sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 17k | content stringlengths 1 200k |
|---|---|---|---|---|
2455742f1fd60a9a7e7cf5f87e083070fc4ea8c7e696725898ddd57c8b8288d4 | MATLAB | 493 | 15 | classdef FreedmanLane < nla.net.mcc.Base
properties (Constant)
name = "Freedman-Lane"
end
methods
function [is_sig_vector, p_max] = correct(obj, net_atlas, input_struct, prob)
p_max = input_struct.prob_max;
is_sig_vector = prob.v < p_max;
end
... |
8d39f4585dbc5af5f39abe50d4abe8e03a5af76951c42c7f94abe19c69bf69c8 | MATLAB | 493 | 15 | function data_down_sampled = loc_list_down_sample(data,scatter_num)
if length(data.x_data)>scatter_num
vec = 1:length(data.x_data);
vec = vec(randperm(length(vec)));
I = vec(1:scatter_num);
data_down_sampled.x_data = data.x_data(I);
data_down_sampled.y_data = data.y_data(I);
data_down... |
ddd93a9f00cc6ab7fcf07294dc40ace1baef11e74547fa04079186c68ec2a998 | MATLAB | 493 | 20 | function m_out = resQ_unitSwap(m_in, mode)
% function m_out = resQ_unitSwap(m_in, mode)
b_nrm = 1e-9;
m_nrm = 1e-13;
% S0 D R Vi Vir Va Var Xi Xa
f = [1 b_nrm m_nrm b_nrm^2 m_nrm^2 b_nrm^2 m_nrm^2 (b_nrm*m_nrm) (b_nrm*m_nrm)];
switch mode
case... |
e9031c981d9f247ea72da1119d9bc3bd0b0cfb4ea7b26b57fe434e330ce07af7 | MATLAB | 494 | 15 | classdef WestfallYoung < nla.net.mcc.Base
properties (Constant)
name = "Westfall-Young"
end
methods
function [is_sig_vector, p_max] = correct(obj, net_atlas, input_struct, prob)
p_max = input_struct.prob_max;
is_sig_vector = prob.v < p_max;
end
... |
fe2c06a956711b0e8e3f685d953047882a964d5e1698b43d0f9bd902ea36e2a3 | MATLAB | 495 | 14 | function spm_batch = tc_make_spm_contrast_batch
%% spm_batch = tc_make_spm_contrast_batch
spm_batch = {[]};
spm_batch{1}.spm = [];
spm_batch{1}.spm.stats = [];
spm_batch{1}.spm.stats.con = [];
spm_batch{1}.spm.stats.con.spmmat = {[]};
spm_batch{1}.spm.stats.con.consess = {[]};
spm_batch{1}.spm.stats.con.consess{1}.tc... |
120fe253e80a0c1b989f8478146d17e8460f1d8168da1dd90f724e916386c648 | MATLAB | 496 | 16 | function data_scaled = loc_list_multiply_by_number(data)
answer = inputdlg({'Number:'},'Input',[1 50],{'116'});
if isempty(answer)~=1
number = str2double(answer{1});
data_scaled = cell(1,length(data));
for i = 1:length(data)
data_scaled{i} = loc_list_scale_data_inside(data{i},number);
end... |
431878c7e5f34cbf0693d97ce4ff2a5fcc80982b8771bbbbf671dfed5d89a8a8 | MATLAB | 497 | 16 | function [sndx,ssize,nslices] = pl_sliceblocks(n,slicesize)
%
% Slices a dimension of size n into slices of size 'slicesize'
% This function is part of the permutationlab software:
% Author: Dimitrios Pantazis
% The code is currently under development, please do not share
nslices = ceil(n/slicesize);
for s... |
ad2c3284c7f9694b2f274313498e854a119e50180636e57b155eb5fa1bc6762d | MATLAB | 497 | 17 | function txt=SurfStatDataCursor(empt,event_obj)
pos=get(event_obj,'Position');
h=get(event_obj,'Target');
v=get(h,'Vertices');
x=get(h,'FaceVertexCData');
id1=min(find(v(:,1)==pos(1)&v(:,2)==pos(2)&v(:,3)==pos(3)));
tag=get(get(h,'Parent'),'Tag');
[s,a,id0]=strread(tag,'%s %d %d');
id=id1+id0
txt = {['x: ',num... |
310fff674d179e0859f7a548d801b18200dbdf35821648c58ae6fb69dadd4f02 | MATLAB | 501 | 18 | function channel_data = loc_list_show_in_channels(data)
areas = logspace(0,1,length(data));
for i = 1:length(data)
x{i} = data{i}.x_data;
y{i} = data{i}.y_data;
area{i} = areas(i)*ones(length(data{i}.x_data),1);
end
x = vertcat(x{:});
y = vertcat(y{:});
area = vertcat(area{:});
channel_data{... |
15cefa73c3776ce6b12e085cd799beb42cbf9f2aae0de6c27ced73604b579562 | MATLAB | 503 | 13 | function inputs = genBaseInputs()
%GENBASEINPUTS Generate struct of required network-level inputs with
% default values
inputs = struct(...
'no_permutations', true,...
'full_connectome', true,...
'within_network_pair', true,...
'prob_plot_method',nla.gfx.ProbPlotMethod.DEFA... |
ec35ae95fd7d80fb1f22d5b663cc49451c7b6476cbffdc3fcabeb11505a23226 | MATLAB | 504 | 17 | function astroGpe = gp_display_out_2p(Gpe,labeled,scaleBar)
% Displays the groupe of astrocytes specified in 'Gpe'
% 25/03/2019
astroGpe = zeros(size(labeled));
for Ag = 1:length(Gpe)
Agrp = Gpe(Ag);
% x = find(labeled == Ag);
astroGpe(labeled == Agrp)=1;
end
% Add scale bar 100µm
% insertShape(ast... |
f582d2f0d82a1ffdfc5248d6e5a0d505c23a5e3421c523fec6d852ba113cb5c3 | MATLAB | 506 | 15 | function [id, r_, G_] = set_all_part_traj_v2(pnts, G, dist_thr, num_sources, N, m0, P0)
M=length(pnts);
dist_sources=zeros(1,N);
r_=zeros(3*num_sources,N);
G_=zeros(size(G,1),num_sources,N);
min_dist = 0;
r_1 = repmat(m0, [1 N]) + chol(P0)*randn(3*num_sources, N);
for j=1:N
pos = reshape(r_1(:,j), [3 num_sources])'... |
93641de5dea153c29bcdcce57392e043608f8ad763f41c7d14e3bf5070989c00 | MATLAB | 508 | 21 | function bst_create_test_protocol(use_default_anatomy)
if nargin < 1
use_default_anatomy = 0;
end
%% Ensure that nst_utest protocol exists
ProtocolName = 'nst_utest';
% Delete existing protocol
db_dir = bst_get('BrainstormDbDir');
gui_brainstorm('DeleteProtocol', ProtocolName);
nst_protocol_dir = fullfile(db_dir... |
3c929ebcd9d89fe37d5c5f07d557dd0ca785b38d54e37080f452e5507e0bdfe7 | MATLAB | 512 | 19 | function DynamicBC_write_NIFTI(dat,vm,outname)
datdims = size(dat);
if length(datdims)==3
v1 = vm(1);
v1.dt = [16,0];
v1.n=[1,1];
v1.fname = outname;
spm_write_vol(v1,dat);
elseif length(datdims)==4
for i = 1:datdims(4)
v1(i) = vm(1);
v1(i).dt = [16,0];
v1(i).... |
99355f3c0d5a5036a94597b465ac6508637648149f3cc60a01a07f2a9487ee78 | MATLAB | 514 | 11 | function [startend epidur]=SleepEpisodes(sleep,maxep,mindur,ba)
sleep=find(sleep>0);
sleep=[sleep maxep];
dif=diff(sleep);
dif1=find(dif>ba+1);
endvs=sleep(dif1);
startvs=dif1+1; startvs=[1 startvs maxep];
nepivs=diff(startvs); nepi1vs=find(nepivs>mindur); % episodes longer than interruption
epidurvs=n... |
f1711a3e29acae0855fbb3677de04abcef536328c820b7485094a26635d984d5 | MATLAB | 515 | 14 | function distances = euclidianDistanceROIs(network_atlas)
pos_vec = [network_atlas.ROIs(1:network_atlas.numROIs()).pos]';
dist_comp_sum = zeros(network_atlas.numROIs(), network_atlas.numROIs());
compute_dist_component = @(a, b) (a - b) .^ 2;
for dim = 1:3
pos_comp = pos_vec(:, dim);
dis... |
6540b3daebb084f8d44e2bd5a4922860803c34074b8da3ad9e8858146f299028 | MATLAB | 517 | 6 | function now_print_requested_and_real_times(o)
fprintf(1, '------------ Requested timing parameters: ------------ \n');
fprintf(1, 'DurPre = %5.3f DurPost = %5.3f DurPi = %5.3f [ms]\n\n', o.durationFirstPartRequested, o.durationSecondPartRequested, o.durationZeroGradientRequested);
fprintf(1, '------------ Actual... |
ecfad66e664ed9f79412c4d0e826a020778024115882088121fa5003c844e836 | MATLAB | 518 | 27 | function shape = resQ_1x6_to_shape(a, b)
% function shape = resQ_1x6_to_shape(a, b)
% By Filip Sz
% Funciton returns the shape of the outer product of a * b
% It is (A'*B):shear / (A'*B):bulk / 2
if nargin < 2
b = a;
end
E_iso = eye(3)/3;
E_bulk = E_iso(:) * E_iso(:)';
E_shear = eye(9)/3 - E_bulk;
n = s... |
83569f13198c05ac92ecda40bab0a7055a74900a833a3400ee324099ab66f944 | MATLAB | 520 | 24 | function link = Which_Link_ThisMCIndex(index, MC)
% FUNCTION link = Which_Link_ThisMCIndex(index, MC) returns the directed
% link i=link(1) to j=link(2) corresponding to index in the meta-connectivity
% (MC) representation.
M = size(MC,1);
nregions = (1 + sqrt(1+4*M))/2;
if index > M
disp('please provide valide ... |
2773595f789d2698b55f331637c91552a1556ec90f7ce22022c6f3a81d98e78c | MATLAB | 521 | 19 | function to_group = find_groups(classes,parameters,link,coefficient_of_variation)
if length(link)>1
i = 1;
while true
i = i+1;
if i <= length(link)
to_check = classes(link(1:i),2);
to_check = vertcat(to_check{:});
variation = abs(std(to_check,0,1)./mea... |
2ef5f707aa3a436c824916860bc04c6c85f1ff885bbc1500b89eb532f632404a | MATLAB | 521 | 20 | function spt_tracks_change_tracks_time(data)
input_values = inputdlg({'Tracks Time Step:'},'',1,{'0.05'});
if isempty(input_values)==1
return
else
time_step = str2double(input_values{1});
for i = 1:length(data)
for j=1:length(data{i}.tracks)
data{i}.tracks{j} = chnage_tracks_time... |
c79699dcb1ba0d7ff2975d87f02a40ad45d031e12d92647ca3f3c0d23dd655f0 | MATLAB | 521 | 14 | classdef BenjaminiHochberg < nla.net.mcc.Base
properties (Constant)
name = "Benjamini-Hochberg"
end
methods
function [is_sig_vector, p_max] = correct(obj, net_atlas, input_struct, prob)
[is_sig_vector, p_max] = nla.lib.fdr_bh(prob.v, input_struct.prob_max, 'pdep');
e... |
0c30d9c82235b93a86d917a8ff9cfa499734b9ce342565fd3496d297ac11691c | MATLAB | 523 | 12 | function x = findOriginXFromTwoTangents(a, b, rad_a, rad_b)
%FINDORIGINXFROMTWOTANGENTS Find circle origin X position from two
% tangent lines on the circle
% a: x-y coordinate, tangent point a
% b: x-y coordinate, tangent point b
% rad_a: angle from circle origin to tangent point a
% ra... |
384e02ef84e563e2a2233e43136ffb21bad768baf2357b1ed3bbaa38ea3113fc | MATLAB | 523 | 14 | classdef BenjaminiYekutieli < nla.net.mcc.Base
properties (Constant)
name = "Benjamini-Yekutieli"
end
methods
function [is_sig_vector, p_max] = correct(obj, net_atlas, input_struct, prob)
[is_sig_vector, p_max] = nla.lib.fdr_bh(prob.v, input_struct.prob_max, 'dep');
... |
7b9cb471acedda6cc940f319a3a1ea0f212098541bf3cfe6e5b4a67c405d61c9 | MATLAB | 523 | 11 | function write_surface(surface,file)
% WRITE_SURFACE Reads surface from disk.
%
% surf_out = WRITE_SURFACE(surface,path) writes the surface to a file.
% Accepted file formats are .gii, .mat, .obj. Any file that does not end
% in one of those extensions will be encoded as a Freesurfer file.
%
% For complete d... |
51146ba999a6c850a78951371061bdae1063f8e321278a749f5e94f4298c5ec7 | MATLAB | 528 | 15 | function [ams ass pns] = assemble_paths(assembly_paths, contig_direct, Assemblies, deltafiles,deltafiles_ref, Ref_chrom, overlap)
if isa(assembly_paths,'double')
assembly_paths = {assembly_paths};
end
ams = cell(size(assembly_paths));
ass = cell(size(assembly_paths));
pns = cell(size(assembly_paths));
for li = ... |
04a7fdc6433a671581e71a4be4e8399349af1ce5a5e6d6a91a22ac971b7ae800 | MATLAB | 535 | 13 | prot = tblread('L:\Davi\Christina\Elite\allPeptides.txt','\t')
prot = tblread('L:\Results\Ishita\Copy of Log2abs0.5 GO.txt','\t');
prot = tblread('L:\Results\Ishita\Copy of Log2abs0.5 GO Col Znorm.txt','\t');
protcl=prot(:,1:12);
[x, fval, exitflag, output] = fminunc(fun,x0,'Algorithm','quasi-newton')
cubic = @(x)... |
9ce63c338153f13980f31c78508e9c3c8abe65bfe916975165afba2861cdb2a0 | MATLAB | 538 | 29 | function [data, A] = five_source_model(T)
M = 5; %number of sources;
P=2; %order of the model
T0=1000; %length of ignored start
%Generate stable AR matrix
lambdamax=10;
while lambdamax > 1 || lambdamax < 0.9
A=[];
for k=1:P
aloc = zeros(M);
aloc([1 7 13 19 25]) = -0.9; %diagonal elements
a... |
6a634a954cadabcb148b66f222ee7ec1bbcfc62195b1cf4a9184d8adfd6039d2 | MATLAB | 540 | 27 | function [bastend badur]=BriefAwakenings(wake,maxep)
mindur=1;
maxdur=4;
wake=find(wake>0);
wakep=[wake maxep];
dif=diff(wake);
dif1=find(dif>1);
endvs=wake(dif1);
startvs=dif1+1;
startvs=[1 startvs maxep];
nepivs=diff(startvs);
nepi1vs=find(nepivs>=mindur);
epidurvs=nepivs(nepi1vs);
startep=wake(startvs(nepi... |
672edf74f623e7de59a4901120f8fd7086d38f0704b604fefadc3d2dd48992ff | MATLAB | 541 | 11 | function coefficient_of_variation = shape_classification_iterative_clustering_input_coeff_var(classes)
input_values = inputdlg({'Lengh Coeff. of Variation Threshold:','Width Coeff. of Variation Threshold:'},'',1,{'0.15','0.15'});
if isempty(input_values)~=1
size_c_o_v = size(classes{1,3},2);
coefficient_of_... |
385f8a2afdb3413bdc6cdfce1b46ddfec3393e72fb0cbc1420ccbd332e89e30c | MATLAB | 544 | 20 | function [C_tri]=transitivity_bu(A)
%TRANSITIVITY_BU Transitivity
%
% T = transitivity_bu(A);
%
% Transitivity is the ratio of 'triangles to triplets' in the network.
% (A classical version of the clustering coefficient).
%
% Input: A binary undirected connection matrix
%
% Output: ... |
5b243ea9e46f1de9ef0eb981403ef8d6b3360fa307faae2244b6083588698981 | MATLAB | 546 | 24 | function [CIJ] = makerandCIJ_dir(N,K)
%MAKERANDCIJ_DIR Synthetic directed random network
%
% CIJ = makerandCIJ_dir(N,K);
%
% This function generates a directed random network
%
% Inputs: N, number of vertices
% K, number of edges
%
% Output: CIJ, directed random connect... |
712e3bd72c6d15b52c657bb9c4fb679281a766e36f4a2f6efd051912ebb2513f | MATLAB | 549 | 17 | function Y = nst_misc_convert_to_mumol(Y,DisplayUnits)
%NST_MISC_CONVERT_TO_MUMOL Convert Y from DisplayUnits to mumol.l-1
if strcmp(DisplayUnits, 'mol.l-1')
Y = Y * 1e6;
elseif strcmp(DisplayUnits, 'mmol.l-1')
Y = Y * 1e3;
elseif strcmp(DisplayUnits, 'mumol.l-1') || strcmp(DisplayUnits, '\m... |
7cab679ab850fe849c49a73c4f72cd933d41f78df4d6e823f18f54e4a27b4a69 | MATLAB | 550 | 19 | function utest_clean_bst()
% Clean brainstorm from anything done during unit test
global GlobalData;
GlobalData.Program.isServer = 0;
GlobalData.Program.HandleExceptionWithBst = 1;
% Delete unit test protocol
ProtocolName = 'nst_utest';
gui_brainstorm('DeleteProtocol', ProtocolName);
db_dir = bst_get('BrainstormDbDir'... |
ae3c76e10203dc505aa8eda4525ca217a704d3e366aa1b03a739d9b7c326b67a | MATLAB | 550 | 15 | function settings_struct = getDefaultPlotSettings()
settings_struct = struct();
settings_struct.color_map = flip(parula(1000));
settings_struct.color_map(end+1,:) = [1 1 1];
settings_struct.p_value_plot_max = 0.0500;
settings_struct.name_label = 'plot title';
settings_struct.callba... |
b5b1e0227f48dd4e7692163f91e2eb78deab4706cafd025fa4f6b0833cf32c50 | MATLAB | 551 | 25 | %% read file and info
all=sffread('Ecoli.sff')
info = sffinfo('Ecoli.sff')
%% plot
len = cellfun(@length, {all(:).Sequence});
meanLength = mean(len)
medianLength = median(len)
stdLength = std(len)
figure(); hist(len);
xlabel('Sequence length');
ylabel('Number of reads');
title('Length distribution');
%=== metrics on... |
a4e3a87b5d47cb7b5fd4b6e5a1890c3dddc8baf536df22461bbaf06d96528aed | MATLAB | 554 | 24 |
function O = coregfunc(input) % apparently function need a 0 and no end to run in the cluster (!?), set the name of the function and the input variable
O = [];
addpath('/projects/MINDLAB2017_MEG-LearningBach/scripts/osl/osl-core'); %this add the path (not necessary being in the osl-core directory if you have this ... |
04ef189381d70e1ecaa911499066cd3379444b5900ad1bb51fef22fc2ec8cb6f | MATLAB | 557 | 17 | function classes_new = cluster_classes(idx,classes)
for i=1:length(unique(idx))
classes_new{i,1} = classes(idx==i,1);
classes_new{i,1} = vertcat(classes_new{i,1}{:});
classes_new{i,2} = classes(idx==i,2);
classes_new{i,2} = vertcat(classes_new{i,2}{:});
if size(classes_new{i,2},1)>1
... |
b743332f9bc56e926675bcfbf85684a9bd8953e87493417062d00abe84d4e72b | MATLAB | 559 | 14 | function obj = drawSphere(ax, pos, color, radius)
%DRAWSPHERE Draw sphere
% ax: axes to draw on (currently unused)
% pos: x-y-z position, center of sphere
% color: r-g-b or r-g-b-a color
% radius: radius of sphere
[x, y, z] = sphere(10);
obj = patch(surf2patch(radius * x + pos(1... |
83f3422e4e7dca3dbbf1ad91cc24801f0185eb02406070ac1d8582054b90aa72 | MATLAB | 563 | 26 | function convert_csv_to_gifti(in_tri,in_points, out_gii,io_value)
addpath('gifti')
tri = importdata(in_tri)
vertices = importdata(in_points)
mystruct = struct
mystruct.faces = tri+1
mystruct.vertices = zeros(size(vertices,1),3)
mystruct.vertices(:,1:2) = vertices(:,:);
%set t... |
3465ede4158f25365f436f89367372eed13005fb3946e6c0d64c1bbb5b846b9b | MATLAB | 567 | 29 | function SurfStatColormap( map );
%Colormap function for SurfStatView.
%
% Usage: SurfStatColormap( map );
%
% Same as for matlab's colormap function - see help colormap.
colormap(map);
a=get(gcf,'Children');
k=0;
for i=1:length(a)
tag=get(a(i),'Tag');
if strcmp(tag,'Colorbar')
cb=a(i);
... |
512018549025e83780d4eb4dd72f99dd5c7203fa44a4c0a422b3458723efeafb | MATLAB | 567 | 27 | function [minval maxval]=imrange(inpict)
% [min max]=IMRANGE(INPICT)
% returns the global min and max pixel values in INPICT
%
% INPICT can be a vector or array of any dimension or class
dims=length(size(inpict));
for n=1:1:dims
if n==1
minval=min(inpict);
maxval=max(inpict);
e... |
09754d1813676256d58e4f414f6a47adb7b77b54c9f85c76bcc286afacde6fb9 | MATLAB | 568 | 19 | function nst_save_figure(fig_fn, options, hfig)
if nargin < 3
hfig = gcf;
end
switch options.save_fig_method
case 'export_fig'
if ~isfield(options, 'export_fig_dpi')
options.export_fig_dpi = 90;
end
export_fig(fig_fn, '-transparent', sprintf('-r%d', options.export_fig_dpi),... |
c843908cb20e887cfb0c7a1fc0fb1d18dffbd777e3a01b29db25df9b7010f219 | MATLAB | 571 | 13 | function surf_out = read_surface(file)
% READ_SURFACE Reads surface from disk.
%
% surf_out = READ_SURFACE(file) reads the surface file and outputs it in
% MATLAB format. The file can be (.gii, .mat, .obj, Freesurfer), a loaded
% variable (in SurfStat or MATLAB format), or a cell array containing
% multiple ... |
9b7dddfdcff8cbc06eb8f6e7a1cb18166c6bf8aa3c46dae6aae087cdc5e525c7 | MATLAB | 572 | 23 | function success = nst_download(source_fn, dest_fn, download_msg, bst_interactive)
if nargin < 3
download_msg = sprintf('Downloading "%s" to "%s"...\n', source_fn, dest_fn);
end
fprintf('%s -- ', download_msg)
success = 1;
tstart = tic();
errMsg = bst_websave(dest_fn, sou... |
b32833848dbc86c0d23468f5b6c1e3e374b43f5bdf37785d1d5cfbb37a325556 | MATLAB | 572 | 30 | function O = cluster_Dlabel(S2)
O = [];
addpath('/projects/MINDLAB2017_MEG-LearningBach/scripts/osl/osl-core'); %this add the path (not necessary being in the osl-core directory if you have this one)
osl_startup
D = S2.D; %file to be updated
D2 = S2.D2; %file with original labels
D=D.montage('switch',0);
for ii = ... |
83cedc809d73d08084badafc9bd38fcd9c2b4ebf12edcfe85e415db6c4d95f20 | MATLAB | 573 | 23 | function index = Which_MCIndex_ThisLink(i,j, MC)
% FUNCTION index = Which_MCIndex_ThisLink(i,j) returns the ordinal index of a
% link in the meta-connectivity (MC) representation of a directed link from i to j.
M = size(MC,1);
nregions = (1 + sqrt(1+4*M))/2;
if (i == j)
disp('Careful! Slef-loops are excluded fro... |
0b3667e62f20723d9b98865e3312458f4d48471e89ae43fc5c8da175438466d2 | MATLAB | 574 | 26 | function image(t)
sx=char(t);
x=double(t);
if isempty(x)
return
end
[n,p]=size(x);
minx=min(x);
maxx=max(x);
x=(x-ones(n,1)*minx)./(ones(n,1)*(maxx-minx+(maxx==minx))) ...
+ ones(n,1)*(maxx==minx);
d1=0.66;
clf;
axes('position',[0.06 0.06 0.2 d1]);
imagesc(x); colorbar; colormap(spectral(256)); ... |
bc9e041c7a28bd0917e7f3338b07df34704abe6f8a200d19bbd96869fc30fa94 | MATLAB | 575 | 27 | function [Y, sigma, est_snr] = add_meas_noise(Y, snr)
%UNTITLED2 Summary of this function goes here
%author : Narayan Subramaniyam
%Aalto/NBE
% resonable to use this if you have just one type of sensors
num_chan = size(Y,1);
sig_var = zeros(num_chan,1);
for i = 1:1:num_chan
sig_var(i,1) = var(Y(i,:));
end
avg_v... |
537524f971239139f1e89a8aeb97fc3b81d591865cd3cd86e69f7eb616eaa2e7 | MATLAB | 577 | 18 | classdef KendallB < nla.edge.BaseTest
%KENDALL Edge-level Kendall's tau
properties (Constant)
name = "Kendall's tau-b"
coeff_name = "Kendall's tau-b"
end
methods
function obj = KendallB()
obj@nla.edge.BaseTest();
end
function result = run... |
ed583865faf2bef950e57b81eef133bd155503f1bc1bb9c4c461f91ae3688310 | MATLAB | 581 | 21 | function str = commitString(full)
%COMMITSTRING Output current git commit as a string
if ~exist('full', 'var'), full = false; end
cmd_str = sprintf('cd %s\ngit rev-parse --abbrev-ref HEAD', nla.findRootPath());
[cmd_status, branch_name] = system(sprintf(cmd_str));
if cmd_status ~= 0
st... |
98746a7e5e2d680d56bcdabaf2f41f1b9d81329bf8fbc611650351d1d800c070 | MATLAB | 588 | 28 | function eFC = TS2eFC(TS)
% FUNCTION eFC = TS2eFC(TS)
% takes time-series (TS) as input and calculates edge functional connectivity
% (eFC) as output.
%
% inputs: TS(t,n) --> rows are t different time-points;
% columns are n different regions;
%
% Example: efc = TS2eFC(ts)
t = size... |
c1ce770775e66b7963204672cfb376f6f65ac5a877349ccb2f7429daeae460dd | MATLAB | 590 | 18 | function tc_data2nii(data, ref_nii, filename)
%% ref_nii = tc_data2nii(data, ref_nii, filename)
if ischar(ref_nii)
ref_nii = load_untouch_nii(ref_nii);
end
% set data
ref_nii.img = data;
% define pixel dimensions in standard nifti 8D
ref_nii.hdr.dime.dim = [ndims(data), size(data), ones(1, 8 - ndims(data) - 1)];
... |
d9edb108354cd3aeda7b76fd5b59bb5827a484c3b77ee885a14dc28db133eead | MATLAB | 591 | 11 | function shape_classification_iterative_linkage_clustering(data)
coefficient_of_variation = shape_classification_iterative_clustering_input_coeff_var(data.classes);
if isempty(coefficient_of_variation)~=1
[classes,size_classes] = shape_classification_iterative_clustering(data.classes,coefficient_of_variation,'li... |
dbacc6908a689b400e83f6b29d46504adb6729a25979401fcd7617e86687ac61 | MATLAB | 594 | 25 | function [CIJ] = makerandCIJ_und(N,K)
%MAKERANDCIJ_UND Synthetic directed random network
%
% CIJ = makerandCIJ_und(N,K);
%
% This function generates an undirected random network
%
% Inputs: N, number of vertices
% K, number of edges
%
% Output: CIJ, undirected random co... |
f9af83e82336241bf64a8a65b641505531cdd97c66f025e21a7dbf8ffa249abf | MATLAB | 595 | 14 | function [log_weights] = gpu_logpdf(Y,ypred,sigma, logDetSigma,N,ny)
gputimes = @(A, B) pagefun(@mtimes, A, B); % A*B on GPU
gpurdivide = @(A, B) pagefun(@mrdivide, A, B); % A/B on GPU
gputranspose = @(A) pagefun(@transpose, A); % A'
y1(:,1,:) = ypred - repmat(Y, [1 N]);
a1=gpuArray(y1);
b = gpuArray(sigma);
... |
141e35557c27f47b85cfca7f263af7ff9a11efa2ba973f9540bb0855d14f93ba | MATLAB | 596 | 31 | function pno = mipp_pnam_to_unit(pni, mode)
if nargin < 2
mode = 1;
end
switch mode
case 1
switch pni
case {'s0' 'rare'}
pno = '[a.u.]';
case {'D' 'D_delta' 'Dg'}
pno = '[µm^2/ms]';
case {'Vi' 'Va' 'Vi_delta' 'Va_... |
019339db29d5aafa62260b0aa30fb1357223d9792293f9cb77a190bdc930b58e | MATLAB | 598 | 11 | function shape_classification_iterative_distance_clustering(data)
coefficient_of_variation = shape_classification_iterative_clustering_input_coeff_var(data.classes);
if isempty(coefficient_of_variation)~=1
[classes,size_classes] = shape_classification_iterative_clustering(data.classes,coefficient_of_variatio... |
2af05d76944235db0dd28f649185b44ef58e335c83c524399ca0873d333841d0 | MATLAB | 599 | 27 | %% read list
fo='X:\Elite\Alexey\HCD\protlist.txt';
pl=fopen(fo);
pla=textscan(pl,'%s');
fclose(pl);
%hprot=fastaread('X:\FastaDB\uniprot-human-may-13.fasta')
%% compare lists
%hprot.Header==pla{size(pla{1},2)}{1}
%pla{size(pla{1},2)}{1}
%% write retrieved protein sequences
fw=[fo,'.sequence','.fa... |
701bb7d601e51ceee59af64b78090bd8fb1de8ee59a08fca86c3ce43f5fa9522 | MATLAB | 604 | 25 | % function for map showing signal levels across tissue
function [map]=fluo_map_raw(imagestack)
[rows cols] = size(imagestack(:,:,1))
premap = zeros(rows,cols);
for row = 1:rows
for col = 1:cols
signal=imcomplement(squeeze(imagestack(row,col,:)));
signal = (double(signal)); ... |
7dfaf8e4079ddb8f70fde4fadd256370685d8dea111b7e845605f48ce23cfe4f | MATLAB | 609 | 22 | function inputs = reduce(inputs_unreduced)
%REDUCE Reduce duplicate inputs
i = 2;
while i <= numel(inputs_unreduced)
duplicate = false;
for j = 1:i-1
% if it's equal, it's a duplicate
if strcmp(class(inputs_unreduced{i}), class(inputs_unreduced{j})) && strcmp(inputs_u... |
c75380b51dd5c3c4bb22a287435ef49e32dcb0b36e8ee92641cc8e1e5671a2fd | MATLAB | 610 | 24 | function [n_on_start_new, n_on_end_new]=on_minimum_spikenum(on_start_new, on_end_new,left_spikes)
n_on_start_new=on_start_new;
n_on_end_new=on_end_new;
%%
for u=1:length(on_start_new)
interval=[ on_start_new(u) on_end_new(u) ];
ai = myFind(left_spikes>=interval(1)&left_spikes<=interval(2));
% len... |
ea778963c6e3e224c4d2ac0cfbacf5de984ff2dda28550431207b012bbc725a2 | MATLAB | 613 | 19 | classdef Pearson < nla.edge.BaseTest
%PEARSON Edge-level Pearson correlation
properties (Constant)
name = "Pearson's r"
coeff_name = "Pearson's r (Fisher-Z Transformed)"
end
methods
function obj = Pearson()
obj@nla.edge.BaseTest();
end
fu... |
190ba58c3e62aca7d013d673b96f79bcdc86d159c99c0d0886a5be9df46dee57 | MATLAB | 614 | 24 | %% FILTER A VECTOR NOISE WITH A SPECIFIED STRICTLY CAUSAL MVAR MODEL: Y(n)=A(1)Y(n-1)+...+A(p)Y(n-p)+U(n)
%%% INPUT
% A=[A(1)...A(p)]: M*pM matrix of the MVAR model coefficients (strictly causal model)
% U: M*N matrix of innovations
%%% OUTPUT
% Y: M*N matrix of simulated time series
function [Y]=MVARfilter... |
a075a9cc9d535eeb11f00141e9249053000633f389628b67974d476206c2e0e8 | MATLAB | 614 | 29 | %% simulate power law from uniform
a=0;
b=1000;
alpha=-2/3;
n=rand(1000000,1)';
r=(a.^(alpha+1)+n*(b^(alpha+1)-a.^(alpha+1))).^(1/(alpha+1));
plot(n,r,'.');
hist(n);
hist(r);
%% create samples and species count
sampN=4;
specN=10;
ssc=zeros(sampN,specN);
for i=1:sampN
% sample
m=i*1000;
sample = r(:,ran... |
61296c4cb014d8912a8b6e7d97bd3396b79bb6e1210a8299ede24bb87996b52b | MATLAB | 615 | 20 | function clusters = extract_clusters(data)
% Extract the reference data and its individual clusters
try
DataArray = horzcat(data.x_data,data.y_data,data.area,data.channel); % Set up the reference data
catch
DataArray = horzcat(data.x_data,data.y_data,data.area); % Set up the reference data
end
[~,Idx... |
9ba1f96e6e49517740fa64ff06fea4e3c4b817918affcd445366dd13992ddf37 | MATLAB | 619 | 24 | function [kden,N,K] = density_dir(CIJ)
% DENSITY_DIR Density
%
% kden = density_dir(CIJ);
% [kden,N,K] = density_dir(CIJ);
%
% Density is the fraction of present connections to possible connections.
%
% Input: CIJ, directed (weighted/binary) connection matrix
%
% Output: kden, density
% ... |
409f3b4ad5fc0ea51e76b2c49809dc0b623b935c5e26f602b6633e97a7e9063b | MATLAB | 621 | 19 | classdef Spearman < nla.edge.BaseTest
%SPEARMAN Edge-level Spearman correlation
properties (Constant)
name = "Spearman's rho"
coeff_name = "Spearman's rho (Fisher-Z Transformed)"
end
methods
function obj = Spearman()
obj@nla.edge.BaseTest();
end
... |
274faa248dd176bd95c49a29ec68d453bfd412cfb8e2d3cd72bdeb8b70f4dde7 | MATLAB | 623 | 13 | function parcel_data = full2parcel(data,parcellation)
% FULL2PARCEL Downsamples vertex data to parcel data.
%
% parcel_Data = full2parcel(data,parcellation) takes the mean of columns
% in n-by-m matrix data that have the same value in corresponding 1-by-m
% vector parcellation. If some numbers are missing in th... |
3f2b9ae552269e06a286e5b4b15b43fa2bab9d08afd83debab7d936bf97c4aa0 | MATLAB | 626 | 19 | function M_t = compute_Bt_gpu(P_ks, P_ks1, M, nq, ny, N,T)
gputimes = @(A, B) pagefun(@mtimes, A, B);
M_t = zeros(3*nq+ny, 3*nq+ny, N);
M_t=gpuArray(M_t);
M=gpuArray(M);
P_ks=gpuArray(P_ks);
P_ks1=gpuArray(P_ks1);
M_t(1:nq,1:2*nq,:) = gputimes(1, gpuArray([P_ks, M]));
M_t(1:nq,2*nq+ny+1:... |
a029dfe8a3d8833aa2236aa4f33d639ac470724deff46dad570379017e8e328b | MATLAB | 626 | 25 | function file_path_out = copyfile_path(file_path,output_folder)
[~,file,extension] = fileparts(file_path);
file_path_out = [output_folder file extension];
if ~isfile(file_path)
error('file to copy does not exist');
end
if exist(file_path_out,'file')
warning('output file alrea... |
2a111fa78078e51866a0b6e7dfc9a45befde3e46d38bac187e70cfffbf3aeb63 | MATLAB | 627 | 18 | function data_simulated = loc_list_random_point_pattern_simulation()
input_values = inputdlg({'x_i,x_f','number of localizations:'},'',1,{'-3 3','100'});
if isempty(input_values)==1
data_simulated = [];
else
x = str2num(input_values{1});
N = str2num(input_values{2});
if N<1
N=1;
end... |
553304d2bc0d2a45faabdb9a5798d43e7f02a241290097dbda624d259f618403 | MATLAB | 629 | 19 | %%%% EXTERNAL FUNCTION
% function taken from the fastICA package
% complete software may be downloaded from http://research.ics.tkk.fi/ica/fastica/
function [newVectors, meanValue] = remmean(vectors);
%REMMEAN - remove the mean from vectors
%
% [newVectors, meanValue] = remmean(vectors);
%
% Removes the mean ... |
f8f9b30318e06c30d06a8453238f30166d22e48f99ad46180a2c86e92aa43353 | MATLAB | 632 | 22 | function [source_inds] = select_source_inds(pnts, num_sources, dist_thr)
%UNTITLED4 Summary of this function goes here
% Detailed explanation goes here
msize = length(pnts);
distMat = inf*eye(num_sources,num_sources);
dmin = 0;
while dmin < dist_thr % just to ensure the dipoles drawn are not too close (in mm )
i... |
47e6e078b05bcf83be91225ba7f7d2894f08a30b06ebb8037b5598488821fffa | MATLAB | 634 | 24 | function y=convert_back_to_time(fyo, Nsamples, freq_indtest)
% Nsamples=109;yo=demean(randn(Nsamples,1));
% NumUniquePts=ceil((Nsamples+1)/2); freq_indtest=2:25; fyo=fft(yo); fyo=fyo(freq_indtest);
% y=convert_back_to_time(fyo,Nsamples, freq_indtest);
% sfigure;plot(y);ho;plot(yo,'r--')
NumUniquePts=ceil((Nsamples+1... |
bec6beeb84cffc87c6ba312fcccfe5d772ec75a1bd4c2d40495b87917d8b3c51 | MATLAB | 634 | 10 | function plot_size_clusters_iteraion(size_classes)
figure();
set(gcf,'color','w','name','Iterative_clustering','NumberTitle','off','color','w','units','normalized','position',[0.1 0.3 0.3 0.5])
scatter(1:length(size_classes),size_classes,10,'b','filled')
hold on
plot(1:length(size_classes),size... |
f434c32d30e19a991286b6ca7d5fbe9f2fe162769d1995ddf7ee282a8ca96cd7 | MATLAB | 635 | 31 | function x=bandpasshopf(y,freqrange,fres,do_plot)
% function x=bandpass(y,lp,hp)
%
% does bandpass filtering via FFT
% 0<lp<1 proportion of high freq to cut out
% 0<hp<1 proportion of low freq to cut out
if(nargin<4)
do_plot=0;
end;
Nsamples=length(y);
Nunique_points=ceil((Nsamples+1)/2);
fHz = (0:Nunique_point... |
d7ca798cab67088e770033c1c3e75a1bd9180298d78ee7682eb33de0312bd77c | MATLAB | 636 | 26 | function txt=SurfStatDataCursorQ(empt,event_obj)
pos=get(event_obj,'Position');
h=get(event_obj,'Target');
v=get(h,'Vertices');
x=get(h,'FaceVertexCData');
id1=min(find(v(:,1)==pos(1)&v(:,2)==pos(2)&v(:,3)==pos(3)));
tag=get(get(h,'Parent'),'Tag');
[s,a,id0]=strread(tag,'%s %d %d');
id=id1+id0
c=get(get(h,'par... |
64b466299b823d8d802a58e0bbf225a5e3c5f3c2bcc3435768ff9ec4c77c6474 | MATLAB | 639 | 18 | function sResults = nst_misc_FOV_to_cortex(sResults, nVertex, valid_vertex, isSaveFactor)
mapping = sparse(valid_vertex, 1:length(valid_vertex), 1, nVertex, length(valid_vertex));
for iMap = 1:length(sResults)
if iscell(sResults(iMap).ImageGridAmp)
sResults(iMap).ImageGridAmp = [ {... |
3ac588a4b0248ad3705d2dc719fabc2e92c9597b7247fb345296d012f453426e | MATLAB | 640 | 23 | function mask = SurfStatMaskCut( surf );
%Mask that excludes the inter-hemisphere cut.
%
% Usage: mask = SurfStatMaskCut( surf );
%
% surf.coord = 3 x v matrix of surface coordinates, v=#vertices.
%
% mask = 1 x v vector, 1=inside, 0=outside, v=#vertices.
%
% It looks in -50<y<50 and -20<z<40, and ... |
d839e42ae80fb6b373fdaaef9e02cc3bdfa625574c4eb0f09a53e455dc3f5d17 | MATLAB | 640 | 36 | %% bi-phasic log-sigmoid function
t=100;
b=0;
s=t-b;
f=0.4;
kd1=0.5;
kd2=0.5;
m1=1;
m2=1;
x=0.01:0.001:1;
y=b+(s*f)./(1+10.^(log(kd1-x)*m1))+(s*(1-f))./(1+10.^(log(kd2-x)*m2))
plot(x,y,'r.')
%% data from MST
d=[3.9063 926.4901
7.8125 928.6018
15.6250 926.7656
31.2500 923.3865
62.5000 920.1771
12... |
85cd79694efaae20e6619729cd56dee68eb256ef85170f6ed904ed4fd071fd0e | MATLAB | 641 | 18 | function signal_filt = TemporalFiltering(signal,flp,fhi,tr)
% Temporal Filtering Settings
fnq = 1/(2*tr); % Nyquist frequency
Wn = [fhi/fnq flp/fnq]; % butterworth bandpass non-dimensional frequency
k = 5; % 5th order butterworth filter
[bfilt,afilt] = butter(k,Wn); %... |
8d90430c84be294e77f0f158317faa850c278a958d6640463bb38a5fb4836506 | MATLAB | 643 | 26 | function s = resQ_1d_fit2data(beta, xps)
% function s = resQ_1d_fit2data(beta, xps)
b = xps.b;
b2 = b.^2;
m = xps.m;
m2 = m.^2;
bm = b .* m;
bs = xps.b_shape;
ms = xps.m_shape;
bms = xps.bm_shape;
beta = resQ_unitSwap(beta, 0);
s = beta(1) * exp( ...
- ( b * beta(2) + ... |
c81adfa1cd383f9c7f580b100ab9e64a77ec97f9afdec8dec4798b7876020e47 | MATLAB | 643 | 30 | function m = fl_cell_mean2(Cell1,Cell2)
% Computes the mean difference between two cell elements
%
% The cells must be 1-dimensional
% (The function does not make an internal copy of the cell variable, which
% is critical for large data sets)
% This function is part of the permutationlab software:
% Author: D... |
93b52c31410c7e086ca8922f10c0a1afba5e61ff187e81d87235aed05c357476 | MATLAB | 644 | 12 | function plot_voronoi_area(vor,number_of_bins)
figure()
set(gcf,'name','Voronoi Areas Histogram','NumberTitle','off','color','w','units','normalized','position',[0.3 0.2 0.4 0.65])
voronoi_areas = vor.voronoi_areas;
[y_log,x_log] = hist(log10(voronoi_areas),number_of_bins,'facecolor','b','edgecolor','none');
hist_... |
b816d3d25996d3f18ae9f7bef83e4b8cafdf13f9021f070d01bccea24eccb362 | MATLAB | 648 | 22 | function mat = rotationMatrix(dir, theta)
% Generate a rotation matrix for the direction given
% an angle (in radians).
import nla.Direction
mat = zeros(3);
switch dir
case Direction.X
mat = [1 0 0;...
0 cos(theta) -sin(theta);...
0 sin(t... |
ba31caa711b106cf18ef9b47c2341e6421f77153146e6d463b357942abbb9bf2 | MATLAB | 661 | 21 | function y=C_AbsorBoundAUTO(z, CurrTime, D, halfZ)
%This is a corrected version of equation 16 in Kues and Kubitscheck, Single
%Molecules, 2002 and a corrected version of equation Suppl 5.7 in Mazza et
%al, Nucleic Acids Research, 2012. Both equations are wrong, but they are
%wrong in different ways and the one below i... |
d0d0b056868ff2404451c8ec15d6792a735df042964a3ee4271878f2ecec586c | MATLAB | 661 | 15 | function AS_WTA_SepRes
indir = uigetdir(pwd,'OrigData');
outdir = uigetdir(pwd,'Outputdir');
[TargetROI,FilPa,Filext] = uigetfile({'*.nii';'*.img'},'TargetROI');
[vtar,dtar] = Dynamic_read_dir_NIFTI(fullfile(FilPa,TargetROI));
NIINAMELIST = dir([indir,filesep,'*.nii']);
dtarind = unique(dtar);
for i = 1:length(N... |
96ea651139b5969464ac3ac6ec0f7a859c65686e61e52c4bb7c71632321d9784 | MATLAB | 662 | 25 | classdef WelchT < nla.edge.result.Base
%WELCHT The output result of a WelchTTest
properties
dof
end
methods
function obj = WelchT(size, prob_max, group_names)
if nargin == 0
size = 2;
prob_max = -1;
end
... |
a4ec85f36ca977fd892921c5313fb5b478550bc9f7322054803c6e856ded43b5 | MATLAB | 664 | 29 | function intpctrange = interpercentilerange(data,percentiles)
%%% input
%%% 'data' = vector of data
%%% 'percentiles' = lower and upper percentile values to be determined
if nargin < 2
percentiles = [0.25 0.75];
end
if max(size(percentiles)) ~= 2
error('give upper/lower percentiles as input')
en... |
9dd1b0de758a8b6e350504a2d6e91667ca3aebc5f0cfb6f9816c167684a4d15f | MATLAB | 665 | 34 |
%% Test script to evaluate effect of spike pairing protocol on synaptic
%% strength of a STDP synapse.
%% Synaptic dynamics for STDP synapses according to Abigail Morrison's
%% STDP model (see stdp_rec.pdf).
%% author: Moritz Helias, april 2006
%%
clear;
deltaT=-50:1:50;
lambda=0.1;
w_init=17.0;
alpha=0.11;
mu=0.4;
... |
c7d283beb432e40c0b0563ededa2a69cd14e5fd3a7df170c14b7f91f6d2bef43 | MATLAB | 668 | 19 | function W = threshold_absolute(W, thr)
% THRESHOLD_ABSOLUTE Absolute thresholding
%
% W_thr = threshold_absolute(W, thr);
%
% This function thresholds the connectivity matrix by absolute weight
% magnitude. All weights below the given threshold, and all weights
% on the main diagonal (self-self conn... |
28e549bed879db0620061231b4832dc3eef4cef8d751923f5286f04b0b879d26 | MATLAB | 670 | 25 | function s=plus(m1,m2)
if (~isa(m1,'term') && ~isa(m1,'random') && numel(m1)>1) || ...
(~isa(m2,'term') && ~isa(m2,'random') && numel(m2)>1)
warning('If you don''t convert vectors to terms you can get unexpected results :-(')
end
if ~isa(m1,'random')
m1=random([],m1,[],inputname(1));
end
if ~isa(m... |
6200dadaa80cde61fbe90816af129884ccfef3ca0cc01b7dd2661b85a79fba64 | MATLAB | 670 | 18 | function h = scree_plot(lambdas)
% SCREE_PLOT Plot of the scaled eigenvalues.
%
% h = SCREE_PLOT(lambdas) plots the eigenvalues corresponding to a
% gradient. Lambdas are scaled to a sum of 1. All graphics objects
% created are stored in structure array h.
%
% For more information, please consult our <a
% ... |
482fbec021a59dc039660bb863caef38901040bc08e478885b0de527edaaefe9 | MATLAB | 671 | 25 | function s=minus(m1,m2)
if (~isa(m1,'term') && ~isa(m1,'random') && numel(m1)>1) || ...
(~isa(m2,'term') && ~isa(m2,'random') && numel(m2)>1)
warning('If you don''t convert vectors to terms you can get unexpected results :-(')
end
if ~isa(m1,'random')
m1=random([],m1,[],inputname(1));
end
if ~isa(... |
da81bc43cee3ca881885611bdadea84c45b1a43f5cfc32d5d69861c2b37f8403 | MATLAB | 671 | 19 | classdef Bonferroni < nla.net.mcc.Base
properties (Constant)
name = "Bonferroni"
end
methods
function [is_sig_vector, p_max] = correct(obj, net_atlas, input_struct, prob)
p_max = input_struct.prob_max / net_atlas.numNetPairs();
is_sig_vector = prob.v... |
5303b63e9fee848cd0cc115fd76f2894c0043b5d76b10c42fa1876b197bff068 | MATLAB | 677 | 28 | classdef InputField < handle
%INPUTFIELD Base class of input fields
properties
satisfied = false % whether the given input field has been satisfied
end
properties (Access = protected)
fig
end
methods (Abstract)
draw(obj, x, y, parent, fig)
undraw(obj)
... |
d625fb1e5cbc6e3b20a650c0a64a410b2c8a934e6d4df5ea0772710e43a70de7 | MATLAB | 677 | 26 | function h=plotVirtualChannel(allDataTFR,channelSelection,XLim,Zlim)
%%
tmpDataTFR=[];
tmpDataTFR.cfg=[];
tmpDataTFR.time = allDataTFR.time;
tmpDataTFR.powspctrm = nanmean(squeeze(allDataTFR.powspctrm(:,channelSelection,:,:)),1);
tmpDataTFR.dimord='chan_freq_time';
tmpDataTFR.label=arrayfun(@num2str, channelSelection, ... |
d9dd73aaded0de35438d861cdeeadb6efc31c22ecfc7c084928113ca05efe1b2 | MATLAB | 685 | 27 | function DI=dunns(clusters_number,distM,ind)
%%%Dunn's index for clustering compactness and separation measurement
% dunns(clusters_number,distM,ind)
% clusters_number = Number of clusters
% distM = Dissimilarity matrix
% ind = Indexes for each data point aka cluster to which each data point
% belongs
i=clusters_... |
693026dcda520bd189d3088d9ffeb433e315bbd3fecdd74f1002740d45b3868d | MATLAB | 690 | 23 | function loc_list_filter_no_of_locs(data)
answer = inputdlg({'Enter minimum number of localizations:'},'Input',[1 50],{'1000'});
if isempty(answer)~=1
min_loc = str2double(answer{1});
for i = 1:length(data)
if length(data{i}.x_data)>=min_loc
data_above(i) = i;
else
... |
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