sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
f939e57e9158c8490d0efb2bf606fa9ad942787c0c948330a35c1a53f2b00473 | MATLAB | 2,753 | 56 |
clear all
close all
addpath('/Volumes/LASA/TBI_OpenNeuro/TBI_openneuro/Hopf_model/TBI_homogeneous/outputs/Grange/HC/');
addpath('/Volumes/LASA/TBI_OpenNeuro/Shared_code/Hopf_TBI_ON/')
addpath('/Volumes/LASA/TBI_OpenNeuro/Shared_code/Hopf_TBI_ON/helper_functions/')
addpath('/Volumes/LASA/TBI_OpenNeuro/TBI_openneuro/Hop... |
379dfbe0317e2cb9895e16790ba880fabc9a9a06f383221b0db52f243bc0d5bf | MATLAB | 2,761 | 67 | function [perfmet] = perfmet_maxclass_adaboostrounds(acts,targs,scratchpad,args)
%
% [PERFMET] = PERFMET_MAXCLASS_ADABOOSTROUNDS(ACTS,TARGS,SCRATCHPAD,ARGS)
%
% License:
%=====================================================================
%
% This is part of the Princeton MVPA toolbox, released under
% the GPL. See h... |
3df55de72d7654fb50edac49bb216413817283ebf7d3c0f2d5f9e6f4cc5da037 | MATLAB | 2,761 | 56 |
clear all
close all
addpath('/Volumes/LASA/TBI_OpenNeuro/TBI_openneuro/Hopf_model/TBI_ON_Hopf_hc_tp2/outputs/Grange/');
addpath('/Volumes/LASA/TBI_OpenNeuro/Shared_code/Hopf_TBI_ON/')
addpath('/Volumes/LASA/TBI_OpenNeuro/Shared_code/Hopf_TBI_ON/helper_functions/')
addpath('/Volumes/LASA/TBI_OpenNeuro/TBI_openneuro/Hop... |
dd1d10a40b8ad3f07578078c8681991929768683321d0737da565af25a03a21a | MATLAB | 2,764 | 57 |
clear all
close all
addpath('/Volumes/LASA/TBI_OpenNeuro/TBI_openneuro/Hopf_model/TBI_homogeneous/outputs/Grange/HC/');
addpath('/Volumes/LASA/TBI_OpenNeuro/TBI_openneuro/Hopf_model/TBI_ON_Lesions_Hopf_mask1halfSD_bin_corr/outputs/Grange/');
addpath('/Volumes/LASA/TBI_OpenNeuro/Shared_code/Hopf_TBI_ON/')
addpath('/Vol... |
fd176c8b92dd8836d72f59fd9887722a37a733cad3180672549b0303ecaf5279 | MATLAB | 2,764 | 87 | function [dataTree, procStreamConfigFile] = changeProcStream(datafmt, procStreamCfg, funcName, paramName, newval)
global procStreamStyle
dataTree = [];
% Set globals
if isempty(procStreamStyle)
procStreamStyle = datafmt;
end
% Parse args
if ~exist('datafmt','var')
datafmt = 'nirs';
end
if ~exist('procStreamC... |
b5844cde602afe931be06a849ed2df466281efe36c26a19f14cab77e9d238b11 | MATLAB | 2,767 | 93 | function [] = mr_fmontage(anat,func,mask,varargin)
% Functional montage superimposed on anatomical montage
%
% [] = MR_FMONTAGE(ANAT,FUNC,MASK,SLICES)
%
% ANAT = anatomical information, in 3D form
%
% FUNCT = anatomical information, in 3D form
%
% MASK = booleans for which voxels to display, in 3D form
%
% SLICES (opt... |
1a9f09fbd84ad7e98e349a20b1a1de087d76b1d6c395b3dd4e4a2c338c956df6 | MATLAB | 2,768 | 139 | function typefiles = findTypeFiles(subdir, type, exclList)
if ~exist('subdir','var')
subdir = pwd;
end
if ~exist('exclList','var')
exclList = {};
end
if ~iscell(exclList)
exclList = {exclList};
end
typefiles = {};
if ~ispathvalid(subdir, 'dir')
fprintf('Warning: folder %s doesn''t exist under %s\n', ... |
86be5d6c0b925ef96ea977082f4b08d76d544d5c11fe8f7360bc582fd90f8c67 | MATLAB | 2,768 | 106 | function L = CrossValidationSpatialCheck_HDInfo_Cells(S,lambda,X,Y,ep,ang,angHisto,B,varargin)
nbEp = 10;
dispBar = 1;
binSize = 0.5; %in cms
nbRand = 10;
B = B(:);
exDisp = 0;
if ~isempty(varargin)
exDisp = 1;
end
epV = regIntervals(ep,nbEp);
L = NaN(nbEp,length(S),nbRand);
aep = Restrict(ang,ep);
da = Data(ae... |
bb986b7acf8c7e38852224884c6a62da0e597d52c803a87c7b4e2aeed30b51b0 | MATLAB | 2,768 | 155 | % cc heatmap and nodes for cytoscape(P<0.05,cc>0)
clc
clear
[~,~,raw] = xlsread('/Users/luna/Downloads/data.xlsx');
data = brainregionISOKeta(:,3:2:57);
data= cell2mat(data);
ISOmean=mean(data,2)
STD=std(data,0,2)
dataI = cell2mat(dataI);
dataK= cell2mat(dataK);
cc = [];
P_value = [];
[cc,P_value] = c... |
3ba621a7217b297c36270c01f889bd578c4ead3c6d881702971a1b4264c2f6e8 | MATLAB | 2,769 | 57 | function [data_clean_artifacts] = artifact_rejection_auto(fname, data_seg, trl_visual_only,rd)
% trl_visual_only= trl_visual_only(1:10,:);
% trl_short = trl(1:10,:);
%%%%%%%%%%%%%%%%%% detection of muscle artifacts in taks trials and rest trials %%%%%%%%%%%%%%%%%
% http://www.fieldtriptoolbox.org/tutorial/autom... |
e54a7e34ad1f1bb4f8d0e76268adf9e654aad33863a166867bc3ce15c542c8ac | MATLAB | 2,770 | 72 | %Fetches mni anatomy so can plot electrodes and other in MNI space.
function mni=fetch_mni_anat(root_dir)
mni.anat.fs_outputdir=[root_dir '/mni/mni-icbm152-t1-tal-nlin-asym-09b-hires_freesurfer-output'];
mni.anat.fs_t1=ft_read_mri([mni.anat.fs_outputdir '/mri/orig.mgz']);
mni.anat.pial=ft_read_headshape({[... |
a4b6f6f627b07dcd54a65115dc0d187b1f9f3a2cbde8a12e7d5d3c2f1bc6f2e2 | MATLAB | 2,772 | 57 |
clear all
close all
addpath('/Volumes/LASA/TBI_OpenNeuro/TBI_openneuro/Hopf_model/TBI_ON_Hopf_hc_tp2/outputs/Grange/');
addpath('/Volumes/LASA/TBI_OpenNeuro/TBI_openneuro/Hopf_model/TBI_ON_Lesions_Hopf_mask1halfSD_bin_corr/outputs/Grange/');
addpath('/Volumes/LASA/TBI_OpenNeuro/Shared_code/Hopf_TBI_ON/')
addpath('/Vol... |
1620838057a08598ba218d294f6949e553236eb64c7ec8fc2ebbc0ac99f61666 | MATLAB | 2,774 | 35 | % run from local pc and create gridscore, trajectory plot, and rate map plot
cd ..
initOAT
cd gridscore
preloaded_spk_reader=0; % save time of loading spike reader by using prior loaded one
if preloaded_spk_reader==0 && ~exist('keep_vars','var') clear all; preloaded_spk_reader=0; end; clear keep_vars;
use_hopper_data=0... |
c3d4931ec0ecabed8fb46ed9837c832e96f7a013a73b38d60592119f5a714083 | MATLAB | 2,774 | 77 | function [recon_energy,recon_FC_r]= homology_CC_rewired_digraphs_Human(W,zX_RS,RX_RS,n_null)
% =========================================================================
% Generate 1000 random digraphs (degree-preserving surrogate connectoms) further calculate reconstruction accuracy
% ============================... |
a98f8508f84624218e1cd77cbda878a57f380d20fe6423b6ec816348c7566f0d | MATLAB | 2,775 | 91 | function [scratchpad] = train_ridge(trainpats,traintargs,in_args,cv_args)
% Use ridge regression to predict your regressors
%
% [SCRATCHPAD] = TRAIN_RIDGE(TRAINPATS,TRAINTARGS,IN_ARGS,CV_ARGS)
%
% Uses Matlab's RIDGE function. Linear regression, but
% penalises small weights (like weight regularization in
% backprop),... |
c79cd9669ce1cf049ba13aa92a05969375c4dd55734baa1a5aa197d91ffbc3c9 | MATLAB | 2,779 | 104 | DataPath = '.\Data\Neuro\';
OutPath = '.\Data\Neuro\';
%% PSTH generation
% Output should be a nTrials x nBin x nClu matrix
% ==== PSTH parameters (TRANSITION) =====
minT = 0.5; maxT = 1.5;
bins = 1./(2.^(1:10));
load([DataPath,'AMDSpks_Sal.mat'],'SpkT_all','cids_all','cpos_all','Mouse_all','SortedStim');
tic
fp... |
36c877f55cb17aad4ea8b0e95b7c7f57c29e2290e99524d85960f9a7981d7036 | MATLAB | 2,782 | 91 | function [subj] = remove_object(subj,objtype,objname,varargin)
% This removes an object permanently
%
% [SUBJ] = REMOVE_OBJECT(SUBJ,OBJTYPE,OBJNAME,...)
%
% This will remove the entire object permanently from the subj
% structure - NOT RECOMMENDED
%
% There is no reason to remove the actual cell itself, since it
% tak... |
c84d89d073fc03eef8cb08da34e5e6417a635a2d38e4333b123869b5ec35f7e6 | MATLAB | 2,783 | 82 | function net = glm(nin, nout, outfunc, prior, beta)
%GLM Create a generalized linear model.
%
% Description
%
% NET = GLM(NIN, NOUT, FUNC) takes the number of inputs and outputs for
% a generalized linear model, together with a string FUNC which
% specifies the output unit activation function, and returns a data
% stru... |
d3767185abb7dbaf01bc271e83242b5e8433df81b30eac5f63e304595e2b7d9f | MATLAB | 2,783 | 61 | function [fx] = f_kToM_2modes(x,P,u,inF)
% wrapper around recursive k-ToM evolution function
% function [fx] = f_kToM(x,P,u,inF)
% A k-ToM agent with k>0 learns the unknown parameters (theta) of her
% opponent's learning and decision rules. Typically, theta includes her
% opponent's prior volatility about herself ... |
ad29439b31390e650e2032e26550006c979e8fc49f699c3b368a39cd27cf6e26 | MATLAB | 2,787 | 99 | function [vol] = volumize_pattern(pat,mask,varargin)
% Turns an nVox x nTimepoints pattern into a 4D XYZT matrix
%
% [VOL] = VOLUMIZE_PATTERN(PAT,MASK, ...)
%
% Returns a 4D XYZT matrix containing the pattern
% data. Positions excluded from the mask are set to zero.
%
% The MASK must include the same number of dimensi... |
b73f19ffd4ef911b590cf91d7f1a8319b16553493468e8f15ebd5b212e381692 | MATLAB | 2,787 | 59 | clear,clc
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% This script is used to perform CCA for brain-behavior analysis
%
% Jin
% 11/3/2021
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% set the file path
current_path = pwd;
working_path = current_pa... |
e42d866c495b1686afff4dad72a03d5c9be0de937db8bc27be3b3451b51dbe23 | MATLAB | 2,788 | 103 | function [gammaMod,gammaPh,goodEp] = GammaPhase(lfp,S,ep,freqBand)
% [gammaMod,gammaPh] = GammaPhase(fbasename,channel,ep,freqBand)
% INPUTS:
% channel : LFP channel number (base 1)
% ep : intervalSet object identifying where the signal should be looked at
% freqBand : 2 value vector indicating low and hi... |
2b929acce26395cb0aab0ab7e0e43a05c0ea77e14f67ffd58b19f1c5ea300389 | MATLAB | 2,793 | 83 | function [data_dcAvg, data_dc, data_dod, snirf, procElem] = unitTest_SnirfBasicProcStream(filename)
% Syntax:
% [data_dcAvg, data_dc, data_dod, snirf, procResult] = unitTest_SnirfBasicProcStream(filename)
%
% Example1:
% cd([fileparts(which('Homer3.m')), '/UnitTests/Example9_SessRuns'])
% filename = './Simple_Pr... |
56678bf19c4f4bdbfc15ce1cc5ea249481306979adf2c9a014570ea1585d12cc | MATLAB | 2,806 | 88 | folder = 'E-GEOD-46903.processed.1/';
files = dir(folder);
fileNames = cell(length(files), 1);
matchingFile = false(length(files), 1);
for i = 1:length(files)
fileNames{i} = files(i).name;
matchingFile(i) = contains(files(i).name, '.txt');
end
fileNames(not(matchingFile)) = [];
sampleNames = strrep(fileNames, '_s... |
1a90bb5f196531da1c7368e82a9c23b0f6472cee444467b1cd21b7396b9756cc | MATLAB | 2,809 | 113 | function [perfmet] = perfmet_xcorr(acts,targs,scratchpad,varargin)
% Correlates the acts with the targets
%
% [PERFMET] = PERFMET_XCORR(ACTS,TARGS,SCRATCHPAD,...)
%
% Correlates each condition vector in ACTS with each
% condition vector in TARGS - that's the 'CORRECTS'
%
% Outputs a NaN GUESSES matrix.
%
% License:
%=... |
1edf102a8c97790d2167472fef6f5728c42b9adb97cd982971b773d6b5617ffc | MATLAB | 2,819 | 73 | %Plots PSTH showing response of given neuronal unit to senosry flicker for
%given condition.
%2024/02/25
function plot_flicker_psth(psth_data,conditionIndex,condition,unit_label,max_spikerate)
%determine condition color:
temp=strsplit(condition,'-');
if strcmp(temp{2},'V') %visual in orange
conditi... |
177f80f0443c45de8a4cff07dbfa85902f7112d21b0e54c935995477c0cba7e9 | MATLAB | 2,820 | 86 | function [subj] = peek_feature_select(subj,data_patname,regsname,selname,varargin)
% Just like FEATURE_SELECT, except that it peeks
%
% [SUBJ] = PEEK_FEATURE_SELECT(SUBJ,DATA_PATNAME,REGSNAME,SELNAME,...)
%
% Only creates a single statmap pattern and mask because it runs the
% statmap_anova on all the TRs in the data.... |
9465af3468e42276ab9ecc0c8c69e66aa0ef773cb7b2bc68b94c7d3f6c8784c9 | MATLAB | 2,823 | 117 |
%% Pursuit task
clear
[task_info,supPath] = loadDBAndSpecifyDataPaths('Floc');
figure
req_params.grade = 7;
req_params.cell_type = {'PC cs'};
req_params.num_trials = 50;
req_params.remove_question_marks = 1;
req_params.remove_repeats = false;
req_params.task = 'rwd_direction_tuning';
req_params.ID = 3390:4000;
l... |
b6b4cc2f3e2f62ddd30452e7e5ff0f57e36ca5c8745c27256f7c073fec25172b | MATLAB | 2,826 | 83 | function [subj] = create_pattern_from_mask(subj,old_patname,maskin,new_patname,varargin)
% Creates a new pattern by masking an old one
%
% SUBJ = CREATE_PATTERN_FROM_MASK(SUBJ,OLD_PATNAME,MASKIN,NEW_PATNAME,...)
%
% Creates a new pattern called NEW_PATNAME from an
% existing one called OLD_PATNAME, including only the ... |
443e903b8e2d266226e96fa55289983bd9791a309912dfdf5b428c8fafa48e37 | MATLAB | 2,828 | 91 | function Image3_ColoredFlowImage(imageExportFolder, fileParams, flowFigureImageParams, vxMat, vyMat, relMat, numMicrons)
%% Check exist
fileName = 'WholeImage_WithColoredFlow.png';
%% Import parameters
% File Params
pxPerMicron = fileParams.pxPerMicron;
thetaInDegrees = fileParams.thetaInDegrees;
thetaInRadians = fil... |
e1f10f6ac7cfdd12c3c580379db4fb1e88bdc56c9d725b129a67d22badf699e9 | MATLAB | 2,836 | 108 | function L = CrossValidationSpatialCheck_HDInfo_CtrlSpkTimes_Cells(S,lambda,X,Y,ep,varargin)
nbEp = 10;
dispBar = 1;
binSize = 0.5; %in cms
nbRand = 10;
exDisp = 0;
if ~isempty(varargin)
exDisp = 1;
end
epV = regIntervals(ep,nbEp);
L = zeros(nbEp,length(S),nbRand);
S = Restrict(S,ep);
X = Restrict(X,ep);
... |
91cff78ebe4ab19e80a356b65a1385b3e8ba32f27c517a432f2db1f5bba10b6a | MATLAB | 2,837 | 77 | %% load dataset
% load('dataset_201.mat', 'df'); % load data of 201 brain regions
% load('dataset_53.mat', 'df'); % or load data of 53 brain regions
load('dataset_64.mat', 'df'); % or load data of 64 brain regions
load('onto.mat', 'onto');
ivol = cat(1, df.Volume{:}); % get volume of each... |
65da434e21e9360c44c051b22995bbef413150312062ece66ae2aac817080619 | MATLAB | 2,840 | 94 | function [L,weights] = CrossValidationAssemblyPrediction_Cells(Starget,Qassembly,Qtarget,ep)
%
% [L,weights] = CrossValidationAssemblyPrediction_Cells(Starget,Qassembly,Qtarget,ep)
%
% computs the loglikelihood prediction of the spike train Starget from the
% binned spike train matrix of its peers (Qassembly). The p... |
ec015a68952ec9991000b869d4ec1a8cfadb88caeb14a5fcaf3ad9ed949bdc2a | MATLAB | 2,840 | 83 | function [p, Cost, kX_deltas, PF] = plotCost(fC_matfun, k0, i1, i2, zlims, klabels_plot, complex_on, best_fit_on)
nplot = 151;
apos = [1.5 1.5 .83 .83];
ha = gca;
cla
cs = linspace(-1,1, nplot);
ds = linspace(-1,1, nplot);
if i2==5
ds = linspace(-k0(5), k0(5),nplot);
end
[Cs,Ds] = meshgrid(cs,ds);
Cost = NaN(nplo... |
1e00c6e5eb741019e776a681d1310b778478f27e337dcbebdf0fb802bc8f3f48 | MATLAB | 2,841 | 94 | function [L,weights] = CrossValidationAssemblyPrediction_Cells(Starget,Qassembly,Qtarget,ep)
%
% [L,weights] = CrossValidationAssemblyPrediction_Cells(Starget,Qassembly,Qtarget,ep)
%
% computs the loglikelihood prediction of the spike train Starget from the
% binned spike train matrix of its peers (Qassembly). The p... |
556f81604f10e9db1302aa4efc71f2acdd0f7a61aab60806d7a6749accca4af2 | MATLAB | 2,841 | 98 | clear all
[task_info,supPath,MaestroPath] =...
loadDBAndSpecifyDataPaths('Vermis');
NUM_COND = 20;
req_params.remove_repeats = 0;
req_params.grade = 7;
req_params.cell_type = 'CRB|PC|BG|SNR';
req_params.task = 'choice';
req_params.ID = 4000:5845;
req_params.num_trials = 180;
req_params.remove_question_marks =0;
r... |
fc593314acd37f13c59374867a1aef9b4a5c36c5e77af53372301375e2412292 | MATLAB | 2,842 | 89 | function [scratch] = train_gnb(trainpats,traintargs, in_args, cv_args)
% Use a Gaussian Naive Bayes classifier to learn regressors.
%
% [SCRATCH] = TRAIN_GNB(TRAINPATS, TRAINTARGS, IN_ARGS, CV_ARGS)
%
% The Gaussian Naive Bayes classifier makes the assumption that
% each data point is conditionally independent of the ... |
e2f51ceb34d802cfbabf422fb2530a045c153385f802d51b6b99bf268a80af7f | MATLAB | 2,845 | 77 | function net = mdn(nin, nhidden, ncentres, dim_target, mix_type, ...
prior, beta)
%MDN Creates a Mixture Density Network with specified architecture.
%
% Description
% NET = MDN(NIN, NHIDDEN, NCENTRES, DIMTARGET) takes the number of
% inputs, hidden units for a 2-layer feed-forward network and the
% number of centre... |
0335be32ac03a7036476a55d171d55985ea0c978f39499771e40f2a330970f10 | MATLAB | 2,847 | 95 | function [cData] = get_cData(beh_dat,sess_list,RF)
cData = [];
for iSess = sess_list
tempcData = extract_cData(beh_dat{iSess});
if(RF==1)
tempcData = tempcData(tempcData(:,4) == 3 | tempcData(:,4) == 0,:);
end
cData = [cData;tempcData];
end
end
function cData = extract_cData... |
3d5b0e3a80bbd70fc0d173a8670a4171fcd543b3468ae689adbc0eb9e577b964 | MATLAB | 2,849 | 89 | % first stim mark is start of cue
% second stim mark is start of movie
% third stim mark is start of question
% fourth stim mark is end of question
function plotHistISI(sbjNum)
rawDataDir = ['C:\Users\mn0mn\Documents\ResearchProjects\spatailAttentionProject\RawDatafNIRS\Experiment' num2str(sbjNum)];
load([rawData... |
287fec6278254e796060e94d955d3434c3ed462dbce89788d54908e46a4c63ea | MATLAB | 2,850 | 100 | function play_movie( x, varargin )
% *WAVE*
%
% PLAY MOVIE image/surface animation of the input datacube
%
% INPUT:
% x - datacube (r,c,t)
%
% -- optional string-identified arguments:
%
% plot_method - choose either imagesc or surf plot
% color_range - z-axis scale
% time_range - sample numbers to plot
% ydir - y-di... |
079d664707faff75a10060aaaf812cbf61eeedd07b461642330d266ce79ed70b | MATLAB | 2,851 | 100 | clear
[task_info,supPath] = loadDBAndSpecifyDataPaths('Vermis');
req_params.grade = 7;
req_params.cell_type = {'PC ss', 'PC cs', 'CRB','SNR','BG msn'};
req_params.task = 'speed_2_dir_0,50,100';
req_params.num_trials = 90;
req_params.remove_question_marks = 1;
raster_params.align_to = 'targetMovementOnset';
raster_pa... |
cae168b8be500584b21b230d9e049e3d73353ce13c28fb930f6d962749684f19 | MATLAB | 2,851 | 99 | % Dependency of LoadPosition.
% [CleanWhl GoodRanges] = CleanWhlFR(pos, StretchLen, JumpSize,Gap);
%
% "cleans up" a position file by interpolating missing stretches
% up to StretchLen long (default 20), for which the endpoints
% don't differ by more than JumpSize, (default 30).
%
% also returns the ranges where the w... |
ca3f401744e7aaf03c42d0b9f7671df63ec12a677572968ef59fc93636c494ad | MATLAB | 2,854 | 60 | function msBatchAlignmentIntensity(dirName)
%MSBATCHALIGNMENT Summary of this function goes here
% Detailed explanation goes here
%--------- Alignment parameters ----
[optimizer, metric] = imregconfig('monomodal');%'monomodal');
%filter templates for preprocessing alignment frames
hLarge = fspecial(... |
c38943ebc14fadb2005c28e48978c9e1710a3cba656f3ae29a7858983a347d7d | MATLAB | 2,855 | 79 | %{
Combined plot of multiple neuron groups' spikes
%}
% params
Ts=500;%3000;%500; % start time to plot
Te=Ts+2000;%2500;%size(SPKste,1); % end time to plot
Ns=1; % starting index of spikes to plot
Ne=1600;%1764; % ending index of principle cell spikes to plot
Nei=833;%588; % ending index of interneuron spikes to plo... |
e34c9dea716c9d58207371d5794790683f8b02a9d11973a9d2954788f3d5341b | MATLAB | 2,856 | 86 | function EcodeOverview
% Prints out a pdf overview of the ecodes recorded in a cell
% Morphys code is necessary for this to function
% Bioinformatics toolbox is necessary for the suptitle function to work
% Instruction: insert the filename to be saved (use the cell name)
% Select the abf files % setups... |
77f05c255e164ba3b1025f48c3b657322ad11df8a1b2e1dcbe2c84ceebbce955 | MATLAB | 2,861 | 87 | function [I_si_h, I_ind_i, I_multi] = computeMultiSpinsInfo(eta_vals, eps_vals, j_vals)
% computes the information that the set of spins {couplings j_vals} has about the
% latent field
% warning: number of terms computed goes at 2^length(j_vals)
if length(j_vals) > 15
I_si_h = nan;
I_ind_i = nan;
I_multi =... |
d663a48af67741ebfe4c4be681208d154ae03c74a4a6e0472df0005251e960d1 | MATLAB | 2,865 | 88 | function [A,B]=platt(out,target,prior0,prior1)
% find the coefficients A and B such that the posterior probability
% of P(y=1|x) = 1/(1+exp(A*f(x)+B)), where f(x) is the output
% of the SVM
%
% out: vector of outputs of the SVM on a validation set
% target: validation labels
% prior0: number of negative points
... |
bb34459e62419473ae4d63cec713e3ded4b4cbc2fba794aaca0e928946741c74 | MATLAB | 2,866 | 98 | function [errs warns] = unit_get_group_as_matrix()
errs = {};
warns = {};
subj = init_subj(mfilename,'test');
nObjectsPerGroup = 4;
nVox = 10;
nTimepoints = 100;
p_1 = ones(nVox,nTimepoints) * 10;
p_2 = ones(nVox,nTimepoints) * 20;
p_3 = ones(nVox,nTimepoints) * 30;
p_4 = ones(nVox,nTimepoints) * 40;
nConds = 3;
r... |
ff7c1d80632fa1ae7903da6100f734b350b9610cef3664fe55dc210b8120463a | MATLAB | 2,867 | 75 | function [G,h] = build_G_h_linear2d(b,delta_c,transformationModel)
% BUILD_G_H_LINEAR2D Builds the equation system G*p = h
%
% INPUT ARGUMENTS
% b - b = [b2 b1]'
% delta_c - c_fixed - c_moving
% transformationModel - Transformation model (translation or affine)
%
% OPTIONAL INPUT AR... |
4c7fc6908145862c401f4b1b09ecc0d5ec31abe60a5fd7607c13fad107e6c313 | MATLAB | 2,872 | 88 | function [subj, patstem] = ebc_tutorial_adv_optimize(subj, default_params, varargin)
% Performs averaging to increase EBC generalization performance.
%
% [SUBJ] = EBC_TUTORIAL_ADV_OPTIMIZE(SUBJ, DEFAULT_PARAMS, ...)
%
% Performs spatial and temporal filtering on a given pattern, given
% a set of default parameters fo... |
6f7e2a5e2c5e2b3dc3036843c1c6849eaaf7eace4e6707472b9d4c5295ccb63d | MATLAB | 2,873 | 71 | function [sin_pred, cos_pred, ang_pred] = pSVR_predict(data_psvr, labels_psvr, mask_psvr, missing, p)
% function [sin_pred, cos_pred, ang_pred] = pSVR_predict(data_psvr, labels_psvr, mask_psvr, missing, p)
%
% Initialize multitarget/periodic support vector regression (pSVR). First,
% labels are transformed into a sinu... |
64fb45bf7930014bf079228f87c438036abaff4273b98fdd248a3735df043d64 | MATLAB | 2,874 | 62 | % function select_ind = select_peak(search_range,all_results)
%
% This function selects the index of search_range which is the largest
% (same as max), but if several peaks exist it picks the most stable. The
% function has a tendency to select values at the extremes which is
% slightly corrected by switching a c... |
efd3f3f1db44ed82699fcfd1282ae01461fcc2e5acd51bf57741ca45217f64ea | MATLAB | 2,874 | 94 | function multCond_chosen_action_val(cfg, i_sub, i_run, hbi_model_path, output_path)
%% Load hbi model parameters
cbm_hbi = load(hbi_model_path);
hbi_output = cbm_hbi.cbm.output;
winning_model = 1;
hbi_params = hbi_output.parameters{winning_model,1};
%% Load behav_data .mat
behav_... |
7cfde32d74af8805b9df4dfc820010e10fa7fe1ff8d8d65d141f5ad8b4d0ca21 | MATLAB | 2,878 | 72 | % util unit test
%
% mv_select_train_and_test_data
tol = 10e-10;
% Random data
N = 100;
ntime = 30;
X = randn(N,40,ntime);
clabel = randi(3, N, 1);
train_indices = [1:N/4, 80:N];
test_indices = setdiff(1:N, train_indices);
n_tr = numel(train_indices);
n_te = numel(test_indices);
cfg= [];
cfg.preprocess_fun = []; % ... |
f548eaed4be257ac3990a959280ba8babe9400862ff4977d312eec430c4d30a5 | MATLAB | 2,880 | 80 | function combineOCTHistToOneImage(alignedImagesFolder,outputFolder)
% This function takes image pairs and generates a nice overview image
%% Inputs
% Folder with aligned images (input folder)
if ~exist('alignedImagesFolder','var') || isempty(alignedImagesFolder)
alignedImagesFolder = [pwd '\dataset_oct_histo... |
7c668f05fb37310453ff3fbd008a4dbec2f89a38e17b856995f4286720c622f6 | MATLAB | 2,882 | 77 | % function warningv(msg_id,msg,varargin)
%
% This function makes sure that a warning is only printed once at each
% level of execution. For example, in feature selection a warning message
% should only be printed once. It is similar to warning.m, but does not
% allow input similar to fprintf as would be possible ... |
8a4f6bd69b2bfd00840dce2fc36c4c016432a8d85cd0c374a3f04e1e4bf48a3a | MATLAB | 2,882 | 87 |
load('\\storage1.ris.wustl.edu\kerschensteinerd\Active\Emily\Noise\Analysis\ProcessedData\RFSpatialNL_04242023\Ai148_Grm6Cre_NoiseSpatialNonlinear_radius40_110818_1_5.mat')
%% decide the targeting position
figure;
plot(mean(Rd_posi(:, 3, :), 3, 'omitnan'));
%% decide the targeting ROIs
figure;
plot(squeeze(Rd_p... |
8e6e12a77c51b46f435b793e0f48df56e09040a5297dfca1099ce6cf30919f87 | MATLAB | 2,883 | 112 | % Filename : eegprocessor.m
% Author : Andreas Seas
% Created : 17-11-2021
% Edited : 21-11-2021
% Descr : ***
% *** means fix me
%% clear slate
close all;clear;clc
%% Set up Workspace
disp("Set Up Workspace");
homedir=pwd;
addpath(genpath('/Users/as822/Box/PST Project'));% add the path of al... |
a28900922c4a56b8899262ebb4cab99debcb4603847aa8356b0d13ba27da17cf | MATLAB | 2,889 | 79 | % function [AUC, zAUC, p] = AUCstats(decision_values, true_labels, labels, plot_on)
% computes the area unter the curve (AUC) and the corresponding z-value
% zAUC for the n-by-1 vector of decision values, corresponding n-by-1
% vector of true labels. The order of assigned labels is given by the
% variable "labels. ... |
b364501f4e5cf869067c945375e19ec752832ae7e72f5349970c0644c72bb613 | MATLAB | 2,890 | 105 | %% Pre-Post improvements
clear all;
clc;
mdir=pwd;
cd('../Data')
load PrePost.mat
cd(mdir);
%% Performance pre-post 1) construct mat
ExPP=PrePost;
den=0;
Exprs=[1:4];
avgFxs.PFs=[]; avgFxs.Crv=[]; PCEx=[];
for ex=1:4
expr=Exprs(ex);
for fr=1:2 %%% fr =1 orientation, fr = 2 color
... |
59ef07126e04362476999a648aa5c51dae3b403b8cadf30819c03180e02cf121 | MATLAB | 2,891 | 88 | %% Plotting goodness-of-fit p-val, median split of sessions based on jackknife estimates of sensitivity and bias
%% Code
Trials = ["All","RF"];
split_sessions.parameter = ["sens_cued","LR_cued","sens_opp","LR_opp","sens_cued_mi","LR_cued_mi","sens_opp_mi","LR_opp_mi"];
sessions_list = {[4:15 18 20:24] , [34:45]};... |
7473d0f273af04883ad538f4abba7a8a7798607a0333318dcedd6926d051db02 | MATLAB | 2,892 | 90 | % function save_fig(filename, cfg, fighdl)
%
% This function serves to save a figure (the current axis) as
% matlab-figure + other formats (if desired).
%
% INPUT
% filename: name for the file to be saved to (extensions will be added
% automatically)
%
% OPTIONAL INPUT
% cfg.plot_design_formats: if ... |
cec4aae2fb742a3c0164bbd5cc49a791a462bec51e1148a6e86ecd5bd8ba3451 | MATLAB | 2,892 | 77 | function val = minc_variable(hdr,var_name,att_name)
% Read a MINC variable inside a header
% VAL = MINC_VARIABLE( HDR , [VAR_NAME] , [ATT_NAME] )
%
% HDR (structure) a minc header read with MINC_READ
% VAR_NAME (string, optional) the name of a variable.
% ATT_NAME (string, optional) the name of an attribute.
% VAL (va... |
3eb5d57dd9847a6f02b17bfa0977f1fffbecbc72f41690cd222cb21b2a9faa5e | MATLAB | 2,893 | 90 | function transformationMatrix = create_transformation_matrix3d(varargin)
% CREATE_TRANSFORMATION_MATRIX3D Create a 3D transformation matrix
%
% [transformationMatrix rotationMatrix shearMatrix scaleMatrix] = ...
% create_transformation_matrix3d()
%
% INPUT ARGUMENTS
% N/A
%
% Optional input arguments
% 'phiX' ... |
7c875e39cb161d7a4bdb4d97406f22879c717d34ba79a7b26a6be5f31d22e6e9 | MATLAB | 2,897 | 72 | %This script converts JSON version to the latest version
%File path to search for JSONS
filePath = 's3://delazerdamatlab/Users/OCTHistologyLibrary/LB/';
%% Search for JSON files
fprintf('%s Finding JSON Files\n',datestr(now));
folder = awsModifyPathForCompetability([fileparts(filePath) '/']);
ds = fileData... |
763f6c57246b6fbea662664d796af95312d6f751a5d48cf025bad5472ec8922d | MATLAB | 2,898 | 100 |
% Script to calculate the split-half reliability of the BFCA as a neural
% measure.
delay = p.psvr.delay;
% delay = 12;
p.psvr.label = 'target';
all_results = pSVR_load_results(p, 'grid');
[bfca_cv, voxel_cv, fwhm_cv] = pSVR_nested_cv(all_results, p);
n_perm = 1000;
% Run analysis
for i_sub = 1:numel(p.subjects)
... |
a5c12f92d04c39f10498153f68fa45dc92ac9247b9211c119aa8e204f5929189 | MATLAB | 2,900 | 76 | function [subj results] = ebc_tutorial_adv_xval(subj, patstem, default_params)
% Perform cross-validation experiment for advanced EBC tutorial
%
% [SUBJ RESULTS] = EBC_TUTORIAL_ADV_XVAL(SUBJ)
%
% Performs one cross validation experiment for each regressor in
% the group 'baseregs_grp'. The PATSTEM argument will hav... |
c3fd1be46e2a54d358a294173a63da24de013481a7ff10f7053dea2b3907ff1b | MATLAB | 2,900 | 96 | function fig = plot_dff_fragments(step1, step2, Eventname)
i1 = step2.(Eventname).i1;
i2 = step2.(Eventname).i2;
D = step2.(Eventname).D;
x = step2.(Eventname).x;
mean_frag_raw = step2.(Eventname).dff_PETA.matrix_mean;
std_frag_raw = step2.(Eventname).dff_PETA.matrix_std;
mean_frag_zscore =... |
246597ac354b093f6856c0fd13507816b1369feec67eb589bef3cba7e9e25c3f | MATLAB | 2,903 | 73 | function FlowClusterTracksMovies(fileParams)
%% File Parameters
exportFolder = fileParams.exportFolder;
pxPerMicron = fileParams.pxPerMicron;
secBtwFrames = fileParams.secBtwFrames;
thetaInRadians = fileParams.thetaInRadians;
cmap = flipud(Rainbow(22)); cmap = cmap(3:end, :);
%% Define folders
flowExportFolder = [exp... |
254baeebcdd1d7c55582fe31268a424434729b7efb1ac26734e9f6641e482261 | MATLAB | 2,903 | 108 | % Visualization of spiking activity in our model
%% generate latent process, random coupled spins, covariance matrix
N_F = 5;
N_neur = 128;
J_if = randn(N_neur, N_F)/sqrt(N_F);
%
T = 150;
dt = 0.003; % in seconds
N_T = ceil(T/dt); %
tau_F = 15 + 0*logspace(-1, 1, N_F); % in seconds
h_F = zeros(N_T, N_F);
for i_f =... |
b53b157357fee771780217689fb70d8a2fad24eb9ab42f8a8b87d5daf8442e47 | MATLAB | 2,908 | 126 |
clear
[task_info,supPath,~,task_DB_path] = loadDBAndSpecifyDataPaths('Vermis');
EPOCH = 'reward';
PLOT_CELL = false;
req_params = reqParamsEffectSize("both","both");
lines = findLinesInDB (task_info, req_params);
cells = findPathsToCells (supPath,task_info,lines);
cellType = cell(length(cells),1);
cellID = nan(le... |
08686a4656853370f48310aba57ac9bdca658c5de745e567eba6857b79f491ad | MATLAB | 2,910 | 88 | % This script compares cross plane distance of algorithm vs fine alignment.
%% Inputs
libraryNames = {'LC','LD','LE','LF','LG'};
%% Load data
st = loadStatusReportByLibrary(libraryNames);
%% Filter
goodI = computeOverallSectionQuality(st) == 2;
% Pull data
goodI = find(goodI);
d_StackAlignment = st.se... |
0a00819b8dddac7089140132aaf8cd26d39c9f99b5316846e3f2145929e597c1 | MATLAB | 2,912 | 110 | function [varargout] = size2(x, dim, cellflag)
% SIZE2 Size of cells in cell-array.
%
% Use as:
% [varargout] = size2(x, dim, cellflag)
%
% Input arguments:
% x = cell-array
% dim = dimension argument, default []
% cellflag = true (default) or false. If false, size2 behaves as size,
% otherwise the size... |
38e91fe4955c7e6a4a647180db8e72de0cad3fc1207b8734fefbb23f58885e8b | MATLAB | 2,913 | 73 | function [flavorNameString, priorityStruct] = MachGetPriorityFlavor
% OS X: ___________________________________________________________________
%
% MachGetPriorityFlavor returns the priority flavor mode of the main
% MATLAB thread, either 'THREAD_TIME_CONSTRAINT_POLICY' or
% 'THREAD_STANDARD_POLICY'.
%
% The seco... |
c9830096faf6190458f1a9ab12bdc5b3de520ca6d6a9eabb990d0a429f03259a | MATLAB | 2,914 | 150 | function [errmsgs warnmsgs] = test_perfmet_xcorr(varargin)
% USAGE :[ERRMSGS WARNMSGS] = TEST_(type)
%
% This is a script that tests the cross correlation performance metric.
%
% INPUT ARGUMENTS:
%
% STDOUT (optional, default = true) If true, prints errors and
% warnings to screen as they occur.
%
% OUTPUT ARGUMENTS:... |
005894b75072e8abfe2a29d5dd02fa076aba80459814c48c39f19b0b1dcd5956 | MATLAB | 2,917 | 79 | function allTracks_analyzed = analyzeTracks(allTracks,MotionThresh,obj)
figure
for i = 1:length(allTracks)
%calc change in X coordinate between frames
X = allTracks(i).centroids(:,1);
dX = zeros(length(X),1);
dX(2:end) = X(1:end-1);
dX = dX - X;
dX = dX(2:end);
%remove frames when worm is s... |
9b9b5380c1836e14f5d31e8c55a6918142af7b5ab62495b7f5318225a8571ebe | MATLAB | 2,917 | 74 | function pipeline_patdif_reward_at_cue_decoding()
cfg = load('neurovar_cfg.mat').cfg;
for i_sub = 1:44
fprintf(strcat('Decoding Sub_', string(i_sub),'\n'));
pipeline_dir = fullfile(cfg.global.deriv_dir, 'pipeline_patdif_reward_at_cue_1st', ...
strcat('sub-',sprintf('%0... |
8504c1c5e242be58e85d495ba6f20c5e5e35d84bae56b730a36fd586173c93b0 | MATLAB | 2,921 | 79 | function [results_data] = plot_cue_opp_indivSig_histo(cueMI, oppMI, cue_sig, opp_sig, iMonkey, xl, yl, bin_size, trial_type, prop_flag, plot_flag)
%% Plots V4 f-MI cued and opp histos separately with overlaid shaded histograms showing individually significant units
%% Code
iWindow = 1;
windows = ["cue_onset","pre_bla... |
0c49e8fcb5ca6f3cbc2a9fa1156077e9273bd46b411bd5422b369f5091229baf | MATLAB | 2,924 | 77 | function [null_hom_ratio, null_hom_recon_FC_r]= homology_non_signals_Human(U,demean_signals,RX_signals,null_graph)
% % =========================================================================
% % Generate 1000 random non-homology regions' signal further calculate reconstruction accuracy
% % =====================... |
31cd8736cd8b43ae3d97344cafca80ed11545c7dcf8023af083cc0537bb1c2f5 | MATLAB | 2,926 | 103 | function demmet1(plot_wait)
%DEMMET1 Demonstrate Markov Chain Monte Carlo sampling on a Gaussian.
%
% Description
% The problem consists of generating data from a Gaussian in two
% dimensions using a Markov Chain Monte Carlo algorithm. The points are
% plotted one after another to show the path taken by the chain.
%
% ... |
5542270d6c3f89782d915ed95da7748587266c70a13e328a8d516dd1da84ec4c | MATLAB | 2,926 | 72 | function plotScalingVerification(x_reps_T1, eta_T1_infTC, eps_T1_infTC, T1_tau_vals, T1_taurep_vals, gamma_fit_summary, ii_rep, jj_t, t1_codes)
h_Fig = makeMyFigure(30, 10);
tiledlayout(1, 4, 'TileSpacing','compact');
h_sp = zeros(4, 1);
for ii =1 : 4
h_sp(ii) = nexttile;
end
data_color = [0.4940 0.1840 0.5... |
20f0d4cc5667d15520c591ddee6650aaa2e3e8edc9436b0e082704430d48b822 | MATLAB | 2,937 | 85 | % Preprocessing unit test
%
% oversample
tol = 10e-10;
N = 99;
X = randn(N,1);
% Get default parameters
param = mv_get_preprocess_param('oversample');
%% if data is already balanced then oversampling should have no effect
clabel = ones(N,1);
clabel(N/3+1:2*N/3) = 2;
clabel(2*N/3+1:end) = 3;
[~, ~, clabel2] = mv_pr... |
242d38addf3533a9b9c12074ef47fec06a32aa3058f3c313d223030127e6ba32 | MATLAB | 2,938 | 89 | function guess=find_bpn2_2(neurons,coords,C,guessed)
for i=1:size(coords,1)
stds(i)=std(neurons(i).coords(:,5));
end
[~, sorted_ids]=sort(stds,'descend');
guess=[];
selection=sorted_ids(1:50);
num_neu=3;
additional_factor=1;
selection(ismember(guess(:),selection))=[];
combinations=nchoosek(selection,n... |
3f2022a70856016570cd59a3bfe73d5ed1dae28d392475d6389dea455e5323ca | MATLAB | 2,939 | 86 | function params = ComputeCrossCorrPrediction(preTrain,postTrain,sPost,binT,varargin)
% Finds the optimal gaussian window (s.d. and time-lag) that integrates pre-synaptic spikes to
% predict post synaptic spike train.
%
% USAGE
%
% weights = ComputeCrossCorrPrediction(preTrain,postTrain,sPost,rg,binT,r,binT)
%
% ... |
503113ce226e23463a25871035c601f5243d0942e04793330c0881dfa2fdf55d | MATLAB | 2,939 | 105 | function [A sizes] = fill_clusters(vol, adj_list)
% Fills in all non-zero clusters with unique sequential integers.
%
% Usage:
% [A] = FILL_CLUSTERS(VOL)
% [A] = FILL_CLUSTERS(VOL, ADJ_LIST)
%
% Operates on a 3D mask volume. Iteratively searches out
% connectivity for only the non-zero voxels in VOL, using the
% a... |
b81359b0ab8448d1d3ecc1682f0b192a0f34b1828692804d4e58f3dbd60aa75a | MATLAB | 2,942 | 61 | function RE = estimate_registration_error(fixedCoordinates,movingCoordinates,displacementField,spacing)
% ESTIMATE_REGISTRATION_ERROR Estimates the target registration error
%
% RE = estimate_registration_error(...
% fixedCoordinates,movingCoordinates,displacementField,spacing)
%
% INPUT ARGUMENTS
% fixedCoordinates... |
aa7a62d4834b17490b82fdc06152ee65a9d65d78bcbb14ff3240dad28608383f | MATLAB | 2,943 | 64 | function layer = classificationLayer( varargin )
% classificationLayer Classification output layer for a neural network
%
% layer = classificationLayer() creates a classification output layer for
% a neural network. The classification output layer holds the name of the
% loss function that is used for trai... |
db30c2b2d5e6fb1a28d32fbd9a57ce0edd1581e14319397357b49339faf358d4 | MATLAB | 2,946 | 96 | function [subj] = remove_timepoints(subj,objtype,objin,actives_selname,varargin)
% Remove timepoints from an object or group
%
% [SUBJ] = remove_timepoints(SUBJ,OBJTYPE,OBJIN,ACTIVES_SELNAME,...)
%
% Feed in a selector ACTIVES_SELNAME with 1s for the timepoints that
% you want to keep, and an object/group OBJIN name f... |
ea40952c5e72c758a91ad5fb196647baf559f9402e4cd6888ef755e2c8cbe592 | MATLAB | 2,947 | 81 | %% Plot Sel Fit
ttl{1,1}=["Hits and False Alarms - Without MB"];
ttl{1,2}=["Misses and False Alarms - Without MB"];
ttl{2,1}=["Hits and False Alarms - With MB"];
ttl{2,2}=["Misses and False Alarms - With MB"];
rsq2=zeros(2,2);
rsq2_err=zeros(2,2);
rsq2_jk=zeros(1,64);
fold = ["even","odd"];
subset=["hfa","... |
7d7f0c79000d322bbe7ead8c53cc844693f6242a7ec985062d988bae91b8c95e | MATLAB | 2,955 | 91 | function bhvr = bhvr_load(p)
% function bhvr = bhvr_load(p)
%
% Loads behavioral data from the MRI experiment for all subjects.
%
% Input:
% - p: Structure with analysis parameters.
%
% Output:
% - bhvr: Structure with all relevant behavioral variables. Fields
% include:
% resp_err: Matrix with respons... |
7ca276024d3dc9f6e427645fd2905515dd965f175dd3a559331da050d69e6768 | MATLAB | 2,956 | 70 | function [pparam, X, clabel] = mv_preprocess_oversample(pparam, X, clabel)
% Oversamples the minority class(es) in unbalanced data.
%
%Usage:
% [pparam, X, clabel] = mv_preprocess_oversample(pparam, X, clabel)
%
%Parameters:
% X - [samples x ... x ...] data matrix
% clabel - [samples x 1] vector of... |
6270b842475d154994d8e09dd7ddee0411423c77fd717e629a376cac81c4bb16 | MATLAB | 2,961 | 69 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This script is meant to be used as part of a wrokflow aimed to calculate yap overal expression and YAP
% nuc/cytoplasm ratio in fixed cells seeded on PAA gels.
% Written by Sefora Conti
% Affilition: Xav... |
4cec6932bac00c4d0dd89c6e12364e21e716ec14b42301d4c341031442bdd178 | MATLAB | 2,962 | 99 | % figure 2A: measuring sample dissimilarity matrix
% last update: October 24 2022
% Amirhossein Farzmahdi
clear
close all
clc
% add path
addpath(genpath('functions'))
images_dir = 'mat_files/';
% loading data
load (images_dir+"names")
load(images_dir+"imgs")
load(images_dir+"bw_masks")
% setting
nexemplar = 25;
nvi... |
7f61b256e5b16e9aba6d5aafa3a52464ff49182cadc4252a38cdceeb429b8745 | MATLAB | 2,963 | 46 | function [ mask ] = ConnectPointsAlongPath( InputImg, StartPt, EndPt )
%ConnectPointsAlongPath: Traces path between two points while constrained to
%path of pixels with value one.
%Find the shortest path connecting the centroid and the endoiints.
%This script duplicates the image, and finds the path (following o... |
d6548b599807f23b4e3df4acdf9b12e32973c1c45893f092f1c83dc73bfb3797 | MATLAB | 2,963 | 108 | function [thetaMod,thetaPh,thetaTrghs,thetaPks,goodEp] = ThetaPhase(fbasename,channel,ep,S,varargin)
% [thetaMod,thetaPh,thetaTrghs,thetaPks] = ThetaPhase(fbasename,channel,ep)
% optional:
% [thetaMod,thetaPh,thetaTrghs,thetaPks] = ThetaPhase(fbasename,channel,ep,folder)
% to indicate another folder where the lfp file... |
e22a9ff2507bcf78279313acff424eef8267264e7429572aea875f98cc3d6ebf | MATLAB | 2,965 | 68 | function [decisionStumpClassifier initialweight] = decisionStumpInitialize(trainpats,traintargs,epsilonconstant,seed);
% [DECISIONSTUMPCLASSIFIER INITIALWEIGHT] = DECISIONSTUMPINITIALIZE(TRAINPATS,TRAINTARGS,EPSILONCONSTANT,SEED);
%
% Implements decision stump weak learners. See weaklearnerinitialize_template.m f... |
fce3a64c73d3eb96a90e61228bc5e96947779c3f87e7a78fcf29ce4f88e17eb1 | MATLAB | 2,965 | 83 | function [subj] = shift_regressors(subj,old_regsname,runsname,nShift,varargin)
%
% [SUBJ] = SHIFT_REGRESSORS(SUBJ,OLD_REGSNAME,RUNSNAME,NSHIFT,...)
%
% Shifts regressors ahead in time to account for hemodynamic lag.
% Modifies the regressors OLD_REGSNAME by shifting NTIMEPOINTS forward within
% each run specified by ... |
54cfcc8df8db50c4b469711f743fb6d24df795c8ed5afb266b4383d96577551a | MATLAB | 2,966 | 84 | function [analysis_complete, predictions, results, first_voxel, first_fwhm] = pSVR_check_progress_grid(sub_id, p)
% function [analysis_complete, predictions, results, first_voxel, first_fwhm, first_tr] = pSVR_check_progress_grid(i_sub, p)
%
% Checks whether a final result file or temporary result file for the
% curren... |
c1d4eb0c33da675f1e3e1421554eaa202405c3c0a483e2bf6e664d5c0e17423b | MATLAB | 2,967 | 97 | function awsCopyFile_MW1(source, dest)
%This function is a hack to copy files to the cloud from a Matlab Worker.
%Matlab Parallel Computing Clusterdoes not have AWS CLI support (August 2019), so
%we cannot use awsCopyFileFolder from a Matlab cluster.
%But what if you did want to upload a file to AWS within a worke... |
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