sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
53406505e677ba9fc9d04d3953f5ec0b6d43905d842657811c8e24717100de0f | MATLAB | 1,570 | 43 | function actImg = calcActivityOverviewImg(pixCov, diags, h, w)
% actImg = calcActivityOverviewImg(pixCov, diags, h, w) calculates an image
% displaying the variance of the correlation of a pixel with its neighbors,
% which gives a visual impression of where active cells are.
C = corrcovDiag(pixCov, diags);
actStd = st... |
cc9dc188b1580654c9a57c4ba4b8756395d3f6f983048974f0fe1c04baa0d51f | MATLAB | 1,575 | 53 | function [loss,gradients,mseF1,mseF2,mseF3,mseF4,mseF5]=modelLoss(parameters,net1,net2,CP,CB1,CB2,CBIC,CBt,U0,Ux0,w1,w2,w3,w4,w5)
net1.Learnables=parameters(1:6,:); net2.Learnables=parameters(7:end,:);
% make predictions
U=forward(net1,CP); vel=forward(net2,CBt);
gradientsU=dlgradient(sum(U,"all"),CP,EnableHigher... |
06eb75b3663e179ddb2cf653c9be778fb41d1672a3b8972995dfc373b176d40e | MATLAB | 1,609 | 65 | function Ahat = nearestSPD(A)
% nearestSPD - the nearest (in Frobenius norm) Symmetric Positive Definite matrix to A
% usage: Ahat = nearestSPD(A)
%
% From Higham: "The nearest symmetric positive semidefinite matrix in the
% Frobenius norm to an arbitrary real matrix A is shown to be (B + H)/2,
% where H is the symmetr... |
be750086bd37357f3a817d0947bc02fb25e90e232d6218c2f842cb0d5b391efc | MATLAB | 1,612 | 51 | function [u,flag]=pme2d_rhs(dt,u0,xpt,template)
np=length(xpt);
dx=xpt(2)-xpt(1);
u=u0;
flag=0;
while 1
res=zeros(np+2,1);
ajac=sparse(np+2,np+2);
i=1;
res(i)=u(i);
ajac=ajac+sparse(i,i,1,np+2,np+2);
i=2:np-1;
res(i)=(u(i+1).^2+u(i-1).^2-2.*u(i).^2)./dx.^2.*... |
8759dcb088b0ea9800089b42464a152581a94f508e5ca66bb1d453acd10337e8 | MATLAB | 1,618 | 44 | function centers = randomly_distribute_centerpoints(numberofpatterns, GridSizeN, PatternLength)
% Generated by LLM
%RANDOMLY_DISTRIBUTE_CENTERPOINTS Randomly place segment centers in an NxN area.
%
% centers = randomly_distribute_centerpoints(numberofpatterns, GridSizeN, PatternLength)
%
% Inputs:
% numberofpatterns ... |
fdf2d90150a8ae3ca821c56bb7992b34dbdd1f6fabd7797c590da9c92c1bdcec | MATLAB | 1,633 | 39 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% function assignParams(opts,params)
%
% Assigns all of the fields of the struct params to variables in the
% caller's workspace. Used for overwriting defaults with parameter inputs.
% Note that it expects 'opts', which is a list of poss... |
d22e32b403998ea510fe805ee9e22d8a3e1e5a32c2dbef28a0b1b85d0897577f | MATLAB | 1,639 | 71 | function [perf_s awhist spwhist choice_s audp]=model_CNO(delta1,delta2,aw,spw,side,cnoeffect);
perf_s=[];
choice_s=[];
awhist=aw;
spwhist=spw;
audp=[];
for i=1:length(side);
percept_rate=binornd(1,cnoeffect);
p=aw/(aw+spw);
audp=[audp;p];
awspw=binornd(1,p);
if awspw==1;
... |
361cfdcaf37e60862586e2a429df5682bdc2e739c02383f0b92582a85e231690 | MATLAB | 1,697 | 41 | function [T2_map, S0_map] = T2_fit(x_dim, y_dim, z_dim, sub_boli_number, echotimes, average, gmmask)
T2_map = NaN(x_dim, y_dim, z_dim, sub_boli_number);
S0_map = NaN(x_dim, y_dim, z_dim, sub_boli_number);
% Define the function for fitting
func = @(TE, S0, T2) S0 .* exp(-TE ./ T2);
for z = 1:z_dim... |
54d07f8bdc314531170433790fa3bc0084943dc88d0e264fe9b807868b8fa6df | MATLAB | 1,697 | 53 | function populateMovieList(obj,firstMov)
%Function to add all movies with same base name to the Movies field,
%using the addMovie method for error checking
[movDir, movName movExt] = fileparts(firstMov);
movName = [movName,movExt];
trueCounters = [];
potentialCounters = find(movName == '1');
... |
74beb02a11fd09c1d0140605cbf4f303ac2fe02ddf4d495cff8b547193a9c1f9 | MATLAB | 1,714 | 50 | function ref = meanRef(obj, movNums, sliceNum, channelNum, isAvg, isNormalize)
% Constructs a mean reference image of an acquisition after motion
% correction using the derived data structure, without requiring
% loading movie files
%
% ref = meanRef(obj,movNums,sliceNum,channelNum)
%
% mov... |
0ae12ef640f06adae89f7ac993402f9a6d940e3778f57c65ed133c3204cc1072 | MATLAB | 1,749 | 58 | function cbScrollwheel(sel, ~, evt)
%Allows interactive adjustment of the number of clusters / cuts to perform
% No region has been selected yet:
if isempty(sel.disp.cutVecs)
return
end
%Determine scrolling direction and update cluster count accordingly
nEigs = size(sel.disp.cutVecs, 2);
if strcmpi(evt.Source.Cu... |
19f0dbc2550ff5f4a80d826ed2211559afcfdcc6424a3d0e9e5641025507e4a3 | MATLAB | 1,766 | 62 | function outFile = ft_Orthogonalized_wPLI(roiFile, freqLabel, freqRange, varargin)
% Recompute debiased wPLI after symmetric orthogonalization of ROI time series.
p = inputParser;
p.addParameter('FTPath','',@(x)ischar(x)||isstring(x));
p.addParameter('Fs',600,@(x)isnumeric(x)&&isscalar(x));
p.addParameter('OutFile',''... |
742131c77a631e88ed8194e60ffcc85ffec5f74fbd8c6778f3d5b8358aee1d69 | MATLAB | 1,770 | 42 | function doManualROI(sel)
%Turn off figure click callback while drawing ROI
set(sel.h.fig.main, 'WindowButtonDownFcn', []),
title(sel.h.ax.roi, 'Drawing Manual ROI');
%If a poly somehow wasn't deleted, do it now
if isfield(sel.disp,'manualPoly') && isvalid(sel.disp.manualPoly)
delete(sel.disp.manualPoly)
end
%Dra... |
56d70acd6e2dd41aa5fe1fb768af84eccd5af7aa05c1431d7d7435b944ccf5fc | MATLAB | 1,790 | 79 | numLayers = 3;
numNeurons=20;
layers=featureInputLayer(2);
for i=1:numLayers-1
layers = [layers
fullyConnectedLayer(numNeurons)
tanhLayer];
end
layers = [layers
fullyConnectedLayer(1)];
net1=dlnetwork(layers);
net1 = dlupdate(@double,net1);
% Collocation points
ti=li... |
1182b1ca456cf9db6e80fe45f7fe06bc1ada0dc89e39b0c36bdc792f45376fd9 | MATLAB | 1,797 | 65 | function [covMat, qNhInd] = mmCovMat(pixCov, h, w, nh, qRow, qCol)
persistent mem queryCols indBlock2Diag covMatDiagsInd
if isempty(mem)
mem.h = h;
mem.w = w;
mem.nh = nh;
else
if ~(h==mem.h && w==mem.w && nh==mem.nh)
% If any of these have changed, we need to re-calculate persistent
%... |
d285b510da1f5d55bdc19b8093cfda0672f6a5f0a230a63df9ae023ceeb5a7c9 | MATLAB | 1,797 | 59 | function [loss,gradients,mseF1,mseF2,mseF3,mseF4,mseF5,mseF6]=modelLoss(parameters,net1,net2,CP,CB1,CB2,CBIC,CBt,U0,w1,w2,w3,w4,w5,w6)
net1.Learnables=parameters(1:6,:); net2.Learnables=parameters(7:end,:);
% make predictions
U=forward(net1,CP); AB=forward(net2,CBt);
gradientsU=dlgradient(sum(U,"all"),CP,EnableHi... |
57d93e83d9b6249b44006ece94473af735aabe2fe2deaad4be8ee7dd58f6be11 | MATLAB | 1,799 | 59 | function [loss,gradients,mseF1,mseF2,mseF3,mseF4,mseF5,mseF6]=modelLoss(parameters,net1,net2,CP,CB1,CB2,CBIC,CBt,U0,w1,w2,w3,w4,w5,w6)
net1.Learnables=parameters(1:8,:); net2.Learnables=parameters(9:end,:);
% make predictions
U=forward(net1,CP); AB=forward(net2,CBt);
gradientsU=dlgradient(sum(U,"all"),CP,EnableHi... |
382c5fd79c4c2f92f9a6ad6bfe0c3ff083d4f9e3f9e7f540ff3eb278ba7d5789 | MATLAB | 1,801 | 51 | function u=burgers_rhs(dt,u0,xpt,template,dtemplate)
np=length(xpt);
dx=xpt(2)-xpt(1);
u=u0;
while 1
res=zeros(np+2,1);
ajac=sparse(np+2,np+2);
i=1;
res(i)=0.01.*(2.*u(i+1)-2.*u(i))./dx.^2+u(np+1).*u(i)-(u(i)-u0(i))/dt;
ajac=ajac+sparse(i,i,-1/dt+u(np+1) - 1./(20.*dx.^2),np+2... |
3d08fbccbb97e4d6dcc3b163030bbd1234d7aa93224fa70c19d54bd85e743eae | MATLAB | 1,806 | 47 | function subjData = loadData(numSubjects, baseDir, alignType,taskType,dataType,curr_parc,scanIds,numTRs)
% Function to load data for either 'rest' or 'movie' condition
for scanId = scanIds
if strcmp(taskType,'rest')
numTR = 1800;
else
numTR = numTRs(scanId);
end
if strcmp(dataType,'gra... |
22fd86511baabb86de6fd38d2d5a11b78317eeef3d60eafc1d259d8657e2f6da | MATLAB | 1,807 | 49 | function global_se = get_mag_se(directory,task_type, align_type,curr_parc,scan_id)
% Get a list of files matching the pattern
filePattern = strcat('fc_herit_net_',task_type,'_',align_type,'_parc_',curr_parc,'_scan_',num2str(scan_id),'_jackknife_*.csv');
files = dir(fullfile(directory, filePattern));
% Count the ... |
63fa8fc5abb32071cf95bc774ac089ebdae8e7bad0542f6e5e3f0b8c93c38f9b | MATLAB | 1,808 | 76 | a=xlsread('m2 30s60s zong.xlsx','yali');
b=xlsread('m2 30s60s zong.xlsx','Caxinhao');
fs1=800;
fs2=2000;
movement=a(:,1);
calcium=b(:,1);
% Â˲¨ ÖزÉÑù
b=fir1(fs1*2,0.1/fs1*2,'high');
calcium_f=filtfilt(b,1,calcium);
b=fir1(fs2*5,0.1/fs2*2,'high');
movement_f=filtfilt(b,1,movement);
k=1;calcium_r... |
c660e75750f2abfbdbf355f2f21f7ae4988cf76bc0d24744f348a8c932069631 | MATLAB | 1,811 | 55 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% example script to generate simulated interacting brain regions and
% perform Current-Based Decomposition (CURBD). Ref:
%
% Perich MG et al. Inferring brain-wide interactions using data-constrained
% recurrent neural network models. bioRxiv. D... |
89d37c4dea51d50c3191ee4e08b693146b9053eb84585bc3dfc4b00f884f454c | MATLAB | 1,833 | 59 | function changeAcqPathToLocal(path)
%changePathToLocal.m Looks in path for acq objects necessary for roi
%selection, and changes path in object to allow for proper local
%processing.
%
%INPUTS
%path - path to local folder containing acq objects. If empty, opens
% uigetdir
%
%ASM 12/14
%check if path exists
if nargin... |
e460fc3cf3ce543c095c36680c40b8a0ff573b7d575b685e3d8175eb12303033 | MATLAB | 1,848 | 71 | function [loss,gradients,mseF1,mseF2,mseF3,mseF4,mseF5,f1,f2,f3,f4]=modelLoss(parameters,net1,net2,CP,CB,CBIC,CBt,U0,Ux0,dataVel,A,w1,w2,w3,w4,w5)
net1.Learnables=parameters(1:6,:); net2.Learnables=parameters(7:end,:);
alpha=parameters(end,:).Value{1};
% make predictions
U=forward(net1,CP); vel=forward(net2,CB... |
a2593ea110dc1b58ccba9c7de7fabd6c62f3a051de4a1f74989e7949f7e9bf3f | MATLAB | 1,859 | 42 | function [T2_map_CSF, S0, goodnessFit] = calculate_T2_map_CSF(img_data, echo_times, t2maps_path, wm_average)
% Initialize the dimensions
[x_dim, y_dim, z_dim, num_echoes] = size(img_data);
% Preallocate the T2_map matrix for each echo
T2_map_CSF = zeros(x_dim, y_dim, z_dim);
S0 = zeros(x_dim, y_dim... |
1604936b62140c7929223cb9561bbffc8be9cad6c0c7461b9b6d5c08c059962c | MATLAB | 1,866 | 46 | function [T2_map, S0, goodnessFit] = calculate_T2_map(img_data, echo_times, t2maps_path)
% Initialize the dimensions
[x_dim, y_dim, z_dim, num_echoes] = size(img_data);
% Preallocate the T2_map matrix for each echo
T2_map = zeros(x_dim, y_dim, z_dim);
S0 = zeros(x_dim, y_dim, z_dim);
goodnessFi... |
16b7f476328de131c26b020750fee77501b2012791dae0aba804ddd5cde305c5 | MATLAB | 1,890 | 47 | function plot_voxel_t2_fit(echo_times, T2_map, goodnessFit, x, y, z)
%%
% Extract voxel data
voxel_data = squeeze(T2_map(x, y, z, :));
% Check if the voxel data is all zeros
if all(voxel_data == 0)
disp('The selected voxel data is all zeros.');
return;
end
%%
... |
755d7c8c6c41bafb75cd5b399bcd2e19b9e15878a0382d78536bcb1aae4e0660 | MATLAB | 1,898 | 48 | function saveNewROI(sel,evt)
%saves new roi
if isempty([sel.roiInfo.roi.id])
newRoiNum = 1;
else
newRoiNum = max([sel.roiInfo.roi.id])+1;
end
% Save current ROI:
newInd = numel([sel.roiInfo.roi.id])+1; % Attention: newRoiIndex is no necessarily the same as the roiNumber of the current ROI!
sel.roiInfo.roi(ne... |
2534c832d0e78c36008a3a8b877756d8245b5372ad66c2f416c0159aca572d42 | MATLAB | 1,925 | 80 |
function [finalParms , finDiscrepancy]=wrap_2lp_rand(startParms,ResModData);
% options = optimset('Display','iter')
options = optimset('MaxFunEvals',500*length(startParms),'MaxIter',500*length(startParms));
[finalParms , finDiscrepancy] = fminsearchbnd(@fitModBY,startParms,[0 0 0],[10e7 10e7 10e7],options) ;... |
5818bb0ecd259740e85b43348efd108cca66400c302d0af750c3b416532bfb12 | MATLAB | 1,928 | 81 |
function [finalParms , finDiscrepancy]=wraponeloop(startParms,ResModData);
% options = optimset('Display','iter')
options = optimset('MaxFunEvals',500*length(startParms),'MaxIter',500*length(startParms));
[finalParms , finDiscrepancy] = fminsearchbnd(@fitModBY,startParms,[0 0 0],[10e7 10e7 10e7],options) ;
... |
182d69e9bc2103629b38f4f24cbbcc83263cb9c157da9abf885594c37c1cb39a | MATLAB | 1,930 | 55 | function [u,flag]=burgers_rhs_forward(dt,u0,xpt,template,dtemplate)
np=length(xpt);
dx=xpt(2)-xpt(1);
flag=0;
u=u0;
nu=0.001;
while 1
res=zeros(np+2,1);
ajac=sparse(np+2,np+2);
i=1;
res(i)=u(np+1).*u(i) - (nu.*(2.*u(i) - 2.*u(i+1)))./dx.^2-(u(i)-u0(i))/dt;
ajac=ajac+sparse(i,i,-1... |
557320a0d21507b9e5cd3d55a63b28f23968bae3ec66e2fca43534b2034bda30 | MATLAB | 1,948 | 60 | % [im,path] = loadData(path)
% ---------------------------------------
%
% Generic function to load arbitrary image stacks in the workspace
%
% Inputs:
% path Full path of the file, if not provided the function will call uigetfile
%
% Outputs:
% im Loaded image
% path Image full path
%
% ----------... |
aee54e39eaa4620ac438b2f27e5ad6db6e6e7e96b5f796bf40b2bb6934207ca5 | MATLAB | 1,951 | 57 | function outFile = ft_LateralityAnalysis(subjList, method, freqLabel, varargin)
% Compute paired hemispheric laterality indices for ROI-based connectivity.
p = inputParser;
p.addParameter('BaseDir', pwd, @(x)ischar(x)||isstring(x));
p.addParameter('Condition', 'marker', @(x)ischar(x)||isstring(x));
p.addParameter('Pai... |
b8ce55fe34b1a807efe05c25eec6313ec9fe94175a11e918a36235a8a0beeba6 | MATLAB | 1,968 | 44 | function varargout = withinFile_fullFrame_fft(obj,movStruct, metaMov, movNum, opMode)
%Example of a motion correction function compatable with Acquisition2P class,
%calculates full frame translations within each file independently, then adds
%these to shifts calculated between each file
%
% nSlices = metaMov.stackNumSl... |
3de2295b7db06e921033103fc94237df165bb0a202c24b3b5b7f57138d310368 | MATLAB | 1,986 | 61 | function mMov = avgMov(obj, keyFrames, frameWin)
%% Input handling
sliceNum = 1;
channelNum = 1;
%% Make memory map
movSizes = obj.correctedMovies.slice(sliceNum).channel(channelNum).size;
movLengths = movSizes(:, 3);
% if ~exist(obj.indexedMovie.slice(sliceNum).channel(channelNum).fileName, 'file')
% error('Can... |
815e96f18f1a6be96846767e924a26b66d249705fe5a5d8c171e735440022fba | MATLAB | 2,012 | 85 |
function [finalParms , finDiscrepancy]=wrap_onlyaud(startParms,ResModData);
% options = optimset('Display','iter')
options = optimset('MaxFunEvals',500*length(startParms),'MaxIter',500*length(startParms));
[finalParms , finDiscrepancy] = fminsearchbnd(@fitModBY,startParms,[0 0 0],[10e7 10e7 10e7],options) ;
... |
1413b220f7bfd7ec9867038278f6b07d20e1c3cd43a7f8c7ead072faf6fc1218 | MATLAB | 2,024 | 65 | function [covMat, pxNeighbors] = getCovData(sel, queryNhRad)
% [covMat, pixNeighbors] = getCovData(hFig) extracts the covariance data
% for one "seed" pixel from the covFile. This function deals with the
% different covFile formats.
persistent mem queryCols nElInRow
% Simplify variable names:
h = sel.disp.movSize(1);
... |
963f7a9ed6693f7530cf77c19291f6afa3c7b959739db6918dd51c88e50c0711 | MATLAB | 2,025 | 77 | %% Make avg movie
%tV = vecTrials(stimExpt);
movNeur = unique(tV.nTarg);
targOn = [];
for iNeur = 1:length(movNeur)
nNeur = movNeur(iNeur);
repF = find(tV.nTarg == nNeur);
for f = 1:length(repF)
targOn(iNeur,f) = tV.stimFrames{repF(f)}(1);
end
end
frameWin = -15:84;
%frameWin = -6:43;
mMov = na... |
2d8e551f417a207457d2ba6241692687702acaf655eeeeec47a8b03d97414612 | MATLAB | 2,051 | 55 | function [avgMov, dFmov] = eventTriggeredMovie(obj,avgMovFrames)
% avgMovFrames should be a 1xf cell array, where f is the number of frames
% in the average movie, and each cell contains the (data) frame numbers to
% be averaged together for each triggered movie frame
% Note that the code will not allow 'double count... |
e91fac87ace168a7ace8f3eacef08b77b92c3295bb95820389e20e13020c4631 | MATLAB | 2,062 | 84 |
function [finalParms , finDiscrepancy]=wrap_onlyaud_aversion(startParms,ResModData);
% options = optimset('Display','iter')
options = optimset('MaxFunEvals',500*length(startParms),'MaxIter',500*length(startParms));
[finalParms , finDiscrepancy] = fminsearchbnd(@fitModBY,startParms,[0 0 0 0],[10e7 10e7 10e7 1... |
d8b90708e756171bbbeddf49db9fbd768b03f2b0658dbd90bb70d4c9c84eff38 | MATLAB | 2,067 | 84 | %
%%
% spid=[];
% for j=1:1000
% a=(1:39)';
% spid=[spid;a];
% end
%
% simLearnAnimID=cell(1000,1);
% for i=1:1000
% x=spid(randperm(length(spid)));
% x=x(1:39);
% simLearnAnimID{i}=x;
% end
% save('simLearnAnimID','simLearnAnimID');
%%
%Simulate experimental derived CNO effect l... |
cd6b8dd99ce9a6dde51d3c211c9333427101a077d3ce558d819c1569a8eba8f3 | MATLAB | 2,083 | 60 | function adc_and_fa_masking_threshold(subjPath, subject_code, ADC_map_avg, FA_map_avg);
% goal: convert adc and fa maps to niftis, create frangi filter on inverted
% image
% register t2 maps to the b0
%% Paths
regDir = fullfile(subjPath, 'reg');
fa = fullfile(subjPath, 'fa');
adc = ful... |
11213b7c213e31590dbf274bc9e7438f754a832f15432c79503b71bce152ada6 | MATLAB | 2,087 | 60 | function adc_and_fa_masking_reorientating(subjPath, subject_code, ADC_map_avg, FA_map_avg);
% goal: convert adc and fa maps to niftis, create frangi filter on inverted
% image
% register t2 maps to the b0
%% Paths
regDir = fullfile(subjPath, 'reg');
fa = fullfile(subjPath, 'fa');
adc =... |
8003b5b73c7730ca0b26bed2ffd53aff0e1e2c93a70bff7f18834f9bc0dc7475 | MATLAB | 2,100 | 94 | numLayers = 3;
numNeurons=40;
layers=featureInputLayer(2);
for i=1:numLayers-1
layers = [layers
fullyConnectedLayer(numNeurons)
tanhLayer];
end
layers = [layers
fullyConnectedLayer(1)];
net1=dlnetwork(layers);
net1 = dlupdate(@double,net1);
numLayers = 2;
numNeurons=5; ... |
349ba590fbd20ca21e1df47b86d214283e178a3891339a77e683f6a13615613b | MATLAB | 2,106 | 84 |
function [finalParms , finDiscrepancy]=wrap_2lp_rand_v2(startParms,ResModData);
% options = optimset('Display','iter')
options = optimset('MaxFunEvals',500*length(startParms),'MaxIter',500*length(startParms));
[finalParms , finDiscrepancy] = fminsearchbnd(@fitModBY,startParms,[0 0 0],[10e7 10e7 10e7],options... |
6836f9be2dfe6af25c0f35b65b36588373bcbb811f494cd0898302b82194d8df | MATLAB | 2,113 | 65 | function anchor_starts = computeExactAccelEvents(reward, distance, reset, speed, reward_starts, speed_threshold)
% Parameters
window_size = 2000; % 100ms window
min_distance_cm = 50; % 50 cm minimum
smooth_speed = smooth(speed, 100);
acceleration = [0; diff(smooth_speed)];
global_accel_tolerance = (max(acceleration) -... |
03e9eb3c9cd4ecec09fccb2d562568a94f9f8336212d092c3a5ee85bcc14c76b | MATLAB | 2,150 | 59 | function [u,flag]=burgers_rhs_forward_2(dt,u0,xpt,template,dtemplate)
np=length(xpt);
dx=xpt(2)-xpt(1);
flag=0;
u=u0;
nu=0.001;
while 1
res=zeros(np+2,1);
ajac=sparse(np+2,np+2);
i=1;
res(i)=nu.*(2.*u(i+1)-2.*u(i))./dx.^2-u(i).*(u(i+1)-u(i))./(dx)+u(np+1).*(u(i)+xpt(i).*(u(i+1)-u(i)).... |
b4039c8e01e82c8b4695c2a8586c2fdbde97e04e5471a8b619f5f1e7aaac7494 | MATLAB | 2,158 | 79 | function outFile = ft_GraphMetrics(groupFile, varargin)
% Compute basic weighted graph metrics from a group connectivity matrix.
p = inputParser;
p.addParameter('OutDir', '', @(x)ischar(x)||isstring(x));
p.addParameter('ThresholdDensity', [], @(x)isnumeric(x));
p.parse(varargin{:});
opt = p.Results;
S = load(groupFil... |
55c5bd7a72e6d67890ed8c73e72827bfa1c929b01c3e4574e648d2d26e665879 | MATLAB | 2,176 | 91 | function [perf_s awhist choice_s]=model_act_onlyaud_aversion(delta1,delta2,aw,aversion,side);
% thre=1;
delta1=delta1/10e7;
delta2=delta2/10e7;
aw=aw/10e7;
aversion=aversion/10e7;
rng("default")
% rng(9,"threefry")
perf_s=[];
choice_s=[];
awhist=aw;
fo... |
22ac28d2c80a96d311f0c86ed36bec56a9aefb5b9954e85ca048cee8056d27af | MATLAB | 2,191 | 96 | numLayers = 4;
numNeurons=20;
layers=featureInputLayer(2);
for i=1:numLayers-1
layers = [layers
fullyConnectedLayer(numNeurons)
tanhLayer];
end
layers = [layers
fullyConnectedLayer(1)];
net1=dlnetwork(layers);
net1 = dlupdate(@double,net1);
numLayers = 2;
numNeurons=4; ... |
69c482ad4f4788e33f7cd54afbc4d29e6b8f4cb7edef479788ff7d4d54f97172 | MATLAB | 2,192 | 69 | function points = generate_polygon(center, avg_radius, irregularity, spikiness, num_vertices)
% Start with the center of the polygon at center, then create the
% polygen by sampling points on a circle around the center.
% Random noise is added by varying the angular spacing between
% sequential points, ... |
6f6f579eb9646fe28e024529b49a5fcb6c05a82304818a19e8258f7443f913bc | MATLAB | 2,193 | 53 | function opposite_starts = computeOppositeRewardEvents(reward, distance, reset)
% COMPUTEOPPOSITEREWARDEVENTS Anchor points on the opposite half of the track.
%
% For each reward, returns the sample index on the opposite half of the 2 m
% track, at the mirrored position (reward position +/- 1 m). Used as a
% behaviour... |
b84f85ae61fec4e25a73fa2fb1a16a4b3cfc0fcadf662e964e0bb5312f27c81a | MATLAB | 2,193 | 67 | function location_groups = group_reward_locations(reward, distance, reset)
% Circular mean helper function
function m = circularMean(positions, L)
theta = positions * (2*pi/L);
s = sum(sin(theta));
c = sum(cos(theta));
mean_angle = atan2(s, c);
m = mod(mean_angle * L/(2*p... |
5aa6163e51589121a3fd6d796e4c6123bc5d149e96bc70af2671c790a0366e2a | MATLAB | 2,201 | 66 | function anchor_starts = computeExactSpeedEvents(reward, distance, reset, speed, reward_starts, speed_threshold)
% Parameters
window_size = 20000; % 20000 samples = 1000ms
min_distance_cm = 50; % 50 cm minimum distance
global_speed_tolerance = (max(speed) - min(speed)) / 20; % Dynamic tolerance
% Handle optional inpu... |
f76165647f5786900d4c394063210f8e4fccec492b91677619a0d3bc111acc97 | MATLAB | 2,203 | 92 | function [perf_s awhist spwhist choice_s]=model_act_2lp_rand(delta1,delta2,aw,spw,side);
% thre=1;
delta1=delta1/10e7;
delta2=delta2/10e7;
spw=spw/10e7;
rng("default")
% rng(9,"threefry")
perf_s=[];
choice_s=[];
awhist=aw;
spwhist=spw;
for i=1:length(side);... |
e867cf6cb83d5bc1f8bb63fc029dfe7be5e736b46bbecb95217b86dc17a024fe | MATLAB | 2,219 | 56 | function [shuffled_rest_fc, shuffled_movie_fc, shuffled_rest_covari, shuffled_movie_covari, swap_indices] = shuffleFCData(rest_fc_square, movie_fc_square, rest_covari, movie_covari,iter)
rng(iter)
% Check if FC data is provided
if exist('rest_fc_square', 'var') && exist('movie_fc_square', 'var')
[nROIs, nROIs, num_... |
554d78070aa10991d95601daabe080e7125b39cb4869d4430d3b965545961fa1 | MATLAB | 2,235 | 96 | function [perf_s awhist choice_s phist]=model_act_onlyaud(delta1,delta2,aw,side);
% thre=1;
delta1=delta1/10e7;
delta2=delta2/10e7;
aw=aw/10e7;
rng("default")
% rng(9,"threefry")
perf_s=[];
choice_s=[];
awhist=aw;
phist=[];
for i=1:length(side);
% ma... |
692fd9d5e17bd385fd15360d34bdc2dfabeeea611027b3d097025189ec7a364c | MATLAB | 2,242 | 64 | function B0_to_niftis(subjPath, imageData);
% goal: convert adc and fa maps to niftis
% register t2 maps to the b0
%% Paths
regDir = fullfile(subjPath, 'reg');
anatFolder = fullfile(subjPath, 'anat'); % anat folder path
mhd = fullfile(subjPath, 'mhd');
% Target file path
echo1File = fullfil... |
a0a96190c106a8609c9f5a2a8e58a3c99c4b89f5d41132688d5765f30ba6400f | MATLAB | 2,250 | 95 | function [perf_s awhist spwhist choice_s]=model_act_2lp_rand_v2(delta1,delta2,aw,spw,side);
% thre=1;
delta1=delta1/10e7;
delta2=delta2/10e7;
spw=spw/10e7;
rng("default")
% rng(9,"threefry")
perf_s=[];
choice_s=[];
awhist=aw;
spwhist=spw;
for i=1:length(sid... |
33a2904e239e7c9b68dccd23c0e69f11c64f28cc4042ab6308b8fac2d99ecefd | MATLAB | 2,254 | 101 | numLayers = 4;
numNeurons=40;
layers=featureInputLayer(2);
for i=1:numLayers-1
layers = [layers
fullyConnectedLayer(numNeurons)
tanhLayer];
end
layers = [layers
fullyConnectedLayer(1)];
net1=dlnetwork(layers);
net1 = dlupdate(@double,net1);
numLayers = 2;
numNeurons=4; ... |
2e155be1c682c2d38408adb96e5d45e70f82df237c62b7126c84c466f7acf984 | MATLAB | 2,269 | 93 | function [perf_s awhist spwhist choice_s aw]=model_act_CNO_2lp_rand(delta1,delta2,aw,spw,side,cnoeffect);
% thre=1;
delta1=delta1/10e7;
delta2=delta2/10e7;
spw=spw/10e7;
spw=spw*cnoeffect;
rng("default")
% rng(9,"threefry")
perf_s=[];
choice_s=[];
awhist=aw... |
3a4d4969c69285c8f4a182adac94b7c27031a8541f82d12662f040e7769efdde | MATLAB | 2,278 | 76 | function copyLocalAcqToServer(path,serverPath)
%copyLocalAcqToServer.m Copies local acquisition back to server and fixes
%paths
%
%INPUTS
%path - local path
%
%ASM 12/14
if nargin < 2 || isempty(serverPath)
serverPath = 'Z:\HarveyLab\Ari\2P Data\ResScan';
end
%check if path exists
if nargin < 1 || isempty(path)... |
a26839430a91f3b626d17b1ad2f1799a0d0fc53382c45bbd721483fc0d41f143 | MATLAB | 2,293 | 71 | function T2star_venogram_thresholded(project_directory, project_name, subject_code);
reoriented = fullfile(project_directory, project_name, subject_code, 'reoriented');
veno_dir = fullfile(project_directory, project_name, subject_code, 'veno');
toolbox = fullfile(project_directory, 'scripts', 'toolbox... |
7bfed3ffb6a78411ca62dba7143b058e9c1da47586d7a82d4f74c6dd7526cfc0 | MATLAB | 2,298 | 50 | function [fc_herit, fc_herit_perm,fc_herit_jack,fc_similarity] = computeNetworkHeritability(fc_square, net_ids, subjects,kinship,covari,num_perm,fam_ids)
num_nets = max(net_ids);
n2 = num_nets + ((num_nets * (num_nets - 1)) / 2);
numsubjects = length(subjects);
fc_similarity =... |
577a91644893e71536d19f08d4ad96846383eea5a9f3404d1ae4b9025229a8f0 | MATLAB | 2,341 | 103 | numLayers = 3;
numNeurons=40;
layers=featureInputLayer(2);
for i=1:numLayers-1
layers = [layers
fullyConnectedLayer(numNeurons)
tanhLayer];
end
layers = [layers
fullyConnectedLayer(1)];
net1=dlnetwork(layers);
net1 = dlupdate(@double,net1);
numLayers = 2;
numNeurons=5; ... |
207079dc047a4e2878a7f689cd73737711f50786999573998e09bbf7fffe207c | MATLAB | 2,342 | 71 | function [loss,gradients,mseF1,mseF2,mseF3,mseF4,mseF5,mseF6,mseF7,mseF8]=modelLoss(parameters,net1,net2,CP,CB1,CB2,CB3,CB4,CBIC,CBt,CPin,U0,drdth,w1,w2,w3,w4,w5,w6,w7,w8)
net1.Learnables=parameters(1:8,:); net2.Learnables=parameters(9:end,:);
% make predictions
U=forward(net1,CP); AB=forward(net2,CBt);
gradU = d... |
83d9346c852ac5420ef549fe2778b1ffaff2fcc56f551d04c7e2d6de0d64376f | MATLAB | 2,343 | 85 | function addSIMetaData(folder)
%addSIMetaData.m Looks through folder and adds metadata to all objects
%
%INPUTS
%folder - folder to look through
%
%ASM 12/14
if nargin < 1 || isempty(folder)
folder = '\\research.files.med.harvard.edu\Neurobio\HarveyLab\Ari\2P Data\ResScan';
end
%get subdirectories which match mo... |
0b596d82c9c7ca2589873eb5217694476777b759a74cbd16c1370e05d0be4b99 | MATLAB | 2,353 | 63 | %% Define project paths
project_directory = '/exports/gorter-hpc/users/ninafultz/';
project_name = 'csfdonuts_lydiane';
scripts = fullfile(project_directory, 'scripts');
addpath(genpath(fullfile(scripts, project_name)));
addpath(genpath(fullfile(scripts, 'toolbox', 'nifti_tools-master')));
addpath(genpa... |
d0ac5255c069587f933d3fbb6baaba16c293b5ccd2546e4a4b65ed36a22637db | MATLAB | 2,360 | 69 | function plotConcentricCircles(project_directory, project_name, subject_list, roi_patterns, voxel_size)
% Initialize structure to store data
roi_data = struct();
for roi_name = roi_patterns
roi_data.(roi_name{1}).ADC = {}; % Cell array for variable-length ADC data
end
% Process each subject
for subj = 1:length(su... |
009aa3a384b30b4392a46bce8446ba0be44e41d586044f8a479a5796584c4939 | MATLAB | 2,384 | 87 | function venogram_combiningimages(project_directory, project_name, subject_code);
% Define the switch block for subject-specific operations
switch subject_code
case 'csfdonut01'
subject_code = '';
case 'test'
subject_code = 'test01_angiogramvenogram';
otherwise
... |
7f89b73be772fae7a7c01613e37cc80e6646f320ed2f6576da858b87f34c8de8 | MATLAB | 2,385 | 66 | function selectROIs(acq, img, sliceNum, channelNum, smoothWindow, excludeFrames)
% selectROIs(acq, img, sliceNum, channelNum, smoothWindow, excludeFrames)
% All inputs except acquisition object are optional.
%
% GUI for sorting through pixel-pixel correlation matrices to select
% appropriate ROIs for an acquisition. Re... |
b42cbbdbb16801a9a3889ab21d1f914597f5b03b327ad62633a932c3fb4e5f4a | MATLAB | 2,458 | 51 | function doAllClusterTraces(sel)
% Tell user that we're loading the traces:
title(sel.h.ax.roi, 'Loading Trace for Current ROI');
drawnow
% Get matrix of fluorescence traces for all clusters:
mov = sel.movMap.Data.mov;
F = zeros(sel.disp.clusterNum+1, size(mov, 1));
... |
0e34176f0e7e0c04d5d0f5956221a387f22207972d32b1446f8bb09216abc6d0 | MATLAB | 2,470 | 87 | function calcClusterProps(sel)
doCuts = get(sel.h.ui.autoCalcCuts,'Value');
doClusters = get(sel.h.ui.autoCalcClusters,'Value');
if doCuts
cutVals = sel.disp.cutVals;
X = 1:length(cutVals);
warnState = warning('off', 'stats:statrobustfit:IterationLimit');
[B,stats] = robustfit(X,cutVals,'bisquare',2);... |
70ca8a9f686d722564b971da30ae1157b637fa396cd4bb410bdae8e85e228695 | MATLAB | 2,481 | 74 | function [loss,gradients,mseF1,mseF2,mseF3,mseF4,mseF5,mseF6,mseF7,mseF8]=modelLoss(parameters,net1,net2,CP,CB1,CB2,CB3,CB4,CBIC,CBt,U0,w1,w2,w3,w4,w5,w6,w7,w8)
net1.Learnables=parameters(1:8,:); net2.Learnables=parameters(9:end,:);
% make predictions
U=forward(net1,CP); AB=forward(net2,CBt);
gradU = dlgradient(s... |
facae415725f6c266f213239ae16a4712ad888a6e24e359bd576f7d43130dbf2 | MATLAB | 2,488 | 112 | numLayers = 3;
numNeurons=20;
layers=featureInputLayer(2);
for i=1:numLayers-1
layers = [layers
fullyConnectedLayer(numNeurons)
tanhLayer];
end
layers = [layers
fullyConnectedLayer(1)];
net1=dlnetwork(layers);
net1 = dlupdate(@double,net1);
numLayers = 2;
numNeurons=5; ... |
e5c7507defc1d55cb5e0462e718732d976afb77d0d47670f5d8326fe483d647b | MATLAB | 2,535 | 117 | function [h2, p_perm, jack_se,h2_jack] = h2_mat(P, K, X, n_perm, F)
%%
%
% Heritability estimation from phenotypic and genetic similary mattrices
%
% Input:
% P: an Nsubj x Nsubj phenotypic similarity matrix
% K: an Nsubj x Nsubj genetic similarity matrix
% X: an Nsubj x Ncov matrix of covariates
% n_perm: number of pe... |
de06048bbfa041d3b1b6fcd893b5c601f4290c9df87e3f0079586717fa0b2eb9 | MATLAB | 2,540 | 77 | function plotSixDistributions(all_counts_cell, short_counts_cell, long_counts_cell, ...
bin_size, bin_units, titles, main_title)
% Plot six distributions in 2x3 grid with consistent y-axis
max_range = 1.2; % ±1.2 meters
xlabel_str = ['Distance from anchor (' bin_units ')'];
edges = -max_range:bin_size:m... |
00e50e2c40a374aa4bd720345f0ea92ef8cc432f5eddee88d2637825ae6477c1 | MATLAB | 2,584 | 78 | function varargout = correctLineShift(mov, manualShift)
% [movCorrected, shifts] = correctLineShift(mov) corrects the offset
% between odd and even image lines caused by bidirectional laser scanning.
% Apply this correction before any other processing.
if ~exist('manualShift', 'var') || isempty(manualShift)
manual... |
6ad6b87f07295df842a1db170d139da3b501878dbf163c2b9e706c61312b733b | MATLAB | 2,626 | 121 | numLayers = 4;
numNeurons=40;
layers=featureInputLayer(3);
for i=1:numLayers-1
layers = [layers
fullyConnectedLayer(numNeurons)
tanhLayer];
end
layers = [layers
fullyConnectedLayer(1)];
net1=dlnetwork(layers);
net1 = dlupdate(@double,net1);
numLayers = 2;
numNeurons=5; ... |
770c4a208386ee64319556f911bd8f40e7cb4bebd8ef65865336d125bbcc8684 | MATLAB | 2,628 | 60 | function displayRoi(sel)
%Helper function which updates display of currently selected ROI in overlay
%panel
%Check current status, reflected in title of ROI overlay axes. If trace has
%recently been loaded, reset title to ROI selection, or if currently
%searching for neuropil, do not enforce ROI continuity constraint
... |
165c7e71466bd9adaa9667f9321540519ba376d7bb3aee810a941540d82cd693 | MATLAB | 2,630 | 95 | clear
GENERALDIR = 'R:\- Gorter\CSF_STREAM\Data\';
allSubjects = dir([GENERALDIR '*20201019_Reconstruction']);
subjectNb = 1;
subject = allSubjects(subjectNb);
disp(subject.name)
physioFolder = '\Cardiac\T2prep';
physio = 'cardiac';
threshold = 150;
newFolder = 'R:\- Gorter\- Personal folders\Fultz, N... |
1182f4f5e8ca4c6bb828e993c7bcefd440cd8e925520d2fb59f85f38e7ad1269 | MATLAB | 2,636 | 74 | function mhd_to_niftis(subjPath, referenceScan);
% goal: convert adc and fa maps to niftis
% register t2 maps to the b0
%% Paths
regDir = fullfile(subjPath, 'reg');
fa = fullfile(subjPath, 'fa');
adc = fullfile(subjPath, 'adc');
anatFolder = fullfile(subjPath, 'anat'); % anat fol... |
4a788f93b8e3c87cfb71963595e00c08383e25c7aab43ee0bb4ca74c44259062 | MATLAB | 2,663 | 62 | function [movStruct, nSlices, nChannels] = parseScanimageTiff(mov, siStruct)
% Check for scanimage version before extracting metainformation
if isfield(siStruct, 'VERSION_MAJOR')
% SI2016 or later tiff that was loaded using the new SI tiff reader:
fZ = siStruct.hFastZ.enable;
nChannels =... |
fb1fbb535279aa15346e731b1fa606f87c2e26fff49f04371c6c95112736af5c | MATLAB | 2,684 | 82 | function [dF, traces, rawF, roi, traceNeuropil] = ...
extractROIsBin(obj,roiGroups,sliceNum,channelNum, isGetDF)
% Function for extracting ROIs from movies using grouping assigned by
% selectROIs. This function uses a memorymapped binary file of the entire
% movie, as output by indexMovie. See extractROIsTIFF to ex... |
57b2502829f4967ff27f64fcd3abdb9fcf54cd9f6ec402868e53ede6795873ab | MATLAB | 2,694 | 57 | function [all_counts, short_counts, long_counts, all_plateau_times, ...
short_plateau_times, long_plateau_times, dwell_times] = ...
analyze_anchor_set(anchor_starts, analysis_name, distance, reset, plateaus, widPlat, ...
bin_size, short_thresh_samples, min_plateau_duration_samples, speed, ...
... |
cc5f3d26edb88891ea9b1ef2be0a3bca6519ca46564206f9facf35553f67cf5a | MATLAB | 2,713 | 76 | function [CURBD,CURBDLabels] = computeCURBD(varargin)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Performs Current-Based Decomposition (CURBD) of multi-region data. Ref:
%
% Perich MG et al. Inferring brain-wide interactions using data-constrained
% recurrent neural network models. b... |
a915edd5152677722464f2854fa312da879b241639fe3b9623997f84992beecc | MATLAB | 2,716 | 123 | numLayers = 4;
numNeurons=40;
layers=featureInputLayer(3);
for i=1:numLayers-1
layers = [layers
fullyConnectedLayer(numNeurons)
tanhLayer];
end
layers = [layers
fullyConnectedLayer(1)];
net1=dlnetwork(layers);
net1 = dlupdate(@double,net1);
numLayers = 2;
numNeurons=5; ... |
b3e3f547e09f123c91adc632c4307cdced1948df1c22e5b4d94ca00e90b0c276 | MATLAB | 2,719 | 112 | function [perf_s awhist spwhist choice_s]=model_act_oneloop(delta1,delta2,aw,spw,side);
% thre=1;
delta1=delta1/10e7;
delta2=delta2/10e7;
spw=spw/10e7;
rng("default")
% rng(2,"threefry")
perf_s=[];
choice_s=[];
awhist=aw;
spwhist=spw;
for i=1:length(side);
... |
55736f9ffc792f7a6facfd22503e630b22e2d19aed43462245459310aa86d9d3 | MATLAB | 2,736 | 63 | function anchor_starts = findCrossingsNearRewards(reward, distance, reset, reward_starts, all_crossings)
track_length = 2; % meters
min_dist_before = 0.2; % meters (20 cm)
min_dist_after = 0.8; % meters (80 cm)
anchor_starts = NaN(size(reward_starts));
for i = 1:length(reward_starts)
R ... |
e30a3c7ee92841a1e0cf12f11055d427b6d0b40c72680790d3d3e919b8116f8c | MATLAB | 2,747 | 83 | function anchor_starts = computeSimilarAccelEvents(reward, distance, reset, speed, reward_starts)
% Parameters for trend matching
bin_duration = 0.1; % 100 ms bins
trend_window = 5; % ±5 bins (±500ms)
min_distance_cm = 50; % 50 cm minimum distance
% Get reward starts if not provided
if nargin < 5 || isempty(reward_sta... |
6e10c2a61e3560cb620cf48d1daef7415571dabb30aee17d525d6ee865ee99c7 | MATLAB | 2,772 | 132 | %%
%Do simulation
load mod2lp_control_result
load model_chem_control.mat
parm_results2=parm_results;
parm_results2(:,2:4)=parm_results2(:,2:4)/10e7;
% figure;
% hold on;
cnoeffect=0.559;
% cnoeffect=0.54;
perfday=zeros(39,5,2);
for qq=1:size(parm_results,1)
qq=parm_results(qq,1);
param=parm_resu... |
fa5ee13cdfcea2dc7c4b0232db588e6a06b8099c42b950853950473301efcb85 | MATLAB | 2,773 | 68 | function movStruct = withinFile_segmentConsensus(obj,movStruct, metaMov, movNum, opMode)
%Example of a motion correction function compatable with Acquisition2P class,
%calculates full frame translations within each file independently, then adds
%these to shifts calculated between each file
% Clips off pockels-blanked ... |
14d68bc980484c7546f3502cbec8168b13ed7837937f97ae96126adc9da5b877 | MATLAB | 2,801 | 73 | function [dF, traces, rawF, roi] = extractROIsTIFF(obj,roiGroups,movNums,sliceNum,channelNum)
% Function for extracting ROIs from movies using grouping assigned by
% selectROIs. This function motion corrected tiff files. See extractROI for a
% sometimes faster method using a memory mapped 'indexed' binary file instead... |
5d1c64b24f969d23994fd39dad3a1704e975ac2cfe99eb33955a94a0ddb87f20 | MATLAB | 2,860 | 73 | function plotConcentricCircles(project_directory, project_name, subject_list, roi_patterns, voxel_size)
%% Parameters
num_layers = 20; % Number of concentric circles
layer_thickness = 0.45; % Thickness of each layer in voxel units
% Process each subject
for subj = 1:length(subject_list)
sub... |
e10b4ffa6620e8b2451352e8136cc096286b24609fcba503c50d6051d1fa40e3 | MATLAB | 2,883 | 83 | function anchor_starts = computeSimilarSpeedEvents(reward, distance, reset, speed, reward_starts)
% Parameters for trend matching
bin_duration = 1; % 1000 ms bins (in seconds)
trend_window = 5; % ±5 bins (i.e., ±500ms around reward)
min_distance_cm = 50; % Minimum 50 cm away from reward
% Get reward starts if not prov... |
cd8391a40bfba7585a440c927c0e8fa1fcd1fc04565dcffa72eaf1033687506e | MATLAB | 2,997 | 188 |
%model A--use cum all perf -independent 2 loops, with random noise
clear all
tic
load model_chem_control.mat
parm_results=[];
parfor qq=1:39
ResModData=modeldata{qq,1};
results=[];
for parm1=[0.005 0.01 0.05 0.1 0.5]*10e7;% delta1
for parm2=[0.3 0.4 0.5... |
a159a1dbab99a5464561c3c044798bbec3814cfcdc2105bd4497e0b92892df54 | MATLAB | 3,074 | 79 | function plotADCandB0Values(project_directory, project_name, subject_list, roi_patterns, voxel_size)
%% Process the first subject only
subject_code = subject_list(1).name;
subjPath = fullfile(project_directory, project_name, subject_code);
ROIs_dir = fullfile(subjPath, 'ROIs');
reoriented_dir = full... |
5451a9e7db54be27dfad2ecee4cb569f3ad0139997064a7a00d574e8ad7132c2 | MATLAB | 3,092 | 93 | %% CONVERTPARREC Convert a Philips PARREC image file to a different version
%
% [SUCCESS] = CONVERTPARREC(SRCFILENAME,DSTFILENAME,VERSION)
%
% SRCFILENAME is a string containing a file prefix or name of the PAR header
% file or REC data file, e.g. SURVEY_1 or SURVEY_1.PAR or SURVEY_1.REC
%
% DSTFILENAM... |
a800ff4e2e0d48e9ef5cfcb215fef0d6ab9c50d52e7738dfd95299315e3b9a69 | MATLAB | 3,124 | 83 | function result = XMLRECwritexml(xmlrec_struct,filename)
%% Assumes the following structure format
% xmlrec_struct.Series_Info
% xmlrec_struct.Image_Array(:)
%
% xmlrec_struct.Series_Info.ATTRIBUTE_NAME.subattribute_1
% xmlrec_struct.Series_Info.ATTRIBUTE_NAME.subattribute_2
% ...
% xmlrec_struct.Series_Info.... |
d0dc5857d3780dc5459fdb2377376b31c980f44399b6eb80426fc9d2562b7aa5 | MATLAB | 3,143 | 81 |
function plotADCandB0Values(project_directory, project_name, subject_list, roi_patterns, voxel_size)
%% Process the first subject only
subject_code = subject_list(1).name;
subjPath = fullfile(project_directory, project_name, subject_code);
ROIs_dir = fullfile(subjPath, 'ROIs');
reoriented_dir = ful... |
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