sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 17k | content stringlengths 1 200k |
|---|---|---|---|---|
54d508857a93a5d379811dafe18e8638e8fc018a0565acbc011fa78dcb3af26a | MATLAB | 150 | 4 | function gc = wgr_inv_pwGC_F(pvalue,nobs,porder);
n = nobs - porder;
th = 1+finv(1-pvalue,porder,n-2*porder-1)/(n-2*porder-1)*porder;
gc = log(th); |
9036e56f68891db093f7b7e0b877c6d0d688d4502917bda67f193d508ba62996 | MATLAB | 151 | 7 | function [C]=create_timecell(ro,leng)
%create_timecell(ro,leng)
fn=1000;
vec=-ro/fn:1/fn:ro/fn;
C = cell(1, leng);
C(:) = {vec};
end |
4da6fd073e585eb0077406bd26440bd60ecf652b5541a9ba36ffb5fcba0d2649 | MATLAB | 155 | 7 | function image_sobel_filter(data)
h = fspecial('sobel');
for k=1:length(data)
data{k}.image = imfilter(data{k}.image,h);
end
image_plot(data)
end |
afcaf4992915c610f852f0cc234ebc1dca3b5658ff6615c8d6d0a7011a2bcf56 | MATLAB | 156 | 8 | function time = mipp_date2time(T, do_norm)
% function time = mipp_date2time(T, do_norm)
time = datenum(T.Date);
if do_norm
time = time-min(time);
end
|
2b97050d5fb37a10e92dce8420b7b81c06facb65ee2f711afdafc76ae334b160 | MATLAB | 158 | 4 | addpath(genpath('/usit/titan/u1/ash022/MAIA/'))
addpath(genpath('/usit/titan/u1/ash022/gaimc/'))
maia('list5000','Pwgs6dhmovlcod.scf.fasta.len5000.fasta')
|
0b27df0d983ceaed1900069061ffaa3bbc5e89a8f045df38016b39bbf385a583 | MATLAB | 159 | 7 | function image_prewitt_filter(data)
h = fspecial('prewitt');
for k=1:length(data)
data{k}.image = imfilter(data{k}.image,h);
end
image_plot(data)
end |
9c71fbab03cda6cbc63ea7355625aa701b00524570e14f4052bd9fb0a6851b87 | MATLAB | 159 | 6 | function opt = mipp_resQ_opt_nodt(opt)
opt.resQ.lb([3 5 7 8 9]) = 0;
opt.resQ.ub([3 5 7 8 9]) = 0;
opt.resQ.lg([3 5 7 8 9]) = 0;
opt.resQ.ug([3 5 7 8 9]) = 0; |
0b68de1e658a2e33ea15578d289b49ef21e5ef17062a966bf2201a9df92a638e | MATLAB | 162 | 5 | function virtChannels=selectVirtChannels(virtChannels, index)
for trial=1:size(timelockData.trial,2)
virtChannels{trial}=virtChannels{trial}(index,:);
end
end |
bdd744eb75a122c19500e448aff3229c088f8524fb3fb75d4e00c6e78930e597 | MATLAB | 164 | 5 | function chunk = colorChunk(color, h, w)
%COLORCHUNK create a matrix of size h * w * 3, filled with color
chunk = repmat(reshape(color,1,1,[]), h, w);
end
|
6ab0abde2ec71c4188e9253b8c81ab3ce2e888bd269d52328d8d766df6a2a086 | MATLAB | 166 | 6 | function opt = mipp_resQ_opt_dki(opt)
opt.resQ.lb([3 5 6 7 8 9]) = 0;
opt.resQ.ub([3 5 6 7 8 9]) = 0;
opt.resQ.lg([3 5 6 7 8 9]) = 0;
opt.resQ.ug([3 5 6 7 8 9]) = 0; |
1532284efb8aa228d1ff163c2c1bdc56c6c0bfe0f48e7880a369f81dca9535af | MATLAB | 168 | 5 | function PDC = get_true_pdc(A, nfft, fc, Ns)
[~,~,~,gpdc,~,~,~,~,~,~,~] = fdMVAR(A,eye(Ns),nfft,fc);
PDC=abs(gpdc).^2; % partial directed coherence
PDC=mean(PDC,3);
end |
38760789ccdbeb6c5728aabd7a6e81189a4e1a5baa2cd03d79f2a7dbfec6248d | MATLAB | 169 | 6 | function [x2,inBetween] = error_area(mean,std,x)
curve1 = mean + std;
curve2 = mean - std;
x2 = [x, fliplr(x)];
inBetween = [curve1, fliplr(curve2)];
end |
e6bb1c7b1f82066c39050a95c2345bcec9fbf6644c9915d54e416500fceec726 | MATLAB | 172 | 7 | function q = now_gwf_to_q(gwf, dt)
% function q = now_gwf_to_q(gwf, dt)
%
% Assume gwf is given as the effective waveform.
q = 2 * pi * now_gamma * cumsum(gwf, 1) * dt;
|
bb2c963547aa30040394d3874ebfd8bc86feaf775c5ffdceb55ba9d61a996900 | MATLAB | 173 | 8 | function image_transpose(data)
for k=1:length(data)
for j = 1:length(data{k}.image)
data{k}.image{j} = data{k}.image{j}';
end
end
image_plot(data)
end |
81689a4f80f85ee4d2ea277b1da3350285a0d55e331a43e9bc1f75b69a7b615e | MATLAB | 176 | 10 | classdef PartialVarianceType < int32
% What to partial variance from
enumeration
NONE (0)
FCBX (1)
ONLY_BX (2)
ONLY_FC (3)
end
end
|
095b4911133c4531807404c59e1b006ebf9efa78151804b9a2df0c85c519b057 | MATLAB | 178 | 8 | function image_flip_lr(data)
for k=1:length(data)
for j = 1:length(data{k}.image)
data{k}.image{j} = fliplr(data{k}.image{j});
end
end
image_plot(data)
end |
0a5e0455e6fe7d78913f6e5cf313414ebd61921991da9d745838364b35ccd631 | MATLAB | 178 | 10 | function r=subsref(t,s)
switch s.type
case '()'
cols=s.subs{:};
case '.'
cols=find(ismember(t.names,s.subs));
end
r=t.matrix(:,cols);
|
9058ecab70e5c63b8ba0335d63fc7109f52232309a2760484f1697baa26cf942 | MATLAB | 178 | 6 | function eta = compute_mai(H, noise_cov, data_cov)
inv_noise_cov = inv(noise_cov);
inv_data_cov = inv(data_cov);
eta = trace((H'*inv_noise_cov*H) * inv(H'*inv_data_cov*H));
end
|
94b86f4809fa3600c985b5a4f5db0a68ad65e50dc9ffaef0285f55f4b656c08f | MATLAB | 178 | 5 | function parameters = shape_classification_normalized_parameters(classes)
parameters = classes(:,3);
parameters = vertcat(parameters{:});
parameters = zscore(parameters);
end |
d3fb927f697e8ef87e156a7eb7f8168409fb7f9bb4153b5ccce04af32407368d | MATLAB | 178 | 8 | function image_flip_ud(data)
for k=1:length(data)
for j = 1:length(data{k}.image)
data{k}.image{j} = flipud(data{k}.image{j});
end
end
image_plot(data)
end |
163d7794039bc14dc999ce412f09cc3d29214a347c8b1cdcbaaaabdebe72db18 | MATLAB | 181 | 6 | function h = LABEL_GAP()
%LABEL_GAP The pixel gap of a label. It has to be provided by a
% function because Matlab doesn't support package constants. Sad!
h = 5;
end
|
b6be30bafbe4490045d5f0d9daf9bdb81a321f277018ed08c31e5beaaf560382 | MATLAB | 181 | 6 | function h = LABEL_H()
%LABEL_H The pixel height of a label. It has to be provided by a
% function because Matlab doesn't support package constants. Sad!
h = 22;
end
|
d63fa924bee6c008c0ed757c71dc0aed332351a34e530ce8fa0a0bbbe6d39bc8 | MATLAB | 183 | 8 | classdef TriMatrixDiag < int32
% I really really don't like how this has to be in another file
enumeration
REMOVE_DIAGONAL (-1)
KEEP_DIAGONAL (0)
end
end
|
834aaea882e9b8a0be79d49e8a7dc542592db03aa94fdfbfa5986f3671ffb55b | MATLAB | 185 | 6 | function v = VERSION()
%VERSION The current version number. It has to be provided by a
% function because Matlab doesn't support package constants. Sad!
v = '1.0.0';
end
|
951676f96e08311299e5930c5181e02253debe3587e11f1ef9820d94b39a8e69 | MATLAB | 187 | 10 | classdef (Abstract) IndexGroup < handle
%INDEXGROUP Abstract class for passing to UI interfaces
properties (Abstract)
name
color
indexes
end
end
|
013adbb9985730065e966e583bf7dbdd49158fce79412bcca3848df931bcdf69 | MATLAB | 189 | 7 | function get_capture_from_figure()
[file,path] = uiputfile('*.jpeg');
if path~=0
set(gcf, 'InvertHardcopy', 'off')
print(fullfile(path,file),gcf,'-dpng','-r300');
end
end |
badf680a2314d9f47d37ad23c9cdd5a698e883911730b75b0a18ee95d8557731 | MATLAB | 189 | 7 | function y = FilterAllChannels(x,frq_band,fs)
% x is a signal of shape chans * time
for chans = 1:size(x,1)
y(chans,:) = bandpasshopf(x(chans,:),[frq_band(1) frq_band(2)],fs);
end
end
|
119db43755f6b84ca6fc4f00c06e0d81043e2dd0a714e6e4a11945d6ef36008f | MATLAB | 194 | 6 | classdef Base
methods (Abstract)
[is_sig_vector, p_max] = correct(obj, net_atlas, input_struct, prob)
correction_label = createLabel(obj, net_atlas, input_struct)
end
end |
e616249801e72fb9474b2db3eb00f50b73167edd4c14fab5e61ec13210a58599 | MATLAB | 200 | 8 | function image_flip_color(data)
for k=1:length(data)
for j = 1:length(data{k}.image)
data{k}.image{j} = abs(max(data{k}.image{j})-data{k}.image{j});
end
end
image_plot(data)
end |
a92b0e16e1d482547029650750e4aa494662bbb4e2ca15500e9015b5735b94c0 | MATLAB | 202 | 9 | function [newX] = scaleFunc(X)
% scaleFact = 0.6*max(max(X(:)),-min(X(:))); % another way to do it
% scaleFact = max(abs(X(:)));
scaleFact = prctile(abs(X(:)),95);
newX = X ./ scaleFact;
end |
df0fcfb6b27de5a7d69f4c8f677ed9ad2fa22c1f6eced38bc3cd79882d0b0571 | MATLAB | 204 | 3 | function feature_signal = tc_fit_feature_template(clean_data, feature_templates)
%% feature_signal = tc_fit_feature_template(clean_data, feature_templates)
feature_signal = feature_templates \ clean_data; |
ef015b35fd1777f75cc26edc4c123db23b138cd0c1d6ba97620a6aa0d7345ea2 | MATLAB | 204 | 11 | function varargout = imrotate_slices(img,angle)
% out = zeros(size(img));
for slice = 1:size(img,3)
out(:,:,slice) = imrotate(img(:,:,slice),angle);
end
varargout{1} = out;
end |
d0c2295dd4087ae634944e99fc1cc05aa5f2cac8e4be7e065855086cf8176a12 | MATLAB | 208 | 11 | function [m, s, medi, iqra] = mipp_table2par(datac)
for i = 1:numel(datac)
tmp = datac{i};
m(i) = mean(tmp(:));
s(i) = std(tmp(:));
medi(i) = median(tmp(:));
iqra(i) = iqr(tmp(:));
end
|
1d405786c0d469fb89984c2653eed28d6a7ce1e03920fd1e2852b8b99ddeed1d | MATLAB | 209 | 9 | function data_cov = compute_cov(Y, num_trials, T)
data_cov=0;
for j=1:1:num_trials
data_cov = data_cov + (squeeze(Y(:,:,j))*squeeze(Y(:,:,j))');
end
data_cov = data_cov./T;
data_cov=double(data_cov);
end
|
2cc3e08819a161c8d0b1df2f975de9e4fff60d15e328b1042bfa782d0df43a8c | MATLAB | 211 | 7 | classdef None < nla.edge.permutationMethods.Base
methods
function permuted_input_struct = permute(~, orig_input_struct)
permuted_input_struct = orig_input_struct;
end
end
end
|
4c156f34a35c606d7620c0de36d8c8ad97e12b8fa3dfebb001e10b70ce97af20 | MATLAB | 211 | 12 | function [ftp_site, rfn] = nst_split_ftp(ftp)
ftp = strrep(ftp, 'ftp://', '');
toks = strsplit(ftp, '/');
ftp_site = toks{1};
if length(toks) > 1
rfn = strjoin(toks(2:end), '/');
else
rfn = '';
end
end |
652dceb89a88720375504d77a12eb2b217d216dbeea0f261d760195a4cd71510 | MATLAB | 212 | 6 | function zscore_matrix = tc_zscore(data)
%% zscore_matrix = tc_zscore(data)
%
% zscores values of matrix over all dimensions and rearranges to orignal
% shape
zscore_matrix = reshape(zscore(data(:)), size(data)); |
8b4b22a6c17a758f7f781d5847f51d3185841f65a0d6671432f25179065899bb | MATLAB | 215 | 6 | function template_names = nst_core_get_available_templates()
%TODO: add descriptions
template_names = {'Colin27_4NIRS', ...
'Colin27_4NIRS_lowres', ...
'Colin27_4NIRS_Jan19'};
end |
9b9c48f1af1647a52e88761c5b648bd84a9e9c6fab70ed29c89ff6e15bf3c3b8 | MATLAB | 217 | 7 | function m = ACCURACY_MARGIN()
%VERSION The margin of accuracy for comparing test statistics. It has
% to be provided by a function because Matlab doesn't support package
% constants. Sad!
m = 0;
end
|
9a5463e1b3e5a141078f7bb7d7d23c91d1f57fea0d4c7d9b3b51d4a4a62b8ca0 | MATLAB | 219 | 6 | function formats = nst_get_formats()
formats.pair_re = 'S(?<src_id>\d+)D(?<det_id>\d+)';
formats.pair_fmt = 'S%dD%d';
formats.chan_re = [formats.pair_re '(?<measure>WL\d+|HbO|HbR|HbT)'];
formats.pair_fmt = 'S%dD%d';
end |
cf68615e23d689953a9bac723a0119df4ac4211c8dea25f06531b0a049afc374 | MATLAB | 223 | 6 | data=xlsread('P:\begrenset\HUNTER\MR-metabolomics\NMR_all_cancer_types.xlsx');
dataval=data(2:37,1:66)
[coefs,score] = pca(zscore(dataval));
[coefs,score] = pca((dataval));
biplot(coefs(:,1:2),'scores',score(:,1:2))
|
2e3b60c2364d2424805b9f563f20982a7b5eae1827912485fad0d66688ebb596 | MATLAB | 224 | 8 | function [str, valid] = validateInputStruct(inp, str, valid)
for i = 1:numel(inp)
if ~inp{i}.satisfied
valid = false;
str = [str sprintf('\n - ') inp{i}.disp_name];
end
end
end |
3bf786342821fa14b6c4b9665074c0942aeb277e4cb2ddf5a9bca974f7bafe83 | MATLAB | 226 | 6 | function normalized = normClipped(x, llimit, ulimit)
%NORMCLIPPED clip something between two limits and normalize to 0-1
clipped = min(max(x, llimit), ulimit);
normalized = (clipped - llimit) / (ulimit - llimit);
end
|
4c9323da101d165dfd16826c256be17d4a24d0be376e09d19db169b08c249f12 | MATLAB | 226 | 13 | function save_fig(h,fname)
%function save_fig(h,fname)
% wrapper for export_fig
%
% assumes plot is still up
% makes plot with a transparent background
set(h,'Color','w');
export_fig(h,fname); % matlab central function
end |
effb62efd879344fdbf1468c967a93b7e6fb2e4dace29b699cd3c2e6f9c91666 | MATLAB | 229 | 6 | function usageError(err)
%USAGEERROR Throw an error with a message explaining how to use the
% mex.run function.
error("%s\nmex.run usage: [val1, val2, ... valN] = mex.run(func_name, arg1, arg2, ... argN)", err)
end
|
8ed6049a9161a12dba50ecfccd07fadc07b95bf1c069ac5648c63f5dd71b0caf | MATLAB | 233 | 11 | function [theta]=extremal_Sueveges(Y,p)
u=quantile(Y, p);
q=1-p;
Li=find(Y>u);
Ti=diff(Li);
Si=Ti-1;
Nc=length(find(Si>0));
N=length(Ti);
theta=(sum(q.*Si)+N+Nc- sqrt( (sum(q.*Si) +N+Nc).^2 -8*Nc*sum(q.*Si)) )./(2*sum(q.*Si));
end |
a801868316104b7b5d7b5dfa987ecc39061e44036963cee6be8e02547fd6be79 | MATLAB | 233 | 6 | function z = fisherR2Z(r)
%FISHERR2Z Transforms r-value data into z-value data via the
% transform: z = 1/2 ln([1+r]/[1-r]) = arctanh(r)
% z = arctanh(r);
z = 0.5 .* (log(1 + r) - log(1 - r));
z(~isfinite(z)) = 0; |
4cb0923a5dece7f8aaa4dbff00634d598d9585fb2d56f3e128d7fdadcd20268c | MATLAB | 236 | 10 | function O = mean_D(S)
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 = spm_eeg_average(S);
end
|
2541033c7745b02a9139cf8184d9c7fde9d01e45996b2c9b83c5892a92c3086e | MATLAB | 239 | 6 | function [dist_vec, id] = find_nearest_id(pnts,pos)
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
dist_vec=sqrt(sum((pnts - repmat(pos,[ size(pnts,1) 1])).^2,2));
id = find(dist_vec == min(dist_vec));
end |
140db85fab22b73004c411e683c934633e0fe6cf23cfffac2a863b7543cf2209 | MATLAB | 243 | 11 | %%%% EXTERNAL FUNCTION
% function taken from the LiNGAM package
% complete software may be downloaded from http://www.cs.helsinki.fi/group/neuroinf/lingam/
function q = iperm( p )
for i=1:length(p)
q(i) = find(p==i);
end
|
84d180523ad14495bb70fd671db156f4ef0b4fc9de2825348c9a80225ee5579a | MATLAB | 245 | 14 | if ~isdir('data')
mkdir data
mkdir data\new
mkdir data\original
mkdir data\simulated
mkdir data\new\raw
mkdir data\new\processed
mkdir data\original\processed
mkdir data\simulated\processed
end |
9563e4602d9a3791cd7b121e48d73d271df239ff6a920ddb4a868a59c23964e5 | MATLAB | 245 | 15 | function O = filtering(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 = spm_eeg_filter(S2);
end
|
31a933976263d0b643449642cb47770298906441e412d26b8f5901cbeb57b8d0 | MATLAB | 247 | 7 | function col = valToColor(x, llimit, ulimit, color_map)
%VALTOCOLOR map values to color map
x_indexed = int32(ceil(nla.helpers.normClipped(x, llimit, ulimit) * (size(color_map, 1) - 1)));
col = ind2rgb(x_indexed, color_map);
end
|
b2212ce6d60cfd22eb6ae3e3919d0f7ac79057952ed66dab96e578de19543e99 | MATLAB | 248 | 11 | function O = cluster_spmobject(S)
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 = spm_eeg_convert(S);
end
|
11f9194a3377b9ab9015f03077000a996aad280b286552f995a214c996e92eef | MATLAB | 249 | 10 | function c = mergeStruct(a, b)
%MERGESTRUCT Merge two structures, with the second overwriting the first if
%they set the same key
c = a;
fnames = fieldnames(b);
for n = 1:numel(fnames)
c.(fnames{n}) = b.(fnames{n});
end
end
|
fc386ef9c5e6d9323ea669f880efdbfc67f0bf5b5fb9ffee2c39bd6247d41a8d | MATLAB | 249 | 10 | classdef (Abstract) AbstractSwEStdErrStrategy < handle
methods
stdError = calculate(obj, SwEStdErrInput) %input is SwEStdErrorInput object, output is 2D matrix (numCovariates x numFcEdges)
end
end
|
35d2c893c4759a644a76ed5dc5b214bd5ca508ab245d59580994f704e1e4addb | MATLAB | 252 | 11 | function O = cluster_merging(S)
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 = spm_eeg_merge(S);
D.save();
end |
36565560b423937646f8cd656acaa4214efcb1aadb65d5c57556acebb8a3db2a | MATLAB | 252 | 15 | function O = downsampling(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 = spm_eeg_downsample(S2);
end
|
4de04e19bd28450da7bcdde65c9affb62fb1a09443375cf167f43235476fe042 | MATLAB | 253 | 12 | function O = cluster_epoch(S)
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 = spm_eeg_epochs(S);
D.save();
end
|
ee0ebc4776e654110227f968466d59554294e419544648540931e3e65dbf09c5 | MATLAB | 253 | 8 | function FC_project = get_nroi(FC_project)
disp('Getting number of ROIs...')
cd(FC_project.CONN_results_dir)
FC_project.nroi = numel(load('resultsROI_Subject001_Condition001.mat').names);
disp(['Number of ROIs = ',num2str(FC_project.nroi)])
end |
355849b0003d62c5f2e9172d63988b131530af3dc3340b17f5ea604f6c2d4077 | MATLAB | 254 | 10 | classdef DeepCopyable < handle
methods
function new = copy(obj)
%COPY Deep copy of an object
objByteArray = getByteStreamFromArray(obj);
new = getArrayFromByteStream(objByteArray);
end
end
end
|
6fd21bc511bf99687dcf1a8d13eaaebc7debbb709ba270556bbca1bd0feb7cba | MATLAB | 256 | 10 | function total_size = get_size(data)
%size of structured data in MB
names = fieldnames(data);
total_size = 0;
for i=1:numel(names)
var = getfield(data,names{i});
s = (whos('var').bytes)/(1024^2);
total_size = total_size + s;
end
end |
4d09108b3f1aa1946b795869eef231812232bd6b6e44d92ad82f70391b97aa58 | MATLAB | 258 | 9 | function data = loc_list_make_same_color(data)
f=waitbar(0,'Changing Coloe Value');
for i=1:length(data)
data{i}.area = 0.7*ones(length(data{i}.x_data),1);
waitbar(i/length(data),f,'Changing Coloe Value');
end
close(f)
loc_list_plot(data)
end |
ec6f708cb913d8743cf878aca65993e14f07e7a390420c51293107f91077e783 | MATLAB | 258 | 9 | classdef Integer < nla.inputField.Number
methods
function [w, h] = draw(obj, x, y, parent, fig)
[w, h] = draw@nla.inputField.Number(obj, x, y, parent, fig);
obj.field.RoundFractionalValues = 'on';
end
end
end
|
1d2617649775ce1c8617c55ac91f7b615ba62eaaa0628bdca51147b2c129d189 | MATLAB | 259 | 13 | function s_pval = nst_format_pval(pval)
if pval < 0.001
s_pval = sprintf('%1.1e', pval);
elseif pval < 0.01
s_pval = sprintf('%1.3f', pval);
elseif pval < 0.1
s_pval = sprintf('%1.2f', pval);
else
s_pval = sprintf('%1.1f', pval);
end
end |
fc5f51a73babef86534a53bfe460ab99290f6e1b6a14a88c9f5363e7801897cd | MATLAB | 263 | 8 | %% LODIST from http://online.liebertpub.com/doi/full/10.1089/cmb.2011.0052
[lcs sas na]=PairLcs;%('C:\Users\animeshs\SkyDrive\matlab_toolbox\LODIST\testseqs.fasta')
[distmatrix,asydistmatrix]=DistMat(lcs,[500,50],3)
%% Ecoli genomes
[lcs sas na]=PairLcs |
519fb7aeed80c98dfb609290e1d12448d22739a76052bd7629f61cdb13ad191d | MATLAB | 264 | 12 | function [n, nt, H] = now_cross_term_sensitivity(result)
% function [n, nt, H] = now_cross_term_sensitivity(result)
gwf = result.g;
rf = result.rf;
dt = result.dt;
qt = now_gwf_to_q(gwf, dt);
H = now_gamma * cumsum(rf)*dt;
nt = cumsum(qt.*H)*dt;
n = nt(end,:);
|
85ef3df6a5185cf95c89e38a6debcbaf00a26490cfa908e5818063096ddcd8da | MATLAB | 266 | 10 | function [f ,g] = objFun(x)
f = -x(end); %fmincon minimizes the objective, so a minus sign is used to maximize instead
g = zeros(size(x));
g(end) = -1;
OptTime = toc;
if OptTime > (length(g)^2/.5e3)
error(['Timed out after ' num2str(OptTime) ' seconds!']);
end |
7fa5ef089d66b9c743b92392c603c4f033ab4ee7639ce2cc22582d1782997e19 | MATLAB | 267 | 9 | function pvalue = wgr_pwGC_F(gc,nobs,porder)
n = nobs - porder;
TH = exp(gc);
THnew = TH-1;
THnew2 = THnew/porder*(n-2*porder-1);
pvalue = fcdf(THnew2,porder,n-2*porder-1);
end
% th = 1+finv(1-pvalue,porder,n-2*porder-1)/(n-2*porder-1)*porder;
% gc = log(th); |
8d9562aaed67f9edebe9e0426df501cfb5235f6669ef2c37efa54a1195c303c4 | MATLAB | 267 | 12 | function W = nst_math_fit_AR(x,M)
% Autoregressive all-pole model parameters using Yule-Walker equation
% W = aryule(x,M);
r = xcorr(x,'biased')';
r(1:length(x)-1) = [];
R=toeplitz(r(1:M));
W=R\(-r(2:M+1));
W=[1 W'];
end |
63b2b77384058844bacab159815002776a001858f3c5542f9c4cd5c1ca4d661f | MATLAB | 268 | 12 | function [data, A] = gen_mvar_data(A, T, T0, P, M)
%UNTITLED Summary of this function goes here
% Detailed explanation goes here
x=randn(M,T+T0);
y=x;
for i=P+1:T+T0
yloc=reshape(fliplr(y(:,i-P:i-1)),[],1);
y(:,i)=A*yloc+x(:,i);
end
data=y(:,T0+1:end);
end
|
ccaa012df56ca35463de60362f00839b4e680ad49440876c41376d56763b12af | MATLAB | 278 | 12 | function []=findtroisize(valimg, maskimg, N)
[infoval, dataval]=read4dfp(valimg);
[infomask, datamask]=read4dfp(maskimg);
data=dataval(datamask>0);
data=sort(data(:),1,'descend');
t=data(N+1);
fid=fopen('findtroisize_threshold.txt', 'w');
fprintf(fid,'%f\n',t);
fclose(fid);
|
da09fe434962a6fc90a11c623dbabfc10d795376ebdcfced1a5b8e7b21fc8f1c | MATLAB | 279 | 7 | function image_plot_mouse_up(data,name)
imagesc(data)
colormap(gray)
title({'',regexprep(name,'_',' ')},'Interpreter','latex','fontsize',14)
set(gca,'TickDir','out','TickLength',[0.02 0.02],'FontName','TimesNewRoman','FontSize',12,'TickLabelInterpreter','latex')
box on
end |
6099fa5500be2eef042a074c0eacbbe3aff71e45035b6313693bdf9e130d142e | MATLAB | 284 | 12 | function O = clusterbasedpermutation_osl(S)
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
[ gstats ] = oat_cluster_permutation_testing(S);
end
|
cca2750a1211410abf325a622b7db6c15a8343ed550ee062d97da4b60a5cc87c | MATLAB | 284 | 17 | function s = getrefsize(contig)
% Get the size of a reference node assuming name 'refnode_pos1_pos2'
r = regexp(contig.name,'_','split');
pos1 = str2num(r{2});
pos2 = str2num(r{3});
if pos1 >= pos2
s = 0;
else
s = pos2 - pos1;
end
end |
09dc8e836266629ad88e15608612ad41f6599e1f8f05ea1f376718f860f3b6f2 | MATLAB | 285 | 11 | classdef SwEStdErrorInput
properties
scanMetadata nlaEckDev.swedata.ScanMetadata
residual % [numObservations x numOutputVectors]
pinvDesignMtx % pseudoinverse of design matrix [numCovariates x numObservations]
end
end |
83b0058fa86a635faaf2f0d653327b702c18be470b409f06aabfbb74d1a5d9e1 | MATLAB | 286 | 8 | function a = angle_dist_nc(p2, p1)
%function to calculate the angle on the sphere going from from OP1 to OP2
%for a companion function, see RotateAboutAxis
%equivalent in SUMA code is macro : SUMA_ANGLE_DIST_NC
m_cr = cross(p1, p2);
a = atan2(sqrt(sum(m_cr.^2)),dot(p2, p1));
return;
|
36432d32d6eda372b4dd87de11043529a71fefe4931e7589a0ea436d7472b42d | MATLAB | 289 | 7 | function [link,distance,cophenet_value] = shape_classification_finding_linkage(parameters)
disp('Calculating Linkage')
distance = pdist(parameters);
link = linkage(distance,'average');
cophenet_value = cophenet(link,distance);
disp(['Cophenet Value = ',num2str(cophenet_value)]);
end |
468cf353446709dc335079a9a7ac57631e732cea4196aa99131a0e1f996fc20b | MATLAB | 290 | 6 | SetOptions[$FrontEndSession,
PrivatePaths -> {"SystemResources" -> {ParentList},
"TextResources" -> {FrontEnd`FileName[{"/export/data/animesh/matica8/AddOns\
/Applications/WolframAlphaClient/./FrontEnd", "TextResources"},
"PacletManager" -> True, "Prepend" -> True], ParentList}}
] |
cc8f314b7a36af9ffadb0516a7743a2c0ddb1406e0e5478389b4238765d426ae | MATLAB | 290 | 14 | function hideAxes(ax)
%HIDEAXES Hide axes borders and tick marks
% ax: axes to modify
ax.Box = 'off';
ax.XTick = [];
ax.YTick = [];
ax.XColor = 'w';
ax.YColor = 'w';
ax.TickDir = 'out';
ax.Visible = 'off';
disableDefaultInteractivity(ax)
end
|
bf65d57cc332f0dabc95c05eb27b2aca6fc8fcb4bec172446b117f3aa8b36a1f | MATLAB | 291 | 16 | clear
clc
maindir = '\\uw.lu.se\research\msf_fsz\Data\MIPCART\Pilot MRI\';
%% Data conversion
% Conversion in DSI studio
% bpv_pv2nii_batch(maindir)
%% Denoising and motion correction
mipp_run_preprocess(maindir, '*DWEpiWavev7_MAGNITUDE.nii.gz')
%% model fitting
mipp_run_estimation(); |
c4b948cc01cf29614a9b14540daa870b294ae5312b4d8a4736067fc3866b8720 | MATLAB | 291 | 11 | function matching = findMatching(str)
%FINDMATCHINGFILES Find files matching the given pattern, returns a
% string array of matching files
dir_list = string(ls(str));
if strlength(dir_list) > 0
matching = split(dir_list);
else
matching = {};
end
end
|
dc1e814f69fe42d3436ace1020651b20987e8e1dbbe233079b911eaf09bec5cb | MATLAB | 291 | 9 | function model = nst_glm_initialize_model(time)
model=struct('X',[],'reg_names',{},'n_roi',0,'time',[],'hrf',[],'accept_filter',[], 'ntime',0,'fs',0);
%inizialize the structure
model(1).time=time;
model(1).ntime=length(time);
model(1).fs=1/(time(2)-time(1));
end
|
c0260ac285ee583f28cb90724ede3010f1ca2de8a5808c13018e55f013b75eeb | MATLAB | 293 | 18 | function gamma = now_gamma(nuc)
% function gamma = now_gamma(nuc)
%
% Returns the gyromagnetic constant for given nucleus in units of 1/s/T.
if nargin < 1
nuc = 'H';
end
switch nuc
case 'H'
gamma = 42.6e6;
otherwise
error('Nucleus not recognized')
end
|
ab7862ae27ff3189d2fd71573160a3f2dac9c9ba5be64f73d10aa837f8303266 | MATLAB | 294 | 10 | function handle = firstInstanceOfClass(arr, class_name)
%FIRSTINSTANCEOFCLASS find the first instance of a class in a cell vector
handle = false;
for i = 1:size(arr, 2)
if strcmp(class(arr{i}), class_name)
handle = arr{i};
return
end
end
end
|
b7dc316cf78fb2b4c92bc34ecc12cbd1b23c5d81ca935b4363f03e986b5cad7a | MATLAB | 298 | 11 | [A,map,alpha] = imread('C:\Users\animeshs\Desktop\IMG_20130620_112511.jpg');
image(A)
p=ginput(2)
sqrt(sum((p(:,1) - p(:,2)).^2))
%% source
http://www.mathworks.se/help/matlab/ref/imread.html
http://stackoverflow.com/questions/10695747/distance-between-two-points-in-a-image-in-matalb |
ccfe80676310efe32bd0d661ae0793b0a6bcaad4afd67dcd4eae664ea561e074 | MATLAB | 298 | 16 | function version = nst_get_version()
%NST_GET_VERSION Return the current version of NIRSTORN
version = 'github-master';
major=0;
minor=0;
patches=0;
path = fileparts(which('process_nst_mbll.m'));
id = fopen(fullfile(path, '..','VERSION'));
str=fread(id,'*char' )';
version = str(9:end-1);
end
|
7495631fcee0eadd7395fd123efc00e851408fa8e4d87eae813823a698de9bc0 | MATLAB | 299 | 13 | function O = cluster_subjlevel(oat)
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
oat = osl_check_oat(oat);
oat.to_do = [0 0 1 0];
oat = osl_run_oat(oat);
end
|
419235d988908835573e538e60ab3df0436f5280c8d6bd2924e0abd79884cbaa | MATLAB | 300 | 10 | function [S] = suff_stats_fast(S, q_ks, q_ks1, Y_, M_t_s, N, nq, ny, w_1)
x_t_hat = zeros(3*nq+ny, N);
x_t_hat(1:nq,:) = q_ks;
x_t_hat(nq+1:2*nq,:) = q_ks1;
x_t_hat(2*nq+1:ny+2*nq,:) = Y_;
x_t_hat(2*nq+1+ny:end,:) = q_ks;
x_t_hat_w = w_1.* x_t_hat;
S = S + (x_t_hat_w * x_t_hat' + M_t_s);
end
|
0495149a318cab3f7c9718977ae690c73a9aabea553f67bc18681967d8688abd | MATLAB | 301 | 13 | function O = cluster_beamforming(oat)
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
oat = osl_check_oat(oat);
oat.to_do = [1 0 0 0];
oat = osl_run_oat(oat);
end
|
47a06be203340fe761ce59663832a60f5fda96969bd07c0e8e326e0475cd9c26 | MATLAB | 302 | 9 | function d = pointDist(p1, p2)
%POINTDIST Calculate euclidian distance between points
% p1: 2-tuple containing x and y coordinates of point
% p2: 2-tuple containing x and y coordinates of point
d_x = p1(1) - p2(1);
d_y = p1(2) - p2(2);
d = sqrt((d_x ^ 2) + (d_y ^ 2));
end
|
c3dee86947452a0ec3b31bfb387840891f15f7d0f92195c2a62752cd1890975b | MATLAB | 302 | 11 | function saveErrorObjInTimestampedMatfile(saveFolder, errorObj)
nowTime = datetime;
nowTime.Format = 'yyyyMMdd-hhmmss';
errFilename = sprintf('error_%s.mat',nowTime);
errFullFilename = fullfile(saveFolder, errFilename);
save(errFullFilename, 'errorObj');
end |
e10510d1285bce0dac74b38918c9626b7463c67eb206f62602b3ea04913a9f6b | MATLAB | 303 | 13 | function O = cluster_beamsubjlevel(oat)
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
oat = osl_check_oat(oat);
oat.to_do = [0 0 1 0];
oat = osl_run_oat(oat);
end
|
b6821cd0633c09863625115fb91677a958926ae52e6ff7f0f8e3cd214627ee17 | MATLAB | 304 | 13 | function O = cluster_beamfirstlevel(oat)
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
oat = osl_check_oat(oat);
oat.to_do = [0 1 0 0];
oat = osl_run_oat(oat);
end
|
b7e932fd54aa007ae2115708c371e253bc14fa69db5ae766a8d333924130a3c7 | MATLAB | 307 | 11 | function [poly_area,boundary_points] = loc_list_calculate_boundary_area(x,y)
try
k = boundary(x,y);
boundary_points(:,1) = x(k);
boundary_points(:,2) = y(k);
poly_area = polyarea(boundary_points(:,1),boundary_points(:,2));
catch
poly_area = 0;
boundary_points = [];
end
end |
16065c82d0525223580054a13f5bbf546789e10bc7646633d6f6cc2adf8d2f1c | MATLAB | 308 | 8 | function c = nst_math_dctmtx(n)
% Create a DCT matrix, see
% https://www.mathworks.com/help/images/ref/dctmtx.html
% Simimar output as: %cmat = dctmtx(nsamples)';
[cc,rr] = meshgrid(0:n-1);
c = sqrt(2 / n) * cos(pi * (2*cc + 1) .* rr / (2 * n));
c(1,:) = c(1,:) / sqrt(2);
|
9c156cfc409c3fd3846aa48a27a51af6831b6d9388c3e7be5d10e411eb8baa6a | MATLAB | 314 | 19 | %% simulate fMRI
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);
%% simulate EEG
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);
|
a63a38134ac9a23481ae49df6cded35bfeb0cc06dd1a91d52aa57014de08b48b | MATLAB | 315 | 14 | function display(t)
disp(' ');
if isempty(t.matrix)
disp('[]');
else
d=[];
n=size(t.matrix,1);
for k=1:length(t.names)
d=[d repmat(' ',n+1,1) strvcat(t.names{k},num2str(t.matrix(:,k)))];
end
d=[d(1,:); repmat('-',1,size(d,2)); d((1:n)+1,:)];
disp(d);
end
disp(' '); |
bff99f722f0de80d4e4d4717e301c96778b56915e7fde1ebab0c9755a953e734 | MATLAB | 320 | 12 | function loc_list_change_colormap_limits()
ax = gca;
c_lim = ax.CLim;
input_values = inputdlg({'c-min:','c-max:'},'',1,{num2str(c_lim(1)),num2str(c_lim(2))});
if isempty(input_values)==1
return
else
clim(1)=str2double(input_values{1});
clim(2)=str2double(input_values{2});
caxis(clim);
end
end |
a7801f1ddcb9864ad096a60212ae8a442edc67769990d1cd545a6735eff82fc9 | MATLAB | 321 | 11 | classdef HistBin
% Logarithmic histogram bins for probabilities.
properties (Constant)
BIN_COUNT = 10000
EDGE_COUNT = nla.HistBin.BIN_COUNT + 1
EDGES = [0; logspace(-200, 0, nla.HistBin.BIN_COUNT)']
SIZE = [nla.HistBin.BIN_COUNT, 1]
SMALLEST_POS_EDGE = 1e-200
end
end
... |
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