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value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
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github | lnls-fac/MatlabMiddleLayer-master | setoperationalmode.m | .m | MatlabMiddleLayer-master/Release/machine/SIRIUS/SI.V13/setoperationalmode.m | 6,707 | utf_8 | 0a3b57eb651ea1d195819b96f61c44d3 | function setoperationalmode(ModeNumber)
%SETOPERATIONALMODE - Switches between the various operational modes
% setoperationalmode(ModeNumber)
%
%
% History
%
% 2012-08-29 Ximenes
% Check if the AO exists
checkforao;
% MODES
ModeCell = { ...
'3 GeV - A', ...
'3 GeV - B', ...
'3 GeV - C... |
github | lnls-fac/MatlabMiddleLayer-master | sirius_si_lattice.m | .m | MatlabMiddleLayer-master/Release/machine/SIRIUS/SI.V13/sirius_si_lattice.m | 18,666 | utf_8 | 580e93dfa62ec0091d143f03e805b47f | function [r, lattice_title] = sirius_si_lattice(varargin)
% the_ring = sirius_si_lattice : retorna o modelo atual do anel;
%
% the_ring = sirius_si_lattice(mode) : Ateh o momento, pode ser 'A', 'B' or 'C'(default).
%
% the_ring = sirius_si_lattice(mode,version): mode_version define o ponto de operacao
% ... |
github | lnls-fac/MatlabMiddleLayer-master | sirius_init.m | .m | MatlabMiddleLayer-master/Release/machine/SIRIUS/SI.V13/sirius_init.m | 74,124 | utf_8 | 0483da4e090567b3de561cbd6a0595ef | function sirius_init
%SIRIUSINIT - MML initialization file for the VUV ring at sirius3
%
% See also setoperationalmode
% 2012-07-10 Modificado para sirius3_lattice_e025 - Afonso
setao([]);
setad([]);
% Base on the location of this file
[SIRIUS_ROOT, ~, ~] = fileparts(mfilename('fullpath'));
AD.Dire... |
github | lnls-fac/MatlabMiddleLayer-master | sirius_si_multipole_systematic_errors.m | .m | MatlabMiddleLayer-master/Release/machine/SIRIUS/SI.V13/sirius_si_multipole_systematic_errors.m | 5,983 | utf_8 | 5d5cb3f09ba817592d3daf2fe1389571 | function the_ring = sirius_si_multipole_systematic_errors(the_ring)
% multipole order convention: n=0(dipole), n=1(quadrupole), and so on.
fam_data = sirius_si_family_data(the_ring);
% B1 and B2
% =========
% The default systematic multipoles for the dipoles were changed.
% Now we are using the values of a stand... |
github | lnls-fac/MatlabMiddleLayer-master | setoperationalmode.m | .m | MatlabMiddleLayer-master/Release/machine/SIRIUS/SI.V01/setoperationalmode.m | 6,205 | utf_8 | ef96c49fb6530df275945e02afe5b619 | function setoperationalmode(ModeNumber)
%SETOPERATIONALMODE - Switches between the various operational modes
% setoperationalmode(ModeNumber)
%
% ModeNumber = 1. 3 GeV, AC20 {Default}
% 2. 3 GeV, AC10 betax = 4m
% 3. 3 GeV, AC10 betax = 1.5m
%
% History
%
% 2012-08-29 Ximenes
... |
github | lnls-fac/MatlabMiddleLayer-master | sirius_si_lattice.m | .m | MatlabMiddleLayer-master/Release/machine/SIRIUS/SI.V01/sirius_si_lattice.m | 17,189 | utf_8 | c049c09d24b65a0a731b193d2de3ef09 | function r = sirius_si_lattice(varargin)
% r = sirius_si_lattice : retorna o modelo atual do anel;
%
% r = sirius_si_lattice(mode) : Ateh o momento, pode ser 'AC10'(default) or 'AC20'.
%
% r = sirius_si_lattice(mode,mode_version): mode_version define o ponto de operacao
% e a otimizacao sextupolar a s... |
github | lnls-fac/MatlabMiddleLayer-master | sirius_init.m | .m | MatlabMiddleLayer-master/Release/machine/SIRIUS/SI.V01/sirius_init.m | 42,475 | utf_8 | 556d4f1b2b7ad3491f97e5dc6a4d6d96 | function sirius_init(OperationalMode)
%LNLSINIT - MML initialization file for the VUV ring at sirius3
% lnlsinit(OperationalMode)
%
% See also setoperationalmode
% 2012-07-10 Modificado para sirius3_lattice_e025 - Afonso
if nargin < 1
OperationalMode = 1;
end
setao([]);
setad([]);
% B... |
github | lnls-fac/MatlabMiddleLayer-master | sirius_si_init.m | .m | MatlabMiddleLayer-master/Release/machine/SIRIUS/SI.V22.04/sirius_si_init.m | 37,111 | utf_8 | d7321cdb497889ba8a6c765719ca82db | function sirius_si_init
%SIRIUSINIT - MML initialization file for the VUV ring at sirius3
%
% See also setoperationalmode
% PS: All the Fields 'Position' are built in the 'updateatindex.m' script.
% 2012-07-10 Modificado para sirius3_lattice_e025 - Afonso
setao([]);
setad([]);
[SIRIUS_ROOT, ~, ~] = fi... |
github | lnls-fac/MatlabMiddleLayer-master | setoperationalmode.m | .m | MatlabMiddleLayer-master/Release/machine/SIRIUS/SI.V22.04/setoperationalmode.m | 5,624 | utf_8 | b6deb39bb8d7490593b5fe577d8678dc | function setoperationalmode(ModeNumber)
%SETOPERATIONALMODE - Switches between the various operational modes
% setoperationalmode(ModeNumber)
%
%
% History
%
% 2012-08-29 Ximenes
% Check if the AO exists
checkforao;
% MODES
ModeCell = { ...
'3 GeV - S05.01 (default)', ...
'3 GeV - S10', ..... |
github | lnls-fac/MatlabMiddleLayer-master | sirius_si_lattice.m | .m | MatlabMiddleLayer-master/Release/machine/SIRIUS/SI.V22.04/sirius_si_lattice.m | 22,144 | utf_8 | c1405f24e6b64baf167e19d4992646c9 | function [r, lattice_title] = sirius_si_lattice(varargin)
% the_ring = sirius_si_lattice : retorna o modelo atual do anel;
%
% the_ring = sirius_si_lattice(mode) : Ateh o momento, pode ser 'A', 'B' or 'C'(default).
%
% the_ring = sirius_si_lattice(mode,version): mode_version define o ponto de operacao
% ... |
github | lnls-fac/MatlabMiddleLayer-master | sirius_si_multipole_systematic_errors.m | .m | MatlabMiddleLayer-master/Release/machine/SIRIUS/SI.V22.04/sirius_si_multipole_systematic_errors.m | 9,062 | utf_8 | f0c0a10dece8718256f7f6bd92eaeae0 | function the_ring = sirius_si_multipole_systematic_errors(the_ring,fam_data)
% multipole order convention: n=0(dipole), n=1(quadrupole), and so on.
% ---- current model of sextupoles ---
% SEXTUPOLES COILS
% ================
fams = {'SN'};
r0 = 12/1000;
% systematic multipoles from '2017-02-24_SI_Sextupo... |
github | lnls-fac/MatlabMiddleLayer-master | sirius_si_lattice_errors_analysis.m | .m | MatlabMiddleLayer-master/Release/machine/SIRIUS/SI.V22.04/sirius_si_lattice_errors_analysis.m | 20,017 | utf_8 | 638cbe4351530bf429323034118174db | function machine = sirius_si_lattice_errors_analysis()
fprintf('\n')
fprintf('Lattice Errors Run\n');
fprintf('==================\n');
% first step is to initialize global auxiliary structures
name = 'CONFIG'; name_saved_machines = name;
initializations();
% next a nominal model is chosen for the study
the_ring = c... |
github | lnls-fac/MatlabMiddleLayer-master | sirius_tb_lattice_sept.m | .m | MatlabMiddleLayer-master/Release/machine/SIRIUS/TB.V03.02/sirius_tb_lattice_sept.m | 10,066 | utf_8 | 56cb30a2242b7adeb594fd3d8e6f0670 | function [r, lattice_title, IniCond] = sirius_tb_lattice_sept(varargin)
% 2013-08-19 created Fernando.
% 2013-12-02 V200 - from OPA (Ximenes)
% 2014-10-20 V300 - new version (Liu)
% 2015-08-26 V01 - new version (Liu)
% 2016-09-28 V01.02 - new version (Ximenes)
% 2017-08-25 V02.01 - new dipole model 02 (Ximenes) ... |
github | lnls-fac/MatlabMiddleLayer-master | setoperationalmode.m | .m | MatlabMiddleLayer-master/Release/machine/SIRIUS/TB.V03.02/setoperationalmode.m | 7,020 | utf_8 | 5862d106083ed644366f5cee7d6f20c3 | function setoperationalmode(ModeNumber)
% Check if the AO exists
checkforao;
% MODES
ModeCell = { ...
'150 MeV - M1', ...
'150 MeV - M2', ...
'150 MeV - M3', ...
'150 MeV - M4', ...
'150 MeV - M5', ...
'150 MeV - M6', ...
};
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Accelerator Depend... |
github | lnls-fac/MatlabMiddleLayer-master | sirius_tb_models_from_measurements.m | .m | MatlabMiddleLayer-master/Release/machine/SIRIUS/TB.V03.02/sirius_tb_models_from_measurements.m | 5,926 | utf_8 | 4b513cfe7912abf209a3972fae1e3c14 | function the_ring = sirius_tb_models_from_measurements(the_ring0)
the_ring = the_ring0;
% the_ring = models_from_measurements_dipoles(the_ring);
% the_ring = models_from_measurements_quadrupoles_qf(the_ring);
% the_ring = models_from_measurements_quadrupoles_qd(the_ring);
% the_ring = models_from_measurements_correcto... |
github | lnls-fac/MatlabMiddleLayer-master | sirius_tb_lattice.m | .m | MatlabMiddleLayer-master/Release/machine/SIRIUS/TB.V03.02/sirius_tb_lattice.m | 9,050 | utf_8 | 83a474da6320b9bb0e838fa5351a9379 | function [r, lattice_title, IniCond] = sirius_tb_lattice(varargin)
% 2013-08-19 created Fernando.
% 2013-12-02 V200 - from OPA (Ximenes)
% 2014-10-20 V300 - new version (Liu)
% 2015-08-26 V01 - new version (Liu)
% 2016-09-28 V01.02 - new version (Ximenes)
% 2017-08-25 V02.01 - new dipole model 02 (Ximenes) - see... |
github | lnls-fac/MatlabMiddleLayer-master | sirius_tb_init.m | .m | MatlabMiddleLayer-master/Release/machine/SIRIUS/TB.V03.02/sirius_tb_init.m | 28,077 | utf_8 | ff06486a150693830d55f8388cc45b4b | function sirius_tb_init(OperationalMode)
%SIRIUS_TB_INIT - MML initialization file for the TB at sirius
%
% See also setoperationalmode
% 2013-12-02 Inicio (Ximenes)
setao([]);
setad([]);
AD.Directory.ExcDataDir = '/home/fac_files/lnls-sirius/control-system-constants/magnet/excitation-data';
setad(... |
github | yanweifu/zslearning-master | mapDoMap.m | .m | zslearning-master/mapDoMap.m | 387 | utf_8 | 8a3bf3bec5ca38c218cbbded50fe7390 | % Map image vectors into semantic space.
function [ mapped ] = mapDoMap( images, theta, trainParams )
addpath toolbox/;
[ W, b ] = stack2param(theta, trainParams.decodeInfo);
% Feedforward
if (length(W) == 2)
a2All = trainParams.f(bsxfun(@plus, W{1} * images, b{1}));
mapped = bsxfun(@plus, W{2} * a2All, b{2... |
github | yanweifu/zslearning-master | visualize.m | .m | zslearning-master/plotting/visualize.m | 1,043 | utf_8 | ab33a91d7cb6f3593fea8135c4eb2c09 | % Visualize t-SNE mapped word vectors.
function [] = visualize(mappedX, Y, mappedWordTable, label_names, images, images_to_show)
gscatter(mappedX(:,1), mappedX(:,2), label_names(Y), [], '+o*.xsd^v>', 7);
hold on;
% Add actual images
for j = 1:length(images_to_show)
i = images_to_show(j);
I = displayColorNetw... |
github | yanweifu/zslearning-master | slmetric_pw.m | .m | zslearning-master/toolbox/pwmetric/slmetric_pw.m | 11,906 | utf_8 | e3864f6e2643ac4e2c007a18ef7febc6 | function M = slmetric_pw(X1, X2, mtype, varargin)
%SLMETRIC_PW Compute the metric between column vectors pairwisely
%
% [ Syntax ]
% - M = slmetric_pw(X1, X2, mtype);
% - M = slmetric_pw(X1, X2, mtype, ...);
%
% [ Arguments ]
% - X1, X2: the sample matrices
% - mtype: the string indicating... |
github | yanweifu/zslearning-master | WolfeLineSearch.m | .m | zslearning-master/toolbox/minFunc/WolfeLineSearch.m | 11,395 | utf_8 | 3d2acf1139093fe11df95ccdf888aab8 | function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS,maxLS,tolX,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function value at st... |
github | yanweifu/zslearning-master | minFunc_processInputOptions.m | .m | zslearning-master/toolbox/minFunc/minFunc_processInputOptions.m | 3,704 | utf_8 | dc74c67d849970de7f16c873fcf155bc |
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,tolFun,tolX,method,...
corrections,c1,c2,LS_init,LS,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
DerivativeCheck,Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,use... |
github | yanweifu/zslearning-master | buildFeatures.m | .m | zslearning-master/buildFeatures/buildFeatures.m | 2,308 | utf_8 | e719e7bb68e6d7926b2e55af1ea83509 | % Extract features from CIFAR-10.
% Read README for instructions.
function [] = buildFeatures(dataset)
OUTPUT_DIR = ['../image_data/features/' dataset];
CIFAR_DIR = ['../image_data/images/' dataset];
%%%%% Configuration
addpath ../toolbox/minFunc;
rfSize = 6;
numBases=1600;
CIFAR_DIM=[32 32 3];
alpha = 0.25; %% CV-... |
github | yanweifu/zslearning-master | run_omp1.m | .m | zslearning-master/buildFeatures/run_omp1.m | 1,218 | utf_8 | 669789128ddb30c2edda9bdc2296d0fb | function dictionary = run_omp1(X, k, iterations)
% initialize dictionary
dictionary = randn(k, size(X,2));
dictionary = bsxfun(@rdivide, dictionary, sqrt(sum(dictionary.^2,2)+1e-20));
for itr=1:iterations
fprintf(1,'Running GSVQ: iteration=%d... \n', itr);
% do assignment + accumulation
[summa... |
github | yanweifu/zslearning-master | trainAttributes.m | .m | zslearning-master/attributeTraining/trainAttributes.m | 2,073 | utf_8 | 989645a8c2104b6b30d59b67ed5663a7 | % Train using the attributes model (Lampert et al).
function [thetas, fullTrainParams] = trainAttributes(X, Y, attributes, assignments, trainParams)
fields = {{'imageDataset', 'cifar10'};
{'costFunction', @softmaxCost}; % training cost function
{'lambda', 1E-3}; %... |
github | yanweifu/zslearning-master | evaluateAttributes.m | .m | zslearning-master/attributeTraining/evaluateAttributes.m | 2,184 | utf_8 | 6bc56f404146c9f7bc8628a55ff1b9c4 | % Evaluate attributes model (Lampert et al.)
function [ guessedCategories, results ] = evaluateAttributes( X, Y, thetas, trainParams, ...
assignments, categoriesToConsider, nonZeroCategories, categoryNames, doPrint)
numCategories = length(categoriesToConsider);
numAttributes = length(thetas);
X = ... |
github | yanweifu/zslearning-master | d2p.m | .m | zslearning-master/tsne/d2p.m | 3,155 | utf_8 | dc48b1dd0688d11671d81af50a0970de | function [P, beta] = d2p(D, u, tol)
%D2P Identifies appropriate sigma's to get kk NNs up to some tolerance
%
% [P, beta] = d2p(D, kk, tol)
%
% Identifies the required precision (= 1 / variance^2) to obtain a Gaussian
% kernel with a certain uncertainty for every datapoint. The desired
% uncertainty can be specified... |
github | yanweifu/zslearning-master | x2p.m | .m | zslearning-master/tsne/x2p.m | 3,297 | utf_8 | e0d6a8b9bcdd6ebd97037e46252fe200 | function [P, beta] = x2p(X, u, tol)
%X2P Identifies appropriate sigma's to get kk NNs up to some tolerance
%
% [P, beta] = x2p(xx, kk, tol)
%
% Identifies the required precision (= 1 / variance^2) to obtain a Gaussian
% kernel with a certain uncertainty for every datapoint. The desired
% uncertainty can be specifie... |
github | yanweifu/zslearning-master | predictGaussianDiscriminantMin.m | .m | zslearning-master/gaussianFunctions/predictGaussianDiscriminantMin.m | 957 | utf_8 | 775343a129a60d0e8b6227a8826cb6e8 | % outputs the probability that each image feature
function [logprobability] = predictGaussianDiscriminantMin(projectedImageFeatures, mu, sigma_elem, deletedClasses)
numLabels = size(mu, 1);
numTraining = size(projectedImageFeatures, 2);
dim = size(projectedImageFeatures, 1);
probability = zeros(numLabels - le... |
github | yanweifu/zslearning-master | predictGaussianDiscriminant.m | .m | zslearning-master/gaussianFunctions/predictGaussianDiscriminant.m | 683 | utf_8 | a128c2a53159fcea8a5c375d92c2ce19 | % outputs the probability that each image feature is seen before
function [logprobability] = predictGaussianDiscriminant(projectedImageFeatures, mu, sigma_elem, priors, deletedClasses)
dim = size(mu, 1);
numLabels = size(mu, 1);
numTraining = size(projectedImageFeatures, 2);
probability = zeros(1, numTraining)... |
github | yanweifu/zslearning-master | trainGaussianDiscriminant.m | .m | zslearning-master/gaussianFunctions/trainGaussianDiscriminant.m | 862 | utf_8 | 967dede18367120602c1bfd6272b42a6 | %p(y|x,X_T) = sum_v p(y|v,X_t)*p(v|x,X_T)
%p(y=1|v,x,X_T) = p(x|y=i,mu,Sigma)*p(y=i)/sum_{k=1}^{|V|}p(x|y=k,mu,Sigma)*pi(y=k)
function [mu,sigma_elem,priors] = trainGaussianDiscriminant(projectedImageFeatures, labels, numLabels, wordVectors)
[dim,numTraining] = size(projectedImageFeatures);
%sigma = zeros(numLa... |
github | pseudospectators/spectral2d-m-master | spectral2d.m | .m | spectral2d-m-master/spectral2d.m | 1,461 | utf_8 | 5c25b6d45645cd04cfcf4426302ea491 | function main
close all
clear all
clc
global params
addpath(genpath('./lib_spectral_matlab/'))
time = 0;
it = 1;
%---------------------------
% here, all case-specific options are hidden:
%---------------------------
% PARAMS_turbulent_cylinder;
PARAMS_uniform_cylinder;
% PARAMS_moving_cylinder;
% initialize grid, ... |
github | pseudospectators/spectral2d-m-master | create_mask.m | .m | spectral2d-m-master/create_mask.m | 1,330 | utf_8 | 77999328d1f207ccf21a8fcc99000348 |
function create_mask(time)
global params
switch params.mask_geometry
case ('cylinder')
params.mask=zeros(params.nx,params.ny);
params.us =zeros(params.nx,params.ny,2);
x0 = 0.5*params.Lx;
y0 = 0.5*params.Ly;
R = sqrt( (params.X-x0).^2 + (para... |
github | pseudospectators/spectral2d-m-master | secs2hms.m | .m | spectral2d-m-master/lib_spectral_matlab/secs2hms.m | 717 | utf_8 | 8a54067fa1ccb6067b9fd9ac49c4bce1 | %SECS2HMS - converts a time in seconds to a string giving the time in hours, minutes and second
%Usage TIMESTRING = SECS2HMS(TIME)]);
%Example 1: >> secs2hms(7261)
%>> ans = 2 hours, 1 min, 1.0 sec
%Example 2: >> tic; pause(61); disp(['program took ' secs2hms(toc)]);
%>> program took 1 min, 1.0 secs
function ti... |
github | joramvd/eegpreproc-master | eeglab2ft.m | .m | eegpreproc-master/eeglab2ft.m | 5,879 | utf_8 | ab31aaab25bc081e9b6c34a776e206c7 | function FT_EEG = eeglab2ft(EEG,filepath)
% function FT_EEG = eeglab2ft(EEG,filepath)
% Wrapper function to import EEG lab data to a fieldtrip, including
% conditions
% EEG can either be an eeglab struct, or be the filename which contains the
% eeglab data. In the latter case, filepath must also be specified
%
% J.J.Fa... |
github | joramvd/eegpreproc-master | v2struct.m | .m | eegpreproc-master/v2struct.m | 16,311 | utf_8 | 77b042ae8b342c50880cf0e543b7bd27 | %% v2struct
% v2struct Pack/Unpack Variables to/from a scalar structure.
function varargout = v2struct(varargin)
%% Description
% v2struct has dual functionality in packing & unpacking variables into structures and
% vice versa, according to the syntax and inputs.
%
% Function features:
% * Pack... |
github | RexYing/infant-eye-master | gui.m | .m | infant-eye-master/gui/gui.m | 17,796 | utf_8 | 5962a23995007e3da29b81c96445cc6c | function varargout = gui(varargin)
% GUI MATLAB code for gui.fig
% GUI, by itself, creates a new GUI or raises the existing
% singleton*.
%
% H = GUI returns the handle to a new GUI or the handle to
% the existing singleton*.
%
% GUI('CALLBACK',hObject,eventData,handles,...) calls the local
% ... |
github | RexYing/infant-eye-master | skeleton.m | .m | infant-eye-master/Vessel Analysis/FastMarching_version3b/skeleton.m | 6,325 | utf_8 | c8eb074a2da04508b77583b404a2f489 | function S=skeleton(I,verbose)
% This function Skeleton will calculate an accurate skeleton (centerlines)
% of an object represented by an binary image / volume using the fastmarching
% distance transform.
%
% S=skeleton(I,verbose)
%
% inputs,
% I : A 2D or 3D binary image
% verbose : Boolean, set to true (default) f... |
github | RexYing/infant-eye-master | msfm.m | .m | infant-eye-master/Vessel Analysis/FastMarching_version3b/msfm.m | 4,984 | utf_8 | 4ede2ad83d22018ce07744a23814e1f4 | function [T,Y]=msfm(F, SourcePoints, UseSecond, UseCross)
% This function MSFM calculates the shortest distance from a list of
% points to all other pixels in an image volume, using the
% Multistencil Fast Marching Method (MSFM). This method gives more accurate
% distances by using second order derivatives and cross... |
github | RexYing/infant-eye-master | msfm2d.m | .m | infant-eye-master/Vessel Analysis/FastMarching_version3b/functions/msfm2d.m | 10,712 | utf_8 | d551bc701da08d8e4e3f6a72c427fba8 | function [T,Y]=msfm2d(F, SourcePoints, usesecond, usecross)
% This function MSFM2D calculates the shortest distance from a list of
% points to all other pixels in an image, using the
% Multistencil Fast Marching Method (MSFM). This method gives more accurate
% distances by using second order derivatives and cross ne... |
github | RexYing/infant-eye-master | LineCurvature2D.m | .m | infant-eye-master/Vessel Analysis/measures/LineCurvature2D.m | 4,069 | utf_8 | 5d68750190861c373b2e3ee9972996e6 | function k=LineCurvature2D(Vertices,Lines)
% This function calculates the curvature of a 2D line. It first fits
% polygons to the points. Then calculates the analytical curvature from
% the polygons;
%
% k = LineCurvature2D(Vertices,Lines)
%
% inputs,
% Vertices : A M x 2 list of line points.
% (optional)
% Li... |
github | RexYing/infant-eye-master | xyGradient.m | .m | infant-eye-master/lib/Dijkstra_segmentation/xyGradient.m | 3,017 | utf_8 | efdb9101b35da45f1acdd1600e94cbfe | % function [gx, gy, gm, g, d] = xyGradient(img, sigma)
%
% gx is the partial derivative of img in the horizontal direction (right is
% positive); gy is the same in the vertical direction (up is positive);
% gm, if requested, is the magnitude of the gradient;
% sigma is the standard deviation of the Gaussian function ... |
github | RexYing/infant-eye-master | dijkstra_seg_single.m | .m | infant-eye-master/lib/Dijkstra_segmentation/dijkstra_seg_single.m | 3,417 | utf_8 | 525a4a1e5dcddd09402fed2c71cd8c08 | function [I,v,I3,Ig,I2,P,a,pt2,I4] = dijkstra_seg_single(A,v0,Ig,t,t3,t2,t8,c,t12,I)
%CODE FOR THE PAPER
%"Exploratory Dijkstra forest based automatic vessel segmentation: applications in video indirect ophthalmoscopy (VIO)",
%R. Estrada, C. Tomasi, M. T. Cabrera, D.K. Wallace, S.F. Freedman, S. Farsiu
%Biomedical Opti... |
github | RexYing/infant-eye-master | blk_amp_spec_slope_eo_toy.m | .m | infant-eye-master/lib/S3codes_Oct2011/blk_amp_spec_slope_eo_toy.m | 484 | utf_8 | 16466f33449d41d06fe1924da9ec040e |
function [res] = blk_amp_spec_slope_eo_toy(blk)
persistent N;
persistent wnd;
if (nargin == 0)
N = [];
wnd = [];
return;
end
if (nargin == 2 || isempty(N))
N = size(blk, 1);
wnd = hanning(N);
wnd = wnd * wnd';
end
if (~isa(blk, 'double'))
blk = double(blk);
end
blk_wnd_prod = blk .* wnd;
% blk_wnd_pr... |
github | RexYing/infant-eye-master | disp_progress.m | .m | infant-eye-master/lib/S3codes_Oct2011/disp_progress.m | 1,007 | utf_8 | a6489e6f67c1122df69d9dcbb7197946 |
function disp_progress(p, p_max)
persistent p_last;
if (nargin == 0)
p_last = [];
fprintf(1, '%s\n', '');
return;
end
p_done = p / p_max * 100;
p_done = round(p_done / 10) * 10;
%[p_done p_last]
if (p_done == p_last)
return;
end
if (~isempty(p_last))
% fprintf(1, '%d\n', p_last);
fprintf(1, '%s\b\b\b... |
github | RexYing/infant-eye-master | s3_map.m | .m | infant-eye-master/lib/S3codes_Oct2011/s3_map.m | 5,377 | utf_8 | 0dc569c5fdcc96459de87d5889ccf39f | function [s_map1 s_map2 s3] = s3_map(img, show_res)
% Input: img is a gray scale image, in double type, range from 0 - 255.
% You have to convert to gray scale if your image
% is color. You also have to cast img to double in order to run this code
% Parameter show_res = 1 to show results
% Output:
% s_map1: The sha... |
github | RexYing/infant-eye-master | fdct_wrapping_dispcoef.m | .m | infant-eye-master/Dibyendu/Project - Neonatal Eye/External Codes/Fusion/MST_SR_fusion_toolbox/fdct_wrapping_matlab/fdct_wrapping_dispcoef.m | 1,919 | utf_8 | 2af5a55f76ce583e6879244514db1b37 | function img = fdct_wrapping_dispcoef(C)
% fdct_wrapping_dispcoef - returns an image containing all the curvelet coefficients
%
% Inputs
% C Curvelet coefficients
%
% Outputs
% img Image containing all the curvelet coefficients. The coefficents are rescaled so that
% the largest coefficent... |
github | RexYing/infant-eye-master | extend2.m | .m | infant-eye-master/Dibyendu/Project - Neonatal Eye/External Codes/Fusion/MST_SR_fusion_toolbox/nsct_toolbox/extend2.m | 1,792 | utf_8 | 607c7de17e89483c3983b26b6987cb80 | function y = extend2(x, ru, rd, cl, cr, extmod)
% EXTEND2 2D extension
%
% y = extend2(x, ru, rd, cl, cr, extmod)
%
% Input:
% x: input image
% ru, rd: amount of extension, up and down, for rows
% cl, cr: amount of extension, left and rigth, for column
% extmod: extension mode. The valid modes are:
% 'per': period... |
github | RexYing/infant-eye-master | colorspace.m | .m | infant-eye-master/Dibyendu/Project - Neonatal Eye/External Codes/Fusion/MST_SR_fusion_toolbox/dtcwt_toolbox/colorspace.m | 13,590 | utf_8 | b1a9eb973fa39950345a1df707b5d2c8 | function varargout = colorspace(Conversion,varargin)
%COLORSPACE Convert a color image between color representations.
% B = COLORSPACE(S,A) converts the color representation of image A
% where S is a string specifying the conversion. S tells the
% source and destination color spaces, S = 'dest<-src', or
% alt... |
github | RexYing/infant-eye-master | dtwaveifm.m | .m | infant-eye-master/Dibyendu/Project - Neonatal Eye/External Codes/Fusion/MST_SR_fusion_toolbox/dtcwt_toolbox/dtwaveifm.m | 3,486 | utf_8 | 07a0631bc29c7e92d8255a7f52ecec0b | function Z = dtwaveifm(Yl,Yh,biort,qshift,gain_mask);
% Function to perform an n-level dual-tree complex wavelet (DTCWT)
% 1-D reconstruction.
%
% Z = dtwaveifm(Yl,Yh,biort,qshift,gain_mask);
%
% Yl -> The real lowpass subband from the final level
% Yh -> A cell array containing the complex highpass subban... |
github | RexYing/infant-eye-master | dDTCWT.m | .m | infant-eye-master/Dibyendu/Project - Neonatal Eye/External Codes/Fusion/MST_SR_fusion_toolbox/dtcwt_toolbox/dDTCWT.m | 1,565 | utf_8 | 8c9992a796e21805fbcd88f6acc567bd |
function [Y1 h11]=derotated_dtcwt(I1,n,biot,Qshift)
% X -> 2D real matrix/Image
%
% nlevels -> No. of levels of wavelet decomposition
%
% biort -> 'antonini' => Antonini 9,7 tap filters.
% 'legall' => LeGall 5,3 tap filters.
% 'near_sym_a' => Near-Symmetric 5,7 tap filter... |
github | RexYing/infant-eye-master | dtwavexfm2.m | .m | infant-eye-master/Dibyendu/Project - Neonatal Eye/External Codes/Fusion/MST_SR_fusion_toolbox/dtcwt_toolbox/dtwavexfm2.m | 6,425 | utf_8 | dc2ac3ba8198284e20b2982574de6174 | function [Yl,Yh,Yscale] = dtwavexfm2(X,nlevels,biort,qshift);
% Function to perform a n-level DTCWT-2D decompostion on a 2D matrix X
%
% [Yl,Yh,Yscale] = dtwavexfm2(X,nlevels,biort,qshift);
%
% X -> 2D real matrix/Image
%
% nlevels -> No. of levels of wavelet decomposition
%
% biort -> 'antonini' => Ant... |
github | RexYing/infant-eye-master | dtwaveifm2.m | .m | infant-eye-master/Dibyendu/Project - Neonatal Eye/External Codes/Fusion/MST_SR_fusion_toolbox/dtcwt_toolbox/dtwaveifm2.m | 4,884 | utf_8 | b7b8d7bbbe5209e8550b34297949f93c | function Z = dtwaveifm2(Yl,Yh,biort,qshift,gain_mask);
% Function to perform an n-level dual-tree complex wavelet (DTCWT)
% 2-D reconstruction.
%
% Z = dtwaveifm2(Yl,Yh,biort,qshift,gain_mask);
%
% Yl -> The real lowpass image from the final level
% Yh -> A cell array containing the 6 complex highpass subi... |
github | RexYing/infant-eye-master | ompdemo.m | .m | infant-eye-master/Dibyendu/Project - Neonatal Eye/External Codes/Fusion/MST_SR_fusion_toolbox/sparsefusion/ksvdbox/ompbox/ompdemo.m | 2,294 | utf_8 | 330e3e897edffa6b88c516d30fc4e96b | function ompdemo
%OMPDEMO Demonstration of the OMP toolbox.
% OMPDEMO generates a random sparse mixture of cosines and spikes, adds
% noise, and applies OMP to recover the original signal.
%
% To run the demo, type OMPDEMO from the Matlab prompt.
%
% See also OMPSPEEDTEST.
% Ron Rubinstein
% Computer Science De... |
github | RexYing/infant-eye-master | circle_houghpeaks.m | .m | infant-eye-master/Dibyendu/Project - Neonatal Eye/External Codes/circle_hough/circle_houghpeaks.m | 7,288 | utf_8 | e209d9dc6cb796b41051b98321c58616 | function peaks = circle_houghpeaks(h, radii, varargin)
%CIRCLE_HOUGHPEAKS finds peaks in the output of CIRCLE_HOUGH
% PEAKS = CIRCLE_HOUGHPEAKS(H, RADII, MARGIN, OPTIONS) locates the
% positions of peaks in the output of CIRCLE_HOUGH. The result PEAKS is a
% 3 x N array, where each column gives the position and r... |
github | RexYing/infant-eye-master | circlepoints.m | .m | infant-eye-master/Dibyendu/Project - Neonatal Eye/External Codes/circle_hough/circlepoints.m | 1,081 | utf_8 | 9dc1f35b98cf4625c1f5217a3df9949f | function [x, y] = circlepoints(r)
%CIRCLEPOINTS Returns integer points close to a circle
% [X, Y] = CIRCLEPOINTS(R) where R is a scalar returns coordinates of
% integer points close to a circle of radius R, such that none is
% repeated and there are no gaps in the circle (under 8-connectivity).
%
% If R is a r... |
github | RexYing/infant-eye-master | gaussian3.m | .m | infant-eye-master/Dibyendu/Project - Neonatal Eye/External Codes/CircleHoughTransform_v1.1/CircleHoughTransform_v1.1/gaussian3.m | 883 | utf_8 | b512f4f3f4c81d675f39e039c75c74e3 | % Copyright (C) 2012 Quan Wang <wangq10@rpi.edu>,
% Signal Analysis and Machine Perception Laboratory,
% Department of Electrical, Computer, and Systems Engineering,
% Rensselaer Polytechnic Institute, Troy, NY 12180, USA
%
% You are free to use this software for academic purposes if you cite our paper:
% Quan Wang, Ki... |
github | RexYing/infant-eye-master | circleHough.m | .m | infant-eye-master/Dibyendu/Project - Neonatal Eye/External Codes/CircleHoughTransform_v1.1/CircleHoughTransform_v1.1/circleHough.m | 2,134 | utf_8 | b9220acffc564c65f11aca1afddb5e92 | % Copyright (C) 2012 Quan Wang <wangq10@rpi.edu>,
% Signal Analysis and Machine Perception Laboratory,
% Department of Electrical, Computer, and Systems Engineering,
% Rensselaer Polytechnic Institute, Troy, NY 12180, USA
%
% You are free to use this software for academic purposes if you cite our paper:
% Quan Wang, Ki... |
github | RexYing/infant-eye-master | circle_in_image.m | .m | infant-eye-master/Dibyendu/Project - Neonatal Eye/External Codes/CircleHoughTransform_v1.1/CircleHoughTransform_v1.1/circle_in_image.m | 1,237 | utf_8 | d3e46d4b6e8ed3e4b2bc88c35c598d51 | % Copyright (C) 2012 Quan Wang <wangq10@rpi.edu>,
% Signal Analysis and Machine Perception Laboratory,
% Department of Electrical, Computer, and Systems Engineering,
% Rensselaer Polytechnic Institute, Troy, NY 12180, USA
%
% You are free to use this software for academic purposes if you cite our paper:
% Quan Wang, Ki... |
github | RexYing/infant-eye-master | computeColor.m | .m | infant-eye-master/Dibyendu/Project - Neonatal Eye/Proposed Codes/Registration Codes/SIFT-Flow/computeColor.m | 3,142 | utf_8 | a36a650437bc93d4d8ffe079fe712901 | function img = computeColor(u,v)
% computeColor color codes flow field U, V
% According to the c++ source code of Daniel Scharstein
% Contact: schar@middlebury.edu
% Author: Deqing Sun, Department of Computer Science, Brown University
% Contact: dqsun@cs.brown.edu
% $Date: 2007-10-31 21:20:30 (Wed, 31 O... |
github | RexYing/infant-eye-master | SIFTflowc2f.m | .m | infant-eye-master/Dibyendu/Project - Neonatal Eye/Proposed Codes/Registration Codes/SIFT-Flow/SIFTflowc2f.m | 4,252 | utf_8 | 02b76a36e0a8deb731e006e96648d38a | % function for coarse to fine SIFT flow matching
%
% Usage:
%
% [vx,vy,energylist]=SIFTflowc2f(im1,im2,SIFTflowpara,isdisplay);
%
% Input arguments:
% im1 an im2: SIFT images to match (from im1 to im2). The two images are not required
% to have the same dimension, but it is not likely to have meani... |
github | RexYing/infant-eye-master | dense_sift.m | .m | infant-eye-master/Dibyendu/Project - Neonatal Eye/Proposed Codes/Registration Codes/SIFT-Flow/dense_sift.m | 2,953 | utf_8 | f5d20f6edfcb5fdd61778096fc9d1a9a | function [sift_arr, grid_x, grid_y] = dense_sift(I, patch_size, grid_spacing)
I = double(I);
I = mean(I,3);
I = I /max(I(:));
% parameters
num_angles = 8;
num_bins = 4;
num_samples = num_bins * num_bins;
alpha = 9; %% parameter for attenuation of angles (must be odd)
if nargin < 5
sigma_edge = 1;
end
angle_step... |
github | RexYing/infant-eye-master | warpImage.m | .m | infant-eye-master/Dibyendu/Project - Neonatal Eye/Proposed Codes/Registration Codes/SIFT-Flow/warpImage.m | 481 | utf_8 | fc40d048af1746aef3b59b2ec6fb50a7 | % function to warp images with different dimensions
function [warpI2,mask]=warpImage(im,vx,vy)
[height2,width2,nchannels]=size(im);
[height1,width1]=size(vx);
[xx,yy]=meshgrid(1:width2,1:height2);
[XX,YY]=meshgrid(1:width1,1:height1);
XX=XX+vx;
YY=YY+vy;
mask=XX<1 | XX>width2 | YY<1 | YY>height2;
XX=min(max(XX,1),wid... |
github | amoodie/random-master | case_test.m | .m | random-master/case_test.m | 1,837 | utf_8 | b8f5022f459545c7fb0a937459e2533c | function case_test()
% test ways of having multiple methods in codes, and compare speeds
% preallocate
etc = zeros(1, 5); % elapsed time for switch-case
eti = zeros(1, 5); % elapsed time for if-then
% do five, sequential depths of tests
for i = 1:5
% x is the method 'name'
... |
github | valentinp/PROBE-master | loadCalibrationCamToCam.m | .m | PROBE-master/datasets/extraction/utils/devkit/loadCalibrationCamToCam.m | 1,894 | utf_8 | 88db832a2338f205ea36b1a9f6231aed | function calib = loadCalibrationCamToCam(filename)
% open file
fid = fopen(filename,'r');
if fid<0
calib = [];
return;
end
% read corner distance
calib.cornerdist = readVariable(fid,'corner_dist',1,1);
% read all cameras (maximum: 100)
for cam=1:100
% read variables
S_ = readVariable(fid,['S_' num2s... |
github | valentinp/PROBE-master | loadCalibrationRigid.m | .m | PROBE-master/datasets/extraction/utils/devkit/loadCalibrationRigid.m | 855 | utf_8 | 9148661cd7335b41dace4f57bd25b3a4 | function Tr = loadCalibrationRigid(filename)
% open file
fid = fopen(filename,'r');
if fid<0
error(['ERROR: Could not load: ' filename]);
end
% read calibration
R = readVariable(fid,'R',3,3);
T = readVariable(fid,'T',3,1);
Tr = [R T;0 0 0 1];
% close file
fclose(fid);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | valentinp/PROBE-master | icp.m | .m | PROBE-master/groundtruth/icp.m | 18,342 | utf_8 | 283054154b9888ad4a7e24be82f8851c | function [TR, TT, ER, t] = icp(q,p,varargin)
% Perform the Iterative Closest Point algorithm on three dimensional point
% clouds.
%
% [TR, TT] = icp(q,p) returns the rotation matrix TR and translation
% vector TT that minimizes the distances from (TR * p + TT) to q.
% p is a 3xm matrix and q is a 3xn matrix.
%
% [TR,... |
github | spm/spm99-master | spm_extract.m | .m | spm99-master/spm_extract.m | 2,696 | utf_8 | 36a05826475de9b08356b8349ba135ba | function data = spm_extract(fname,index)
% extracts columns from a matrix file stored on disk
% FORMAT data = spm_extract(fname,index)
% fname - name of data file.
% index - the indexes of the required columns.
% - if index is Inf or NaN, then the whole
% matrix is extracted.
% data - the extracted c... |
github | spm/spm99-master | spm_fmri_spm_ui.m | .m | spm99-master/spm_fmri_spm_ui.m | 25,043 | utf_8 | 5e48fa394a9061de17cec9b159f68eb5 | function [xX,Sess] = spm_fmri_spm_ui
% Setting up the general linear model for fMRI time-series
% FORMAT [xX,Sess] = spm_fmri_spm_ui
%
% xX - structure describing design matrix
% xX.X - design matrix
% xX.dt - time bin {secs}
% xX.RT - Repetition time {secs}
% xX.iH - vector ... |
github | spm/spm99-master | spm_input.m | .m | spm99-master/spm_input.m | 9,413 | utf_8 | 9ec7e64554fcf441e24020493286428f | function varargout = spm_input(varargin)
% Input wrapper function for batch mode input
% FORMAT spm_input(gui_arg,'batch', mat-file, batch_arg)
%
% gui_arg - Standard GUI input arguments (See spm_input_ui.m)
% 'batch' - dummy argument delimit where batch (non GUI) arguments start
% bch_mat - the name of a m... |
github | spm/spm99-master | spm_realign.m | .m | spm99-master/spm_realign.m | 16,656 | utf_8 | 4016abe54968274d7bfd7cb69a9e816b | function spm_realign(P,flags)
% Estimation of within modality rigid body movement parameters
% FORMAT spm_realign(P,flags)
%
% P - matrix of filenames {one string per row}
% All operations are performed relative to the first image.
% ie. Coregistration is to the first image, and resampling
% ... |
github | spm/spm99-master | spm_sn3d.m | .m | spm99-master/spm_sn3d.m | 31,670 | utf_8 | 75db11e2f580a4cefe4d67eda6c10fe8 | function spm_sn3d(P,matname,bb,Vox,params,spms,brainmask,objmask)
% Spatial (stereotactic) normalization
% ___________________________________________________________________________
%
% This module spatially (stereotactically) normalizes MRI, PET or SPECT
% images into a standard space defined by some ideal model or ... |
github | spm/spm99-master | spm_write_sn.m | .m | spm99-master/spm_write_sn.m | 6,793 | utf_8 | c42696bec0c4ea2bc37731e2076238ea | function spm_write_sn(P,matname,bb,Vox,Hold)
% Write Out Normalized Images.
% FORMAT spm_write_sn(P,matname,bb,Vox, Hold)
% P - Image to transform.
% matname - File containing transformation information.
% bb - Bounding box (mm).
% Vox - Required voxel size (mm).
% Hold - Sampling method (se... |
github | spm/spm99-master | spm_DesMtx.m | .m | spm99-master/spm_DesMtx.m | 30,886 | utf_8 | ef34c3a7054673d9ebf348b678675e86 | function [X,Pnames,Index,idx,jdx,kdx]=spm_DesMtx(varargin);
% Design matrix construction from factor level and covariate vectors
% FORMAT [X,Pnames] = spm_DesMtx(<FCLevels-Constraint-FCnames> list)
% FORMAT [X,Pnames,Index,idx,jdx,kdx] = spm_DesMtx(FCLevels,Constraint,FCnames)
%
% <FCLevels-Constraints-FCnames>
% ... |
github | spm/spm99-master | spm_imatrix.m | .m | spm99-master/spm_imatrix.m | 1,448 | utf_8 | 8d740b0688b1ac297afe11aa4941e0ca | function P = spm_imatrix(M)
% returns the parameters for creating an affine transformation
% FORMAT P = spm_imatrix(M)
% M - Affine transformation matrix
% P - Parameters (see spm_matrix for definitions)
%___________________________________________________________________________
% @(#)spm_imatrix.m 2.1 John ... |
github | spm/spm99-master | spm_input_ui.m | .m | spm99-master/spm_input_ui.m | 83,196 | utf_8 | 21db1edbf5518e72e8aa9aa42a832643 | function varargout = spm_input_ui(varargin)
% Comprehensive graphical and command line input function
% FORMATs (given in Programmers Help)
%_______________________________________________________________________
%
% spm_input_ui handles most forms of interactive user input for SPM.
% (File selection is handled by spm_... |
github | spm/spm99-master | spm_powell.m | .m | spm99-master/spm_powell.m | 8,421 | utf_8 | 05aa7e5977aee68a7f8ea97ac41ab852 | function [p,f] = spm_powell(p,xi,tolsc,func,varargin)
% Powell optimisation method
% FORMAT [p,f] = spm_powell(p,xi,tolsc,func,varargin)
% p - Starting parameter values
% xi - columns containing directions in which to begin
% searching.
% tolsc - stopping criteria
% - optimisat... |
github | spm/spm99-master | spm_get_ons.m | .m | spm99-master/spm_get_ons.m | 18,363 | utf_8 | 689a241b3fcc6b3d5c5510320a400e96 | function [sf,Cname,Pv,Pname,DSstr] = spm_get_ons(k,T,dt,STOC,Fstr,v,Cname,s)
% returns onset times for events
% FORMAT [sf,Cname,Pv,Pname,DSstr] = spm_get_ons(k,T,dt,STOC,Fstr,v,Cname,s)
%
% k - number of scans
% T - time bins per scan
% dt - time bin length (secs)
% STOC - flag to enable stochastic designs... |
github | spm/spm99-master | spm_vol.m | .m | spm99-master/spm_vol.m | 3,172 | utf_8 | 4500012ba460fb1a63e37b9445184ac7 | function V = spm_vol(P)
% Get header information etc for images.
% FORMAT V = spm_vol(P)
% P - a matrix of filenames.
% V - a vector of structures containing image volume information.
% The elements of the structures are:
% V.fname - the filename of the image.
% V.dim - the x, y and z dimensions of the vo... |
github | spm/spm99-master | spm_realign_ui.m | .m | spm99-master/spm_realign_ui.m | 16,587 | utf_8 | c82f35b8c07e627c56ff1973c7ea7831 | function spm_realign_ui(arg1)
% Within Mode Image Realignment
%___________________________________________________________________________
%
% This routine realigns a time-series of images acquired from the same subject
% using a least squares approach and a 6 parameter (rigid body) spatial
% transformation. The fir... |
github | spm/spm99-master | spm_create_image.m | .m | spm99-master/spm_create_image.m | 2,482 | utf_8 | a1dce4c970e435a27d1057788d5a15c8 | function Vo=spm_create_image(Vi)
% Create an image file.
% FORMAT Vo = spm_create_image(Vi)
% Vi - data structure containing image information.
% - see spm_vol for a description.
% Vo - data structure after modification for writing.
%_______________________________________________________________________
% @(#... |
github | spm/spm99-master | spm_get.m | .m | spm99-master/spm_get.m | 59,149 | utf_8 | 2ab77cef0f77edf02ca7c3c8d78fe2f2 | function varargout = spm_get(varargin)
% User interface : filename selection
% FORMAT P = spm_get(n,Filter,Prompt,NewWDir,CmdLine);
% n - 0 - returns with empty P
% - [mn,mx] : prompts for between mn & mx items
% - scalar n => [n,n], i.e. must select n items
% - all(n) positiv... |
github | spm/spm99-master | spm_append.m | .m | spm99-master/spm_append.m | 4,396 | utf_8 | d9226818ad1970818f041a2eacfa2fc9 | function spm_append(data,fname,options)
% appends columns to a matrix file stored on disk
% FORMAT spm_append(data,fname,options)
% data - the data to be appended.
% fname - name of data file.
% default file name is ``dat.mad''.
% options(1) - datatype used to represent the data
% d... |
github | spm/spm99-master | spm_xbrain.m | .m | spm99-master/spm_xbrain.m | 11,256 | utf_8 | d4b40e86f35b2d6e5aca4d542b7c44eb | function spm_xbrain(P,mode)
% Brain extraction.
% FORMAT spm_xbrain
%
% A rough and ready routine for extracting the brain from segmented
% images. It begins by taking the white matter, and eroding it a
% couple of times to get rid of any odd voxels. The algorithm
% continues on to do conditional dilations for severa... |
github | spm/spm99-master | spm_read_netcdf.m | .m | spm99-master/spm_read_netcdf.m | 3,640 | utf_8 | 9339212a576f5d67662107cebb087054 | function cdf = spm_read_netcdf(fname)
% Read the header information from a NetCDF file into a data structure.
% FORMAT cdf = spm_read_netcdf(fname)
% fname - name of NetCDF file
% cdf - data structure
%
% See: http://www.unidata.ucar.edu/packages/netcdf/
% _____________________________________________________________... |
github | spm/spm99-master | spm_mireg.m | .m | spm99-master/spm_mireg.m | 9,068 | utf_8 | 7072e31d54ebb8c7d5cb7c476c56c2ba | function x = spm_mireg(varargin)
% Between modality coregistration using Mutual Information
% FORMAT x = spm_mireg(VG,VF,params)
% VG - handle for first image (see spm_vol).
% VF - handle for second image.
% x - the parameters describing the rigid body rotation.
% such that a mapping from voxels in G to voxels in F... |
github | spm/spm99-master | spm_P_RF.m | .m | spm99-master/spm_P_RF.m | 5,737 | utf_8 | 02fa99a3479a2d593b10511b8b3b7c25 | function [P,p,Em,En,EN] = spm_P_RF(c,k,Z,df,STAT,R,n)
% Returns the [un]corrected P value using unifed EC theory
% FORMAT [P p Em En EN] = spm_P_RF(c,k,Z,df,STAT,R,n)
%
% c - cluster number
% k - extent {RESELS}
% Z - height {minimum over n values}
% df - [df{interest} df{error}]
% STAT - Statisical fe... |
github | spm/spm99-master | spm_image.m | .m | spm99-master/spm_image.m | 20,098 | utf_8 | 9ce014c27278d6253ff43e2956fa5e56 | function spm_image(op,varargin)
% image and header display
% FORMAT spm_image
%_______________________________________________________________________
%
% spm_image is an interactive facility that allows orthogonal sections
% from an image volume to be displayed. Clicking the cursor on either
% of the three images mov... |
github | spm/spm99-master | spm_coreg_ui.m | .m | spm99-master/spm_coreg_ui.m | 8,089 | utf_8 | 38c4ea3754f83549019b359f6723c723 | function spm_coregister(opt)
% Between and within mode image coregistration.
%
%____________________________________________________________________________
%
% The TARGET image is the image to which the OBJECT image is realigned.
% If there are any OTHER images, then the same transformations are applied to
% these ima... |
github | spm/spm99-master | spm_transverse.m | .m | spm99-master/spm_transverse.m | 14,021 | utf_8 | 94efdde5c588a579d85ee94178405943 | function spm_transverse(SPM,VOL,hReg,junk1)
% Rendering of regional effects [SPM{T/F}] on transverse sections
% FORMAT spm_transverse(SPM,VOL,hReg)
%
% SPM - structure containing SPM, distribution & filtering details
% - required fields are:
% .Z - minimum of n Statistics {filtered on u and k}
% .STAT - ... |
github | spm/spm99-master | spm_coregister.m | .m | spm99-master/spm_coregister.m | 12,840 | utf_8 | 5248f4b18fc5400c1dcd651779227811 | function spm_coregister(PGF, PFF, PGG, PFG, others,flags)
% Between and within mode image coregistration.
% FORMAT spm_coregister(PGF, PFF, PGG, PFG, others,flags)
% PGF - target image (the image to act as template).
% PFF - object image (the image to reposition).
% PGG - image to affine normalise target... |
github | spm/spm99-master | spm_sp.m | .m | spm99-master/spm_sp.m | 38,276 | utf_8 | 36bb45251125c441deb01f147ed27238 | function varargout = spm_sp(varargin)
% Orthogonal (design) matrix space setting & manipulation
% FORMAT varargout = spm_spc(action,varargin)
%
% This function computes the different projectors related to the row
% and column spaces X. It should be used to avoid redundant computation
% of svd on large X matrix. It is ... |
github | spm/spm99-master | spm_dbm.m | .m | spm99-master/spm_dbm.m | 19,662 | utf_8 | 61880e374a33a51156b89ecda3d00751 | function morphin
% Deformation Based Morphometry
%
% **** Extract Shapes *****
% Extracts the brain shapes from the nonlinear deformations.
%
% The deformation fields are defined by both non-linear and affine
% components. In order to proceed, it is necessary to decompose
% the transformation into components rela... |
github | spm/spm99-master | spm_platform.m | .m | spm99-master/spm_platform.m | 8,481 | utf_8 | 7fc85baada6b1c36c83825a5976f4b1d | function varargout=spm_platform(varargin)
% Platform specific configuration parameters for SPM
%
% FORMAT ans = spm_platform(arg)
% arg - optional string argument, can be
% - 'bigend' - return whether this architecture is bigendian
% - Inf - is not IEEE floating point
% ... |
github | spm/spm99-master | spm_orthviews.m | .m | spm99-master/spm_orthviews.m | 32,776 | utf_8 | 799a2ee6c8fedfc6e5bebd2f476ae73c | function varargout = spm_orthviews(action,varargin)
% Display Orthogonal Views of a Normalized Image
% FORMAT H = spm_orthviews('Image',filename[,position])
% filename - name of image to display
% area - position of image
% - area(1) - position x
% - area(2) - position y
% - area... |
github | spm/spm99-master | spm_FcUtil.m | .m | spm99-master/spm_FcUtil.m | 32,592 | utf_8 | d245bed90b513dbdedb8140bb8fbe83c | function varargout = spm_FcUtil(varargin)
% Contrast utilities
% FORMAT varargout = spm_FcUtil(action,varargin)
%_______________________________________________________________________
%
% spm_FcUtil is a multi-function function containing various utilities
% for contrast construction and manipulation. In general, it a... |
github | spm/spm99-master | spm_reslice.m | .m | spm99-master/spm_reslice.m | 22,009 | utf_8 | 59c8e49469de72f5f5af82e6bee4a2ae | function spm_reslice(P,flags)
% Rigid body reslicing of images
% FORMAT spm_reslice(P,flags)
%
% P - matrix of filenames {one string per row}
% All operations are performed relative to the first image.
% ie. Coregistration is to the first image, and resampling
% of images is into the space o... |
github | spm/spm99-master | spm_write_plane.m | .m | spm99-master/spm_write_plane.m | 3,043 | utf_8 | aaad4d709d0ad9ca4f88cb9d4fd46661 | function VO = spm_write_plane(V,A,p)
% Write a transverse plane of image data.
% FORMAT spm_write_plane(V,A,p)
% V - data structure containing image information.
% - see spm_vol for a description.
% A - the two dimensional image to write.
% p - the plane number (beginning from 1).
%
% VO - (possibly) modif... |
github | spm/spm99-master | spm_hread.m | .m | spm99-master/spm_hread.m | 4,435 | utf_8 | e2e242922cb3ff21f5c085dc95aa18f9 | function [DIM,VOX,SCALE,TYPE,OFFSET,ORIGIN,DESCRIP] = spm_hread(P)
% reads a header
% FORMAT [DIM VOX SCALE TYPE OFFSET ORIGIN DESCRIP] = spm_hread(P);
%
% P - filename (e.g spm or spm.img)
% DIM - image size [i j k [l]] (voxels)
% VOX - voxel size [x y z [t]] (mm [secs])
% SCALE - sca... |
github | spm/spm99-master | spm_SpUtil.m | .m | spm99-master/spm_SpUtil.m | 26,495 | utf_8 | cd848e0765d134d197212801818555d3 | function varargout = spm_SpUtil(varargin)
% Space matrix utilities
% FORMAT varargout = spm_SpUtil(action,varargin)
%
%_______________________________________________________________________
%
% spm_SpUtil is a multi-function function containing various utilities
% for Design matrix and contrast construction and manipu... |
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