sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
12c689c44a8164d3a931e4c3fb1af3247e21d6e407f9488a7ba4a05571ec330c | MATLAB | 9,023 | 171 | function update_burst_axis(app, keep_xl)
%UNTITLED Summary of this function goes here
% Detailed explanation goes here
cla(app.ax_msna_int), cla(app.ax_burst_res)
cla(app.ax_burst_interval_his), cla(app.ax_burst_duration_his)
cla(app.ax_burst_amplitude_his), cla(app.ax_burst_integral_his)
cla(app.ax_burst_res), cla... |
7d9c48aca87f571ad10ef79c7d76915175f37bd3271599a5b14e1b40a6dadef4 | MATLAB | 9,027 | 233 | function neuron_data = BF_IQR_V5 (BF_single_peak,downsample_A1,downsample_AAF,downsample_A2)
% first variable input created by extract_animals, other inputs downsample factor if you want to randomly exclude neurons
% 100 um rings
min_neuron_number = 5; % less than that per ring and the neuron is not used for o... |
efb7eb06557e5293d34bbae47adeb799292480144bfaf384036eb0c5bd803daf | MATLAB | 9,028 | 271 | function map = cubehelixmap(varargin)
% FORMAT: map = cubexhelixmap(type,value)
%
% INPUT: cubehelixmap('increase',size)
% cubehelixmap('increase_lowbound',value,size)
% cubehelixmap('decrease',size)
% cubehelixmap('decrease_highbound',value,size)
% cubehelixmap('continuous,size')
% ... |
dcc1e37345b863b4dba059929b42aa1f048bb9ff1be23919296e1c7b33190a96 | MATLAB | 9,029 | 187 | function SpikeOn_Touch_Random_CV20200303
path=['C:\Users\C238\Desktop\touchcellLtRt_terashita'];
cd(path);
LS=ls;
Seq5LtouchTime=[];
Seq5RtouchTime=[];
MeanCVLtouchArrayArray=[];
MeanRandomCVLtouchArrayArray=[];
MeanCVRtouchArrayArray=[];
MeanRandomCVRtouchArrayArray=[];
CVLtouchPeakArray=[];
CVRtouc... |
bdd9217c96f0157ccf4a0d5da4a99629877bb8206b65609246b2f532cadfb11f | MATLAB | 9,036 | 256 | function matsuda20190902
global CCresultRtouchWon CCresultLtouchWon RpegTouchallWon LpegTouchallWon SpikeArrayWon tfile
% cd('C:\Users\C238\Desktop\CheetahData\2019-8-12_16-26-19 17201-02');
% dpath=('C:\Users\C238\Desktop\CheetahData\2019-8-12_16-26-19 17201-02\');
% dpath3=('C:\Users\C238\Desktop\CheetahData\20... |
8e5620e86908694408b6b6e094508ca224e834562e2dbc8d42c47a23ec2fb422 | MATLAB | 9,040 | 238 | %% SortData_Behaviour
% This script sorts behavioural data according to the different modalities
% investigated:
% 'VpOpFp',... visual cues (see-through), feeding
% 'VpOpFm',... visual cues (see-through), not feeding
% 'VmOpFp',... no visual cues(opaque), feeding
% 'VmOpFm',... no vis... |
0d4ebe60c01fe73c182cdb96a795d4a242d0d43871f9949c72c488f5741206e2 | MATLAB | 9,059 | 214 | function save_annotate_results(app)
[int_idxs,~] = listdlg('PromptString',{'Please select intervals ',...
'to be plotted/saved.',''},...
'SelectionMode','multiple','ListString',app.popup_int_spect.Items);
[form_idxs,~] = listdlg('PromptString',{'Please select fileformat ',...
''},...
'SelectionMode','... |
402fcedef5bbc4fc26bca6211df870b43f1deef56f1e09cb6e0cd3df8749b051 | MATLAB | 9,064 | 255 | % Statistical test for functional modularizations of a CA1 pyramidal cell model.
%
% Reproduces results shown in Fig 4 discussed in the section "Inferring flat modularizations in dendrites of pyramidal neurons" in
% "Correlations reveal the hierarchical organization of networks with latent binary variables" (2023) S... |
8f3377f656ffbc58b8679141531efe918658985a25cd834c106465fde1fd3c09 | MATLAB | 9,071 | 246 | function [gdata, st, newData, phaseTranslations] = applyRetroMC_nufft(kdata_in, alignMats, alignDim, alignIndices, useTable, dataDims_mm, fullMatrixDims, kspaceCentre, cgIterations, oversampFactor, bKeepPhaseTranslations)
% The alignMats are a series of 4x4 (rigid-body) matrices describing the
% motion, one for each se... |
e8ffeda56939033d8af81fadc1cb3c6d7ae3cbbf29e585a4d685bac6ac5c8254 | MATLAB | 9,075 | 209 | %% 1. retrieve data
disp('select a brain region about spike data');
Folder_region = uigetdir('E:\Wang_SpikDat\');
FileList_SpikMat = dir(fullfile(Folder_region, '*.mat'));
FileList_BehavDat = dir(fullfile('E:\Wang_BehavDat\**\*.mat'));
%% 2. set parameters
r1 = 1000; r2 = 5000; %baseline:-r1:DO; test:DO:... |
4181d6fc2d5ddd13c0877e7ae4291bcdd1cc16582d13f3c7488d4f9d6f7c591d | MATLAB | 9,086 | 226 | % Funtion to Detrend Fluorescence Signals
% Input
% X: signals of Ns x Frames
% Output
% XD: Detrended Signals
function XD=only_detrending(X)
[Ns,Frames]=size(X); % Ns-Signals of Frames
% Initialize Outputs:
XD=zeros(Ns,Frames);
%% MAIN LOOP
for i=1:Ns
x=X(i,:); % Read Single Fluorescence Signal
... |
2817ce4523e5b5720c862b861facef0deea4539b9150444ff8054787a53ae202 | MATLAB | 9,089 | 237 | function RLphase190227
global ACresult ACresultW CCresultDrinkOn SpikeArray TurnMarkerTime RpegTouchallWon LpegTouchallWon...
RpNum LpNum CCresultSpike locsP1time locsP2time locsP1time2 locsP2time2 pksA1 pksA2 CCresultRtouchWon CCresultLtouchWon dpath3...
StartTime FinishTime DiffMaxMinR DiffMaxMinL fname... |
6541279581c269d95ca673188c4e97bd05b7e5c142d0f4d1db779cadf07f91c6 | MATLAB | 9,089 | 222 | % This script analyzes the result of the vSP framework which was applied
% on all patients from the MNI and CHUGA. The results are found in
% virtual_removal_perturbation.mat
addpath('functions');
addpath('demo_data')
addpath('perturbation')
addpath(genpath('results_figures\sp_results'))
load('virtual_remov... |
1885b11b0aa63674952b6242186971be81f7f8938dc239d4afa44ef285e72550 | MATLAB | 9,093 | 261 | function varargout = rst_rep_anova_plot(Data,factors,gp,alpha)
% plots effects for a repeated measure anova
% plots include confidence intervals for within-subjects
%
% INPUT
% Data = 2D matrix (n*p) of repeated measures
% gp = vector (n*1) indicating gp belonging - doesn't do nested gp
% factors = vector ind... |
5c55bbaf5a789a593790a46fcbd7b875dafe5cd991639016befc4088e35aff76 | MATLAB | 9,096 | 260 | function [x_smoothed1, x_smoothed2, x_smoothed3, optim_gammas1, optim_gammas2] = ECGSmoothnessPriorsDenoiserBWFiltFilt(x, SmoothnessFactor, varargin)
% Block-wise ECG denoising based on smoothness priors
%
% [x_smoothed1, x_smoothed2] = ECGSmoothnessPriorsDenoiserBW(x, SmoothnessFactor, mode, FilterParam, KnotsParam, A... |
5234e9a8a93b58d3feef650029a2ca9f36a1891a0cb8b9b58303122f12f540b3 | MATLAB | 9,139 | 184 | int_idx = 1;
xl = [app.burst_res.ts(1), app.burst_res.ts(3)];
figure
ax(1) = subplot (2,1,1);
if ~isempty(app.hb_res)
hb_idx = app.hb_res.use_beats(:,1) & app.hb_res.use_beats(:,int_idx);
end
burst_idx = app.burst_res.use_burst(:,1) & app.burst_res.use_burst(:,2) & app.burst_res.use_burst(:,int_idx+1);
if int_i... |
fc6bc38255ff0ede1aa98de0d9c205a58eff80c33ed482e895dc61fc17438bf4 | MATLAB | 9,141 | 225 | function [TimeStamps EventStrings Evt]=readEvents(fnameEV)
% cd('C:\Users\kit\Documents\MATLAB\WR LV')
% fnameEV='C:\Users\kit\Desktop\2015-7-1_15-32-53\Events.Nev';
% fnameEV='C:\Users\kit\Desktop\2008-9-25_20-28-25\Events.Nev';
% fnameEV='C:\Users\kit\Documents\MATLABoriginal\Events.Nev';
% fnameEV='C:\Cheetah... |
47dbcff237bfa9431b838711be5a104d385425cc01bbe6cc5e6cb5caf498c528 | MATLAB | 9,142 | 165 | clear;
close all;
clc;
addpath 'G:\Sameni\Documents\Papers\Journal 40 (Spline ECG Filtering)\CODES'
randn('seed', 0);
rand('seed', 0);
% % % dbstop if warning
type = {'Normal', 'Arrhythmia', 'PTB'};
INITIAL_PARAM_LIST_NUM = 1000;
MAX_ITR = 100;
for ttp = 1 : length(type),
switch(type{ttp})
case 'Normal'... |
d9efe9bf2cf68e8c914b01260e6b3e4b640b50d1a61384031244889d747c5741 | MATLAB | 9,152 | 204 | close all
clearvars -except Behav_path Path_figures Path_variables Code_path MEG_path
SubjectID={'S01' 'S02' 'S05' 'S15' 'S19' 'S20' 'S22' 'S24','S21','S25','S23'};
Drug=[3 1 1 2 3 2 3; 3 1 3 2 1 2 0; 2 1 1 3 2 3 0; 1 3 3 2 2 1 0; 2 3 1 2 1 3 0; 1 2 3 1 3 2 0; 3 1 2 2 1 3 0; 3 1 1 2 3 2 0; 2 1 1 2 3 3 0; 3 2 1 3 1 2 ... |
67e40a2d5ac3a5385e3ab9bcda82eb9c71807744daa3b1008dfbca77fcd5a369 | MATLAB | 9,182 | 270 | function varargout = rst_single_subject(rawAcc, rawRT, varargin)
% rab function to clean up and extract relevant information per subject
% 1st the raw data are cleaned using the Median Absolute Deviation (MAD)
% - a cutoff can be specified for RT and each condition is cleaned separately
% 2nd the mean, trimmed mean an... |
f48305e52d0fabd5a128ae6624198fefdf87bce4d488e10e549d2cc3d77658c7 | MATLAB | 9,187 | 266 | function [f,status]=GetWritableFolder(varargin)
%Return a folder with write permission or error.
% If the output folder doesn't already exist, this function will attempt to create it. This
% function should provide a reliable and repeatable location to write files.
%
% Syntax:
% f=GetWritableFolder
% [f,status]=GetWrit... |
d2dceaf9c2e4b8c2fc5bfb7c3184aa7ce90daa3f9a04de8d00f897ca8afe7789 | MATLAB | 9,188 | 190 | % Train Classifier for Each Outcome on Multiple Time-points
% and Apply to Epoched Task Choice Data
% Enter path of MEG_Decision_Study folder
study_folder = '/Users/erussek/Dropbox/MEG_Decision_Study_WC';
% add path to util folder
util_folder = fullfile(study_folder, 'MEG_analysis_scripts', 'utils');
addpath(genpath(... |
e680850fe40ef6ea8b0116905bb9fba056c528b0a2f9cb9d65decbe830d47c17 | MATLAB | 9,192 | 250 |
clear
[file, path] = uigetfile('*.mat');%When the user clicks the load data button, a window should open to enable the user to select a file.
datafile = fullfile(path, file); %save path
load(datafile)
close all
%%
binsize = 0.1;
downsampling = 5;
[file, path] = uigetfile('*.mat','MultiSelect','on');%When the user cli... |
f63390439248f85d522b8efa095d09e2a1a6567ca7825ed760cd8435955a312e | MATLAB | 9,195 | 214 | function save_annotate_results(app)
[int_idxs,~] = listdlg('PromptString',{'Please select intervals ',...
'to be plotted/saved.',''},...
'SelectionMode','multiple','ListString',app.popup_int_spect.Items);
[form_idxs,~] = listdlg('PromptString',{'Please select fileformat ',...
''},...
'SelectionMode','... |
887dab26ccbad6db9d3285d37e039704f1cf64b5ded19bc2d1c2644f9c9c8c02 | MATLAB | 9,199 | 232 | function Aij = run_timeSeries2mat(timeSeriesInput,inputTR,passBand,windowSizeInput,windowOverlapInput,analysisTypeInput,outputDir,subjectNameInput,taskNameInput,timeLag)
% PURPOSE: Converts time series data to multilayer network
%
% INPUT:
% timeSeriesInput: Time series data
% * Rows ... |
da2ea8fc22caff48753fa06545c990ce86d9db6a3aeefe8655b091b90b78411a | MATLAB | 9,202 | 248 | %% Neural Ensemble General Features Display
% Script that read and boxplot GENERAL Ensembles Features
% between or among Conditions of one Several Experiments
% Figs
% 'N ensembles','Threshold','Dunn',... [1]
% 'MaxIV','ErrorClass','CoreRatio',... ... |
dbeda7beef250c9076c8e9670fe5ea3d8cac4846c1e3ccf28e8ad26ff092be29 | MATLAB | 9,203 | 314 | %%
clear
robust_indicator_list = ["no", "yes"];
dataset_list = ["leuven", "oxford"];
for robust_indicator_counter = 1 : length(robust_indicator_list)
clearvars -except robust_indicator_list dataset_list robust_indicator_counter
robust_indicator = robust_indicator_list(robust_indicator_counter);
for d... |
06464f3c8bddac9aaed6a523e1eb7624495c607373248497f9a705c21707e5d7 | MATLAB | 9,231 | 257 | function spmup = spmup_cfg_tbx
% SPM Configuration file for the spm utility plus toolbox
% version 1 - july 2015
% ------------------------------------------------------
% Copyright (C) spmup team 2015
if ~isdeployed, addpath(fullfile(spm('dir'),'toolbox','spmup')); end
% ------------------------
% generic T1 image ... |
f66ad07617c1be1e80d05e866d75296f9126477794ac2303a117c9822a9485cb | MATLAB | 9,233 | 261 | %% eria stats for all_psthT
%load all psthT
clear
clc
[file, path] = uigetfile('all_psthT.mat');%When the user clicks the load psetion button, a window should open to enable the user to select a file.
cd(path)
load(file)
%% load all psthT
[file, path] = uigetfile('all_cells_structers_probabilty.mat');%When ... |
ad182ece78bf8f9fb18797477843ce607e426bcd318917bed4fd54ee81055ce1 | MATLAB | 9,275 | 210 | function AllLFP
%WheelAnalyzer24chSpikeÅðÍðs¢AʪÉcÁÄ¢éóÔÅs¤
%
global TurnMarkerTime OneTurnTime StartTime FinishTime fname RpegTimeArray LpegTimeArray RPegTouchAll LPegTouchAll DrinkOnArray WaterOnArrayOriginal WaterOffArrayOriginal filename
%2,3,6,8,9,13
CrossCorrValue=2
% CrossCo... |
001b822832335b6ddc0c9420871b30ae1622e8d17101ea5812850da452ea383a | MATLAB | 9,281 | 218 | function M = warping_transform(in_vector_boundaries, out_vector_boundaries, order)
% warping_transform - Generates matrices for time-warping one time-series to another (with different lengths).
% This function can be used for ECG phase wrapping, mapping ECG beats of different lengths
% into ECG beats of a fixed length,... |
a2ed78d8eaa2306c97cf439d0b43ffa543f295252ce0afd3e2a90b619d8fe5f4 | MATLAB | 9,284 | 254 | clear
clc
[file, path] = uigetfile('*.mat','MultiSelect','on');%When the user clicks the load psetion button, a window should open to enable the user to select a file.
cd(path)
%%
n = length(file);
all_psth = cell(7,2);
all_cells = cell(1,2);
ploting = 0;
window_times = {40:50, 65:125, 140:150};
win_n... |
5655066fb8fbbeab35110ccd2c26ec1d06533e19b9d9a41edbc1069ea6723480 | MATLAB | 9,285 | 242 | function RLphase190227
global ACresult ACresultW CCresultDrinkOn SpikeArray TurnMarkerTime RpegTouchallWon LpegTouchallWon...
RpNum LpNum CCresultSpike locsP1time locsP2time locsP1time2 locsP2time2 pksA1 pksA2 CCresultRtouchWon CCresultLtouchWon dpath3...
StartTime FinishTime DiffMaxMinR DiffMaxMinL fname... |
2b42d0c82496b6434bbff3538e25f31a6830e55c24c92e667c71cdaec672c4bd | MATLAB | 9,289 | 251 | % Usage: tableMonteCarlo = montecarlo_fornewdata_adaptation(presec,postsec,fs,woody,data_control,data_noxious)
% Inputs:
% - presec: start time of epoch in s (eg -0.5)
% - postsec: end time of epoch in s (eg 1)
% - woodystart: time to start woodyfiltering in seconds.
% - woodyend: end time of woodyfiltering in second... |
3928b2007551e821dc7e58d14db1b057b7dcf19bece87d0aae8b5caa22f40292 | MATLAB | 9,296 | 181 | close all
clearvars -except Behav_path Path_figures Path_variables Code_path MEG_path
SubjectID={'S01' 'S02' 'S04' 'S05' 'S06' 'S07' 'S08' 'S10' 'S11' 'S12' 'S15' 'S17' 'S18' 'S19' 'S20' 'S22' 'S24','S21','S25','S23'};
Drug=[3 1 1 2 3 2 3; 3 1 3 2 1 2 0; 1 3 2 1 3 2 0; 2 1 1 3 2 3 0; 2 1 3 1 2 3 0; 1 3 2 3 1 2 0; 3... |
154689c7db267c79397faa471b526d2f2e017ea923fcc21b2d4aa212fb176ef5 | MATLAB | 9,298 | 219 | % Function to Indentify Ensembles in Neural Activity
% by Clustering (hierarchichally) Neural *Coactivity*
% 2 or more cells in experiment
% Ignores cells that fires less than 0.25% of the ensemble time
% Applies to the Raster after Selection
% *** OUTPUT has to be named with the sufix: _Analysis *****
% Neither ne... |
3aee54653d13cecf26339adbbd2fbb1fb130d63f5565f723adf02e3f0d1bc2c8 | MATLAB | 9,300 | 188 | function Rectangles = Find_Vertex_Angles(Data,v,Cxy,Rc,Scale_Factor,Im_Rows,Im_Cols)
Plot1 = 0;
Plot2 = 0;
Plot3 = 0;
Peaks_Max_Num = Data.Vertices(v).Order;
SmoothingParameter = Data.Parameters.Auto_Tracing_Parameters(1).Vertex_Angles_SmoothingParameter;
MinWidth = Data.Parameters.Auto_Tracing_Parameters(1... |
27e651a4336275ba900d06b3612be00c116c4aac35031f5ea4396101368e5816 | MATLAB | 9,310 | 234 | function [Final_Curve,Approved] = Find_Center_Line(Im,BW)
% D:\Dropbox (Technion Dropbox)\Omer Yuval\Neuronalizer\PVD Images\Sharon\DEG_for_Menorah_analyser_Filtered_Format_Names
% TODO:
% Get rid of very small objects (see image 6 BW).
% Detect the 2 longest perimeter lines after deleting perimeter pixels.
... |
c484b10faa453e6e497103b24ca6161c9c46bc373d6e84b768c44cb5079a7eb3 | MATLAB | 9,311 | 236 | % Script to read in MEG data for further analysis using FieldTrip.
% Triggers on movement offset using the optitrack markers.
% Bad trials are removed and data are saved out for further analysis.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Housekeeping
clear all; close all; clc... |
522d71738c25e57576aafb79e46ee1c0ca7cd1cfd9a89203b0b02b324709f0a3 | MATLAB | 9,314 | 273 | function untitled20190311JS
global ACresult ACresultW CCresultDrinkOn SpikeArray TurnMarkerTime RpegTouchallWon LpegTouchallWon...
RpNum LpNum CCresultSpike locsP1time locsP2time locsP1time2 locsP2time2 pksA1 pksA2 CCresultRtouchWon CCresultLtouchWon dpath3...
StartTime FinishTime DiffMaxMinR DiffMaxMinL... |
75ef306688fb227fe62543af27342823e7abec09019d539acb77be93e2180394 | MATLAB | 9,317 | 266 | %__________________________________________________________________________
% violin.m - Simple violin plot using matlab default kernel density estimation
% Last update: 10/2015
%__________________________________________________________________________
% This function creates violin plots based on kernel density e... |
7a6a1b714992e5a76ca628a2cd3397692c3d6e92c14eb522397a9a0730ac607f | MATLAB | 9,344 | 187 |
classdef SonoNerfLayer < nnet.layer.Layer % ...
% & nnet.layer.Formattable ... % (Optional)
% & nnet.layer.Acceleratable % (Optional)
properties
layersSonoNerf
directionsSampling
frequencySamples
sampleRateAcoustic
numDirections
dbCutOutput
... |
8f6cd8bb156a056cb7c31dfc36064df01c890d1ad88dd8fbe388603199fd9f06 | MATLAB | 9,371 | 249 | function [gsidx,p] = correct_grn_network(grnnet,par);
% CORRECT_GRN_NETWORK Correction to the evidence for gene interactions predicted from gene expression data.
% [GSIDX,P] = CORRECT_GRN_NETWORK(GRNNET,PAR) for gene expression data GRNNET and correction parameters PAR returns
%
% GSIDX prediction... |
8f8ed7630f279595f8a102af0167bb582175549ed0c7340877f317babf283287 | MATLAB | 9,375 | 284 | function [ q ,trial_idx_ses,subject_ves,joint_ves,dim_sims,projs] = dr_into_joint_subspace( p,av_trials,err_trials,trans_data,n_pcs)
%% split trials into dr and dc per session (needed for VE per ses analysis)
% take half for DR and half for DC
% structure: sub / ses / (av|err|t) / (dr|dc)
t... |
e7fc269c2f5635681442723e56970ca0115ef69b4588ee08b3bd328937e4a195 | MATLAB | 9,376 | 247 | %% load and prepare the data
cd('I:\My Drive\ELYASHIV\PFC_paper_results_after_revision\DREEDDS_VS_CONTROL_PHb\new_IR_calculations')
load("all_cells_before_after_fit_parameters_control.mat")
load("all_cells_before_after_fit_parameters_DREEDDS.mat")
dreadds_all = importdata("all_cells_PHb_DREADD.mat");
control_all =... |
558be1e5201b0b9bf296a2b75feabb910e352c52c80527609ef37696aec390d2 | MATLAB | 9,379 | 172 | %% Speed Time series analysis https://www.sciencedirect.com/science/article/pii/S2211124718316437#fig3
%Similar to Zé Henrique T.D. Góis - Characterizing Speed Cells in the Rat Hippocampus
rng shuffle % Seed the random number generator
clear all % Clear workspace
close all ... |
aefdfc0ef5a8618cae8b23c7ea9892ea26a8ba6fe5b70b2ce64efed902afff51 | MATLAB | 9,379 | 286 | % Save NIFTI dataset. Support both *.nii and *.hdr/*.img file extension.
% If file extension is not provided, *.hdr/*.img will be used as default.
%
% Usage: save_nii(nii, filename, [old_RGB])
%
% nii.hdr - struct with NIFTI header fields (from load_nii.m or make_nii.m)
%
% nii.img - 3D (or 4D) matrix of NIFTI... |
7b943480e553c12ae0073bafa19743604964d312ebc362a77ffd27e6e2ba4fe1 | MATLAB | 9,412 | 293 | function [regions,dim_1_rot] = eq_regions(dim,N,varargin)
%EQ_REGIONS Recursive zonal equal area (EQ) partition of sphere
%
%Syntax
% [regions,dim_1_rot] = eq_regions(dim,N,options);
%
%Description
% REGIONS = EQ_REGIONS(dim,N) uses the recursive zonal equal area sphere
% partitioning algorithm to partition S^dim (the ... |
6091eab54cbf75ff334d51ef224fce03eb4aa26f4581861acbf4c9679e7f6126 | MATLAB | 9,417 | 289 | %% Test code to make sure that all folds have projections scores that correspond in direction
for fold = 1:10
tl = w.train_labels{fold};
pr = w.projected{fold};
con = (tl ==2);
del = (tl ==1);
dup = (tl ==3);
hold all;
if sum(sign(pr(del,1))) >0 && sum(sign(pr(dup,1)))<0... |
01436d608b68097e3eb1164968e593014515e811ac6b980f418103f166a77b3e | MATLAB | 9,422 | 154 | function Repeat_heatmap_analysis_eachwindow
%%% q[g}bv0~5(½ñÄÍÜÁ½©)»ê¼êÌðÍ
%%% »ê¼êÌq[g}bvÌêÔå«ÈlorãÊ1/4Æ©Å»Ì×EÌJèÔµÌÎpxðvZ·éB
path='C:\Users\C238\CheetahWRLV20220213\touchcellLtRt_Hirokane';
Left_FR = double.empty(5,0);
Right_FR = double.emp... |
2295a79930b8b766c371ab48b3b292e6a285a5c1151f79d025efeb1ab61cc8f0 | MATLAB | 9,425 | 216 | function save_annotate_results(app)
[int_idxs,~] = listdlg('PromptString',{'Please select intervals ',...
'to be plotted/saved.',''},...
'SelectionMode','multiple','ListString',app.popup_int_spect.Items);
[form_idxs,~] = listdlg('PromptString',{'Please select fileformat ',...
''},...
'SelectionMode','... |
3426d49adac209b737b45bfecb3462fc08ad3481b718d6763279d6235f4eb06d | MATLAB | 9,436 | 241 | clear;
clc;
binsize = 1; %sec
samp_rate = 40; %smapling rate in ms
%% load files
% [file, path] = uigetfile('*.mat');
% datafile = fullfile(path, file);
% load(datafile)
[file, path] = uigetfile('spike_clusters.npy');
datafile = fullfile(path, file);
spike_clusters = readNPY(datafile);
cd(path)
[file,... |
70e385f20d5914aa62e0ae9b7e8a93f6621fd22332fb8a50ba6bab9cfa725e47 | MATLAB | 9,437 | 216 | function save_annotate_results(app)
[int_idxs,~] = listdlg('PromptString',{'Please select intervals ',...
'to be plotted/saved.',''},...
'SelectionMode','multiple','ListString',app.popup_int_spect.Items);
[form_idxs,~] = listdlg('PromptString',{'Please select fileformat ',...
''},...
'SelectionMode','... |
7fb3f9a6c67eaf11809767067cbef8cb17e6ae65eb4ac412ae43e4b237f86d95 | MATLAB | 9,449 | 192 | function Peak_Variance_ALLTouch_20201208
path=['C:\Users\C238\Desktop\touchcellLtRt_Hirokane'];
cd(path);
LS=ls;
Seq5LtouchTime=[];
Seq5RtouchTime=[];
MeanCVLtouchArrayArray=[];
MeanRandomCVLtouchArrayArray=[];
MeanCVRtouchArrayArray=[];
MeanRandomCVRtouchArrayArray=[];
CVLtouchPeakArray=[];
CVRtouch... |
a9f9630e40ddef1a42444dd31f7e019f1b87beb955f3e98c67716f1b65cb30fa | MATLAB | 9,451 | 241 | %Shinjini Kundu (c) 2018
%Complete cross-validation
function [accuracy, kappa, sensitivity, specificity, w,alpha,actual,predicted] = PLDA_knn_CV(X, labels, alpha, k, mode,ITER)
%This function performs classification in the discriminant subspace of the
%PLDA classifier
%inputs:
% ... |
8b66a31827fd3a2a39e28ff9a71ffc66f74925d4ff5b04d2be194fde062751b9 | MATLAB | 9,457 | 286 | clear
p = params_2DAA;
fpath = fullfile(p.processed_data_dir,'allData.mat');
load(fpath)
[cols,alpha] = chooseColors;
%% find transitions and record some data about them
for sub = 1:p.nSubjects
for ses = 1:p.nSessions
% time
qdiff = [0 diff(bvs{sub,ses}(5,:))];
transition_times = find(abs... |
b4316a84fd2a7668e4c77fc4c02786c4bb7a74198be0e06100b93f77acd01864 | MATLAB | 9,462 | 225 | %% use_calculate_stats_before_and_after_from_all_cells
[file, path] = uigetfile('all_cells_control.mat');%When the user clicks the load psetion button, a window should open to enable the user to select a file.
cd(path)
load(file)
all_cells_control = all_cells;
[all_parameters_diff_cont, all_parameters_diff_percent... |
5412d46da05e5d400517576b9502e2b2e9bbe4f2bb50159a4418398b0eb6a662 | MATLAB | 9,463 | 213 | %% LIFonly for Pixels. E is divided into S and C cells
%% Input:
% Th idea of this is similar to MF+v, but all L4 input
% LGNFreq on/off frequencies of LFN input
% L4E, L4I Total E/I input kick numbers
% Assume L4SE/CE/IE are proportional to presynaptic ne... |
0fa8f135ad029d64ac67829d49a480072b8fec5ae31138db610400a436ec93ed | MATLAB | 9,474 | 322 | %% $\bf{FinderSpiker}$
%
% *SOURCE* :
% <https://github.com/vladscript/FinderSpiker.git>
%
% |Steps Guide|
%
%% 0. SETTTINGS
%
% * Import ALL functions >>Import_FinderSpiker
% * Experiments Directory: >>edit Load_Default_Directories.m
% * Deconvolution Parameters: >>edit Load_Default_Values_SP
% * Clu... |
5cebe730d33b89c5291d723211dce4e9483d28c084a3094c53ff59a584337f06 | MATLAB | 9,484 | 445 | % Run and advance each cell and output is generated appropriately
clear all;
figure;
%% Plotting single bar
clear all;
clf;
superbar(5);
xlim([0 2]);
ylim([-1 6]);
title('Plotting a single bar');
%% Plot 2 bars, one at a time
clear all;
clf;
superbar(5);
hold on;
superbar(2, 5.5);
xlim([0 3]);
ylim([-1 6]);
title('Pl... |
9a4a9ffd4e5ed24976580b19f95390cad64c9985a36fa18d4bc6ed7e68b27d90 | MATLAB | 9,490 | 277 | function varargout = Ragu_DetectOutlier(varargin)
% RAGU_DETECTOUTLIER MATLAB code for Ragu_DetectOutlier.fig
% RAGU_DETECTOUTLIER, by itself, creates a new RAGU_DETECTOUTLIER or raises the existing
% singleton*.
%
% H = RAGU_DETECTOUTLIER returns the handle to a new RAGU_DETECTOUTLIER or the handle to
%... |
4cc560a4c16b7ef25c28105903af829896f987455bc112d1706f828f5fcf30b9 | MATLAB | 9,497 | 295 | % double KF for fetal denoising
clear;
close all;
load('FOETAL_ECG.dat'); data = FOETAL_ECG(:,2:end)'; time = FOETAL_ECG(:,1)'; clear FOETAL_ECG; fs = 250;
L1 = size(data,1);
L2 = size(data,2);
bins = fs;
% R-peak detection
ref = data(1,:);
b = LPFilter(ref,1.5/fs);
ref = ref - b;
f = 1.1;
peaks = P... |
5e6f50e7c671ace5bd938c79566f130dfdb2f6b89651bc2de45af9f02b71d0da | MATLAB | 9,502 | 297 | function [new_files,FD,glo] = spmup_realign_qa(P,varargin)
% implement different quality control 'tools' for
% fMRI realigned data using SPM
%
% FORMAT realign_qa ( = do it all)
% realign_qa(P,flags)
%
% INPUT P indicate the timeseries to load. Can be
% - path to a 4D nifti
% - cellstring of pa... |
ff4cff8566f52efb9dbc88fe326b60816a6a33bc829651c4f29ed9f766cbc89f | MATLAB | 9,506 | 264 | function FigureS3d_NeuralSequenceTest_NormRescaleFilter
parent='G:/Ishizu_data';
dataPath='/Revise_ishizu/output/SpikeMark_longtone';
outPath='/Revise_ishizu/output/NeuralSequenceTest3';
% hulistic parameter %
shuffNum=100;
p_threshold = 1.0e-10;
figsaveTYPE='-dsvg';
%--------------------%
%%% setting... |
e297bbffe1007a41790cdd9b5683eb89dc1a6538aea263f00a8e763085b95448 | MATLAB | 9,509 | 303 | function select_bases(session, channel, unit)
% Select bases
%
% Parameters
% ----------
% - session: scalar
% Session number with format: yymmdd
% - channel: scalar
% Channel number
% - unit: scalar
% Unit number
info = get_info();
times = info.times;... |
7a42103c0513db9696005ca4eefa4ca26ebf85ca98b56854ba2d3b9c12630da4 | MATLAB | 9,511 | 389 | function ob = Gsparse(arg1, varargin)
%function ob = Gsparse(file.wtf | sparse | cell, options)
%|
%| Construct Gsparse object, either from a sparse matrix itself,
%| or from the arguments that would be passed to matlab's sparse() command,
%| or from an Aspire binary .wtf file.
%|
%| The Gsparse object overcomes some ... |
7f2ffb8fcf66389d669075e60135ab23de3dedd1b984569101c2f958a0c68872 | MATLAB | 9,519 | 262 | function FigureS3d_NeuralSequenceTest_shuffleTimeCourse
parent='G:/Ishizu_data';
dataPath='/Revise_ishizu/output/SpikeMark_shuff_longtone';
outPath='/Revise_ishizu/output/NeuralSequenceTest_shuffleTimeCourse3';
% hulistic parameter %
shuffNum=100;
p_threshold = 1.0e-10;
figsaveTYPE='-dsvg';
%------------... |
69d7b681b6b7cc65a62c531229bf794e0775054e1edef47c38fd1e698637324f | MATLAB | 9,523 | 268 | function data_quality = data_quality_func(sub,ses,project_dir)
restoredefaultpath
close all
clc
% project_dir = 'P:\Paediatric_OPM_Notts\';
% sub = '001';
% ses = '001';
run = 'run-001';
data_quality.ID = sub;
data_quality.task = 'braille';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
02c342b62cd21da93350abe2aaabc04505f99d9f07011245050b594ba1fefbb2 | MATLAB | 9,532 | 270 | % this script calculates the average value of Free Water fraction in
% lesions and perilesional tissue
clearvars
close all
addpath(genpath('/storage/stella/MATLAB_toolboxes/'));
addpath('/storage/stella/Analysis/matlab_scripts/');
run('/storage/stella/Analysis/DTI_FreeWaterFraction_May2022/SCRIPTS_MATLAB/T1_automatic... |
a904cf94d7ee38dfe0122d3db3e4f80684c07474f9d1509212ba1d4f06b38cbc | MATLAB | 9,533 | 267 |
%{
----------------------------------------------------------------------------
First_take number of frames in each tif files
Analyzing imaging data simply
At least for the correct rate
----------------------------------------------------------------------------
%}
function Task_kaiseki_tokyo1_20230701_sound_c... |
6f53d94c815d26099d87a65121f21f33f1c1ec13e2268dbcaf69b53451a66ca0 | MATLAB | 9,538 | 257 |
%{
----------------------------------------------------------------------------
Determine the time window for task relevant neurons
%Start of task
%Sound on
%Sound off
%Before choice
%After choice (0sec)
%After choice (1sec)
%After choice (2sec)
%p = 0.001
%Each epoch, predict the prior, sensory and choice... |
f98f661d9d795029f0966d106e132b37ac0ec991fd1057b069a17b78be75e76f | MATLAB | 9,550 | 254 | function [outFiles] = convertROIForFS(inFile, weight)
% Converts an ROI map from MNI space to FreeSurfer fsaverage space.
%
% This function converts a volumetric ROI map in the MNI152 template space to
% FreeSurfer's fsaverage surface space. It separates ROIs based on intensities in
% the input file, calculates their... |
15ecb3e489dd11d554772642754021b5d9fc9bdd7bef964247e9b26fabd0b673 | MATLAB | 9,560 | 223 | function [feature_vec, feature_info] = ecg_hrv_features( data, R_peaks_indexes, fs, ...
MinRPeaks, MinRRFactor, MaxRRFactor, ...
MinRRSeconds, MaxRRSeconds, ...
... |
5fb8150705cb32d87397c8022b1e39f114f24a5dd9a7f4bc814e85d14561582e | MATLAB | 9,580 | 203 | % Cardiac model ToR-ORd
% Copyright (C) 2019 Jakub Tomek. Contact: jakub.tomek.mff@gmail.com
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the Licens... |
ba0fd3648bcabb361964904a31b01743ca2324be05af678059db01bf82b95b2b | MATLAB | 9,600 | 354 | %
% ANALISI A COMPONENTI PRINCIPALI
%
% Ingressi alla funzione:
% x : matrice dei dati, sulle righe vanno gli individui e sulle colonne le variabili
% prob : probabilità con cui viene tracciato il cluster, se 0
% traccia il cluster corrispondente al punto più lontano (esclude qll + lo... |
8bcf7908ddaf56f975d55dea15501c636459327f1a4887c58822a79a00611dde | MATLAB | 9,610 | 272 | function pretouch20190608JS
global ACresult ACresultW CCresultDrinkOn SpikeArray TurnMarkerTime RpegTouchallWon LpegTouchallWon...
RpNum LpNum CCresultSpike locsP1time locsP2time locsP1time2 locsP2time2 pksA1 pksA2 CCresultRtouchWon CCresultLtouchWon dpath3...
StartTime FinishTime DiffMaxMinR DiffMaxMin... |
e892e0d7719638a25de4dbed08dac7979ad3a41d6de81274d5a9c28c124a3683 | MATLAB | 9,611 | 202 | function [rawS] = JT_extractRaw_fft(varargin)
%%Extract raw photometry data from multiple recordings into a single
%%structure and parse by specified behavioral state, for FFT analysis with
%%function getFft
%
% [rawS] = extractRaw_fft() - extract new data into structure
% [rawS] = extractRaw_fft(fPath, fName) - ... |
cf36c0098b42a6287f2cdbf91d168518e787a3183ec716dc08965a1eeed49eee | MATLAB | 9,613 | 287 | function [DimensionSummary]=mnl_SimulationPairFinding6(MaxNumPoints,nDim,NumSim)
%Spreads=[0.1 0.2 0.5 1 2 4 6 8];
%Spreads=[0.2 0.5 1 2 4 6 8];
%Spreads=[2 4 6 8];
Spreads=[0.5 1 2 4];
%% Create colour map
[cmap]=mnl_CalculateColourMap(nDim);
%Spreads=[2]; %Down to 2 Copy Numbers
szSp=size(Spreads);
%% Generate Poissi... |
da253c8f9bc96c690ca85fdd4a3a9d62017c6948a35bc5615bafa623cfe287a5 | MATLAB | 9,614 | 347 | function [peaks, peak_indexes] = peak_det_pan_tompkins(ecg_data, fs, varargin)
% peak_det_pan_tompkins - R-peak detector based on Pan-Tompkins method.
% QRS Detection with Pan-Tompkins algorithm.
% Pan et al. 1985: A Real-Time QRS Detection Algorithm.
% [peaks, peak_indexes] = peak_det_pan_tompkins(data, fs, varargin... |
2e71e147325ccb98523187426f1ae358692e2b2a577602c84bb1fa60b955a8e1 | MATLAB | 9,621 | 191 | %Section 1 used for individual file; Section 2 used for all files
%
%Prerequest: spike csv and plx file
%% 1. individual file
%{
% read file
disp('select a RHD file');
read_Intan_RHD2000_file
disp('select a plx file');
addpath(genpath('C:\Users\ywang28\Documents\MATLAB\Matlab Offline Files SDK'))
addpath(genp... |
1cae13dbcffb366829758b9c88aa418013f0889a4ab1cff9a2714561353dfed4 | MATLAB | 9,623 | 204 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This script performs post-processing of volumetric LSFM data with optical
% scattering supression and single sided illumination.
% The script is separated into 5 sections.
% Section 1:
% Define list of paths to tiff or tif images in a user-defined f... |
27b7380096a699e9b191f23b3fbfd212617e971e5cd4826a37c33537e347595a | MATLAB | 9,624 | 354 | classdef rightcircularconeFit<quadricFit
%A class for executing and managing right circular cone fits
properties (SetAccess=protected)
vertex (1,3)
cone_angle (1,1)
height (1,1) %upper and lower extremes along axis of the cone
end
properties (Dependent, Hidden)
... |
287d8519e6ee75967dd599afbc4eb4fe94191b6f2528eaf4e66b5fe0b8f49809 | MATLAB | 9,625 | 321 | function processSession_correlationAnalysis_v2(inputFileName,outputFileName,dataDir)
%% load session
addpath('X:\ibn-vision\DATA\PROJECTS\AllenData\gitcopy\AnalyseData\PopulationAnalyses')
load(fullfile(dataDir,inputFileName))
%% filter trials to those for analysis and classify according to behavioural state
trialD... |
b101263fe0908c67f3779ef247f2f6f9b0f4efca46c5730f9c5c274fc926b418 | MATLAB | 9,629 | 281 | function untitled20190311
%¼àQFXpCNª Á½¼ãÌ^b`©çXpCNÜÅÌÔðp¢½\ª
global ACresult ACresultW CCresultDrinkOn SpikeArray TurnMarkerTime RpegTouchallWon LpegTouchallWon...
RpNum LpNum CCresultSpike locsP1time locsP2time locsP1time2 locsP2time2 pksA1 pksA2 CCresultRtouchWon CCresu... |
99f30d1d4f47f389bce669f2a83c672c099d8a082537015871b8a6421879ae7d | MATLAB | 9,635 | 260 | function varargout = Ragu_TMaps(varargin)
% RAGU_TMAPS M-file for Ragu_TMaps.fig
% RAGU_TMAPS, by itself, creates a new RAGU_TMAPS or raises the existing
% singleton*.
%
% H = RAGU_TMAPS returns the handle to a new RAGU_TMAPS or the handle to
% the existing singleton*.
%
% RAGU_TMAPS('CALLBACK'... |
83b7cd968053e49a1f72f2ebc7a1da1fb35eeabe2b9293c09ec2744c45a15e7c | MATLAB | 9,641 | 267 | function data = TDTthresh(data, STREAM, varargin)
%TDTTHRESH TDT snippet extractor.
% data = TDTthresh(DATA, STREAM, 'parameter', value, ...), where DATA is
% the output of TDTbin2mat, STREAM is the name of the stream store to
% convert to snippets, and parameter value pairs define the snippet
% extracti... |
f6470ab8271f458f6ef552466e447b234106e1df5f78928b8ee4d184569af951 | MATLAB | 9,645 | 270 |
%{
----------------------------------------------------------------------------
First_take number of frames in each tif files
Analyzing imaging data simply
At least for the correct rate
----------------------------------------------------------------------------
%}
function Task_kaiseki_tokyo1_20230701_sound_c... |
4ef570ae5ddb10523983a3cc5e57db4bb45328079cda14f40a2c3950a0a09923 | MATLAB | 9,646 | 263 | function vcg = vcg_construction(ecg, lead_names, varargin)
% VCG_CONSTRUCTION - Constructs the Vectorcardiogram (VCG) from ECG leads.
%
% This function constructs the Vectorcardiogram (VCG) from a set of ECG
% leads. It uses a weighted sum of lead vectors corresponding to the
% available ECG leads to create the VCG. Th... |
4fd96445bd29a25053bc9d9afec1d7613894623e4baaba1e89f594798e67e4ef | MATLAB | 9,646 | 317 | function burst_res = burst_analysis(app)
%BURST_ANALYSIS Summary of this function goes here
% Detailed explanation goes here
cla(app.ax_msna_raw), cla(app.ax_burst_interval_his), cla(app.ax_burst_duration_his),...
cla(app.ax_burst_amplitude_his),cla(app.ax_burst_integral_his), cla(app.ax_burst_res),...
cla(app.ax_msn... |
405e1b0c0a3096dfcc99823948e5991e55141fdef1b42ef194ed64ebe35fd05e | MATLAB | 9,650 | 293 | function [fig] = CirHeatmap23(Data, varargin)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% CircHeatmap written by Joshua A. Welsh
% Created: 1 June 2018
% Last Edit: 13 June 2018
%
% INPUTS:
%
% 'CircType' - 'o' full circle,'tq' 3/4 circle, 'half' semi-circle
%
% 'Outer... |
082f6a9ba1434c4fca771fde8c0d944d4ac7a4d328d2e0e6d0de0c4e59f13653 | MATLAB | 9,664 | 256 | % Function to accumulate Rate of Activity (RoA)
% of each Cell For each Experiment of the same
% experimental setup, i.e., same experimental conditions
% --------------------------------------------------------------------
% Input
% Load experiment manually from different folder: one by one
% R_Condition: Selected... |
3ae64e0f7993cb6c01ee237fd02e529b4032784973e9cc78118bf20a725da6b8 | MATLAB | 9,683 | 211 | function spike_res = transduction_analysis (app, update)
if nargin == 1
update = false;
end
MIN_CLUSTER=10;
% MSNA = app.data(app.settings.channel_idx.msna).data;
% data = app.data(app.settings.channel_idx.msna).data(app.settings.interval(1,1)/app.data(app.settings.channel_idx.msna).ts(1):app.settings.interval(1,2... |
178b4dca0411331704b13931f471bfa2a3f831c74593acdd66edd012d34177ee | MATLAB | 9,686 | 273 | function [newD C lat] = reducedims(D,alg, dims, handles)
% REDUCEDIMS a dimensionality reduction tool
% REDUCEDIMS(D,DIMS,ALG,DATATYPE), where D is the high dimensional data to be
% reduced, DIMS is the number of dimensions to reduce to, ALG is an
% integer representing the reduction algorithm to be used, and CLU... |
07e15f8266a3fa8109301101f941e3ea127409c3cc46c7b3f467ce10aed1061b | MATLAB | 9,687 | 291 | classdef (Abstract) Clustering < handle
%CLUSTERING A clustering of spike data
%% CONFIGURATION
properties (Hidden, SetObservable)
hCfg; % Config object
end
%% CLASS INTROSPECTION
properties (SetAccess=protected)
unitFields; % data fields related to cluster... |
d680897101384c3c411394a5fb10cd93cb5eecca93fbd266185e82d7732d676a | MATLAB | 9,715 | 252 | clear all
close all
clc
% Daniel Sazhin
% ISTART Project (Shared Reward)
% DVS Lab
% 05/13/2022
% Temple University
% This code generates a composite reward sensitivity score using BAS and SR
% data. This code also generates a composite substance use score for AUDIT
% and DUDIT.
%% Inputs
currentdir = pwd;
output_... |
2d7b2fa7c987a16aad8fb50073529e8057c21d07d64cb2a09d0d52052b06d950 | MATLAB | 9,719 | 270 | function [varargout] = polarPcolor(R,theta,Z,varargin)
% [h,c] = polarPcolor1(R,theta,Z,varargin) is a pseudocolor plot of matrix
% Z for a vector radius R and a vector angle theta.
% The elements of Z specify the color in each cell of the
% plot. The goal is to apply pcolor function with a polar grid, which
% prov... |
5c5c3f969c2b0fa7ab349136a05935bf8628e3805a1ae68b302959c7599d6086 | MATLAB | 9,742 | 306 |
%{
----------------------------------------------------------------------------
Determine the time window for task relevant neurons
%Start of task
%Sound on
%Sound off
%Before choice
%After choice (0sec)
%After choice (1sec)
%After choice (2sec)
%p = 0.001
%Each epoch, predict the prior, sensory and choice... |
4f5de3259ce05fd1c196e801e8cfd070fd4ffad117e9990974e5aedb763d6428 | MATLAB | 9,743 | 350 | function st = nufft_init(om, Nd, Jd, Kd, varargin)
%function st = nufft_init(om, Nd, Jd, Kd, [n_shift,] ...)
%|
%| Initialize structure for d-dimension NUFFT using KB interpolator,
%| particularly the interpolation matrix in sparse format.
%| caution: this routine can require a lot of memory!
%| in
%| om [M d] "digita... |
46951a77e1a8dd7d6c21d5c80b44af9c49e235f8f4bee1aef16a7e7da2b8ff3d | MATLAB | 9,768 | 357 | %% analyse train/test decoding
load('rLDA_trainTest_withConsensus.mat')
%% get mean of permutations for each session
for isession = 1:5
session(isession).meanStat = mean(cat(3,session(isession).stat.perm.perf),3);
session(isession).meanRun = mean(cat(3,session(isession).run.perm.perf),3);
end
%% plot consen... |
1a03f73dbe927c098b220a42dd5e0a933659d5a1047459416745adec9becda15 | MATLAB | 9,769 | 249 | function hText = xticklabel_rotate(XTick,rot,varargin)
%hText = xticklabel_rotate(XTick,rot,XTickLabel,varargin) Rotate XTickLabel
%
% Syntax: xticklabel_rotate
%
% Input:
% {opt} XTick - vector array of XTick positions & values (numeric)
% uses current XTick values or XTick... |
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