sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
c979a6f41e296d312d7d6fd9e16caa76fd1a31c70c175eb7ceac5197d0dc4aad | MATLAB | 5,698 | 115 | %% IR74 Processing Variables
[root_dir, app_dir] = fn_get_root_dir(); ft_dir = [app_dir 'fieldtrip/'];
if isempty(strfind(path,'fieldtrip')); addpath(ft_dir); ft_defaults; end
%--------------------------------------
% Basics
%--------------------------------------
SBJ_vars.SBJ = 'IR74';
SBJ_vars.raw_file = {'... |
8e3ba2af5f7226228c84ba03fe1c9155e9e685ebbb734da49f1106fce9b6c7ea | MATLAB | 5,703 | 194 | %% Time-Frequency on Raw Data
% #######################################################################
% Steps:
% 1. Load Raw Data
% 2. Perform TFR
% 3. Save TFR for each subject
% #######################################################################
%%
clear
ft_defaults
%% Subjects
% ID 18 missing because of... |
16a8053df94089952de4a03d34963612ad440212d9b503e0b0a434fdb17ac5a2 | MATLAB | 5,705 | 130 | % bdnfSynchData_forRM.m
%% PART 0 - set colors
clc; clear all; close all;
% actual colors to use
myColors(1,:) = [0 0 0]; % black
myColors(2,:) = [1 0 0]; % red
myColors(3,:) = [0 0 1]; % blue
%% PART 1a - GATHER DATA
% % % * * * * * * * * * * % % %
ready2save = 1; % * * * * * * %
% % % * * * * *... |
98a9feee98d652449df7139556875c46cead19b56eb7fcd6526d6c4a2e8a978b | MATLAB | 5,705 | 168 | function nc_Figure5a(bsIter)
% Figure 5a - Symmetry of R1 over the lifespan
%
% nc_Figure5a([bsIter])
%
% This function will reproduce Figure 5a showing that a parabola is a good
% fit of the R1 lifespan data and Figure 5b showing that R1 changes are
% symmetric over the lifespan.
%
% Inputs:
%
% bsIter - Number of b... |
c17429e94486cbfc36b78bf7f9fe746e606292d83dae6fa7a9a2e9c21d8f539c | MATLAB | 5,709 | 179 | % Demo of a linear Hawkes process where the E neurons are organized on a
% ring
% Wen-Hao Zhang, July 2, 2019
% University of Pittsburgh
%% Parameters of the model
parsHawkesNet;
% parsMdl.Ne = 180 * 40;
% parsMdl.tLen = 2e3*1e3; % unit: ms
parsMdl.tLen = 100*1e3; % unit: ms
parsMdl.bSample_ufwd = 1;
parsMdl.jxe =... |
eba4afb077eef2d9cd3cf08ad6fa8c0ceb7b73d9a663c8fb7d63f2e2a5301b2c | MATLAB | 5,711 | 177 | %% Analyzing Neural Time Series Data
% Matlab code for Chapter 15
% Mike X Cohen
%
% This code accompanies the book, titled "Analyzing Neural Time Series Data"
% (MIT Press). Using the code without following the book may lead to confusion,
% incorrect data analyses, and misinterpretations of results.
% Mike X Cohen... |
76fb744b398d4e6070c6ac3621959344657e71a5a052e05b4a5e0d2f40f27695 | MATLAB | 5,712 | 149 | function example_cell_plot_tsc_II(ID,tWindow1,tWindow2)
%EXAMPLE_CELL_PLOT_TSC_II Demonstration of a rhythmicity group member.
% EXAMPLE_CELL_PLOT_TSC_II(ID,TWINDOW) plots the activity of the given cell
% (ID) from a recording between TWINDOW(1) and TWINDOW(2) seconds; and
% also the the autocorrelation of it... |
fc453fea1453a94ff300e5b408fa5b11645819e155f3cdc774161d9dca62c1db | MATLAB | 5,718 | 137 | function nf = fun_graph_get_node_features(cc_ind, vessel_image, vessel_mask_dt, image_size, compute_feature)
% fun_graph_get_node_features computes the node features including average
% intensity, average radius, center of mass, radial node density
% distribution, local neighbor distance and label, nearest neighbor
% d... |
9b4953ea37f6eb33fde662affde3b69e305e726d0ad69a18ffeb02f8d1f62aa3 | MATLAB | 5,721 | 238 | function [C, ugibb] = Csparse(arg0, arg1, arg2, arg3)
%function C = Csparse('b2info', kappa, mask, chat)
%function C = Csparse('center', Cscale, mask, chat)
%function C = Csparse('maskleak', mask, chat)
%function C = Csparse('maskleak3', [beta_xy beta_z], mask, chat)
%function C = Csparse('maskbar', mask, chat)
%funct... |
e795ccd6053ec511285c93d9b8059d4bc290fd601d782d354c876ba1f4bb5eb7 | MATLAB | 5,721 | 180 | %== main function for plotting rasters.
%
% SPIKERASTER - Spike raster of multiple neurons
% SPIKERASTER(spikes, options), plot spike trains given in variable
% SPIKES, which is in format [neuron time] or in a sparse matrix
% with time down columns and neuron number across rows. If
% there are no spikes (S... |
d86d741a6999ac4209b011f16cab938b7f2314b32c29f9fa337cb134bee9c408 | MATLAB | 5,724 | 131 | function [Im, wtCoarse, wtFull] = CoilAdaptiveCombine_V2(Im_coil, blocksize, trainingsize, bInterp, Rn)
% 20230615 update: line 98 changed: delete sqrt
% Input:
% Im_coil: Nrow x Ncol x Ncoil 256 256 32
% blocksize: size of the image block
% trainingsize: size of the training region
% bInterp: if true, interpol... |
b80a3788d6f03675425142c5402af01555d5147c14c1521d09a3fe399a736c6b | MATLAB | 5,725 | 182 | function [L,D] = spm_eeg_lgainmat(D,Is,channels)
% Load or compute if necessary a gain matrix
% FORMAT [L,D] = spm_eeg_lgainmat(D,Is,channels)
% D - Data structure
% Is - indices of vertices
%
% L - Lead-field or gain matrix L(:,Is)
%__________________________________________________________________________
% C... |
3ccfb502e744ed319debff8858517fd86ef50af1548ba281c3b788cd062a6887 | MATLAB | 5,732 | 165 | function EEG = c_EEG_resample(varargin)
p = inputParser();
p.addRequired('EEG',@isstruct);
p.addRequired('newFs',@isscalar);
p.addParameter('method','eeglab');
p.addParameter('doResampleAuxData',true,@islogical);
p.parse(varargin{:});
EEG = p.Results.EEG;
if p.Results.newFs == EEG.srate
c_saySingle('New sampling rate... |
e2c5f65bca9623e18aedb4ed05b448985b02aea7261fbb31b9f8c5719ea1997a | MATLAB | 5,732 | 145 | % 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
% compari... |
d3066952fd64e407c3a81702913bd54817a6cdaef75a35689a2a6d3e71ae11c3 | MATLAB | 5,740 | 143 |
phase_regions_list = {'TEMP'}
power_regions_list = {'CA3'}
%% initializa group matrices for each pair of regions and each condition
% cortex w/ MTL
if strcmp('OFC', phase_regions_list) | strcmp('FRO', phase_regions_list) | strcmp('TEMP', phase_regions_list) && strcmp('MTL', power_regions_list)
group = {'3... |
a5cc50631ad80d3de7241494381287b673c57bf83c6c5508226e6476165f2c9b | MATLAB | 5,741 | 183 | function imfs=emdx(data,varargin)
% EMDX - perform empirical mode decomposition on channels x time x trial data
%
% Usage:
% imfs = emdx(data[,maxorder,standdev]);
%
% Inputs:
% data = [chans time trials] input data (can also input [time trials]
% or [time] data)
% maxorder = (optional) ... |
39f6fbd2b392fab6e69d2de6b8f3dc9d29fb3ecd74f9e941f6eacc38168c1b02 | MATLAB | 5,743 | 142 | function compute_lgi (pial, outersmoothed, fnormal, nsteps, radius, outdir)
% Original Author: Marie Schaer
% Date: 2007/11/15
%
% This function computes final local GI calculations, based on previously
% measured area for outer surfaces ROIs (outer_ROIs_area.mat) and on their
% corresponding regions of interest on t... |
1cba334c04c89912e3f3c72306a2531b1366b088f59b4a5b72484e5a88709e58 | MATLAB | 5,746 | 128 | function Gen_OCT_coord(datapath,disp,mosaic,pxlsize,islice,pattern, plane1, plane2, plane3)
%%% ---------------------- %%%
% function version of mosaicing & blending
% input: displacement parameters: four elements array [xx xy yy yx]
% mosaic parameters: four elemetns array [number of x tile, number of y tile,
%... |
a5ee48549a32f12ccf1a8402850fb510e9875780edba3b1255712f20cdacaf53 | MATLAB | 5,748 | 221 | function [coef codes] = ir_odwt2(x, varargin)
%function [coef codes] = ir_odwt2(x, varargin)
%|
%| 2D orthonormal discrete wavelet transform (ODWT) of 2D image x.
%| Also provides the adjoint, which is the inverse ODWT.
%|
%| in
%| x [N M] image
%|
%| option
%| 'level' int default: 1
%| 'wname' char default: 'haar'
%|... |
3d5e2c2adb676fad3532feed2634cada001ce483cc073d3e096c773c79100c90 | MATLAB | 5,750 | 137 | function [ens_nodes, results] = find_temporal_ens_nodes(best_model,shuffle_model,data,stimuli, num_controls)
% FIND_TEMPORAL_ENS_NODES Find multi-timeframe neuron ensembles with CRF models.
%
% Input
% best_model: Trained CRF model to analyze.
% shuffle_model: Shuffled dataset CRFs.
% data: Expected to be a timef... |
ab56027b6b0953acd498d7b4b53d737112a613cff3ae6a3a892bf7d7489c2cad | MATLAB | 5,752 | 155 | % Optimality deviation plots for paper
clear all
clc
close all
no_tit = 1;
input_options = struct;
input_options.show_tit = ~no_tit;
input_options.rank_regression = 0;
plot_transition_dist_also = 0; % (0)
listLength_inclusion_criteria_spans_both_task = 0; % (0) if 1 then excludes any subject who... |
c57b6fad47ffa15d6a447e486566638e2d3ef914ce5e193cb044154cb5831268 | MATLAB | 5,757 | 190 | function [pos] = plot_stats_permtest(stat,data_avg1, data_avg2,fsample,timestep,csd,length_k)
% plot_stats_permtest(stat,data_avg1, data_avg2,fsample,se_data_rare_cp, se_data_freq_cp, se_data_rare_fc, se_data_freq_fc, csd)
cl = cbrewer('div','RdBu',100);
% substract conditions from each other
cfg = [];
... |
7109a25aa9fa63b4ced81332057a706003251ee1f05dde64e41bae7b9fed307c | MATLAB | 5,758 | 156 | function [vertices, faces] = freesurfer_read_surf(fname)
% freesurfer_read_surf - FreeSurfer I/O function to read a surface file
%
% [vertices, faces] = freesurfer_read_surf(fname)
%
% Reads the vertex coordinates (mm) and face lists from a surface file.
%
% Surface files are stored as either triangulations or quad... |
b44c5fbbe9c09115d99becf528835460e2fe796191842d4858f273dd777d67b0 | MATLAB | 5,758 | 158 | function pointsI = circOpposingNearestPoints(pointsI, x, startI, endI, ...
searchLength, varargin)
%CIRCOPPOSINGNEARESTPOINTS Find the nearest equivalent point indices on the
% opposing side (within a search window) of a circular vector.
%
% POINTSI = CIRCOPPOSINGNEARESTPOINTS(POINTSI, X, STARTI, ENDI,
% ... |
e6d26a676f555d0e4f862b08b04488483bd61eecf8e7fc46f2f0cc00e842fc10 | MATLAB | 5,759 | 95 | function PV_corr_leave_one_lap_out
% MH 2021
% Runs population vector analysis comparing within a track, each lap's ratemap to the remaining laps ratemap. E.g. Track 1 lap 2 compared to
% laps 1 to 16, but excluding lap 2.
% Load name of data folders
sessions = data_folders;
session_names = fieldnames(sessions);
ses... |
5fc106956cf751dd395619ff0b6e9b3a1db49b2c3b10fc1d0b82716aa5496be1 | MATLAB | 5,760 | 56 | close all
clear variables
clc
%Varun Mannam
%date: taken from 29th Sep 2019
%date: 27th July 2020
%DnCNN model with diff lr in keras
%calculate the PSNR on test-mix data raw nbn
%1e-3 100 epochs with adjust LR and weights initialization
%5e-4 200 epochs with adjust LR and weights initialization
%1e-4 200 epochs with ... |
b6ef64543545a06c806b48233e0009e9ff95cfed98ea36285f1d52ca95b59d8c | MATLAB | 5,767 | 180 | % run A003, then A011, then this
subj = '57'
cd(['/mnt/yassamri/iEEG/sandra/subj_' subj])
if strcmp(subj, '55')
load('elec_loc_wrkspc.mat')
end
addpath(genpath('/mnt/yassamri/iEEG/sandra/analysis_pipeline_final/fieldtrip-20181130'))
load(['/mnt/yassamri/iEEG/sandra/subj_' subj '/FT_Pipeline/Electrodes/IR' subj '_... |
ce0e209c93e939d45e7dd0f87bed3949701ae3d12a38ecdf448e23c10a43ac90 | MATLAB | 5,771 | 188 | function examplestim
clear
close all;
E.levelsC5 = [48 0 48 48]./100;
E.levelsC7 = [0 48 48 48]./100;
stimorient = 360.*(rand(36,1));
ST.npixelsperdegree = 36; % VPixx pixel resolution at 57cm
ST.gratingsize = ST.npixelsperdegree*2; %
ST.gratingradiusfactor = ST.npixelsperdegree*2; % previously 128
... |
d2330dfbadf483e0d5119c6d74a3d8f1f519d245417b8c45cec686fab3ff7598 | MATLAB | 5,777 | 128 | function [globalBursts_beginI, globalBursts_endI, globalBursts_Elecs, globalBursts_NumBInGB, ...
globalBursts_width_iter, globalBursts_AvgWidth_iter, globalBursts_AvgWidth_sec, globalBursts_AvgNumBInGB, ...
NumGlobalBursts, globalIBI_iter, globalIBI_iterAvg, globalIBI_secAvg] ...
= detectGlobalBursts(Num... |
44b8ebd2b027c321b683c2293e361f0bb5197303c0110eaec365716a99f542be | MATLAB | 5,783 | 160 | function FunctionalConnectivity_Fig5
rootdir = 'C:\TKlab_agingPFC\';
cd(rootdir)
load FunctionalConnectivity
%% Pairwise correlation as a function of distance: Across age groups -Ver2
figure( ...
'Position', [0 100 1050 1250], ...
'PaperOrientation', 'landscape', ..... |
21f2b80c8be073fd1735206b7cff6c3d981e7a31aa066a7307b9bfae26bb53e8 | MATLAB | 5,784 | 182 | function [] = genGenData(genAll,meta)
% This function creates a general file for each genotype with information
% about all flies for that genotype
%
% Inputs:
% genAll: cell array of genotypes to consider
% meta: Type of points we want to return (3 options)
% meta.foldRaw = folder with the original data fi... |
ff3d52742478378b2cb26295b13a5a26ae3cac9be85a993d1be77f3dd65cd9d6 | MATLAB | 5,787 | 198 | function [probas, Isubj,nf]=taldir(dirname, th_pval, DirTable)
%
% Computes the probability of the Talairach transform matrices
% of all the subjects found in the directory "dirname".
% Uses the mean vector and covariance matrix obtained with talairachin_table.m from
% the data set (default data set: /space/... |
0bf5fd31e26d3102533e75f571dfc22295b9a34f7b9218e754c6fdacc15d7b65 | MATLAB | 5,792 | 181 | function[]=ret_vol_stitch_from_AB_no_ds(id,datapath)
% mosaic parameters
Xsize=1000;
Ysize=1000;
Xoverlap=0.15;
Yoverlap=0.15;
% 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_code/NIfTI_20... |
4e6ee8d9bf1f638f8057e6f1f8e4f9aab22fefe7f07a545a24187da625287255 | MATLAB | 5,792 | 140 | function SVM_object_decoding_full_experiment_searchlight(subject,task)
%SVM_OBJECT_DECODING_FULL_EXPERIMENT Perform object decoding (average of pairwise object decoding) using the SVM classifier.
%
%Input: subject ID, task (1=categorization, 2=fixation)
%
%Output: NxNxP vector of accuracies in %, where N is the number ... |
38f1839d9be768eaa8279656ada57b849f2974063a0c6f63199b7e8951955622 | MATLAB | 5,799 | 128 | function Gen_2P_coord(datapath,disp,mosaic,pxlsize,islice,pattern, plane1, plane2, plane3)
%%% ---------------------- %%%
% function version of mosaicing & blending
% input: displacement parameters: four elements array [xx xy yy yx]
% mosaic parameters: four elemetns array [number of x tile, number of y tile,
% ... |
aea9188f20438dd1ae3455d9b8a155543b078f6275b220ea7bf7b4d426105a89 | MATLAB | 5,799 | 160 | function sulcus_vertices = get_sulcus_info(session, run, neurovascular_map, UF, varargin)
% GET_SULCUS_INFO Return the ROI matrix for a given session/run combo.
% If no ROI matrix exists for a given session/run combo, allow user
% to create and save the ROI table.
%
% INPUTS:
% session: scalar double;... |
e55b8995a829c48c0ce11fb8449e42fbc6ade391d47fa6c296f27b34dc0a09f5 | MATLAB | 5,803 | 155 | function Stitch_2P(datapath,disp,mosaic,pxlsize,islice,pattern)
%%% ---------------------- %%%
% function version of mosaicing & blending
% input: displacement parameters: four elements array [xx xy yy yx]
% mosaic parameters: four elemetns array [number of x tile, number of y tile,
% ... |
3ffe2469e0d1a6953344ca44ddc4dfa053d8bfad5b76c011e8d4d22ea95b5e4e | MATLAB | 5,806 | 147 | function BHV02d_RL_model_plot_grp_predictions_bar(SBJ_id,proc_id,model_id,plt_id,save_fig,varargin)
%% Plot model predictors across group averaged within condition
% INPUTS:
% SBJs [cell array] - ID list of subjects to run
% proc_id [str] - ID of preprocessing pipeline
% model_id [str] - ID of the model parameter... |
65837d8708c398c1e45e8b7e5147d2692b9d1aa234a7f2ec10d5a3777ee4d788 | MATLAB | 5,810 | 112 | %% ${SBJ} Processing Variables
[root_dir, app_dir] = fn_get_root_dir(); ft_dir = [app_dir 'fieldtrip/'];
if isempty(strfind(path,'fieldtrip')); addpath(ft_dir); ft_defaults; end
%--------------------------------------
% Basics
%--------------------------------------
SBJ_vars.SBJ = '';
SBJ_vars.raw_file = {''};
SBJ_var... |
d5bca3bd9f5037cdf58e4ef23b4ada0afa5b3b2f5e9850afa5b08acd80184726 | MATLAB | 5,810 | 131 | % bgluSynchData_forRM.m
%% PART 0 - set colors
clc; clear all; close all;
% actual colors to use
myColors(1,:) = [0 0 0]; % black
myColors(2,:) = [0.6 0.6 0.6]; % grey
myColors(3,:) = [1 0 0]; % red
myColors(4,:) = [1 0.6 0.6]; % light red
%% PART 1a - GATHER DATA
% % % * * * * * * * * * * % % %
rea... |
b52fc89aae855912a0f90d1f126a1a019e508e768f25e9f3042aa12dff1a024a | MATLAB | 5,820 | 200 | function create_cum_sum_replay_figure
cd 'X:\BendorLab\Drobo\Lab Members\Marta\Analysis\HIPP\Chapter 2\Raw_replay_analysis\figures\combined figures'
axn = replay_cum_sum_makeFigure;
cd 'X:\BendorLab\Drobo\Lab Members\Marta\Analysis\HIPP\Chapter 2\Raw_replay_analysis\figures\Sum cumulative replay'
sleep = openfig('Cum... |
55d2e1d3774058a6ed75aa37d2381cbc5f03a9e008cf82fb5f1e0b0ae7019197 | MATLAB | 5,826 | 131 |
function [transpa,hIm]=plotResponses(roiHSV,peakParameter,peakResponse,peakParameterStd,im,totalCells,mapData,roiHSVRescaled,peakResponseLabels,peakParameterStdLabels,cellper,pixROIs,mx,remap,transpa,hIm)
figure
screensize = get( 0, 'Screensize' );
set(gcf,'Position',[screensize(1:4)])
hIm=subplot(1,... |
fdeaf190fcdfda67765e19109e17100e193190ea7bf8eaf2e276f129fe10e841 | MATLAB | 5,827 | 178 | % close all
clear all
npx_init;
clear all
npx_init;
%General parameters
color_shanks=0;
starting_point_plot=300; %in seconds
%%%% KIMI
% % mouseid='104638';
% % list = dir(strcat('W:\npxwaves\MEC\',mouseid));
% % % Parameters
% % type_of_unit='good';
% % probe_num=1;
% % dt=1;
% % thr=2.5;
% % chunk_ini=1200;
% % chu... |
9987e1d90bd36615e2a77941182094715b01ea31fa227d0c6733d37dc79a57a0 | MATLAB | 5,828 | 225 | % This function centers all projections and superposes them on top
% of each other
function [H,majorAng,mmRatio,fig] = superposedProjection(obj,RGCidx,showRange)
if(~exist('RGCidx') | isempty(RGCidx))
RGCidx = 1:obj.nRGC;
allRGC = 1;
else
allRGC = 0;
end
if(obj.plotFigures == 0)
disp('super... |
887e919e5d67edf2bf3db332c4c81d1cb00cf385f10b6355caa57627c85d8ef2 | MATLAB | 5,829 | 172 | clear all;close all;clc
addpath('/mnt/yassamri/iEEG/sandra/analysis_pipeline_final')
exp_type = 'encoding' %encoding, tuning_correct
reg = 'CA3'
lock = 'onset' %onset response
ref = 'LM'
fs = 500;
if strcmp('encoding', exp_type);
cond_num=2; titles = {'lure+','lure-... |
5a9588c69a060a826afd7f9b9ee2e012ac8ea0dbe7912c720a3ec071908baec1 | MATLAB | 5,840 | 145 | function mds_peak_decoding(subjects,task,analysis,filetype)
%MDS_PEAK_DECODING Create MDS plot from the object decoding data at
%peak decoding, and as a video across all timepoints
%
%Input: subject IDs, task (1=categorization,2=distraction), analysis
%('object_decoding' or 'category_decoding'),filetype ('image' or 'vi... |
c87729c883b833801f8777c43e797ee84edbcea5f8a62c900e1c5e660f1365d0 | MATLAB | 5,841 | 126 | function new_link_str = fun_graph_split_node_get_new_link(cc_ind_1, cc_ind_2, node_ind, vessel_mask_dt, vessel_skl, num_voxel_fit)
% fun_graph_delete_node_get_new_link find the shortest path that starts at
% one point, go through a point and end at the other point.
% Input:
% midpoint_str: output of fun_graph_delet... |
f12fc6c77cb7caa7bcad1abc70e31aed4a466ff9f8c40a7888439b68b3bf7f9f | MATLAB | 5,845 | 218 | function [xs, info] = qpwls_bb1(x, A, W, yi, C, varargin)
%|function [xs, info] = qpwls_bb1(x, A, W, yi, C, [options])
%|
%| quadratic penalized weighted least squares (QPWLS) via
%| preconditioned (todo) Barzilai and Borwein gradient method.
%| cost(x) = (y-Ax)'W(y-Ax) / 2 + x'C'Cx / 2
%|
%| in
%| x [np 1] initial ... |
20a2128cc05b7ec3aa0ce185e4002a8123253f00bb95fe2053486492903badf7 | MATLAB | 5,849 | 172 | function[]=ret_vol_stitch_from_AB_window_smoothing(id,datapath)
% mosaic parameters
id=3*id-2;
Xsize=1000;
Ysize=1000;
Xoverlap=0.15;
Yoverlap=0.15;
% add path of functions
addpath('/projectnb/npbssmic/s/Matlab_code/PSOCT_code');
addpath('/projectnb/npbssmic/s/Matlab_code/ThorOCT_code');
addpath('/projectnb/npbssmic/s... |
b1cb762be0baf8a268801ff271de5bf93ab767c66f0a60e1d446fa720ecc44d7 | MATLAB | 5,853 | 83 | % for ploting probability density of spikes
%
% INPUTS
%
% A : spikes time courses (time in second dimension)
%
% OPTIONAL
% times : vector with times for spikes ([])
% winP : length of Gaussian window in percentage of range (1)
% doploting : if ==1 plots spike PDF
% bins : number of bi... |
fa31ed9463f126e21e8ba7e6f49836e88918804d42294e96066ad63c84a46612 | MATLAB | 5,854 | 137 | function nf = fun_graph_get_node_features(cc_ind, vessel_image, vessel_mask_dt, image_size, compute_feature)
% fun_graph_get_node_features computes the node features including average
% intensity, average radius, center of mass, radial node density
% distribution, local neighbor distance and label, nearest neighbor
... |
d011bece42072503ca9db0060adac64fab1a62f9398645d1ec58d26c3a9eb3f0 | MATLAB | 5,856 | 198 | function [out1,out2,out3] = ginput(arg1)
%GINPUT Graphical input from mouse.
% [X,Y] = GINPUT(N) gets N points from the current axes and returns
% the X- and Y-coordinates in length N vectors X and Y. The cursor
% can be positioned using a mouse (or by using the Arrow Keys on some
% systems). Data points ar... |
c80a405e8ecfcd04ace567a5a18099c1b78615b122b6a2a10da3a1c40da461d4 | MATLAB | 5,865 | 170 | function [data]=rdk_scan_preprocessing(data,type)
%% realign
if strcmp(type,'realign_reslice')
for s_=1
for r_=1:size(data.run,2)
clear P;
clear files;
for i=1:size(data.scans,3)
files{... |
ba68af72eb33b61ecbbc4c3b0ec7c7688fca84465825220d2f29888d39b61edc | MATLAB | 5,868 | 184 | %% Frequency of single cell oscillations - All sessions
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\... |
b7b0386b9be4595a38d66ac12f4bbf95e77fe6c198105b95152765216665db28 | MATLAB | 5,870 | 159 | function doRICA(matPath)
addpath(genpath('/Volumes/groups/giocomo/export/data/Users/KMasuda/Neuropixels/MalcolmFxn/'));
addpath(genpath('/Users/KeiMasuda/Documents/MATLAB/Add-Ons/Functions/gramm (complete data visualization toolbox, ggplot2_R-like)/code'));
addpath(genpath('/Volumes/groups/giocomo/export/data/Projects... |
bf71f7070991e9e8153e27cfcc79293076acf55bccfd09731fd8b23949df9e47 | MATLAB | 5,870 | 159 | function rsa_rnn_eeg(subjects,with_stats)
%RSA_RNN_EEG Compute and plots the RSA between RNN and EEG with stats, if desired.
%
%Input: subjects' ID (e.g., 1:13), add stats (1 with, 0 without)
%
%Author: Agnessa Karapetian, 2021
%% Add paths
addpath(genpath('/home/agnek95/SMST/PDM_PILOT_2/ANALYSIS_Full_experiment'));
... |
59ee2106f0e98d3f94d8cf1c9a107d22abf0a4b669ce50ee02100b75b6126831 | MATLAB | 5,874 | 148 | classdef TestTablePicker <handle
% AUTHORSHIP
% Primary Developer: Stephen Meehan <swmeehan@stanford.edu>
% Math Lead & Secondary Developer: Connor Meehan <connor.gw.meehan@gmail.com>
% Bioinformatics Lead: Wayne Moore <wmoore@stanford.edu>
% Copyright (c) 2022 The Board of Trustees of the Leland Stanford ... |
bf0231a4ecfc1715e19d736d69d7aed78b972548e67f2925e558da970f06e121 | MATLAB | 5,875 | 119 | % SPIKE PHASE HISTOGRAM
% MH, 2020
% Method to find center of theta sequences by finding the theta phase with max firing rate. For each theta sequence, find active spikes and their
% phases, and find at which phase there are more spikes. Then plots histogram of all phases per track and direction, and gives the peak pha... |
0cc877d609149c3a5576606fbeeb0c984828429cdf9b11c50e116df7028f0135 | MATLAB | 5,880 | 131 | clear all;close all;clc
subj_list = {'57'}
for iSubj = 1:length(subj_list)
subj = subj_list{iSubj}
exp_type = 'tuning_correct' %encoding, tuning_correct =
reg_list = {'HC'} %{'OFC' 'FRO' 'TEMP' 'CING' 'INS' 'EC' 'HC' 'CA3'}
lock = 'onset' %onset response
ref = '... |
58c69d1ffe51c8bbea378c923a0b48b2bf667f9841d2c765b42d29354b2c461c | MATLAB | 5,882 | 178 | function D = spm_eeg_tf_rescale(S)
% Rescale (avg) spectrogram with nonlinear and/or difference operator
% FORMAT [D] = spm_eeg_tf_rescale(S)
%
% S - input structure (optional)
% fields of S:
% S.D - MEEG object or filename of M/EEG mat-file
% S.method - 'LogR', 'Diff', '... |
8dda561b072017b93c93a8d19195cd33283fed76a1fffd0b9f974356efcf118e | MATLAB | 5,885 | 145 | function bootstrap_peak_latency_rsa(subjects,conditions_int) %OBSOLETE - use bootstrap_peak_latency_rsa_avg_tps.m instead
%BOOTSTRAP_PEAK_LATENCY_RSA Apply bootstrapping to calculate the 95% confidence
%interval of peak latency difference (for RSA between RNN and EEG-categorization task).
%
%Input: subject IDs (e.g., 1... |
ad0b5d685c428649dd0dad58e0ca3552af8c80be9b6da8ce33c730b3ed71dab5 | MATLAB | 5,890 | 152 | function item = cfg_entry(varargin)
% This is the entry configuration item class
%
% Data structure
% ==============
% Description fields
% * name - display name of config item
% * tag - tag of the menu item
% * val - 1x1 cell array
% * check - (optional) function handle to implement configuration
% ... |
da18451be4bb1cbf23dc141bea3944d4d1318ffd629f2ee686d511458ce4ab3b | MATLAB | 5,891 | 134 | function startTime = initMRIChatPart2(mmap, scans2Wait)
% Wait for the scanner and return the time the experiment starts. Call this
% AFTER initWindow and after initMRIChatPart1.
%
% The function shows a waiting message, then blocks until the trigger counter
% has seen scans2Wait scans, and returns the time stamp of th... |
3f7b4e2bcd160205cef70437a2ac5a521207482bfcbc69de860e4680e755230b | MATLAB | 5,893 | 239 | %% Figure S8
%%
clear
ft_defaults
%% - SUBJECTS - %%
% ID 18 missing because of LP at 50Hz
subjects = {'OSL13', 'OSL14', 'OSL17', 'OSL21', 'OSL24', 'OSL26', 'OSL27', 'OSL28', ...
'OSL29', 'OSL30', 'OSL31', 'OSL32', 'OSL34', 'OSL35', 'OSL36', 'OSL38', 'OSL39', 'OSL40'};
%% - PATH SETTINGS - %%
path_data = '/Vo... |
f26577a8e98129ecb21c8fe54bbdc5fdc523d6df4f28a6e32dce3ca535d54284 | MATLAB | 5,894 | 151 | function plot_Projections(Animal)
SavePathName = '/Users/rupesh/Dropbox/AC_Paper/OutlineDarft/Fig_2/Results/PopDecode/SessionWiseData/';
SavePath = [SavePathName(1:31) 'Comments1/Fig_2/Results/Projections/SessionWise/'];
%% Get All Projections
[IncludeSess] = Get_Sess(Animal);
[PassiveProj,ActiveProj] = GetAllSessPro... |
a8a45ee5f4b6528799314e5f7fb23fc25d080f2fec90588ffee3a1d4e142f8c5 | MATLAB | 5,899 | 191 | function [wmap, wconv] = mri_field_map_reg_2d(yi, etime, varargin)
%function [wmap, wconv] = mri_field_map_reg_2d(yik, etime, varargin)
%
% MRI field map estimation using regularization and optimization transfer,
% based on a multiple image model:
% y_ik = mag_i * phase_ik * decay_ik + noise_ik
% phase_ik = exp(1i * w... |
de61b04f00af1820ba814b84717d0a5bec5d9f6b64f0d08021e7ba3a17c17864 | MATLAB | 5,905 | 137 | function [outPatterns,outPatternLengths,outPatternFreq] = IPEMMECExtractPatterns (varargin)
% Usage:
% [outPatterns,outPatternLenghts,outPatternFreq] = ...
% IPEMMECExtractPatterns(inAnalyzedSignal,inSampleFreq,inPeriods,...
% inBestPeriodIndices,inAnalysisFreq,...
% ... |
2878ab384aa4d208fd1bbe606483a20b962f3d7c73cbf7d17382596b9eab569a | MATLAB | 5,914 | 162 | % 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
% 2013-01-02... |
8e593f53b46ad0ccd35382e9af39b308d9d8ca8375a28f09c433c0e6ba22aaea | MATLAB | 5,920 | 162 | % plot behavior
clear all; close all; clc
patient_num = [39 44 57 63 66 84 85 87] ;%83
%% plot test behav data
figure;hold on
% plot response distributions and over all accuracy for all patients
for subj = 1:length(patient_num)
cd(['/mnt/yassamri/iEEG/sandra/subj_' num2str(patient_num(subj))])
load(['b... |
cc67800a4c1f2f71115b162597ef99e2227fe54e63f1525fc060a50c1fb8f849 | MATLAB | 5,926 | 198 | function handles = fix_FixTrackFlies(flies,f0,f1,handles)
% track multiple flies
% splintered from fixerrorsgui 6/23/12 JAB
guidata(handles.figure1,handles);
MINPRIOR = .01;
se = strel('disk',1);
nflies = length(flies);
mu0 = zeros(nflies,2);
S0 = zeros([2,2,nflies]);
priors0 = zeros(1,nflies);
for i = 1:nflies,
fl... |
209bff29351ea46732e20e3773c480d1cfa2b429a99e979fef0201141c1a0c97 | MATLAB | 5,928 | 205 | function data = spm_eeg_inv_get_vol_sens(D, val, space, gradsource, modality)
% Retrieves data for leadfield computation from D.inv structure
% Format:
% D - @meeg object
% val - inversion index (overrides D.val)
% space - one of 'MNI-aligned', 'Head', 'Native' (default 'MNI-aligned')
% gradsource - 'inv... |
f4db3693208312a6eec33f50c0b393e607ee3dbbb0a73fff68198ed259b1edf1 | MATLAB | 5,929 | 83 |
function replay_decoding_split_events_final_lap
% Splits each replay event approximately in half, based on when the MUA is
% lowest in the middle third of the event. This is used to seperately analyze the
% first and second half of long duration replay events
% Load parameters
parameters = list_of_parameters;
load('... |
0014b107e5a6fdfd5f1c0e2ae9c4c9f2bfe35f2712b3b95c8a04633385ac3c0a | MATLAB | 5,933 | 223 | function [phantom, params] = cuboid_im(ig, params, varargin)
%function [phantom, params] = cuboid_im(ig, params, varargin)
%|
%| generate cuboid phantom image from parameters:
%| [x_center y_center z_center x_diameter y_diameter z_diameter
%| xy_angle_degrees z_angle_degrees amplitude]
%| in
%| ig image_geom()
%| ... |
9f889b4374ad378dc1a48de64ecc48f0a8708000bae7616ca79389dada94bc58 | MATLAB | 5,933 | 195 | % plots and statistically compares individually saved proboscis data from separate conditions
% (data generated with plot_heat_map_proboscis_track and analyseProboscisMotion)
clear all
close all
ylabelText='rel. contacts';
animalInd=5;
dataInd=[1 2];
med=0;
animalOverlap=1;%put 1 if same animals in b... |
071b1b173e7b13ceac7b93f56d8081674ab9190433264b90b7189164ec46c16f | MATLAB | 5,935 | 131 | clear all;
close all hidden
prompt = "Using Existing Experimental Data, Y or N:";
txt = input(prompt,"s");
%Initialization
if txt =="Y"
prompt = "Enter Destination File Path:";
Destination_path = input(prompt,"s");
while ~exist(Destination_path, 'dir')
Destination_path = input("Entered De... |
bd89b925261066bc22c975e3bc2622964e0b82b1c1165e8d33cb09b1886a042b | MATLAB | 5,945 | 132 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% MAIN - Analysi fatta sul dataset a disposizione - PLV - 20 soggetti - Sess. 4 - MEG - Desikan-Killiany
% Authors: MCC
% Date: 26/10/2022
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
close all; clear... |
d7e678266bfe33328afcff878921ff03e63ad0b0e9f701290aaf6bb9f2e1a8f8 | MATLAB | 5,945 | 216 | %% Table S1 (PFC)
%%
clear
ft_defaults
%% Subjects
% ID 18 missing because of LP at 50Hz
subjects = {'OSL13', 'OSL14', 'OSL17', 'OSL21', 'OSL24', 'OSL26', 'OSL27', 'OSL28', ...
'OSL29', 'OSL30', 'OSL31', 'OSL32', 'OSL34', 'OSL35', 'OSL36', 'OSL38', 'OSL39', 'OSL40'};
%% Paths
path_in = '/Volumes/IMPECOG/iEEG_... |
6bc71896d901ceee75d9d0b69fdfa115c18e3dc4cff911ff3e082794c375413e | MATLAB | 5,946 | 230 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%
%% 4-level multiresulution nonrigid image registration with no constraint
%% Conjugate gradient with Newton's first step was used
%% Cubic B-spline bases are used for both image and deformation models
%%
%% Copyright July 2007, Se Youn... |
4015473b73f20bdaf39cec40df2c4e60b534db7c6cb192371600120b171d50ff | MATLAB | 5,947 | 183 | function [ umapOrEppOrMatch, reduction, clusterIdentifiers, extras]=...
suh_pipelines(csvFileOrData, varargin)
%SUH_PIPELINES brokers ALL input and output argument of either run_umap or
% run_epp or SuhMatch.Run depending on the named argument 'pipeline'
% which defaults to 'umap'.
% For usage of UMAP, see u... |
cbbf9935ee2d55f1fc858159ecc93373682d8a585a30d898d66f73f9972ac8c6 | MATLAB | 5,952 | 103 | %% PRJ_Error BIDS Conversion: CP24
% Convert CP24 data from original CWH to BIDS directory format
SBJ = 'CP24';
% Set Up Directories
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/fieldtri... |
dacb1a676c084a5f8f3530c55b71fd74806c06fd996c61d71c682179cd17afb1 | MATLAB | 5,954 | 119 | %% IR84 Processing Variables
[root_dir, app_dir] = fn_get_root_dir(); ft_dir = [app_dir 'fieldtrip/'];
if isempty(strfind(path,'fieldtrip')); addpath(ft_dir); ft_defaults; end
%--------------------------------------
% Basics
%--------------------------------------
SBJ_vars.SBJ = 'IR84';
SBJ_vars.raw_file = {'IR84_erro... |
935db31de7c6ad43ce29817762e964698091b9a9162eb6da4a884dfb899edb20 | MATLAB | 5,957 | 148 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% "Batch" code to compute PWelch & PLV after spatial downsampling - 20 subjects - MEG & DK only
% Authors: MCC
% Date: 25/10/2022
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
close all; clc;
%% to be a... |
1db5c909d238cd5e084d70a848c9024e15a992eced54a23d1c88adbd8ac63049 | MATLAB | 5,959 | 159 | function Fmenu = spm_Menu(action, varargin)
% SPM Menu window
% FORMAT Fmenu = spm_Menu('Create',Vis)
% Create the SPM Menu window (tag property set to 'Menu')
% Vis - visibility: {'on'} or 'off'
% Fmenu - handle of figure created
%
% FORMAT Fmenu = spm_Menu('Switch',Modality)
% Switch the SPM Menu window to the sp... |
5544d7a36c90f2ea6f8794db8c26babec5c134021ff893020f1d0259cb7e165b | MATLAB | 5,959 | 90 | % THETA SEQUENCE DECODING ERRROR
% MH, 2020
% Finds real position at the time of the theta sequence, and calculates the decoding error during the theta sequence
% INPUTS:
% decoded_thetaSeq - uses decoded theta sequences that have passed velocity and number of active units thresholds.
% If em... |
2a72d5c09967d05468b632013146f05943e8ea512e47d11d4f9b853abcb9b67e | MATLAB | 5,960 | 217 | %% Stacked Single Trials Motor Cortex
%%
clear
ft_defaults
%% Subjects
% ID 18 missing because of LP at 50Hz
subjects = {'OSL13', 'OSL14', 'OSL17', 'OSL21', 'OSL24', 'OSL26', 'OSL27', 'OSL28', ...
'OSL29', 'OSL30', 'OSL31', 'OSL32', 'OSL34', 'OSL35', 'OSL36', 'OSL38', 'OSL39', 'OSL40'};
%% Paths
path_in = '/V... |
a85f8c381166c576051b6bed2428066a0ed8da785420c5a444ba82907c4da7d5 | MATLAB | 5,960 | 115 |
function scored_replay = replay_scoring(data,analysis_type)
% Runs up to 4 scoring methods for candidate replay events: line fitting, weighted correlation, path finding (or pacman), and spearman correlation coefficient.
% Modify_decoding.m subroutine is run to zero probability if no spikes from good place fields occur... |
045c6400b50d8860c0b17951cc45b9ef05f7671efad826a137d6c99c9222122a | MATLAB | 5,966 | 126 | function new_link_str = fun_graph_split_node_get_new_link(cc_ind_1, cc_ind_2, node_ind, vessel_mask_dt, vessel_skl, num_voxel_fit)
% fun_graph_delete_node_get_new_link find the shortest path that starts at
% one point, go through a point and end at the other point.
% Input:
% midpoint_str: output of fun_graph_d... |
8947316ba93a779d9eaec5b530672ce25c20f4a9ee1a201951f32f20d4ef8c81 | MATLAB | 5,970 | 129 | function IPEMBatchExtractSoundFragment(varargin)
% Usage:
% IPEMBatchExtractSoundFragment(inFragment,inInputDirectory,inFilePattern,...
% inOutputDirectory,inNewNameFormat,...
% inCreateDir,inFadeInOutTime,...
% inPre... |
fdb4d86a7c0135e98838693426c98d37e0538cb1392e395b096e9c36d17e3dd8 | MATLAB | 5,973 | 136 | % This script warns the user that something went wrong in a program.
% It should be called from a catch-statement, where it saves all
% workspace variables (so no data is lost), shows an error msgbox and
% rethrows the original error.
%
% The workspace is saved to ErrorTermination_YYYYmmDDHHMMSS.mat in the
% working di... |
b8ee32e2bde0a221c6e39349541aa5783a6ade19d5981c3e2b1a1c1ad49b79f8 | MATLAB | 5,980 | 229 | clear;
%{
This script sweeps through bound and lambda values and saves psychometric paramaters
(conditioned and unconditioned)
Can be easily configured to run free/choice interrogation tasks
Can be easily configured to run for continuous or discrete stimuli
Can be configured to change the drift instead of initial po... |
a58f082e1d62b7a19f9af605b64bbb88ed17526a6afb3c1c4823d07f921ec0ba | MATLAB | 5,988 | 231 | function sh = de_ftab_s_iter(fit, fh, varargin)
%|function sh = de_ftab_s_iter(fit, fh, [options])
%| estimate s from fh by iterative LS
%| sh = argmin{s} | fh - fm(s) |^2
%| in
%| fit from de_ftab_fit() / de_ftab_curv()
%| fh [(Nd) M] estimates of f (nonlinear BH function)
%| option
%| 'niter' # of iterations (d... |
0ca74a3ad6c50595185b16dffd866bec186f38a52a4ffaf95b8fb6827de96987 | MATLAB | 5,990 | 180 |
cond1a = cell(1,length(group));
cond2a = cell(1,length(group));
cond3a = cell(1,length(group));
cond4a = cell(1,length(group));
cond1a_null = cell(1,length(group));
cond2a_null = cell(1,length(group));
cond3a_null = cell(1,length(group));
cond4a_null = cell(1,length(group));
cond2a_pacz = cell(1,... |
cad59122c797dede91bdd8f65e0d0a32e195cdc95e31fdb3dd886b5e9578c173 | MATLAB | 5,992 | 143 | clear all; close all;clc
subj_chan_name = cell(1600,1);
X = nan(1600,1);
Y = nan(1600,1);
Z = nan(1600,1);
anat_selec_index_group = nan(1600,1);
cue_resp_index_group = nan(1600,1);
subj_list = {'39' '44' '57' '63' '66' '84' '85' '87'};
addpath('/mnt/yassamri/iEEG/sandra/analysis_pipeline_final')
i=0
for iSubj = 1:l... |
d11dbbbebd403bee846918a177f16b9a94d1d5d849effee47725b0b9ef6a066d | MATLAB | 5,992 | 176 | % Plot Firing Rates of Cells
addpath(genpath('/Volumes/groups/giocomo/export/data/Users/KMasuda/Neuropixels/MalcolmFxn/'));
addpath(genpath('/Users/KeiMasuda/Documents/MATLAB/Add-Ons/Functions/gramm (complete data visualization toolbox, ggplot2_R-like)/code'));
%%
% sessions = dir('/Volumes/groups/giocomo/export/data... |
ba666032631bb38b5f0fec55c6d2715c4d823fd933800af2f0d64321592ccd2b | MATLAB | 5,995 | 126 | function [df_leaving_all,df_entering_all,f_leaving_all,f_entering_all,...
kinematics_leaving,kinematics_entering,kinematics_leaving_std,kinematics_entering_std] = ...
getDf_aligned_kinematics(f_orco,thresh,delay,yl,type,singleTrial,plotFig)
%getDf_aligned_kinematics calculates the firing rate, change in firing... |
e3d56c9adb285ac887aa6efa1c1fc1b07b97fc9293b016a26d7b8803212b22ca | MATLAB | 5,996 | 133 | %% Plot preprocessed data for cleaning with Bob
% 1) Load preproc data (downsample, highpass, trim, notch)
% 2) Plot data to identify bad channels and epochs
clear all; %close all;
%addpath(genpath('/home/knight/hoycw/PRJ_Stroop/scripts/_TOOLBOXES/'));
addpath('/home/knight/hoycw/Apps/fieldtrip/fieldtrip/');
ft_de... |
ef7c7724cdae61a3082869be840e1870413a89f20044bf47720918a6af4e1a05 | MATLAB | 5,998 | 228 | function [x, mask0] = mri_phase_denoise(yi, varargin)
%function [x, mask0] = mri_phase_denoise(yi, [options]) <- recommended usage
%|function [x] = mri_phase_denoise(yi, l2b, niter, chat, wthresh) <- old way
%|
%| in
%| yi [(N)] noisy complex image: y = mag .* exp(1i * x)
%| can be any size: 1D, 2D, 3D, ...
%|
%| op... |
a8ebe5c4150cb4bb0437eec7b017f3bb6843facc4526a2391d89b7552a4247ef | MATLAB | 5,999 | 133 | function xk = modelStepMultiMealT1D(xkm1,I,CHOB,CHOL,CHOD,CHOS,CHOH,hourOfTheDay,mP,xk,model)
% function modelStepMultiMealT1D(xkm1,I,CHOB,CHOL,CHOD,CHOS,CHOH,hourOfTheDay,mP,xk,model)
% Simulates a step of the multi-meal t1d physiological model.
%
% Inputs:
% - xkm1: is a vector containing the model state at time k... |
fd8adb9b0a2c6bac9f190542af5ecc5059e5f66b1b385bfeb87b0dfbdefff7aa | MATLAB | 6,001 | 225 | % this script first generates fake psychometric data with lapse rate, number of trials, range and discretization of diifficulties.
% then it fits a psychometric curve and reports the lapse rate
% it also estimates the lapse rate based on the end point at the highest trial difficulty
% and then reports how good the reco... |
e8c9b09921019ae33a7de3bfb42d0c6d6640e3c680255836b612931cfebfb56a | MATLAB | 6,005 | 160 | function BHV02d_RL_model_plot_grp_predictions(SBJ_id,proc_id,model_id,plt_id,save_fig,varargin)
%% Plot model predictors across group averaged within condition
% INPUTS:
% SBJs [cell array] - ID list of subjects to run
% proc_id [str] - ID of preprocessing pipeline
% model_id [str] - ID of the model parameters to... |
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