sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
8b4d25f4dbe1384e39587b7571727da729f9a5d470731b6be5a620430f660198 | MATLAB | 3,856 | 183 | clear
% Load wfs from method file
seq = bpv_method2seq('method_example');
[gwfc, rfc, dtc] = bpv_seq2gwf(seq);
for i = 1:numel(gwfc)
gwfc{i} = fwf.gwf.force.bval(gwfc{i}, rfc{i}, dtc{i}, 2e9, 'amp');
end
xps = fwf.gwf.toXps(gwfc, rfc, dtc);
clist = fix.cmap.blackredwhite;
if 0 % gray
clist = mean(clist, 2... |
17aa3c7f6de1fdc0ddd6771fc7ecf10ea3b51de3b952cf0074f6a8e82a7cddd5 | MATLAB | 3,858 | 107 | function spt_video_plot(data)
if isempty(data)~=1
for j = 1:length(data)
input_data = data{j}.tracks;
video_slider_figure(input_data)
end
end
end
function video_slider_figure(input_data)
for i=1:length(input_data)
time{i} = input_data{i}(:,1);
end
time = vertcat(time{:});
... |
f8c0d64aae18f987eb77753ef5f06cc66f8b0ec900f516f0cce0e6f8af7ebb0f | MATLAB | 3,878 | 111 | function string = user_string(string_name, string)
%USER_STRING Get/set a user specific string
%
% Examples:
% string = user_string(string_name)
% isSaved = user_string(string_name, new_string)
%
% Function to get and set a string in a system or user specific file. This
% enables, for example, system specific pat... |
75f3f4f4875ccdeda0767f23ff2b4442cba632397efdf6ced977b0df3e279997 | MATLAB | 3,889 | 111 | % current working directory needs to be /path/to/scripts
mainpath = pwd;
disp('setting up environment...')
addpath([mainpath filesep 'toolboxes' filesep 'analyzePRF'])
addpath([mainpath filesep 'toolboxes' filesep 'analyzePRF' filesep 'utilities'])
addpath([mainpath filesep 'toolboxes' filesep 'knkutils'])
addpath([mai... |
bf474108e0465a1a53ca8da14e48724bd5540ce167fe42dc6934a11cb9277036 | MATLAB | 3,891 | 106 | function [ pval, peak, clus, clusid ] = SurfStatP( slm, mask, clusthresh );
%Corrected P-values for vertices and clusters.
%
% Usage: [ pval, peak, clus, clusid ] =
% SurfStatP( slm [, mask, [, clusthresh] ] );
%
% slm.t = l x v matrix of test statistics, v=#vertices; the first
% ... |
9cf2fa3b2a386a317b270e635fda570c0cebb78c4e78297dba067c1259cd86a3 | MATLAB | 3,896 | 129 | function [v]=arsim(w,A,C,n_ntr,ndisc)
%ARSIM Simulation of AR process.
%
% v=ARSIM(w,A,C,n) simulates n time steps of the AR(p) process
%
% v(k,:)' = w' + A1*v(k-1,:)' +...+ Ap*v(k-p,:)' + eta(k,:)',
%
% where A=[A1 ... Ap] is the coefficient matrix, and w is a vector of
% intercept terms that is included to a... |
6babc8c36489943848bbfc095c74717949d810d42cfdf00937761b1764f8c909 | MATLAB | 3,899 | 180 | function [beta, tstr, ha] = mipp_resQ_plot_v2(S, xps, h, h2, opt)
% function beta = resQ_plot(S, xps, h, h2, opt)
if (nargin < 3), h = gca; end
if (nargin < 4), h2 = []; end
if nargin < 5
opt = mdm_opt();
opt = resQ_opt(opt);
end
ind = ones(xps.n, 1, 'logical');
S = abs(S);
if ~isfield(xps, 'b_delta')
... |
286dca7770e071d7ae6e09560e451f9cb2c49692fa563fc8b89d1793d60caf7f | MATLAB | 3,907 | 106 | function [ pval, peak, clus, clusid ] = SurfStatP( slm, mask, clusthresh );
%Corrected P-values for vertices and clusters.
%
% Usage: [ pval, peak, clus, clusid ] =
% SurfStatP( slm [, mask, [, clusthresh] ] );
%
% slm.t = l x v matrix of test statistics, v=#vertices; the first
% ... |
6e7f16aa73323d3a3c9dbb1441ab489bcf5e12cfd35164aacb72c625e44d8a5f | MATLAB | 3,911 | 126 | function surrogateData = generate_surrogate_iaaft(originalData, varargin)
% GENERATE_SURROGATE_IAAFT Generates surrogates of a time series.
%
% surrogateData = GENERATE_SURROGATE_IAAFT(originalData, ...)
% Generates, by means of the Iterative Amplitude Adjusted Fourier Transform
% (IAAFT) algorithm, surrogate time ser... |
a9acefd61db7a4c9130682a5acb93f4c6f082f38e6bc013336b80db060b19cd6 | MATLAB | 3,911 | 106 |
if ~(exist([subjectDataDir filesep '4_retinotopy' filesep 'results_analyzePRF_all.mat'], 'file') == 2)
disp('waiting for parts to complete...')
parts=dir([subjectDataDir filesep '4_retinotopy' filesep 'results_analyzePRF_part_*_of_*.mat']);
while size(parts,1)<num_parts
parts=dir([subjectDataD... |
3f83f26ab425962d3c4bc1c603c645a6654fe11fe387bb26945bec6bae3a6a5a | MATLAB | 3,924 | 68 | classdef PermutationNode < handle
%PERMUTATIONNODE Node which defines one grouping of each permutatino tree
%
% node = permutationNode(level, input_data, permutation_groups)
% node = One grouping of data for each permutation group. Each group contains the data (functional connectivity)
% along wit... |
d85be93d416a7d3a1863b7c04838a31b54b9b5d57524b692c006df67b45f145d | MATLAB | 3,937 | 101 | function J = nst_mne_lcurve_MAP(HM,OPTIONS)
% nst_mne_lcurve - this function solve the inverse probleme using a l-curve
% approach in the MAP formalism. This approach is inefficient and should
% not be used. Consider using nst_mne_lcurve instead.
% Input: HM - struct
% | - HM.Gain : Gain matrix
% OPTIONS... |
d788000132dbbef7aeb087a8f92e02f4e3b5328ca2678fc6202f92dc180c2b68 | MATLAB | 3,938 | 99 | function spt_particles_velocity(data)
for i=1:length(data)
input_data = data{i}.tracks;
for k = 1:length(input_data)
v{k} = calculate_velocity(input_data{k});
end
velocity{i}.velocity = v;
velocity{i}.name = data{i}.name;
velocity{i}.type = 'spt_velocity';
clear input_data v... |
0faf3b68346ea8e7698e1420615f6b7a0111df42d89b1bd304dbc0f281f584c4 | MATLAB | 3,940 | 111 | function loc_list_distance_clustering(data)
answer = inputdlg({'Minimum Number of Points to Cluster:','Epsilon:'},'Input',[1 50],{'5','0.5'});
if isempty(answer)~=1
N = str2double(answer{1});
epsilon = str2double(answer{2});
for i=1:length(data)
counter(1) = i;
counter(2) = length(dat... |
0a220c660deb509765a365f7543613406603848842ca01810a82865e4b04459f | MATLAB | 3,951 | 140 | function [R] = randomizer_bin_und(R,alpha)
%RANDOMIZER_BIN_UND Random graph with preserved in/out degree distribution
%
% R = randomizer_bin_und(A,alpha);
%
% This function randomizes a binary undirected network, while preserving
% the degree distribution. The function directly searches for rewirable
% ed... |
30bbe41d34ddb024ebb00fb5d3493018705b0973f5cc1819de936ee69d1536f0 | MATLAB | 3,957 | 140 | function hhh=vline_new(x,in1,in2,in3)
% function h=vline(x, linetype, linewidth, label)
%
% Draws a vertical line on the current axes at the location specified by 'x'. Optional arguments are
% 'linetype' (default is 'r:'), linewidth, which specifies the width of the line plotted
% and 'label', which applies a te... |
e89b83198b103ff222304833a784312dbe053c360ea2645d4cb8a814ae594338 | MATLAB | 3,957 | 83 | classdef SpearmanEstimator < nla.edge.BaseTest
%SPEARMANESTIMATOR Fast estimator of the edge-level Spearman correlation
% Similar to the internal implementation of SpearmanTest, but removes
% an expensive call to tiedrank. This causes it to run several times
% faster, but produce slightly incorrec... |
ac105905a72e5454990abf96bc5f40a09d8d6e2bc002df56de1ccbb2b450d111 | MATLAB | 3,969 | 119 | function [ silhouettes,alternativeid ] = silhouette_mod( parcels, D, neigh )
%SILHOUETTE_COEF Silhouette coefficient of a parcellation.
% For each vertex in a cortical surface, SILHOUETTE_COEF compares the
% within-parcel dissimilarity defined as the average distance to all
% vertices in the same parcel, to the i... |
4bfc8b667198fe473c3411bafeffb5af5a1d83ca677b45e17f0a4b771073b2c2 | MATLAB | 3,979 | 172 | clear
% Get paths and some info
paths = mipp_getDataPaths(2);
% paths = mipp_getDataPaths(103);
I_rare = mdm_nii_read(paths.fn_rare);
I_rare = mipp_signalEveningFilter(I_rare);
p_qti = mdm_dps_load(paths.fn_qti);
p_resq = mdm_dps_load(paths.fn_resq);
I_roi = mdm_nii_read(paths.fn_roi_plt);
I_tum = mdm_... |
7c2c67df505f5ee5c1055dd829a6494262e72521789122292312d0e69da8e51c | MATLAB | 3,988 | 106 | function dam_index = computeDAMindex(A)
% COMPUTEDAMINDEX Compute a data-availability/adjacency-missingness (DAM) index
%
% dam_index = computeDAMindex(A) computes a normalized index in the range
% [0, 1] that summarizes how "missing" a 2-D matrix is, taking into account
% the number of missing elements and... |
bc69b6fbc60e90a9caf369972e6ef0a70f1e165670e1bd5644cf09000ae979ac | MATLAB | 3,989 | 128 | function slm = SurfStatF( slm1, slm2 );
%F statistics for comparing two uni- or multi-variate fixed effects models.
%
% Usage: slm = SurfStatF( slm1, slm2 );
%
% slm1 and slm2 must have the following fields:
% slm.X = n x p design matrix.
% slm.df = degrees of freedom.
% slm.SSE = k*(k+1)/2 x v matrix of... |
fd06b3c944f73ed0194cc5da50bd52f732f653db9af46b5da74cfd92e0553f8e | MATLAB | 3,996 | 92 | function spt_motion_classification(data)
input_values = inputdlg({'r2 threshold','percentage of data to fit:','confined motion slope threshold (<):','directed motion slope threshold (>=):'},'',1,{'0.8','25','0.8','1.2'});
if isempty(input_values)==1
return
else
r2_threshold = str2double(input_values{1});
... |
307626c6f288c5eb7457d1a25cc2057c031e2e6c7d36ac9422cb888b4e59bc34 | MATLAB | 4,008 | 101 | function spt_particles_velocity_correlation(data)
for i=1:length(data)
input_data = data{i}.tracks;
for k = 1:length(input_data)
v_corr{k} = calculate_velocity_correlation(input_data{k});
end
velocity_correlation{i}.velocity_correlation = v_corr;
velocity_correlation{i}.name = data{i}... |
c2734d2a275971181dae23f74a7035dffc5f278525ad37a65206a0888ab686a4 | MATLAB | 4,014 | 114 | %%
clear
% current working directory needs to be /path/to/scripts
mainpath= pwd;
thisSubject='S2';
computeForMultipleSubjects=[1:52];
visualROIs={'V1','V2','V3'};
hemiROIs={'lhV','rhV'};
tapsmofrq=5;
freqAnalysisToi=-1:0.05:2;
slidingWindow=0.5;
method={'plv','coh','granger'};
%
addpath([mainpath filesep 'toolboxes' ... |
450f75b2d338146a9d28ac2cc93000ba4ed8f7ae8cd50e7930e572d3a6757c94 | MATLAB | 4,015 | 89 | classdef PermutationTestPlotter < handle
properties
network_atlas
end
methods
function obj = PermutationTestPlotter(network_atlas)
if nargin > 0
obj.network_atlas = network_atlas;
end
end
function [w, h, matrix_plot] = plotProbabilit... |
5010ad711aafd0f5446d1472673d5236aa7a147942b526a0536a61eb6bd04f91 | MATLAB | 4,020 | 76 | classdef WilcoxonTest < handle
%WILCOXON Wilcoxon rank sum test
properties (Constant)
name = "wilcoxon"
display_name = "Wilcoxon Rank Sum"
statistics = ["ranksum_statistic", "single_sample_ranksum_statistic", "z_statistic"]
ranking_statistic = "z_statistic"
allows_within_... |
dc88d566dd1118bc981033435aad03b6f34820393d68b207d81259cbcb0b5858 | MATLAB | 4,020 | 117 | function SI = search_information(W, L, has_memory)
% SEARCH_INFORMATION Search information
%
% SI = search_information(W, L,has_memory)
%
% Computes the amount of information (measured in bits) that a random
% walker needs to follow the shortest path between a given pair of nodes.
%
% ... |
0954005b712d928581fa695fd7e65ddbc649e1bfb723e9ae0364e6509b5200ef | MATLAB | 4,028 | 84 | function [Z, P] = AS_TFRtoZ(Data,Flag,Df1,Df2)
% This is from software rest.
% The orginal version is [Z P Header] = rest_TFRtoZ(ImgFile,OutputName,Flag,Df1,Df2,Header)
% The bellow is the orginal annotation.
% function [Z P Header] = rest_TFRtoZ(ImgFile,OutputName,Flag,Df1,Df2,Header)
% FORMAT [Z P Header] = y_... |
82d8e3ef1e1d22d2bbcff94295ac6a88e59e818b8fccbc70ca1ea95c94313582 | MATLAB | 4,032 | 177 | %% file
TMS2=xlsread('X:\Qexactive\Alexey\20130827_tMS2_JPT_01.xlsx');
[~,~,IL]=tblread('X:\Qexactive\Alexey\incl.CSV',',');
FMS2EX=xlsread('X:\Qexactive\Alexey\20130827_FullMS-MS2_JPT_Ecxl_01.xlsx');
%% compare inclusion list with search input
hist(mze)
a=unique(sort(str2num(IL)));
%mze=a+(rand(size(a,1),1)... |
d53f694a64132f619641a6d34afd20146fc8230b5753540df55f99501125a081 | MATLAB | 4,034 | 140 | function hhh=hline_new(y,in1,in2,in3)
% function h=hline(y, linetype, linewidth, label)
%
% Draws a horizontal line on the current axes at the location specified by 'y'. Optional arguments are
% 'linetype' (default is 'r:'), linewidth, which specifies the width of the plotted line
% and 'label', which applies a ... |
f8ab2c43d3413cd093e07d2a39e3009dc22e43944c1a972f422b2c1864f791f8 | MATLAB | 4,035 | 162 | %% load data
load('/media/DATA/documents/courses_uib/bmed360/presentation/svm_mat555.mat');
[s v t]=size(svm_mat);
sn=Design_Para(:,6)';
mn=Design_Para(:,4)';
corrcoef(mn,sn)
crosscorr(mn,sn)
autocorr(mn)
autocorr(sn)
for i = 1:size(svm_mat,1)
for j = 1:size(svm_mat,3)
svm_mat_norm(i,:,j)=mat2gray(svm_mat(i,:,j... |
31affcd82e76bdeb45f96767ec876b2d9c697b818c47ab8732487a68e33129b3 | MATLAB | 4,041 | 108 | function [h,C] = gradient_in_euclidean(gradients,surface,parcellation)
% GRADIENT_IN_EUCLIDEAN Plots gradient values as a scatter plot.
%
% h = GRADIENT_IN_EUCLIDEAN(gradients) plots a scatter plot of n-by-2 or
% n-by-3 matrix gradients where n is the number of datapoints. Each point
% is colored by their posit... |
37a21ca97f0f601451d973e99f7e4990a1be33d7ff4dec32dde61ccd7d890a0b | MATLAB | 4,050 | 80 | %%
% current working directory needs to be /path/to/scripts
mainpath = pwd;
addpath(genpath([mainpath filesep 'toolboxes' filesep 'analyzePRF']))
addpath([mainpath filesep 'toolboxes' filesep 'tc_functions'])
%%
fcont=dir([subjectDataDir filesep '4_retinotopy']);
fcont = fcont([fcont.isdir] & contains({fcont.name}, 're... |
5da752cfc1442adc060086172653730b00ca292ad9718a4d33dca22dfda43d54 | MATLAB | 4,053 | 166 | function [ pcntvar, U, V ] = SurfStatPCA( Y, mask, X, k );
%Principal Components Analysis (PCA).
%
% Usage: [ pcntvar, U, V ] = SurfStatPCA( Y [,mask [,X [,k] ] ] );
%
% Y = n x v matrix or n x v x k array of data, v=#vertices,
% or memory map of same.
% mask = 1 x v vector, 1=inside, 0=outside, def... |
7f31acacb596bea8d0865fdcfd67a073ce005097aa13daa66accc559fd64b45a | MATLAB | 4,069 | 139 | %% data
[~,~,ss]=xlsread('X:\Elite\Aida\SS_1R\SS1RPGsortMGUS2MMmedvals.xls');
[cu,~,~]=xlsread('L:\Elite\gaute\test\CDS_CU_EntrezID.xls');
od=xlsread('L:\Elite\Aida\CellLines\cell-lines.xlsx');
comb=xlsread('L:\Elite\kamila\SILACmRNA.xlsx');
comb=xlsread('L:\Elite\kamila\Heart\combolfqs.xlsx');
comb=xlsread('L:\E... |
f74498d341147b05bf8abf5b83ed8f58c3f739370f37d890bdb17b395898cf91 | MATLAB | 4,094 | 109 | % current working directory needs to be /path/to/scripts
mainpath = pwd;
%%
addpath([mainpath filesep 'toolboxes' filesep 'OpenFmriAnalysis'])
addpath([mainpath filesep 'toolboxes' filesep 'spm12'])
addpath([mainpath filesep 'toolboxes' filesep 'tc_functions'])
tvm_installOpenFmriAnalysisToolbox;
freeSurferFolder = [... |
bfafcaa5ba624d0f0d7ac55312af66d16b6841525eb084a8b9a26b69e532116e | MATLAB | 4,099 | 93 | function O = InducedResponses_Morlet_WholeBrain_LBPD_D( S )
O = [];
% It computes time-frequency analysis (induced responses) using Morlet
% wavelet.
% It computes Morlet wavelet transform independently on each voxel of the brain
% and each trial.
% This should be used in connection with LBPD functions, after computi... |
7da42b648b93c93ffb64d0f838f426618cca4a2b3aef15b86f574e2904928a25 | MATLAB | 4,100 | 107 | %% define parameter sets for analysis
if ~(exist('mainpath','var') && exist('thisSubject','var'))
clearvars
% current working directory needs to be /path/to/scripts
mainpath= pwd;
thisSubject = 'S1';
else
clearvars -except mainpath thisSubject
end
load([mainpath filesep '..' filesep 'subjectData' f... |
5f1e14bd4e1309b41b375c2784d03bade889bed11cae15d637b66ebac9010dc4 | MATLAB | 4,114 | 58 | function fn = digest(s,e,m,l,dct,tct,cl,ch,ntlc,mod,msite)
fprintf('Sequence %s\t Enzyme %s\t Missed Cleavage %d\t Minimum Length %d MZ Range %d-%d\t Charge Range %d-%d\t N-term truncation %d\tModification %s-%s',s,e,m,l,dct,tct,cl,ch, ntlc, mod,msite);
fn=[s,e,'MC',int2str(m),'L',int2str(l),'MZl',int2str(dct... |
3a246880d4e7185e294eb83257d91e886ed383c57664ae0aeb02a25730f7800d | MATLAB | 4,116 | 91 | function [I,Q,F]=motif4struct_wei(W)
%MOTIF4STRUCT_WEI Intensity and coherence of structural class-4 motifs
%
% [I,Q,F] = motif4struct_wei(W);
%
% Structural motifs are patterns of local connectivity in complex
% networks. Such patterns are particularly diverse in directed networks.
% The motif fre... |
289ffa6774aaabbe6e9096525713495675b095b283558d6ca4fb88a8f09ed14a | MATLAB | 4,123 | 135 | function MC = dFCstream2MC(dFCstream, verbose)
% FUNCTION MC = dFCstream2MC(dFCstream, [verbose])
% Thus function takes as input a dFCstream, either 3D or 2D,
% and outputs a square MC matrix (in a redundant form to find more easily
% meta-links impinging a region
% NB: Set optional argument 'verbose' to zero for sile... |
b74ac1e5019946e957a5666f03ef43443530482cbae42208ffef1abbfe730478 | MATLAB | 4,123 | 115 | function [ MATF ] = signROIdegree_otherROI_prepareplotting_LBPD_D( POS, NEG, label_path )
% It creates an ROIs x ROIs matrix, ordering ROIs according to labels
% provided (example that I provide consists in AAL ordered like LRLRLR),
% showing the number of significant connections between couple of ROIs
% over time.
%... |
db170c170fc0862a624b91a68906451b1c61c959bb898acebd0a5fda8ceedafc | MATLAB | 4,129 | 119 | function [Rlatt,Rrp,ind_rp,eff] = latmio_dir_connected(R,ITER,D)
%LATMIO_DIR_CONNECTED Lattice with preserved in/out degree distribution
%
% [Rlatt,Rrp,ind_rp,eff] = latmio_dir_connected(R,ITER,D);
%
% This function "latticizes" a directed network, while preserving the in-
% and out-degree distributions... |
809432cfb5dd16b8310a111ab1e1bb2ff67db0c0f6453b2547f32671d2538c17 | MATLAB | 4,132 | 105 | classdef PermBase < handle
properties
avg_prob_sig = NaN
perm_count = uint32(0)
coeff = []
prob = []
prob_sig = []
perm_seed
end
properties (Access = protected)
lastResultIdx = 0;
end
methods
function obj = PermBase()
... |
d296e2290d90fa94f64bda27dc1f00e8b090ef82cd18944c94a5397c41042457 | MATLAB | 4,140 | 121 | function varargout = process_nst_cpt_cortex_to_head_distance( varargin )
% Compute the distance of each vertex of the cortical surface to the head
% in mm
% @=============================================================================
% This software is part of the Brainstorm software:
% http://neuroimage.usc.edu/b... |
40ad9d2ee84d48fa805993142579fe4ef196d434ee3b886cd5be37ca138fcabb | MATLAB | 4,148 | 124 | function shape_classification_supervised_clustering(data)
classes = data.classes;
if size(classes,1)<200
disp('calculating nodes and edges')
parameters = classes(:,3);
parameters = vertcat(parameters{:});
parameters = zscore(parameters);
number_of_nodes = size(parameters,1);
[x,y] = mesh... |
d7c59a1cfcf3940a685766b510d680c629a458ef711d3474e5294dd3f13de09e | MATLAB | 4,152 | 109 | function [siglev,res, lmp, dof_lmp]=arres(w,A,v,k)
%ARRES Test of residuals of fitted AR model.
%
% [siglev,res]=ARRES(w,A,v) computes the time series of residuals
%
% res(k,:)' = v(k+p,:)'- w - A1*v(k+p-1,:)' - ... - Ap*v(k,:)'
%
% of an AR(p) model with A=[A1 ... Ap]. If v has three dimensions,
% the 3rd... |
09d6fb7fdbb34197af5d5476bb49810fa7e1868fad1709e75b605676fa318aac | MATLAB | 4,153 | 179 | % Imbed a file menu to any figure. If file menu exist, it will append
% to the existing file menu. This file menu includes: Copy to clipboard,
% print, save, close etc.
%
% Usage: rri_file_menu(fig);
%
% rri_file_menu(fig,0) means no 'Close' menu.
%
% - Jimmy Shen (jimmy@rotman-baycrest.on.ca)
%
... |
a7fdcc2505225df4498cd65f3ac134e8b856a02e5ff3face02d5602f1db413a6 | MATLAB | 4,170 | 110 | function [XN,IOTA,EPS,U]=quasi_idempotence(X,K)
%QUASI_IDEMPOTENCE Connection matrix quasi-idempotence
%
% [XN,IOTA,EPS,U]=quasi_idempotence(X)
% [XN,IOTA,EPS,U]=quasi_idempotence(X,K)
%
% The degree of quasi-idempotence of a matrix represents how close it
% is to being idempotent, i.e. invariant to squari... |
58e888dab6e75d514624697e2b49d3cf4512700553e20c5e7f3577cb8b8bf374 | MATLAB | 4,178 | 124 | %% in silico digestion UNG2 with trypsin, lys-c and arg-c
name=digest('P13051','trypsin',2,8,400,2000,2,3,20,'HPO3','[STY]')
name=digestNphosphorylate('P05387','trypsin',2,8,400,2000);
name=digest('P13051','trypsin',2,8,400,2000,1,4,'HPO3','[STY]')
fprintf('Results written to file %s\n',name);
%% xrcc1
... |
0fe5265d830c639971d5637244431cba80017c3c92bcb66b7cf82568c75970c6 | MATLAB | 4,232 | 135 | %% load example of training data
load('training_set/data749.mat')
%visualize absorption and scattering targets
figure
subplot 121
patch('Faces',mesh.H,'Vertices',mesh.r,'FaceVertexCData', mua, 'FaceColor', 'flat', 'EdgeColor', 'none');
axis tight
axis square
title('\mu_a')
subplot 122
patch('Faces',mesh.H,'Vertices',m... |
439e961cc424fbe63a84c34a64dfc235da5defca3ead8bebb850b6ec5acd2807 | MATLAB | 4,248 | 81 |
function [isSignificant,adjusted_pvals,alpha]= bonferroni_holm(pvals,optional_alpha)
%% bonferroni_holm, bonferroni_holm(pvals), bonferroni_holm(pvals, alpha);
% Corrects for testing multiple hypotheses.
% This method is more powerful, but less conservative than the Bonferroni method.
% Fixed so that a... |
a39e8b2a7a9d4bea5e27a1ec31041b64a9072ee1bcb6ad39954ff7b92258c8f5 | MATLAB | 4,252 | 112 | %% Plot and count Fig 3D-F data points
% Reproduces the D-F scatter/trend plots and reports exact counts
if ~exist('DATA_ROOT','var'), run(fullfile(pwd, 'matlab', 'config.m')); end
% load(FIG3_DATA); % uncomment if workspace not already loaded
metrics = {mergedDFF, mergedTrendsOnset, mergedTrendsDur};
metricNames = {... |
4074d7419c54a11bbe12dfc57df28f6bbd55f0b99b644e1c059fd1ac02e0da2a | MATLAB | 4,253 | 121 | % This script processes shows an example of how to process several
% recorded sessions (in this example the video data is already converted to
% .mat format but the script works for any matlab supported video format,
% you should just adapt step 0 below for the loading part).
% files and concatenates the results (step... |
651e9e6b91cb380d7286f644b1ccb761834ae3cb751756082378eecb7bf5c345 | MATLAB | 4,255 | 103 | function [ MAG_data_pos, MAG_data_neg, GRAD_data, MAG_GRAD_tval ] = MEG_sensors_MCS_reshapingdata_LBPD_D( S )
% It reshapes the order of data according to labels that are
% submitted, providing 2D approximation of MEG sensors layout.
% This function works in combination with a following function that finds
% spatio-te... |
299e8848a365689ea2fd6290ae5675bedb37629f0a3fe1ffbef549e6438a66e9 | MATLAB | 4,259 | 103 | classdef SandwichEstimator < nla.edge.result.Base
properties
% test result specific properties go here (things that are
% specific to a particular data input, ie: results of running the sandwich
% estimator on said data, or covariates which are specific to a
% particular data se... |
0274c972a544ed0566cbd55629a49a18ec85663cdc6addd1b84e1fcb12758ecb | MATLAB | 4,269 | 146 | function BCCT_makefinalMask
clc;clear all;close all;
ASMASK.fig = figure('Name','Make final mask for calculation',...
'units','normalized',...
'menubar','none',...
'numbertitle','off',...
'color',[0.95 0.95 0.95],...
'position',[0.20 0.15 0.5 0.5]);
movegui(ASMASK.f... |
f98286a5893b2308fc56722f16a1eb691e8e8acb28212fae1fd88709b43be445 | MATLAB | 4,281 | 121 | function [Kernel, M, J] = nst_mne_lcurve(HM,OPTIONS)
% nst_mne_lcurve - this function solve the inverse probleme using a l-curve
% approach
% Input: HM - struct
% | - HM.Gain : Gain matrix
% OPTIONS - struct
% | - OPTIONS.Data : data matrix (nChannel x nTimes)
% | - OPTIONS.DataTime: Corres... |
cf77906e3fda39214ef344ecb01efd7d39475c45c1b3bf24b9865bd79fd489d4 | MATLAB | 4,282 | 147 | function [ peak, clus, clusid ] = SurfStatPeakClus( slm, mask, thresh, ...
reselspvert, edg );
%Finds peaks (local maxima) and clusters for surface data.
%
% Usage: [ peak, clus, clusid ] = SurfStatPeakClus( slm, mask, thresh ...
% [, reselspvert [, edg ] ] );
%
% slm.t ... |
a567f53b0512cc0192b317a2a0d904ab090548a876d831e455371d8ad3062305 | MATLAB | 4,283 | 117 | function [cellpos,trac,modnum,aflow,polyrate,gactin,trec,Forcec,t,msd,sem,gof,rmc,traction,rgflow,modulenum,modulel,csl,gact,area,aratio]=...
analyzeCMSoutputv1_0(tint,tanalysis,texc,nucr,mc,trec,Forcec,cellpos,polyrate,gactin,trac,aflow,modnum,sdist,mdist)
% Original code written by Benjamin Bangasser and ... |
9f5f55f6a220f25c2b7ba2558c7e2ecb5698e73dc8f9585fd0de986f234c24ef | MATLAB | 4,291 | 96 | function [Z,out] = AS_PtoTRF(pval,Flag,Df1,Df2)
% This is from software rest.
% The orginal version is [Z P Header] = rest_TFRtoZ(ImgFile,OutputName,Flag,Df1,Df2,Header)
% The bellow is the orginal annotation.
% function [Z P Header] = rest_TFRtoZ(ImgFile,OutputName,Flag,Df1,Df2,Header)
% FORMAT [Z P Header] = y... |
2649d7f4de8dd06182b49199f179f4ec8dcd0d6828a461737aaf22f8c0a7ca00 | MATLAB | 4,305 | 87 | function [O] = GED_single_subject_singletrial(S)
O = [];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Loads a single subject, computed GED on it and store the data in a .mat file
% the covariance matrix is computed on each trial independently and then averaged
% INPUTS : -S.fil... |
d8f8eea3c0d19b7059b079591521cde06069a8307a1cc699714833877e242f35 | MATLAB | 4,310 | 192 | function [efs,F,cdfs,p,eps,dfs,b,y2,sig,mse]=repanova5(d,D,fn,gg,alpha);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Version5 has return of mse added (eg for plotting Loftus error bars)
%
% [efs,F,cdfs,p,eps,dfs,b,y2,sig] = repanova(d,D,fn,gg,alpha);
%
% R.Henson, 17/3/03; rik.henson@mrc-cbu.cam.ac.uk
%
%... |
639cfe7bad96c9ce8463ca8dde2f09fd18b22c0a9d951eb1bdf5a4c22a3556b1 | MATLAB | 4,336 | 111 | function [ a, cb ] = SurfStatView( struct, surf, title, background);
%Viewer for surface data or P-value or Q-value structures.
%
% Usage: [ a, cb ] = SurfStatView( struct, surf [,title [,background] ] );
%
% struct = 1 x v vector of data, v=#vertices, zeros(1,v) if empty,
% or one of t... |
f10128d3b8d8608da56d4ff22ed7b7fd552c025349517438a42b8e73aaed297b | MATLAB | 4,338 | 144 | clear
% NOTES
% v5 is with updated file names so that dt and non dt dont overwrite
% v6 has homogenized fitting bounds and includes biQTI
% v7 is using the new resQ functions
% v8 is same as v7 but fixes overwrite issue
maindir =[path_ludisc '\Common\DATA\MIPCART pilot\Pilot MRI\'];
dir_name = 'analysis_v8_';
% This... |
7da3f5d37edc193ee2e9b63cdb741a12484040aa61aabe66aa6cd4c89dba5136 | MATLAB | 4,341 | 135 | function slidetraj = slideFilter(type,data,win,dimorig)
%%% J. Otterstrom, Matlab 2012a, July 2013
% sliding filter that makes a window around each time point in a
% trajectory of width 2*win. If a difference filter is used, then the
% difference between the later half of the window from the first half of
% t... |
8919d562a364ab95327d5faa07596fce1cfbebcb0251c67e1c0257641435bbaa | MATLAB | 4,348 | 119 | function surface_group_pipeline_V1()
% Example for the template- and surface-based full pipeline, using the
% function NST_PPL_SURFACE_TEMPLATE_V1.
%
% This script downloads some sample data of 10 "dummy" subjects (27 Mb),
% as well as the Colin27_4NIRS template (19 Mb) if not available.
% All downloaded data wi... |
ac039282ba05a7c5bf4dc3d67b2b4be8ad2905b5c88b5af8a03acdd00b7fd9a9 | MATLAB | 4,364 | 88 | function [O] = GED_single_subject_LandR_separately(S)
O = [];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Why this dumb name? I chose it because I wanted to distinguish the
% function to perform GED when Listening and resting were divided before
% source reconstruction. This re... |
48ef3eb4af009e3797002fd17279242b472b83c2002d907913f5ae0644fa8f7c | MATLAB | 4,369 | 120 | function [anat_hr_seg, vc_mask] = Segment_anatomy(anat_hr,output_folder)
% Segment_anatomy - Segments anatomy, normalizes to MNI,
% and extracts visual cortex mask from atlas.
%
% INPUT:
% anat_hr (string) - full path to anatomical T1w NIfTI image
%
% OUTPUT:
% anat_hr_seg (struct) - GM/WM/CSF probability maps
% ... |
290328b184ba4198f0bde8bc6502bb2480aa1c8ebfdde5225e416eedf1235b7a | MATLAB | 4,371 | 133 | function [surf,ab]=SurfStatReadSurf(filenames,ab,numfields,dirname,maxmem);
%Reads coordinates and triangles from an array of .obj or FreeSurfer files.
%
% Usage: [ surf, ab ] = SurfStatReadSurf( filenames [,ab [,numfields ...
% [, dirname [, maxmem ] ] ] ] );
%
% filenames = .o... |
651b4dcd988e43ffbd8aa2e32a6754583da8230e40bff1d9f948fa62d248fc50 | MATLAB | 4,372 | 160 | function varargout=shadedErrorBar(x,y,errBar,lineProps,transparent)
% function H=shadedErrorBar(x,y,errBar,lineProps,transparent)
%
% Purpose
% Makes a 2-d line plot with a pretty shaded error bar made
% using patch. Error bar color is chosen automatically.
%
% Inputs
% x - vector of x values [optional, can be left em... |
9526a7a2787cc469f084cd7cc50bb4d8dfc1b6d2e72bae7eeb613bdef0d6db70 | MATLAB | 4,388 | 141 | function [ t, df, pval ] = SurfStatPlot( x, y, M, g, varargin );
%Numeric or string variables against numeric, adjusted and/or by groups.
%
% Usage: [ t, df, pval ] = SurfStatPlot( x, y [,M [,g [,varargin]]] );
%
% x = n x 1 vector of numbers or cell array of strings or a term of these.
% y = n x 1 vector of ... |
8fe5d78896cc000713a58848bf996f2a4e40ab07781ddcc1db6398695187bd4c | MATLAB | 4,400 | 126 | classdef SpreeTest < matlab.unittest.TestCase
properties
tmp_dir
end
methods(TestMethodSetup)
function setup(testCase)
tmpd = tempname;
mkdir(tmpd);
testCase.tmp_dir = tmpd;
utest_bst_setup();
end
end
methods(Test... |
7628bd32c8e21a8d818dd88b979bd7600c090b8e81ef6c4a3fecc107af161001 | MATLAB | 4,409 | 184 | function [preimages,images,averages,mask] = OMimprocess(fname,im,rect,num_images,cropchoice,mask,sfilt,sfiltsize,inversion,tfilt,removef,camopt,sfiltsigma,pbefore,pafter,n)
%funtion for reading in and processimg all images in the tif stack/ rhs
[rows cols] = size(im);
[qq,token,remain] = fileparts(fname);
fileisrsh... |
692377fb269fa62055f86b38eca8868231fb3b5d1282af9279f27f61f54846c6 | MATLAB | 4,414 | 114 | function jds_normPopulationActivityREM(animalprefixlist,area)
%JDS_NORMPOPULATIONACTIVITYREM Ripple-aligned normalized population activity in REM.
% jds_normPopulationActivityREM(animalprefixlist,area) plots cortical
% REM-ripple-aligned population activity ('CA1' or PFC), normalized to
% the mean population rate... |
ed21a5be65513153c65f44528da04bb149d34820ae4387124416a868dbc836c1 | MATLAB | 4,417 | 130 | function tc_show_single_boundary_frame(configuration)
% Hack to show single frame of video.
%
% TVM_VOLUMEWITHBOUNDARIESTOMOVIE(configuration)
% TVM_VOLUMEWITHBOUNDARIESTOMOVIE(configuration)
% @todo Add description
%
% Input:
% i_SubjectDirectory
% i_ReferenceVolume
% i_Boundaries
% i_Axis
% i_FramesPerS... |
d648767b3434cc3f2808e16de68931b2a140e62b0d8c2c316892add1ad497738 | MATLAB | 4,425 | 134 | function run_tests(to_run, stop_on_error, do_coverage, re_match_filter)
% Run unit test suites from files located in './test'
% Test scenarios to run are specificied by given to_run:
% - package: tests related to functions in bst_plugin
% - source: tests of tools used on package sources (eg dist_tools)
% - scr... |
cfa265768ae0802935c73bb665d026ed2d64a3358dcbb4c31244011edea7b447 | MATLAB | 4,430 | 113 | function Plot_K_diffs_transitions(data_dir,selectedK)
%
% Plot the the results from the hypothesis tests obtained from comparing
% the mean state-to-state transition probability between conditions.
% INPUT:
% data_dir directory with the results from running the hypothesis
% tests on the state-to-sta... |
dc13f0a4bca07912f626cb60404dcf49e576a111fc82b8477bf5c74f2801a6a7 | MATLAB | 4,433 | 126 | function [ POS, NEG ] = signROIs_degree_connotherROIs_LBPD_D( INT, COUP, lab_ab, r, X )
% It identifies the connections between the ROIs significantly central within
% the brain network and the other ROIs.
% Currently, it is implemented for the contrast between two conditions or for
% working with only condition 1 or ... |
0b3c91bfbae680c5a1a2cbebda89a2111b6c201b107a27c5df9ab6a765dfcd4c | MATLAB | 4,443 | 110 | function Plot_K_state_time(data_dir,save_dir,selectedK)
%
% Plot the state time courses for all participants and make pie plots with
% the percentage of time spent in each state.
%
% INPUT:
% data_dir directory where LEiDA results are stored
% save_dir directory to save results for selected optimal K
% select... |
9598ddcc6094020945ed75e186576d0d8400ea09a0dd70ee98d0833dc16aa11b | MATLAB | 4,444 | 102 | function EEGpav_prepStan4Reeg(dirs)
% Generate EEG4stan.mat
% Input:
% dirs.singletrial = '.../EEGsingletrial.mat'
% dirs.stan = '.../EEG4stan.mat'
% ---- Load single-trial data ----
S = load(dirs.singletrial, 'data', 'par');
data = S.data;
par = S.par;
% ---- Feedback r: support outco... |
a4eaf8a6f32636df90658da62a9138488a0e5f54c378a4f82ea69ef7e633c5f5 | MATLAB | 4,444 | 118 | function [isrcs, idets, measures, measure_type] = nst_unformat_channels(channel_labels, warn_bad_channels)
% NST_UNFORMAT_CHANNELS extract sources, dectectors and measures information
% from channel labels with *homogeneous* type (eg wavelength or Hb).
%
% [ISRCS, IDETS, MEAS, CHAN_TYPE] = NST_UNFORMAT_CHANNELS(CHAN... |
9c63ed840e917637655631e941327c5fdc3901f2da7e3fe7e20a0de524d9e855 | MATLAB | 4,445 | 102 | classdef ResultPool
%RESULTPOOL Summary of this class goes here
% TODO Detailed explanation goes here
properties
network_atlas
test_options
network_test_options
edge_test_results
network_test_results
permutation_edge_test_results
permutation_net... |
426bbc7a4018e0f93461468c07376daf71b12c65890f487c620f401a50552c97 | MATLAB | 4,450 | 131 | function [clustersize,clusters,numcluster] = pl_conncomp(map)
%
% Finds clusters (connected components) in maps of any dimension. The clusters consist of map locations with non-zero elements.
% Useful for cluster-based statistical procedures
%
% This function is part of the permutationlab software:
% Author:... |
e6a2a83ad9368ddc1f8c2dadf4652f4dc370963f8cb30bcf4d943a2aa89eaf2a | MATLAB | 4,466 | 119 | function [ a, cb ] = SurfStatView( struct, surf, title, background);
%Viewer for surface data or P-value or Q-value structures.
%
% Usage: [ a, cb ] = SurfStatView( struct, surf [,title [,background] ] );
%
% struct = 1 x v vector of data, v=#vertices, zeros(1,v) if empty,
% or one of t... |
0d1067cccbc17b7f03e18c8f3502201cb558c9aa8735f3ec6a620086e8f3a692 | MATLAB | 4,467 | 107 | %jds_assemblyFieldMetricsShuffle
%Computes 2D spatial rate maps for PFC assembly activation events (run
%epochs, reactivation strength > 5) and quantifies their spatial
%information and sparsity against a null distribution from circularly
%shifted activation times (n=1000 shuffles, minimum 20 s shift). Plots
%z-scored ... |
3d681876799ad893b08e4b6ad0cbf37568ec2cf150baaa8a8f7aa5fed57affd0 | MATLAB | 4,472 | 164 | function BCCT_SCN_statGUI
D.fig = figure('Name','BCCT stastic(two groups)',...
'units','normalized',...
'menubar','none',...
'numbertitle','off',...
'color',[0.95 0.95 0.95],...
'position',[0.3 0.4 0.4 0.3]);
movegui(D.fig,'center');
%%
D.OutputText = uicon... |
428ccc697ff846d5e9a6f3ffa9f3786706704c8e8b7ebe5133e6a3c6b1350762 | MATLAB | 4,476 | 164 | function BCCT_CaSCN_statGUI
D.fig = figure('Name','BCCT stastic(two groups CaSCN)',...
'units','normalized',...
'menubar','none',...
'numbertitle','off',...
'color',[0.95 0.95 0.95],...
'position',[0.3 0.4 0.4 0.3]);
movegui(D.fig,'center');
%%
D.OutputText... |
6dc5c6fddf9011016d2368e2dba1d76cf7948bd923b667794c27c0d9b1101e6b | MATLAB | 4,482 | 129 | %--------------------------------------------------------------------------
% Till Habersetzer, 23.06.2025
% Communication Acoustics, CvO University Oldenburg
% till.habersetzer@uol.de
%
% This script performs a quality control (QC) check by visualizing the
% outputs of the MRI processing and MEG/MRI coregistration pi... |
9f37f684b04854f355b227a4c54cc176170c25cc094eefd7cbb8fd795a2d18c5 | MATLAB | 4,485 | 132 | function dm_prep(dataFolder,subName,taskName,icaTriggers,icaWindow,filters,reference,badChannels,appendString,exportAsBV)
%DM_PREP Preprocess DM lab data
% dataFolder: high-level BIDS folder
% subName: subject name, e.g. 'sub-01'
% taskName: task name, e.g. 'cooltask'
% icaTriggers: the triggers around w... |
d3d1fdf5ce2c107ada30f35fcec6f4a6555ed885a8e5f7917db5e983da081864 | MATLAB | 4,497 | 164 | % Return time frame of a NIFTI dataset. Support both *.nii and
% *.hdr/*.img file extension. If file extension is not provided,
% *.hdr/*.img will be used as default.
%
% It is a lightweighted "load_nii_hdr", and is equivalent to
% hdr.dime.dim(5)
%
% Usage: [ total_scan ] = get_nii_frame(filename)
%
... |
4a5b0cce5059a16ee73340824cd168ad21d6891eb62fbad7a2f9c5e837f2b9d2 | MATLAB | 4,511 | 92 | function vox_coor = get_schaefer_neurosynth_vox_coor()
vox_coor = [
43.2445652173913 86.6413043478261 39.7364130434783
47.8008849557522 86.6637168141593 41.7300884955752
21.3996840442338 31.9447077409163 53.4849921011058
69.8309037900875 32.7026239067055 53.5393586005831
41.3212435233161 34.2849740932642 42.953... |
a1f97f95fb8220d017e0a62455416a9b96417cab2e9a4f3f3706da5f8f7d218c | MATLAB | 4,521 | 104 | function O = Replay_TG_Plot_Stats(S)
O = [];
%It computes plots (and/or statistics against 50% chance level (time
%consuming)) for temporal generalisation (TG) decoding.
%It is designed to work on the Aarhus Cluster
% INPUT:
% -S structure with folloring fields:
% -list_TG: directory wi... |
4025cd66adb3e0cc6cb99f2c2ef991bd88ee73f2f3e39ec0f6832829707c4cae | MATLAB | 4,522 | 116 | function b_first_level_contrasts(parameters, delete_spm_files)
%% b_first_level_contrasts(mainpath, subject, delete_spm_files)
%mainpath = '/project/3018037.01/Experiment3.2_ERC/AnalysisFolder/scripts/scriptTemplates/..';
tc_struct2ws('caller', parameters.main);
%% paths
tc_struct2ws('caller', parameters.paths);
tc_st... |
be7b876c7361b1ba212da99d337a9b321ef86a9d85c86a95654231d88ecb2e1e | MATLAB | 4,523 | 115 | %%
% current working directory needs to be /path/to/scripts
mainpath= pwd;
addpath([mainpath filesep 'toolboxes' filesep 'tc_functions'])
addpath([mainpath filesep 'fmriRegAnalysis'])
addpath([mainpath filesep 'toolboxes' filesep 'fieldtrip'])
addpath([mainpath filesep 'toolboxes' filesep 'OpenFmriAnalysis'])
tvm_inst... |
7a9922ebd1ece42cf5d2a8c8e6b17968c13667dd511ebf0c398e99b01fc1b34f | MATLAB | 4,526 | 101 | function [I,Q,F]=motif3funct_wei(W)
%MOTIF3FUNCT_WEI Intensity and coherence of functional class-3 motifs
%
% [I,Q,F] = motif3funct_wei(W);
%
% *Structural motifs* are patterns of local connectivity in complex
% networks. In contrast, *functional motifs* are all possible subsets of
% patterns of lo... |
618224917eedb5c50ac44476c524e6c534cbdf86ea46bb22f8a89e3dcfda98fa | MATLAB | 4,549 | 190 | clear;clc;close all;
load('trait_score.mat')
load distance_beta.mat
%%
%% all subjects vs 0
result_permuation=zeros(85,1001,4,2);
for condition=1:4
for i=1:1001
for time=1:85
a11=squeeze(distance_beta(:,time,i,condition));
[h,p,ci,stats] = ttest(a11);
result_permuation(time,i,co... |
f1d77f3fa33bf861d066352c86815f9d2645713e3481b07cf886dacca5d1b5ce | MATLAB | 4,549 | 113 | % -------------------------------------------------------------------------
% Script: make_figureS3.m
%
% Description:
% This script generates Figure S3 for the manuscript, comparing the
% circular distribution of respiratory phases in newly collected data
% according to 4 separate groupings: W trials, M trials, first ... |
e60e944dd0de2179b0b6941a25ee5ee9ce506d71dee142e82d4bedab561e1075 | MATLAB | 4,555 | 107 | function data_simulated = loc_list_storm_image_simulation()
input_values = inputdlg({'clusters density (um^-2):','clusters radius (nm):','clusters localizations density (um^-2):','background density (um^-2):','connections density (um^-2):','connections distance (um):','connections probability [0 1]:','image size (um):... |
c122b128bcb2f91992344a4d2beb9eb055300c8ccdbda55dcd9cbfccc08035dd | MATLAB | 4,556 | 94 | function image_laplacian_filter(data)
figure()
set(gcf,'name','Laplacian Filter','NumberTitle','off','color','w','units','normalized','position',[0.25 0.15 0.4 0.6],'menubar','none','toolbar','figure')
if length(data)>1
slider_step=[1/(length(data)-1),1];
slider_one = uicontrol('style','slider','unit... |
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