogno\MATLAB\Flavio2\Waves\Sorting\';
dpath='C:\Users\xscogno\MATLAB\F... |
43eb80c2c82eab1bb8bfe48ee5307c8c95af63465554cc8e6939919daaf2dcb6 | MATLAB | 11,334 | 270 | function dth_all_distances_median_RT(subjects,task_distance,task_RT,with_stats,with_error_bars,varargin) %distance art, distance nat, RT
%DTH_ALL_DISTANCES_MEDIAN_RT Performs the distance-to-hyperplane analysis using
%the distances-to-hyperplane for each subject and the median RT across subjects.
%
%Input: subjects' ID... |
d765c04c05447e77922c2475b612a0cf018051d4dbe29fdf2b7a8f8d9e739658 | MATLAB | 11,335 | 424 | function R = Rmem(kappa, varargin)
%function R = Rmem(kappa, [options])
%
% Build roughness penalty regularization "object" based on C = Cdiff() object,
% for regularized solutions to inverse problems.
% This version is somewhat less flexible than Robject() but uses less memory.
%
% General form of nonquadratic penalt... |
3d2e5f10a661b044a5f51bd8a95156032e9bc5de7d7fe3d5a78608b4eb2922b4 | MATLAB | 11,353 | 314 | function fn_view_recon_atlas_grp_stat_ROI_LME_cat(SBJ_id, proc_id, an_id, model_id, stat_id, cat_id, reg_type, show_labels,...
hemi, atlas_id, roi_id, plot_roi, mirror, varargin)
%% Plot a reconstruction with electrodes colored according to statistics
% This version uses categories based o... |
5fc8462a2457bae5766e95e98bc7c2678f2ef80f387f2eae0aea0c62f720d99c | MATLAB | 11,362 | 399 | %| spect_3d_example.m
%|
%| Illustrate 3D SPECT image reconstruction with compensation
%| for depth-dependent detector response and nonuniform attenuation.
%|
%| For simplicity, there is no "model mismatch" in this example:
%| we use a discrete object and we simulate the projection views
%| with exactly the same system... |
bc9771145c9e02a15af853e1988901d2c2159e3cfa645cb9cb244339d57fcd15 | MATLAB | 11,375 | 419 | %%%
% Morelet wavelet FWHM definition
%
% This code accompanies the paper:
% "A better way to define and describe Morlet wavelets for time-frequency analysis"
% by MX Cohen (NeuroImage, 2019)
%
% Questions? -> mikexcohen@gmail.com
%%%
%% Figure 1: The happy time-frequency ma... |
344c9d9dad08521e544f7f7c08d3659af50856345db1434f1cda268366032728 | MATLAB | 11,376 | 259 | %% NWB_SB_import_main
% A sample import script that computes the following:
% - Behavioral metrics
% - Spike sorting metrics
% - SU selectivity metrics (SB)
% - SU selectivity metrics (SC)
%
% SB refers to sternberg main task, and SC to sternberg screen task
% throughout.
%
%
% Michael Kyzar 6/30/23
clea... |
dfe6734c1d3c55bb4ea725db7bc38b57b2e1bbe1d9ca5b8adf6b205b3dbdac85 | MATLAB | 11,381 | 306 | function triggerCounter3
% Standalone application that counts the MRI triggers and saves the number
% of triggers and the time into a memory mapped file.
% The presenting application is then able to read out these values.
% This version uses a virtual or real keyboard!
% The keys that are counted as a trigger are ´, t ... |
dd0b4158553f663ad76418cb06360b637dadfe99fb5a54de3fed179cd8c7ea12 | MATLAB | 11,392 | 319 | function DCM = spm_dcm_fmri_csd(P)
% Estimates parameters of a DCM using cross spectral fMRI densities
% FORMAT DCM = spm_dcm_fmri_csd(DCM)
% DCM - DCM structure
%
% Expects
%--------------------------------------------------------------------------
% DCM.a % switch on endogenous connecti... |
1732c1bd96bdcab64c5dad1ce593f8f6ecea7064cde6f3c4d23b186a8500004a | MATLAB | 11,401 | 271 | function derivedPic = standalone_segmentImage( varargin )
%%%%% Start: Process the Input Arguments
if isequal(length(varargin),1) % Only an input image is given; default arguments
handles.basePic.data = varargin{1} ;
handles.basePic.ySize = 1024 ;
handles.basePic.xSize = 1024 ;
if size(handles.basePic.data,3)==... |
dba93f9eaa14e618f612311e2be2a20d7318ce997981444019d321f4d150614b | MATLAB | 11,406 | 322 | clear all
close all
[big_table, waves] = get_big_table();
%% Calculates the COM for different temporal resolutions and without shuffling
N_sh=100;
count=0;
for w=1:length(waves)
row_w=waves(w);
disp(w);
count=count+1;
mouse=['L',num2str(big_table(waves(w),1)),'M',num2str(big_table(waves(w),2))]... |
02b8354aa706002d0c83689fa5f6cbb66b9b1e1588b878bc17eecf36f994b1f3 | MATLAB | 11,421 | 263 | function [TRACKS, EXIT_STRUCT] = get_enter_exit_events_SPLINEMETHOD2( TRACKS, stat_int, before_sec, after_sec , bg_struct)
%GET_ENTER_EXIT_EVENTS_SPLINEMETHOD.M This function takes in a set of tracks and
%identifies the indices of lawn exits and entries .
% Also returns a set of durations outside the lawn on a per tr... |
1e7d4349a68f9262de522551c7c1a4d0866ba6268318e86009f994985b726153 | MATLAB | 11,429 | 283 | %this plots all the flightORI_data from flight_orientation.m from individuals in the same
%condition (or at least conditions that CAN be pooled)
clear all
close all
%load data: rotated data
[filenames, path]=uigetfile('flightORI*.mat','multiselect','on');
%if only one selected turn filenames into cell
if i... |
1a0180ac41053838a1dcf09ea972d4cfe8943aa6758b7b54f63553cdd6f7fe4b | MATLAB | 11,434 | 360 | %% Figure 5b
% this script rungs additional analysis required for the plots of figure
% 5b.
%% - HOUSE KEEPING - %%
clear
close all;
ft_defaults;
%% -SUBJECTS - %%
% ID 18 missing because of LP at 50Hz
subjects = {'OSL13', 'OSL14', 'OSL17', 'OSL21', 'OSL24', 'OSL26', 'OSL27', 'OSL28', ...
'OSL29', 'OSL30', 'OS... |
53dd791e9c3f9a40e1b814f1cc4273c8c37ac21c435722142dd1a18eb47d0129 | MATLAB | 11,435 | 307 | function [embedding, method] = choose_optimize_layout(head_embedding,...
tail_embedding, head, tail, n_epochs, n_vertices,epochs_per_sample,...
a, b, gamma, initial_alpha, negative_sample_rate, verbose, method,...
progress_callback, epoch_reports, random_state, min_dist, ...
move_point, dataDims, sgd_ta... |
74b3eb84dc79959dc9e41d6d773f67de93508d88a7249c825b26a9150c87e405 | MATLAB | 11,447 | 358 | %
% Phenotype can be WT,Isl2heterozygous,Isl2homozygous,ephrinA2mm,
% ephrinA5mm, ephrinA2mmA5mm, TKO
%
% Action can be: run, analyse
%
% expNum is used if you want to run multiple runs with same
% parameter from another script to do statistics. This allows you
% to separate the runs.
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
fb9be9c536b4384a72e7c568634df90d30aff86fa90a87df859158a96e7d8e95 | MATLAB | 11,451 | 399 | function loadGradients(obj,phenoType)
% This loads the table that David Sterrat had prepared
% Added the B-system
% infoFile = 'gradients/Eph-ephrins-both-axis-JH.csv';
fid = fopen(obj.gradientInfoFile,'r');
fprintf('Reading gradient info file: %s\n', obj.gradientInfoFile)
header = fgets(fid);
if(... |
f26af70dc45328681b7c520f201cd51123b0b2562688d304c04e0651ddab92af | MATLAB | 11,456 | 274 | function [text2display, escape] = inputTextBox(varargin)
% This function creates a box that allows the user to type a freely defined
% text - only the letters a to z (lower case) and space, other keys are ignored!
% The input is terminated by pressing enter.
% The text is returned as a char array. Use the ESC key to ex... |
6e98189590c958b384cd5e9d51a06c09af4f5230416c97e1cec83f2d684b7631 | MATLAB | 11,468 | 243 | function POKE_STRUCT = get_head_pokes_from_summary( bgvidindex, pixpermm, IN_OR_OUT, Lawn_Boundary_Pts_x, Lawn_Boundary_Pts_y, Lawn_Boundary_Dist, Head_cent, Midbody_cent, forward, reverse, speed )
%GET_HEAD_POKES_FROM_SUMMARY.m This function identifies times that the worm pokes its
%head out of the lawn while the rest... |
522378a061889a1f3a7d26196ab32430e4174e3544a97d8bcfc579fe5e4e7072 | MATLAB | 11,470 | 369 | function [MDP] = spm_MDP_check(MDP)
% MDP structure checking
% FORMAT [MDP] = spm_MDP_check(MDP)
%
% MDP.V(T - 1,P,F) - P allowable policies of T moves over F factors
% or
% MDP.U(1,P,F) - P allowable actions at each move
% MDP.T - number of outcomes
%
% MDP.A{G}(O,N1,...,NF) - likelihood ... |
b8a14918a0611caac30866d242b8e5b1bb363cdf3c0a974c13deefda9338cd30 | MATLAB | 11,483 | 370 | function [C,P,F] = spm_PEB(y,P,HP,OPT)
% parametric empirical Bayes (PEB) for hierarchical linear models
% FORMAT [C,P,F] = spm_PEB(y,P,[hP],OPT))
%
% y - (n x 1) response variable
%
% MODEL SPECIFICATION
%
% P{i}.X - (n x m) ith level design matrix i.e: constraints on <Eb{i - 1}>
% P{i}.C - {q}(n x n) ... |
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