sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
838bb037dd74d43049b5379abb499de6552982e64368b6c974a3e65f297acd1b | MATLAB | 868 | 25 | %% Function to load video of fluorescence .avi
% Input
% FileName: Name of the video
% PathName: Directory of the video
% Output
% mov: Video, Structure with pixel's fluorescence (grayscale)
function [mov]=Video_Load(FileName,PathName)
%% Read Movie
disp('****Loading video***')
% [FileName,PathName] = uigetfile('... |
d12d8b91963868a8870f5a4dd343ab0262520a6923eaa9d8fc3574e2e5a81bdc | MATLAB | 869 | 30 | function [S, Q, lAlambda] = multislice_static_unsigned(A,gplus)
% INPUTS
% A is the (weighted) connectivity matrix
% it is assumsed that all values of the connectivity matrix are positive
% Gplus is the resolution parameter. If unsure, use default value of 1.
% OUTPUTS
% S is the partition (or community assignment of... |
03cb07b3d918bf908084165b5ebdd66fe533cba11feda930b730a75ffc490f70 | MATLAB | 870 | 33 | function [Ps,time,frequency]=Spectrogram_(s,fs,window_size,shift_size,w)
dft_size=window_size;
length_s=length(s);
nframe=floor(((length_s-window_size)/shift_size)+1); % St[:shiftÆDFT·éwindow_sizeÅÜé
x=zeros(1,window_size);
% w=HanningWindow_(window_size); % Ö
Ps=zeros(dft_size/2+1,n... |
72ceebc50082f0b625a3916f951a35c6a413d0edd747d0bb258b4a19e960798e | MATLAB | 871 | 26 | % 准备数据（替换为你的数据）
X = selected_variables_values; % 特征数据，大小为 m x n，其中 m 是样本数，n 是特征数
y = ycal; % 目标数据，大小为 m x 1
% 拆分数据为训练集和测试集
cv = cvpartition(size(X, 1), 'HoldOut', 0.2); % 80% 训练集，20% 测试集
X_train = X(cv.training, :);
y_train = y(cv.training, :);
X_test = X(cv.test, :);
y_test = y(cv.test, :);
% 创建SVM回归模型
mdl = fit... |
03c0f70960d2bbf0a845d9134853b58661d96bf4fe57917d6f7bf27bfd1f5d19 | MATLAB | 873 | 28 | function derived_resp_signals(app)
idx = 0;
tmp = nan(1,length(app.data));
for i = 1: length(app.data)
tmp(i) = app.data(i).tic_multipl;
if strcmp (app.data(i).name,'Respiration Rate')
idx = i;
end
end
if idx == 0
idx = length(app.data)+1;
end
max_fs_idx = find(tmp ==1,1);
app.data(idx).name... |
8dbfe07ba3529355ab28ff2cc7371945ab19f6022c50f05950c7d62d10cc8a95 | MATLAB | 873 | 27 | function diagonstic = diagnostic_partitions(P)
mx = max(max(P)); % max communities per sample
bins = 1:mx; % bins. number of points supporting the distribution
h = hist(P',bins); % node histogram
p = bsxfun(@rdivide,h,sum(h)); % probabilities
e = -nan... |
909f80f6dc3a81a9959245d4327e25183289ebbc569331bdc9cbcddd3c416b82 | MATLAB | 873 | 17 | function Fourierfunction4touchCC(n,U,M)
%AutocorÍSpikeArrayADrinkÍSpikeArrayWon,WaterOn/OffÅÍSpikeArray
%TouchÅÍSpikeArrayÆTðgpB
global StartTime FinishTime fname SpikeUnits TurnMarkerTime dpath3...
SpikeArray RpegTouchallWon LpegTouchallWon CCresultRtouchWon CCresultLtouchWon
[CrossCoRtouchWon]... |
36c5c60596c55c179d06748927752f11462596694931058358b897c9103454f3 | MATLAB | 874 | 23 | function flag = isEqual(val1, val2)
%ISEQUAL Generalized check for equality
if isnumeric(val1)
flag = isnumeric(val2) && ...
ndims(val1) == ndims(val2) && ...
all(size(val1) == size(val2)) && ...
all(val1(:) == val2(:));
elseif ischar(val1)
flag =... |
bf5d94718248e7e353b08389b5bcd8ff41e62fe02a1178423f84b8d592ee6eac | MATLAB | 876 | 31 | % Make grid of values for SVM prediction
%
% Input:
% Xpca matrix pf Principal Components
% PC_i i-th PC
% PC_j j-th PC
% delta Resolution
% Npts (optional): default=100
% Output
%
%
function [x1Grid,x2Grid]=makepcagrid(Xpca,ONEpca,TWOpca,deltaPlus,varargin)
% N pts:
if isempty(varargin)
N... |
c659f0f298e5aad56239a28d9f3ec7fa4749f7e750ffbaaae570b2861d6a125d | MATLAB | 876 | 32 | function TBvol=hbf_TM_Bvol_Linear(DB,Tphi_full, ci,co)
% HBF_TM_BVOL_LINEAR makes transfer matrix for B_vol
%
%TBvol=HBF_TM_Bvol_LINEAR(DB,Tfull,ci,co)
% DB: DB matrix, [N x 1] cell array
% Tphi_full: BEM matrix for potential
% ci: Conductivity inside each BEM surface, [N x 1]
% co: Conductivity outside each B... |
240bdb1a5f485508e248b53919c9befe3f3f5cb402e9e1cf7d60ec07f257c291 | MATLAB | 878 | 33 | function [ loading_similarity ] = compute_load_sim( fa_model )
%
% computes the loading similarity of each dimension
%
% Input:
% fa_model - (struct), factor analysis model parameters
%
% Output:
% loading_similarity - (list), loading similarities of each dimension's
% loading vector
%
% @ Ak... |
aa32c6ee98c0bb9788c8c9502c4456231361c485e756183d51c199d26e3676e3 | MATLAB | 878 | 42 | function [node,link,cell] = OCY_cell_nodes(node,link,cells)
w=size(cells,1);
l=size(cells,2);
h=size(cells,3);
cells=uint16(cells);
% get list of cell coordinates
cc=bwconncomp(cells);
n_cells = cc.NumObjects;
cell = [];
% generate cell structure
if(n_cells>0)
for i=1:n_cells
cells(cc.Pixe... |
688a1e3a3f1e1b0bc870c838fefbb741b70e3e12f17814ac84d779fa1ca6bff1 | MATLAB | 879 | 33 | function X = dtft1(x, omega, n_shift)
%function X = dtft1(x, omega, n_shift)
% Compute DTFT of 1D signal x at frequency locations wx
% In
% x [N,ncol] signal values
% omega [M,1] frequency locations
% n_shift [1,1] use [0:(N-1)]-n_shift indices
% Out
% X [M,ncol] DTFT values
%
% This needs enough memory to store... |
653cbee3a494144864f3fe1c8675bc15a818116fc6a277dd32d7562673f4fc36 | MATLAB | 880 | 23 | % Functions that check repeated pair of coordinates
% Ouput
% repetidas: indexes of repeated coordinates
% Input
% Matrix of Coordinates [X,Y]
function IndxRepeated=repxychekcker(XY_selected)
IndxRepeated=[];
[~,IndexSortedUN]=unique(XY_selected,'rows','stable');
if numel(IndexSortedUN)<size(XY_selected,1)
fpri... |
c2c77112031ffd27b089a9e67e559613513c7d5e05de1265d44b66495baeb2d8 | MATLAB | 880 | 32 | function [subtimeseries] = extROITS(targetpath, StimType, GroupType)
% This program is intended to extract ROI time series from .mat
%
% input:
% targetpath:
% StimType:
% GroupType:
%
% output:
% subtimeseries:
%
% By Hiroaki Hamada, Araya, 2019/08/29th,
temporalSub = load(targetpath);
if strcmp(GroupType, ... |
624f87e7109ad6af712102a310961a63c8d26bb2f0935973b608a5f5e8e052d9 | MATLAB | 881 | 21 | % plotDistToNearestTip.m:
% lines 213 - 214
% my changes - return the whole table data to the calling function
borders = borders_table;
% Display_Probe_Track.m
% lines 231 - 238
% my changes - save relevant data to csv files
mm_reference_probe_length_tip = reference_probe_length_tip/100;
mm_probe_length = pro... |
1280df1d6e250c1bb7b3d9db1fd6265c40edd23bff2c087290840ab9e9076cb3 | MATLAB | 882 | 22 | function spike_res_out = sel_spikes(spike_res_in, ts, burst_ints)
%SEL_SPIKES Summary of this function goes here
% Detailed explanation goes here
spike_res_in.use_spikes = [];
ts_msna = ts(1) : ts(1) : ts(2);
spike_res_in.use_spikes(1:length(spike_res_in.spike_idx),1) = ones(size(spike_res_in.spike_idx));
for i =1... |
385223fc6f02c79a8e68c690dac8da1f2ae673ceb6da8903fa055e14e492db95 | MATLAB | 882 | 33 | function miout=MIhigherdim(x,kneig,emb_dim,emb_tau);
% Calculate MI value of any high-dimensional vector - to include also
% dependencies in time use embedding dimension >1 (see[1], Fig 14)
% (rectangular version)
% x....input data mxn m...channelnummer n...sampling points m<<n
% kneig... k nearest neigbor for MI ... |
a1ea3cd1665405721a73df6b33bc58d2b989f0cb948967537af2e63c2f3ff71e | MATLAB | 882 | 28 | % Funtion to get oval pixels in retangular ROIs
% Input
% sROI: Cell of ROIs from ImageJ
% Output
% XYcenter: Coordinates of ROI center
% ROIpixel: pixels Coordinates in Elliptical ROI
function [XYcenter,ROIpixel]=getpixelsatROI(sROI)
N= numel(sROI);
XYcenter=[];
for n=1:N
% Get Rectangular Coordinates
xy... |
d0d893e098e14fe1681b170588167d35fbb5c129c3d3ef9f243f3c07e4dcd60f | MATLAB | 882 | 33 | clear;
close all;
%//////////////////////////////////////////////////////////////////////////
% generate input data file
x = cumsum(randn(10000,1));
x = x + sin(2*pi*(1:length(x))'*60/1000);
hin = fopen('testinput.txt','w');
fprintf(hin,'%14.10f\n',x);
fclose(hin);
%//////////////////////////////////////... |
1ac8ad8cfe5c16699008195bd619716f9b8a8c7db413b9e2cddc743ab1f8390c | MATLAB | 883 | 27 | function data=createdatamatc(data,E,Fs,win)
% Helper function to create an event triggered matrix from univariate
% continuous data
% Usage: data=createdatamatc(data,E,Fs,win)
% Inputs:
% data (input time series as a column vector) - required
% E (events to use as triggers) - required
% Fs (sampling frequen... |
44933050c6e0b499680c4df4f484c35c30d8e500b176815c760f4cd8bad26504 | MATLAB | 883 | 35 | function EyeClosed = detect_eyeclosure(Eye, SampleRate, Threshold)
% Classifies eyes as open or closed, and if stretch longer than 1s, as a
% microsleep.
% in Lapse-Causes
Nans = isnan(Eye);
% eye event can't be shorter than 50 ms
MinEO = .05;
% Convert pupils into 1s for opne and 0s for closed.
EyeOpen = Eye > Thres... |
5962c9869faa8be2b77f867c900d74647808a350351688ab358555555a2014e9 | MATLAB | 883 | 30 | % test bids processing pipeline on the ds000117 dataset
%% update path
% in case spmup was not added to the path
location = fullfile(pwd, '..');
addpath(fullfile(location, 'adaptative_threshold'), ...
fullfile(location, 'bids'), ...
fullfile(location, 'external'), ...
fullfile(location, 'hrf')... |
d50386a9e202f72dc9151cf8b760e6843daa49512e6915a3be5c2d294c452639 | MATLAB | 883 | 27 | % Copyright 2024 Eelke Spaak, Donders Institute.
% See https://github.com/Spaak/rift-phase-and-visibility for readme/license.
% Belongs with:
% Spaak, E., Bouwkamp, F. G., & de Lange, F. P. (2024). Perceptual foundation and extension
% to phase tagging for rapid invisible frequency tagging (RIFT). Imaging Neuroscience,... |
b5e9658b5f5dcc1c6dfb2d07e06b8e497b99952f063d5e0f638202b4fb12cd3f | MATLAB | 884 | 33 | function plot_junction(ax,x,y,angles)
% angles = [pi/4,pi/2,pi];
angles = sort(angles);
Alpha = 0.5;
CM2 = [0.8,0,0 ; 0,0.8,0 ; 0,0,0.8];
t0 = linspace(0,2*pi,100); % Angle range 1.
t1 = linspace(angles(1),angles(2),100); % Angle range 1.
t2 = linspace(angles(2),angles(3),100); % Angle range 2.
t3 = [li... |
6a4e705789e0b06e5a8a979c61be3463c0d53bdf01017f32cd0a7c3d5432e0ef | MATLAB | 885 | 30 | function [map,nodes] = trimmap(map,nodes)
%% Remove disconnected nodes from the network
% This function removes all-zero rows and columns from the adjacency
% matrix. This effectively removes disconnected nodes from the network
%
% [map,nodes] = trimmap(map,[nodes])
%
% Input:
% map: projections matrix
% nod... |
86b7fb34fa68dc7df079a2756bcb4a023fa830509ef5af48b0e5c3602ccd5995 | MATLAB | 885 | 33 | function plotsingle_tf(hdl,~)
D=get(hdl,'UserDat');
time = D{1,1};
timeindx = D{1,2};
freq = D{1,3};
powdata = D{1,4};
powlims = D{1,5};
currchan = D{1,6};
blstring = D{1,7};
cone_oi = D{1,8};
f1=figure; set(f1,'PaperUnits','normalized','PaperPosition',[0.0235308 0.0272775 ... |
da82550aa709e0a463959dadcd50ac92cd64826a935e1fe816f38cd481fe595b | MATLAB | 885 | 35 | function file_writer = createFile(file_path,varargin)
%
% file_writer = adi.createFile(file_path)
%
% This is a work in progress
%
in.copy_blank_when_new = false;
in = adi.sl.in.processVarargin(in,varargin);
is_new = ~exist(file_path,'file');
%Possibilities
%1) create new file de novo
%2) create new file using a... |
23fa5313bd66a01dced2439bc99a7553d52887b3cf229474b48c15707e82f834 | MATLAB | 886 | 33 | function new_file_path = changeFileExtension(file_path,new_extension)
%
% new_file_path = sl.dir.changeFileExtension(file_path,new_extension)
%
% This is a simple helper function that makes it obvious what is being
% done (i.e. changing the file extension).
%
% Inputs:
% -------
% file_path: str
% Pat... |
9d8fd36888509c77178e560d27c1b86b27f41610e5da3b5c26948eee39b80fe6 | MATLAB | 886 | 32 | function Generate_Result_Demo(Workspace)
Scale_Factor = 50/140;
CB_GS2BW_Threshold = .9;
figure(1);
clf(1);
imshow(Workspace.Image0);
set(gca,'YDir','normal');
hold on;
for s=1:numel(Workspace.Segments)
for r=1:numel(Workspace.Segments(s).Rectangles)
plot([Workspace.Segments(s).Rectangles.X],[Workspa... |
f02547fd6e34d50c6dc06e54e99ca23ac0c4dcd360143bf9ba30fcbb932672a6 | MATLAB | 886 | 28 | function bootstrap_se = se_of_median(sample_dist,bno)
%SE_OF_MEDIAN Standard error of median.
% BOOTSTRAP_SE = SE_OF_MEDIAN(SAMPLE_DIST) calculates bootstrap standard
% error of median (BOOTSTRAP_SE) from a bootstrap sample of 1000
% resamplings from the empirical sample (SAMPLE_DIST).
%
% BOOTSTRAP_SE ... |
121b0cc070e74f4d515c53f14a7acd9f9fdb1f17eda486b0dad6a7cf1d693192 | MATLAB | 888 | 36 | function pv_acg(cellids,resdir,issave)
%PV_ACG Auto-correlation analysis.
% PV_ACG calculates auto-correlations for PV(+) neurons in 500 ms windows.
% ACG results are saved.
%
% See also ACG.
% Balazs Hangya, Panna Hegedus
% Institute of Experimental Medicine, Hungarian Academy of Sciences
% hangya.balaz... |
c28f47da83417d6acf2534cb7d3643434071e619fa3a683b0eb16820502302e6 | MATLAB | 889 | 25 | function t = tvalue(data1,data2,dim,vartype)
if nargin < 3
dim = 1;
end
if nargin < 4
vartype = 'e';
end
if isempty(data2)
t = mean(data1,dim) ./ std(data1,0,dim) * sqrt(size(data1,dim));
else
switch vartype
case 'e'
v = (var(data1,0,dim) * (size(data1,dim)-1) + var(data2,0,dim)... |
c72edd64b30b6f916fe752655e7a7594517960bd86da3803ec6ab0030d1fe034 | MATLAB | 890 | 25 | function Trace_Neuron_New(Im,Segments)
% TODO:
% In Segment_Skeleton.m, prepare skeleton coordinates in the same format as the final segments.
% Their coordinates and width will only be tweaked by this algorithm.
for s=1:numel(Segments)
for r=1:numel(Segments(s).Rectangles)
P = Segments(s).Rectangle... |
eeee8806593a619f9add6815b0f852e530b0c817de71229e54225b207dcf1d00 | MATLAB | 890 | 18 | function [gsidx] = truncate_predictions(gsidx);
% TRUNCATE_PREDICTIONS Truncate gene interactions to at most 100000 interactions.
% GSIDX = TRUNCATE_PREDICTIONS(GSIDX) for gene interactions GSIDX returns
%
% GSIDX predictions of interactions (#interactions-by-3) with rows
% ... |
1d8485aeca5fbece63ef4f8d23cb7edd12c2eff54fe101649aca4761648fd050 | MATLAB | 891 | 28 | function [Power, Freqs] = discrete_time_frequency(Data, SampleRate, EpochLength, WindowLength, Overlap)
% Calculates a time-frequency plot for data, but nothing fancy, just
% non-overlapping windows.
% Data is ch x t. Power is a ch x Freq x t matrix or Freq x t if only 1
% channel provided.
nChans = size(Data, 1);
Tim... |
33352028ae00abc629bf430bef011639050480de9e46ed78540e667404f6a6ca | MATLAB | 896 | 27 | function W3distribution
global RTroughlocsInterval LTroughlocsInterval LlocsInterval RlocsInterval W3distance LtWindowintervaL RtWindowintervaL
%%%%¶«W3iâÎlªÅଳ¢jÌo
[InterbalLtouch IndexLtouch]=sort(abs(LlocsInterval),'ascend');
LtouchW3=LlocsInterval(IndexLtouch(1:5)); %%%%%%%W3ÌXpCN... |
e61ea108e5d48094328e9a9159b463ad949441eadbdf0f0b57b8bec874ec7d5c | MATLAB | 896 | 23 | function sel_beats(app)
%SEL_BEATS Summary of this function goes here
% Detailed explanation goes here
if ~isempty(app.burst_ints) && ~isempty(app.hb_res)
app.hb_res.use_beats = [];
ts_beats = app.hb_res.ts(1) : app.hb_res.ts(2) : app.hb_res.ts(3);
app.hb_res.use_beats = true(size(app.hb_res.idx));
... |
e9e1b34da99289a6c0fa31141d326fefa20fbd7fd1c4ed9ff92a3d52663b1ab7 | MATLAB | 896 | 45 | function [gl_node gl distance binary]=global_efficiency_wd(w)
%calculate global efficiency
%INPUT: w =inverse FCG (functional connectivity graph)
%OUTPUT:gl_node = global efficiency per node
% gl = total global efficiency of the network
% distance = matrix which preserve the weighted shortest path length
... |
e0f0355781ea3e1ec973df3a7e6af2073296cc2d9ebdda12a7bab44d4b4b0342 | MATLAB | 897 | 44 | %function to generate a block diagonal matrix of size p
function [Sigma] = Generateblkdiag(ncliques,cliquesize,theta)
p = ncliques * cliquesize;
sizes = repelem(cliquesize, ncliques);
Sigma = zeros(p);
lims = [0, cumsum(sizes)];
for i = 1:ncliques
ii = (lims(i) + 1):lims(i + 1);
signs = 2 * (normrn... |
2c80ddfa3353cd86098595b8821f2ead8c10434769a486dcde7612dd1341d3a2 | MATLAB | 898 | 25 | function inconsistencies = inconsistentFields(obj)
%INCONSISTENTFIELDS Check all fields have the correct sizes
inconsistencies = {};
vectorFields = obj.unitFields.vectorFields;
hFun = @(vals) numel(vals) == obj.nClusters;
for i = 1:numel(vectorFields)
fn = vectorFields{i};
if ~isemp... |
e9c6ffd84d0fb12eac07b084903f3326712b4a04839239528f2da6850f0e4d93 | MATLAB | 898 | 25 | function [FDR, h, crit_p] = fdr_matrix(pValues, StatsParameters)
% applies FDR correction to a N x N matrix of pvalues, by turning them
% first into a vector, then back into the original matrix.
Dim1 = size(pValues, 1);
% turn matrix into vector
Indexes = 1:numel(pValues); % a number for every p value
Indexes = resha... |
ffa3519ae3b7f6e2d3930cde79f9aa0033fc8103c3192d2bd004dd2b4b7b6e7f | MATLAB | 898 | 32 | function ica_A = compute_ica_weights(data, num_ICs, term_tol, max_iter)
% Reference: Hyvarinen (1999), FastICA (look it up!)
num_sources = size(data,1);
num_samples = size(data,2);
ica_A = orth(randn(num_sources, num_ICs)); % Random seed
BOld = zeros(size(ica_A));
min_abs_cos = 0;
iter = ... |
3a1a11c705115e27bb081aa74dd18ecf62ad9e5f18c10f3ec0e54a7898a7d9ba | MATLAB | 899 | 18 | function exportMeanWf(obj, exportAll)
%EXPORTMEANWF Export mean waveforms to workspace
if exportAll
jrclust.utils.exportToWorkspace(struct('meanWfGlobal', obj.hClust.meanWfGlobal), obj.hCfg.verbose);
elseif ~isempty(obj.selected)
mwf = obj.hClust.meanWfGlobal;
primarySites = obj.hCf... |
6cda5539edbea8adb7e08c0850d6cd1e5084388a17ee6bc3803af58b5a16dcd8 | MATLAB | 899 | 30 | function Update_All_MAT_Files_In_Dir()
Dir1 = uigetdir; % Let the user choose a directory.
Files_List = List_All_Files(Dir1,'mat'); % List all .mat files under this directory (including subdirectories).
N = length(Files_List);
Multiple_NN_WaitBar = waitbar(0,'Please Wait');
for f=1:numel(Files_List) % For ... |
bce8d4c56f6de5d1285ee62e1a2e14459096f3f588ce72d9ff64fdf865f59564 | MATLAB | 899 | 48 | function miout=MIxnyn(x,y,kneig)
% Calculate MI value between 2 vector of any dimension (rectangular
% version)
% x....input data mxn m...channelnummer n...sampling points m<<n
% kneig... k nearest neigbor for MI algorithm
%default-values
if ~exist('kneig'), kneig=6; end
% check input data if format is correct... |
9079b03c9a0f210d8dac58bf158626c5882791a01b186281108984bc1c91e2eb | MATLAB | 900 | 25 | function hFigWav = setFigWavXTicks(hFigWav, hClust, displayCount)
%SETFIGWAVXTICKS Set X axis ticks for main view
if ~isfield(hFigWav.figData, 'showSubset')
hFigWav.figData.showSubset = 1:hClust.nClusters;
end
showSubset = hFigWav.figData.showSubset;
if displayCount
xTickLabels = a... |
ced44d7a1fe9bd496b304ca6a65d87e9eb1683356d099f06c9e8bd6af178944b | MATLAB | 901 | 22 | % plotting one person scalpmap at a given timepoint (CompLat)
clear all;
load('D:\BORI\bedrest\RENDEZETT\A) Bedrest_EEG\EEGÖsszes\5.P3a_P3b_abrak\Grandavg_VisualPersons_P3aSkalphoz.mat');
EEG = pop_loadset('D:\BORI\bedrest\RENDEZETT\A) Bedrest_EEG\EEGÖsszes\5.P3a_P3b_abrak\scalptemplate.set'); %az EEG.times-ot ... |
46f44ff9974aa091ebf7cc2081e89403149cf0e14f1414954402e9e1f121d580 | MATLAB | 902 | 37 | function [ spd_dims ] = get_speed_dims_task_balance(p,trans_data_all,q)
%% pool trials per task
task_sessions{1} = 3:4;
task_sessions{2} = 5:9;
t_avs = {};
t_pool = {};
for t = 1:2
t_pools = {};
for sub = 1:p.nSubjects
t_pools{sub} = [];
for ses... |
c3935a348857d963376d192dcb31687c6927cb987fe79e2956deff07ab2edf59 | MATLAB | 902 | 20 | function EEGNarrowbands = filter_eeg_narrowbands(EEGBroadband, NarrowbandRanges)
% filters EEG data into the specified narrow bands (a structure, each field
% indicating a lower and upper bound like so: NarrowbandRanges.Alpha = [8 12];
SampleRate = EEGBroadband.srate;
BandLabels = fieldnames(NarrowbandRanges);
% cre... |
f067df9da0a4ed2bf996bbef57a8fd79759f690b97c60dd12d971bfb9f19a63f | MATLAB | 902 | 26 | function [binIdx,binEdges]=BINfrequency(input_vect,nBins)
%Limitation: it may in separate in different bins repeated numbers. Shouldnt be much problem with real numbers were the p
%of 2 numbers being equal is small.
Q=fix(numel(input_vect)/nBins);
R=rem(numel(input_vect),nBins);
binCounts=[Q*ones(1,R)+1 Q*ones(1,nBins... |
9ef94d7601f354094ef7b40fc5bef43e6a8dd594847063f098da82a05a80d019 | MATLAB | 903 | 33 | function newmeshes=hbf_SortNestedMeshes(meshes)
%HBF_SORTNESTEDMESHES sorts nested triangle meshes ("small to large")
%
% meshes_out=HBF_SORTNESTEDMESHES(meshes_in)
% meshes_in: the meshes to be sorted, cell array of hbf structs
% meshes_out: sorted meshes, cell array of hbf structs
%
% v160229 Matti Stenroos
Nm... |
c28a8b7f9107d78519651ae455a7352abd87c4b9e288e6aa452bf6989a7486d0 | MATLAB | 903 | 34 | clear
clc
close all
% generate a random signal
SampleRate = 1000;
Duration = 10; % seconds
t = linspace(0, Duration, Duration*SampleRate);
Signal = randn(size(t));
% filter the signal in narrow range (8-12 Hz)
FilteredSignal = cycy.utils.lowpass_filter(Signal, SampleRate, 12);
FilteredSignal = cycy.utils.highpass_fil... |
0eca329b8f6642c4ce5cd95aa7d64d1f1a7d930909988b98e4e6cb7c4e711f32 | MATLAB | 904 | 30 | function [corrVals, pVals] = cal_sliding_corr(x, y, winSize)
% for two given vector x, y of the same length N, calculate the sliding-window correlation with length winSize, the sliding step is 1
% Ensure x and y are row vectors for simplicity
if iscolumn(x)
x = x';
end
if iscolumn(y)
... |
aabe47252417f6ec33e5644565c0bc9d2bc885c256eb4529659549d36514d046 | MATLAB | 904 | 30 | % Matlab script that uses the TikhonovRegularization.m;
% It reads a .csv file produced by testTikhonovRegularization.ipynb that
% contains a noisy ecg and it's corresponding reconstruction obtained by
% the python function TikhonovRegularization.m . It calls the
% function TikhonovRegularization.m and adds as the thi... |
deefcf9f00646468aa0b844e82b77786206e515e2bdc03659a8af979b15efd98 | MATLAB | 904 | 35 | function c = get_water_raw(c,lcdir)
for npool = 1:length(c)
pool_dir = fullfile(lcdir,c(npool).pool);
for nsuj=1:length(c(npool).suj)
fh2o=get_subdir_regex_files(pool_dir, [c(npool).suj{nsuj}(11:end),'.*H2O$']);
if isempty(fh2o)
keyboard
end
f=read_raw(char(fh2o));
... |
3ee0fd34f1f0cd8c0565e6bd0586f3c3d2b4139a0faf97d7133828caccd8f7b7 | MATLAB | 905 | 34 | function [im_corrected, st, newData] = apply_2DMotionCorrection(kdata_in,mpars,cgIters,oversampFactor)
if nargin < 4
oversampFactor = 2;
end
if nargin < 3
cgIters = 1;
end
[Nx,Ny] = size(kdata_in);
Npars = size(mpars,2);
alignMats = zeros(4,4,Npars);
this_mpars = mpars;
for iT = 1:Npars
alignMats(1:3,1:... |
4463ca91193872175eb24664163998dade467e4891e18343c1c0e3de292c6654 | MATLAB | 905 | 24 | function saveprediction20190730
%\ªlÆÀªlðärµÄs[N̸êÆïüÌJSDÌl̽Ïðzñi¼à6ªjɵÄpredicttH_ÉÛ¶
global meanXaxiserror1 Envedist1 meanXaxiserror2 Envedist2 meanXaxiserror3 Envedist3 meanXaxiserror4 Envedist4 meanXaxiserror5 Envedist5 tfile...
meanXaxiserror6 Envedist6 FL... |
8c30123bed887724b78d0c955daf2cabd6bffa4d697ae1ded4bc6742677c1f55 | MATLAB | 906 | 25 | function Save_Multiple_Demos
Dir1 = uigetdir; % Let the user choose a directory.
Files_List = List_All_Files(Dir1,'mat');
SaveDir = 'D:\Dropbox (Technion Dropbox)\Omer Yuval\Neuronalizer\PVD Images\Sharon\DEG_for_Menorah_analyser_Filtered_Format_Names\Results_Demos\0.65';
% SaveDir = uigetdir;
for f=1:numel(... |
9e3c0b7c151bb1f948c454f984c1f26ee9fb073aa4ff27cc6669046b41a176cd | MATLAB | 906 | 24 | function positions = spikePos(obj, spikeSites, spikeFeatures)
%SPIKEPOS Compute a feature-weighted position for each spike
positions = [];
if isempty(spikeSites) || isempty(spikeFeatures)
return;
end
nSitesEvt = 1 + obj.hCfg.nSiteDir*2 - obj.hCfg.nSitesExcl;
featuresSquared = squeeze(s... |
07b27b2ebf9220f9f292624e3974d50bb76488fc3bc45606aa24a27e05860d99 | MATLAB | 907 | 27 | function [datMemMap,datSize] = getDatMemMap(datFF,nChan)
%datMemMap - Create a memory map to dat files
%
% Usage:
% datMemMap = getDatMemMap(datFF,nChan);
%
% Description: This function uses the full path to the dat file including
% the number of channels and creates a memory map to it. This allows
... |
884076bfc15b76b5d11b948f9cfe3ca16e6ca1e3e91624a87d3dd94f183e117f | MATLAB | 907 | 23 | function SemSegPic_ThinSection = MainFunc_ThinSections(A,B)
load E:\SemanticSegmentation_Scale\CPL\GTruth.mat;
load E:\trainedNet_resnet50.mat;
cmap = GTruth.LabelDefinitions.LabelColor;
% A = imread('E:\SemanticSegmentation_Scale\CPL\Train_896\1_18_4.png');
% B = imread('E:\SemanticSegmentation_Scale\XPL\Train_... |
884bdc4397fa0efded27e83ddc89911253413b3293af109da958be16b650e32a | MATLAB | 908 | 46 | function ro = mean_raw()
dir_sel = spm_select(inf,'dir','select a dirs to get result from','',pwd)
% s=get_subdir_regex('PROTO_SPECTRO_DYST/LCmodel/Raw',{'pat','control','suj'});
% dir_sel=get_subdir_regex(s,'basis_MEGA_edit_MM_Gln$');
dir_sel=cellstr(dir_sel);
pp.gessfile=2;
for nd = 1:length(dir_se... |
2b2148f0f6fd4784e745c069318d9579e6d8bdf54d6b3326d344ca5bddc4dee3 | MATLAB | 909 | 33 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% [ normLabeledDataSet rMin rMax ] = dataSetNormalize(labeledDataSet, vMin, vMax, M)
%
% The labeledDataSet must have the form:
%
% Sensor1 Sensor2 ... SensorN labelIndex1 ... labelIndexM
%
% Only sensors will be normalized using their min and max (also ... |
4927c39cfade75c8a0b83181e718a459ad8f1eea278e271a818e48b4f14860af | MATLAB | 909 | 44 | function sp = sparse(ob)
%function sp = sparse(ob)
% return sparse version of object
% this uses less memory than full(ob)
if ~isempty(ob.handle_sparse)
sp = ob.handle_sparse(ob);
return
end
% extract one column at time, determine number of nonzeros, then combine
% todo: use blocks of 10 or 100 at a time instead of... |
bb870cd46287c5790c29aba7a76ebd9455a715af3731dc325b08e74c7a343e62 | MATLAB | 909 | 33 | %% script gets mean PDF of Distance:
% between or among different conditions of
% rasters:
% A vector is fromed where Active Cells makes 1
% Input
% R_Condition: Cell of rasters
% Distance (from matlab): 'hamming' (default), 'euclidean', 'cosine'
% Output
% MatDist: Matrix Of Dsitacne among Conditions:
% MatDis(1... |
1f52e857983fb05dfe002ffd8499cc65ac4e9a2687543b74e41c64ea74f779a0 | MATLAB | 910 | 38 | % example video tracking setup
%
% Used here for gap-crossing experiments with an AVT Pike F-032B
% using the image aq. toolbox
% http://www.mathworks.com/hardware-support/allied-vision-technologies.html
%
% 2014 Jakob Voigts (jvoigts@mit.edu)
imaqreset
vid = videoinput('avtmatlabadaptor64_r2009b', 1, 'F7M0_Mono8_64... |
3683f519d26b858e386e61c7e33e20756a3dd4fe5f32328fe6788d072229ff7a | MATLAB | 910 | 24 | function varargout = spikeCov(spikeWindows, hCfg)
%SPIKECOV Compute covariance feature for spikes
% spikeWindows: nSamples x nSpikes x nSites
vnDelay_fet = hCfg.getOr('vnDelay_fet', [0, 3]);
[nSamples, ~, nSites] = size(spikeWindows);
[shiftsBack, shiftsForward] = jrclust.utils.shiftRange(nSample... |
60927e05bef961b230a66da04383fddb7e15b07880a31980e53702abeeb2e950 | MATLAB | 910 | 28 | function distribution = rst_vincentize(data,quantile,flag)
% compute and return a vincentized distribution
sorted_data = sort(data);
nbin = floor(length(data) / quantile);
index1 = 1;
index2 = nbin;
for i=1:(quantile-1)
distribution{i} = sorted_data(index1:index2); % data per quantile
index1 = index2+1; i... |
cf2321bcf4ef50022179574b04865d3a43808f66a43956337a93f77784d8d02c | MATLAB | 910 | 25 | function auto_bp(app)
%auto_Bb checks if bb is already detected and runs if
%not
% Detailed explanation goes here
if isempty(app.bp_res)
if ~isnan(app.settings.channel_idx.bldp)
[data,ts,~, ~] = current_signal(app, app.settings.channel_idx.bldp);
data = lowpass(data,3,1/ts(1));
try
... |
363aab9cd05962204c08c13939e14154ee046a336e805abcdd726d69e09ca56f | MATLAB | 911 | 39 | function [Xorth, Lorth, TT] = orthogonalize(X, L)
%
% [Xorth, Lorth, TT] = orthogonalize(X, L)
%
% Orthonormalize the columns of the loading matrix and
% apply the corresponding linear transform to the latent variables.
%
% yDim: data dimensionality
% xDim: latent dimensionality
%
% INPUTS:
%
% X - latent v... |
12a21ca167c351d0dd3301d9a25e279bc42485ce5c6cd01f6c2e5dc4370b970c | MATLAB | 913 | 36 | function S = str2struct(strval)
%STR2STRUCT Convert a string to a struct
% (only numerical expressions supported for now)
strval = strval';
strval = strval(:)';
keysVals = textscan(strtrim(strval), '%s%s', 'Delimiter', '=;', 'ReturnOnError', 0);
structKeys = keysVals{1};
structVals = ke... |
ca820f5c1e00b15f59339c77f76343db86bde14c9b631f7512cbddebbc3d2f1a | MATLAB | 914 | 33 | function [Data,Scale,dim,metadata]=mnl_Load4Dimage
%Function to load the image, and relevant OME metadata using bfopen
% Outputs
% Data - The image as a matrix (x*y*c*z)
% Scale - the size of a voxel (x*y*z)
% dim - the dimensions of the image
% metadata - the OME metadata
file=bfopen;
series1=file{1, 1};
metadata=fil... |
288040711507289c8540f779627e4c27fc9d066a6b5b27fb07bbbe2c238744f6 | MATLAB | 916 | 34 | function [MSClass,MSFit,gfp] = RaguFitMicrostates(gm,MSMaps,Smooth,b,lambda,Start,Stop)
% Copyright 2009-2011 Thomas Koenig
% distributed under the terms of the GNU AFFERO General Public License
MSMaps = NormDimL2(MSMaps,2) / sqrt(size(MSMaps,2));
ng = size(gm,1);
nc = size(gm,2);
ne = size(gm,3);
nt = size(gm,4);
... |
22bc4251856fa8d8164e2423aa493d81354f504c3e47399039adcf9001a63c14 | MATLAB | 918 | 41 | function [s,peaks] = PiCA4(dat,ref,t0,w,th,wlen,Itr)
N = length(ref);
for k = 1:Itr
refframe = ref(max(1,t0-w):min(length(ref),t0+w));
error = zeros(size(ref));
for i = 1:N
ind = max(1,i-w):min(length(ref),i+w);
%error(i) = std(refframe(1:length(ind)) - ref(ind),1);
c... |
f9dfb683d843f817fde15a6a9cb6b8cd480c53117dd7886cbcaf33e7c7475098 | MATLAB | 918 | 35 | % create Gamma model
% ------------------------------------------
% This file is part of the adaptative_threshold SPM plug-in
% Copyright (C) Cyril Pernet and Chris Gorgolewski 2012
classdef Gamma < Distribution
properties
shape
scale
mu
end
properties(Constant)
free_paramet... |
9c69ea53f442be59ac60def629dfc1db789f550b02fc1a28809f030241188e1a | MATLAB | 919 | 45 | classdef file_comparison
%
% Class:
% adi.file_comparison
%{
fc = adi.file_comparison(p1,p2);
%}
properties
file_path1
file_path2
f1
f2
n_records1
n_records2
end
methods
function obj = file_... |
7a6139a38248676728caaa51c351858c309de647fe3ae99ddceee93e20f6cf43 | MATLAB | 924 | 31 | function VIP=vip(X,y,T,W,Q)
%+++ Calculate the vip for each variable to the response;
%+++ T,W,Q are output from plsnipals.m
%+++ T: socres, which can be obtained by pls_nipals.m
%+++ W: weight, which can be obtained by pls_nipals.m
%+++ Q: Y-loadings
%+++ VIP=sqrt(p*q/s);
%+++ where, p is a constant, den... |
d0c24184ff10b2496ad8ab4682aa35f1f874d9a2e9ee4919db257da6c6934e95 | MATLAB | 924 | 31 | function cluster_quality_stats(cellids, resdir, str)
%CLUSTER_QUALITY_STATS ID and L-ratio statistics.
% CLUSTER_QUALITY_STATS(CELLIDS,RESDIR,STR) calculates and saves median
% and SE of median of Isolation Distance and L-ratio for CELLIDS. These
% parameters are stored in CellBase by LRATIO2. STR string in... |
bff68370028995b99af4e497275da75d7678d0b4f80b7549ce18ae3fc1f3714d | MATLAB | 925 | 55 | clear
clc
[fname,chemin]=uigetfile('*.*','MultiSelect','on');%loads file
if isequal([fname chemin],[0,0])
return
else
datname=[chemin fname];
d=importdata(datname,',',1);
data=d.data;
figure
% hold on
% k=1; factor=k*data(1,1);
% plot(data(:,1))
% for i=2:size(data,2)
%
%
%
% k= k+1... |
8a04def8cc03c50163df81069ad7f61e372e27c663c4250d893654e3efd85e0d | MATLAB | 927 | 28 | function lfp = loadLFP(nChan,chan2pull,acqFs,dsFs)
%loadLFP - Load LFPs from In Vivo Ephys Recordings
%
% Usage:
% fp = loadLFP(nChan,chan2pull,acqFs,dsFs)
%
% Description: This function pulls a single channel recording from an In
% Vivo Ephys recording and decimates the trace to allow for LFP analys... |
1778b8de70a44a89cde5b88be5ab16d0a7c7f73f6e601d04855f2b1f60baea08 | MATLAB | 928 | 34 |
function wat_ppm = find_watter_peack(fids,spec_info)
f_inf=4.6; % lower bound in ppm
f_sup=4.8; % upper bound in ppm
for nbser = 1:length(fids)
fid= fids{nbser}(:,1);
info = spec_info{nbser};
spec = info.spectrum;
spec_metab=abs(fftshift(fft(fid)));
ppm_center = spec.ppm_center;
zero_filling = spec... |
4e1006145414fb2254a692fe4ecd0422d99eae5a6d0c3118686e02ddfb4dfb01 | MATLAB | 928 | 27 | % Copyright 2024 Eelke Spaak, Donders Institute.
% See https://github.com/Spaak/rift-phase-and-visibility for readme/license.
% Belongs with:
% Spaak, E., Bouwkamp, F. G., & de Lange, F. P. (2024). Perceptual foundation and extension
% to phase tagging for rapid invisible frequency tagging (RIFT). Imaging Neuroscie... |
567ee5759b4bc0343a133932be21829a342ad3041ef5c4ead77ac093608cbf6a | MATLAB | 928 | 28 | % function that builds a table to save ti as CSV
% from several data cells of Raster Features
% Input
% RASTER_NAME: cell with ExpIDs
% RASTER_FEATURES: cell of rastrfeatures
% Names_Conditions: cell of Condition Names
function TableFeatures=maketableraster_ensembles(RASTER_NAMES,RASTER_FEATURES,Nen... |
f0f31fba279e4b0a6f86348046aac1654171a08f80169fdb6706c4d16a4f2076 | MATLAB | 928 | 24 | function auto_etco2(app)
%AUTO_ETCO2 Summary of this function goes here
% Detailed explanation goes here
if ~isnan(app.settings.channel_idx.co2)
if isempty(find(contains(vertcat(app.data.name),'etCO2')))
[data,ts,~, ~] = current_signal(app, app.settings.channel_idx.co2);
t= ts(1):ts(1):ts(2);
... |
99732349c810c3450889f0afe96657edc843aec540dcc4ff0888660ad6325cb1 | MATLAB | 929 | 45 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% [ trainingSet, testSet ] = dataSetSplit(labeledDataSet, percTest)
%
% The labeledDataSet must have the form:
%
% Sensor1 Sensor2 ... SensorN labelIndex
%
% for each class, the last percTest is used as testSet
%
function [ trainingSet, testSet ] = dataS... |
3a56a3f06313b76698bdbffed19f0984ae56b1418a2eb58eb054a2f6f493be98 | MATLAB | 930 | 38 | function [KSmax] = test_ks(x)
%
% Calculates the CDF (expcdf)
%[y_expcdf,x_expcdf]=cdfcalc(x);
yCDF = [];
xCDF = [];
x = x(~isnan(x));
n = length(x);
x = sort(x(:));
% Get cumulative sums
yCDF = (1:n)' / n;
% Remove duplicates; only need final one with total count
notdup = ([diff(x(:)); 1] > 0);
x_expcdf = x(notdup);... |
55497de09ccaa0eaa9c4a587762bc8e8fa3081a3bd8316a4ce0f4abb3e6f0a86 | MATLAB | 931 | 33 | function [] = visualizeContrastMadinoHamada(val1, val2, texts, color)
% preparation for the
count = 1;
clear COPE
nROI = length(val1);
% blue stim control
COPE.first(count:nROI, 1) = val1;
COPE.second(count:nROI, 1) = val2;
COPE.labels(count:nROI, 1) = {'Anesthesia (Mandino2022vsSession3)'};
COPE.color(count:nROI... |
ade802680b6b704d1b7c6726e29ec379f2d74a380fc5fed91ebc53bcedc0bb47 | MATLAB | 931 | 42 | function plotMpars(Amats,t,iRef)
% Plot the effect of a series of affine matrices on 3 points located at
% (x/y/z) = 50mm
nT = size(Amats,3);
if nargin < 2
t = 1:nT;
end
if nargin < 3
% iRef = round(nT/2);
iRef = 1;
end
pts = [1 0 0; 0 1 0; 0 0 1]*50;
pts(:,4) = 1;
Vpts = zeros(nT,3,3);
for iT = 1:nT
... |
154ce2b63d5549db13f25e3070b43252de349d2f185fd0126c40b83773ed8a33 | MATLAB | 932 | 24 | function data = XxReadTiffSmallerThan4GB(TiffPath,start_frame,end_frame,interval)
pic_info = imfinfo(TiffPath);
[frame_CCD,~] = size(pic_info);
if nargin < 4, interval = 1; end
if nargin < 3, end_frame = frame_CCD; end
if nargin < 2, start_frame = 1; end
row_CCD = pic_info.Height;
colum_CCD = pic_info.Width;
temp=pic... |
0bc9e1351c05cee9ae0ac035d02e41bd9ca2329189dadf9f478d2d0e0458e11c | MATLAB | 933 | 20 | function abstr = about()
%ABOUT Get an about string
md = jrclust.utils.info();
abstr = strjoin({sprintf('%s v%s', md.program, jrclust.utils.version());
sprintf('Created by %s', md.authors{1});
'Maintained by:';
sprintf(' %s', strjoin(md.maint... |
03e5e0bc3fc47f1f598501f866d562ac9b0739fbf867bf4984e75deeb83b7aea | MATLAB | 934 | 41 |
kWorkspaceFolder = 'C:\Users\sergeyd\Workspaces\CIFAR-100\data';
load(fullfile(kWorkspaceFolder, 'train.mat'));
kTrainNum = size(data, 1);
imagesize = [32 32 3];
TrainX = double(data) / 255;
TrainX = reshape(TrainX', [imagesize kTrainNum]);
TrainX = permute(TrainX, [2 1 3 4]);
classnum = 100;
TrainY = zeros(kTrainN... |
94d8fe9d33650ced2f96341aba25616d76c7d5de3285b3558e77c486adf6c00f | MATLAB | 934 | 31 | function [T0,T1] = find_in_phase_samples(peaks,phase)
L = length(peaks); % the signal length
rr_intervals = diff(find(peaks)); % the RR-intervals
% Avg, Min and Max RR-intervals
avg_rr_interval = round(mean(rr_intervals));
min_rr_interval = min(rr_intervals);
max_rr_interval = max(rr_intervals);
wlen = max_r... |
34470bcc7dd3d8da4ab89fe1c5de64fad667d698e252b17249ca4aff0b823ab8 | MATLAB | 936 | 30 | function tfilecount20191125
path=('C:\Users\C238\Desktop\CheetahWRLV20180729Part1');
cd(path)
LS1=ls;
tfilecount=0;
for i=1:length(LS1(:,1))
trimFolder1=strtrim(LS1(i,:));
if ~strcmp(trimFolder1,'.') && ~strcmp(trimFolder1,'..') && isempty(strfind(trimFolder1,'.mat')) && isempty(strfind(trimFolder... |
bb854bca8ebaeef03b38ed0c2e9a3ae2b62be4345eee9aa7cbb77f9acff0970b | MATLAB | 937 | 22 | [app.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.
datafile = fullfile(path, app.file); %save path
file_numes = length(app.datafile);
all_files = cell(1, file_numes);
for i = 1:file_numes
all_files{1, i} ... |
33223e356ad6b865f475ccba39c2f82d92b04e74ab4ce55ddc8765811af8e487 | MATLAB | 940 | 23 | function [ inputFiltered ] = apply_lowpass( p,fr )
%APPLY_LOWPASS Summary of this function goes here
% Detailed explanation goes here
padSize = size(fr,1);
filterSize = [size(fr,1) + 2*padSize , size(fr,2) + 2*padSize];
cutoffFilter = mat2gray(fspecial('gaussian',filterSize,p.filtering.lowpassFreq));
... |
819ea40a96567341460a86b5a6434dd780239615f81e8dc0a8dd415cdd5063d4 | MATLAB | 940 | 32 | function boxplot_subplot(h, data, regs, labels)
%% Boxplot subfigure
load('structures', 'regions');
subplot(h);
cvg_inf = isinf(data);
cvg_zero = data == 0;
cvg_nan = isnan(data);
include = ~(cvg_inf | cvg_zero | cvg_nan);
boxplot(data(include), regs(include), 'BoxStyle', 'outline', ...
'Colors', regions.color*0.7... |
92cc612e796a98041c4223a794c632f7a7cfa388f62b6473f63693412616e7ec | MATLAB | 940 | 32 | %plot clusters across leyers and leyeres acroos clusters
clc
clear
[filename, path] = uigetfile("MultiSelect","off", ".csv");
datafile = fullfile(path, filename); %save path
T = readtable(datafile);
cluster_names = {'On Sustained', 'ON Suppressed 1', 'ON-OFF', 'ON Suppressed 2'};
%%
%plot clusters across erias
figure
... |
5f88f41650209bf4ac7c90c58bf69e1d7ae2ea0833d31ec40dda92eab56d389c | MATLAB | 941 | 44 | function MSNplot20200107
path=['G:\CheetahWRLV20200121'];
cd(path)
load('widthPtoT.mat');
X(:,1)=X(:,1)*1/32;
X(:,2)=X(:,2)*1/32;
X
fig1=figure;
sz=25;
scatter(X(:,1),X(:,2),sz,'k','o');hold on
xlabel('Half Peak Width (ms)')
ylabel('Peak to Trough (ms)')
ThresholdWidth=5*1/32;
ThresholdPtoT=10*... |
b46a2dbe4206dff300a271445286632f5b8ce80866f9aa6e716d42c3201121a2 | MATLAB | 941 | 26 | function [myState]=Model_RL_Init(N_state,N_action,N_transition)
myState.N_state=N_state;
myState.N_action=N_action;
myState.N_transition=N_transition;
myState.index=1; % current index
myState.state_history=zeros(N_transition,1); myState.state_history(1,1)=1;
myState.action_history=zeros(N_transition,1);
myS... |
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