sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
3416e5e4244f28a6cdc315eee652b63b903180bfbb68465b7465c44279c4f31b | MATLAB | 433 | 23 | %{
aodpre.ExtractSpikes (computed) # my newest table
-> aodpre.ComputeTraces
-> pre.SpikeInference
-----
# add additional attributes
%}
classdef ExtractSpikes < dj.Relvar & dj.AutoPopulate
properties
popRel = aodpre.ComputeTraces * pre.SpikeInference & 'language="matlab"'
end
methods(Access=protected)
funct... |
6ee5fc54f3904fa58d81092bb91d99377327f6eec9b2cff6f681eef63fc24a42 | MATLAB | 433 | 19 | %{
preprocess.ComputeTracesTrace (computed) # final calcium trace but before spike extraction or filtering
-> preprocess.ComputeTraces
-> preprocess.ExtractRawTrace
---
trace=null : longblob # leave null same as ExtractRaw.Trace
%}
classdef ComputeTracesTrace < dj.Relvar
methods... |
d5bf8b62619aae9b563d597339779ce0af94a61bbd23667753e41e993d0cc5b3 | MATLAB | 434 | 12 | % This is a test script for the cvdproc MATLAB pipeline.
% This script will copy a test image to the output floder and smooth it.
input_image = 'placeholder/for/nipype/input_image'
output_dir = 'placeholder/for/nipype/output_dir'
if ~exist(output_dir, 'dir')
mkdir(output_dir);
end
output_image = fullfile(output_d... |
2c53524a3c3a47f6561a463945a57c89a9e5e7a53b2d456746bd931d77fc63bc | MATLAB | 435 | 13 | %{
# population statistics parameters
opt_idx : smallint #
---
rsz=10 : smallint # movie resizing factor
algorithm=null : enum('opticalFlowFarneback','opticalFlowLK') #
params=null : varchar(255) ... |
51c742b7b7cc081a5b578d847c87a6e826da11c3ed40027644b1386c7b1d9ebb | MATLAB | 436 | 8 | %% This file specifies the Checkpoint of xQSM
% Specify the directory of the xQSM code from Github
deepMRI_HOME = '/home/common/matlab/sepia/external/deepMRI/';
% If you have your own trained network, specify the file that contains the trained parameters
% otherwise, uses the one provided with the tool
checkpoints ... |
b4b47b1a3d18512a68b63ce2782e81fee078ce4e02b434f5b18c95120d5556ed | MATLAB | 436 | 19 | function neurons = eventLockRADICaL(neurons, R, tPts, tPtsRADICaL, field)
% Only works for the original locking RADICaL was run on: liftTimeDLC
if nargin < 4
field = 'radical_rates';
end
% tPtsRADICaL = -200:10:790; % From the paper
tBins2Use = ismember(tPtsRADICaL, tPts);
% Format neural
for tr = 1:length(neuro... |
c95aad74eedc7d5cdade96373d54fd914889d9c23818043d210b9d4d278b0869 | MATLAB | 436 | 20 | %{
tuning.DirectionalResponseTrial (computed) # the response for each trial and each trace
-> tuning.DirectionalResponse
-> preprocess.SpikesRateTrace
-> tuning.DirectionalTrial
---
response : float # integrated response
%}
classdef DirectionalResponseTrial < dj.Relvar
meth... |
dc76cd5ac6d131f72dfbcb09f1a5a0a7d4a9a88f11ee5f3722f2c8a43dffa350 | MATLAB | 436 | 20 | %{
# table for fixing faulty calculation in preprocess.PrepareGalvo
-> preprocess.PrepareGalvo
---
um_width : float # width in microns
um_height : float # height in microns
%}
classdef FixZoom < dj.Computed
methods(Access=protected... |
172918308e80d0069d888fed2eb7abcaacc99cc3797d0792f70a696802281faf | MATLAB | 437 | 18 |
M = spherefactory(5, 10);
x = M.rand();
v = M.randvec(x);
t = .1;
y = M.retr(x, v, t);
u1 = M.randvec(x);
u2 = M.randvec(x);
Tu1 = M.transp(x, y, u1);
Tu2 = M.transp(x, y, u2);
fprintf('Probably not zero: %g\n', (M.inner(x, u1, u2) - M.inner(y, Tu1, Tu2))/M.inner(x, u1, u2));
Tu1 = M.isotransp(x, y, u1);
Tu2 = M.i... |
7e592763fe183fdd3b953b1041404ab2574c972fdaf8b7954a99baf95cd5e2de | MATLAB | 437 | 32 | function [njm] = tapas_zeromat(jm)
%% Zeros all but the first entry in each column of jm.
%
% Input
% jm -- Matrix.
%
% Output
% njm -- Matrix.
%
% aponteeduardo@gmail.com
% copyright (C) 2015
%
njm = zeros(size(jm));
j = 0;
i = 0;
while j < size(jm, 2)
j = j + 1;
i = 0;
while i < size(jm, ... |
a0055b41f9916b3e5e521615e03eca658330ce81f8a1f5188036012e91dbc1bd | MATLAB | 437 | 17 | function a=imdilate_special(a)
ind=find(abs(a));
ind_other=[1:prod(size(a))];
ind_other=setdiff(ind_other,ind)';
[x,y,z]=ind2sub(size(a),ind);
[xx,yy,zz]=ind2sub(size(a),ind_other);
for i=1:length(ind_other)
tmp=sqrt(sum((repmat([xx(i),yy(i),zz(i)],length(x),1)-[x,y,z]).^2,2));
ind_min=find... |
481ea06aa9a563407b2925030a51a58461ee2b4858601f9aa552b8b38154e0e2 | MATLAB | 438 | 10 | %% really quick and dirty to get screenshots of the gradunwarp
bet DATECENSORED_125001T1wMPRs022a1001.nii.gz test.nii.gz -m
fslmaths test_mask.nii.gz -kernel box 9 -dilM test_mask_dil.nii.gz
res is from itksnap quick and dirty
fslmaths DATECENSORED_125001T1wMPRs022a1001.nii.gz -mas res.nii.gz out1_masked.nii.gz -od... |
496b0c635b3687ed6a7a022afae4671d291c217e800984f128a332d6627013b7 | MATLAB | 438 | 18 | function spikeMatrix = spikeTimesToSpikeMatrix(spikeTimes, duration, fs)
start_time = 0;
end_time = duration;
bin_edges = start_time:1/fs:end_time;
numUnits = length(spikeTimes);
num_bins = length(bin_edges) - 1;
spikeMatrix = zeros(num_bins, numUnits);
for unit = 1:numUnits
channel_spike_times = spikeTimes{unit... |
8bcbc27450877f84f0bd17d082353f6548545466e14fcf79386e8723dcb00d0c | MATLAB | 438 | 10 | classdef FakeApp < handle
% Headless stand-in for the MEA-NAP GUI app object, used only by
% MEApipeline_parity.m so the pipeline's guiMode==1 status-text lines
% have somewhere to write when running under `matlab -batch`.
% Purely a sink for status strings — touches no analysis parameter.
propertie... |
bd400ca27946e0001a13b6fc991f3c53b66f4ca42bde5141bcbbf35a25d499aa | MATLAB | 438 | 17 | function [M] = cell2mat3d(C)
% converts a cell with nbx1 or 1xnb same-sized matrices into a 3d matrix by
% stacking the cell entries upon each other along the 3rd dimension.
assert(iscell(C), "error: input must be a cell array.")
assert(min(size(C))==1, "error: input must be a row or column cell array.")
nb = ... |
1bb25cb3fffb3230c87334a4c4ed6d15f7a59ab3d62065a4252dc5d628a1ab39 | MATLAB | 439 | 13 | function tf = isfield(this,field)
% Isfield method for GIfTI objects
% FORMAT tf = isfield(this,field)
% this - GIfTI object
% field - string of cell array
% tf - logical array
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimagi... |
5524971f34b7113640526271711c45fca190b8267112050e3143c8a24ef3e162 | MATLAB | 439 | 20 | //
// MetalCompatibility.m
// wxVulkanTex
//
// Created by takashi kawase on 5/9/19.
//
#import <Cocoa/Cocoa.h>
#import <QuartzCore/CAMetalLayer.h>
void makeViewMetalCompatible(void* handle)
{
NSView* view = (NSView*)handle;
assert([view isKindOfClass:[NSView class]]);
if (![view.layer isKindOfClas... |
7252ac115b288c8493034f7f9f10e30b3324e0294c8e5b4488bda026ab25c9cd | MATLAB | 439 | 16 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Requires: https://github.com/anders-s-olsen/psilocybin_dynamic_FC
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear; clc;
ROI_sig = readmatrix('time_series.txt');
ROI_sig = ROI_sig(:,1:5);
allpair = 0; parallel = 0;
tic
[ H... |
9b32f96c2ae99f180c786d43d1db03f9752369daa9b0761a084c14e05ff9d2c8 | MATLAB | 439 | 21 | %{
# spatiotemporal receptive field map
-> tuning.RF
-> preprocess.SpikesRateTrace
---
scale : float # receptive field scale
map : longblob # int8 data map scaled by scale
%}
classdef RFMap < dj.Computed
methods(Access=protec... |
d0f03ceca01dac56367e2853230d64503a60ad3b64e5d1188c5fd6f14b12cbc1 | MATLAB | 439 | 26 | % PLAN B
rho = 1055;
Q0 = 15*1e-6;
A1 = 1.98857e-6;
A2 = 1.85632e-5;
d1 = sqrt(4*A1/pi);
d2 = sqrt(4*A2/pi);
Q = (0:.1:20)*1e-6;
dpv = 8*rho*Q.^2/pi^2*(1/d1^2 - 1/d2^2)^2;
R = 16*rho/pi^2*(1./d1.^2 - 1./d2.^2)^2*Q0;
R2 = 8*rho/pi^2*(1./d1.^2 - 1./d2.^2)^2;
dp0 = 8*rho*Q0^2/pi^2*(1/d1^2 - 1/d2^2)^2;
Q_lin = (5:.1:... |
9207d5222027f6c03d0213e8b57643d8890272a32484df77afe32d78c4326e40 | MATLAB | 440 | 8 | % Please cite this paper if you use any component of this software:
% D. Cunefare, L. Fang, R.F. Cooper, A. Dubra, J. Carroll, S. Farsiu, "Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks," Scientific Reports, 7, 6620, 2017.
% Re... |
6e27d857eaf5ee4237b21f4cb829c17b45b3991da019aef6cfd1a5dcf68641ef | MATLAB | 441 | 33 | function [njm] = tapas_ti_zeromat(jm)
%% Zeros all but the first entry in each column of jm.
%
% Input
% jm -- Matrix.
%
% Output
% njm -- Matrix.
%
% aponteeduardo@gmail.com
% copyright (C) 2015
%
njm = zeros(size(jm));
j = 0;
i = 0;
while j < size(jm, 2)
j = j + 1;
i = 0;
while i < size(j... |
e55518fff6a3c3ba3837710ef4b5999d88edeff060b795c6b4e73e845082a3bd | MATLAB | 441 | 10 | % we used this to determine where invalid data might be in the NSD dataset (so that we can report it).
% edit as necessary
cd ~/Dropbox/nsddata/ppdata/subj08/func1mm/
% look at all
itksnap -g T1_to_func1mm.nii.gz -o valid.nii.gz valid_nsdimagery.nii.gz valid_nsdsynthetic.nii.gz valid_prffloc.nii.gz mean.nii.gz brainm... |
f8e893e047edd9ec1bcf3aabae5c6a09a60c3ed3fa74f032b5a6470ecc68e6df | MATLAB | 441 | 24 | %Creado por Luisca Mayo09.
%SRactive() genera una matriz nXm donde
%n:Número de células activas;
%m:número de picos
function [SR_active]=SRactive(A)
%S_Rasterbin=Rb*Si; %Células totalesXnúmero de picos
% Rasterbin y S_index tienen que tener 1 frame de resolución.
N=size(A,1); %Número de células totales
j=1;
for... |
f98551923990a7113345dfdc2bac7735213be1cb524e668777667e0158f1b513 | MATLAB | 441 | 10 | function g = compute_gratio(D)
% Ikeda M, Oka Y. Brain Behav, 2012. "The relationship between nerve conduction velocity and fiber morphology during peripheral nerve regeneration."
% g = 0.220 .* log10(2*D) + 0.508;
% g = 0.7 * ones(size(D)); % constant g-ratio
g = 0.6 + 0.1 * rand(size(D)); % random g-ratio betwe... |
5f944a89ef6e55a949c6746e4941b4e123c50088e5cf4fe96251a62b91a2a0bd | MATLAB | 442 | 26 | for i=1:20
i
n1=num2str(900+i);
file=strcat('fort.',n1);
x=load(file);
Fs=100000;
inh=x(:,1)-mean(x(:,1));
amp=x(:,2)-mean(x(:,2));
nmd=x(:,3)-mean(x(:,3));
an=(amp+nmd)/2;
w=400;
ov=90;
[lags,mcorre]=ccorrelation(inh,an,Fs,w,ov);
lags=lags/100;
cc(i,:)=mcorre;
... |
7289ac84c49eb41ca8d428d754343b3c73cd9cc4b8014c822199a030cd98dde0 | MATLAB | 442 | 22 | function [vec] = sqmat2vec(mat, format)
%vectorizes square matrix
% INPUT:
% format (str) = which part of matrix shall be vectorized (e.g. triu, tril, full)
if nargin ==1
format = triu;
end
switch format
case 'triu'
mask = triu(true(size(mat)),1);
vec = mat(mask);
case 'tril'
... |
c4955f472c6855ee5a6b08ebc50122813aaa00870bd2190d416eef8a7f267dcf | MATLAB | 442 | 25 |
function [u,s,v]=ss_svds(x,n);
if size(x,1) < size(x,2)
if n < size(x,1)
[u,d] = eigs(x*x',n);
else
[u,d] = eig(x*x'); u=fliplr(u); d=flipud(fliplr(d));
end
s = sqrt(abs(d));
v = x' * (u * diag((1./diag(s))));
else
if n < size(x,2)
[v,d] = eigs(x'*x,n);
else
[v,d] =... |
c693ecd808d8024d109b17b3bad0dc6986bcbdab5b3374d6d2c7c1ee7f1b193d | MATLAB | 444 | 21 | function [tensor_idx, mdl_idx] = findZeroNF(model, IDmap)
%finds cells with a neuron factor of 0
%INPUTS:
% model = ktensor, this is already sorted within components
% IDmap = provides a mapping of the neurons in the model
%OUTPUTS:
% tensor_idx = index of the cells with 0 nf in the tensor as constructed
% ... |
6587e46fa2ddfb8b4983a6c49e7f16858c06f736d8c928430a45d2f2e52a9a70 | MATLAB | 445 | 26 | function r = mrdivide(p,q)
%RDIVIDE this function divides the x, y, and z values of the structures that I use in
% the FVtool.
%
% SYNOPSIS:
%
%
% PARAMETERS:
%
%
% RETURNS:
%
%
% EXAMPLE:
%
% SEE ALSO:
%
% Copyright (c) 2012-2016 Ali Akbar Eftekhari
% See the license file
if isa(p, 'FaceVariable') && (~isa(q, 'FaceV... |
ce763649b5c023be1db0b3056cd39f966bf5e7ff73d27341d197cb8b815ce8a5 | MATLAB | 445 | 26 | function r = mrdivide(p,q)
%RDIVIDE this function divides the x, y, and z values of the structures that I use in
% the FVtool.
%
% SYNOPSIS:
%
%
% PARAMETERS:
%
%
% RETURNS:
%
%
% EXAMPLE:
%
% SEE ALSO:
%
% Copyright (c) 2012-2016 Ali Akbar Eftekhari
% See the license file
if isa(p, 'CellVariable') && (~isa(q, 'CellV... |
0bfd295651e35949a40a686d70128dcd62457e768715727d11b93a85c9f67b9f | MATLAB | 446 | 17 | function x = un_pca(y,vecs,mu)
%
%function x = un_pca(y,vecs,mu)
%
% Undo Principal Component Analysis transform
%
% inputs:
% y - input data n_dim x n_samples
% vecs - pca vectors n_dim x n_dim_orig (or n_dim_orig x n_dim_orig)
% mu - mean of original space data n_dim_orig x 1
%
% outputs:
% x - y transformed back... |
270d2694a34b6e3853b16f9ffbcfd0922b9f8271e778d3935501bb628e528864 | MATLAB | 446 | 15 | %% BehaviorScripts
%% Test whether an animal shows more avoid behavior at the end of the session
% use with batch to accumulate behavior data
numTrials = zeros(40,1);
for session = 1 : 40
numTrials(session) = numel(output{session});
end
escapeGraph = zeros(40, max(numTrials));
for session = 1 : 40
escapeGrap... |
1ac0c76b8d37e3e8cab2b95eac1388fd7bc533dd21e79a863753611770ec7ba7 | MATLAB | 447 | 16 | % Expects hh and cc to be defined.
% Creates <somedata> in the workspace.
% h5read parameters
arg1 = [(cc-1)*numinchunk+1 1];
if cc==ceil(datasizes(hh,1)/numinchunk)
arg2 = [datasizes(hh,1)-(cc-1)*numinchunk Inf];
else
arg2 = [numinchunk Inf];
end
% load the data
tic;
somedata = h5read(sprintf('~/ext/figurefiles/... |
8fa2aae1b32caedc084355455f05759531a9b2523cb95986126c893ab7a1c7e6 | MATLAB | 447 | 16 | function epochs = findContPatches(dataInds)
% localizes beginnings and ends of continouus patches in vector of data indices
% (c) Jiri, Oct16
% include discrete edges
dataInds = dataInds(:); % column vector
d = cat(1, -666, dataInds, max(dataInds)+10);
% determine jumps in data index continuity
i_jump = find(dif... |
d9c2dae2fbf37c443f5d54a45a8113c6f736e0f2c2daa092d3d2e5c6c7b1313b | MATLAB | 447 | 14 | %{
vis.Trial (manual) # visual stimulus trial
-> vis.Session
trial_idx : int # trial index within sessions
---
-> vis.Condition
flip_times : mediumblob # (s) row array of flip times
last_flip_count : int unsigned # the last flip n... |
3ae8eb5919ebd08b73ed05c618293ba85212c7382842267b5c047e834b4b92cd | MATLAB | 448 | 21 | %% set_non_nan_value(h,field,val)
%
% Input
% --------------
% h : structure contains all SEPIA handles
% field : handle field to be changed
% val : new value
%
% Description: only set the parameter when it is valid
%
% Kwok-shing Chan @ DCCN
% k.chan@donders.ru.nl
% Date created: 6 March ... |
5d838d21840e8feb79c79a67117d71d48fcba9343d6db0dc2ed4017a3f5e5cde | MATLAB | 448 | 12 | function epochWindowSizePopup_Callback(hObject, eventdata, handles)
dropdown_items = cellstr(get(hObject,'String'));
page_seconds = regexp(dropdown_items{get(hObject,'Value')},'([\d*.])*','match');
page_seconds = str2double(page_seconds{1});
handles.data.settings.pageSize = page_seconds;
handles.data.saveSettings();
... |
397a1004cc1bb9bee734bf503e139ae575050ac713e95ebc904dc8309d9761d9 | MATLAB | 449 | 40 | go to analysis_drawrois_prf_part02.m
for subj03:
do manual:
angle (4), q (with white edges)
ecc (5), o, (with white edges)
and only curvature version.
do two clicks for fatter edges.
=============
go to analysis_drawroi_faces.m
for subj03:
same deal. and with only curvature.
============
"anat/"
t1 0.8
.... |
98620c0872a18d6f06161d6c87514abe80ca812d92898768d096f5f700c65df4 | MATLAB | 449 | 14 | function plot_time_amp(rez,i)
Spike_id = find(rez.st(:,end)==i);
f=figure;
channel = rez.Merge_cluster{i,5};
Nchan = length(channel);
for k=1:Nchan
subplot(Nchan,1,k)
plot(rez.st(Spike_id,2),rez.best_channel_M(Spike_id,channel(k)),'.')
xlabel('Time in ms')
y... |
24df19dd5b4bcf876bda0f4d680fdeedc7456c8c43a0c8a760de57bdd16b7796 | MATLAB | 450 | 30 | clear;
rho = 1055;
Q0 = 15*1e-6;
d1 = 0.00204;
d2 = 0.00382;
Q = (0:.1:20)*1e-6;
A1 = d1*d1*pi/4;
A2 = d2*d2*pi/4;
v1 = Q/A1;
v2 = Q/A2;
dpv = 8*rho*Q.^2/pi^2*(1/d1^2 - 1/d2^2)^2;
R = 16*rho/pi^2*(1./d1.^2 - 1./d2.^2)^2*Q0;
R2 = 8*rho/pi^2*(1./d1.^2 - 1./d2.^2)^2;
dp0 = 8*rho*Q0^2/pi^2*(1/d1^2 - 1/d2^2)^2;
Q_lin ... |
3b35e7476e3196ed21617af8dd76d4266c94daff30bc4accbbf2283532aa5d8b | MATLAB | 450 | 15 | function output = loadTargetPartialClassifier(dataPath, mouse, session)
% Wrapper around targetPartialClassifier
% Load preprocessed data
% This data is already subsampled to the best 100 neurons and the desired
% time points
load(sprintf('%s%s_%s_cueLocked', dataPath, mouse, session), 'kinematics', 'neurons', 'M')
% ... |
46639e7a01ae9765805e0a1867b206ccc9a29be8fe2eb0b9482a2f13b5703090 | MATLAB | 450 | 10 | function add_path_STIsuitev3(STISuite_dir)
addpath([STISuite_dir filesep 'Core_Functions_P']);
addpath([STISuite_dir filesep 'GUI_Functions_P']);
addpath([STISuite_dir filesep 'Support_Functions']);
addpath(genpath([STISuite_dir filesep 'Support_Functions' filesep 'qsm_kiwi_1']));
addpath(genpath([STISuite_dir filesep... |
90796032370f5bc219302e3058ac857b912c3ac34cef8604fdeef5995876f64a | MATLAB | 450 | 13 | function startTime = fetchTime(obj)
assert( length(obj)==1 );
[path,name] = fetch1( Experiments*obj, 'directory','file_name' );
files = dir(getLocalPath([path '/' name '_ts*']));
if isempty(files)
timestamp = GetFileTime(getLocalPath([path '/' name 'p0.h5']));
save([path '/' name '_ts'],'timestamp');
st... |
5c6ec469a8c002b77a8cb67d768deaceaaa690770984dfa43a809aab2f47c3d8 | MATLAB | 451 | 20 | function M = perform_blurring(M, sigma, options)
% perform_blurring - gaussian blurs an image
%
% M = perform_blurring(M, sigma, options);
%
% M is the original data
% sigma is the width of blurs (in pixels)
%
% Copyright (c) 2007 Gabriel Peyre
options.null = 0;
n = size(M,1);
eta = 4;
p = round((sigma*eta)/... |
5fa8701e12e2eb40c2a842889b48266739cefcc9adfbfb368cc4634a478371b4 | MATLAB | 451 | 17 | xyz = randn(3,12);
figure
hold on
%plot dot cloud
plot3(xyz(1,:),xyz(2,:),xyz(3,:),'ko')
%plot projections
projection_constant = -5;
%projection onto X
plot3(xyz(1,:)*0+projection_constant,xyz(2,:),xyz(3,:),'bo')
%projection onto Y
plot3(xyz(1,:),xyz(2,:)*0+projection_constant,xyz(3,:),'go')
%projection onto Z
plot3(... |
7a5dbaac6669f067c546256ae829b2e56ce411f25ca2f3a5b9ff48b9eb32c5a7 | MATLAB | 452 | 14 | function [spectogram_y_lims, s, f] = cutSpectogramFrequency(s,f, handles)
upper_freq = find(f>=handles.data.settings.HighFreq*1000,1);
if isempty(upper_freq)
upper_freq = length(f);
end
lower_freq = find(f>=handles.data.settings.LowFreq*1000,1);
% Extract the region within the frequency ra... |
c8e54e6578b49ab3c1cba67cdde290c8583a92a68cbdd8dd7148749e92a22269 | MATLAB | 452 | 21 | %{
# Each pixel shows the (average) temporal correlation between that pixel and its four neighbors
-> preprocess.ExtractRaw
-> preprocess.Channel
-> preprocess.Slice
---
correlation_image : longblob # correlation image
%}
classdef ExtractRawGalvoCorrelationImage < dj.Imported
methods(... |
a9f48e56e798d2048e562be6a7119257f9e282999527748165cf8ceea9eb9817 | MATLAB | 453 | 11 | function [NetMetMinMax] = findMinMaxAllNetMetStruct(NetMetAllDat)
% find min and max for NetMet values and edges (adjM
% values) across the batch dataset
NetMetPlots = {'ND','MEW','NS','Eloc','BC','PC','Z'};
for i = 1:length(NetMetPlots)
NetMetMinMax.(sprintf(NetMetPlots{i})) = [min(NetMetAllDat.(sprintf(NetMetP... |
df17381864c9f27b52672c4abac62a9bb41fd1a05e92ef7ea59d960d0a61941f | MATLAB | 453 | 12 | function focusWindowSizePopup_Callback(hObject, eventdata, handles)
dropdown_items = cellstr(get(hObject,'String'));
focus_seconds = regexp(dropdown_items{get(hObject,'Value')},'([\d*.])*','match');
focus_seconds = str2double(focus_seconds{1});
handles.data.settings.focus_window_size = focus_seconds;
handles.data.saveS... |
3de1e87139bc304e31fa98674d75b98b4e75667552a3e45dabf77cb6ba76e072 | MATLAB | 454 | 17 | function [loss] = computeLoss(o, utest)
% Computes loss and t*.
% get number of input variables D and input length T
T = size(utest, 2);
% compute absolute deviation at each time step scaled by std
loss = abs((o-utest))./std(utest, 0, 2);
% scale by time exponentially to account for inevitable error increas... |
4f39c7f1976195985b517f26f0e363ead4e1b427da9ab0f1da5feac60896c8e9 | MATLAB | 454 | 11 | % Compute y = log( sum(exp(x),2) ), the softmax in a numerically safe way by
% subtracting the row maximum to avoid cancelation after taking the exp
% the sum is done along the rows.
%
% Copyright (c) by Hannes Nickisch, 2013-10-16.
function [y,x] = logsumexp2(logx)
N = size(logx,2); max_logx = max(logx,[],2);
%... |
2098d219dea390983a7c7b80d5f1490f7c900d3c0c27e9e5ebfda3378ee6c071 | MATLAB | 455 | 16 | =============== sessionwise
√ PNG "one png from each session", 8x40.
copy folders from NSD134-glmdataLOW-glmBASIC
rename to subj01
rm subj*/orth*
rm subj*/psc*
rm subj*/varexpcumUP.png
rm subj*/varexporthMID*.png
rm subj*/pairwise.png
meanvol00.png -> mean_session00.png
grandmean.png -> mean.png
grandvalid.png -> val... |
f33ba4e4ac238e59ea6ac9d5fdf9fe1260e0cbda21535af715f5a6d5b1013785 | MATLAB | 455 | 23 | function [v] = tapas_mc3_arc(ollh, olpp, T, mc3it)
%%
%
% aponteeduardo@gmail.com
% copyright (C) 2016
%
v = 1:numel(ollh);
nc = size(ollh, 2);
for l = 1:mc3it
s = ceil(rand()*(nc - 1));
p = min(1, exp(ollh(s) * T(s+1) + ollh(s+1) * T(s) ...
- ollh(s) * T(s) - ollh(s+1) * T(s+1)));
if rand() < p
... |
9c8a7e7e9589a8374a18a9f6b6aca0283565031bdbff3ecfa5e031cfcdfe6f09 | MATLAB | 456 | 18 | function [SD, baseline_mean] = findsigma2(trace, eventthreshold)
% recursively determine SD for the baseline period of a calcium signal
% after removing elements that fall above the n sigma threshold.
SD = std(trace);
trace = trace-mean(trace);
while true
baseline = trace(trace<eventthreshold*SD);
if... |
094f69b353714dd9fb94fcd5c47e68b6209d13685a33868d0344670ac3af7b77 | MATLAB | 457 | 23 | function [C, C_indiv] = multilevel_clustering(FC,thres_list)
arguments
FC double
thres_list double
end
n = size(FC,1);
FC(1:n+1:end) = nan;
ML_clustering = nan(n,numel(thres_list));
C_indiv = nan(n,1);
for threshold = 1:numel(thres_list)
ML_clustering(:,threshold) = clustering_coef_bu_fast(F... |
0b76c458ce886c3880b517511374f7f67754eb365d689ea0e166da409b295989 | MATLAB | 457 | 18 | function dvh = detector_vs_human_confs(hsi,det_out,filt,halo)
%function dvh = detector_vs_human_confs(hsi,det_out,filt,halo)
%
% Gets detector confidence for each target, put it in a matrix vs human conf and human cat
% for easy comparison
%
% outputs:
% dvh - n_targets x 3, [detector_output | human_conf | human_cat... |
50fb8a839f464cd31c61193c6f73c2b1273495c4b01794b442cdec14ff56addf | MATLAB | 457 | 18 | function plot_proba(rez,i)
f=figure;
channel = rez.Merge_cluster{i,5};
Nchan = length(channel);
h=0.01;
for k=1:Nchan
PDF = rez.PDF{i,k};
subplot(Nchan,1,k)
N = histcounts(PDF,0:h:1);
bar(0:h:1-h,N)
title(['Channel:', num2str(channel(k))]);... |
eb8d8a45f2eb09cb34e473111e58f7e817e65888f450d5b5e4d255f8dc029da2 | MATLAB | 457 | 21 | function [sstate, si] = tapas_linear_get_stored_state(data, model, ...
inference, state)
%% Get the state that should be stored
%
% aponteeduardo@gmail.com
% copyright (C) 2016
%
sstate = struct('graph', {cell(4, 1)}, 'llh', {cell(4, 1)});
sstate.graph{2} = state.graph{2}.y;
sstate.graph{3} = state.graph{3}.y;
... |
9b62ae468188cd997bfbc3b14eda500be917ca9390549049b6b7ad000f588dd2 | MATLAB | 458 | 21 | %{
# slice-specific scan information
-> reso.ScanInfo
-> `pipeline_shared`.`#field`
---
z : float # (um) absolute depth with respect to the surface of the cortex
delay_image : longblob #
%}
classdef ScanInfoField < dj.Imported
m... |
03d357d987687796daa35285e8d059293c9ff9f4ffc83da00c5ea160c7d86e92 | MATLAB | 459 | 22 | function [lags,mcorre]=ccorrelation(x,y,Fs,w,ov)
%w:windows (ms)
%ov:overlap (%)
%x:signal n
%Fs=sampling frequency
%Xcorr:maximum of the cross-correlation
w=w/1000;
nw=w*Fs;
npf=1+floor((length(x)-nw)/((1-ov/100)*nw));
m=length(x(:,1)); % number of signals
ji=1;
jf=nw;
for i=1:npf
npf-i
[Xcorr,lags]=xcorr(x(j... |
bcf5351f048811aa28d9a2c0e21915e000b8521cde76e7894f5e43dff3f221b4 | MATLAB | 459 | 14 | function X=violaPoints(X,Y,jitter)
% Variable jitter according to how many points occupy each range of values.
[counts,~,bins] = histcounts(Y,10);
inds = find(counts~=0);
counts = counts(inds);
Xr = X;
for jj=1:length(inds)
tWidth = jitter * (1-exp(-0.1 * (counts(jj)-1)));
xpoi... |
efb1bf35306d3ab94af750722ad92f7348ce98e62d631c7fb0e375bec71c22d8 | MATLAB | 459 | 10 | % mexGPUall. For these to complete succesfully, you need to configure the
% Matlab GPU library first (see README files for platform-specific
% information)
mexcuda -largeArrayDims mexconv.cu
% mexcuda -largeArrayDims mexTemplate.cu
% If you get uninterpretable errors like "An unexpected error occurred duri... |
3c7330d6af3e7afdb7340555622340c1927f07d7d3a3b40b6ea84f8fd38bb781 | MATLAB | 460 | 17 | scalp_ud=get(findobj('Tag','ve_eeg'),'UserData');
if isempty(scalp_ud.data2)
comp_ud=get(findobj('Tag','ve_ica'),'UserData');
comp_keep=~comp_ud.chan_marks_struct(1).flags;
comp_proj=EEG.data;
comp_proj(EEG.icachansind,:) = EEG.icawinv(:, find(comp_keep))*eeg_getdatact(EEG, 'component', find(comp_keep... |
77e018f0cc0a9ea2cd77df93f840b1950563ebfea693510b6ece5228ab87eef7 | MATLAB | 460 | 6 | function likelihood = mfunc_Likelihood(h, J, binarizedData)
dataLength = size(binarizedData, 2);
%likelihood = sum(sum(binarizedData.*(0.5*J*binarizedData),2)) + sum(h' * binarizedData, 2) - 2*sum(sum(log(cosh(0.5*J*binarizedData+h*ones(1,dataLength))),2));
likelihood = sum(sum(binarizedData.*(J*binarizedData),2))... |
b3eac6762ac45f9de7034d45568d10da51bd886aadc2f45bd60141de5fa61e48 | MATLAB | 460 | 9 | function [y1,y2,y3] = rsa_affine_transform(x1,x2,x3,M)
% function [y1,y2,y3] = affine_transform(x1,x2,x3,M)
% -----------------------------------------------------------------
% Affine Transform for input stuff in any format (N-dim strcutures)
% -----------------------------------------------------------------
y1 = M(1... |
d23465724baa03fe2b324cdb2e3ce8c86a56948b609c3cf0c766261f61a30a70 | MATLAB | 460 | 15 | function boxOut = bbox_scale2(boxIn,scale,szOut)
% Copyright (C) 2016 Hakan Bilen.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
if isempty(boxIn), boxOut = []; return; end
boxOut = scale * (boxIn-1) + 1;
boxOut =... |
2e531eee2994c357181ea88a29f4b16fd4528444efa634dca57d2785bf4e0c91 | MATLAB | 461 | 18 | function Y = multisym(X)
% Returns the symmetric parts of the matrices in the 3D matrix X
%
% function Y = multisym(X)
%
% Y is a 3D matrix the same size as X. Each slice Y(:, :, i) is the
% symmetric part of the slice X(:, :, i).
%
% See also: multiprod multitransp multiscale multiskew
% This file is part of Manopt: ... |
6dc62778b799fd1ad7287d3652598cee1b570fa0e1b63172b22bff22a1a2559e | MATLAB | 461 | 34 | function jitterExamples
%
% % Jitter examples
% % default jitter is 0.3
%
% clf
%
% R = randn(40,5);
%
% subplot(3,1,1)
% notBoxPlot(R,'jitter',0.15)
%
% subplot(3,1,2)
% notBoxPlot(R,'jitter',0.3); % The default
%
% subplot(3,1,3)
% notBoxPlot(R,'jitter',0.6);
help(['NBP.',mfilename])
clf
R = randn(40,5);
subp... |
6fdc3975afab1f882490c3e24d944cbce10f6c1d743bb5698556c91416f6d641 | MATLAB | 461 | 31 | function r = plus(p,q)
% this function adds the x, y, and z values of the structures that I use in
% the FVtool.
%
% SYNOPSIS:
%
%
% PARAMETERS:
%
%
% RETURNS:
%
%
% EXAMPLE:
%
% SEE ALSO:
%
% Copyright (c) 2012-2016 Ali Akbar Eftekhari
% See the license file
if (isa(p, 'CellVariable')&&isa(q, 'CellVariable'))
r=... |
feb213902902bf0657c21ab2b16de09b9e89b5c02161fbeedb029bd7108ea289 | MATLAB | 461 | 15 | function [newVectors, meanValue] = remmean(vectors);
%REMMEAN - remove the mean from vectors
%
% [newVectors, meanValue] = remmean(vectors);
%
% Removes the mean of row vectors.
% Returns the new vectors and the mean.
%
% This function is needed by FASTICA and FASTICAG
% @(#)$Id: remmean.m,v 1.2 2003/04/05 14:23:58 ja... |
1d69bec6fd205d3a46b60407dfdb87bd5206691514cad2d93264164e37515fd8 | MATLAB | 462 | 25 | function nfu = du2nfu(du, axis)
% Converts data units of an axes to notmalized figure units (0 to 1).
ax_layout = get(gca, 'Position');
switch axis
case 'x'
ax_lims = get(gca, 'XLim');
ndu = (du-ax_lims(1)) / diff(ax_lims);
nfu = ax_layout(1) + ndu * ax_layout(3);
case 'y'
... |
b49bb53189f0edfc4078c3d3de43417b959abecb60629f9459b365c985b76f98 | MATLAB | 462 | 23 | %{
monet.DriftResponseSet (computed) # my newest table
-> monet.DriftTrialSet
-> pre.ExtractSpikes
---
%}
classdef DriftResponseSet < dj.Relvar & dj.AutoPopulate
properties
popRel = monet.DriftTrialSet*pre.Segment*pre.ExtractSpikes & pre.Spikes
end
methods(Access=protected)
... |
189196b941c3b87448b95f2e281df6f4bc1eabeb59984734399fdb40cf2b881e | MATLAB | 463 | 31 | function r = or(p,q)
% this function compares the x, y, and z values of the structures that I use in
% the FVtool.
%
% SYNOPSIS:
%
%
% PARAMETERS:
%
%
% RETURNS:
%
%
% EXAMPLE:
%
% SEE ALSO:
%
% Copyright (c) 2012-2016 Ali Akbar Eftekhari
% See the license file
if (isa(p, 'CellVariable')&&isa(q, 'CellVariable'))
... |
478a9bcdc1f885e4485744ebea084858dcc091b24650ce461f8c402983c90e74 | MATLAB | 463 | 22 | function stringOut = removeSpaces(stringIn);
%
% removeSpaces will remove all spaces from a string
%
% Cai Wingfield 1-2010
%__________________________________________________________________________
% Copyright (C) 2010 Medical Research Council
import rsa.*
import rsa.fig.*
import rsa.fmri.*
import rsa.rdm.*
import r... |
94ccbadcafb0593f0b7b73967177be44fdf4dfa084407ed976aa5ca3409ad190 | MATLAB | 463 | 17 | function [f df] = fitt_optnu(nu, delta, p)
% function to solve for ML nu estimate
nu2 = nu/2;
pnu2 = (p+nu)/2;
w = (p + nu) ./ (delta + nu);
f = -psi(nu2) + log(nu2) + (sum(log(w)-w)/length(delta)) + 1 ...
+ psi(pnu2) - log(pnu2);
% jacobian
if nargout > 1
dp = delta - p;
dnu = delta + nu;
sumterm =... |
b1bacb9b5a81e482638ca17ddaf578178f26bb284990ff28bff64fb7df7432f0 | MATLAB | 463 | 29 | function vox = ellipsoidMask(a,b,c)
xP = [];
yP = [];
zP = [];
for z = -c:c
ymax = sqrt(b.^2 * (1 - z.^2 ./ c.^2));
for y = -ymax:ymax
xmax = sqrt(a ^ 2 * (1 - z^2 / c^2 - y^2 / b^2));
for x = -xmax:xmax
xP = [xP;x];
... |
ba647b0d5bef89b5546a3c36d4aa97f8c7fee8dabe99e3c2ed900f49e893bcae | MATLAB | 463 | 16 | function [uniq_bval_arr, dwi_img_new] = avgRepeatBvalVols(bval_arr,dwi_img)
disp('Averaging multiple bval vols to increase signal')
[uniq_bval_arr,IA,~] = unique(bval_arr);
dwi_img_new = [];
for i = 1:numel(uniq_bval_arr)
if i < numel(uniq_bval_arr)
dwi_avg_vol = mean(dwi_img(:,:,:,IA(i):IA(i+... |
bbcf050ae54e91dd6097fb8bf784790393c9c027b315d59d488b257d6d7158af | MATLAB | 463 | 28 | function [llh] = tapas_ti_llh(y, x, u, theta, ptheta)
%% General method to iterate across chains and subjects
%
% aponteeduardo@gmail.com
% copyright (C) 2016
%
fllh = ptheta.method_llh;
ns = size(theta, 1);
nc = size(theta, 2);
llh = zeros(1, nc);
for i = 1:nc
tllh = zeros(ns, 1);
for j = 1:ns
tll... |
6ecc2def0290a230f91b526cd83adc6f864ce3f3fb4030ad09ae06b5ea17b958 | MATLAB | 464 | 12 | function [Q, R] = stiefelgeneralized_retraction_MGS_twice(B, X, U, t)
% Retraction for generalized Stiefel based on Modified Gram-Schmidt.
% More accurate but twice as slow as stiefelgeneralized_retraction_MGS.
% NB, Dec. 16, 2018.
if ~exist('t', 'var') || isempty(t)
t = 1;
end
[Q1, R1] = stiefelgen... |
ae7965c3cfedb0b9b169a5e5567fa601e569b41f25b4ca6ef8022dc22a50ef25 | MATLAB | 464 | 34 | function out = MySmooth(x, N)
% operates on first dimension only
if N==1
out = x;
return;
end
Ncol = size(x, 2);
Nel = size(x, 1);
if mod(N, 2)==0
N = N+1;
end
kern = ones(1, N);
if N>1
kern(1:floor(N/2)) = 0; %causal
end
kern = kern./sum(kern);
out = zeros(Nel, Ncol);
... |
c6d6647dcb7f6d744432f185b5b442e39005173fe6a619fc749cbde2c706e4e6 | MATLAB | 464 | 18 | function output = brain_region_features(data, original_data, mask)
%generate cifti based spatial features according to different masks
% Detailed explanation goes here
mean_data=mean(data);
std_data=std(data);
std_outlier_data=std_outlier(data);
if sum(original_data)==0
region_data_1=0;
else
region_data_1=s... |
fc54afa7c7d080c54d778fe1eb0a47bd3265378bdb8ceef9f10cdddfe5682e40 | MATLAB | 464 | 31 | function r = and(p,q)
% this function compares the x, y, and z values of the structures that I use in
% the FVtool.
%
% SYNOPSIS:
%
%
% PARAMETERS:
%
%
% RETURNS:
%
%
% EXAMPLE:
%
% SEE ALSO:
%
% Copyright (c) 2012-2016 Ali Akbar Eftekhari
% See the license file
if (isa(p, 'CellVariable')&&isa(q, 'CellVariable'))
... |
fe7a370f4bbdda589b5fca84cf9fcd228df10949617b4fdb9978a81aa7d049dc | MATLAB | 464 | 9 | function [filt_onset_frames,filt_offset_frames] = filter_event_timestamps(onset_frames,offset_frames,min_interval)
% merge events that are too close in time into one long event
filt_onset_frames = onset_frames; filt_offset_frames = offset_frames;
event_intervals = onset_frames(2:end) - offset_frames(1:end-1);
merge... |
1486e9531f5cd149b0765a73bb7fdbb9a5e8dfdd1a087dd749a85f08f617e725 | MATLAB | 465 | 13 | function varargout = sparseinv_mex(varargin) % compile and run function
loc = fileparts(mfilename('fullpath'));
src = ['-outdir ',loc,' ',loc,'/sparseinv_mex.c ',loc,'/sparseinv.c'];
if ~isempty(strfind(computer,'64'))
fprintf ('Compiling sparseinv (64-bit)\n') ;
eval(['mex -largeArrayDims ',src])
else
... |
0ca00f4a4e917749be50b0353a16c80450f488eb2961ee638356bb900a9fb434 | MATLAB | 466 | 21 | function [v] = tapas_mh_mc3_hier_arc(ollh, olpp, nllh, nlpp, ...
ratio, T)
%% Acceptance rejection criterion for metropolis hastings in the context of
% population mcmc.
%
% aponteeduardo@gmail.com
% copyright (C) 2016
%
v = sum(nllh, 1) .* T + nlpp - (sum(ollh, 1) .* T + olpp) + ratio;
nansv = isnan(v);
v(nansv... |
2c4019628536f614fb1022dbe9d49c29030f3b061804b4a28fbd4839417faac3 | MATLAB | 466 | 8 | % --- Executes on selection change in popupmenuColorMap.
function popupmenuColorMap_Callback(hObject, eventdata, handles)
handles.data.cmapName = get(handles.popupmenuColorMap, 'String');
handles.data.cmapName = handles.data.cmapName(get(handles.popupmenuColorMap, 'Value'));
handles.data.cmap = feval(handle... |
8092d14215fc3fbace48ad37a14b192c8666f84d8e8f0e938db7fd5f6c254e72 | MATLAB | 466 | 12 | function boxes = bbox_clip(boxes, im_size)
% bbox_clip Clip boxes to image boundaries.
%
% Copyright (C) 2016 Hakan Bilen.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
boxes(:,1) = max(min(boxes(:,1),im_size(2)),1)... |
c8331f95be7a82dac4383b21f094cde3d6928aab2c2f2085210e5524e656a807 | MATLAB | 466 | 19 | % Compute the log intensity for the inverse link function g(f) = exp(f).
% Output range: 0 <= g(f).
%
% The function can be used in GLM likelihoods such as likPoisson, likGamma, and
% likInvGauss.
%
% Copyright (c) by Hannes Nickisch, 2016-10-04.
function [lg,dlg,d2lg,d3lg] = glm_invlink_exp(f)
lg = f;
if nargout>... |
4262cad2af4ee00aa0f76f022b9e13162a63c4af77e383ad3289abc5e8ea871d | MATLAB | 467 | 20 | function drawTrace(Ftrace,traceX, traceY,current)
plot(Ftrace,[traceX' traceY']);
hold on;
Ftrace.NextPlot = 'add';
maxL = max([traceX(:); traceY(:)]);
minL = min([traceX(:); traceY(:)]);
plot(Ftrace,[current current],[minL maxL],'g');
xlabel('Frame #');
title('Traces (z-scored)');
% difference
% plot(Ftrace,traceX-t... |
89c427e36e1404c7a49020baf6d8c59dc7f79520eb84357e4a548ec5be1e8302 | MATLAB | 467 | 24 | %{
trk.EyeFrame (computed) # eye tracking info for each frame of a movie
-> rf.Eye
-> trk.SVM
frame : int # frame number in movie
---
eye_frame_ts=CURRENT_TIMESTAMP: timestamp # automatic
%}
classdef EyeFrame < dj.Relvar & dj.AutoPopulate
properties
popRel = rf.Eye*t... |
97146f72f989fe164db4590b0edf0ccbfe16b8169752074242d5807e05b1fb82 | MATLAB | 467 | 13 | function [m,dm] = meanOne(hyp, x)
% One mean function. The mean function does not have any parameters.
%
% m(x) = 1
%
% Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch, 2016-04-15.
%
% See also meanFunctions.m.
if nargin<2, m = '0'; return; end % report number of hyperparameters
m = ones(size(x... |
27482f41ba1cfc931fb2285586f50040dcb31e0a65a1585e35ba986d7fea5df5 | MATLAB | 468 | 22 | function [av, moes, bci] = bootmoes(vec)
% Takes in a vector (that can be NaN-contaminated), calculates mean, moes,
% and Cis by bootstrap
%% For testing
% clear all; close all; clc
% vec=[0.455973541079959;0.607332882904319;0.504551752988633;0.590645277791557;0.541899635550893;0.482477113888613;NaN];
%% Calculate m... |
36206c7e2117692928da68061a15322dacdf24c108317c2e0652124d003e5922 | MATLAB | 468 | 24 | %{
# Calcium activity for a single field within a scan
-> `pipeline_experiment`.`scan`
-> `pipeline_shared`.`#pipeline_version`
-> `pipeline_shared`.`#field`
-> `pipeline_shared`.`#channel`
-> `pipeline_shared`.`#segmentation_method`
-> `pipeline_shared`.`#spike_method`
---
-> fuse.Pipe
%}
classdef Activity < dj.Comp... |
50000136ca6389c1bee4ca554a63f2016a753dc4abd7a84950f9dbf2ffc74ae5 | MATLAB | 468 | 33 | function [llh] = tapas_linear_hier_llh(data, theta, ptheta)
%%
%
% aponteeduardo@gmail.com
% copyright (C) 2016
%
y = data.y;
u = data.u;
theta = theta.y;
[np, nc] = size(y);
llh = zeros(np, nc);
ln2pi = log(2 * pi);
% Precision
pe = ptheta.pe;
lpe = log(ptheta.pe);
for j = 1:nc;
for i = 1:np
r = y... |
638218b409e263c379b56abf6d4d11bf56df1f129703d3c26043e1c61ebfe7ff | MATLAB | 468 | 24 | function blockdiag = consensuscheck(D)
% H Smith, Cambridge, 2020
% Check if matrix is binary and block diagonal
%
% INPUTS:
% D: adjacency matrix
%
% OUTPUTS:
% blockdiag:
% = 0: matrix is not block diagonal
% = 1: matrix is block diagonal
v1 = sum(D,1);
v1 = v1';
v2 = sum(D,2);
check =... |
ecdab53ba5c1195564cbebd2719524fc3ac1ba8b563630c1dbf8f71305daa63f | MATLAB | 468 | 23 | %{
tuning.DirectionalResponse (computed) # response to directional stimulus
-> tuning.Directional
-> preprocess.Spikes
---
latency : float # latency used (ms)
%}
classdef DirectionalResponse < dj.Relvar & dj.AutoPopulate
properties
popRel = tuning.Directional*preprocess... |
9b66b76fd4934abfdce578954ef6ff64eca4aa2b919505ae4a620c2632de6bf9 | MATLAB | 469 | 23 | clear, clc
%raw data to stack of 2D images
fn_dat = 'location of raw data file';
fn_seq = 'SERO_example_sequence.seq';
fn_xps = 'location of xps file';
fn_nii = '2D_example.nii';
sero_pulseq_data2nii_adaptiveCombine(fn_dat, fn_seq, fn_nii, fn_xps)
%%
%2D stack to SRR
new_nii = 'SRR_example.nii';
m... |
f171731d24c7761851a580ac7b3f703aa03db7269ca9d1570822bdb69b268d6b | MATLAB | 470 | 25 | %clear all
%close all
path(path,'.\FUZZCLUST')
%data set
data.X = Z(:,[1 2]);
%parameters
param.c=ii;
param.m=2;
param.e=1e-3;
param.ro=ones(1,param.c);
param.val=3;
%normalization
data=clust_normalize(data,'range');
result = GKclust(data,param);
result = validity(result,data,param);
plot(data.X(:,1),data.X(:,2),'b.... |
6b6b4b0047457e64b1b9b71b9ed28eab7a3933d0cbde18442404a2adc2dc209d | MATLAB | 471 | 17 | function [] = error_patch(x, y, yerr, color, varargin)
% draws a symmetric transparent area around a plotted line e.g. to
% indicate std, confidence interval etc.
% INPUT
% x = x data of the plotted line;
% y = y data of the plotted line;
% yerr = error/deviation from y at each point in x;
% VARARGIN
% any nam... |
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