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stringlengths 3
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stringlengths 12
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github
|
jianxiongxiao/ProfXkit-master
|
dirSmart.m
|
.m
|
ProfXkit-master/dirSmart/dirSmart.m
| 1,476 |
utf_8
|
937e09fdced78e2b7c9bb27706116c62
|
% example usage: imageFiles = dirSmart(fullfile(...),'jpg');
function files = dirSmart(page, tag)
[files, status] = urldir(page, tag);
if status == 0
files = dir(fullfile(page, ['*.' tag]));
end
end
function [files, status] = urldir(page, tag)
if nargin == 1
tag = '/';
else
tag = lower(tag);
if strcmp(tag, 'dir')
tag = '/';
end
if strcmp(tag, 'img')
tag = 'jpg';
end
end
nl = length(tag);
nfiles = 0;
files = [];
% Read page
page = strrep(page, '\', '/');
[webpage, status] = urlread(page);
if status
% Parse page
j1 = findstr(lower(webpage), '<a href="');
j2 = findstr(lower(webpage), '</a>');
Nelements = length(j1);
if Nelements>0
for f = 1:Nelements
% get HREF element
chain = webpage(j1(f):j2(f));
jc = findstr(lower(chain), '">');
chain = deblank(chain(10:jc(1)-1));
% check if it is the right type
if length(chain)>length(tag)-1
if strcmp(chain(end-nl+1:end), tag)
nfiles = nfiles+1;
chain = strrep(chain, '%20', ' '); % replace space character
files(nfiles).name = chain;
files(nfiles).bytes = 1;
end
end
end
end
end
end
|
github
|
jianxiongxiao/ProfXkit-master
|
duplicateRemoval.m
|
.m
|
ProfXkit-master/duplicateRemoval/duplicateRemoval.m
| 13,686 |
utf_8
|
9a2d5d0eca38443523c43d23346de6a4
|
function [images, image2keep, image2delete]=duplicateRemoval(rootPath,category)
% find all duplicates under the path fullfile(rootPath,category)
% and DELETE all the duplicated images!!! (file deletion will happen!!!)
% Written by Jianxiong Xiao @ 20130812
if ~exist('rootPath','var')
rootPath = '/data/vision/torralba/gigaSUN/imageGood/';
end
if ~exist('category','var')
%category = 'o/office_cubicles/';
category = 'o/office_cubicles';
end
% parameters
threshold_1st = 0.0005; % threshold for the first pca components
threshold_gist = 0.02; % threshold for the gist square distance
% parameters for gist
param.imageSize = [256 256]; % it works also with non-square images
param.orientationsPerScale = [8 8 8 8];
param.numberBlocks = 4;
param.fc_prefilt = 4;
param.boundaryExtension = 32; % number of pixels to pad
param.G = createGabor(param.orientationsPerScale, param.imageSize+2*param.boundaryExtension);
folders = regexp(genpath(fullfile(rootPath,category)), pathsep, 'split');
folders = folders(1:end-1);
cnt = 0;
for f=1:length(folders)
files = dir(folders{f});
for i=1:length(files)
if ~files(i).isdir
cnt = cnt + 1;
images{cnt} = fullfile(folders{f},files(i).name);
end
end
end
if matlabpool('size')==0
try
matlabpool
catch e
end
end
fprintf('# CPU threads = %d\n',matlabpool('size'));
% current implementation load all images into the memory
% it can be improved by loading individual images into the memory
fprintf('computing gist for %d images',cnt);
tic
gistMatrix =single(zeros(cnt,512));
parfor i=1:cnt
gistMatrix(i,:) = LMgist(imread(images{i}), '', param);
end
toc
try
load('pca_result.mat');
catch e
% training
mu = mean(gistMatrix,1);
fprintf('PCAing');
tic
coeff = princomp(bsxfun(@minus, gistMatrix,mu));
toc
cnt = length(images);
save('pca_result.mat','mu','coeff','rootPath','category','cnt','-v7.3');
end
% reproject
score_reproject = bsxfun(@minus, gistMatrix,mu)*coeff;
score_1st = score_reproject(:,1);
cnt = length(images);
time_complexity = zeros(1,cnt-1);
duplicates = cell(cnt-1,1);
parfor i=1:cnt-1
candidates = find(abs(score_1st(i+1:end) - score_1st(i))<threshold_1st);
time_complexity(i)=length(candidates);
candidates = candidates + i;
diff = sum(bsxfun(@minus, gistMatrix(candidates,:),gistMatrix(i,:)) .^ 2,2);
candidates_subset = find(diff < threshold_gist);
if isempty(candidates_subset)
duplicates{i} = single(zeros(0,3));
else
duplicates{i} =[repmat(i,length(candidates_subset),1) candidates(candidates_subset) diff(candidates_subset)];
end
end
fprintf('\nTime complexity = %f\n',mean(time_complexity));
duplicates = cell2mat(duplicates);
[~,ind] = sort(duplicates(:,3));
duplicates = duplicates(ind,:);
%save('debugThreshold.mat','duplicates','images','-v7.3');
duplicates = duplicates(duplicates(:,3)<threshold_gist,:);
%{
% visualization for duplicate pairs. duplicates, images
close all
for i=1:max(1,round(size(duplicates,1)/30)):size(duplicates,1)
figure(i);
subplot(1,2,1);
imshow(imread(images{duplicates(i,1)}));
title(duplicates(i,3));
subplot(1,2,2);
imshow(images{duplicates(i,2)});
end
%}
fprintf('connected components');
tic
G = sparse(double(duplicates(:,1)), double(duplicates(:,2)),true,length(images),length(images));
[numOfComponents, componentID] = graphconncomp(G, 'Weak', true);
toc
% connected component
image2keep = [];
image2delete = cell(0,0);
for i=1:numOfComponents
ids = find(componentID==i);
if length(ids)>1
area = zeros(1,length(ids));
for j=1:length(ids)
im = imread(images{ids(j)});
area(j) = size(im,1)*size(im,2);
end
[~,maxj] = max(area);
image2keep = [image2keep ids(maxj)];
image2delete{end+1} = ids( 1:length(ids) ~= maxj);
end
end
% visualize for connected components
%{
for i=1:length(image2keep)
figure(i)
num = length(image2delete{i})+1;
subplot(1,num,1);
imshow(imread(images{image2keep(i)}));
title(images{image2keep(i)})
for j=1:num-1
subplot(1,num,j+1);
imshow(imread(images{image2delete{i}(j)}));
title(images{image2delete{i}(j)})
end
end
%}
% delete the duplicated images
disp('Deleting images');
cntDeletedImages = 0;
for i=1:length(image2delete)
cntDeletedImages = cntDeletedImages + length(image2delete{i});
for j=1:length(image2delete{i})
delete(images{image2delete{i}(j)});
disp(images{image2delete{i}(j)});
end
end
disp('Image-based duplicate removal is finished!');
fprintf('Removed %d images (from %d images to %d images)\n', cntDeletedImages, length(images), length(images)-cntDeletedImages);
end
%% gist computation functions from Antonio
function [gist, param] = LMgist(D, HOMEIMAGES, param, HOMEGIST)
%
% [gist, param] = LMgist(D, HOMEIMAGES, param);
% [gist, param] = LMgist(filename, HOMEIMAGES, param);
% [gist, param] = LMgist(filename, HOMEIMAGES, param, HOMEGIST);
%
% For a set of images:
% gist = LMgist(img, [], param);
%
% When calling LMgist with a fourth argument it will store the gists in a
% new folder structure mirroring the folder structure of the images. Then,
% when called again, if the gist files already exist, it will just read
% them without recomputing them:
%
% [gist, param] = LMgist(filename, HOMEIMAGES, param, HOMEGIST);
% [gist, param] = LMgist(D, HOMEIMAGES, param, HOMEGIST);
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Modeling the shape of the scene: a holistic representation of the spatial envelope
% Aude Oliva, Antonio Torralba
% International Journal of Computer Vision, Vol. 42(3): 145-175, 2001.
if nargin==4
precomputed = 1;
% get list of folders and create non-existing ones
%listoffolders = {D(:).annotation.folder};
%for i = 1:length(D);
% f{i} = D(i).annotation.folder;
%end
%[categories,b,class] = unique(f);
else
precomputed = 0;
HOMEGIST = '';
end
% select type of input
if isstruct(D)
% [gist, param] = LMgist(D, HOMEIMAGES, param);
Nscenes = length(D);
typeD = 1;
end
if iscell(D)
% [gist, param] = LMgist(filename, HOMEIMAGES, param);
Nscenes = length(D);
typeD = 2;
end
if isnumeric(D)
% [gist, param] = LMgist(img, HOMEIMAGES, param);
Nscenes = size(D,4);
typeD = 3;
if ~isfield(param, 'imageSize')
param.imageSize = [size(D,1) size(D,2)];
end
end
param.boundaryExtension = 32; % number of pixels to pad
if nargin<3
% Default parameters
param.imageSize = 128;
param.orientationsPerScale = [8 8 8 8];
param.numberBlocks = 4;
param.fc_prefilt = 4;
param.G = createGabor(param.orientationsPerScale, param.imageSize+2*param.boundaryExtension);
else
if ~isfield(param, 'G')
param.G = createGabor(param.orientationsPerScale, param.imageSize+2*param.boundaryExtension);
end
end
% Precompute filter transfert functions (only need to do this once, unless
% image size is changes):
Nfeatures = size(param.G,3)*param.numberBlocks^2;
% Loop: Compute gist features for all scenes
gist = zeros([Nscenes Nfeatures], 'single');
for n = 1:Nscenes
g = [];
todo = 1;
% if gist has already been computed, just read the file
if precomputed==1
filegist = fullfile(HOMEGIST, D(n).annotation.folder, [D(n).annotation.filename(1:end-4) '.mat']);
if exist(filegist, 'file')
load(filegist, 'g');
todo = 0;
end
end
% otherwise compute gist
if todo==1
if Nscenes>1 disp([n Nscenes]); end
% load image
try
switch typeD
case 1
img = LMimread(D, n, HOMEIMAGES);
case 2
img = imread(fullfile(HOMEIMAGES, D{n}));
case 3
img = D(:,:,:,n);
end
catch
disp(D(n).annotation.folder)
disp(D(n).annotation.filename)
rethrow(lasterror)
end
% convert to gray scale
img = single(mean(img,3));
% resize and crop image to make it square
img = imresizecrop(img, param.imageSize, 'bilinear');
%img = imresize(img, param.imageSize, 'bilinear'); %jhhays
% scale intensities to be in the range [0 255]
img = img-min(img(:));
%img = 255*img/max(img(:));
img = 255*img/max(1,max(img(:)));
if Nscenes>1
imshow(uint8(img))
title(n)
end
% prefiltering: local contrast scaling
output = prefilt(img, param.fc_prefilt);
% get gist:
g = gistGabor(output, param);
% save gist if a HOMEGIST file is provided
if precomputed
mkdir(fullfile(HOMEGIST, D(n).annotation.folder))
save (filegist, 'g')
end
end
gist(n,:) = g;
drawnow
end
end
function output = prefilt(img, fc)
% ima = prefilt(img, fc);
% fc = 4 (default)
%
% Input images are double in the range [0, 255];
% You can also input a block of images [ncols nrows 3 Nimages]
%
% For color images, normalization is done by dividing by the local
% luminance variance.
if nargin == 1
fc = 4; % 4 cycles/image
end
w = 5;
s1 = fc/sqrt(log(2));
% Pad images to reduce boundary artifacts
img = log(img+1);
img = padarray(img, [w w], 'symmetric');
[sn, sm, c, N] = size(img);
n = max([sn sm]);
n = n + mod(n,2);
img = padarray(img, [n-sn n-sm], 'symmetric','post');
% Filter
[fx, fy] = meshgrid(-n/2:n/2-1);
gf = fftshift(exp(-(fx.^2+fy.^2)/(s1^2)));
gf = repmat(gf, [1 1 c N]);
% Whitening
output = img - real(ifft2(fft2(img).*gf));
clear img
% Local contrast normalization
localstd = repmat(sqrt(abs(ifft2(fft2(mean(output,3).^2).*gf(:,:,1,:)))), [1 1 c 1]);
output = output./(.2+localstd);
% Crop output to have same size than the input
output = output(w+1:sn-w, w+1:sm-w,:,:);
end
function g = gistGabor(img, param)
%
% Input:
% img = input image (it can be a block: [nrows, ncols, c, Nimages])
% param.w = number of windows (w*w)
% param.G = precomputed transfer functions
%
% Output:
% g: are the global features = [Nfeatures Nimages],
% Nfeatures = w*w*Nfilters*c
img = single(img);
w = param.numberBlocks;
G = param.G;
be = param.boundaryExtension;
if ndims(img)==2
c = 1;
N = 1;
[nrows ncols c] = size(img);
end
if ndims(img)==3
[nrows ncols c] = size(img);
N = c;
end
if ndims(img)==4
[nrows ncols c N] = size(img);
img = reshape(img, [nrows ncols c*N]);
N = c*N;
end
[ny nx Nfilters] = size(G);
W = w*w;
g = zeros([W*Nfilters N]);
% pad image
img = padarray(img, [be be], 'symmetric');
img = single(fft2(img));
k=0;
for n = 1:Nfilters
ig = abs(ifft2(img.*repmat(G(:,:,n), [1 1 N])));
ig = ig(be+1:ny-be, be+1:nx-be, :);
v = downN(ig, w);
g(k+1:k+W,:) = reshape(v, [W N]);
k = k + W;
drawnow
end
if c == 3
% If the input was a color image, then reshape 'g' so that one column
% is one images output:
g = reshape(g, [size(g,1)*3 size(g,2)/3]);
end
end
function y=downN(x, N)
%
% averaging over non-overlapping square image blocks
%
% Input
% x = [nrows ncols nchanels]
% Output
% y = [N N nchanels]
nx = fix(linspace(0,size(x,1),N+1));
ny = fix(linspace(0,size(x,2),N+1));
y = zeros(N, N, size(x,3));
for xx=1:N
for yy=1:N
v=mean(mean(x(nx(xx)+1:nx(xx+1), ny(yy)+1:ny(yy+1),:),1),2);
y(xx,yy,:)=v(:);
end
end
end
function img = imresizecrop(img, M, METHOD)
%
% img = imresizecrop(img, M, METHOD);
%
% Output an image of size M(1) x M(2).
if nargin < 3
METHOD = 'bilinear';
end
if length(M) == 1
M = [M(1) M(1)];
end
scaling = max([M(1)/size(img,1) M(2)/size(img,2)]);
%scaling = M/min([size(img,1) size(img,2)]);
newsize = round([size(img,1) size(img,2)]*scaling);
img = imresize(img, newsize, METHOD);
[nr nc cc] = size(img);
sr = floor((nr-M(1))/2);
sc = floor((nc-M(2))/2);
img = img(sr+1:sr+M(1), sc+1:sc+M(2),:);
end
function G = createGabor(or, n)
%
% G = createGabor(numberOfOrientationsPerScale, n);
%
% Precomputes filter transfer functions. All computations are done on the
% Fourier domain.
%
% If you call this function without output arguments it will show the
% tiling of the Fourier domain.
%
% Input
% numberOfOrientationsPerScale = vector that contains the number of
% orientations at each scale (from HF to BF)
% n = imagesize = [nrows ncols]
%
% output
% G = transfer functions for a jet of gabor filters
Nscales = length(or);
Nfilters = sum(or);
if length(n) == 1
n = [n(1) n(1)];
end
l=0;
for i=1:Nscales
for j=1:or(i)
l=l+1;
param(l,:)=[.35 .3/(1.85^(i-1)) 16*or(i)^2/32^2 pi/(or(i))*(j-1)];
end
end
% Frequencies:
%[fx, fy] = meshgrid(-n/2:n/2-1);
[fx, fy] = meshgrid(-n(2)/2:n(2)/2-1, -n(1)/2:n(1)/2-1);
fr = fftshift(sqrt(fx.^2+fy.^2));
t = fftshift(angle(fx+sqrt(-1)*fy));
% Transfer functions:
G=zeros([n(1) n(2) Nfilters]);
for i=1:Nfilters
tr=t+param(i,4);
tr=tr+2*pi*(tr<-pi)-2*pi*(tr>pi);
G(:,:,i)=exp(-10*param(i,1)*(fr/n(2)/param(i,2)-1).^2-2*param(i,3)*pi*tr.^2);
end
if nargout == 0
figure
for i=1:Nfilters
contour(fx, fy, fftshift(G(:,:,i)),[1 .7 .6],'r');
hold on
end
axis('on')
axis('equal')
axis([-n(2)/2 n(2)/2 -n(1)/2 n(1)/2])
axis('ij')
xlabel('f_x (cycles per image)')
ylabel('f_y (cycles per image)')
grid on
end
end
|
github
|
kevinalai/AgnosticMeanAndCovarianceCode-master
|
meanTesterGeneral.m
|
.m
|
AgnosticMeanAndCovarianceCode-master/meanTesterGeneral.m
| 2,260 |
utf_8
|
6cf708f98619aa376136460d3c3e2e55
|
% Output: Several vectors of values corresponding to the performance of
% the algorithm, the coordinate-wise median, and the mean of the true
% samples
% vecs contains the vectors for the algorithm's estimate and the mean of
% the true samples for dimension equal to the largest value of drange
% This code also plots the performance of the different estimators over a
% range of values of the dimension.
function [algError, medError, trueSampleError, vecs] = meanTesterGeneral()
drange = [50:50:200 300:100:600];
%drange = [500];
algError = zeros(size(drange));
medError = zeros(size(drange));
sampleError = zeros(size(drange));
trueSampleError = zeros(size(drange));
vecs = zeros(3, drange(end));
eta = .1;
for i = 1:size(drange,2)
d = drange(i);
m = 10*d;
%X = noisyG(zeros(d,1), eye(d), 2*ones(d,1), eta, m);
I = eye(d);
trueMu = I(1,:);
trueMu = zeros(1,d);
Y = genTruePoints(d, trueMu, (1-eta)*m);
Z = cauchyrnd(2, 1, eta*m, d);
X = [Y;Z];
est = agnosticMeanGeneral(X, eta);
algError(i) = norm(est-trueMu);
medError(i) = norm(median(X)-trueMu);
sampleError(i) = norm(mean(X)-trueMu);
trueSampleError(i) = norm(mean(Y)-trueMu);
if d == drange(end)
vecs(1,:) = est;
vecs(2,:) = mean(Y);
vecs(3,:) = median(Y);
end
end
plot(drange, algError, drange, medError, drange, trueSampleError);%, drange, vals3);
legend('Algorithm norm', 'Coord-median', 'True sample mean norm', 'Location', 'NorthEastOutside');
end
% trueMu parameter only transfers to mvnrnd
function [Y] = genTruePoints(d, trueMu, numPts)
%Gaussian
Y = mvnrnd(trueMu, 3*eye(d), numPts);
%GMM
%Y = useGMM(d, numPts);
%Uniform in simplex
%Y = useSimplex(d, numPts);
end
function Y = useSimplex(d, numPts)
Z = gamrnd(1, 1, numPts, d+1); % generate points from d+1 dimensional Dirichlet
a = sum(Z, 2);
Z = bsxfun(@rdivide, Z, a); % normalize
Y = Z(:, 1:d); % Only use the first d coordinates to get a point inside the simplex
end
function [Y] = useGMM(d, numPts)
A = eye(d);
w1 = .2; mu1 = zeros(1, d); Sigma1 = eye(d);
mu2 = A(1, :); Sigma2 = 2*eye(d);
Y = generateGMMsamples(numPts, w1, mu1, Sigma1, mu2, Sigma2);
end
|
github
|
kevinalai/AgnosticMeanAndCovarianceCode-master
|
covTesterGeneral.m
|
.m
|
AgnosticMeanAndCovarianceCode-master/covTesterGeneral.m
| 3,978 |
utf_8
|
526f5f44d82b36c11eada85bfbe4473e
|
% Output: Several vectors of values corresponding to the performance of
% the algorithm, the coordinate-wise median, and the mean of the true
% samples
% vecs contains the vectors for the algorithm's estimate and the mean of
% the true samples for dimension equal to the largest value of drange
% This code also plots the performance of the different estimators over a
% range of values of the dimension.
function [algError, medError, trueSampleError] = covTesterGeneral()
drange = [40:10:60];
%drange = [500];
algError = zeros(size(drange));
medError = zeros(size(drange));
trueSampleError = zeros(size(drange));
vecs = zeros(3, drange(end));
eta = .1;
for i = 1:size(drange,2)
d = drange(i);
m = d^2;
%X = noisyG(zeros(d,1), eye(d), 2*ones(d,1), eta, m);
I = eye(d);
%trueMu = I(1,:);
trueMu = zeros(1,d); % Set the mean of the true distribution
trueCov = 3*eye(d); % Set the covariance of the true distribution
mD = floor((1-eta)*m);
mN = m - mD;
trueDistType = 1; % Set the type of the true distribution
% 1: Gaussian
% 2: GMM (to set mixture parameters, see code below)
% 3: Uniform in standard simplex
% trueMu and trueCov variables are only used for Gaussian
Y = genTruePoints(d, trueMu, trueCov, mD, trueDistType);
Z = cauchyrnd(2, 1, mN, d); %noise distribution
%Z = 10000*randn(mN, d);
X = [Y;Z];
%X = Y;
[muHat, SigmaHat, ~] = agnosticCovarianceGeneral(X, eta);
fprintf('%d : %f\n', d, norm(muHat));
C = num2cell(X, 2);
XX = cell2mat(cellfun(@outerProdToVec, C, 'UniformOutput', 0));
% XX is a matrix where each row is the d^2 length vector corresponding
% to the outer product of a sample
CC = num2cell(Y, 2);
YY = cell2mat(cellfun(@outerProdToVec, CC, 'UniformOutput', 0));
% YY is a matrix where each row is the d^2 length vector corresponding
% to the outer product of a true sample
medvec = median(XX);
med = reshape(medvec, [d d]);
trueSampleMeanVec = mean(YY);
trueSampleMean = reshape(trueSampleMeanVec, [d d]);
algError(i) = norm(SigmaHat - trueCov, 'fro');
medError(i) = norm(med - trueCov, 'fro');
trueSampleError(i) = norm(trueSampleMean - trueCov, 'fro');
clf
plot(drange(1:i), algError(1:i),'--o', drange(1:i), medError(1:i),'-o', drange(1:i), trueSampleError(1:i),'-*');
legend('Algorithm norm', 'Coord-median', 'True sample mean norm', 'Location', 'NorthEastOutside');
axis([0 drange(end) 0 14])
drawnow
end
%plot(drange, algError, drange, medError, drange, trueSampleError);%, drange, vals3);
%legend('Algorithm norm', 'Coord-median', 'True sample mean norm', 'Location', 'NorthEastOutside');
end
% trueMu parameter only transfers to mvnrnd
% 1: Gaussian
% 2: GMM (to set mixture parameters, see code below)
% 3: Uniform in standard simplex
function [Y] = genTruePoints(d, trueMu, trueCov, numPts, type)
if type == 1
%Gaussian
Y = mvnrnd(trueMu, trueCov, numPts);
elseif type == 2
%GMM
Y = useGMM(d, numPts);
elseif type == 3
%Uniform in simplex
Y = useSimplex(d, numPts);
else
fprintf('Invalid type provided. Using Gaussian.\n')
Y = mvnrnd(trueMu, trueCov, numPts);
end
end
function Y = useSimplex(d, numPts)
Z = gamrnd(1, 1, numPts, d+1); % generate points from d+1 dimensional Dirichlet
a = sum(Z, 2);
Z = bsxfun(@rdivide, Z, a); % normalize
Y = Z(:, 1:d); % Only use the first d coordinates to get a point inside the simplex
end
function [Y] = useGMM(d, numPts)
A = eye(d);
w1 = .2; mu1 = zeros(1, d); Sigma1 = eye(d);
mu2 = A(1, :); Sigma2 = 2*eye(d);
Y = generateGMMsamples(numPts, w1, mu1, Sigma1, mu2, Sigma2);
end
%transform row vector v to outer product and then reshape into vector
function out = outerProdToVec(v)
len = size(v, 2);
V = v'*v;
out = reshape(V,[1, len*len]);
end
|
github
|
kevinalai/AgnosticMeanAndCovarianceCode-master
|
noisyG.m
|
.m
|
AgnosticMeanAndCovarianceCode-master/noisyG.m
| 646 |
utf_8
|
2ec7a1383e477d64b10e6b615992815c
|
% Method for generating points from a spherical Gaussian with noise placed
% at a single point
%
% Input: mean, covariance matrix, noise fraction eta, number of samples m,
% and a noise point z. Mean and z are column vectors in n dimensions
%
% Output: a matrix X of samples, where in expectation, first 1-eta
% fraction are from N(mu, var), and the last eta fraction are repeats of
% the vector z
% The output has m rows (one per sample point) and n columns (one per
% dimension)
function [X] = noisyG(mu, Sigma, z, eta, m)
mN = binornd(m, eta); %ceil(eta*m);
mG = m - mN;
Y = mvnrnd(mu', Sigma, mG);
Z = repmat(z', mN, 1);
X = [Y; Z];
end
|
github
|
kevinalai/AgnosticMeanAndCovarianceCode-master
|
agnosticMeanGeneral.m
|
.m
|
AgnosticMeanAndCovarianceCode-master/agnosticMeanGeneral.m
| 857 |
utf_8
|
d77761516d49c3db9c7ed9e63c4ef926
|
% Agnostic algorithm for computing mean of a general distribution with
% bounded fouth moments
%
% Input: X = noisy data from a general distribution with bounded fourth
% moments, noise fraction eta
% Output: est = estimate for the mean
function est = agnosticMeanGeneral(X, eta)
n = size(X,2);
if n <= 1
est = estGeneral1D(X, 1, eta);
return;
end
w = outRemBall(X, eta);
newX = X(w>0,:);
%newX = bsxfun(@times, X, sqrt(w));
S = cov(newX);
[V,D] = eig(S);
if ~issorted(diag(D)) % check if eigvecs are in ascending order
[~,inds] = sort(diag(D));
V = V(:, inds);
end
PW = V(:, 1:floor(n/2))*V(:, 1:floor(n/2))';
%weightedProjX = bsxfun(@times, X*PW, w);
weightedProjX = newX*PW;
est1 = mean(weightedProjX); %weighted mean
QV = V(:, floor(n/2)+1:end);
est2 = agnosticMeanGeneral(X*QV, eta);
est2 = est2*QV';
est = est1 + est2;
end
|
github
|
kevinalai/AgnosticMeanAndCovarianceCode-master
|
generateGMMsamples.m
|
.m
|
AgnosticMeanAndCovarianceCode-master/generateGMMsamples.m
| 303 |
utf_8
|
679bd5af6f168a8fa24843e805386e31
|
% Generates m samples from a general GMM with the given parameters
function x = generateGMMsamples(m, w1, mu1, Sigma1, mu2, Sigma2)
d = size(mu1, 2);
x = zeros(m, d);
numOnes = binornd(m, w1);
x(1:numOnes,:) = mvnrnd(mu1, Sigma1, numOnes);
x(numOnes+1:end,:) = mvnrnd(mu2, Sigma2, m - numOnes);
end
|
github
|
kevinalai/AgnosticMeanAndCovarianceCode-master
|
recursivePCA.m
|
.m
|
AgnosticMeanAndCovarianceCode-master/recursivePCA.m
| 701 |
utf_8
|
69d5d69cc551640850c7441656b65498
|
% Agnostic algorithm for computing mean of a Gaussian
%
% Input: data X from a Gaussian, outlierRemoval procedure
% Output: estimate for the mean
function est = recursivePCA(X,sig,outlierRemoval)
m = length(X);
n = size(X,2);
if n<=2
est = median(X);
return;
end
% iter = ceil(m/2);
% R = zeros(iter,1);
% for i=1:iter
% i1 = ceil(rand()*m); j1 = ceil(rand()*m);
% R(i) = norm(X(i1,:) - X(j1,:));
% end
% sig = median(R)/sqrt(n);
r = sig*sqrt(n);
[X] = outlierRemoval(X,r);
S = cov(X);
[V,~] = eig(S);
PW = V(:, 1:floor(n/2))*V(:, 1:floor(n/2))';
est1 = mean(X*PW);
QV = V(:, floor(n/2)+1:end);
est2 = recursivePCA(X*QV,sig);
est2 = est2*QV';
est = est1 + est2;
%est = mean(X);
end
|
github
|
kevinalai/AgnosticMeanAndCovarianceCode-master
|
estG1D.m
|
.m
|
AgnosticMeanAndCovarianceCode-master/estG1D.m
| 590 |
utf_8
|
2a0a2d58bf29151c01e9792ec94f14c6
|
% Algorithm for estimating 1D mean and variance of a Gaussian in a
% direction v
%
% Input: Noisy samples from a general Gaussian
% Output: estimate of the mean and variance along the direction v
function [mu, sigma2] = estG1D(X, v)
v = v/norm(v); %normalize
m = size(X,1);
Z = X*v;
mu = median(Z);
Z = Z - repmat(mu, m, 1);
% subtract 60th quantile location from 40th quantile
topQuant = .6;
botQuant = .4;
diff = quantile(Z,topQuant) - quantile(Z,botQuant);
sigma2 = (diff/(norminv(topQuant, 0, 1) - norminv(botQuant, 0, 1)))^2;
end
|
github
|
kevinalai/AgnosticMeanAndCovarianceCode-master
|
agnosticCovarianceGeneral.m
|
.m
|
AgnosticMeanAndCovarianceCode-master/agnosticCovarianceGeneral.m
| 821 |
utf_8
|
3933997e1758b32366fb3ee0b79cf9ee
|
% Algorithm for estimating general covariance
% Assume mean of X's is 0
function [muHat, SigmaEst, centeredX] = agnosticCovarianceGeneral(X, eta)
m = size(X, 1);
n = size(X, 2);
%muHat = agnosticMeanGeneral(X, eta);
muHat = zeros(1, n);
tic;
Z = X - repmat(muHat, m, 1);
C = num2cell(Z, 2);
centeredX = cellfun(@outerProdToVec, C, 'UniformOutput', 0);
centeredX = cell2mat(centeredX);
toc;
fprintf('%d %d\n', size(centeredX));
w = outRemBall(centeredX, eta);
tic;
SigmaEst = agnosticMeanGeneral(centeredX, eta);
toc;
SigmaEst = reshape(SigmaEst, [n n]);
end
%transform row vector v to outer product and then reshape into vector
function out = outerProdToVec(v)
len = size(v, 2);
V = v'*v;
out = reshape(V,[1, len*len]);
end
|
github
|
kevinalai/AgnosticMeanAndCovarianceCode-master
|
estGeneral1D.m
|
.m
|
AgnosticMeanAndCovarianceCode-master/estGeneral1D.m
| 718 |
utf_8
|
c3c13e6cc219840e6d497e99c10eb60c
|
% Algorithm for estimating the 1D mean of a general distribution with
% bounded fourth moments in a direction v
%
% Input: Noisy samples from a general distribution with bounded fourth
% moments, column vector v, noise fraction eta
% Output: estimate of the mean and variance along the direction v
function mu = estGeneral1D(X, v, eta)
v = v/norm(v); %normalize
m = size(X,1);
Z = X*v;
Z = sort(Z);
intervalWidth = floor(m*(1-eta)^2);
lengths = zeros(m - intervalWidth + 1, 1);
for i = 1:m - intervalWidth + 1
lengths(i) = Z(i + intervalWidth - 1) - Z(i);
end
[~,ind] = min(lengths);
ind = ind(1);
mu = mean(Z(ind:ind + intervalWidth - 1));
end
|
github
|
kevinalai/AgnosticMeanAndCovarianceCode-master
|
outRemBall.m
|
.m
|
AgnosticMeanAndCovarianceCode-master/outRemBall.m
| 673 |
utf_8
|
3f50404576c02f2e35495df7a4d97486
|
% Removes points outside of a ball containing (1-eta)^2 fraction of the
% points. The ball is centered at the coordinate-wise median.
% The weight vector returned has 0 weight for points from X that are
% outside this ball.
%
% Input: X = sample from a distribution with bounded fourth moments,
% noise fraction eta
%
% Output: weight (column) vector w that is 0 for "removed" points
function [w] = outRemBall(X, eta)
m = size(X, 1);
med = median(X);
w = ones(m, 1);
Z = X - repmat(med, m, 1);
T = sum(Z.^2,2);
thresh = prctile(T, 100*(1-eta)^2);
w(T > thresh) = 0;
%fprintf('dim = %d and numOverThresh = %d, median norm = %f\n',n,sum(T> C*thresh), norm(med));
end
|
github
|
kevinalai/AgnosticMeanAndCovarianceCode-master
|
agnosticMeanG.m
|
.m
|
AgnosticMeanAndCovarianceCode-master/agnosticMeanG.m
| 1,177 |
utf_8
|
1544f8ae91b45b7dfd7a2dec75b3129c
|
% Agnostic algorithm for computing mean of a general Gaussian
%
% Input: X = noisy data from a general Gaussian
% Output: est = estimate for the mean
function est = agnosticMeanG(X, eta)
m = size(X,1);
n = size(X,2);
if n<=2
est = median(X);
return;
end
w = outlierRemoval(X, eta);
muHat = w'*X/m;
norm(muHat);
C = X - repmat(muHat, m, 1);
C = bsxfun(@times, C, sqrt(w));
%for i=1:m
% S = S + (X(i,:) - muHat)' * (X(i,:) - muHat);
%end
S = C'*C; %does the outer product from above
S = S/m; % weighted covariance matrix
[V,~] = eig(S);
PW = V(:, 1:floor(n/2))*V(:, 1:floor(n/2))';
weightedProjX = bsxfun(@times, X*PW, w);
est1 = mean(weightedProjX);
QV = V(:, floor(n/2)+1:end);
est2 = agnosticMeanG(X*QV, eta);
est2 = est2*QV';
%fprintf('dim = %d, mean norm here = %f\n', n, norm(est2));
est = est1 + est2;
end
function [w] = outlierRemoval(X, eta)
m = size(X,1);
n = size(X,2);
w = outlierDamping(X);
%w = ones(m, 1);
%w = outRemBall(X, eta);
%w = outRemSpherical(X, sqrt(n));
%size(w)
%r = sqrt(n);
%w = ones(m, 1);
%Z = X - repmat(med,m,1);
%T = sum(Z.^2,2);
%w(T > 2*r*r) = 0;
%size(w)
end
|
github
|
kevinalai/AgnosticMeanAndCovarianceCode-master
|
tester.m
|
.m
|
AgnosticMeanAndCovarianceCode-master/tester.m
| 946 |
utf_8
|
d7f0f0d73a362c7cb7e40db450157d48
|
% Testing code for agnosticMeanG
% Compares the quality of agnosticMeanG's output to the sample mean and
% sample median for noise all at the ones vector times 100
%
% Input: eta = noise fraction
% m = number of samples to test
% Output: norms of agnosticMeanG estimate, sample mean, and sample median
% for various values of the dimension n
function [est, sMean, sMed] = tester(eta, m)
numVals = 10;
range = ceil(linspace(100, 10000, numVals));
sMean = zeros(numVals, 1);
sMed = zeros(numVals, 1);
est = zeros(numVals, 1);
for i=1:numVals
n = range(i);
fprintf('Working on i=%d, n=%d\n',i, n);
mu = zeros(n, 1);
I = eye(n);
z = 100*ones(n, 1);
X = noisyG(mu, I, z, eta, m);
sMean(i) = norm(mean(X));
sMed(i) = norm(median(X));
est(i) = norm(agnosticMeanG(X));
end
plot(range, est, range, sMean, range, sMed);
end
|
github
|
kevinalai/AgnosticMeanAndCovarianceCode-master
|
cauchyfit.m
|
.m
|
AgnosticMeanAndCovarianceCode-master/Cauchy code/cauchyfit.m
| 5,565 |
utf_8
|
026e4e274726fdf96ecf7abf0cddf87b
|
function [mlepars, output]= cauchyfit(varargin)
% USAGE:
% [mlepars, res]= cauchyfit(x) Fit parameters to data x.
% [mlepars, res]= cauchyfit(x, xpars) Fit parameters to data x, with one known parameter.
% [mlepars, res]= cauchyfit(n, npars) Debugging: generate a n-size sample and fit it...
% [mlepars, res]= cauchyfit(..., i) Info about execution.
%
% Parameter estimates (one or both parameters) for Cauchy distributed data.
%
% Parameters are estimated thru MLE, using Matlab optimization fminsearch (fmincon,
% if the Optimization Toolbox is available).
%
% NOTE: No confidence interval yet, I got to find the math for it first...
%
% ARGUMENTS:
% - x (vector of length 2 or more) The data to fit.
% - xpars: [a NaN], [NaN b], or [NaN NaN] (b>0) NaN-parameters are calculated, others are given.
% - n (scalar) Generate a n-sized random sample and fit.
% - npars, [a b] (b>0) The parameters to use for the random generation.
% - i (string) Information ('info') or detailed information ('info2')
% about execution. Generates a nice figure too!
% - mlepars, the mle parameter estimation.
% - res (structure) is the 'output' structure of the optimization call with two additions:
% .exitflag is the exitflag value returned by the optimization call.
% .call is the name of the called function, see its reference for the other fields.
%
% EXAMPLE:
% x= cauchyrnd(1, 0.3, [1 100]);
% params1= cauchyfit(x, [1 NaN], 'info2'); % Fits b, given that a equals 1.
% params2= cauchyfit(x, 'info2'); % Fits a and b.
%
% SEE ALSO: cauchycdf, cauchyinv, cauchypdf, cauchyrnd, cauchystat.
%
% Copyright (C) Peder Axensten <peder at axensten dot se>
%
% HISTORY:
% Version 1.1, 2006-07-26.
% - Added cauchyfit to the cauchy package.
% Version 1.2, 2006-08-06:
% - Can now estimate one parameter when the other is given.
% - The arrangement of arguments now follows the ways of Statistics Toolbox.
% - Put the actual mle in a separate file.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Check the arguments
argok= true;
dbg= 0; % No execution information displayed.
dbgstr= '';
if((nargin > 1) && ischar(varargin{end}))
% Display execution information.
switch(varargin{end})
case 'info', dbg= 1;
case 'info2', dbg= 2;
otherwise, argok= false;
end
dbgstr= varargin{end};
varargin= {varargin{1:end-1}};
end
if((length(varargin) == 2) && all(cellfun('isreal', varargin)) && ...
~isempty(varargin{1}) && (length(varargin{2}) == 2) ...
)
tpars= varargin{2};
if(~any(isnan(varargin{2})) && (length(varargin{1}) == 1))
% All parameters given: generate random numbers to fit.
x= cauchyrnd(tpars(1), tpars(2), [1 varargin{1}]);
elseif(any(isnan(varargin{2})) && (length(varargin{1}) >= 2))
x= varargin{1};
else
argok= false;
end
elseif((length(varargin) == 1) && all(all(isreal(varargin{1}))))
% This is a "real" run.
tpars= [NaN, NaN];
x= varargin{1};
else
argok= false;
end
if(~argok)
error('Incorrect arguments, check ''help cauchyfit''.');
end
% Initial parameter values and stuff.
ipars= [ median(x), ... % Initial a.
max([std(x)/10 0.2]) ... % initial b.
];
lBounds= [-Inf, 1e-20];
n= length(x);
negloglikeshort= @(pp)negloglike(pp(1), pp(2), x, n, 3);
if(isnan(tpars(1)) && ~isnan(tpars(2)))
ipars= ipars(1);
lBounds= lBounds(1);
negloglikeshort= @(a)negloglike(a, tpars(2), x, n, 1);
elseif(~isnan(tpars(1)) && isnan(tpars(2)))
ipars= ipars(2);
lBounds= lBounds(2);
negloglikeshort= @(b)negloglike(tpars(1), b(1), x, n, 2);
end
% Info on the data.
if(dbg)
value(' ', 'size', 'mean', 'median', 'std');
value('Data:', numel(x), mean(x), median(x), std(x));
disp(' ');
end
% Find parameters.
[mlepars, output]= paxmle(ipars, negloglikeshort, lBounds, dbgstr);
% Result info.
if(dbg)
if(isnan(tpars(1)) && ~isnan(tpars(2))), [l, dl]= negloglikeshort(tpars(2));
else [l, dl]= negloglikeshort(tpars);
end
value('True params:', [l, sqrt(sum(dl.^2)), tpars]);
disp(' ');
% Add to figure.
legend('Initial point', 'Best fit', 'Location', 'ne');
hold off;
end
end
function value(gs, varargin)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% For debugging purposes.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
fprintf(1, '\n%-20s', gs);
for i= 1:length(varargin)
if(ischar(varargin{i})), fprintf(1, '%15s', varargin{i});
else fprintf(1, '%15.6f', varargin{i});
end
end
end
function [L, dL, ddL]= negloglike(a, b, x, n, whatab)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Calculate the log-likelihood and, if need be, the derivates and second derivates.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
k1= (x-a)/b;
kL= 1 + k1.*k1;
L= n*log(pi*b) + sum(log(kL)); % The log-likelihood
if(nargout >= 2)
k2= 1./kL;
k3= k1.*k2;
if(whatab == 1) % Only fitting a.
dL= -2*sum(k3)/b;
if(nargout >= 3), ddL= 2*sum(k2)/(b*b); end
elseif(whatab == 2) % Only fitting b.
k4= k1.*k3;
dL= (n-2*sum(k4))/b;
if(nargout >= 3) ddL= (-n+sum(k4.*(6-4*k4)))/(b*b); end
else % Fitting a and b.
k4= k1.*k3;
dL= [-2*sum(k3), n-2*sum(k4)]/b;
if(nargout >= 3)
k5= 4*sum(k3.*(1-k4));
ddL= [2*sum(k2), k5; k5, -n+sum(k4.*(6-4*k4))]/(b*b);
end
end
end
end
|
github
|
kevinalai/AgnosticMeanAndCovarianceCode-master
|
paxmle.m
|
.m
|
AgnosticMeanAndCovarianceCode-master/Cauchy code/paxmle.m
| 6,174 |
utf_8
|
e3a492151051b3c6fb196ba07e8436ef
|
function [mlepars, output]= paxmle(pars, negloglike, varargin)
% USAGE:
% [mlepars, output]= paxmle(pars, negloglike)
% [mlepars, output]= paxmle(pars, negloglike, lBounds)
% [mlepars, output]= paxmle(pars, negloglike, lBounds, uBounds)
% [mlepars, output]= paxmle(..., options)
%
% Calculate the best parameter fit given the negative log-likelihood.
%
% The parameter(s) is/are estimated thru MLE, using Matlab optimization fminsearch (fmincon,
% if the Optimization Toolbox is available).
%
% NOTE: No confidence interval yet, I got to find the math for it first...
%
% ARGUMENTS:
% - pars (non empty vector) The initial parameter, the starting value.
% - negloglike is a function of the type [negL, negDL, negDDL]= negloglike(p), where
% negL, negDL, and negDDL are the value, first derivate (Jacobian), and second derivate
% (Hessian) respectively for the (log-)likelihood function at p. If you don't want to
% calculate the Jacobian and/or the Hessian, return NaN for these instead. They are only
% used when the Optimisation Toolbox is available.
% - lBounds (default is -Inf), the lower bounds for mlepars.
% - uBounds (default is Inf), the upper bounds for mlepars.
% - options (string) Information ('info') or detailed information ('info2')
% about execution. Generates a nice figure too!
% - mlepars is the maximum likelihood estimated parameter values, or NaNs if none was found.
% - output (structure) is the 'output' structure of the optimization call with two additions:
% .exitflag is the exitflag value returned by the optimization call.
% .call is the name of the called function, see its reference for the other fields.
%
% EXAMPLE:
% [v, res]= paxmle([mean(x) std(x)], myfunhandle, 'info2');
%
% Copyright (C) Peder Axensten <peder at axensten dot se>
%
% HISTORY:
% Version 1.0, 2006-08-03.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Default values.
options= optimset( ...
'Display', 'off', ...
'MaxIter', 2000, ...
'TolX', eps, ...
'TolFun', 0 ...
);
% Check the arguments
argok= all(all(isreal(pars))) && ~isempty(pars) && strcmp(class(negloglike), 'function_handle');
pLen= length(pars); % Number of parameters.
if((nargin > 2) && ischar(varargin{end})) % Display execution information?
dbg= true;
switch(varargin{end})
case 'info', options= optimset(options, 'Display', 'final');
case 'info2', options= optimset(options, 'Display', 'iter');
case '', dbg= false;
otherwise, argok= false;
end
varargin= {varargin{1:end-1}};
else dbg= false;
end
if(length(varargin) >= 1) % Lower bounds?
lBounds= varargin{1};
argok= argok && all(all(isreal(lBounds))) && all(all(size(lBounds) == size(pars)));
else
lBounds= repmat(-Inf, [1 pLen]);
end
if(length(varargin) >= 2) % Upper bounds?
uBounds= varargin{2};
argok= argok && all(all(isreal(uBounds))) && all(all(size(uBounds) == size(pars)));
else
uBounds= repmat( Inf, [1 pLen]);
end
if(~argok || (length(varargin) > 2)) % Argument error?
error('Incorrect arguments, check ''help paxmle''.');
end
% Do we have the Jacobian ? The Hessian?.
[L, dL, ddL]= negloglike(pars);
if(~isnan(dL))
options= optimset(options, 'LargeScale', 'on', 'GradObj', 'on');
if(~isnan(ddL))
options= optimset(options, 'Hessian', 'on');
end
end
% Find parameters.
divzero= warning('query', 'MATLAB:divideByZero');
warning('off', 'MATLAB:divideByZero');
if(exist('fmincon', 'file') == 2) % Optimization Toolbox is available.
[mlepars,fval,exitflag,output]= fmincon(negloglike, pars, ...
[], [], [], [], lBounds, uBounds, [], options);
output.call= 'fmincon';
else % Standard Matlab.
[mlepars,fval,exitflag,output]= fminsearch(negloglike, pars, options);
output.call= 'fminsearch';
end
warning(divzero.state, 'MATLAB:divideByZero');
output.exitflag= exitflag;
% Diverged?
if(exitflag <= 0), mlepars= repmat(NaN, [1 pLen]); end % We did not find a solution...
% Debug info.
if(dbg)
% Textual information.
value('ALGORITHM:', output.algorithm);
value('Iterations:', output.iterations);
value('Function calls:', output.funcCount);
disp(' ');
value('', '-loglike', '-Jacobian', 'parameter(s)');
[l, dl]= negloglike(pars);
value('Initial value(s):', [l, sqrt(sum(dl.^2)), pars]);
[l, dl]= negloglike(mlepars);
value('Best fit:', [l, sqrt(sum(dl.^2)), mlepars]);
% Prepare for figure.
st= 0.05;
pmin= max([lBounds; min([mlepars; pars; uBounds]) - 0.5 - st*3]);
pmax= min([uBounds; max([mlepars; pars; lBounds]) + 0.5 + st*3]);
if(pLen == 1) % Draw 2-d figure.
mark= {'MarkerFaceColor', 'r', 'MarkerSize', 8};
xx= linspace(pmin(1), pmax(1), 50);
LL= zeros(1, length(xx));
for nx= 1:length(xx)
LL(nx)= negloglike(xx(nx));
end
plot(xx, LL);
hold on;
plot(pars, negloglike(pars), '^r', mark{:});
plot(mlepars, negloglike(mlepars), 'vr', mark{:});
xlabel('Parameter'); ylabel('negative log-likelihood');
else % Draw 3-d figure.
mark= {'MarkerFaceColor', 'k', 'MarkerSize', 12};
aa= linspace(pmin(1), pmax(1), 50);
bb= linspace(pmin(2), pmax(2), 50);
LL= zeros(length(bb), length(aa));
for na= 1:length(aa)
for nb= 1:length(bb)
LL(nb, na)= negloglike([aa(na), bb(nb)]);
end
end
[aa, bb]= meshgrid(aa, bb);
plot3(pars(1), pars(2), negloglike(pars), '^k', mark{:});
hold on
plot3(mlepars(1), mlepars(2), negloglike(mlepars), 'vk', mark{:});
meshz(aa, bb, LL);
contour3(aa, bb, LL, 'LineSpec', 'k');
shading interp; colormap hsv;
xlabel('Parameter 1'); ylabel('Parameter 2'); zlabel('negative log-likelihood');
end
end
end
function value(gs, varargin)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% For debugging purposes.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
fprintf(1, '\n%-20s', gs);
for i= 1:length(varargin)
if(ischar(varargin{i})), fprintf(1, '%15s', varargin{i});
else fprintf(1, '%15.6f', varargin{i});
end
end
end
|
github
|
joe-of-all-trades/czifinfo-master
|
czifinfo.m
|
.m
|
czifinfo-master/czifinfo.m
| 5,263 |
utf_8
|
77e512aa3e745b1d6c0d642e3ee955ab
|
function fileInfo = czifinfo( filename, varargin )
%CZIFINFO returns informaion of Zeiss CZI file
%
% czifinfo returns information of czi file includingl pixel type,
% compression method, fileGUID, file version number, a structure
% recording various information of raw image data including data start
% position within the czi file, data size and spatial coordinates. Also
% the function returns associated metadata in the field named 'XML_text'.
% This can be saved as an .xml file and examined in web browser.
%
% Version 1.0
% Copyright Chao-Yuan Yeh, 2016
fID = fopen(filename);
while true
segHeader = readSegHeader(fID);
if strfind(segHeader.SID, 'ZISRAWSUBBLOCK')
fileInfo.genInfo = readMinSUBBLOCKHeader(fID);
break
end
fseek(fID, segHeader.currPos + segHeader.allocSize, 'bof');
end
count = 0;
frewind(fID);
flag = 1;
sBlockCount_P0 = 0;
sBlockCount_P2 = 0;
while flag
segHeader = readSegHeader(fID);
if segHeader.allocSize
if strfind(segHeader.SID, 'ZISRAWSUBBLOCK')
[sBlockHeader, pyramidType] = readPartSUBBLOCKHeader(fID);
switch pyramidType
case 0
sBlockCount_P0 = sBlockCount_P0 + 1;
fileInfo.sBlockList_P0(sBlockCount_P0) = sBlockHeader;
case 2
if strcmpi(varargin,'P2')
sBlockCount_P2 = sBlockCount_P2 + 1;
fileInfo.sBlockList_P2(sBlockCount_P2) = sBlockHeader;
end
end
elseif strfind(segHeader.SID, 'ZISRAWFILE')
fileInfo.fileHeader = readFILEHeader(fID);
elseif strfind(segHeader.SID, 'ZISRAWATTACH')
count = count + 1;
readAttach(fID);
end
flag = fseek(fID, segHeader.currPos + segHeader.allocSize, 'bof') + 1;
else
flag = 0;
end
end
fseek(fID, 92, 'bof');
fseek(fID, fileInfo.fileHeader.mDataPos, 'bof');
fseek(fID, fileInfo.fileHeader.mDataPos + 32, 'bof');
XmlSize = uint32(fread(fID, 1, '*uint32'));
fseek(fID, fileInfo.fileHeader.mDataPos + 288, 'bof');
fileInfo.metadataXML = fread(fID, XmlSize, '*char')';
fclose(fID);
disp(count)
end
function segHeader = readSegHeader(fID)
segHeader.SID = fread(fID, 16, '*char')';
segHeader.allocSize = fread(fID, 1, '*uint64');
fseek(fID, 8, 'cof');
segHeader.currPos = ftell(fID);
end
function sBlockHeader = readMinSUBBLOCKHeader(fID)
fseek(fID, 18, 'cof');
sBlockHeader.pixelType = getPixType(fread(fID, 1, '*uint32'));
fseek(fID, 12, 'cof');
sBlockHeader.compression = getCompType(fread(fID, 1, '*uint32'));
fseek(fID, 6, 'cof');
sBlockHeader.dimensionCount = fread(fID, 1, '*uint32');
end
function [sBlockHeader, pyramidType] = readPartSUBBLOCKHeader(fID)
currPos = ftell(fID);
mDataSize = fread(fID, 1, '*uint32');
fseek(fID, 4, 'cof');
sBlockHeader.dataSize = fread(fID, 1, '*uint64');
fseek(fID, 22, 'cof');
pyramidType = fread(fID, 1, '*uint8');
fseek(fID, 5, 'cof');
dimensionCount = fread(fID, 1, '*uint32');
for ii = 1 : dimensionCount
dimension = fread(fID, 4, '*char');
sBlockHeader.([dimension(1),'Start']) = fread(fID, 1, '*uint32');
if ~strcmp(dimension(1),'X') && ~strcmp(dimension(1),'Y')
fseek(fID, 12, 'cof');
else
sBlockHeader.([dimension(1),'Size']) = fread(fID, 1, '*uint32');
fseek(fID, 8, 'cof');
end
end
sBlockHeader.dataStartPos = currPos + 256 + mDataSize;
end
function fileHeader = readFILEHeader(fID)
fileHeader.major = fread(fID, 1, '*uint32');
fileHeader.minor = fread(fID, 1, '*uint32');
fseek(fID, 8, 'cof');
fileHeader.primaryFileGuid = fread(fID, 2, '*uint64');
fileHeader.fileGuid = fread(fID, 2, '*uint64');
fileHeader.filePart = fread(fID, 1, '*uint32');
fileHeader.dirPos = fread(fID, 1, '*uint64');
fileHeader.mDataPos = fread(fID, 1, '*uint64');
fseek(fID, 4, 'cof');
fileHeader.attDirPos = fread(fID, 1, '*uint64');
end
function readAttach(fID)
dataSize = fread(fID, 1, '*uint32');
fseek(fID, 24, 'cof');
filePos = fread(fID, 1, '*uint64');
fseek(fID, 20, 'cof');
contentType = fread(fID, 8, '*char')';
disp(contentType)
name = fread(fID, 80, '*char')';
disp(name)
if strfind(contentType, 'JPG')
fseek(fID, 112, 'cof');
fout = fopen('thumbnail.jpg', 'wb');
fwrite(fout, fread(fID, dataSize, '*uint8'), 'uint8');
fclose(fout);
end
end
function pixType = getPixType(index)
switch index
case 0
pixType = 'Gray8';
case 1
pixType = 'Gray16';
case 2
pixType = 'Gray32Float';
case 3
pixType = 'Bgr24';
case 4
pixType = 'Bgr48';
case 8
pixType = 'Bgr96Float';
case 9
pixType = 'Bgra32';
case 10
pixType = 'Gray64ComplexFloat';
case 11
pixType = 'Bgr192ComplexFloat';
case 12
pixType = 'Gray32';
case 13
pixType = 'Gray64';
end
end
function compType = getCompType(index)
if index >= 1000
compType = 'System-RAW';
elseif index >= 100 && index < 999
compType = 'Camera-RAW';
else
switch index
case 0
compType = 'Uncompressed';
case 1
compType = 'JPEG';
case 2
compType = 'LZW';
case 4
compType = 'JPEG-XR';
end
end
end
|
github
|
onalbach/caffe-deep-shading-master
|
classification_demo.m
|
.m
|
caffe-deep-shading-master/matlab/demo/classification_demo.m
| 5,412 |
utf_8
|
8f46deabe6cde287c4759f3bc8b7f819
|
function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% ****************************************************************************
% For detailed documentation and usage on Caffe's Matlab interface, please
% refer to Caffe Interface Tutorial at
% http://caffe.berkeleyvision.org/tutorial/interfaces.html#matlab
% ****************************************************************************
%
% input
% im color image as uint8 HxWx3
% use_gpu 1 to use the GPU, 0 to use the CPU
%
% output
% scores 1000-dimensional ILSVRC score vector
% maxlabel the label of the highest score
%
% You may need to do the following before you start matlab:
% $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64
% $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
% Or the equivalent based on where things are installed on your system
%
% Usage:
% im = imread('../../examples/images/cat.jpg');
% scores = classification_demo(im, 1);
% [score, class] = max(scores);
% Five things to be aware of:
% caffe uses row-major order
% matlab uses column-major order
% caffe uses BGR color channel order
% matlab uses RGB color channel order
% images need to have the data mean subtracted
% Data coming in from matlab needs to be in the order
% [width, height, channels, images]
% where width is the fastest dimension.
% Here is the rough matlab for putting image data into the correct
% format in W x H x C with BGR channels:
% % permute channels from RGB to BGR
% im_data = im(:, :, [3, 2, 1]);
% % flip width and height to make width the fastest dimension
% im_data = permute(im_data, [2, 1, 3]);
% % convert from uint8 to single
% im_data = single(im_data);
% % reshape to a fixed size (e.g., 227x227).
% im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');
% % subtract mean_data (already in W x H x C with BGR channels)
% im_data = im_data - mean_data;
% If you have multiple images, cat them with cat(4, ...)
% Add caffe/matlab to you Matlab search PATH to use matcaffe
if exist('../+caffe', 'dir')
addpath('..');
else
error('Please run this demo from caffe/matlab/demo');
end
% Set caffe mode
if exist('use_gpu', 'var') && use_gpu
caffe.set_mode_gpu();
gpu_id = 0; % we will use the first gpu in this demo
caffe.set_device(gpu_id);
else
caffe.set_mode_cpu();
end
% Initialize the network using BVLC CaffeNet for image classification
% Weights (parameter) file needs to be downloaded from Model Zoo.
model_dir = '../../models/bvlc_reference_caffenet/';
net_model = [model_dir 'deploy.prototxt'];
net_weights = [model_dir 'bvlc_reference_caffenet.caffemodel'];
phase = 'test'; % run with phase test (so that dropout isn't applied)
if ~exist(net_weights, 'file')
error('Please download CaffeNet from Model Zoo before you run this demo');
end
% Initialize a network
net = caffe.Net(net_model, net_weights, phase);
if nargin < 1
% For demo purposes we will use the cat image
fprintf('using caffe/examples/images/cat.jpg as input image\n');
im = imread('../../examples/images/cat.jpg');
end
% prepare oversampled input
% input_data is Height x Width x Channel x Num
tic;
input_data = {prepare_image(im)};
toc;
% do forward pass to get scores
% scores are now Channels x Num, where Channels == 1000
tic;
% The net forward function. It takes in a cell array of N-D arrays
% (where N == 4 here) containing data of input blob(s) and outputs a cell
% array containing data from output blob(s)
scores = net.forward(input_data);
toc;
scores = scores{1};
scores = mean(scores, 2); % take average scores over 10 crops
[~, maxlabel] = max(scores);
% call caffe.reset_all() to reset caffe
caffe.reset_all();
% ------------------------------------------------------------------------
function crops_data = prepare_image(im)
% ------------------------------------------------------------------------
% caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat contains mean_data that
% is already in W x H x C with BGR channels
d = load('../+caffe/imagenet/ilsvrc_2012_mean.mat');
mean_data = d.mean_data;
IMAGE_DIM = 256;
CROPPED_DIM = 227;
% Convert an image returned by Matlab's imread to im_data in caffe's data
% format: W x H x C with BGR channels
im_data = im(:, :, [3, 2, 1]); % permute channels from RGB to BGR
im_data = permute(im_data, [2, 1, 3]); % flip width and height
im_data = single(im_data); % convert from uint8 to single
im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear'); % resize im_data
im_data = im_data - mean_data; % subtract mean_data (already in W x H x C, BGR)
% oversample (4 corners, center, and their x-axis flips)
crops_data = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;
n = 1;
for i = indices
for j = indices
crops_data(:, :, :, n) = im_data(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :);
crops_data(:, :, :, n+5) = crops_data(end:-1:1, :, :, n);
n = n + 1;
end
end
center = floor(indices(2) / 2) + 1;
crops_data(:,:,:,5) = ...
im_data(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:);
crops_data(:,:,:,10) = crops_data(end:-1:1, :, :, 5);
|
github
|
antipa/proxMin-master
|
tv3d_iso_Haar.m
|
.m
|
proxMin-master/tv3d_iso_Haar.m
| 2,349 |
utf_8
|
9dbca1f3c36fa28e8cf93711c0874c42
|
function y = tv3d_iso_Haar(x, tau, alpha)
% Private functions here
% circshift does circular shifting
% indexing: x(5:10), 1 indexed. Use x(5:4:end-6) to index in strides of 4
% to the 6th-to-last element
D = 3;
gamma = 1; %step size
thresh = sqrt(2) * 2 * D * tau * gamma;
y = zeros(size(x), 'like', x);
for axis = 1 : 3
if axis == 3
t_scale = alpha;
else
t_scale = 1;
end
y = y + iht3(ht3(x, axis, false, thresh*t_scale), axis, false);
y = y + iht3(ht3(x, axis, true, thresh*t_scale), axis, true);
end
y = y / (2 * D);
return
function w = ht3(x, ax, shift, thresh)
s = size(x);
w = zeros(s, 'like', x);
C = 1 / sqrt(2);
if shift
x = circshift(x, -1, ax);
end
m = floor(s(ax) / 2);
if ax == 1
w(1:m, :, :) = C * (x(2:2:end, :, :) + x(1:2:end, :, :)); % use diff or circhisft?
w((m + 1):end, :, :) = hs_soft(C * (x(2:2:end, :, :) - x(1:2:end, :, :)), thresh);
%w((m + 1):end, :, :) = hs_soft(w((m + 1):end, :, :), thresh);
elseif ax == 2
w(:, 1:m, :) = C * (x(:, 2:2:end, :) + x(:, 1:2:end, :));
w(:, (m + 1):end, :) = hs_soft(C * (x(:, 2:2:end, :) - x(:, 1:2:end, :)), thresh);
%w(:, (m + 1):end, :) = hs_soft(w(:, (m + 1):end, :), thresh);
else
w(:, :, 1:m) = C * (x(:, :, 2:2:end) + x(:, :, 1:2:end));
w(:, :, (m + 1):end) = hs_soft(C * (x(:, :, 2:2:end) - x(:, :, 1:2:end)), thresh);
%w(:, :, (m + 1):end) = hs_soft(w(:, :, (m + 1):end), thresh);
end
return
function y = iht3(w, ax, shift)
s = size(w);
y = zeros(s, 'like', w);
C = 1 / sqrt(2);
m = floor(s(ax) / 2);
if ax == 1
y(1:2:end, :, :) = C * (w(1:m, :, :) - w((m + 1):end, :, :));
y(2:2:end, :, :) = C * (w(1:m, :, :) + w((m + 1):end, :, :));
elseif ax == 2
y(:, 1:2:end, :) = C * (w(:, 1:m, :) - w(:, (m + 1):end, :));
y(:, 2:2:end, :) = C * (w(:, 1:m, :) + w(:, (m + 1):end, :));
else
y(:, :, 1:2:end) = C * (w(:, :, 1:m) - w(:, :, (m + 1):end));
y(:, :, 2:2:end) = C * (w(:, :, 1:m) + w(:, :, (m + 1):end));
end
if shift
y = circshift(y, 1, ax);
end
return
function threshed = hs_soft(x,tau)
threshed = max(abs(x)-tau,0);
threshed = threshed.*sign(x);
return
|
github
|
antipa/proxMin-master
|
proxMin.m
|
.m
|
proxMin-master/proxMin.m
| 8,566 |
utf_8
|
31302ab2b5b63c6185b13bd08ca43337
|
function [out,varargout] = proxMin(GradErrHandle,ProxFunc,xk,b,options)
% Out = proxMin(GradErrHanle,ProxHandle,AxyTxy0,measurement,options)
%
% GradErrHandle: handle for function that computes error and gradient at
% each step
%
% ProxFunc: handle for function that does projection step
%
% AxyTxy0: initialization Nx x Ny x 2
% where the first matrix is the amplitude of the diffuser A(x,y)
% and the second matrix is the thickness of the diffuser T(x,y)
%
% options: similar to minFunc, but won't support all of the same options.
%
% Nick Antipa, summer 2016
if ~isa(GradErrHandle,'function_handle')
GradErrHandle = @(x) matrixError(GradErrHandle,transpose(GradErrHandle),x,b);
end
if ~isfield(options,'convTol')
options.convTol = 1e-9;
end
if ~isfield(options,'residTol')
options.residTol = 1e-2;
end
if ~isfield(options,'xsize')
options.xsize = size(A,2);
end
if ~isfield(options,'momentum')
options.momentum = 'nesterov';
end
if ~isfield(options,'disp_figs')
options.disp_figs = 0;
end
if ~isfield(options,'restarting')
options.restarting = 0;
end
if ~isfield(options,'print_interval')
options.print_interval = 1;
end
if ~isfield(options,'color_map')
options.color_map = 'parula';
end
if ~isfield(options,'save_progress')
options.save_progress = 0;
end
if ~isfield(options,'restart_interval')
options.restart_interval = 0;
end
if ~isfield(options,'disp_crop')
options.disp_crop = @(x)x;
end
if ~isfield(options,'disp_prctl')
options.disp_prctl = 99.999;
end
if options.save_progress
if ~isfield(options,'save_progress')
options.progress_file = 'prox_progress.avi';
end
if exist(options.progress_file,'file')
overwrite_mov = input('video file exists. Overwrite? y to overwrite, n to abort.');
if strcmpi(overwrite_mov,'n')
new_mov_name = input('input new name (no extension): ');
options.progress_file = [new_mov_name,'.avi'];
end
end
options.vidObj = VideoWriter(options.progress_file);
open(options.vidObj);
end
step_num = 0;
yk = xk;
%h1 = figure(1);
fun_val = zeros([options.maxIter,1],'like',xk);
%step_size = .0000000008;
step_size = options.stepsize*ones(1,'like',xk);
fm1 = zeros(1,'like',xk);
f = inf;
switch lower(options.momentum)
case('linear')
while (step_num < options.maxIter) && (f>options.residTol)
step_num = step_num+1;
[ f, g ] = GradErrHandle( yk );
fun_val(step_num) = f;
x_t1 = yk - step_size*g;
yk = ProxFunc(x_t1);
if ~mod(step_num,options.disp_fig_interval)
if options.disp_figs
draw_figures(yk,options)
end
end
if abs(fm1-f)<options.convTol
fprintf('Answer is stable to within convTol. Stopping.\n')
out = yk;
break
end
fm1 = f;
fprintf('%i\t%6.4e\n',step_num,f)
end
case ('nesterov')
tk = ones(1,'like',xk);
yk = xk;
f = 1e12*ones(1,'like',xk);
f_kp1 = f;
tic
while (step_num < options.maxIter) && (f>options.residTol)
step_num = step_num+1;
[f_kp1, g] = GradErrHandle(yk);
fun_val(step_num) = f_kp1;
%fun_val(step_num) = norm(options.xin-options.crop(yk),'fro')/norm(options.xin,'fro');
[x_kp1, norm_x] = ProxFunc(yk-options.stepsize*g);
fun_val(step_num) = fun_val(step_num)+norm_x;
t_kp1 = (1+sqrt(1+4*tk^2))/2;
beta_kp1 = (tk-1)/t_kp1;
restart = (yk(:)-x_kp1(:))'*vec(x_kp1 - xk); %dx(:);
yk = x_kp1+beta_kp1*(x_kp1 - xk);
if step_num == 1
if options.known_input
fprintf('Iteration \t objective \t ||x|| \t momentum \t MSE \t PSNR\n');
else
fprintf('Iter\t ||Ax-b|| \t ||x|| \t Obj \t sparsity \t momentum \t elapsed time\n');
end
end
if restart<0 && mod(step_num,options.restart_interval)==0
fprintf('reached momentum reset interval\n')
restart = Inf;
end
%restart = f_kp1-f;
if ~mod(step_num,options.print_interval)
if options.known_input
fprintf('%i\t %6.4e\t %6.4e\t %.3f\t %6.4e\t %.2f dB\n',...
step_num,f,norm_x,tk,...
norm(options.xin(:) - yk(:)),...
psnr(gather(yk),options.xin,max(options.xin(:))));
else
telapse = toc;
fprintf('%i\t%6.4e\t%6.4e\t%6.4e\t%6.4e\t%.3f\t%.4f\n',step_num,f,norm_x,fun_val(step_num), nnz(x_kp1)/numel(x_kp1)*100,tk,telapse)
end
tic
end
if ~mod(step_num,options.disp_fig_interval)
if options.disp_figs
draw_figures(yk,options);
end
if options.save_progress
frame = getframe(options.fighandle);
writeVideo(options.vidObj,frame);
end
end
if restart>0 && options.restarting
tk = 1;
fprintf('restarting momentum \n')
yk = x_kp1;
else
tk = t_kp1;
%yk = y_kp1;
end
xk = x_kp1;
f = fun_val(step_num);
if abs(restart)<options.convTol
fprintf('Answer is stable to within convTol. Stopping.\n')
out = yk;
draw_figures(out,options);
break
end
end
end
if (f<options.residTol)
fprintf('Residual below residTol. Stopping. \n')
end
if step_num>=options.maxIter
fprintf('Reached max number of iterations. Stopping. \n');
end
out = yk;
if nargout>1
varargout{1} = fun_val;
end
draw_figures(out,options)
if options.save_progress
close(options.vidObj);
end
return
function draw_figures(xk,options)
set(0,'CurrentFigure',options.fighandle)
if numel(options.xsize)==2
imagesc(options.disp_crop(xk))
axis image
colorbar
colormap(options.color_map);
%caxis(gather([prctile(xk(:),.1) prctile(xk(:),90)]))
elseif numel(options.xsize)==3
xk = gather(xk);
set(0,'CurrentFigure',options.fighandle)
subplot(1,3,1)
im1 = squeeze(max(xk,[],3));
imagesc(im1);
hold on
axis image
colormap parula
%colorbar
caxis([0 prctile(im1(:),options.disp_prctl)])
set(gca,'fontSize',6)
axis off
hold off
set(0,'CurrentFigure',options.fighandle)
subplot(1,3,2)
im2 = squeeze(max(xk,[],1));
imagesc(im2);
hold on
%axis image
colormap parula
%colorbar
set(gca,'fontSize',8)
caxis([0 prctile(im2(:),options.disp_prctl)])
axis off
hold off
drawnow
set(0,'CurrentFigure',options.fighandle)
subplot(1,3,3)
im3 = squeeze(max(xk,[],2));
imagesc(im3);
hold on
%axis image
colormap parula
colorbar
set(gca,'fontSize',8)
caxis([0 prctile(im3(:),options.disp_prctl)]);
axis off
hold off
elseif numel(options.xsize) == 4
xkr = reshape(xk,options.xsize);
subplot(2,2,1)
imagesc(transpose(squeeze(xkr(end,ceil(options.xsize(2)/2),:,:))))
hold on
axis image
colorbar
colormap gray
caxis([0 prctile(xkr(:),99)]);
hold off
subplot(2,2,2)
imagesc(transpose(squeeze(xkr(1,ceil(options.xsize(2)/2),:,:))))
hold on
axis image
colorbar
colormap gray
caxis([0 prctile(xkr(:),99)]);
hold off
subplot(2,2,3)
imagesc(transpose(squeeze(xkr(ceil(options.xsize(2)/2),1,:,:))))
hold on
axis image
colorbar
colormap gray
caxis([0 prctile(xkr(:),99)]);
hold off
subplot(2,2,4)
imagesc(transpose(squeeze(xkr(ceil(options.xsize(2)/2),end,:,:))))
hold on
axis image
colorbar
colormap gray
caxis([0 prctile(xkr(:),99)]);
hold off
elseif numel(options.xsize)==1
plot(xk)
end
drawnow
|
github
|
antipa/proxMin-master
|
tv2d_aniso_haar.m
|
.m
|
proxMin-master/tv2d_aniso_haar.m
| 2,249 |
utf_8
|
3d7154b33f0fde5be8941b98e3824b62
|
function y = tv2dApproxHaar(x, tau)
% Private functions here
% circshift does circular shifting
% indexing: x(5:10), 1 indexed. Use x(5:4:end-6) to index in strides of 4
% to the 6th-to-last element
D = 2;
gamma = 1; %step size
thresh = sqrt(2) * 2 * D * tau * gamma;
y = zeros(size(x), 'like', x);
for axis = 1 : 2
% if axis == 3
% t_scale = alpha;
% else
% t_scale = 1;
% end
y = y + iht2(ht2(x, axis, false, thresh), axis, false);
y = y + iht2(ht2(x, axis, true, thresh), axis, true);
end
y = y / (2 * D);
return
function w = ht2(x, ax, shift, thresh)
s = size(x);
w = zeros(s, 'like', x);
C = 1 / sqrt(2);
if shift
x = circshift(x, -1, ax);
end
m = floor(s(ax) / 2);
if ax == 1
w(1:m, :) = C * (x(2:2:end, :) + x(1:2:end, :)); % use diff or circhisft?
w((m + 1):end, :) = soft(C * (x(2:2:end, :) - x(1:2:end, :)), thresh);
%w((m + 1):end, :) = hs_soft(w((m + 1):end, :), thresh);
elseif ax == 2
w(:, 1:m) = C * (x(:, 2:2:end) + x(:, 1:2:end));
w(:, (m + 1):end) = soft(C * (x(:, 2:2:end) - x(:, 1:2:end)), thresh);
%w(:, (m + 1):end, :) = hs_soft(w(:, (m + 1):end, :), thresh);
% else
% w(:, :, 1:m) = C * (x(:, :, 2:2:end) + x(:, :, 1:2:end));
% w(:, :, (m + 1):end) = C * (x(:, :, 2:2:end) - x(:, :, 1:2:end));
% w(:, :, (m + 1):end) = hs_soft(w(:, :, (m + 1):end), thresh);
end
return
function y = iht2(w, ax, shift)
s = size(w);
y = zeros(s, 'like', w);
C = 1 / sqrt(2);
m = floor(s(ax) / 2);
if ax == 1
y(1:2:end, :) = C * (w(1:m, :) - w((m + 1):end, :));
y(2:2:end, :) = C * (w(1:m, :) + w((m + 1):end, :));
elseif ax == 2
y(:, 1:2:end) = C * (w(:, 1:m) - w(:, (m + 1):end));
y(:, 2:2:end) = C * (w(:, 1:m) + w(:, (m + 1):end));
% else
% y(:, :, 1:2:end) = C * (w(:, :, 1:m) - w(:, :, (m + 1):end));
% y(:, :, 2:2:end) = C * (w(:, :, 1:m) + w(:, :, (m + 1):end));
end
if shift
y = circshift(y, 1, ax);
end
return
function threshed = hs_soft(x,tau)
threshed = max(abs(x)-tau,0);
threshed = threshed.*sign(x);
return
|
github
|
antipa/proxMin-master
|
conv2c.m
|
.m
|
proxMin-master/conv2c.m
| 1,666 |
utf_8
|
fbd814f6dbc22cf39b0304b1630681a6
|
function y = conv2c(x,h)
% Circular 2D convolution
x=wraparound(x,h);
y=conv2(x,h,'valid');
function y = wraparound(x, m)
% Extend x so as to wrap around on both axes, sufficient to allow a
% "valid" convolution with m to return the cyclical convolution.
% We assume mask origin near centre of mask for compatibility with
% "same" option.
[mx, nx] = size(x);
[mm, nm] = size(m);
if mm > mx | nm > nx
error('Mask does not fit inside array')
end
mo = floor((1+mm)/2); no = floor((1+nm)/2); % reflected mask origin
ml = mo-1; nl = no-1; % mask left/above origin
mr = mm-mo; nr = nm-no; % mask right/below origin
me = mx-ml+1; ne = nx-nl+1; % reflected margin in input
mt = mx+ml; nt = nx+nl; % top of image in output
my = mx+mm-1; ny = nx+nm-1; % output size
y = zeros(my, ny);
y(mo:mt, no:nt) = x; % central region
if ml > 0
y(1:ml, no:nt) = x(me:mx, :); % top side
if nl > 0
y(1:ml, 1:nl) = x(me:mx, ne:nx); % top left corner
end
if nr > 0
y(1:ml, nt+1:ny) = x(me:mx, 1:nr); % top right corner
end
end
if mr > 0
y(mt+1:my, no:nt) = x(1:mr, :); % bottom side
if nl > 0
y(mt+1:my, 1:nl) = x(1:mr, ne:nx); % bottom left corner
end
if nr > 0
y(mt+1:my, nt+1:ny) = x(1:mr, 1:nr); % bottom right corner
end
end
if nl > 0
y(mo:mt, 1:nl) = x(:, ne:nx); % left side
end
if nr > 0
y(mo:mt, nt+1:ny) = x(:, 1:nr); % right side
end
|
github
|
oussamamoslah/Democratic-RPSO-master
|
simeditcb.m
|
.m
|
Democratic-RPSO-master/simeditcb.m
| 14,986 |
utf_8
|
e773b67088c3cf2660ecb16b0639b3c6
|
function simeditcb(action)
nameSim = 'MRSim - Multi-Robot Simulator v1.0';
switch action
case 'import'
% ***************** Import bitmap ********************
h = findobj('Tag','ListStore'); % We need to check the list
list = get(h,'UserData'); % Get it
if ~isempty(list) % If it is not empty, ask a question
answer = questdlg('This will delete all robots. Continue?','Question','Yes','No','No');
else
answer = 'Yes';
end
if strcmp(answer,'Yes')
set(h,'UserData',[]); % Delete the list
[filename,pathname] = uigetfile('*.bmp','Open map');
if filename ~= 0 % if any file selected
matrix = matrprep([pathname filename]);
for i = 1:3
matrix = rot90(matrix); % Ensures normal position of the matrix
end % (as it is stored in bitmap)
LocalDraw(matrix,2);
list = [];
h = findobj('Tag','EditorWindow');
set(h,'UserData',matrix);
set(h,'Name',['Editor Window - [untitled.mat - ' pwd '\untitled.mat]'])
h = findobj('Tag','EditPath');
set(h,'UserData',[pwd '\untitled.mat'])
save([pwd '\untitled.mat'],'matrix')
% ******** Enable disabled context menu items *********
h = findobj('Type','uimenu','Enable','off');
set(h,'Enable','on')
h = findobj('Tag','SaveMenu');
set(h,'Enable','off')
h = findobj('Tag','StepsMenu');
set(h,'UserData',Inf)
h = findobj('Tag','DelayMenu');
set(h,'UserData',0.001)
% *****************************************************
end
end
% ****************************************************
case 'load'
[filename,pathname] = uigetfile('*.mat','Open simulation');
robotT = simrobot('',2,2,2,'',[1 1 0],1,10,10); % Create object
if filename ~= 0
% ****** Check the file structure ******
vars = who('-file',[pathname filename]);
load([pathname filename],'list')
if isempty(strmatch('list',vars)) | isempty(strmatch('no_steps',vars)) | isempty(strmatch('delay',vars)) | isempty(strmatch('matrix',vars)) | isempty(strmatch('type',vars)) | isempty(list)
dispstr = strvcat(['"' pathname filename '"'],' ');
dispstr = strvcat(dispstr,'Cannot open this file - invalid file structure!');
h = msgbox(dispstr,'Error','error');
return
end
% **************************************
load([pathname filename]);
% type = 'replay'; % This is necessary !!
% if strcmp(type,'replay')
% answer = questdlg('You are going to edit a replay file. This will delete replay data. Continue?','Question','Yes','No','No');
% if strcmp(answer,'Yes')
% type = 'simulation';
% for i = 1:length(list)
% list(i) = delhist(list(i));
% end
% else
% return
% end
% end
path = [pathname filename];
save(path,'list','matrix','no_steps','delay','type');
h = findobj('Tag','EditPath');
set(h,'UserData',path);
simeditcb loadfile;
end
case 'loadfile'
% ******** New part ********
h = findobj('Tag','EditPath');
path = get(h,'UserData');
[pathname,filename,ext] = fileparts(path);
pathname = [pathname '\'];
filename = [filename ext];
% **************************
% robotT = simrobot('',2,2,2,'',[1 1 0],1,10,10); % Create object
load([pathname filename]);
h = findobj('Tag','EditorWindow');
set(h,'UserData',matrix)
set(h,'Name',['Editor Window - [' filename ' - ' pathname filename ']'])
LocalDraw(matrix,2);
for i = 1:length(list)
list(i) = putrob(list(i),getpos(list(i)),matrix);
end
h = findobj('Tag','ListStore');
set(h,'UserData',list) % Store list
h = findobj('Tag','StepsMenu');
if iscell(no_steps)
no_steps = no_steps{1};
end
set(h,'Label',['Steps limit: ' num2str(no_steps)]);
set(h,'UserData',no_steps)
h = findobj('Tag','DelayMenu');
if iscell(delay)
delay = delay{1};
end
set(h,'Label',['Step time: ' num2str(delay)]);
set(h,'UserData',delay)
% ******* Enable disabled context menu items ********
h = findobj('Type','uimenu','Enable','off');
set(h,'Enable','on')
% ***************************************************
% ***********************************************************
case 'save'
h = findobj('Tag','EditPath');
path = get(h,'UserData');
if ~isempty(path)
loadstr = load(path); % Load (matrix) from original file
[pathn,file,ext] = fileparts(path);
file = [file ext];
% ***************************************************************************
if strcmp(file,'untitled.mat')
[filename,pathname] = uiputfile('newsave.mat','Save simulation');
if strcmp(filename,'untitled.mat')
h = msgbox('Error: Cannot save as ''untitled.mat''. Please select another name','Error','error');
return
end
if filename ~= 0
delete(path)
path = [pathname filename];
h = findobj('Tag','EditPath');
set(h,'UserData',path);
h = findobj('Tag','EditorWindow');
if isempty(findstr(filename,'.mat'))
filename = [filename '.mat'];
end
set(h,'Name',['Editor Window - [' filename ' - ' pathname filename ']']);
else
return
end
end
h = findobj('Tag','StepsMenu');
no_steps = get(h,'UserData');
no_steps = no_steps{1}; % Convert from cell
h = findobj('Tag','ListStore');
list = get(h,'UserData'); % Get list
matrix = loadstr.matrix;
h = findobj('Tag','DelayMenu');
delay = get(h,'UserData'); % Get delay
delay = delay{1}; % Convert from cell
type = 'simulation';
save(path,'list','matrix','no_steps','delay','type');
end
case 'saveas'
% *********** Select mat file and save simulation ***********
h = findobj('Tag','EditPath');
path = get(h,'UserData');
loadstr = load(path);
[path,name,ext] = fileparts(path);
if strcmp(name,'untitled')
name = 'newsave';
end
[filename,pathname] = uiputfile([name ext],'Save simulation');
if strcmp(filename,'untitled.mat')
h = msgbox('Error: Cannot save as ''untitled.mat''. Please select another name','Error','error');
return
end
if filename ~= 0 % if any file selected
h = findobj('Tag','StepsMenu');
no_steps = get(h,'UserData');
path = [pathname filename];
h = findobj('Tag','ListStore');
list = get(h,'UserData'); % Get list
matrix = loadstr.matrix;
h = findobj('Tag','DelayMenu');
delay = get(h,'UserData'); % Get delay
type = 'simulation';
save(path,'list','matrix','no_steps','delay','type');
h = findobj('Tag','EditPath'); % Store the path
set(h,'UserData',path);
h = findobj('Tag','EditorWindow');
title = ['Editor Window - [' filename ' - ' pathname filename ']'];
set(h,'Name',title)
end
% ***********************************************************
case 'add'
gui_addr; % Open the window
gui_addrcb('initialize');
case 'add_ok'
% ********* User pressed OK button on robot-adding screen *********
h = findobj('Tag','Store');
robot = get(h,'UserData');
if ~isempty(robot)
load cur_put % Load cursor shape data
set(gcf,'Pointer','custom',...
'PointerShapeCData',cdata,... % User data defining pointer shape
'PointerShapeHotSpot',[11 5]); % Pointer active area (center of the p.)
h = findobj('Tag','Axes'); % Main window axes
set(h,'ButtonDownFcn','simeditcb add_click') % Set callback fcn & wait for a click
end
% *****************************************************************
case 'add_click'
% *********** Put down the new robot *************
cpoint = get(gca,'CurrentPoint');
cpoint = cpoint(1,1:2);
h = findobj('Tag','Axes'); % Main window axes
set(h,'ButtonDownFcn','') % Delete callback
set(gcf,'Pointer','arrow',...
'PointerShapeHotSpot',[1 1])
h = findobj('Tag','EditorWindow');
matrix = get(h,'UserData');
h = findobj('Tag','Store');
data = get(h,'UserData');
h = findobj('Tag','ListStore');
list = get(h,'UserData');
if length(list) > 0
number = getnum(list(length(list))) + 1;
else number = 2; % First robot
end
robot = simrobot(data.name,number,data.heading,data.power,data.af,...
data.color,1,data.xdata,data.ydata); % Create object
robot = putrob(robot,cpoint,matrix); % Put it
robot = addsenss(robot,data.sensors);
if ~isempty(robot)
if isempty(list)
list = robot;
else
list = list + robot;
end
end
set(h,'UserData',list);
h = findobj('Tag','SaveMenu');
set(h,'Enable','on')
% **************************************************
case 'steps'
h = findobj('Tag','StepsMenu');
def = get(h,'UserData');
steps = inputdlg('Steps limit:','Number of steps',[1 14],{num2str(def{1})});
if ~isempty(steps)
if ~strcmp(steps{1},'Inf')
steps = str2num(steps{1});
if isempty(steps) | steps <= 0 | strcmpi(num2str(steps),'NaN')
h = msgbox('Please enter a valid number','Error','error');
waitfor(h)
simeditcb steps
return
else
steps = round(steps(1));
end
else
steps = Inf;
end
set(h,'UserData',steps)
set(h,'Label',['Steps limit: ' num2str(steps)])
end
case 'delay'
h = findobj('Tag','DelayMenu');
def = get(h,'UserData');
steps = inputdlg('Step time (in secs):','Minimum step length',[1 25],{num2str(def{1})});
if ~isempty(steps)
if ~strcmp(steps{1},'Inf')
steps = strrep(steps{1},',','.');
steps = str2num(steps);
if isempty(steps) | steps < 0 | strcmpi(num2str(steps),'NaN')
h = msgbox('Please enter a valid number','Error','error');
waitfor(h)
simeditcb delay
return
else
steps = steps(1);
end
else
steps = Inf;
end
set(h,'UserData',steps)
set(h,'Label',['Step time: ' num2str(steps)])
end
case 'run'
h = findobj('Tag','ListStore');
list = get(h,'UserData');
if ~isempty(list)
simeditcb save
h = findobj('Tag','EditPath');
path = get(h,'UserData');
[path,file,ext] = fileparts(path);
if ~strcmp([file ext],'untitled.mat')
simview
h = findobj('Tag','SimPath');
set(h,'UserData',path)
close(gcbf)
pathname = [path '\'];
filename = [file ext];
h = findobj('Tag','SimPath');
set(h,'UserData',[pathname filename]);
simviewcb loadfile;
simviewcb sim;
end
else
h = msgbox('Please insert at least one robot','Request','warn');
end
% ****************************
case 'close'
% Maybe some question here ??
h = findobj('Tag','SensWindow');
delete(h)
h = findobj('Tag','PowerWindow');
delete(h)
h = findobj('Tag','HeadWindow');
delete(h)
h = findobj('Tag','AddrWindow');
delete(h)
h = findobj('Tag','Info');
delete(h)
h = findobj('Tag','EditorWindow');
delete(h)
end
function LocalDraw(matrix,size_of_marker);
[xmax, ymax] = size(matrix);
[x, y] = find(matrix);
figNumber = findobj('Tag','EditorWindow');
figure(figNumber);
axHndl = findobj('Tag','Axes');
color = get(figNumber,'Color');
plotHndl = plot(x,y,'s', ...
'Color','black', ...
'MarkerFaceColor','black',...
'Tag','mapplot',...
'MarkerSize',size_of_marker,...
'Parent',axHndl);
axis equal
set(axHndl, ...
'XLim',[0 xmax+1],'YLim',[0 ymax+1], ...
'XDir','normal','YDir','normal', ...
'Drawmode','fast', ...
'Visible','on', ...
'NextPlot','replace', ...
'Tag','Axes',...
'TickLength',[0 0],...
'XColor',color,...
'YColor',color);
drawnow;
|
github
|
oussamamoslah/Democratic-RPSO-master
|
gui_senscb.m
|
.m
|
Democratic-RPSO-master/gui_senscb.m
| 9,298 |
utf_8
|
22009f4c455b3ff73eacfc8a5718e513
|
function gui_senscb(action);
if nargin == 0
action = 'initialize';
end
% Data - in SensStore (static text)
% SensAdd (add button) - 1 if new sensor
% Shape in SensCancel
% SensOK = OK Button - number of callbacking robot (when called from robot's uicm)
switch(action)
case 'initialize'
h = findobj('Tag','SensStore');
sensors = get(h,'UserData');
h = findobj('Tag','SensAdd');
new = get(h,'UserData');
if isempty(sensors) | new
if new
h = findobj('Tag','NamesMenu');
name = get(h,'String');
s = size(name);
name = name(s(1),:);
else
name = 'sensor_1';
end
sensor = struct( 'name',name,'position',[5 0],'axisangle',0,...
'scanangle',60,'range',30,'resolution',30);
sensors = [sensors sensor];
h = findobj('Tag','SensStore');
set(h,'UserData',sensors);
h = findobj('Tag','SensAdd');
set(h,'UserData',0);
i = length(sensors);
else
h = findobj('Tag','NamesMenu');
i = get(h,'Value');
end
% ********* Fill in the popup menu with names of sensors *********
names = sensors(1).name;
for j = 2:length(sensors)
names = [names '|' sensors(j).name];
end
% ****************************************************************
h = findobj('Tag','NamesMenu'); % Needed when adding new sensor
set(h,'Value',i);
set(h,'String',names)
h = findobj('Tag','XPos');
set(h,'String',num2str(sensors(i).position(1)))
h = findobj('Tag','YPos');
set(h,'String',num2str(sensors(i).position(2)))
h = findobj('Tag','AxisSlider');
set(h,'Value',sensors(i).axisangle)
h = findobj('Tag','AxisVal');
set(h,'String',num2str(sensors(i).axisangle))
h = findobj('Tag','ScanSlider');
set(h,'Value',sensors(i).scanangle)
h = findobj('Tag','ScanVal');
set(h,'String',num2str(sensors(i).scanangle));
h = findobj('Tag','EditRes');
set(h,'String',num2str(sensors(i).resolution));
h = findobj('Tag','EditRange');
set(h,'String',num2str(sensors(i).range));
h = findobj('Tag','SensCancel');
shape = get(h,'UserData');
UpdatePreview(shape,sensors)
case 'xpos'
h = findobj('Tag','XPos');
xpos = get(h,'String');
xpos = str2num(xpos);
h = findobj('Tag','NamesMenu'); % Get index of the sensor in sensors' structure
i = get(h,'Value');
h = findobj('Tag','SensStore');
sensors = get(h,'UserData');
sensors(i).position(1) = xpos;
set(h,'UserData',sensors)
case 'ypos'
h = findobj('Tag','YPos');
ypos = get(h,'String');
ypos = str2num(ypos);
h = findobj('Tag','NamesMenu'); % Get index of the sensor in sensors' structure
i = get(h,'Value');
h = findobj('Tag','SensStore');
sensors = get(h,'UserData');
sensors(i).position(2) = ypos;
set(h,'UserData',sensors)
case 'res'
h = findobj('Tag','EditRes');
res = get(h,'String');
res = str2num(res);
h = findobj('Tag','NamesMenu'); % Get index of the sensor in sensors' structure
i = get(h,'Value');
h = findobj('Tag','SensStore');
sensors = get(h,'UserData');
sensors(i).resolution = res;
set(h,'UserData',sensors)
case 'range'
h = findobj('Tag','EditRange');
range = get(h,'String');
range = str2num(range);
h = findobj('Tag','NamesMenu'); % Get index of the sensor in sensors' structure
i = get(h,'Value');
h = findobj('Tag','SensStore');
sensors = get(h,'UserData');
sensors(i).range = range;
set(h,'UserData',sensors)
case 'axisangle'
h = findobj('Tag','AxisSlider');
angle = get(h,'Value');
angle = round(angle);
h = findobj('Tag','AxisVal');
set(h,'String',num2str(angle));
h = findobj('Tag','NamesMenu'); % Get index of the sensor in sensors' structure
i = get(h,'Value');
h = findobj('Tag','SensStore');
sensors = get(h,'UserData');
sensors(i).axisangle = angle;
set(h,'UserData',sensors)
case 'scanangle'
h = findobj('Tag','ScanSlider');
angle = get(h,'Value');
angle = round(angle);
h = findobj('Tag','ScanVal');
set(h,'String',num2str(angle));
h = findobj('Tag','NamesMenu'); % Get index of the sensor in sensors' structure
i = get(h,'Value');
h = findobj('Tag','SensStore');
sensors = get(h,'UserData');
sensors(i).scanangle = angle;
set(h,'UserData',sensors)
case 'ok'
% The callback routine is assigned by caller of the editor
case 'cancel'
close(gcbf)
case 'select'
gui_senscb initialize;
case 'sensdel'
answer = questdlg('Really delete selected sensor ?','Question','Yes','No','No');
if strcmp(answer,'Yes')
h = findobj('Tag','NamesMenu');
i = get(h,'Value');
set(h,'Value',1)
h = findobj('Tag','SensStore');
sensors = get(h,'UserData');
sensors(i) = [];
set(h,'UserData',sensors)
gui_senscb initialize;
end
case 'sensren'
h = findobj('Tag','NamesMenu');
i = get(h,'Value');
names = get(h,'String');
name = inputdlg('Name:','Enter name',[1 15],{names(i,:)});
if ~isempty(name)
h = findobj('Tag','SensStore');
sensors = get(h,'UserData');
sensors(i).name = name{1};
set(h,'UserData',sensors)
gui_senscb initialize;
end
case 'updprev'
h = findobj('Tag','SensStore');
sensors = get(h,'UserData');
h = findobj('Tag','SensCancel');
shape = get(h,'UserData');
UpdatePreview(shape,sensors)
case 'add'
name = inputdlg('Name:','Enter name',[1 15]);% This returns CELL
if ~isempty(name{1})
h = findobj('Tag','NamesMenu');
names = get(h,'String'); % Get list of names
if names == ' ' % This is value for new-defined menu
names = ''; % and we need empty string instead
end % because otherwise we'll have an empty line
names = strvcat(names,name{1}); % Add new name to menu's list
set(h,'String',names,'Value',length(names))
h = findobj('Tag','SensAdd');
set(h,'UserData',1)
gui_senscb initialize;
end
end
function UpdatePreview(shape,sensors)
axHndl = findobj('Tag','SensAxes');
scale = get(axHndl,'UserData');
axes(axHndl);
cla; % Clear Axes
h = findobj('Tag','NamesMenu');
active = get(h,'Value');
xmax = 0;
% ****** Determine plotting order of sensors - the active one goes last ******
order = 1:length(sensors);
order(active) = []; % Delete active
order = [order active]; % ... and put it at the end
% This all is necessarry when drawing two indentical sensors
% (the active one could be overlapped by non-active one)
% ****************************************************************************
for j = 1:length(order)
i = order(j);
angles = sensors(i).axisangle - sensors(i).scanangle/2:1:sensors(i).axisangle + sensors(i).scanangle/2;
angles = angles*pi/180;
% *** Points of ending arcs ***
x = sensors(i).position(1) * scale + sensors(i).range * cos(angles);
y = sensors(i).position(2) * scale + sensors(i).range * sin(angles);
% *****************************
tx = max(max(x),abs(min(x)));
ty = max(max(y),abs(min(y)));
if tx > xmax
xmax = tx;
end
if ty > xmax
xmax = ty;
end
x1 = sensors(i).position(1) * scale;
y1 = sensors(i).position(2) * scale;
x2 = x1 + sensors(i).range * cos((sensors(i).axisangle + sensors(i).scanangle/2)*pi/180);
y2 = y1 + sensors(i).range * sin((sensors(i).axisangle + sensors(i).scanangle/2)*pi/180);
line1 = line([x1 x2],[y1 y2]);
x2 = x1 + sensors(i).range * cos((sensors(i).axisangle - sensors(i).scanangle/2)*pi/180);
y2 = y1 + sensors(i).range * sin((sensors(i).axisangle - sensors(i).scanangle/2)*pi/180);
line2 = line([x1 x2],[y1 y2]);
plot1 = plot(x,y);
set(line1,'Color','b')
set(line2,'Color','b')
set(plot1,'Color','b')
end
% ***** The last drawn sensor is the active one, color it red *****
set(line1,'Color','r')
set(line2,'Color','r')
set(plot1,'Color','r')
% *****************************************************************
% ******* Now set axes limits ********
set(axHndl, 'XLim',[-xmax-0.25*xmax xmax+0.25*xmax],...
'YLim',[-xmax-0.25*xmax xmax+0.25*xmax],...
'XTickMode','auto','YTickMode','auto')
% ************************************
rob = patch(shape.xdata,shape.ydata,[1 1 1]);
|
github
|
joe-of-all-trades/xml2struct-master
|
xml2struct.m
|
.m
|
xml2struct-master/xml2struct.m
| 6,351 |
utf_8
|
0feee43c103f51c376a7dab2ac8457d0
|
function outStruct = xml2struct(input)
%XML2STRUCT converts xml file into a MATLAB structure
%
% outStruct = xml2struct2(input)
%
% xml2struct2 takes either a java xml object, an xml file, or a string in
% xml format as input and returns a parsed xml tree in structure.
%
% Please note that the following characters are substituted
% '-' by '_dash_', ':' by '_colon_' and '.' by '_dot_'
%
% Originally written by W. Falkena, ASTI, TUDelft, 21-08-2010
% Attribute parsing speed increase by 40% by A. Wanner, 14-6-2011
% Added CDATA support by I. Smirnov, 20-3-2012
% Modified by X. Mo, University of Wisconsin, 12-5-2012
% Modified by Chao-Yuan Yeh, August 2016
errorMsg = ['%s is not in a supported format.\n\nInput has to be',...
' a java xml object, an xml file, or a string in xml format.'];
% check if input is a java xml object
if isa(input, 'org.apache.xerces.dom.DeferredDocumentImpl') ||...
isa(input, 'org.apache.xerces.dom.DeferredElementImpl')
xDoc = input;
else
try
if exist(input, 'file') == 2
xDoc = xmlread(input);
else
try
xDoc = xmlFromString(input);
catch
error(errorMsg, inputname(1));
end
end
catch ME
if strcmp(ME.identifier, 'MATLAB:UndefinedFunction')
error(errorMsg, inputname(1));
else
rethrow(ME)
end
end
end
% parse xDoc into a MATLAB structure
outStruct = parseChildNodes(xDoc);
end
% ----- Local function parseChildNodes -----
function [children, ptext, textflag] = parseChildNodes(theNode)
% Recurse over node children.
children = struct;
ptext = struct;
textflag = 'Text';
if hasChildNodes(theNode)
childNodes = getChildNodes(theNode);
numChildNodes = getLength(childNodes);
for count = 1:numChildNodes
theChild = item(childNodes,count-1);
[text, name, attr, childs, textflag] = getNodeData(theChild);
if ~strcmp(name,'#text') && ~strcmp(name,'#comment') && ...
~strcmp(name,'#cdata_dash_section')
% XML allows the same elements to be defined multiple times,
% put each in a different cell
if (isfield(children,name))
if (~iscell(children.(name)))
% put existsing element into cell format
children.(name) = {children.(name)};
end
index = length(children.(name))+1;
% add new element
children.(name){index} = childs;
textfields = fieldnames(text);
if ~isempty(textfields)
for ii = 1:length(textfields)
children.(name){index}.(textfields{ii}) = ...
text.(textfields{ii});
end
end
if(~isempty(attr))
children.(name){index}.('Attributes') = attr;
end
else
% add previously unknown (new) element to the structure
children.(name) = childs;
% add text data ( ptext returned by child node )
textfields = fieldnames(text);
if ~isempty(textfields)
for ii = 1:length(textfields)
children.(name).(textfields{ii}) = text.(textfields{ii});
end
end
if(~isempty(attr))
children.(name).('Attributes') = attr;
end
end
else
ptextflag = 'Text';
if (strcmp(name, '#cdata_dash_section'))
ptextflag = 'CDATA';
elseif (strcmp(name, '#comment'))
ptextflag = 'Comment';
end
% this is the text in an element (i.e., the parentNode)
if (~isempty(regexprep(text.(textflag),'[\s]*','')))
if (~isfield(ptext,ptextflag) || isempty(ptext.(ptextflag)))
ptext.(ptextflag) = text.(textflag);
else
% This is what happens when document is like this:
% <element>Text <!--Comment--> More text</element>
%
% text will be appended to existing ptext
ptext.(ptextflag) = [ptext.(ptextflag) text.(textflag)];
end
end
end
end
end
end
% ----- Local function getNodeData -----
function [text,name,attr,childs,textflag] = getNodeData(theNode)
% Create structure of node info.
%make sure name is allowed as structure name
name = char(getNodeName(theNode));
name = strrep(name, '-', '_dash_');
name = strrep(name, ':', '_colon_');
name = strrep(name, '.', '_dot_');
name = strrep(name, '_', 'u_');
attr = parseAttributes(theNode);
if (isempty(fieldnames(attr)))
attr = [];
end
%parse child nodes
[childs, text, textflag] = parseChildNodes(theNode);
% Get data from any childless nodes. This version is faster than below.
if isempty(fieldnames(childs)) && isempty(fieldnames(text))
text.(textflag) = char(getTextContent(theNode));
end
% This alterative to the above 'if' block will also work but very slowly.
% if any(strcmp(methods(theNode),'getData'))
% text.(textflag) = char(getData(theNode));
% end
end
% ----- Local function parseAttributes -----
function attributes = parseAttributes(theNode)
% Create attributes structure.
attributes = struct;
if hasAttributes(theNode)
theAttributes = getAttributes(theNode);
numAttributes = getLength(theAttributes);
for count = 1:numAttributes
% Suggestion of Adrian Wanner
str = char(toString(item(theAttributes,count-1)));
k = strfind(str,'=');
attr_name = str(1:(k(1)-1));
attr_name = strrep(attr_name, '-', '_dash_');
attr_name = strrep(attr_name, ':', '_colon_');
attr_name = strrep(attr_name, '.', '_dot_');
attributes.(attr_name) = str((k(1)+2):(end-1));
end
end
end
% ----- Local function xmlFromString -----
function xmlroot = xmlFromString(iString)
import org.xml.sax.InputSource
import java.io.*
iSource = InputSource();
iSource.setCharacterStream(StringReader(iString));
xmlroot = xmlread(iSource);
end
|
github
|
inria-larsen/toolbox-probabilistic_movement_primitives-master
|
visualisation.m
|
.m
|
toolbox-probabilistic_movement_primitives-master/toolbox_promps/visualisation.m
| 510 |
utf_8
|
0100f613e186d950ded2e0809db7067d
|
% Function that plot the matrix with the color col1. x is the line of the
% matrix, y the number of colonnes
function y = visualisation(matrix, x,y, z, col1, nameFig)
tall = size(nameFig,2);
for i=1:x
for j=1:y
val(i,j) = matrix(y*(i-1)+j);
end
end
if(isa(col1, 'char'))
%for i=1:x
i=z;
nameFig(tall + 1) = plot(val(i,:), col1); hold on;
%end
else
%for i=1:x
i=z;
nameFig(tall + 1) = plot(val(i,:), 'Color', col1); hold on;
%end
end
y = nameFig;
end
|
github
|
inria-larsen/toolbox-probabilistic_movement_primitives-master
|
computeBasisFunction.m
|
.m
|
toolbox-probabilistic_movement_primitives-master/toolbox_promps/computeBasisFunction.m
| 1,618 |
utf_8
|
854c5ead441fdfc45654ce74ccef107d
|
%In this function, we create basis function matrix corresponding to the
%number of input information we have and the number of basis function we
%have defined with their bandwith h.
function PSI = computeBasisFunction(z,nbFunctions, nbDof, alpha, totalTime, center_gaussian, h, nbData)
%creating the center of basis function model
for k = 1 : size(nbFunctions,2)
for i = 1 : nbFunctions(k)
c(k,i) = center_gaussian(k)*(i-1);
end
for t = 1 : z / alpha
%creating a basis functions model (time*nbFunctions)
for i = 1 : nbFunctions(k)
val{k} = -(alpha*t*0.01-c(k,i))*(alpha*t*0.01-c(k,i))/(h(k));
basis{k}(t,i) = exp(val{k});
end
sumBI = sum(basis{k}(t,:));
for i = 1 : nbFunctions(k)
phi{k}(t,i) = basis{k}(t,i) / sumBI;
end
end
end
for i=1:size(nbDof,2)
for j =1:nbDof(i)
if and(i==1,j==1)
PSI = phi{i}(1:nbData,:);
else
PSI = blkdiag(PSI, phi{i}(1:nbData,:));
end
end
end
% %draw the basis function
% figure;
% for k=1:size(nbFunctions,2)
% for i=1:nbFunctions(k)
% plot(phi{k}(:,i), 'color', [0, k/size(nbFunctions,2), 0]); hold on;
% plot(basis{k}(:,i),'.', 'color', [0, k/size(nbFunctions,2), 0]);
% end
% end
% title('representation of the basis function used for each type of data')
% xlabel('time')
% ylabel('basis normalized')
%TODO ameliorate here to pu as much as we have trajectories!
%CREATING THE MATRIX BLOCK FOR ALL DOF
end
|
github
|
inria-larsen/toolbox-probabilistic_movement_primitives-master
|
visualisation2.m
|
.m
|
toolbox-probabilistic_movement_primitives-master/toolbox_promps/visualisation2.m
| 674 |
utf_8
|
dd838d01f4acf9892cc1fd8ad967d9da
|
% Function that plot the matrix with the color col1. x is the line of the
% matrix, y the number of colonnes
%take into account the alpha
function y = visualisation2(matrix, x,y, z, col1, alpha, nameFig)
tall = size(nameFig,2);
for i=1:x
for j=1:y
val(i,j) = matrix(y*(i-1)+j);
end
end
if(isa(col1, 'char'))
% for i=1:x
i=z;
nameFig(tall + 1) = plot([alpha : alpha : 100], val(i,:), col1); hold on;
% end
else
% for i=1:x
i=z;
nameFig(tall + (2*1) - 1) = plot([alpha : alpha : 100], val(i,:), col1); hold on;
nameFig(tall + 2*1) = plot(tmp, val(i,:), 'Color', col1); hold on;
% end
end
y = nameFig;
end
|
github
|
inria-larsen/toolbox-probabilistic_movement_primitives-master
|
visualisation3D.m
|
.m
|
toolbox-probabilistic_movement_primitives-master/toolbox_promps/visualisation3D.m
| 668 |
utf_8
|
4acca5e107cb4216bd2b761c1339c88a
|
% Function that plot the matrix with the color col1. x is the number of line of the
% matrix, y the number of colonnes, type is the reference of the kind of data you want to plot, nameFig is the fig
function y = visualisation3D(matrix, x, y, type, nbDof, col1, nameFig)
tall= size(nameFig,2);
for i=1:x
for j=1:y
val(i,j) = matrix(y*(i-1)+j);
end
end
nb=0;
for cpt =1:type-1
nb = nb + nbDof(cpt);
end
if(isa(col1, 'char'))
nameFig(tall + 1 ) = plot3(val(nb + 1,:) ,val(nb + 2,:),val(nb + 3,:), col1); hold on;
else
nameFig(tall+ 1) = plot3(val(nb + 1,:) ,val(nb + 2,:),val(nb + 3,:), 'Color', col1); hold on;
end
y = nameFig;
end
|
github
|
inria-larsen/toolbox-probabilistic_movement_primitives-master
|
visualisation3D2.m
|
.m
|
toolbox-probabilistic_movement_primitives-master/toolbox_promps/visualisation3D2.m
| 816 |
utf_8
|
60fec35072c7d3c8166bb3632e59270e
|
% Function that plot the matrix with the color col1. x is the number of line of the
% matrix, y the number of colonnes, bool=1 if we want forces, 0 if we want
% cartesian position, col1 is the color of the fig, nameFig is the fig
function y = visualisation3D2(matrix, , y, type, col1, alpha, nameFig)
tall= size(nameFig,2);
for i=1:x
for j=1:y
val(i,j) = matrix(y*(i-1)+j);
end
end
nb=0;
for cpt =1:type-1
nb = nb + nbDof(cpt);
end
sizeV = size(val, 2);
if(isa(col1, 'char'))
nameFig(tall + 1 ) = plot3(val(nb + 1,1:alpha:sizeV), val(nb + 2,1:alpha:sizeV),val(nb + 3,1:alpha:sizeV), col1); hold on;
else
nameFig(tall+ 1) = plot3( val(nb + 1,1:alpha:sizeV), val(nb + 2,1:alpha:sizeV),val(nb + 3,1:alpha:sizeV), 'Color', col1); hold on;
end
y = nameFig;
end
|
github
|
inria-larsen/toolbox-probabilistic_movement_primitives-master
|
logLikelihood.m
|
.m
|
toolbox-probabilistic_movement_primitives-master/toolbox_promps/logLikelihood.m
| 276 |
utf_8
|
b0809bc3dbdd70c471b3ca61227a78cd
|
%function that compute the log likelihood
% If A positif symetric
% R = chol(A) where R'*R = A
function log_p = logLikelihood(x,mu,S)
Sigma = chol(2*pi*S);
logdetSigma = sum(log(diag(Sigma))); % logdetSigma
log_p = -2*logdetSigma -(1/2)*(x-mu)*(S\(x-mu)');
end
|
github
|
sergiocastellanos/switch_mexico_data-master
|
UIExample.m
|
.m
|
switch_mexico_data-master/SAM/sam-sdk-2016-3-14-r3/languages/matlab/UIExample.m
| 586,602 |
utf_8
|
0aa9c0ca12306d99f27e822c55971cd2
|
function varargout = UIExample(varargin)
% UIEXAMPLE MATLAB code for UIExample.fig
% UIEXAMPLE, by itself, creates a new UIEXAMPLE or raises the existing
% singleton*.
%
% H = UIEXAMPLE returns the handle to a new UIEXAMPLE or the handle to
% the existing singleton*.
%
% UIEXAMPLE('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in UIEXAMPLE.M with the given input arguments.
%
% UIEXAMPLE('Property','Value',...) creates a new UIEXAMPLE or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before UIExample_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to UIExample_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help UIExample
% Last Modified by GUIDE v2.5 01-Dec-2014 04:23:19
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @UIExample_OpeningFcn, ...
'gui_OutputFcn', @UIExample_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before UIExample is made visible.
function UIExample_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to UIExample (see VARARGIN)
% Choose default command line output for UIExample
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes UIExample wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = UIExample_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
function txtData_Callback(hObject, eventdata, handles)
% hObject handle to txtData (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of txtData as text
% str2double(get(hObject,'String')) returns contents of txtData as a double
% --- Executes during object creation, after setting all properties.
function txtData_CreateFcn(hObject, eventdata, handles)
% hObject handle to txtData (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in btnVersion.
function btnVersion_Callback(hObject, eventdata, handles)
% hObject handle to btnVersion (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
sscAPI = SSC.API();
set(handles.txtData,'String',{sprintf('Version = %d',sscAPI.Version);sscAPI.BuildInfo});
% --- Executes on button press in btnModuleList.
function btnModuleList_Callback(hObject, eventdata, handles)
% hObject handle to btnModuleList (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
sscEntry = SSC.Entry();
names = {};
while (sscEntry.Get())
module_name = sscEntry.Name();
description = sscEntry.Description();
version = sscEntry.Version();
names{end+1} = sprintf('Module: %s, version: %d', module_name, version );
names{end+1} = description ;
end
set(handles.txtData,'String',names);
% --- Executes on button press in btnModuleAndVariables.
function btnModuleAndVariables_Callback(hObject, eventdata, handles)
% hObject handle to btnModuleAndVariables (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
sscEntry = SSC.Entry();
names = {};
while (sscEntry.Get())
moduleName = sscEntry.Name();
description = sscEntry.Description();
version = sscEntry.Version();
names{end+1} = sprintf('Module: %s, version: %d', moduleName, version );
names{end+1} = description ;
sscModule = SSC.Module(moduleName);
sscInfo = SSC.Info(sscModule);
while (sscInfo.Get())
names{end+1} = sprintf('\t%s: "%s" ["%s"] %s (%s)',sscInfo.VariableType(), sscInfo.Name(), sscInfo.DataType(), sscInfo.Label(), sscInfo.Units());
end
end
set(handles.txtData,'String',names);
% --- Executes on button press in btnTestArrays.
function btnTestArrays_Callback(hObject, eventdata, handles)
% hObject handle to btnTestArrays (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
names = {};
sscData = SSC.Data();
arr = [];
for i = 1:10
arr(i) = i / 10.0;
end
sscData.SetArray('TestArray', arr);
retArray = sscData.GetArray('TestArray');
names{end+1} = 'Testing SetArray and GetArray';
for i = 1:10
names{end+1} = sprintf('\treturned array element: %d = %g',i, retArray(i));
end
set(handles.txtData,'String',names);
% --- Executes on button press in btnTestMatrices.
function btnTestMatrices_Callback(hObject, eventdata, handles)
% hObject handle to btnTestMatrices (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
names = {};
sscData = SSC.Data();
matrix = [ 1 2 ; 3 4 ; 5 6 ; 7 8; 9 10];
sscData.SetMatrix('TestMatrix', matrix);
retMatrix = sscData.GetMatrix('TestMatrix');
[nrows ncols] = size(retMatrix);
names{end+1} = sprintf('Testing SetMatrix and GetMatrix size %d x %d', nrows,ncols);
for i = 1: nrows
for j = 1: ncols
names{end+1} = sprintf('\treturned matrix element: (%d,%d) = %g', i,j, retMatrix(i,j));
end
end
set(handles.txtData,'String',names);
% --- Executes on button press in btnPVWatts.
function btnPVWatts_Callback(hObject, eventdata, handles)
% hObject handle to btnPVWatts (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
names = {};
sscData = SSC.Data();
sscData.SetString('solar_resource_file', '../../examples/abilene.tm2');
sscData.SetNumber('system_capacity', 4.0);
sscData.SetNumber('dc_ac_ratio', 1.1);
sscData.SetNumber('tilt', 20);
sscData.SetNumber('azimuth', 180);
sscData.SetNumber('inv_eff', 96 );
sscData.SetNumber('losses', 14.0757 );
sscData.SetNumber('array_type', 0 );
sscData.SetNumber('gcr', 0.4 );
sscData.SetNumber('adjust:factor', 1 );
mod = SSC.Module('pvwattsv5');
if (mod.Exec(sscData)),
tot = sscData.GetNumber('ac_annual');
ac = sscData.GetArray('ac_monthly');
for i = 1:size(ac)
names{end+1} = sprintf('[%d]: %g kWh', i,ac(i));
end
names{end+1} = sprintf('AC total: %g kWh', tot);
names{end+1} = 'PVWatts test OK';
else
idx = 0;
[result, msg, type, time] = mod.Log(idx);
while (result)
names{end+1} = sprintf('[%s at time:%g ]: %s', type, time, msg);
idx = idx + 1;
[result, msg, type, time] = mod.Log(idx);
end
names{end+1} = 'PVWatts example failed';
end
set(handles.txtData,'String',names);
% --- Executes on button press in bntPVWattsFunc.
function bntPVWattsFunc_Callback(hObject, eventdata, handles)
% hObject handle to bntPVWattsFunc (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
names = {};
sscData = SSC.Data();
sscModule = SSC.Module('pvwattsv1_1ts');
sscData.SetNumber('year', 1970); % general year (tiny effect in sun position)
sscData.SetNumber('month', 1); % 1-12
sscData.SetNumber('day', 1); %1-number of days in month
sscData.SetNumber('hour', 9); % 0-23
sscData.SetNumber('minute', 30); % minute of the hour (typically 30 min for midpoint calculation)
sscData.SetNumber('lat', 33.4); % latitude, degrees
sscData.SetNumber('lon', -112); % longitude, degrees
sscData.SetNumber('tz', -7); % timezone from gmt, hours
sscData.SetNumber('time_step', 1); % time step, hours
% solar and weather data
sscData.SetNumber('beam', 824); % beam (DNI) irradiance, W/m2
sscData.SetNumber('diffuse', 29); % diffuse (DHI) horizontal irradiance, W/m2
sscData.SetNumber('tamb', 9.4); % ambient temp, degree C
sscData.SetNumber('wspd', 2.1); % wind speed, m/s
sscData.SetNumber('snow', 0); % snow depth, cm (0 is default - when there is snow, ground reflectance is increased. assumes panels have been cleaned off)
% system specifications
sscData.SetNumber('system_size', 4); % system DC nameplate rating (kW)
sscData.SetNumber('derate', 0.77); % derate factor
sscData.SetNumber('track_mode', 0); % tracking mode 0=fixed, 1=1axis, 2=2axis
sscData.SetNumber('azimuth', 180); % azimuth angle 0=north, 90=east, 180=south, 270=west
sscData.SetNumber('tilt', 20); % tilt angle from horizontal 0=flat, 90=vertical
% previous timestep values of cell temperature and POA
sscData.SetNumber('tcell', 6.94); % calculated cell temperature from previous timestep, degree C, (can default to ambient for morning or if you don't know)
sscData.SetNumber('poa', 84.5); % plane of array irradiance (W/m2) from previous time step
if (sscModule.Exec(sscData))
poa = sscData.GetNumber('poa');
tcell = sscData.GetNumber('tcell');
dc = sscData.GetNumber('dc');
ac = sscData.GetNumber('ac');
names{end+1} = sprintf('poa: %g W/m2', poa);
names{end+1} = sprintf('tcell: %g C', tcell);
names{end+1} = sprintf('dc: %g W', dc);
names{end+1} = sprintf('ac: %g W', ac);
end
set(handles.txtData,'String',names);
% --- Executes on button press in btnPVSamV1.
function btnPVSamV1_Callback(hObject, eventdata, handles)
% hObject handle to btnPVSamV1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
names={};
data = SSC.Data();
% pvsamv1 compute module call from 2014.11.24 "Photovoltaic, Residential" configuration
data.SetNumber( 'system_capacity', 3.8745 );
data.SetString( 'solar_resource_file', '../../examples/USA AZ Phoenix (TMY2).csv' );
data.SetNumber( 'use_wf_albedo', 0 );
data.SetArray( 'albedo', [ 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2 ] );
data.SetNumber( 'irrad_mode', 0 );
data.SetNumber( 'sky_model', 2 );
data.SetNumber( 'ac_loss', 1 );
data.SetNumber( 'modules_per_string', 9 );
data.SetNumber( 'strings_in_parallel', 2 );
data.SetNumber( 'inverter_count', 1 );
data.SetNumber( 'enable_mismatch_vmax_calc', 0 );
data.SetNumber( 'subarray1_tilt', 20 );
data.SetNumber( 'subarray1_tilt_eq_lat', 0 );
data.SetNumber( 'subarray1_azimuth', 180 );
data.SetNumber( 'subarray1_track_mode', 0 );
data.SetNumber( 'subarray1_rotlim', 45 );
data.SetNumber( 'subarray1_shade_mode', 1 );
data.SetNumber( 'subarray1_gcr', 0.3 );
data.SetArray( 'subarray1_soiling', [ 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5 ] );
data.SetNumber( 'subarray1_dcloss', 4.4402 );
data.SetNumber( 'subarray1_mismatch_loss', 2 );
data.SetNumber( 'subarray1_diodeconn_loss', 0.5 );
data.SetNumber( 'subarray1_dcwiring_loss', 2 );
data.SetNumber( 'subarray1_tracking_loss', 0 );
data.SetNumber( 'subarray1_nameplate_loss', 0 );
data.SetNumber( 'subarray2_mismatch_loss', 2 );
data.SetNumber( 'subarray2_diodeconn_loss', 0.5 );
data.SetNumber( 'subarray2_dcwiring_loss', 2 );
data.SetNumber( 'subarray2_tracking_loss', 0 );
data.SetNumber( 'subarray2_nameplate_loss', 0 );
data.SetNumber( 'subarray3_mismatch_loss', 2 );
data.SetNumber( 'subarray3_diodeconn_loss', 0.5 );
data.SetNumber( 'subarray3_dcwiring_loss', 2 );
data.SetNumber( 'subarray3_tracking_loss', 0 );
data.SetNumber( 'subarray3_nameplate_loss', 0 );
data.SetNumber( 'subarray4_mismatch_loss', 2 );
data.SetNumber( 'subarray4_diodeconn_loss', 0.5 );
data.SetNumber( 'subarray4_dcwiring_loss', 2 );
data.SetNumber( 'subarray4_tracking_loss', 0 );
data.SetNumber( 'subarray4_nameplate_loss', 0 );
data.SetNumber( 'acwiring_loss', 1 );
data.SetNumber( 'transformer_loss', 0 );
data.SetNumber( 'subarray1_mod_orient', 0 );
data.SetNumber( 'subarray1_nmodx', 9 );
data.SetNumber( 'subarray1_nmody', 2 );
data.SetNumber( 'subarray1_backtrack', 0 );
data.SetNumber( 'subarray2_enable', 0 );
data.SetNumber( 'subarray2_nstrings', 0 );
data.SetNumber( 'subarray2_tilt', 20 );
data.SetNumber( 'subarray2_tilt_eq_lat', 0 );
data.SetNumber( 'subarray2_azimuth', 180 );
data.SetNumber( 'subarray2_track_mode', 0 );
data.SetNumber( 'subarray2_rotlim', 45 );
data.SetNumber( 'subarray2_shade_mode', 1 );
data.SetNumber( 'subarray2_gcr', 0.3 );
data.SetArray( 'subarray2_soiling', [ 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5 ] );
data.SetNumber( 'subarray2_dcloss', 4.4402 );
data.SetNumber( 'subarray2_mod_orient', 0 );
data.SetNumber( 'subarray2_nmodx', 9 );
data.SetNumber( 'subarray2_nmody', 2 );
data.SetNumber( 'subarray2_backtrack', 0 );
data.SetNumber( 'subarray3_enable', 0 );
data.SetNumber( 'subarray3_nstrings', 0 );
data.SetNumber( 'subarray3_tilt', 20 );
data.SetNumber( 'subarray3_tilt_eq_lat', 0 );
data.SetNumber( 'subarray3_azimuth', 180 );
data.SetNumber( 'subarray3_track_mode', 0 );
data.SetNumber( 'subarray3_rotlim', 45 );
data.SetNumber( 'subarray3_shade_mode', 1 );
data.SetNumber( 'subarray3_gcr', 0.3 );
data.SetArray( 'subarray3_soiling', [ 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5 ] );
data.SetNumber( 'subarray3_dcloss', 4.4402 );
data.SetNumber( 'subarray3_mod_orient', 0 );
data.SetNumber( 'subarray3_nmodx', 9 );
data.SetNumber( 'subarray3_nmody', 2 );
data.SetNumber( 'subarray3_backtrack', 0 );
data.SetNumber( 'subarray4_enable', 0 );
data.SetNumber( 'subarray4_nstrings', 0 );
data.SetNumber( 'subarray4_tilt', 20 );
data.SetNumber( 'subarray4_tilt_eq_lat', 0 );
data.SetNumber( 'subarray4_azimuth', 180 );
data.SetNumber( 'subarray4_track_mode', 0 );
data.SetNumber( 'subarray4_rotlim', 45 );
data.SetNumber( 'subarray4_shade_mode', 1 );
data.SetNumber( 'subarray4_gcr', 0.3 );
data.SetArray( 'subarray4_soiling', [ 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5 ] );
data.SetNumber( 'subarray4_dcloss', 4.4402 );
data.SetNumber( 'subarray4_mod_orient', 0 );
data.SetNumber( 'subarray4_nmodx', 9 );
data.SetNumber( 'subarray4_nmody', 2 );
data.SetNumber( 'subarray4_backtrack', 0 );
data.SetNumber( 'module_model', 1 );
data.SetNumber( 'spe_area', 0.74074 );
data.SetNumber( 'spe_rad0', 200 );
data.SetNumber( 'spe_rad1', 400 );
data.SetNumber( 'spe_rad2', 600 );
data.SetNumber( 'spe_rad3', 800 );
data.SetNumber( 'spe_rad4', 1000 );
data.SetNumber( 'spe_eff0', 13.5 );
data.SetNumber( 'spe_eff1', 13.5 );
data.SetNumber( 'spe_eff2', 13.5 );
data.SetNumber( 'spe_eff3', 13.5 );
data.SetNumber( 'spe_eff4', 13.5 );
data.SetNumber( 'spe_reference', 4 );
data.SetNumber( 'spe_module_structure', 0 );
data.SetNumber( 'spe_a', -3.56 );
data.SetNumber( 'spe_b', -0.075 );
data.SetNumber( 'spe_dT', 3 );
data.SetNumber( 'spe_temp_coeff', -0.5 );
data.SetNumber( 'spe_fd', 1 );
data.SetNumber( 'cec_area', 1.244 );
data.SetNumber( 'cec_a_ref', 1.9816 );
data.SetNumber( 'cec_adjust', 20.8 );
data.SetNumber( 'cec_alpha_sc', 0.002651 );
data.SetNumber( 'cec_beta_oc', -0.14234 );
data.SetNumber( 'cec_gamma_r', -0.407 );
data.SetNumber( 'cec_i_l_ref', 5.754 );
data.SetNumber( 'cec_i_mp_ref', 5.25 );
data.SetNumber( 'cec_i_o_ref', 1.919e-010 );
data.SetNumber( 'cec_i_sc_ref', 5.75 );
data.SetNumber( 'cec_n_s', 72 );
data.SetNumber( 'cec_r_s', 0.105 );
data.SetNumber( 'cec_r_sh_ref', 160.48 );
data.SetNumber( 'cec_t_noct', 49.2 );
data.SetNumber( 'cec_v_mp_ref', 41 );
data.SetNumber( 'cec_v_oc_ref', 47.7 );
data.SetNumber( 'cec_temp_corr_mode', 0 );
data.SetNumber( 'cec_standoff', 6 );
data.SetNumber( 'cec_height', 0 );
data.SetNumber( 'cec_mounting_config', 0 );
data.SetNumber( 'cec_heat_transfer', 0 );
data.SetNumber( 'cec_mounting_orientation', 0 );
data.SetNumber( 'cec_gap_spacing', 0.05 );
data.SetNumber( 'cec_module_width', 1 );
data.SetNumber( 'cec_module_length', 1.244 );
data.SetNumber( 'cec_array_rows', 1 );
data.SetNumber( 'cec_array_cols', 10 );
data.SetNumber( 'cec_backside_temp', 20 );
data.SetNumber( '6par_celltech', 1 );
data.SetNumber( '6par_vmp', 30 );
data.SetNumber( '6par_imp', 6 );
data.SetNumber( '6par_voc', 37 );
data.SetNumber( '6par_isc', 7 );
data.SetNumber( '6par_bvoc', -0.11 );
data.SetNumber( '6par_aisc', 0.004 );
data.SetNumber( '6par_gpmp', -0.41 );
data.SetNumber( '6par_nser', 60 );
data.SetNumber( '6par_area', 1.3 );
data.SetNumber( '6par_tnoct', 46 );
data.SetNumber( '6par_standoff', 6 );
data.SetNumber( '6par_mounting', 0 );
data.SetNumber( 'snl_module_structure', 0 );
data.SetNumber( 'snl_a', -3.62 );
data.SetNumber( 'snl_b', -0.075 );
data.SetNumber( 'snl_dtc', 3 );
data.SetNumber( 'snl_ref_a', -3.62 );
data.SetNumber( 'snl_ref_b', -0.075 );
data.SetNumber( 'snl_ref_dT', 3 );
data.SetNumber( 'snl_fd', 1 );
data.SetNumber( 'snl_a0', 0.94045 );
data.SetNumber( 'snl_a1', 0.052641 );
data.SetNumber( 'snl_a2', -0.0093897 );
data.SetNumber( 'snl_a3', 0.00072623 );
data.SetNumber( 'snl_a4', -1.9938e-005 );
data.SetNumber( 'snl_aimp', -0.00038 );
data.SetNumber( 'snl_aisc', 0.00061 );
data.SetNumber( 'snl_area', 1.244 );
data.SetNumber( 'snl_b0', 1 );
data.SetNumber( 'snl_b1', -0.002438 );
data.SetNumber( 'snl_b2', 0.0003103 );
data.SetNumber( 'snl_b3', -1.246e-005 );
data.SetNumber( 'snl_b4', 2.11e-007 );
data.SetNumber( 'snl_b5', -1.36e-009 );
data.SetNumber( 'snl_bvmpo', -0.139 );
data.SetNumber( 'snl_bvoco', -0.136 );
data.SetNumber( 'snl_c0', 1.0039 );
data.SetNumber( 'snl_c1', -0.0039 );
data.SetNumber( 'snl_c2', 0.291066 );
data.SetNumber( 'snl_c3', -4.73546 );
data.SetNumber( 'snl_c4', 0.9942 );
data.SetNumber( 'snl_c5', 0.0058 );
data.SetNumber( 'snl_c6', 1.0723 );
data.SetNumber( 'snl_c7', -0.0723 );
data.SetNumber( 'snl_impo', 5.25 );
data.SetNumber( 'snl_isco', 5.75 );
data.SetNumber( 'snl_ixo', 5.65 );
data.SetNumber( 'snl_ixxo', 3.85 );
data.SetNumber( 'snl_mbvmp', 0 );
data.SetNumber( 'snl_mbvoc', 0 );
data.SetNumber( 'snl_n', 1.221 );
data.SetNumber( 'snl_series_cells', 72 );
data.SetNumber( 'snl_vmpo', 40 );
data.SetNumber( 'snl_voco', 47.7 );
data.SetNumber( 'inverter_model', 0 );
data.SetNumber( 'inv_snl_c0', -6.57929e-006 );
data.SetNumber( 'inv_snl_c1', 4.72925e-005 );
data.SetNumber( 'inv_snl_c2', 0.00202195 );
data.SetNumber( 'inv_snl_c3', 0.000285321 );
data.SetNumber( 'inv_snl_paco', 4000 );
data.SetNumber( 'inv_snl_pdco', 4186 );
data.SetNumber( 'inv_snl_pnt', 0.17 );
data.SetNumber( 'inv_snl_pso', 19.7391 );
data.SetNumber( 'inv_snl_vdco', 310.67 );
data.SetNumber( 'inv_snl_vdcmax', 600 );
data.SetNumber( 'inv_ds_paco', 4000 );
data.SetNumber( 'inv_ds_eff', 96 );
data.SetNumber( 'inv_ds_pnt', 1 );
data.SetNumber( 'inv_ds_pso', 0 );
data.SetNumber( 'inv_ds_vdco', 310 );
data.SetNumber( 'inv_ds_vdcmax', 600 );
data.SetNumber( 'inv_pd_paco', 4000 );
data.SetNumber( 'inv_pd_pdco', 4210.53 );
data.SetArray( 'inv_pd_partload', [ 0, 0.404, 0.808, 1.212, 1.616, 2.02, 2.424, 2.828, 3.232, 3.636, 4.04, 4.444, 4.848, 5.252, 5.656, 6.06, 6.464, 6.868, 7.272, 7.676, 8.08, 8.484, 8.888, 9.292, 9.696, 10.1, 10.504, 10.908, 11.312, 11.716, 12.12, 12.524, 12.928, 13.332, 13.736, 14.14, 14.544, 14.948, 15.352, 15.756, 16.16, 16.564, 16.968, 17.372, 17.776, 18.18, 18.584, 18.988, 19.392, 19.796, 20.2, 20.604, 21.008, 21.412, 21.816, 22.22, 22.624, 23.028, 23.432, 23.836, 24.24, 24.644, 25.048, 25.452, 25.856, 26.26, 26.664, 27.068, 27.472, 27.876, 28.28, 28.684, 29.088, 29.492, 29.896, 30.3, 30.704, 31.108, 31.512, 31.916, 32.32, 32.724, 33.128, 33.532, 33.936, 34.34, 34.744, 35.148, 35.552, 35.956, 36.36, 36.764, 37.168, 37.572, 37.976, 38.38, 38.784, 39.188, 39.592, 39.996, 40.4, 40.804, 41.208, 41.612, 42.016, 42.42, 42.824, 43.228, 43.632, 44.036, 44.44, 44.844, 45.248, 45.652, 46.056, 46.46, 46.864, 47.268, 47.672, 48.076, 48.48, 48.884, 49.288, 49.692, 50.096, 50.5, 50.904, 51.308, 51.712, 52.116, 52.52, 52.924, 53.328, 53.732, 54.136, 54.54, 54.944, 55.348, 55.752, 56.156, 56.56, 56.964, 57.368, 57.772, 58.176, 58.58, 58.984, 59.388, 59.792, 60.196, 60.6, 61.004, 61.408, 61.812, 62.216, 62.62, 63.024, 63.428, 63.832, 64.236, 64.64, 65.044, 65.448, 65.852, 66.256, 66.66, 67.064, 67.468, 67.872, 68.276, 68.68, 69.084, 69.488, 69.892, 70.296, 70.7, 71.104, 71.508, 71.912, 72.316, 72.72, 73.124, 73.528, 73.932, 74.336, 74.74, 75.144, 75.548, 75.952, 76.356, 76.76, 77.164, 77.568, 77.972, 78.376, 78.78, 79.184, 79.588, 79.992, 80.396, 80.8, 81.204, 81.608, 82.012, 82.416, 82.82, 83.224, 83.628, 84.032, 84.436, 84.84, 85.244, 85.648, 86.052, 86.456, 86.86, 87.264, 87.668, 88.072, 88.476, 88.88, 89.284, 89.688, 90.092, 90.496, 90.9, 91.304, 91.708, 92.112, 92.516, 92.92, 93.324, 93.728, 94.132, 94.536, 94.94, 95.344, 95.748, 96.152, 96.556, 96.96, 97.364, 97.768, 98.172, 98.576, 98.98, 99.384, 99.788, 100.192, 100.596, 101 ] );
data.SetArray( 'inv_pd_efficiency', [ 0, 0, 34.42, 55.2, 65.59, 71.82, 75.97, 78.94, 81.17, 82.9, 84.28, 85.42, 86.36, 87.16, 87.84, 88.44, 88.95, 89.41, 89.82, 90.18, 90.51, 90.81, 91.08, 91.32, 91.55, 91.75, 91.95, 92.12, 92.29, 92.44, 92.58, 92.72, 92.84, 92.96, 93.07, 93.17, 93.27, 93.37, 93.45, 93.54, 93.62, 93.69, 93.76, 93.83, 93.9, 93.96, 94.02, 94.08, 94.13, 94.18, 94.23, 94.28, 94.33, 94.37, 94.42, 94.46, 94.5, 94.54, 94.57, 94.61, 94.64, 94.68, 94.71, 94.74, 94.77, 94.8, 94.83, 94.86, 94.89, 94.91, 94.94, 94.96, 94.98, 95.01, 95.03, 95.05, 95.07, 95.09, 95.11, 95.13, 95.15, 95.17, 95.19, 95.21, 95.23, 95.24, 95.26, 95.28, 95.29, 95.31, 95.32, 95.34, 95.35, 95.36, 95.38, 95.39, 95.4, 95.42, 95.43, 95.44, 95.45, 95.47, 95.48, 95.49, 95.5, 95.51, 95.52, 95.53, 95.54, 95.55, 95.56, 95.57, 95.58, 95.59, 95.6, 95.61, 95.62, 95.63, 95.64, 95.64, 95.65, 95.66, 95.67, 95.68, 95.68, 95.69, 95.7, 95.71, 95.71, 95.72, 95.73, 95.73, 95.74, 95.75, 95.75, 95.76, 95.77, 95.77, 95.78, 95.78, 95.79, 95.8, 95.8, 95.81, 95.81, 95.82, 95.82, 95.83, 95.83, 95.84, 95.84, 95.85, 95.85, 95.86, 95.86, 95.87, 95.87, 95.88, 95.88, 95.89, 95.89, 95.89, 95.9, 95.9, 95.91, 95.91, 95.91, 95.92, 95.92, 95.93, 95.93, 95.93, 95.94, 95.94, 95.94, 95.95, 95.95, 95.96, 95.96, 95.96, 95.97, 95.97, 95.97, 95.98, 95.98, 95.98, 95.98, 95.99, 95.99, 95.99, 96, 96, 96, 96.01, 96.01, 96.01, 96.01, 96.02, 96.02, 96.02, 96.02, 96.03, 96.03, 96.03, 96.03, 96.04, 96.04, 96.04, 96.04, 96.05, 96.05, 96.05, 96.05, 96.06, 96.06, 96.06, 96.06, 96.06, 96.07, 96.07, 96.07, 96.07, 96.07, 96.08, 96.08, 96.08, 96.08, 96.08, 96.09, 96.09, 96.09, 96.09, 96.09, 96.09, 96.1, 96.1, 96.1, 96.1, 96.1, 96.1, 96.11, 96.11, 96.11, 96.11, 96.11, 96.11, 96.12, 96.12, 96.12, 96.12, 96.12 ] );
data.SetNumber( 'inv_pd_pnt', 0 );
data.SetNumber( 'inv_pd_vdco', 310 );
data.SetNumber( 'inv_pd_vdcmax', 600 );
data.SetNumber( 'adjust:factor', 1 );
module = SSC.Module('pvsamv1');
if (module.Exec(data))
enet = data.GetNumber('annual_energy');
cf = data.GetNumber('capacity_factor');
kWhperkW = data.GetNumber('kwh_per_kw');
names{end+1} = sprintf('Annual energy : %g kWh', enet);
names{end+1} = sprintf('Capacity factor : %g %%', cf);
names{end+1} = sprintf('First year kWhAC/kWDC : %g ', kWhperkW);
names{end+1} = 'PVSamV1 test OK';
else
idx = 0;
[result, msg, type, time] = module.Log(idx);
while (result)
names{end+1} = sprintf('[%s at time:%g ]: %s', type, time, msg);
idx = idx + 1;
[result, msg, type, time] = module.Log(idx);
end
names{end+1} = 'pvsamv1 example failed';
end
set(handles.txtData,'String',names);
% --- Executes on button press in btnBelpe.
function btnBelpe_Callback(hObject, eventdata, handles)
% hObject handle to btnBelpe (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% belpe compute module call from 2014.11.24 "Photovoltaic, Residential" configuration
names={};
data = SSC.Data();
data.SetNumber( 'en_belpe', 0 );
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data.SetString( 'solar_resource_file', '../../examples/USA AZ Phoenix (TMY2).csv' );
data.SetNumber( 'floor_area', 2000 );
data.SetNumber( 'Stories', 2 );
data.SetNumber( 'YrBuilt', 1980 );
data.SetNumber( 'Retrofits', 0 );
data.SetNumber( 'Occupants', 4 );
data.SetArray( 'Occ_Schedule', [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ] );
data.SetNumber( 'THeat', 68 );
data.SetNumber( 'TCool', 76 );
data.SetNumber( 'THeatSB', 68 );
data.SetNumber( 'TCoolSB', 76 );
data.SetArray( 'T_Sched', [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ] );
data.SetNumber( 'en_heat', 1 );
data.SetNumber( 'en_cool', 1 );
data.SetNumber( 'en_fridge', 1 );
data.SetNumber( 'en_range', 1 );
data.SetNumber( 'en_dish', 1 );
data.SetNumber( 'en_wash', 1 );
data.SetNumber( 'en_dry', 1 );
data.SetNumber( 'en_mels', 1 );
data.SetArray( 'Monthly_util', [ 725, 630, 665, 795, 1040, 1590, 1925, 1730, 1380, 1080, 635, 715 ] );
module = SSC.Module('belpe');
if (module.Exec(data))
pload = data.GetArray('p_load');
eload = data.GetArray('e_load');
for i = 1:size(eload)
names{end+1} = sprintf('[%d]: %g kWh', i,eload(i));
end
for i = 1:size(pload)
names{end+1} = sprintf('[%d]: %g kW', i,pload(i));
end
names{end+1} = 'belpe test OK';
else
idx = 0;
[result, msg, type, time] = module.Log(idx);
while (result)
names{end+1} = sprintf('[%s at time:%g ]: %s', type, time, msg);
idx = idx + 1;
[result, msg, type, time] = module.Log(idx);
end
names{end+1} = 'belpe example failed';
end
set(handles.txtData,'String',names);
% --- Executes on button press in btnUtilityRate3.
function btnUtilityRate3_Callback(hObject, eventdata, handles)
% hObject handle to btnUtilityRate3 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
%utilityrate3 compute module call from 2014.11.24 "Photovoltaic, Residential" configuration
names={};
data = SSC.Data();
data.SetNumber( 'analysis_period', 25 );
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0.382957, 0.40006, 0.438811, 0.562122, 0.755715, 0.898462, 0.854154, 0.834903, 0.77473, 0.644096, 0.533066, 0.408129, 0.358652, 0.340414, 0.335314, 0.325711, 0.36051, 0.440728, 0.543027, 0.478959, 0.388294, 0.379927, 0.379118, 0.375988, 0.382957, 0.40006, 0.438811, 0.562122, 0.755715, 0.898462, 0.854154, 0.834903, 0.77473, 0.644096, 0.533066 ] );
data.SetNumber( 'inflation_rate', 2.5 );
data.SetArray( 'degradation', [ 0.5 ] );
data.SetArray( 'load_escalation', [ 0 ] );
data.SetArray( 'rate_escalation', [ 0 ] );
data.SetNumber( 'ur_enable_net_metering', 1 );
data.SetNumber( 'ur_nm_yearend_sell_rate', 0.02789 );
data.SetNumber( 'ur_monthly_fixed_charge', 16.68 );
data.SetNumber( 'ur_flat_buy_rate', 0 );
data.SetNumber( 'ur_flat_sell_rate', 0 );
data.SetNumber( 'ur_monthly_min_charge', 0 );
data.SetNumber( 'ur_annual_min_charge', 0 );
data.SetNumber( 'ur_ec_enable', 1 );
data.SetMatrix( 'ur_ec_sched_weekday',[ 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 4 4 4 4 4 ; 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 4 4 4 4 4 ; 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 4 4 4 4 4 ; 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 4 4 4 4 4 ; 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 2 2 2 2 ; 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 2 2 2 2 ; 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 2 2 2 2 ; 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 2 2 2 2 ; 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 2 2 2 2 ; 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 2 2 2 2 ; 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 4 4 4 4 4 ; 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 4 4 4 4 4 ] );
data.SetMatrix( 'ur_ec_sched_weekend', [ 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 ; 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 ; 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 ; 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 ; 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ; 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ; 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ; 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ; 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ; 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ; 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 ; 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 ] );
data.SetNumber( 'ur_ec_p1_t1_br', 0.26687 );
data.SetNumber( 'ur_ec_p1_t1_sr', 0 );
data.SetNumber( 'ur_ec_p1_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p1_t2_br', 0 );
data.SetNumber( 'ur_ec_p1_t2_sr', 0 );
data.SetNumber( 'ur_ec_p1_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p1_t3_br', 0 );
data.SetNumber( 'ur_ec_p1_t3_sr', 0 );
data.SetNumber( 'ur_ec_p1_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p1_t4_br', 0 );
data.SetNumber( 'ur_ec_p1_t4_sr', 0 );
data.SetNumber( 'ur_ec_p1_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p1_t5_br', 0 );
data.SetNumber( 'ur_ec_p1_t5_sr', 0 );
data.SetNumber( 'ur_ec_p1_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p1_t6_br', 0 );
data.SetNumber( 'ur_ec_p1_t6_sr', 0 );
data.SetNumber( 'ur_ec_p1_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p2_t1_br', 0.08328 );
data.SetNumber( 'ur_ec_p2_t1_sr', 0 );
data.SetNumber( 'ur_ec_p2_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p2_t2_br', 0 );
data.SetNumber( 'ur_ec_p2_t2_sr', 0 );
data.SetNumber( 'ur_ec_p2_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p2_t3_br', 0 );
data.SetNumber( 'ur_ec_p2_t3_sr', 0 );
data.SetNumber( 'ur_ec_p2_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p2_t4_br', 0 );
data.SetNumber( 'ur_ec_p2_t4_sr', 0 );
data.SetNumber( 'ur_ec_p2_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p2_t5_br', 0 );
data.SetNumber( 'ur_ec_p2_t5_sr', 0 );
data.SetNumber( 'ur_ec_p2_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p2_t6_br', 0 );
data.SetNumber( 'ur_ec_p2_t6_sr', 0 );
data.SetNumber( 'ur_ec_p2_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p3_t1_br', 0.22057 );
data.SetNumber( 'ur_ec_p3_t1_sr', 0 );
data.SetNumber( 'ur_ec_p3_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p3_t2_br', 0 );
data.SetNumber( 'ur_ec_p3_t2_sr', 0 );
data.SetNumber( 'ur_ec_p3_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p3_t3_br', 0 );
data.SetNumber( 'ur_ec_p3_t3_sr', 0 );
data.SetNumber( 'ur_ec_p3_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p3_t4_br', 0 );
data.SetNumber( 'ur_ec_p3_t4_sr', 0 );
data.SetNumber( 'ur_ec_p3_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p3_t5_br', 0 );
data.SetNumber( 'ur_ec_p3_t5_sr', 0 );
data.SetNumber( 'ur_ec_p3_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p3_t6_br', 0 );
data.SetNumber( 'ur_ec_p3_t6_sr', 0 );
data.SetNumber( 'ur_ec_p3_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p4_t1_br', 0.08326 );
data.SetNumber( 'ur_ec_p4_t1_sr', 0 );
data.SetNumber( 'ur_ec_p4_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p4_t2_br', 0 );
data.SetNumber( 'ur_ec_p4_t2_sr', 0 );
data.SetNumber( 'ur_ec_p4_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p4_t3_br', 0 );
data.SetNumber( 'ur_ec_p4_t3_sr', 0 );
data.SetNumber( 'ur_ec_p4_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p4_t4_br', 0 );
data.SetNumber( 'ur_ec_p4_t4_sr', 0 );
data.SetNumber( 'ur_ec_p4_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p4_t5_br', 0 );
data.SetNumber( 'ur_ec_p4_t5_sr', 0 );
data.SetNumber( 'ur_ec_p4_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p4_t6_br', 0 );
data.SetNumber( 'ur_ec_p4_t6_sr', 0 );
data.SetNumber( 'ur_ec_p4_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p5_t1_br', 0 );
data.SetNumber( 'ur_ec_p5_t1_sr', 0 );
data.SetNumber( 'ur_ec_p5_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p5_t2_br', 0 );
data.SetNumber( 'ur_ec_p5_t2_sr', 0 );
data.SetNumber( 'ur_ec_p5_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p5_t3_br', 0 );
data.SetNumber( 'ur_ec_p5_t3_sr', 0 );
data.SetNumber( 'ur_ec_p5_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p5_t4_br', 0 );
data.SetNumber( 'ur_ec_p5_t4_sr', 0 );
data.SetNumber( 'ur_ec_p5_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p5_t5_br', 0 );
data.SetNumber( 'ur_ec_p5_t5_sr', 0 );
data.SetNumber( 'ur_ec_p5_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p5_t6_br', 0 );
data.SetNumber( 'ur_ec_p5_t6_sr', 0 );
data.SetNumber( 'ur_ec_p5_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p6_t1_br', 0 );
data.SetNumber( 'ur_ec_p6_t1_sr', 0 );
data.SetNumber( 'ur_ec_p6_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p6_t2_br', 0 );
data.SetNumber( 'ur_ec_p6_t2_sr', 0 );
data.SetNumber( 'ur_ec_p6_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p6_t3_br', 0 );
data.SetNumber( 'ur_ec_p6_t3_sr', 0 );
data.SetNumber( 'ur_ec_p6_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p6_t4_br', 0 );
data.SetNumber( 'ur_ec_p6_t4_sr', 0 );
data.SetNumber( 'ur_ec_p6_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p6_t5_br', 0 );
data.SetNumber( 'ur_ec_p6_t5_sr', 0 );
data.SetNumber( 'ur_ec_p6_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p6_t6_br', 0 );
data.SetNumber( 'ur_ec_p6_t6_sr', 0 );
data.SetNumber( 'ur_ec_p6_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p7_t1_br', 0 );
data.SetNumber( 'ur_ec_p7_t1_sr', 0 );
data.SetNumber( 'ur_ec_p7_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p7_t2_br', 0 );
data.SetNumber( 'ur_ec_p7_t2_sr', 0 );
data.SetNumber( 'ur_ec_p7_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p7_t3_br', 0 );
data.SetNumber( 'ur_ec_p7_t3_sr', 0 );
data.SetNumber( 'ur_ec_p7_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p7_t4_br', 0 );
data.SetNumber( 'ur_ec_p7_t4_sr', 0 );
data.SetNumber( 'ur_ec_p7_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p7_t5_br', 0 );
data.SetNumber( 'ur_ec_p7_t5_sr', 0 );
data.SetNumber( 'ur_ec_p7_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p7_t6_br', 0 );
data.SetNumber( 'ur_ec_p7_t6_sr', 0 );
data.SetNumber( 'ur_ec_p7_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p8_t1_br', 0 );
data.SetNumber( 'ur_ec_p8_t1_sr', 0 );
data.SetNumber( 'ur_ec_p8_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p8_t2_br', 0 );
data.SetNumber( 'ur_ec_p8_t2_sr', 0 );
data.SetNumber( 'ur_ec_p8_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p8_t3_br', 0 );
data.SetNumber( 'ur_ec_p8_t3_sr', 0 );
data.SetNumber( 'ur_ec_p8_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p8_t4_br', 0 );
data.SetNumber( 'ur_ec_p8_t4_sr', 0 );
data.SetNumber( 'ur_ec_p8_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p8_t5_br', 0 );
data.SetNumber( 'ur_ec_p8_t5_sr', 0 );
data.SetNumber( 'ur_ec_p8_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p8_t6_br', 0 );
data.SetNumber( 'ur_ec_p8_t6_sr', 0 );
data.SetNumber( 'ur_ec_p8_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p9_t1_br', 0 );
data.SetNumber( 'ur_ec_p9_t1_sr', 0 );
data.SetNumber( 'ur_ec_p9_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p9_t2_br', 0 );
data.SetNumber( 'ur_ec_p9_t2_sr', 0 );
data.SetNumber( 'ur_ec_p9_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p9_t3_br', 0 );
data.SetNumber( 'ur_ec_p9_t3_sr', 0 );
data.SetNumber( 'ur_ec_p9_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p9_t4_br', 0 );
data.SetNumber( 'ur_ec_p9_t4_sr', 0 );
data.SetNumber( 'ur_ec_p9_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p9_t5_br', 0 );
data.SetNumber( 'ur_ec_p9_t5_sr', 0 );
data.SetNumber( 'ur_ec_p9_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p9_t6_br', 0 );
data.SetNumber( 'ur_ec_p9_t6_sr', 0 );
data.SetNumber( 'ur_ec_p9_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p10_t1_br', 0 );
data.SetNumber( 'ur_ec_p10_t1_sr', 0 );
data.SetNumber( 'ur_ec_p10_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p10_t2_br', 0 );
data.SetNumber( 'ur_ec_p10_t2_sr', 0 );
data.SetNumber( 'ur_ec_p10_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p10_t3_br', 0 );
data.SetNumber( 'ur_ec_p10_t3_sr', 0 );
data.SetNumber( 'ur_ec_p10_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p10_t4_br', 0 );
data.SetNumber( 'ur_ec_p10_t4_sr', 0 );
data.SetNumber( 'ur_ec_p10_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p10_t5_br', 0 );
data.SetNumber( 'ur_ec_p10_t5_sr', 0 );
data.SetNumber( 'ur_ec_p10_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p10_t6_br', 0 );
data.SetNumber( 'ur_ec_p10_t6_sr', 0 );
data.SetNumber( 'ur_ec_p10_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p11_t1_br', 0 );
data.SetNumber( 'ur_ec_p11_t1_sr', 0 );
data.SetNumber( 'ur_ec_p11_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p11_t2_br', 0 );
data.SetNumber( 'ur_ec_p11_t2_sr', 0 );
data.SetNumber( 'ur_ec_p11_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p11_t3_br', 0 );
data.SetNumber( 'ur_ec_p11_t3_sr', 0 );
data.SetNumber( 'ur_ec_p11_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p11_t4_br', 0 );
data.SetNumber( 'ur_ec_p11_t4_sr', 0 );
data.SetNumber( 'ur_ec_p11_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p11_t5_br', 0 );
data.SetNumber( 'ur_ec_p11_t5_sr', 0 );
data.SetNumber( 'ur_ec_p11_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p11_t6_br', 0 );
data.SetNumber( 'ur_ec_p11_t6_sr', 0 );
data.SetNumber( 'ur_ec_p11_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p12_t1_br', 0 );
data.SetNumber( 'ur_ec_p12_t1_sr', 0 );
data.SetNumber( 'ur_ec_p12_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p12_t2_br', 0 );
data.SetNumber( 'ur_ec_p12_t2_sr', 0 );
data.SetNumber( 'ur_ec_p12_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p12_t3_br', 0 );
data.SetNumber( 'ur_ec_p12_t3_sr', 0 );
data.SetNumber( 'ur_ec_p12_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p12_t4_br', 0 );
data.SetNumber( 'ur_ec_p12_t4_sr', 0 );
data.SetNumber( 'ur_ec_p12_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p12_t5_br', 0 );
data.SetNumber( 'ur_ec_p12_t5_sr', 0 );
data.SetNumber( 'ur_ec_p12_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p12_t6_br', 0 );
data.SetNumber( 'ur_ec_p12_t6_sr', 0 );
data.SetNumber( 'ur_ec_p12_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_enable', 0 );
data.SetMatrix( 'ur_dc_sched_weekday', [ 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ] );
data.SetMatrix( 'ur_dc_sched_weekend', [ 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ] );
data.SetNumber( 'ur_dc_p1_t1_dc', 0 );
data.SetNumber( 'ur_dc_p1_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p1_t2_dc', 0 );
data.SetNumber( 'ur_dc_p1_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p1_t3_dc', 0 );
data.SetNumber( 'ur_dc_p1_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p1_t4_dc', 0 );
data.SetNumber( 'ur_dc_p1_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p1_t5_dc', 0 );
data.SetNumber( 'ur_dc_p1_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p1_t6_dc', 0 );
data.SetNumber( 'ur_dc_p1_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p2_t1_dc', 0 );
data.SetNumber( 'ur_dc_p2_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p2_t2_dc', 0 );
data.SetNumber( 'ur_dc_p2_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p2_t3_dc', 0 );
data.SetNumber( 'ur_dc_p2_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p2_t4_dc', 0 );
data.SetNumber( 'ur_dc_p2_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p2_t5_dc', 0 );
data.SetNumber( 'ur_dc_p2_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p2_t6_dc', 0 );
data.SetNumber( 'ur_dc_p2_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p3_t1_dc', 0 );
data.SetNumber( 'ur_dc_p3_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p3_t2_dc', 0 );
data.SetNumber( 'ur_dc_p3_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p3_t3_dc', 0 );
data.SetNumber( 'ur_dc_p3_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p3_t4_dc', 0 );
data.SetNumber( 'ur_dc_p3_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p3_t5_dc', 0 );
data.SetNumber( 'ur_dc_p3_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p3_t6_dc', 0 );
data.SetNumber( 'ur_dc_p3_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p4_t1_dc', 0 );
data.SetNumber( 'ur_dc_p4_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p4_t2_dc', 0 );
data.SetNumber( 'ur_dc_p4_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p4_t3_dc', 0 );
data.SetNumber( 'ur_dc_p4_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p4_t4_dc', 0 );
data.SetNumber( 'ur_dc_p4_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p4_t5_dc', 0 );
data.SetNumber( 'ur_dc_p4_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p4_t6_dc', 0 );
data.SetNumber( 'ur_dc_p4_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p5_t1_dc', 0 );
data.SetNumber( 'ur_dc_p5_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p5_t2_dc', 0 );
data.SetNumber( 'ur_dc_p5_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p5_t3_dc', 0 );
data.SetNumber( 'ur_dc_p5_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p5_t4_dc', 0 );
data.SetNumber( 'ur_dc_p5_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p5_t5_dc', 0 );
data.SetNumber( 'ur_dc_p5_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p5_t6_dc', 0 );
data.SetNumber( 'ur_dc_p5_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p6_t1_dc', 0 );
data.SetNumber( 'ur_dc_p6_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p6_t2_dc', 0 );
data.SetNumber( 'ur_dc_p6_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p6_t3_dc', 0 );
data.SetNumber( 'ur_dc_p6_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p6_t4_dc', 0 );
data.SetNumber( 'ur_dc_p6_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p6_t5_dc', 0 );
data.SetNumber( 'ur_dc_p6_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p6_t6_dc', 0 );
data.SetNumber( 'ur_dc_p6_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p7_t1_dc', 0 );
data.SetNumber( 'ur_dc_p7_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p7_t2_dc', 0 );
data.SetNumber( 'ur_dc_p7_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p7_t3_dc', 0 );
data.SetNumber( 'ur_dc_p7_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p7_t4_dc', 0 );
data.SetNumber( 'ur_dc_p7_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p7_t5_dc', 0 );
data.SetNumber( 'ur_dc_p7_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p7_t6_dc', 0 );
data.SetNumber( 'ur_dc_p7_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p8_t1_dc', 0 );
data.SetNumber( 'ur_dc_p8_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p8_t2_dc', 0 );
data.SetNumber( 'ur_dc_p8_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p8_t3_dc', 0 );
data.SetNumber( 'ur_dc_p8_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p8_t4_dc', 0 );
data.SetNumber( 'ur_dc_p8_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p8_t5_dc', 0 );
data.SetNumber( 'ur_dc_p8_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p8_t6_dc', 0 );
data.SetNumber( 'ur_dc_p8_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p9_t1_dc', 0 );
data.SetNumber( 'ur_dc_p9_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p9_t2_dc', 0 );
data.SetNumber( 'ur_dc_p9_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p9_t3_dc', 0 );
data.SetNumber( 'ur_dc_p9_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p9_t4_dc', 0 );
data.SetNumber( 'ur_dc_p9_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p9_t5_dc', 0 );
data.SetNumber( 'ur_dc_p9_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p9_t6_dc', 0 );
data.SetNumber( 'ur_dc_p9_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p10_t1_dc', 0 );
data.SetNumber( 'ur_dc_p10_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p10_t2_dc', 0 );
data.SetNumber( 'ur_dc_p10_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p10_t3_dc', 0 );
data.SetNumber( 'ur_dc_p10_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p10_t4_dc', 0 );
data.SetNumber( 'ur_dc_p10_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p10_t5_dc', 0 );
data.SetNumber( 'ur_dc_p10_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p10_t6_dc', 0 );
data.SetNumber( 'ur_dc_p10_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p11_t1_dc', 0 );
data.SetNumber( 'ur_dc_p11_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p11_t2_dc', 0 );
data.SetNumber( 'ur_dc_p11_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p11_t3_dc', 0 );
data.SetNumber( 'ur_dc_p11_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p11_t4_dc', 0 );
data.SetNumber( 'ur_dc_p11_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p11_t5_dc', 0 );
data.SetNumber( 'ur_dc_p11_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p11_t6_dc', 0 );
data.SetNumber( 'ur_dc_p11_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p12_t1_dc', 0 );
data.SetNumber( 'ur_dc_p12_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p12_t2_dc', 0 );
data.SetNumber( 'ur_dc_p12_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p12_t3_dc', 0 );
data.SetNumber( 'ur_dc_p12_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p12_t4_dc', 0 );
data.SetNumber( 'ur_dc_p12_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p12_t5_dc', 0 );
data.SetNumber( 'ur_dc_p12_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p12_t6_dc', 0 );
data.SetNumber( 'ur_dc_p12_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_jan_t1_dc', 0 );
data.SetNumber( 'ur_dc_jan_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_jan_t2_dc', 0 );
data.SetNumber( 'ur_dc_jan_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_jan_t3_dc', 0 );
data.SetNumber( 'ur_dc_jan_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_jan_t4_dc', 0 );
data.SetNumber( 'ur_dc_jan_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_jan_t5_dc', 0 );
data.SetNumber( 'ur_dc_jan_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_jan_t6_dc', 0 );
data.SetNumber( 'ur_dc_jan_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_feb_t1_dc', 0 );
data.SetNumber( 'ur_dc_feb_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_feb_t2_dc', 0 );
data.SetNumber( 'ur_dc_feb_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_feb_t3_dc', 0 );
data.SetNumber( 'ur_dc_feb_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_feb_t4_dc', 0 );
data.SetNumber( 'ur_dc_feb_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_feb_t5_dc', 0 );
data.SetNumber( 'ur_dc_feb_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_feb_t6_dc', 0 );
data.SetNumber( 'ur_dc_feb_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_mar_t1_dc', 0 );
data.SetNumber( 'ur_dc_mar_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_mar_t2_dc', 0 );
data.SetNumber( 'ur_dc_mar_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_mar_t3_dc', 0 );
data.SetNumber( 'ur_dc_mar_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_mar_t4_dc', 0 );
data.SetNumber( 'ur_dc_mar_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_mar_t5_dc', 0 );
data.SetNumber( 'ur_dc_mar_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_mar_t6_dc', 0 );
data.SetNumber( 'ur_dc_mar_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_apr_t1_dc', 0 );
data.SetNumber( 'ur_dc_apr_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_apr_t2_dc', 0 );
data.SetNumber( 'ur_dc_apr_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_apr_t3_dc', 0 );
data.SetNumber( 'ur_dc_apr_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_apr_t4_dc', 0 );
data.SetNumber( 'ur_dc_apr_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_apr_t5_dc', 0 );
data.SetNumber( 'ur_dc_apr_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_apr_t6_dc', 0 );
data.SetNumber( 'ur_dc_apr_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_may_t1_dc', 0 );
data.SetNumber( 'ur_dc_may_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_may_t2_dc', 0 );
data.SetNumber( 'ur_dc_may_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_may_t3_dc', 0 );
data.SetNumber( 'ur_dc_may_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_may_t4_dc', 0 );
data.SetNumber( 'ur_dc_may_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_may_t5_dc', 0 );
data.SetNumber( 'ur_dc_may_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_may_t6_dc', 0 );
data.SetNumber( 'ur_dc_may_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_jun_t1_dc', 0 );
data.SetNumber( 'ur_dc_jun_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_jun_t2_dc', 0 );
data.SetNumber( 'ur_dc_jun_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_jun_t3_dc', 0 );
data.SetNumber( 'ur_dc_jun_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_jun_t4_dc', 0 );
data.SetNumber( 'ur_dc_jun_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_jun_t5_dc', 0 );
data.SetNumber( 'ur_dc_jun_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_jun_t6_dc', 0 );
data.SetNumber( 'ur_dc_jun_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_jul_t1_dc', 0 );
data.SetNumber( 'ur_dc_jul_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_jul_t2_dc', 0 );
data.SetNumber( 'ur_dc_jul_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_jul_t3_dc', 0 );
data.SetNumber( 'ur_dc_jul_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_jul_t4_dc', 0 );
data.SetNumber( 'ur_dc_jul_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_jul_t5_dc', 0 );
data.SetNumber( 'ur_dc_jul_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_jul_t6_dc', 0 );
data.SetNumber( 'ur_dc_jul_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_aug_t1_dc', 0 );
data.SetNumber( 'ur_dc_aug_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_aug_t2_dc', 0 );
data.SetNumber( 'ur_dc_aug_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_aug_t3_dc', 0 );
data.SetNumber( 'ur_dc_aug_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_aug_t4_dc', 0 );
data.SetNumber( 'ur_dc_aug_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_aug_t5_dc', 0 );
data.SetNumber( 'ur_dc_aug_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_aug_t6_dc', 0 );
data.SetNumber( 'ur_dc_aug_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_sep_t1_dc', 0 );
data.SetNumber( 'ur_dc_sep_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_sep_t2_dc', 0 );
data.SetNumber( 'ur_dc_sep_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_sep_t3_dc', 0 );
data.SetNumber( 'ur_dc_sep_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_sep_t4_dc', 0 );
data.SetNumber( 'ur_dc_sep_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_sep_t5_dc', 0 );
data.SetNumber( 'ur_dc_sep_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_sep_t6_dc', 0 );
data.SetNumber( 'ur_dc_sep_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_oct_t1_dc', 0 );
data.SetNumber( 'ur_dc_oct_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_oct_t2_dc', 0 );
data.SetNumber( 'ur_dc_oct_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_oct_t3_dc', 0 );
data.SetNumber( 'ur_dc_oct_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_oct_t4_dc', 0 );
data.SetNumber( 'ur_dc_oct_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_oct_t5_dc', 0 );
data.SetNumber( 'ur_dc_oct_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_oct_t6_dc', 0 );
data.SetNumber( 'ur_dc_oct_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_nov_t1_dc', 0 );
data.SetNumber( 'ur_dc_nov_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_nov_t2_dc', 0 );
data.SetNumber( 'ur_dc_nov_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_nov_t3_dc', 0 );
data.SetNumber( 'ur_dc_nov_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_nov_t4_dc', 0 );
data.SetNumber( 'ur_dc_nov_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_nov_t5_dc', 0 );
data.SetNumber( 'ur_dc_nov_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_nov_t6_dc', 0 );
data.SetNumber( 'ur_dc_nov_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_dec_t1_dc', 0 );
data.SetNumber( 'ur_dc_dec_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_dec_t2_dc', 0 );
data.SetNumber( 'ur_dc_dec_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_dec_t3_dc', 0 );
data.SetNumber( 'ur_dc_dec_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_dec_t4_dc', 0 );
data.SetNumber( 'ur_dc_dec_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_dec_t5_dc', 0 );
data.SetNumber( 'ur_dc_dec_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_dec_t6_dc', 0 );
data.SetNumber( 'ur_dc_dec_t6_ub', 1e+038 );
module = SSC.Module('utilityrate3');
if (module.Exec(data))
salespurchases = data.GetArray('year1_monthly_salespurchases');
ns = data.GetNumber('savings_year1');
names{end+1} = 'ear 1 monthly sales/purchases with system : ';
for i = 1:size(salespurchases)
names{end+1} = sprintf('[%d]: $%g', i,salespurchases(i));
end
names{end+1} = sprintf('Net savings : $%g', ns);
names{end+1} = 'UtilityRate3 example OK';
else
idx = 0;
[result, msg, type, time] = module.Log(idx);
while (result)
names{end+1} = sprintf('[%s at time:%g ]: %s', type, time, msg);
idx = idx + 1;
[result, msg, type, time] = module.Log(idx);
end
names{end+1} = 'UtilityRate3 example failed';
end
set(handles.txtData,'String',names);
% --- Executes on button press in btnCashLoan.
function btnCashLoan_Callback(hObject, eventdata, handles)
% hObject handle to btnCashLoan (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
%cashloan compute module call from 2014.11.24 "Photovoltaic, Residential" configuration
names={};
data = SSC.Data();
data.SetNumber( 'analysis_period', 25 );
data.SetNumber( 'federal_tax_rate', 30 );
data.SetNumber( 'state_tax_rate', 7 );
data.SetNumber( 'property_tax_rate', 1 );
data.SetNumber( 'prop_tax_cost_assessed_percent', 100 );
data.SetNumber( 'prop_tax_assessed_decline', 0 );
data.SetNumber( 'sales_tax_rate', 5 );
data.SetNumber( 'real_discount_rate', 5.5 );
data.SetNumber( 'inflation_rate', 2.5 );
data.SetNumber( 'insurance_rate', 1 );
data.SetNumber( 'system_capacity', 3.8745 );
data.SetNumber( 'loan_term', 25 );
data.SetNumber( 'loan_rate', 5 );
data.SetNumber( 'debt_fraction', 100 );
data.SetArray( 'om_fixed', [ 0 ] );
data.SetNumber( 'om_fixed_escal', 0 );
data.SetArray( 'om_production', [ 0 ] );
data.SetNumber( 'om_production_escal', 0 );
data.SetArray( 'om_capacity', [ 20 ] );
data.SetNumber( 'om_capacity_escal', 0 );
data.SetArray( 'om_fuel_cost', [ 0 ] );
data.SetNumber( 'om_fuel_cost_escal', 0 );
data.SetNumber( 'itc_fed_amount', 0 );
data.SetNumber( 'itc_fed_amount_deprbas_fed', 1 );
data.SetNumber( 'itc_fed_amount_deprbas_sta', 1 );
data.SetNumber( 'itc_sta_amount', 0 );
data.SetNumber( 'itc_sta_amount_deprbas_fed', 0 );
data.SetNumber( 'itc_sta_amount_deprbas_sta', 0 );
data.SetNumber( 'itc_fed_percent', 30 );
data.SetNumber( 'itc_fed_percent_maxvalue', 1e+038 );
data.SetNumber( 'itc_fed_percent_deprbas_fed', 1 );
data.SetNumber( 'itc_fed_percent_deprbas_sta', 1 );
data.SetNumber( 'itc_sta_percent', 25 );
data.SetNumber( 'itc_sta_percent_maxvalue', 1e+038 );
data.SetNumber( 'itc_sta_percent_deprbas_fed', 0 );
data.SetNumber( 'itc_sta_percent_deprbas_sta', 0 );
data.SetArray( 'ptc_fed_amount', [ 0 ] );
data.SetNumber( 'ptc_fed_term', 10 );
data.SetNumber( 'ptc_fed_escal', 0 );
data.SetArray( 'ptc_sta_amount', [ 0 ] );
data.SetNumber( 'ptc_sta_term', 10 );
data.SetNumber( 'ptc_sta_escal', 0 );
data.SetNumber( 'ibi_fed_amount', 0 );
data.SetNumber( 'ibi_fed_amount_tax_fed', 1 );
data.SetNumber( 'ibi_fed_amount_tax_sta', 1 );
data.SetNumber( 'ibi_fed_amount_deprbas_fed', 0 );
data.SetNumber( 'ibi_fed_amount_deprbas_sta', 0 );
data.SetNumber( 'ibi_sta_amount', 0 );
data.SetNumber( 'ibi_sta_amount_tax_fed', 1 );
data.SetNumber( 'ibi_sta_amount_tax_sta', 1 );
data.SetNumber( 'ibi_sta_amount_deprbas_fed', 0 );
data.SetNumber( 'ibi_sta_amount_deprbas_sta', 0 );
data.SetNumber( 'ibi_uti_amount', 0 );
data.SetNumber( 'ibi_uti_amount_tax_fed', 1 );
data.SetNumber( 'ibi_uti_amount_tax_sta', 1 );
data.SetNumber( 'ibi_uti_amount_deprbas_fed', 0 );
data.SetNumber( 'ibi_uti_amount_deprbas_sta', 0 );
data.SetNumber( 'ibi_oth_amount', 0 );
data.SetNumber( 'ibi_oth_amount_tax_fed', 1 );
data.SetNumber( 'ibi_oth_amount_tax_sta', 1 );
data.SetNumber( 'ibi_oth_amount_deprbas_fed', 0 );
data.SetNumber( 'ibi_oth_amount_deprbas_sta', 0 );
data.SetNumber( 'ibi_fed_percent', 0 );
data.SetNumber( 'ibi_fed_percent_maxvalue', 1e+038 );
data.SetNumber( 'ibi_fed_percent_tax_fed', 1 );
data.SetNumber( 'ibi_fed_percent_tax_sta', 1 );
data.SetNumber( 'ibi_fed_percent_deprbas_fed', 0 );
data.SetNumber( 'ibi_fed_percent_deprbas_sta', 0 );
data.SetNumber( 'ibi_sta_percent', 0 );
data.SetNumber( 'ibi_sta_percent_maxvalue', 1e+038 );
data.SetNumber( 'ibi_sta_percent_tax_fed', 1 );
data.SetNumber( 'ibi_sta_percent_tax_sta', 1 );
data.SetNumber( 'ibi_sta_percent_deprbas_fed', 0 );
data.SetNumber( 'ibi_sta_percent_deprbas_sta', 0 );
data.SetNumber( 'ibi_uti_percent', 0 );
data.SetNumber( 'ibi_uti_percent_maxvalue', 1e+038 );
data.SetNumber( 'ibi_uti_percent_tax_fed', 1 );
data.SetNumber( 'ibi_uti_percent_tax_sta', 1 );
data.SetNumber( 'ibi_uti_percent_deprbas_fed', 0 );
data.SetNumber( 'ibi_uti_percent_deprbas_sta', 0 );
data.SetNumber( 'ibi_oth_percent', 0 );
data.SetNumber( 'ibi_oth_percent_maxvalue', 1e+038 );
data.SetNumber( 'ibi_oth_percent_tax_fed', 1 );
data.SetNumber( 'ibi_oth_percent_tax_sta', 1 );
data.SetNumber( 'ibi_oth_percent_deprbas_fed', 0 );
data.SetNumber( 'ibi_oth_percent_deprbas_sta', 0 );
data.SetNumber( 'cbi_fed_amount', 0 );
data.SetNumber( 'cbi_fed_maxvalue', 1e+038 );
data.SetNumber( 'cbi_fed_tax_fed', 1 );
data.SetNumber( 'cbi_fed_tax_sta', 1 );
data.SetNumber( 'cbi_fed_deprbas_fed', 0 );
data.SetNumber( 'cbi_fed_deprbas_sta', 0 );
data.SetNumber( 'cbi_sta_amount', 0 );
data.SetNumber( 'cbi_sta_maxvalue', 1e+038 );
data.SetNumber( 'cbi_sta_tax_fed', 1 );
data.SetNumber( 'cbi_sta_tax_sta', 1 );
data.SetNumber( 'cbi_sta_deprbas_fed', 0 );
data.SetNumber( 'cbi_sta_deprbas_sta', 0 );
data.SetNumber( 'cbi_uti_amount', 0 );
data.SetNumber( 'cbi_uti_maxvalue', 1e+038 );
data.SetNumber( 'cbi_uti_tax_fed', 1 );
data.SetNumber( 'cbi_uti_tax_sta', 1 );
data.SetNumber( 'cbi_uti_deprbas_fed', 0 );
data.SetNumber( 'cbi_uti_deprbas_sta', 0 );
data.SetNumber( 'cbi_oth_amount', 0 );
data.SetNumber( 'cbi_oth_maxvalue', 1e+038 );
data.SetNumber( 'cbi_oth_tax_fed', 1 );
data.SetNumber( 'cbi_oth_tax_sta', 1 );
data.SetNumber( 'cbi_oth_deprbas_fed', 0 );
data.SetNumber( 'cbi_oth_deprbas_sta', 0 );
data.SetArray( 'pbi_fed_amount', [ 0 ] );
data.SetNumber( 'pbi_fed_term', 0 );
data.SetNumber( 'pbi_fed_escal', 0 );
data.SetNumber( 'pbi_fed_tax_fed', 1 );
data.SetNumber( 'pbi_fed_tax_sta', 1 );
data.SetArray( 'pbi_sta_amount', [ 0 ] );
data.SetNumber( 'pbi_sta_term', 0 );
data.SetNumber( 'pbi_sta_escal', 0 );
data.SetNumber( 'pbi_sta_tax_fed', 1 );
data.SetNumber( 'pbi_sta_tax_sta', 1 );
data.SetArray( 'pbi_uti_amount', [ 0 ] );
data.SetNumber( 'pbi_uti_term', 0 );
data.SetNumber( 'pbi_uti_escal', 0 );
data.SetNumber( 'pbi_uti_tax_fed', 1 );
data.SetNumber( 'pbi_uti_tax_sta', 1 );
data.SetArray( 'pbi_oth_amount', [ 0 ] );
data.SetNumber( 'pbi_oth_term', 0 );
data.SetNumber( 'pbi_oth_escal', 0 );
data.SetNumber( 'pbi_oth_tax_fed', 1 );
data.SetNumber( 'pbi_oth_tax_sta', 1 );
data.SetNumber( 'market', 0 );
data.SetNumber( 'mortgage', 1 );
data.SetNumber( 'total_installed_cost', 12746.7 );
data.SetNumber( 'salvage_percentage', 0 );
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-0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0228486, 0.596429, 1.41305, 2.01139, 1.54031, 2.31804, 2.13488, 1.45011, 1.14562, 0.302929, 0.00178418, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0043009, 0.190437, 0.456663, 0.774345, 0.706195, 0.750254, 0.227952, 0.347005, 0.296926, 0.0424597, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.00197333, 0.204573, 0.438987, 0.817599, 1.39033, 1.58748, 2.36097, 1.58063, 0.643465, 0.153036, 0.00123386, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0478906, 0.617714, 1.44506, 2.22169, 2.64015, 2.76753, 2.72533, 2.10653, 1.48583, 0.624074, 0.0140018, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0684115, 0.831108, 1.64862, 2.11006, 2.23473, 2.5439, 2.41996, 2.1085, 1.3753, 0.551184, 0.0141597, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0621342, 0.801802, 1.66097, 2.2744, 2.58507, 2.64147, 2.53203, 2.11848, 1.42249, 0.600005, 0.00850035, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0155231, 0.581836, 0.296733, 0.422429, 0.374893, 0.551731, 0.39159, 0.319519, 0.185209, 0.0673856, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.046428, 0.266905, 0.385405, 0.835716, 1.0922, 0.852107, 0.495775, 1.37224, 0.59438, 0.136954, 0.0061824, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0249813, 0.69457, 1.55655, 2.11814, 2.63947, 2.31963, 2.51107, 2.05978, 1.4301, 0.580408, 0.016515, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0273457, 0.710806, 1.58519, 2.29257, 2.6472, 2.72238, 2.59093, 2.1224, 1.29532, 0.587764, 0.00953753, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0460713, 0.376908, 1.00585, 1.95558, 1.64906, 2.57991, 2.60255, 2.28628, 1.63853, 0.765819, 0.0403367, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.00573357, 0.652154, 1.14825, 1.76315, 2.42017, 2.11687, 1.3265, 1.52103, 1.42792, 0.669677, 0.0299071, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0651526, 0.831276, 1.56939, 2.111, 2.42803, 2.39902, 2.57196, 2.28312, 1.63737, 0.744143, 0.0352433, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0193468, 0.415173, 1.3696, 1.41055, 1.83304, 2.2093, 1.35684, 1.81534, 1.26016, 0.453816, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0834133, 0.308908, 0.683149, 0.688705, 0.62197, 0.67926, 0.485344, 0.312526, 0.0695502, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0997101, 0.24553, 0.292913, 1.14435, 0.989543, 0.57329, 1.26016, 0.542071, 0.171351, 0.00539238, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0481958, 0.778754, 1.47179, 2.1002, 2.02692, 2.59275, 2.56468, 2.08318, 1.48502, 0.770173, 0.0568852, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.016664, 0.620281, 1.46676, 1.97059, 2.4413, 2.57254, 2.42621, 2.0301, 1.42493, 0.617651, 0.025802, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683 ] );
data.SetArray( 'degradation', [ 0.5 ] );
data.SetNumber( 'system_use_lifetime_output', 0 );
module = SSC.Module('cashloan');
if (module.Exec(data))
lcoe = data.GetNumber('lcoe_nom');
npv = data.GetNumber('npv');
names{end+1} = sprintf('Levelized COE (nominal) : %g cents/kWh', lcoe);
names{end+1} = sprintf('Net present value : $%g', npv);
names{end+1} = 'CashLoan example OK';
else
idx = 0;
[result, msg, type, time] = module.Log(idx);
while (result)
names{end+1} = sprintf('[%s at time:%g ]: %s', type, time, msg);
idx = idx + 1;
[result, msg, type, time] = module.Log(idx);
end
names{end+1} = 'CashLoan example failed';
end
set(handles.txtData,'String',names);
|
github
|
sergiocastellanos/switch_mexico_data-master
|
ssccall.m
|
.m
|
switch_mexico_data-master/SAM/sam-sdk-2016-3-14-r3/languages/matlab/+SSC/ssccall.m
| 8,963 |
utf_8
|
61a4af2373d48538fcfc4a2dd42eda7c
|
function [result] = ssccall(action, arg0, arg1, arg2 )
% SAM Simulation Core (SSC) MATLAB API
% Copyright (c) 2012 National Renewable Energy Laboratory
% author: Aron P. Dobos and Steven H. Janzou
% automatically detect architecture to load proper dll.
[pathstr, fn, fext] = fileparts(mfilename('fullpath'));
if ( strcmp(computer(), 'PCWIN') ) % Windows 32-bit
ssclibpath = '../../../win32/';
ssclib = 'ssc';
elseif ( strcmp(computer(), 'PCWIN64') ) % Windows 64-bit
ssclibpath = '../../../win64/';
ssclib = 'ssc';
elseif ( strcmp(computer(), 'MACI64') ) % Mac Intel 64-bit
ssclibpath = '../../../osx64/';
ssclib = 'ssc';
elseif ( strcmp(computer(), 'GLNXA64') ) % Linux 64-bit
ssclibpath = '../../../linux64/';
ssclib = 'ssc';
end
% load proper ssc library for all functions
if ~libisloaded(ssclib)
oldFolder = cd(pathstr);
loadlibrary(strcat(ssclibpath,ssclib),strcat(ssclibpath,'../sscapi.h'));
cd(oldFolder);
end
if strcmp(action,'load')
if ~libisloaded(ssclib)
oldFolder = cd(pathstr);
loadlibrary(strcat(ssclibpath,ssclib),strcat(ssclibpath,'../sscapi.h'));
cd(oldFolder);
end
elseif strcmp(action,'unload')
if libisloaded(ssclib)
unloadlibrary(ssclib)
end
elseif strcmp(action,'version')
result = calllib(ssclib,'ssc_version');
elseif strcmp(action,'build_info')
result = calllib(ssclib, 'ssc_build_info');
elseif strcmp(action,'data_create')
result = calllib(ssclib, 'ssc_data_create');
if ( isnullpointer(result) )
result = 0;
end
elseif strcmp(action,'data_free')
result = calllib(ssclib, 'ssc_data_free', arg0);
elseif strcmp(action,'data_unassign')
result = calllib(ssclib, 'ssc_data_unassign', arg0, arg1);
elseif strcmp(action,'data_query')
result = calllib(ssclib, 'ssc_data_query', arg0, arg1 );
elseif strcmp(action,'data_first')
result = calllib(ssclib, 'ssc_data_first', arg0 );
elseif strcmp(action,'data_next')
result = calllib(ssclib, 'ssc_data_next', arg0 );
elseif strcmp(action,'data_set_string')
result = calllib(ssclib, 'ssc_data_set_string', arg0, arg1, arg2 );
elseif strcmp(action,'data_set_number')
result = calllib(ssclib, 'ssc_data_set_number', arg0, arg1, single(arg2) );
elseif strcmp(action,'data_set_array')
len = length(arg2);
arr = libpointer( 'singlePtr', arg2 );
result = calllib(ssclib,'ssc_data_set_array',arg0,arg1,arr,len);
elseif strcmp(action,'data_set_matrix')
[nr nc] = size(arg2);
mat = zeros(nr*nc, 1);
ii = 1;
for r=1:nr,
for c=1:nc,
mat(ii) = arg2(r,c);
ii=ii+1;
end
end
arr = libpointer( 'singlePtr', mat );
result = calllib(ssclib,'ssc_data_set_matrix',arg0,arg1,arr,nr,nc);
elseif strcmp(action,'data_set_table')
result = calllib(ssclib,'ssc_data_set_table',arg0,arg1,arg2);
elseif strcmp(action,'data_get_string')
result = calllib(ssclib,'ssc_data_get_string',arg0,arg1);
elseif strcmp(action,'data_get_number')
p = libpointer('singlePtr',0);
calllib(ssclib,'ssc_data_get_number', arg0,arg1,p);
result = get(p,'Value');
elseif strcmp(action,'data_get_array')
p_count = libpointer('int32Ptr',0);
[xobj] = calllib(ssclib,'ssc_data_get_array',arg0,arg1,p_count);
setdatatype(xobj,'int32Ptr',p_count.Value,1);
len = p_count.Value;
result = zeros( len, 1 );
for i=1:len,
pidx = xobj+(i-1);
setdatatype(pidx,'singlePtr',1,1);
result(i) = pidx.Value;
end
elseif strcmp(action,'data_get_matrix')
p_rows = libpointer('int32Ptr',0);
p_cols = libpointer('int32Ptr',0);
[xobj] = calllib(ssclib,'ssc_data_get_matrix',arg0,arg1,p_rows,p_cols);
setdatatype(xobj,'int32Ptr',p_rows.Value*p_cols.Value,1);
nrows = p_rows.Value;
ncols = p_cols.Value;
if ( nrows*ncols > 0 )
result = zeros( nrows, ncols );
ii=1;
for r=1:nrows,
for c=1:ncols,
pidx = xobj+(ii-1);
setdatatype(pidx,'singlePtr',1,1);
result(r,c) = pidx.Value;
ii=ii+1;
end
end
end
elseif strcmp(action,'data_get_table')
result = calllib(ssclib,'ssc_data_get_table',arg0,arg1);
elseif strcmp(action,'module_entry')
result = calllib(ssclib,'ssc_module_entry',arg0);
if isnullpointer( result ),
result = 0;
end
elseif strcmp(action,'entry_name')
result = calllib(ssclib,'ssc_entry_name',arg0);
elseif strcmp(action,'entry_description')
result = calllib(ssclib,'ssc_entry_description',arg0);
elseif strcmp(action,'entry_version')
result = calllib(ssclib,'ssc_entry_version',arg0);
elseif strcmp(action,'module_var_info')
result = calllib(ssclib,'ssc_module_var_info',arg0,arg1);
if isnullpointer( result ),
result = 0;
end
elseif strcmp(action,'info_var_type')
ty = calllib(ssclib,'ssc_info_var_type',arg0);
if (ty == 1)
result = 'input';
elseif ( ty==2 )
result = 'output';
else
result = 'inout';
end
elseif strcmp(action,'info_data_type')
dt = calllib(ssclib,'ssc_info_data_type',arg0);
if (dt == 1)
result = 'string';
elseif (dt == 2)
result = 'number';
elseif (dt == 3)
result = 'array';
elseif (dt == 4)
result = 'matrix';
elseif (dt == 5)
result = 'table';
else
result = 'invalid';
end
elseif strcmp(action,'info_name')
result = calllib(ssclib,'ssc_info_name',arg0);
elseif strcmp(action,'info_label')
result = calllib(ssclib,'ssc_info_label',arg0);
elseif strcmp(action,'info_units')
result = calllib(ssclib,'ssc_info_units',arg0);
elseif strcmp(action,'info_meta')
result = calllib(ssclib,'ssc_info_meta',arg0);
elseif strcmp(action,'info_group')
result = calllib(ssclib,'ssc_info_group',arg0);
elseif strcmp(action,'info_required')
result = calllib(ssclib,'ssc_info_required',arg0);
elseif strcmp(action,'info_constraints')
result = calllib(ssclib,'ssc_info_constraints',arg0);
elseif strcmp(action,'info_uihint')
result = calllib(ssclib,'ssc_info_uihint',arg0);
elseif strcmp(action,'exec_simple')
result = calllib(ssclib,'ssc_module_exec_simple',arg0,arg1);
elseif strcmp(action,'exec_simple_nothread')
result = calllib(ssclib,'ssc_module_exec_simple_nothread',arg0,arg1);
elseif strcmp(action,'module_create')
result = calllib(ssclib,'ssc_module_create',arg0);
if ( isnullpointer(result) )
result = 0;
end
elseif strcmp(action,'module_free')
result = calllib(ssclib,'ssc_module_free',arg0);
elseif strcmp(action,'module_exec')
result = calllib(ssclib,'ssc_module_exec',arg0,arg1);
elseif strcmp(action,'module_log')
p_type = libpointer('int32Ptr',1);
p_time = libpointer('singlePtr',1);
result = calllib(ssclib,'ssc_module_log', arg0, arg1, p_type, p_time);
elseif strcmp(action,'module_log_detailed')
p_type = libpointer('int32Ptr',1);
p_time = libpointer('singlePtr',1);
text = calllib(ssclib,'ssc_module_log', arg0, arg1, p_type, p_time);
typetext = 'notice';
if (p_type.Value == 2)
typetext = 'warning';
elseif (p_type.Value == 3)
typetext = 'error';
end
if ( strcmp(text,'') )
result = 0;
else
result = {text , typetext , p_time.Value};
end
else
disp( sprintf('ssccall: invalid action %s', action) );
result = 0;
end
end
function bb = isnullpointer(p)
bb = false;
try
setdatatype(p, 'voidPtr', 1, 1);
deref = get(p);
catch
e = lasterror();
if strcmp(e.identifier, 'MATLAB:libpointer:ValueNotDefined')
bb = true;
end
end
end
|
github
|
sergiocastellanos/switch_mexico_data-master
|
UIExample.m
|
.m
|
switch_mexico_data-master/SAM/SDK/languages/matlab/UIExample.m
| 586,602 |
utf_8
|
0aa9c0ca12306d99f27e822c55971cd2
|
function varargout = UIExample(varargin)
% UIEXAMPLE MATLAB code for UIExample.fig
% UIEXAMPLE, by itself, creates a new UIEXAMPLE or raises the existing
% singleton*.
%
% H = UIEXAMPLE returns the handle to a new UIEXAMPLE or the handle to
% the existing singleton*.
%
% UIEXAMPLE('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in UIEXAMPLE.M with the given input arguments.
%
% UIEXAMPLE('Property','Value',...) creates a new UIEXAMPLE or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before UIExample_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to UIExample_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help UIExample
% Last Modified by GUIDE v2.5 01-Dec-2014 04:23:19
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @UIExample_OpeningFcn, ...
'gui_OutputFcn', @UIExample_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before UIExample is made visible.
function UIExample_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to UIExample (see VARARGIN)
% Choose default command line output for UIExample
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes UIExample wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = UIExample_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
function txtData_Callback(hObject, eventdata, handles)
% hObject handle to txtData (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of txtData as text
% str2double(get(hObject,'String')) returns contents of txtData as a double
% --- Executes during object creation, after setting all properties.
function txtData_CreateFcn(hObject, eventdata, handles)
% hObject handle to txtData (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in btnVersion.
function btnVersion_Callback(hObject, eventdata, handles)
% hObject handle to btnVersion (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
sscAPI = SSC.API();
set(handles.txtData,'String',{sprintf('Version = %d',sscAPI.Version);sscAPI.BuildInfo});
% --- Executes on button press in btnModuleList.
function btnModuleList_Callback(hObject, eventdata, handles)
% hObject handle to btnModuleList (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
sscEntry = SSC.Entry();
names = {};
while (sscEntry.Get())
module_name = sscEntry.Name();
description = sscEntry.Description();
version = sscEntry.Version();
names{end+1} = sprintf('Module: %s, version: %d', module_name, version );
names{end+1} = description ;
end
set(handles.txtData,'String',names);
% --- Executes on button press in btnModuleAndVariables.
function btnModuleAndVariables_Callback(hObject, eventdata, handles)
% hObject handle to btnModuleAndVariables (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
sscEntry = SSC.Entry();
names = {};
while (sscEntry.Get())
moduleName = sscEntry.Name();
description = sscEntry.Description();
version = sscEntry.Version();
names{end+1} = sprintf('Module: %s, version: %d', moduleName, version );
names{end+1} = description ;
sscModule = SSC.Module(moduleName);
sscInfo = SSC.Info(sscModule);
while (sscInfo.Get())
names{end+1} = sprintf('\t%s: "%s" ["%s"] %s (%s)',sscInfo.VariableType(), sscInfo.Name(), sscInfo.DataType(), sscInfo.Label(), sscInfo.Units());
end
end
set(handles.txtData,'String',names);
% --- Executes on button press in btnTestArrays.
function btnTestArrays_Callback(hObject, eventdata, handles)
% hObject handle to btnTestArrays (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
names = {};
sscData = SSC.Data();
arr = [];
for i = 1:10
arr(i) = i / 10.0;
end
sscData.SetArray('TestArray', arr);
retArray = sscData.GetArray('TestArray');
names{end+1} = 'Testing SetArray and GetArray';
for i = 1:10
names{end+1} = sprintf('\treturned array element: %d = %g',i, retArray(i));
end
set(handles.txtData,'String',names);
% --- Executes on button press in btnTestMatrices.
function btnTestMatrices_Callback(hObject, eventdata, handles)
% hObject handle to btnTestMatrices (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
names = {};
sscData = SSC.Data();
matrix = [ 1 2 ; 3 4 ; 5 6 ; 7 8; 9 10];
sscData.SetMatrix('TestMatrix', matrix);
retMatrix = sscData.GetMatrix('TestMatrix');
[nrows ncols] = size(retMatrix);
names{end+1} = sprintf('Testing SetMatrix and GetMatrix size %d x %d', nrows,ncols);
for i = 1: nrows
for j = 1: ncols
names{end+1} = sprintf('\treturned matrix element: (%d,%d) = %g', i,j, retMatrix(i,j));
end
end
set(handles.txtData,'String',names);
% --- Executes on button press in btnPVWatts.
function btnPVWatts_Callback(hObject, eventdata, handles)
% hObject handle to btnPVWatts (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
names = {};
sscData = SSC.Data();
sscData.SetString('solar_resource_file', '../../examples/abilene.tm2');
sscData.SetNumber('system_capacity', 4.0);
sscData.SetNumber('dc_ac_ratio', 1.1);
sscData.SetNumber('tilt', 20);
sscData.SetNumber('azimuth', 180);
sscData.SetNumber('inv_eff', 96 );
sscData.SetNumber('losses', 14.0757 );
sscData.SetNumber('array_type', 0 );
sscData.SetNumber('gcr', 0.4 );
sscData.SetNumber('adjust:factor', 1 );
mod = SSC.Module('pvwattsv5');
if (mod.Exec(sscData)),
tot = sscData.GetNumber('ac_annual');
ac = sscData.GetArray('ac_monthly');
for i = 1:size(ac)
names{end+1} = sprintf('[%d]: %g kWh', i,ac(i));
end
names{end+1} = sprintf('AC total: %g kWh', tot);
names{end+1} = 'PVWatts test OK';
else
idx = 0;
[result, msg, type, time] = mod.Log(idx);
while (result)
names{end+1} = sprintf('[%s at time:%g ]: %s', type, time, msg);
idx = idx + 1;
[result, msg, type, time] = mod.Log(idx);
end
names{end+1} = 'PVWatts example failed';
end
set(handles.txtData,'String',names);
% --- Executes on button press in bntPVWattsFunc.
function bntPVWattsFunc_Callback(hObject, eventdata, handles)
% hObject handle to bntPVWattsFunc (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
names = {};
sscData = SSC.Data();
sscModule = SSC.Module('pvwattsv1_1ts');
sscData.SetNumber('year', 1970); % general year (tiny effect in sun position)
sscData.SetNumber('month', 1); % 1-12
sscData.SetNumber('day', 1); %1-number of days in month
sscData.SetNumber('hour', 9); % 0-23
sscData.SetNumber('minute', 30); % minute of the hour (typically 30 min for midpoint calculation)
sscData.SetNumber('lat', 33.4); % latitude, degrees
sscData.SetNumber('lon', -112); % longitude, degrees
sscData.SetNumber('tz', -7); % timezone from gmt, hours
sscData.SetNumber('time_step', 1); % time step, hours
% solar and weather data
sscData.SetNumber('beam', 824); % beam (DNI) irradiance, W/m2
sscData.SetNumber('diffuse', 29); % diffuse (DHI) horizontal irradiance, W/m2
sscData.SetNumber('tamb', 9.4); % ambient temp, degree C
sscData.SetNumber('wspd', 2.1); % wind speed, m/s
sscData.SetNumber('snow', 0); % snow depth, cm (0 is default - when there is snow, ground reflectance is increased. assumes panels have been cleaned off)
% system specifications
sscData.SetNumber('system_size', 4); % system DC nameplate rating (kW)
sscData.SetNumber('derate', 0.77); % derate factor
sscData.SetNumber('track_mode', 0); % tracking mode 0=fixed, 1=1axis, 2=2axis
sscData.SetNumber('azimuth', 180); % azimuth angle 0=north, 90=east, 180=south, 270=west
sscData.SetNumber('tilt', 20); % tilt angle from horizontal 0=flat, 90=vertical
% previous timestep values of cell temperature and POA
sscData.SetNumber('tcell', 6.94); % calculated cell temperature from previous timestep, degree C, (can default to ambient for morning or if you don't know)
sscData.SetNumber('poa', 84.5); % plane of array irradiance (W/m2) from previous time step
if (sscModule.Exec(sscData))
poa = sscData.GetNumber('poa');
tcell = sscData.GetNumber('tcell');
dc = sscData.GetNumber('dc');
ac = sscData.GetNumber('ac');
names{end+1} = sprintf('poa: %g W/m2', poa);
names{end+1} = sprintf('tcell: %g C', tcell);
names{end+1} = sprintf('dc: %g W', dc);
names{end+1} = sprintf('ac: %g W', ac);
end
set(handles.txtData,'String',names);
% --- Executes on button press in btnPVSamV1.
function btnPVSamV1_Callback(hObject, eventdata, handles)
% hObject handle to btnPVSamV1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
names={};
data = SSC.Data();
% pvsamv1 compute module call from 2014.11.24 "Photovoltaic, Residential" configuration
data.SetNumber( 'system_capacity', 3.8745 );
data.SetString( 'solar_resource_file', '../../examples/USA AZ Phoenix (TMY2).csv' );
data.SetNumber( 'use_wf_albedo', 0 );
data.SetArray( 'albedo', [ 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2 ] );
data.SetNumber( 'irrad_mode', 0 );
data.SetNumber( 'sky_model', 2 );
data.SetNumber( 'ac_loss', 1 );
data.SetNumber( 'modules_per_string', 9 );
data.SetNumber( 'strings_in_parallel', 2 );
data.SetNumber( 'inverter_count', 1 );
data.SetNumber( 'enable_mismatch_vmax_calc', 0 );
data.SetNumber( 'subarray1_tilt', 20 );
data.SetNumber( 'subarray1_tilt_eq_lat', 0 );
data.SetNumber( 'subarray1_azimuth', 180 );
data.SetNumber( 'subarray1_track_mode', 0 );
data.SetNumber( 'subarray1_rotlim', 45 );
data.SetNumber( 'subarray1_shade_mode', 1 );
data.SetNumber( 'subarray1_gcr', 0.3 );
data.SetArray( 'subarray1_soiling', [ 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5 ] );
data.SetNumber( 'subarray1_dcloss', 4.4402 );
data.SetNumber( 'subarray1_mismatch_loss', 2 );
data.SetNumber( 'subarray1_diodeconn_loss', 0.5 );
data.SetNumber( 'subarray1_dcwiring_loss', 2 );
data.SetNumber( 'subarray1_tracking_loss', 0 );
data.SetNumber( 'subarray1_nameplate_loss', 0 );
data.SetNumber( 'subarray2_mismatch_loss', 2 );
data.SetNumber( 'subarray2_diodeconn_loss', 0.5 );
data.SetNumber( 'subarray2_dcwiring_loss', 2 );
data.SetNumber( 'subarray2_tracking_loss', 0 );
data.SetNumber( 'subarray2_nameplate_loss', 0 );
data.SetNumber( 'subarray3_mismatch_loss', 2 );
data.SetNumber( 'subarray3_diodeconn_loss', 0.5 );
data.SetNumber( 'subarray3_dcwiring_loss', 2 );
data.SetNumber( 'subarray3_tracking_loss', 0 );
data.SetNumber( 'subarray3_nameplate_loss', 0 );
data.SetNumber( 'subarray4_mismatch_loss', 2 );
data.SetNumber( 'subarray4_diodeconn_loss', 0.5 );
data.SetNumber( 'subarray4_dcwiring_loss', 2 );
data.SetNumber( 'subarray4_tracking_loss', 0 );
data.SetNumber( 'subarray4_nameplate_loss', 0 );
data.SetNumber( 'acwiring_loss', 1 );
data.SetNumber( 'transformer_loss', 0 );
data.SetNumber( 'subarray1_mod_orient', 0 );
data.SetNumber( 'subarray1_nmodx', 9 );
data.SetNumber( 'subarray1_nmody', 2 );
data.SetNumber( 'subarray1_backtrack', 0 );
data.SetNumber( 'subarray2_enable', 0 );
data.SetNumber( 'subarray2_nstrings', 0 );
data.SetNumber( 'subarray2_tilt', 20 );
data.SetNumber( 'subarray2_tilt_eq_lat', 0 );
data.SetNumber( 'subarray2_azimuth', 180 );
data.SetNumber( 'subarray2_track_mode', 0 );
data.SetNumber( 'subarray2_rotlim', 45 );
data.SetNumber( 'subarray2_shade_mode', 1 );
data.SetNumber( 'subarray2_gcr', 0.3 );
data.SetArray( 'subarray2_soiling', [ 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5 ] );
data.SetNumber( 'subarray2_dcloss', 4.4402 );
data.SetNumber( 'subarray2_mod_orient', 0 );
data.SetNumber( 'subarray2_nmodx', 9 );
data.SetNumber( 'subarray2_nmody', 2 );
data.SetNumber( 'subarray2_backtrack', 0 );
data.SetNumber( 'subarray3_enable', 0 );
data.SetNumber( 'subarray3_nstrings', 0 );
data.SetNumber( 'subarray3_tilt', 20 );
data.SetNumber( 'subarray3_tilt_eq_lat', 0 );
data.SetNumber( 'subarray3_azimuth', 180 );
data.SetNumber( 'subarray3_track_mode', 0 );
data.SetNumber( 'subarray3_rotlim', 45 );
data.SetNumber( 'subarray3_shade_mode', 1 );
data.SetNumber( 'subarray3_gcr', 0.3 );
data.SetArray( 'subarray3_soiling', [ 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5 ] );
data.SetNumber( 'subarray3_dcloss', 4.4402 );
data.SetNumber( 'subarray3_mod_orient', 0 );
data.SetNumber( 'subarray3_nmodx', 9 );
data.SetNumber( 'subarray3_nmody', 2 );
data.SetNumber( 'subarray3_backtrack', 0 );
data.SetNumber( 'subarray4_enable', 0 );
data.SetNumber( 'subarray4_nstrings', 0 );
data.SetNumber( 'subarray4_tilt', 20 );
data.SetNumber( 'subarray4_tilt_eq_lat', 0 );
data.SetNumber( 'subarray4_azimuth', 180 );
data.SetNumber( 'subarray4_track_mode', 0 );
data.SetNumber( 'subarray4_rotlim', 45 );
data.SetNumber( 'subarray4_shade_mode', 1 );
data.SetNumber( 'subarray4_gcr', 0.3 );
data.SetArray( 'subarray4_soiling', [ 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5 ] );
data.SetNumber( 'subarray4_dcloss', 4.4402 );
data.SetNumber( 'subarray4_mod_orient', 0 );
data.SetNumber( 'subarray4_nmodx', 9 );
data.SetNumber( 'subarray4_nmody', 2 );
data.SetNumber( 'subarray4_backtrack', 0 );
data.SetNumber( 'module_model', 1 );
data.SetNumber( 'spe_area', 0.74074 );
data.SetNumber( 'spe_rad0', 200 );
data.SetNumber( 'spe_rad1', 400 );
data.SetNumber( 'spe_rad2', 600 );
data.SetNumber( 'spe_rad3', 800 );
data.SetNumber( 'spe_rad4', 1000 );
data.SetNumber( 'spe_eff0', 13.5 );
data.SetNumber( 'spe_eff1', 13.5 );
data.SetNumber( 'spe_eff2', 13.5 );
data.SetNumber( 'spe_eff3', 13.5 );
data.SetNumber( 'spe_eff4', 13.5 );
data.SetNumber( 'spe_reference', 4 );
data.SetNumber( 'spe_module_structure', 0 );
data.SetNumber( 'spe_a', -3.56 );
data.SetNumber( 'spe_b', -0.075 );
data.SetNumber( 'spe_dT', 3 );
data.SetNumber( 'spe_temp_coeff', -0.5 );
data.SetNumber( 'spe_fd', 1 );
data.SetNumber( 'cec_area', 1.244 );
data.SetNumber( 'cec_a_ref', 1.9816 );
data.SetNumber( 'cec_adjust', 20.8 );
data.SetNumber( 'cec_alpha_sc', 0.002651 );
data.SetNumber( 'cec_beta_oc', -0.14234 );
data.SetNumber( 'cec_gamma_r', -0.407 );
data.SetNumber( 'cec_i_l_ref', 5.754 );
data.SetNumber( 'cec_i_mp_ref', 5.25 );
data.SetNumber( 'cec_i_o_ref', 1.919e-010 );
data.SetNumber( 'cec_i_sc_ref', 5.75 );
data.SetNumber( 'cec_n_s', 72 );
data.SetNumber( 'cec_r_s', 0.105 );
data.SetNumber( 'cec_r_sh_ref', 160.48 );
data.SetNumber( 'cec_t_noct', 49.2 );
data.SetNumber( 'cec_v_mp_ref', 41 );
data.SetNumber( 'cec_v_oc_ref', 47.7 );
data.SetNumber( 'cec_temp_corr_mode', 0 );
data.SetNumber( 'cec_standoff', 6 );
data.SetNumber( 'cec_height', 0 );
data.SetNumber( 'cec_mounting_config', 0 );
data.SetNumber( 'cec_heat_transfer', 0 );
data.SetNumber( 'cec_mounting_orientation', 0 );
data.SetNumber( 'cec_gap_spacing', 0.05 );
data.SetNumber( 'cec_module_width', 1 );
data.SetNumber( 'cec_module_length', 1.244 );
data.SetNumber( 'cec_array_rows', 1 );
data.SetNumber( 'cec_array_cols', 10 );
data.SetNumber( 'cec_backside_temp', 20 );
data.SetNumber( '6par_celltech', 1 );
data.SetNumber( '6par_vmp', 30 );
data.SetNumber( '6par_imp', 6 );
data.SetNumber( '6par_voc', 37 );
data.SetNumber( '6par_isc', 7 );
data.SetNumber( '6par_bvoc', -0.11 );
data.SetNumber( '6par_aisc', 0.004 );
data.SetNumber( '6par_gpmp', -0.41 );
data.SetNumber( '6par_nser', 60 );
data.SetNumber( '6par_area', 1.3 );
data.SetNumber( '6par_tnoct', 46 );
data.SetNumber( '6par_standoff', 6 );
data.SetNumber( '6par_mounting', 0 );
data.SetNumber( 'snl_module_structure', 0 );
data.SetNumber( 'snl_a', -3.62 );
data.SetNumber( 'snl_b', -0.075 );
data.SetNumber( 'snl_dtc', 3 );
data.SetNumber( 'snl_ref_a', -3.62 );
data.SetNumber( 'snl_ref_b', -0.075 );
data.SetNumber( 'snl_ref_dT', 3 );
data.SetNumber( 'snl_fd', 1 );
data.SetNumber( 'snl_a0', 0.94045 );
data.SetNumber( 'snl_a1', 0.052641 );
data.SetNumber( 'snl_a2', -0.0093897 );
data.SetNumber( 'snl_a3', 0.00072623 );
data.SetNumber( 'snl_a4', -1.9938e-005 );
data.SetNumber( 'snl_aimp', -0.00038 );
data.SetNumber( 'snl_aisc', 0.00061 );
data.SetNumber( 'snl_area', 1.244 );
data.SetNumber( 'snl_b0', 1 );
data.SetNumber( 'snl_b1', -0.002438 );
data.SetNumber( 'snl_b2', 0.0003103 );
data.SetNumber( 'snl_b3', -1.246e-005 );
data.SetNumber( 'snl_b4', 2.11e-007 );
data.SetNumber( 'snl_b5', -1.36e-009 );
data.SetNumber( 'snl_bvmpo', -0.139 );
data.SetNumber( 'snl_bvoco', -0.136 );
data.SetNumber( 'snl_c0', 1.0039 );
data.SetNumber( 'snl_c1', -0.0039 );
data.SetNumber( 'snl_c2', 0.291066 );
data.SetNumber( 'snl_c3', -4.73546 );
data.SetNumber( 'snl_c4', 0.9942 );
data.SetNumber( 'snl_c5', 0.0058 );
data.SetNumber( 'snl_c6', 1.0723 );
data.SetNumber( 'snl_c7', -0.0723 );
data.SetNumber( 'snl_impo', 5.25 );
data.SetNumber( 'snl_isco', 5.75 );
data.SetNumber( 'snl_ixo', 5.65 );
data.SetNumber( 'snl_ixxo', 3.85 );
data.SetNumber( 'snl_mbvmp', 0 );
data.SetNumber( 'snl_mbvoc', 0 );
data.SetNumber( 'snl_n', 1.221 );
data.SetNumber( 'snl_series_cells', 72 );
data.SetNumber( 'snl_vmpo', 40 );
data.SetNumber( 'snl_voco', 47.7 );
data.SetNumber( 'inverter_model', 0 );
data.SetNumber( 'inv_snl_c0', -6.57929e-006 );
data.SetNumber( 'inv_snl_c1', 4.72925e-005 );
data.SetNumber( 'inv_snl_c2', 0.00202195 );
data.SetNumber( 'inv_snl_c3', 0.000285321 );
data.SetNumber( 'inv_snl_paco', 4000 );
data.SetNumber( 'inv_snl_pdco', 4186 );
data.SetNumber( 'inv_snl_pnt', 0.17 );
data.SetNumber( 'inv_snl_pso', 19.7391 );
data.SetNumber( 'inv_snl_vdco', 310.67 );
data.SetNumber( 'inv_snl_vdcmax', 600 );
data.SetNumber( 'inv_ds_paco', 4000 );
data.SetNumber( 'inv_ds_eff', 96 );
data.SetNumber( 'inv_ds_pnt', 1 );
data.SetNumber( 'inv_ds_pso', 0 );
data.SetNumber( 'inv_ds_vdco', 310 );
data.SetNumber( 'inv_ds_vdcmax', 600 );
data.SetNumber( 'inv_pd_paco', 4000 );
data.SetNumber( 'inv_pd_pdco', 4210.53 );
data.SetArray( 'inv_pd_partload', [ 0, 0.404, 0.808, 1.212, 1.616, 2.02, 2.424, 2.828, 3.232, 3.636, 4.04, 4.444, 4.848, 5.252, 5.656, 6.06, 6.464, 6.868, 7.272, 7.676, 8.08, 8.484, 8.888, 9.292, 9.696, 10.1, 10.504, 10.908, 11.312, 11.716, 12.12, 12.524, 12.928, 13.332, 13.736, 14.14, 14.544, 14.948, 15.352, 15.756, 16.16, 16.564, 16.968, 17.372, 17.776, 18.18, 18.584, 18.988, 19.392, 19.796, 20.2, 20.604, 21.008, 21.412, 21.816, 22.22, 22.624, 23.028, 23.432, 23.836, 24.24, 24.644, 25.048, 25.452, 25.856, 26.26, 26.664, 27.068, 27.472, 27.876, 28.28, 28.684, 29.088, 29.492, 29.896, 30.3, 30.704, 31.108, 31.512, 31.916, 32.32, 32.724, 33.128, 33.532, 33.936, 34.34, 34.744, 35.148, 35.552, 35.956, 36.36, 36.764, 37.168, 37.572, 37.976, 38.38, 38.784, 39.188, 39.592, 39.996, 40.4, 40.804, 41.208, 41.612, 42.016, 42.42, 42.824, 43.228, 43.632, 44.036, 44.44, 44.844, 45.248, 45.652, 46.056, 46.46, 46.864, 47.268, 47.672, 48.076, 48.48, 48.884, 49.288, 49.692, 50.096, 50.5, 50.904, 51.308, 51.712, 52.116, 52.52, 52.924, 53.328, 53.732, 54.136, 54.54, 54.944, 55.348, 55.752, 56.156, 56.56, 56.964, 57.368, 57.772, 58.176, 58.58, 58.984, 59.388, 59.792, 60.196, 60.6, 61.004, 61.408, 61.812, 62.216, 62.62, 63.024, 63.428, 63.832, 64.236, 64.64, 65.044, 65.448, 65.852, 66.256, 66.66, 67.064, 67.468, 67.872, 68.276, 68.68, 69.084, 69.488, 69.892, 70.296, 70.7, 71.104, 71.508, 71.912, 72.316, 72.72, 73.124, 73.528, 73.932, 74.336, 74.74, 75.144, 75.548, 75.952, 76.356, 76.76, 77.164, 77.568, 77.972, 78.376, 78.78, 79.184, 79.588, 79.992, 80.396, 80.8, 81.204, 81.608, 82.012, 82.416, 82.82, 83.224, 83.628, 84.032, 84.436, 84.84, 85.244, 85.648, 86.052, 86.456, 86.86, 87.264, 87.668, 88.072, 88.476, 88.88, 89.284, 89.688, 90.092, 90.496, 90.9, 91.304, 91.708, 92.112, 92.516, 92.92, 93.324, 93.728, 94.132, 94.536, 94.94, 95.344, 95.748, 96.152, 96.556, 96.96, 97.364, 97.768, 98.172, 98.576, 98.98, 99.384, 99.788, 100.192, 100.596, 101 ] );
data.SetArray( 'inv_pd_efficiency', [ 0, 0, 34.42, 55.2, 65.59, 71.82, 75.97, 78.94, 81.17, 82.9, 84.28, 85.42, 86.36, 87.16, 87.84, 88.44, 88.95, 89.41, 89.82, 90.18, 90.51, 90.81, 91.08, 91.32, 91.55, 91.75, 91.95, 92.12, 92.29, 92.44, 92.58, 92.72, 92.84, 92.96, 93.07, 93.17, 93.27, 93.37, 93.45, 93.54, 93.62, 93.69, 93.76, 93.83, 93.9, 93.96, 94.02, 94.08, 94.13, 94.18, 94.23, 94.28, 94.33, 94.37, 94.42, 94.46, 94.5, 94.54, 94.57, 94.61, 94.64, 94.68, 94.71, 94.74, 94.77, 94.8, 94.83, 94.86, 94.89, 94.91, 94.94, 94.96, 94.98, 95.01, 95.03, 95.05, 95.07, 95.09, 95.11, 95.13, 95.15, 95.17, 95.19, 95.21, 95.23, 95.24, 95.26, 95.28, 95.29, 95.31, 95.32, 95.34, 95.35, 95.36, 95.38, 95.39, 95.4, 95.42, 95.43, 95.44, 95.45, 95.47, 95.48, 95.49, 95.5, 95.51, 95.52, 95.53, 95.54, 95.55, 95.56, 95.57, 95.58, 95.59, 95.6, 95.61, 95.62, 95.63, 95.64, 95.64, 95.65, 95.66, 95.67, 95.68, 95.68, 95.69, 95.7, 95.71, 95.71, 95.72, 95.73, 95.73, 95.74, 95.75, 95.75, 95.76, 95.77, 95.77, 95.78, 95.78, 95.79, 95.8, 95.8, 95.81, 95.81, 95.82, 95.82, 95.83, 95.83, 95.84, 95.84, 95.85, 95.85, 95.86, 95.86, 95.87, 95.87, 95.88, 95.88, 95.89, 95.89, 95.89, 95.9, 95.9, 95.91, 95.91, 95.91, 95.92, 95.92, 95.93, 95.93, 95.93, 95.94, 95.94, 95.94, 95.95, 95.95, 95.96, 95.96, 95.96, 95.97, 95.97, 95.97, 95.98, 95.98, 95.98, 95.98, 95.99, 95.99, 95.99, 96, 96, 96, 96.01, 96.01, 96.01, 96.01, 96.02, 96.02, 96.02, 96.02, 96.03, 96.03, 96.03, 96.03, 96.04, 96.04, 96.04, 96.04, 96.05, 96.05, 96.05, 96.05, 96.06, 96.06, 96.06, 96.06, 96.06, 96.07, 96.07, 96.07, 96.07, 96.07, 96.08, 96.08, 96.08, 96.08, 96.08, 96.09, 96.09, 96.09, 96.09, 96.09, 96.09, 96.1, 96.1, 96.1, 96.1, 96.1, 96.1, 96.11, 96.11, 96.11, 96.11, 96.11, 96.11, 96.12, 96.12, 96.12, 96.12, 96.12 ] );
data.SetNumber( 'inv_pd_pnt', 0 );
data.SetNumber( 'inv_pd_vdco', 310 );
data.SetNumber( 'inv_pd_vdcmax', 600 );
data.SetNumber( 'adjust:factor', 1 );
module = SSC.Module('pvsamv1');
if (module.Exec(data))
enet = data.GetNumber('annual_energy');
cf = data.GetNumber('capacity_factor');
kWhperkW = data.GetNumber('kwh_per_kw');
names{end+1} = sprintf('Annual energy : %g kWh', enet);
names{end+1} = sprintf('Capacity factor : %g %%', cf);
names{end+1} = sprintf('First year kWhAC/kWDC : %g ', kWhperkW);
names{end+1} = 'PVSamV1 test OK';
else
idx = 0;
[result, msg, type, time] = module.Log(idx);
while (result)
names{end+1} = sprintf('[%s at time:%g ]: %s', type, time, msg);
idx = idx + 1;
[result, msg, type, time] = module.Log(idx);
end
names{end+1} = 'pvsamv1 example failed';
end
set(handles.txtData,'String',names);
% --- Executes on button press in btnBelpe.
function btnBelpe_Callback(hObject, eventdata, handles)
% hObject handle to btnBelpe (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% belpe compute module call from 2014.11.24 "Photovoltaic, Residential" configuration
names={};
data = SSC.Data();
data.SetNumber( 'en_belpe', 0 );
data.SetArray( 'e_load', [ 0.408648, 0.357441, 0.342144, 0.337044, 0.327441, 0.359645, 0.443928, 0.549341, 0.482851, 0.386304, 0.378283, 0.377475, 0.374345, 0.381314, 0.395735, 0.42627, 0.532543, 0.715238, 0.878051, 0.872057, 0.836114, 0.774817, 0.646085, 0.531682, 0.408319, 0.357241, 0.342024, 0.336944, 0.32728, 0.359345, 0.443328, 0.548196, 0.480997, 0.384159, 0.376266, 0.375708, 0.372778, 0.37981, 0.394452, 0.425067, 0.531289, 0.713878, 0.87626, 0.869992, 0.834284, 0.773291, 0.645058, 0.531056, 0.408319, 0.357241, 0.342024, 0.336944, 0.32728, 0.359345, 0.443328, 0.548196, 0.480997, 0.384159, 0.376266, 0.375708, 0.372778, 0.37981, 0.394452, 0.425067, 0.531289, 0.713878, 0.87626, 0.869992, 0.834284, 0.773291, 0.645058, 0.531056, 0.408319, 0.357241, 0.342024, 0.336944, 0.32728, 0.359345, 0.443328, 0.548196, 0.480997, 0.384159, 0.376266, 0.375708, 0.372778, 0.37981, 0.394452, 0.425067, 0.531289, 0.713878, 0.87626, 0.869992, 0.834284, 0.773291, 0.645058, 0.531056, 0.408319, 0.357241, 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0.382957, 0.40006, 0.438811, 0.562122, 0.755715, 0.898462, 0.854154, 0.834903, 0.77473, 0.644096, 0.533066, 0.408129, 0.358652, 0.340414, 0.335314, 0.325711, 0.36051, 0.440728, 0.543027, 0.478959, 0.388294, 0.379927, 0.379118, 0.375988, 0.382957, 0.40006, 0.438811, 0.562122, 0.755715, 0.898462, 0.854154, 0.834903, 0.77473, 0.644096, 0.533066 ] );
data.SetString( 'solar_resource_file', '../../examples/USA AZ Phoenix (TMY2).csv' );
data.SetNumber( 'floor_area', 2000 );
data.SetNumber( 'Stories', 2 );
data.SetNumber( 'YrBuilt', 1980 );
data.SetNumber( 'Retrofits', 0 );
data.SetNumber( 'Occupants', 4 );
data.SetArray( 'Occ_Schedule', [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ] );
data.SetNumber( 'THeat', 68 );
data.SetNumber( 'TCool', 76 );
data.SetNumber( 'THeatSB', 68 );
data.SetNumber( 'TCoolSB', 76 );
data.SetArray( 'T_Sched', [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ] );
data.SetNumber( 'en_heat', 1 );
data.SetNumber( 'en_cool', 1 );
data.SetNumber( 'en_fridge', 1 );
data.SetNumber( 'en_range', 1 );
data.SetNumber( 'en_dish', 1 );
data.SetNumber( 'en_wash', 1 );
data.SetNumber( 'en_dry', 1 );
data.SetNumber( 'en_mels', 1 );
data.SetArray( 'Monthly_util', [ 725, 630, 665, 795, 1040, 1590, 1925, 1730, 1380, 1080, 635, 715 ] );
module = SSC.Module('belpe');
if (module.Exec(data))
pload = data.GetArray('p_load');
eload = data.GetArray('e_load');
for i = 1:size(eload)
names{end+1} = sprintf('[%d]: %g kWh', i,eload(i));
end
for i = 1:size(pload)
names{end+1} = sprintf('[%d]: %g kW', i,pload(i));
end
names{end+1} = 'belpe test OK';
else
idx = 0;
[result, msg, type, time] = module.Log(idx);
while (result)
names{end+1} = sprintf('[%s at time:%g ]: %s', type, time, msg);
idx = idx + 1;
[result, msg, type, time] = module.Log(idx);
end
names{end+1} = 'belpe example failed';
end
set(handles.txtData,'String',names);
% --- Executes on button press in btnUtilityRate3.
function btnUtilityRate3_Callback(hObject, eventdata, handles)
% hObject handle to btnUtilityRate3 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
%utilityrate3 compute module call from 2014.11.24 "Photovoltaic, Residential" configuration
names={};
data = SSC.Data();
data.SetNumber( 'analysis_period', 25 );
data.SetArray( 'hourly_energy', [ -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.237892, 1.55848, 2.07121, 2.54933, 2.77945, 2.42071, 1.25773, 0.593002, 0.235599, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0131131, 0.701084, 1.32711, 1.76305, 2.5502, 2.29087, 1.83336, 2.01766, 0.881524, 0.298536, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.00528433, 0.737449, 1.51351, 2.14093, 2.39439, 2.32012, 1.83616, 0.712188, 0.32981, 0.087085, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.163208, 0.438056, 0.343575, 1.18182, 1.85202, 1.85609, 0.842696, 0.641464, 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1.74739, 2.31801, 2.38827, 2.23093, 1.90488, 1.21141, 0.498004, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.00198372, 0.593078, 1.31965, 2.07685, 2.40343, 2.53277, 2.29068, 2.13211, 1.5245, 0.603361, 0.0113138, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0032201, 0.661057, 1.43, 2.03944, 2.29233, 2.56232, 2.47674, 2.22701, 1.32747, 0.482224, 0.0189405, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.14109, 0.281369, 0.61837, 1.17956, 1.35886, 1.24448, 0.674592, 0.60305, 0.306165, 0.0125432, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.00466503, 0.668332, 1.4492, 2.1348, 2.45217, 2.67319, 2.67974, 2.32841, 1.71872, 0.753559, 0.0235207, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.031651, 0.710846, 1.51631, 2.0761, 2.59069, 2.73589, 2.61101, 2.27566, 1.28793, 0.596088, 0.0825364, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0251314, 0.674564, 1.48159, 2.16343, 2.52639, 2.6672, 2.58934, 2.19609, 1.67186, 0.858018, 0.0881195, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.207666, 0.579903, 0.912926, 1.00532, 0.884244, 0.696492, 0.608002, 0.459878, 0.217412, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.188015, 0.714458, 1.06288, 1.94864, 2.57486, 2.31493, 1.9987, 1.61288, 0.662761, 0.0476338, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0803296, 0.341384, 0.735301, 0.726501, 0.711979, 1.14964, 1.19626, 0.328706, 0.0181347, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0305592, 0.156043, 0.341021, 0.474378, 0.914521, 0.580048, 0.433983, 0.686068, 0.103174, 0.0121079, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0060497, 0.703844, 1.48762, 2.17949, 2.59902, 2.78725, 2.62568, 2.43746, 1.82643, 0.823967, 0.0825833, -0.0001683, 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data.SetNumber( 'inflation_rate', 2.5 );
data.SetArray( 'degradation', [ 0.5 ] );
data.SetArray( 'load_escalation', [ 0 ] );
data.SetArray( 'rate_escalation', [ 0 ] );
data.SetNumber( 'ur_enable_net_metering', 1 );
data.SetNumber( 'ur_nm_yearend_sell_rate', 0.02789 );
data.SetNumber( 'ur_monthly_fixed_charge', 16.68 );
data.SetNumber( 'ur_flat_buy_rate', 0 );
data.SetNumber( 'ur_flat_sell_rate', 0 );
data.SetNumber( 'ur_monthly_min_charge', 0 );
data.SetNumber( 'ur_annual_min_charge', 0 );
data.SetNumber( 'ur_ec_enable', 1 );
data.SetMatrix( 'ur_ec_sched_weekday',[ 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 4 4 4 4 4 ; 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 4 4 4 4 4 ; 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 4 4 4 4 4 ; 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 4 4 4 4 4 ; 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 2 2 2 2 ; 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 2 2 2 2 ; 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 2 2 2 2 ; 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 2 2 2 2 ; 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 2 2 2 2 ; 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 2 2 2 2 ; 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 4 4 4 4 4 ; 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 4 4 4 4 4 ] );
data.SetMatrix( 'ur_ec_sched_weekend', [ 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 ; 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 ; 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 ; 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 ; 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ; 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ; 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ; 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ; 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ; 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ; 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 ; 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 ] );
data.SetNumber( 'ur_ec_p1_t1_br', 0.26687 );
data.SetNumber( 'ur_ec_p1_t1_sr', 0 );
data.SetNumber( 'ur_ec_p1_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p1_t2_br', 0 );
data.SetNumber( 'ur_ec_p1_t2_sr', 0 );
data.SetNumber( 'ur_ec_p1_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p1_t3_br', 0 );
data.SetNumber( 'ur_ec_p1_t3_sr', 0 );
data.SetNumber( 'ur_ec_p1_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p1_t4_br', 0 );
data.SetNumber( 'ur_ec_p1_t4_sr', 0 );
data.SetNumber( 'ur_ec_p1_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p1_t5_br', 0 );
data.SetNumber( 'ur_ec_p1_t5_sr', 0 );
data.SetNumber( 'ur_ec_p1_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p1_t6_br', 0 );
data.SetNumber( 'ur_ec_p1_t6_sr', 0 );
data.SetNumber( 'ur_ec_p1_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p2_t1_br', 0.08328 );
data.SetNumber( 'ur_ec_p2_t1_sr', 0 );
data.SetNumber( 'ur_ec_p2_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p2_t2_br', 0 );
data.SetNumber( 'ur_ec_p2_t2_sr', 0 );
data.SetNumber( 'ur_ec_p2_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p2_t3_br', 0 );
data.SetNumber( 'ur_ec_p2_t3_sr', 0 );
data.SetNumber( 'ur_ec_p2_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p2_t4_br', 0 );
data.SetNumber( 'ur_ec_p2_t4_sr', 0 );
data.SetNumber( 'ur_ec_p2_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p2_t5_br', 0 );
data.SetNumber( 'ur_ec_p2_t5_sr', 0 );
data.SetNumber( 'ur_ec_p2_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p2_t6_br', 0 );
data.SetNumber( 'ur_ec_p2_t6_sr', 0 );
data.SetNumber( 'ur_ec_p2_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p3_t1_br', 0.22057 );
data.SetNumber( 'ur_ec_p3_t1_sr', 0 );
data.SetNumber( 'ur_ec_p3_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p3_t2_br', 0 );
data.SetNumber( 'ur_ec_p3_t2_sr', 0 );
data.SetNumber( 'ur_ec_p3_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p3_t3_br', 0 );
data.SetNumber( 'ur_ec_p3_t3_sr', 0 );
data.SetNumber( 'ur_ec_p3_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p3_t4_br', 0 );
data.SetNumber( 'ur_ec_p3_t4_sr', 0 );
data.SetNumber( 'ur_ec_p3_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p3_t5_br', 0 );
data.SetNumber( 'ur_ec_p3_t5_sr', 0 );
data.SetNumber( 'ur_ec_p3_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p3_t6_br', 0 );
data.SetNumber( 'ur_ec_p3_t6_sr', 0 );
data.SetNumber( 'ur_ec_p3_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p4_t1_br', 0.08326 );
data.SetNumber( 'ur_ec_p4_t1_sr', 0 );
data.SetNumber( 'ur_ec_p4_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p4_t2_br', 0 );
data.SetNumber( 'ur_ec_p4_t2_sr', 0 );
data.SetNumber( 'ur_ec_p4_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p4_t3_br', 0 );
data.SetNumber( 'ur_ec_p4_t3_sr', 0 );
data.SetNumber( 'ur_ec_p4_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p4_t4_br', 0 );
data.SetNumber( 'ur_ec_p4_t4_sr', 0 );
data.SetNumber( 'ur_ec_p4_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p4_t5_br', 0 );
data.SetNumber( 'ur_ec_p4_t5_sr', 0 );
data.SetNumber( 'ur_ec_p4_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p4_t6_br', 0 );
data.SetNumber( 'ur_ec_p4_t6_sr', 0 );
data.SetNumber( 'ur_ec_p4_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p5_t1_br', 0 );
data.SetNumber( 'ur_ec_p5_t1_sr', 0 );
data.SetNumber( 'ur_ec_p5_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p5_t2_br', 0 );
data.SetNumber( 'ur_ec_p5_t2_sr', 0 );
data.SetNumber( 'ur_ec_p5_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p5_t3_br', 0 );
data.SetNumber( 'ur_ec_p5_t3_sr', 0 );
data.SetNumber( 'ur_ec_p5_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p5_t4_br', 0 );
data.SetNumber( 'ur_ec_p5_t4_sr', 0 );
data.SetNumber( 'ur_ec_p5_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p5_t5_br', 0 );
data.SetNumber( 'ur_ec_p5_t5_sr', 0 );
data.SetNumber( 'ur_ec_p5_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p5_t6_br', 0 );
data.SetNumber( 'ur_ec_p5_t6_sr', 0 );
data.SetNumber( 'ur_ec_p5_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p6_t1_br', 0 );
data.SetNumber( 'ur_ec_p6_t1_sr', 0 );
data.SetNumber( 'ur_ec_p6_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p6_t2_br', 0 );
data.SetNumber( 'ur_ec_p6_t2_sr', 0 );
data.SetNumber( 'ur_ec_p6_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p6_t3_br', 0 );
data.SetNumber( 'ur_ec_p6_t3_sr', 0 );
data.SetNumber( 'ur_ec_p6_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p6_t4_br', 0 );
data.SetNumber( 'ur_ec_p6_t4_sr', 0 );
data.SetNumber( 'ur_ec_p6_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p6_t5_br', 0 );
data.SetNumber( 'ur_ec_p6_t5_sr', 0 );
data.SetNumber( 'ur_ec_p6_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p6_t6_br', 0 );
data.SetNumber( 'ur_ec_p6_t6_sr', 0 );
data.SetNumber( 'ur_ec_p6_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p7_t1_br', 0 );
data.SetNumber( 'ur_ec_p7_t1_sr', 0 );
data.SetNumber( 'ur_ec_p7_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p7_t2_br', 0 );
data.SetNumber( 'ur_ec_p7_t2_sr', 0 );
data.SetNumber( 'ur_ec_p7_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p7_t3_br', 0 );
data.SetNumber( 'ur_ec_p7_t3_sr', 0 );
data.SetNumber( 'ur_ec_p7_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p7_t4_br', 0 );
data.SetNumber( 'ur_ec_p7_t4_sr', 0 );
data.SetNumber( 'ur_ec_p7_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p7_t5_br', 0 );
data.SetNumber( 'ur_ec_p7_t5_sr', 0 );
data.SetNumber( 'ur_ec_p7_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p7_t6_br', 0 );
data.SetNumber( 'ur_ec_p7_t6_sr', 0 );
data.SetNumber( 'ur_ec_p7_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p8_t1_br', 0 );
data.SetNumber( 'ur_ec_p8_t1_sr', 0 );
data.SetNumber( 'ur_ec_p8_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p8_t2_br', 0 );
data.SetNumber( 'ur_ec_p8_t2_sr', 0 );
data.SetNumber( 'ur_ec_p8_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p8_t3_br', 0 );
data.SetNumber( 'ur_ec_p8_t3_sr', 0 );
data.SetNumber( 'ur_ec_p8_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p8_t4_br', 0 );
data.SetNumber( 'ur_ec_p8_t4_sr', 0 );
data.SetNumber( 'ur_ec_p8_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p8_t5_br', 0 );
data.SetNumber( 'ur_ec_p8_t5_sr', 0 );
data.SetNumber( 'ur_ec_p8_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p8_t6_br', 0 );
data.SetNumber( 'ur_ec_p8_t6_sr', 0 );
data.SetNumber( 'ur_ec_p8_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p9_t1_br', 0 );
data.SetNumber( 'ur_ec_p9_t1_sr', 0 );
data.SetNumber( 'ur_ec_p9_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p9_t2_br', 0 );
data.SetNumber( 'ur_ec_p9_t2_sr', 0 );
data.SetNumber( 'ur_ec_p9_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p9_t3_br', 0 );
data.SetNumber( 'ur_ec_p9_t3_sr', 0 );
data.SetNumber( 'ur_ec_p9_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p9_t4_br', 0 );
data.SetNumber( 'ur_ec_p9_t4_sr', 0 );
data.SetNumber( 'ur_ec_p9_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p9_t5_br', 0 );
data.SetNumber( 'ur_ec_p9_t5_sr', 0 );
data.SetNumber( 'ur_ec_p9_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p9_t6_br', 0 );
data.SetNumber( 'ur_ec_p9_t6_sr', 0 );
data.SetNumber( 'ur_ec_p9_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p10_t1_br', 0 );
data.SetNumber( 'ur_ec_p10_t1_sr', 0 );
data.SetNumber( 'ur_ec_p10_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p10_t2_br', 0 );
data.SetNumber( 'ur_ec_p10_t2_sr', 0 );
data.SetNumber( 'ur_ec_p10_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p10_t3_br', 0 );
data.SetNumber( 'ur_ec_p10_t3_sr', 0 );
data.SetNumber( 'ur_ec_p10_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p10_t4_br', 0 );
data.SetNumber( 'ur_ec_p10_t4_sr', 0 );
data.SetNumber( 'ur_ec_p10_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p10_t5_br', 0 );
data.SetNumber( 'ur_ec_p10_t5_sr', 0 );
data.SetNumber( 'ur_ec_p10_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p10_t6_br', 0 );
data.SetNumber( 'ur_ec_p10_t6_sr', 0 );
data.SetNumber( 'ur_ec_p10_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p11_t1_br', 0 );
data.SetNumber( 'ur_ec_p11_t1_sr', 0 );
data.SetNumber( 'ur_ec_p11_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p11_t2_br', 0 );
data.SetNumber( 'ur_ec_p11_t2_sr', 0 );
data.SetNumber( 'ur_ec_p11_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p11_t3_br', 0 );
data.SetNumber( 'ur_ec_p11_t3_sr', 0 );
data.SetNumber( 'ur_ec_p11_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p11_t4_br', 0 );
data.SetNumber( 'ur_ec_p11_t4_sr', 0 );
data.SetNumber( 'ur_ec_p11_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p11_t5_br', 0 );
data.SetNumber( 'ur_ec_p11_t5_sr', 0 );
data.SetNumber( 'ur_ec_p11_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p11_t6_br', 0 );
data.SetNumber( 'ur_ec_p11_t6_sr', 0 );
data.SetNumber( 'ur_ec_p11_t6_ub', 1e+038 );
data.SetNumber( 'ur_ec_p12_t1_br', 0 );
data.SetNumber( 'ur_ec_p12_t1_sr', 0 );
data.SetNumber( 'ur_ec_p12_t1_ub', 1e+038 );
data.SetNumber( 'ur_ec_p12_t2_br', 0 );
data.SetNumber( 'ur_ec_p12_t2_sr', 0 );
data.SetNumber( 'ur_ec_p12_t2_ub', 1e+038 );
data.SetNumber( 'ur_ec_p12_t3_br', 0 );
data.SetNumber( 'ur_ec_p12_t3_sr', 0 );
data.SetNumber( 'ur_ec_p12_t3_ub', 1e+038 );
data.SetNumber( 'ur_ec_p12_t4_br', 0 );
data.SetNumber( 'ur_ec_p12_t4_sr', 0 );
data.SetNumber( 'ur_ec_p12_t4_ub', 1e+038 );
data.SetNumber( 'ur_ec_p12_t5_br', 0 );
data.SetNumber( 'ur_ec_p12_t5_sr', 0 );
data.SetNumber( 'ur_ec_p12_t5_ub', 1e+038 );
data.SetNumber( 'ur_ec_p12_t6_br', 0 );
data.SetNumber( 'ur_ec_p12_t6_sr', 0 );
data.SetNumber( 'ur_ec_p12_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_enable', 0 );
data.SetMatrix( 'ur_dc_sched_weekday', [ 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ] );
data.SetMatrix( 'ur_dc_sched_weekend', [ 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ; 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ] );
data.SetNumber( 'ur_dc_p1_t1_dc', 0 );
data.SetNumber( 'ur_dc_p1_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p1_t2_dc', 0 );
data.SetNumber( 'ur_dc_p1_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p1_t3_dc', 0 );
data.SetNumber( 'ur_dc_p1_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p1_t4_dc', 0 );
data.SetNumber( 'ur_dc_p1_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p1_t5_dc', 0 );
data.SetNumber( 'ur_dc_p1_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p1_t6_dc', 0 );
data.SetNumber( 'ur_dc_p1_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p2_t1_dc', 0 );
data.SetNumber( 'ur_dc_p2_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p2_t2_dc', 0 );
data.SetNumber( 'ur_dc_p2_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p2_t3_dc', 0 );
data.SetNumber( 'ur_dc_p2_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p2_t4_dc', 0 );
data.SetNumber( 'ur_dc_p2_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p2_t5_dc', 0 );
data.SetNumber( 'ur_dc_p2_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p2_t6_dc', 0 );
data.SetNumber( 'ur_dc_p2_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p3_t1_dc', 0 );
data.SetNumber( 'ur_dc_p3_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p3_t2_dc', 0 );
data.SetNumber( 'ur_dc_p3_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p3_t3_dc', 0 );
data.SetNumber( 'ur_dc_p3_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p3_t4_dc', 0 );
data.SetNumber( 'ur_dc_p3_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p3_t5_dc', 0 );
data.SetNumber( 'ur_dc_p3_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p3_t6_dc', 0 );
data.SetNumber( 'ur_dc_p3_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p4_t1_dc', 0 );
data.SetNumber( 'ur_dc_p4_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p4_t2_dc', 0 );
data.SetNumber( 'ur_dc_p4_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p4_t3_dc', 0 );
data.SetNumber( 'ur_dc_p4_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p4_t4_dc', 0 );
data.SetNumber( 'ur_dc_p4_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p4_t5_dc', 0 );
data.SetNumber( 'ur_dc_p4_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p4_t6_dc', 0 );
data.SetNumber( 'ur_dc_p4_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p5_t1_dc', 0 );
data.SetNumber( 'ur_dc_p5_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p5_t2_dc', 0 );
data.SetNumber( 'ur_dc_p5_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p5_t3_dc', 0 );
data.SetNumber( 'ur_dc_p5_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p5_t4_dc', 0 );
data.SetNumber( 'ur_dc_p5_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p5_t5_dc', 0 );
data.SetNumber( 'ur_dc_p5_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p5_t6_dc', 0 );
data.SetNumber( 'ur_dc_p5_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p6_t1_dc', 0 );
data.SetNumber( 'ur_dc_p6_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p6_t2_dc', 0 );
data.SetNumber( 'ur_dc_p6_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p6_t3_dc', 0 );
data.SetNumber( 'ur_dc_p6_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p6_t4_dc', 0 );
data.SetNumber( 'ur_dc_p6_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p6_t5_dc', 0 );
data.SetNumber( 'ur_dc_p6_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p6_t6_dc', 0 );
data.SetNumber( 'ur_dc_p6_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p7_t1_dc', 0 );
data.SetNumber( 'ur_dc_p7_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p7_t2_dc', 0 );
data.SetNumber( 'ur_dc_p7_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p7_t3_dc', 0 );
data.SetNumber( 'ur_dc_p7_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p7_t4_dc', 0 );
data.SetNumber( 'ur_dc_p7_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p7_t5_dc', 0 );
data.SetNumber( 'ur_dc_p7_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p7_t6_dc', 0 );
data.SetNumber( 'ur_dc_p7_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p8_t1_dc', 0 );
data.SetNumber( 'ur_dc_p8_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p8_t2_dc', 0 );
data.SetNumber( 'ur_dc_p8_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p8_t3_dc', 0 );
data.SetNumber( 'ur_dc_p8_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p8_t4_dc', 0 );
data.SetNumber( 'ur_dc_p8_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p8_t5_dc', 0 );
data.SetNumber( 'ur_dc_p8_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p8_t6_dc', 0 );
data.SetNumber( 'ur_dc_p8_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p9_t1_dc', 0 );
data.SetNumber( 'ur_dc_p9_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p9_t2_dc', 0 );
data.SetNumber( 'ur_dc_p9_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p9_t3_dc', 0 );
data.SetNumber( 'ur_dc_p9_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p9_t4_dc', 0 );
data.SetNumber( 'ur_dc_p9_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p9_t5_dc', 0 );
data.SetNumber( 'ur_dc_p9_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p9_t6_dc', 0 );
data.SetNumber( 'ur_dc_p9_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p10_t1_dc', 0 );
data.SetNumber( 'ur_dc_p10_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p10_t2_dc', 0 );
data.SetNumber( 'ur_dc_p10_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p10_t3_dc', 0 );
data.SetNumber( 'ur_dc_p10_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p10_t4_dc', 0 );
data.SetNumber( 'ur_dc_p10_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p10_t5_dc', 0 );
data.SetNumber( 'ur_dc_p10_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p10_t6_dc', 0 );
data.SetNumber( 'ur_dc_p10_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p11_t1_dc', 0 );
data.SetNumber( 'ur_dc_p11_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p11_t2_dc', 0 );
data.SetNumber( 'ur_dc_p11_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p11_t3_dc', 0 );
data.SetNumber( 'ur_dc_p11_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p11_t4_dc', 0 );
data.SetNumber( 'ur_dc_p11_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p11_t5_dc', 0 );
data.SetNumber( 'ur_dc_p11_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p11_t6_dc', 0 );
data.SetNumber( 'ur_dc_p11_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_p12_t1_dc', 0 );
data.SetNumber( 'ur_dc_p12_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_p12_t2_dc', 0 );
data.SetNumber( 'ur_dc_p12_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_p12_t3_dc', 0 );
data.SetNumber( 'ur_dc_p12_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_p12_t4_dc', 0 );
data.SetNumber( 'ur_dc_p12_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_p12_t5_dc', 0 );
data.SetNumber( 'ur_dc_p12_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_p12_t6_dc', 0 );
data.SetNumber( 'ur_dc_p12_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_jan_t1_dc', 0 );
data.SetNumber( 'ur_dc_jan_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_jan_t2_dc', 0 );
data.SetNumber( 'ur_dc_jan_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_jan_t3_dc', 0 );
data.SetNumber( 'ur_dc_jan_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_jan_t4_dc', 0 );
data.SetNumber( 'ur_dc_jan_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_jan_t5_dc', 0 );
data.SetNumber( 'ur_dc_jan_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_jan_t6_dc', 0 );
data.SetNumber( 'ur_dc_jan_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_feb_t1_dc', 0 );
data.SetNumber( 'ur_dc_feb_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_feb_t2_dc', 0 );
data.SetNumber( 'ur_dc_feb_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_feb_t3_dc', 0 );
data.SetNumber( 'ur_dc_feb_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_feb_t4_dc', 0 );
data.SetNumber( 'ur_dc_feb_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_feb_t5_dc', 0 );
data.SetNumber( 'ur_dc_feb_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_feb_t6_dc', 0 );
data.SetNumber( 'ur_dc_feb_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_mar_t1_dc', 0 );
data.SetNumber( 'ur_dc_mar_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_mar_t2_dc', 0 );
data.SetNumber( 'ur_dc_mar_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_mar_t3_dc', 0 );
data.SetNumber( 'ur_dc_mar_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_mar_t4_dc', 0 );
data.SetNumber( 'ur_dc_mar_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_mar_t5_dc', 0 );
data.SetNumber( 'ur_dc_mar_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_mar_t6_dc', 0 );
data.SetNumber( 'ur_dc_mar_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_apr_t1_dc', 0 );
data.SetNumber( 'ur_dc_apr_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_apr_t2_dc', 0 );
data.SetNumber( 'ur_dc_apr_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_apr_t3_dc', 0 );
data.SetNumber( 'ur_dc_apr_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_apr_t4_dc', 0 );
data.SetNumber( 'ur_dc_apr_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_apr_t5_dc', 0 );
data.SetNumber( 'ur_dc_apr_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_apr_t6_dc', 0 );
data.SetNumber( 'ur_dc_apr_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_may_t1_dc', 0 );
data.SetNumber( 'ur_dc_may_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_may_t2_dc', 0 );
data.SetNumber( 'ur_dc_may_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_may_t3_dc', 0 );
data.SetNumber( 'ur_dc_may_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_may_t4_dc', 0 );
data.SetNumber( 'ur_dc_may_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_may_t5_dc', 0 );
data.SetNumber( 'ur_dc_may_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_may_t6_dc', 0 );
data.SetNumber( 'ur_dc_may_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_jun_t1_dc', 0 );
data.SetNumber( 'ur_dc_jun_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_jun_t2_dc', 0 );
data.SetNumber( 'ur_dc_jun_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_jun_t3_dc', 0 );
data.SetNumber( 'ur_dc_jun_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_jun_t4_dc', 0 );
data.SetNumber( 'ur_dc_jun_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_jun_t5_dc', 0 );
data.SetNumber( 'ur_dc_jun_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_jun_t6_dc', 0 );
data.SetNumber( 'ur_dc_jun_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_jul_t1_dc', 0 );
data.SetNumber( 'ur_dc_jul_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_jul_t2_dc', 0 );
data.SetNumber( 'ur_dc_jul_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_jul_t3_dc', 0 );
data.SetNumber( 'ur_dc_jul_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_jul_t4_dc', 0 );
data.SetNumber( 'ur_dc_jul_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_jul_t5_dc', 0 );
data.SetNumber( 'ur_dc_jul_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_jul_t6_dc', 0 );
data.SetNumber( 'ur_dc_jul_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_aug_t1_dc', 0 );
data.SetNumber( 'ur_dc_aug_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_aug_t2_dc', 0 );
data.SetNumber( 'ur_dc_aug_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_aug_t3_dc', 0 );
data.SetNumber( 'ur_dc_aug_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_aug_t4_dc', 0 );
data.SetNumber( 'ur_dc_aug_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_aug_t5_dc', 0 );
data.SetNumber( 'ur_dc_aug_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_aug_t6_dc', 0 );
data.SetNumber( 'ur_dc_aug_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_sep_t1_dc', 0 );
data.SetNumber( 'ur_dc_sep_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_sep_t2_dc', 0 );
data.SetNumber( 'ur_dc_sep_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_sep_t3_dc', 0 );
data.SetNumber( 'ur_dc_sep_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_sep_t4_dc', 0 );
data.SetNumber( 'ur_dc_sep_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_sep_t5_dc', 0 );
data.SetNumber( 'ur_dc_sep_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_sep_t6_dc', 0 );
data.SetNumber( 'ur_dc_sep_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_oct_t1_dc', 0 );
data.SetNumber( 'ur_dc_oct_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_oct_t2_dc', 0 );
data.SetNumber( 'ur_dc_oct_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_oct_t3_dc', 0 );
data.SetNumber( 'ur_dc_oct_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_oct_t4_dc', 0 );
data.SetNumber( 'ur_dc_oct_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_oct_t5_dc', 0 );
data.SetNumber( 'ur_dc_oct_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_oct_t6_dc', 0 );
data.SetNumber( 'ur_dc_oct_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_nov_t1_dc', 0 );
data.SetNumber( 'ur_dc_nov_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_nov_t2_dc', 0 );
data.SetNumber( 'ur_dc_nov_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_nov_t3_dc', 0 );
data.SetNumber( 'ur_dc_nov_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_nov_t4_dc', 0 );
data.SetNumber( 'ur_dc_nov_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_nov_t5_dc', 0 );
data.SetNumber( 'ur_dc_nov_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_nov_t6_dc', 0 );
data.SetNumber( 'ur_dc_nov_t6_ub', 1e+038 );
data.SetNumber( 'ur_dc_dec_t1_dc', 0 );
data.SetNumber( 'ur_dc_dec_t1_ub', 1e+038 );
data.SetNumber( 'ur_dc_dec_t2_dc', 0 );
data.SetNumber( 'ur_dc_dec_t2_ub', 1e+038 );
data.SetNumber( 'ur_dc_dec_t3_dc', 0 );
data.SetNumber( 'ur_dc_dec_t3_ub', 1e+038 );
data.SetNumber( 'ur_dc_dec_t4_dc', 0 );
data.SetNumber( 'ur_dc_dec_t4_ub', 1e+038 );
data.SetNumber( 'ur_dc_dec_t5_dc', 0 );
data.SetNumber( 'ur_dc_dec_t5_ub', 1e+038 );
data.SetNumber( 'ur_dc_dec_t6_dc', 0 );
data.SetNumber( 'ur_dc_dec_t6_ub', 1e+038 );
module = SSC.Module('utilityrate3');
if (module.Exec(data))
salespurchases = data.GetArray('year1_monthly_salespurchases');
ns = data.GetNumber('savings_year1');
names{end+1} = 'ear 1 monthly sales/purchases with system : ';
for i = 1:size(salespurchases)
names{end+1} = sprintf('[%d]: $%g', i,salespurchases(i));
end
names{end+1} = sprintf('Net savings : $%g', ns);
names{end+1} = 'UtilityRate3 example OK';
else
idx = 0;
[result, msg, type, time] = module.Log(idx);
while (result)
names{end+1} = sprintf('[%s at time:%g ]: %s', type, time, msg);
idx = idx + 1;
[result, msg, type, time] = module.Log(idx);
end
names{end+1} = 'UtilityRate3 example failed';
end
set(handles.txtData,'String',names);
% --- Executes on button press in btnCashLoan.
function btnCashLoan_Callback(hObject, eventdata, handles)
% hObject handle to btnCashLoan (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
%cashloan compute module call from 2014.11.24 "Photovoltaic, Residential" configuration
names={};
data = SSC.Data();
data.SetNumber( 'analysis_period', 25 );
data.SetNumber( 'federal_tax_rate', 30 );
data.SetNumber( 'state_tax_rate', 7 );
data.SetNumber( 'property_tax_rate', 1 );
data.SetNumber( 'prop_tax_cost_assessed_percent', 100 );
data.SetNumber( 'prop_tax_assessed_decline', 0 );
data.SetNumber( 'sales_tax_rate', 5 );
data.SetNumber( 'real_discount_rate', 5.5 );
data.SetNumber( 'inflation_rate', 2.5 );
data.SetNumber( 'insurance_rate', 1 );
data.SetNumber( 'system_capacity', 3.8745 );
data.SetNumber( 'loan_term', 25 );
data.SetNumber( 'loan_rate', 5 );
data.SetNumber( 'debt_fraction', 100 );
data.SetArray( 'om_fixed', [ 0 ] );
data.SetNumber( 'om_fixed_escal', 0 );
data.SetArray( 'om_production', [ 0 ] );
data.SetNumber( 'om_production_escal', 0 );
data.SetArray( 'om_capacity', [ 20 ] );
data.SetNumber( 'om_capacity_escal', 0 );
data.SetArray( 'om_fuel_cost', [ 0 ] );
data.SetNumber( 'om_fuel_cost_escal', 0 );
data.SetNumber( 'itc_fed_amount', 0 );
data.SetNumber( 'itc_fed_amount_deprbas_fed', 1 );
data.SetNumber( 'itc_fed_amount_deprbas_sta', 1 );
data.SetNumber( 'itc_sta_amount', 0 );
data.SetNumber( 'itc_sta_amount_deprbas_fed', 0 );
data.SetNumber( 'itc_sta_amount_deprbas_sta', 0 );
data.SetNumber( 'itc_fed_percent', 30 );
data.SetNumber( 'itc_fed_percent_maxvalue', 1e+038 );
data.SetNumber( 'itc_fed_percent_deprbas_fed', 1 );
data.SetNumber( 'itc_fed_percent_deprbas_sta', 1 );
data.SetNumber( 'itc_sta_percent', 25 );
data.SetNumber( 'itc_sta_percent_maxvalue', 1e+038 );
data.SetNumber( 'itc_sta_percent_deprbas_fed', 0 );
data.SetNumber( 'itc_sta_percent_deprbas_sta', 0 );
data.SetArray( 'ptc_fed_amount', [ 0 ] );
data.SetNumber( 'ptc_fed_term', 10 );
data.SetNumber( 'ptc_fed_escal', 0 );
data.SetArray( 'ptc_sta_amount', [ 0 ] );
data.SetNumber( 'ptc_sta_term', 10 );
data.SetNumber( 'ptc_sta_escal', 0 );
data.SetNumber( 'ibi_fed_amount', 0 );
data.SetNumber( 'ibi_fed_amount_tax_fed', 1 );
data.SetNumber( 'ibi_fed_amount_tax_sta', 1 );
data.SetNumber( 'ibi_fed_amount_deprbas_fed', 0 );
data.SetNumber( 'ibi_fed_amount_deprbas_sta', 0 );
data.SetNumber( 'ibi_sta_amount', 0 );
data.SetNumber( 'ibi_sta_amount_tax_fed', 1 );
data.SetNumber( 'ibi_sta_amount_tax_sta', 1 );
data.SetNumber( 'ibi_sta_amount_deprbas_fed', 0 );
data.SetNumber( 'ibi_sta_amount_deprbas_sta', 0 );
data.SetNumber( 'ibi_uti_amount', 0 );
data.SetNumber( 'ibi_uti_amount_tax_fed', 1 );
data.SetNumber( 'ibi_uti_amount_tax_sta', 1 );
data.SetNumber( 'ibi_uti_amount_deprbas_fed', 0 );
data.SetNumber( 'ibi_uti_amount_deprbas_sta', 0 );
data.SetNumber( 'ibi_oth_amount', 0 );
data.SetNumber( 'ibi_oth_amount_tax_fed', 1 );
data.SetNumber( 'ibi_oth_amount_tax_sta', 1 );
data.SetNumber( 'ibi_oth_amount_deprbas_fed', 0 );
data.SetNumber( 'ibi_oth_amount_deprbas_sta', 0 );
data.SetNumber( 'ibi_fed_percent', 0 );
data.SetNumber( 'ibi_fed_percent_maxvalue', 1e+038 );
data.SetNumber( 'ibi_fed_percent_tax_fed', 1 );
data.SetNumber( 'ibi_fed_percent_tax_sta', 1 );
data.SetNumber( 'ibi_fed_percent_deprbas_fed', 0 );
data.SetNumber( 'ibi_fed_percent_deprbas_sta', 0 );
data.SetNumber( 'ibi_sta_percent', 0 );
data.SetNumber( 'ibi_sta_percent_maxvalue', 1e+038 );
data.SetNumber( 'ibi_sta_percent_tax_fed', 1 );
data.SetNumber( 'ibi_sta_percent_tax_sta', 1 );
data.SetNumber( 'ibi_sta_percent_deprbas_fed', 0 );
data.SetNumber( 'ibi_sta_percent_deprbas_sta', 0 );
data.SetNumber( 'ibi_uti_percent', 0 );
data.SetNumber( 'ibi_uti_percent_maxvalue', 1e+038 );
data.SetNumber( 'ibi_uti_percent_tax_fed', 1 );
data.SetNumber( 'ibi_uti_percent_tax_sta', 1 );
data.SetNumber( 'ibi_uti_percent_deprbas_fed', 0 );
data.SetNumber( 'ibi_uti_percent_deprbas_sta', 0 );
data.SetNumber( 'ibi_oth_percent', 0 );
data.SetNumber( 'ibi_oth_percent_maxvalue', 1e+038 );
data.SetNumber( 'ibi_oth_percent_tax_fed', 1 );
data.SetNumber( 'ibi_oth_percent_tax_sta', 1 );
data.SetNumber( 'ibi_oth_percent_deprbas_fed', 0 );
data.SetNumber( 'ibi_oth_percent_deprbas_sta', 0 );
data.SetNumber( 'cbi_fed_amount', 0 );
data.SetNumber( 'cbi_fed_maxvalue', 1e+038 );
data.SetNumber( 'cbi_fed_tax_fed', 1 );
data.SetNumber( 'cbi_fed_tax_sta', 1 );
data.SetNumber( 'cbi_fed_deprbas_fed', 0 );
data.SetNumber( 'cbi_fed_deprbas_sta', 0 );
data.SetNumber( 'cbi_sta_amount', 0 );
data.SetNumber( 'cbi_sta_maxvalue', 1e+038 );
data.SetNumber( 'cbi_sta_tax_fed', 1 );
data.SetNumber( 'cbi_sta_tax_sta', 1 );
data.SetNumber( 'cbi_sta_deprbas_fed', 0 );
data.SetNumber( 'cbi_sta_deprbas_sta', 0 );
data.SetNumber( 'cbi_uti_amount', 0 );
data.SetNumber( 'cbi_uti_maxvalue', 1e+038 );
data.SetNumber( 'cbi_uti_tax_fed', 1 );
data.SetNumber( 'cbi_uti_tax_sta', 1 );
data.SetNumber( 'cbi_uti_deprbas_fed', 0 );
data.SetNumber( 'cbi_uti_deprbas_sta', 0 );
data.SetNumber( 'cbi_oth_amount', 0 );
data.SetNumber( 'cbi_oth_maxvalue', 1e+038 );
data.SetNumber( 'cbi_oth_tax_fed', 1 );
data.SetNumber( 'cbi_oth_tax_sta', 1 );
data.SetNumber( 'cbi_oth_deprbas_fed', 0 );
data.SetNumber( 'cbi_oth_deprbas_sta', 0 );
data.SetArray( 'pbi_fed_amount', [ 0 ] );
data.SetNumber( 'pbi_fed_term', 0 );
data.SetNumber( 'pbi_fed_escal', 0 );
data.SetNumber( 'pbi_fed_tax_fed', 1 );
data.SetNumber( 'pbi_fed_tax_sta', 1 );
data.SetArray( 'pbi_sta_amount', [ 0 ] );
data.SetNumber( 'pbi_sta_term', 0 );
data.SetNumber( 'pbi_sta_escal', 0 );
data.SetNumber( 'pbi_sta_tax_fed', 1 );
data.SetNumber( 'pbi_sta_tax_sta', 1 );
data.SetArray( 'pbi_uti_amount', [ 0 ] );
data.SetNumber( 'pbi_uti_term', 0 );
data.SetNumber( 'pbi_uti_escal', 0 );
data.SetNumber( 'pbi_uti_tax_fed', 1 );
data.SetNumber( 'pbi_uti_tax_sta', 1 );
data.SetArray( 'pbi_oth_amount', [ 0 ] );
data.SetNumber( 'pbi_oth_term', 0 );
data.SetNumber( 'pbi_oth_escal', 0 );
data.SetNumber( 'pbi_oth_tax_fed', 1 );
data.SetNumber( 'pbi_oth_tax_sta', 1 );
data.SetNumber( 'market', 0 );
data.SetNumber( 'mortgage', 1 );
data.SetNumber( 'total_installed_cost', 12746.7 );
data.SetNumber( 'salvage_percentage', 0 );
data.SetArray( 'annual_energy_value', [ 812.892, 832.234, 852.04, 872.322, 892.814, 913.738, 935.158, 957.085, 979.531, 1002.51, 1026.03, 1050.11, 1074.76, 1100, 1125.83, 1152.28, 1179.35, 1207.07, 1235.44, 1264.49, 1294.22, 1324.67, 1355.83, 1387.74, 1428.58 ] );
data.SetArray( 'hourly_energy', [ -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.237892, 1.55848, 2.07121, 2.54933, 2.77945, 2.42071, 1.25773, 0.593002, 0.235599, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0131131, 0.701084, 1.32711, 1.76305, 2.5502, 2.29087, 1.83336, 2.01766, 0.881524, 0.298536, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.00528433, 0.737449, 1.51351, 2.14093, 2.39439, 2.32012, 1.83616, 0.712188, 0.32981, 0.087085, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.163208, 0.438056, 0.343575, 1.18182, 1.85202, 1.85609, 0.842696, 0.641464, 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-0.0001683, -0.0001683, 0.0997101, 0.24553, 0.292913, 1.14435, 0.989543, 0.57329, 1.26016, 0.542071, 0.171351, 0.00539238, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.0481958, 0.778754, 1.47179, 2.1002, 2.02692, 2.59275, 2.56468, 2.08318, 1.48502, 0.770173, 0.0568852, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, 0.016664, 0.620281, 1.46676, 1.97059, 2.4413, 2.57254, 2.42621, 2.0301, 1.42493, 0.617651, 0.025802, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683, -0.0001683 ] );
data.SetArray( 'degradation', [ 0.5 ] );
data.SetNumber( 'system_use_lifetime_output', 0 );
module = SSC.Module('cashloan');
if (module.Exec(data))
lcoe = data.GetNumber('lcoe_nom');
npv = data.GetNumber('npv');
names{end+1} = sprintf('Levelized COE (nominal) : %g cents/kWh', lcoe);
names{end+1} = sprintf('Net present value : $%g', npv);
names{end+1} = 'CashLoan example OK';
else
idx = 0;
[result, msg, type, time] = module.Log(idx);
while (result)
names{end+1} = sprintf('[%s at time:%g ]: %s', type, time, msg);
idx = idx + 1;
[result, msg, type, time] = module.Log(idx);
end
names{end+1} = 'CashLoan example failed';
end
set(handles.txtData,'String',names);
|
github
|
sergiocastellanos/switch_mexico_data-master
|
ssccall.m
|
.m
|
switch_mexico_data-master/SAM/SDK/languages/matlab/+SSC/ssccall.m
| 8,963 |
utf_8
|
61a4af2373d48538fcfc4a2dd42eda7c
|
function [result] = ssccall(action, arg0, arg1, arg2 )
% SAM Simulation Core (SSC) MATLAB API
% Copyright (c) 2012 National Renewable Energy Laboratory
% author: Aron P. Dobos and Steven H. Janzou
% automatically detect architecture to load proper dll.
[pathstr, fn, fext] = fileparts(mfilename('fullpath'));
if ( strcmp(computer(), 'PCWIN') ) % Windows 32-bit
ssclibpath = '../../../win32/';
ssclib = 'ssc';
elseif ( strcmp(computer(), 'PCWIN64') ) % Windows 64-bit
ssclibpath = '../../../win64/';
ssclib = 'ssc';
elseif ( strcmp(computer(), 'MACI64') ) % Mac Intel 64-bit
ssclibpath = '../../../osx64/';
ssclib = 'ssc';
elseif ( strcmp(computer(), 'GLNXA64') ) % Linux 64-bit
ssclibpath = '../../../linux64/';
ssclib = 'ssc';
end
% load proper ssc library for all functions
if ~libisloaded(ssclib)
oldFolder = cd(pathstr);
loadlibrary(strcat(ssclibpath,ssclib),strcat(ssclibpath,'../sscapi.h'));
cd(oldFolder);
end
if strcmp(action,'load')
if ~libisloaded(ssclib)
oldFolder = cd(pathstr);
loadlibrary(strcat(ssclibpath,ssclib),strcat(ssclibpath,'../sscapi.h'));
cd(oldFolder);
end
elseif strcmp(action,'unload')
if libisloaded(ssclib)
unloadlibrary(ssclib)
end
elseif strcmp(action,'version')
result = calllib(ssclib,'ssc_version');
elseif strcmp(action,'build_info')
result = calllib(ssclib, 'ssc_build_info');
elseif strcmp(action,'data_create')
result = calllib(ssclib, 'ssc_data_create');
if ( isnullpointer(result) )
result = 0;
end
elseif strcmp(action,'data_free')
result = calllib(ssclib, 'ssc_data_free', arg0);
elseif strcmp(action,'data_unassign')
result = calllib(ssclib, 'ssc_data_unassign', arg0, arg1);
elseif strcmp(action,'data_query')
result = calllib(ssclib, 'ssc_data_query', arg0, arg1 );
elseif strcmp(action,'data_first')
result = calllib(ssclib, 'ssc_data_first', arg0 );
elseif strcmp(action,'data_next')
result = calllib(ssclib, 'ssc_data_next', arg0 );
elseif strcmp(action,'data_set_string')
result = calllib(ssclib, 'ssc_data_set_string', arg0, arg1, arg2 );
elseif strcmp(action,'data_set_number')
result = calllib(ssclib, 'ssc_data_set_number', arg0, arg1, single(arg2) );
elseif strcmp(action,'data_set_array')
len = length(arg2);
arr = libpointer( 'singlePtr', arg2 );
result = calllib(ssclib,'ssc_data_set_array',arg0,arg1,arr,len);
elseif strcmp(action,'data_set_matrix')
[nr nc] = size(arg2);
mat = zeros(nr*nc, 1);
ii = 1;
for r=1:nr,
for c=1:nc,
mat(ii) = arg2(r,c);
ii=ii+1;
end
end
arr = libpointer( 'singlePtr', mat );
result = calllib(ssclib,'ssc_data_set_matrix',arg0,arg1,arr,nr,nc);
elseif strcmp(action,'data_set_table')
result = calllib(ssclib,'ssc_data_set_table',arg0,arg1,arg2);
elseif strcmp(action,'data_get_string')
result = calllib(ssclib,'ssc_data_get_string',arg0,arg1);
elseif strcmp(action,'data_get_number')
p = libpointer('singlePtr',0);
calllib(ssclib,'ssc_data_get_number', arg0,arg1,p);
result = get(p,'Value');
elseif strcmp(action,'data_get_array')
p_count = libpointer('int32Ptr',0);
[xobj] = calllib(ssclib,'ssc_data_get_array',arg0,arg1,p_count);
setdatatype(xobj,'int32Ptr',p_count.Value,1);
len = p_count.Value;
result = zeros( len, 1 );
for i=1:len,
pidx = xobj+(i-1);
setdatatype(pidx,'singlePtr',1,1);
result(i) = pidx.Value;
end
elseif strcmp(action,'data_get_matrix')
p_rows = libpointer('int32Ptr',0);
p_cols = libpointer('int32Ptr',0);
[xobj] = calllib(ssclib,'ssc_data_get_matrix',arg0,arg1,p_rows,p_cols);
setdatatype(xobj,'int32Ptr',p_rows.Value*p_cols.Value,1);
nrows = p_rows.Value;
ncols = p_cols.Value;
if ( nrows*ncols > 0 )
result = zeros( nrows, ncols );
ii=1;
for r=1:nrows,
for c=1:ncols,
pidx = xobj+(ii-1);
setdatatype(pidx,'singlePtr',1,1);
result(r,c) = pidx.Value;
ii=ii+1;
end
end
end
elseif strcmp(action,'data_get_table')
result = calllib(ssclib,'ssc_data_get_table',arg0,arg1);
elseif strcmp(action,'module_entry')
result = calllib(ssclib,'ssc_module_entry',arg0);
if isnullpointer( result ),
result = 0;
end
elseif strcmp(action,'entry_name')
result = calllib(ssclib,'ssc_entry_name',arg0);
elseif strcmp(action,'entry_description')
result = calllib(ssclib,'ssc_entry_description',arg0);
elseif strcmp(action,'entry_version')
result = calllib(ssclib,'ssc_entry_version',arg0);
elseif strcmp(action,'module_var_info')
result = calllib(ssclib,'ssc_module_var_info',arg0,arg1);
if isnullpointer( result ),
result = 0;
end
elseif strcmp(action,'info_var_type')
ty = calllib(ssclib,'ssc_info_var_type',arg0);
if (ty == 1)
result = 'input';
elseif ( ty==2 )
result = 'output';
else
result = 'inout';
end
elseif strcmp(action,'info_data_type')
dt = calllib(ssclib,'ssc_info_data_type',arg0);
if (dt == 1)
result = 'string';
elseif (dt == 2)
result = 'number';
elseif (dt == 3)
result = 'array';
elseif (dt == 4)
result = 'matrix';
elseif (dt == 5)
result = 'table';
else
result = 'invalid';
end
elseif strcmp(action,'info_name')
result = calllib(ssclib,'ssc_info_name',arg0);
elseif strcmp(action,'info_label')
result = calllib(ssclib,'ssc_info_label',arg0);
elseif strcmp(action,'info_units')
result = calllib(ssclib,'ssc_info_units',arg0);
elseif strcmp(action,'info_meta')
result = calllib(ssclib,'ssc_info_meta',arg0);
elseif strcmp(action,'info_group')
result = calllib(ssclib,'ssc_info_group',arg0);
elseif strcmp(action,'info_required')
result = calllib(ssclib,'ssc_info_required',arg0);
elseif strcmp(action,'info_constraints')
result = calllib(ssclib,'ssc_info_constraints',arg0);
elseif strcmp(action,'info_uihint')
result = calllib(ssclib,'ssc_info_uihint',arg0);
elseif strcmp(action,'exec_simple')
result = calllib(ssclib,'ssc_module_exec_simple',arg0,arg1);
elseif strcmp(action,'exec_simple_nothread')
result = calllib(ssclib,'ssc_module_exec_simple_nothread',arg0,arg1);
elseif strcmp(action,'module_create')
result = calllib(ssclib,'ssc_module_create',arg0);
if ( isnullpointer(result) )
result = 0;
end
elseif strcmp(action,'module_free')
result = calllib(ssclib,'ssc_module_free',arg0);
elseif strcmp(action,'module_exec')
result = calllib(ssclib,'ssc_module_exec',arg0,arg1);
elseif strcmp(action,'module_log')
p_type = libpointer('int32Ptr',1);
p_time = libpointer('singlePtr',1);
result = calllib(ssclib,'ssc_module_log', arg0, arg1, p_type, p_time);
elseif strcmp(action,'module_log_detailed')
p_type = libpointer('int32Ptr',1);
p_time = libpointer('singlePtr',1);
text = calllib(ssclib,'ssc_module_log', arg0, arg1, p_type, p_time);
typetext = 'notice';
if (p_type.Value == 2)
typetext = 'warning';
elseif (p_type.Value == 3)
typetext = 'error';
end
if ( strcmp(text,'') )
result = 0;
else
result = {text , typetext , p_time.Value};
end
else
disp( sprintf('ssccall: invalid action %s', action) );
result = 0;
end
end
function bb = isnullpointer(p)
bb = false;
try
setdatatype(p, 'voidPtr', 1, 1);
deref = get(p);
catch
e = lasterror();
if strcmp(e.identifier, 'MATLAB:libpointer:ValueNotDefined')
bb = true;
end
end
end
|
github
|
fizyr-forks/caffe-master
|
classification_demo.m
|
.m
|
caffe-master/matlab/demo/classification_demo.m
| 5,466 |
utf_8
|
45745fb7cfe37ef723c307dfa06f1b97
|
function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% ****************************************************************************
% For detailed documentation and usage on Caffe's Matlab interface, please
% refer to the Caffe Interface Tutorial at
% http://caffe.berkeleyvision.org/tutorial/interfaces.html#matlab
% ****************************************************************************
%
% input
% im color image as uint8 HxWx3
% use_gpu 1 to use the GPU, 0 to use the CPU
%
% output
% scores 1000-dimensional ILSVRC score vector
% maxlabel the label of the highest score
%
% You may need to do the following before you start matlab:
% $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64
% $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
% Or the equivalent based on where things are installed on your system
% and what versions are installed.
%
% Usage:
% im = imread('../../examples/images/cat.jpg');
% scores = classification_demo(im, 1);
% [score, class] = max(scores);
% Five things to be aware of:
% caffe uses row-major order
% matlab uses column-major order
% caffe uses BGR color channel order
% matlab uses RGB color channel order
% images need to have the data mean subtracted
% Data coming in from matlab needs to be in the order
% [width, height, channels, images]
% where width is the fastest dimension.
% Here is the rough matlab code for putting image data into the correct
% format in W x H x C with BGR channels:
% % permute channels from RGB to BGR
% im_data = im(:, :, [3, 2, 1]);
% % flip width and height to make width the fastest dimension
% im_data = permute(im_data, [2, 1, 3]);
% % convert from uint8 to single
% im_data = single(im_data);
% % reshape to a fixed size (e.g., 227x227).
% im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');
% % subtract mean_data (already in W x H x C with BGR channels)
% im_data = im_data - mean_data;
% If you have multiple images, cat them with cat(4, ...)
% Add caffe/matlab to your Matlab search PATH in order to use matcaffe
if exist('../+caffe', 'dir')
addpath('..');
else
error('Please run this demo from caffe/matlab/demo');
end
% Set caffe mode
if exist('use_gpu', 'var') && use_gpu
caffe.set_mode_gpu();
gpu_id = 0; % we will use the first gpu in this demo
caffe.set_device(gpu_id);
else
caffe.set_mode_cpu();
end
% Initialize the network using BVLC CaffeNet for image classification
% Weights (parameter) file needs to be downloaded from Model Zoo.
model_dir = '../../models/bvlc_reference_caffenet/';
net_model = [model_dir 'deploy.prototxt'];
net_weights = [model_dir 'bvlc_reference_caffenet.caffemodel'];
phase = 'test'; % run with phase test (so that dropout isn't applied)
if ~exist(net_weights, 'file')
error('Please download CaffeNet from Model Zoo before you run this demo');
end
% Initialize a network
net = caffe.Net(net_model, net_weights, phase);
if nargin < 1
% For demo purposes we will use the cat image
fprintf('using caffe/examples/images/cat.jpg as input image\n');
im = imread('../../examples/images/cat.jpg');
end
% prepare oversampled input
% input_data is Height x Width x Channel x Num
tic;
input_data = {prepare_image(im)};
toc;
% do forward pass to get scores
% scores are now Channels x Num, where Channels == 1000
tic;
% The net forward function. It takes in a cell array of N-D arrays
% (where N == 4 here) containing data of input blob(s) and outputs a cell
% array containing data from output blob(s)
scores = net.forward(input_data);
toc;
scores = scores{1};
scores = mean(scores, 2); % take average scores over 10 crops
[~, maxlabel] = max(scores);
% call caffe.reset_all() to reset caffe
caffe.reset_all();
% ------------------------------------------------------------------------
function crops_data = prepare_image(im)
% ------------------------------------------------------------------------
% caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat contains mean_data that
% is already in W x H x C with BGR channels
d = load('../+caffe/imagenet/ilsvrc_2012_mean.mat');
mean_data = d.mean_data;
IMAGE_DIM = 256;
CROPPED_DIM = 227;
% Convert an image returned by Matlab's imread to im_data in caffe's data
% format: W x H x C with BGR channels
im_data = im(:, :, [3, 2, 1]); % permute channels from RGB to BGR
im_data = permute(im_data, [2, 1, 3]); % flip width and height
im_data = single(im_data); % convert from uint8 to single
im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear'); % resize im_data
im_data = im_data - mean_data; % subtract mean_data (already in W x H x C, BGR)
% oversample (4 corners, center, and their x-axis flips)
crops_data = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;
n = 1;
for i = indices
for j = indices
crops_data(:, :, :, n) = im_data(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :);
crops_data(:, :, :, n+5) = crops_data(end:-1:1, :, :, n);
n = n + 1;
end
end
center = floor(indices(2) / 2) + 1;
crops_data(:,:,:,5) = ...
im_data(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:);
crops_data(:,:,:,10) = crops_data(end:-1:1, :, :, 5);
|
github
|
nmovshov/CMS-planet-master
|
CMSPlanet.m
|
.m
|
CMS-planet-master/CMSPlanet.m
| 45,067 |
utf_8
|
5572d78661126b36c626c37bf3e4baf2
|
classdef CMSPlanet < handle
%CMSPLANET Interior model of rotating fluid planet.
% This class implements a model of a rotating fluid planet using
% Concentric Maclaurin Spheroids to calculate the hydrostatic equilibrium
% shape and resulting gravity field. A CMSPlanet object is defined by a
% densiy profile rho(a), supplied by the user and stored in the column
% vectors obj.ai and obj.rhoi, indexed from the surface in. To complete
% the definition the user must also specify a mass, equatorial radius,
% and rotation period. With these a gravity field and equilibrium shape
% can be determined, with a call to obj.relax_to_HE(). Note, however,
% that the oblate shape calculated with obj.relax_to_HE() preserves the
% equatorial radius of the planet, but not the total mass. A call to
% obj.renormalize() will multiply obj.rhoi by a constant to yield the
% correct reference mass at the cost of modifying the assigned density.
% It is not possible to define mass, radius, and density simultaneously.
%
% Alternatively the user may supply a barotrope object, stored in
% obj.eos, and call obj.relax_to_barotrope() to iteratively find a
% density profile consistent with the calculated equilibrium pressure. To
% this end a boundary pressure must also be given, in obj.P0, and an
% initial density profile guess is still required (can be a simple one).
% Again, we can't simultaneously impose an exact mass, radius, and
% barotrope. By default the reference mass and radius will be honored, by
% renormalizing the converged density profile, and this will modify the
% _effective_ barotrope (to override set obj.opts.renorm=false).
%% Properties
properties
name % model name
mass % reference mass
radius % reference radius (equatorial!)
period % refernce rotation period
P0 % reference pressure
ai % vector of equatorial radii (top down, a0=ai(1) is outer radius)
rhoi % vector of layer densities
eos % barotrope(s) (tip: help barotropes for options)
bgeos % optional background barotrope
fgeos % optional foreground barotrope
opts % holds user configurable options (tip: help cmsset)
end
properties (SetAccess = private)
N % convenience name for length(obj.ai)
CMS % spheroid shape and other information returned by cms.m
Js % external gravity coefficients (returned by cms.m)
betam % mass renormalization factor returned by obj.renormalize()
alfar % radius renormalization factor returned by obj.renormalize()
end
properties (Dependent)
M % calculated mass (equal to mass *after* renorm)
mi % cumulative mass below ai
a0 % another name for obj.ai(1)
s0 % calculated mean radius, another name for obj.si(1)
si % calculated spheroid mean radii
rhobar % calculated mean density
wrot % rotation frequency, 2pi/period
qrot % rotation parameter wrot^2a0^3/GM
mrot % rotation parameter, wrot^2s0^3/GM
J2 % convenience alias to obj.Js(2)
J4 % convenience alias to obj.Js(3)
J6 % convenience alias to obj.Js(4)
J8 % convenience alias to obj.Js(5)
end
properties (GetAccess = private)
G % Gravitational constant
end
%% A simple constructor
methods
function obj = CMSPlanet(varargin)
% A simple constructor of CMSPlanet objects.
% CMSPlanet('OPTION1', VALUE, 'OPTION2', VALUE2,...)
% Populate options struct
obj.opts = cmsset(varargin{:});
% Init privates
obj.G = 6.67430e-11; % m^3 kg^-1 s^-2 (2018 NIST reference)
end
end % End of constructor block
%% Public methods
methods (Access = public)
function obj = set_J_guess(obj, jlike)
% Use with caution
obj.CMS.JLike = jlike;
end
function obj = set_observables(obj, obs)
% Copy physical properties from an +observables struct.
obj.mass = obs.M;
obj.radius = obs.a0;
obj.period = obs.P;
obj.P0 = obs.P0;
try
obj.bgeos = obs.bgeos;
obj.fgeos = obs.fgeos;
catch
end
end
function ET = relax_to_barotrope(obj)
% Relax equilibrium shape, Js, and density simultaneously.
% First some checks.
if isempty(obj.eos)
warning('CMSPLANET:assertion',...
'Set valid barotrope first (<obj>.eos = <barotrope>).')
return
end
if numel(obj.renormalize()) < 2
warning('CMSPLANET:assertion',...
'First set reference mass and equatorial radius.')
return
end
if isempty(obj.qrot)
warning('CMSPLANET:assertion',...
'First set rotation period (<obj>.period).')
return
end
if isempty(obj.P0)
warning('CMSPLANET:P0',...
'First set reference pressure (<obj>.P0).')
return
end
% Optional communication
verb = obj.opts.verbosity;
if (verb > 0)
fprintf('Relaxing to desired barotrope...\n\n')
end
% Ready, set,...
warning('off','CMS:maxiter')
t_rlx = tic;
% Main loop
iter = 1;
while (iter <= obj.opts.MaxIterBar)
t_pass = tic;
if (verb > 0)
fprintf('Baropass %d (of max %d)...\n',...
iter, obj.opts.MaxIterBar)
end
old_Js = obj.Js;
old_Js(abs(old_Js) < 1e-12) = 0; % a hack for nonrotating planets
if isempty(old_Js)
old_Js = [-1,zeros(1,15)];
end
old_ro = obj.rhoi;
% Call the cms algorithm
if isempty(obj.CMS), obj.CMS.JLike = struct(); end
[obj.Js, obj.CMS] = cms(obj.ai, obj.rhoi, obj.qrot,...
'tol',obj.opts.dJtol, 'maxiter',obj.opts.MaxIterHE,...
'xlayers', obj.opts.xlayers, 'J0s', obj.CMS.JLike,...
'prerat', obj.opts.prerat);
% Update density from barotrope and renormalize
obj.update_densities();
obj.renormalize();
% Calculate changes in shape/density
dJs = abs((obj.Js - old_Js)./old_Js);
dJs = max(dJs(isfinite(dJs(1:6))));
dro = obj.rhoi./old_ro;
dro = var(dro(isfinite(dro)));
if (verb > 0)
fprintf('Baropass %d (of max %d)...done. (%g sec)\n',...
iter, obj.opts.MaxIterBar, toc(t_pass))
fprintf('var(drho) = %g (%g required); dJ = %g (%g required).\n\n',...
dro, obj.opts.drhotol, dJs, obj.opts.dJtol)
end
% The stopping criterion is to satisfy both J and rho tolerance
if (dro < obj.opts.drhotol) && dJs < obj.opts.dJtol
break
end
% end the main loop
iter = iter + 1;
end
ET = toc(t_rlx);
if iter > obj.opts.MaxIterBar
warning('CMSPLANET:maxiter','Pressure/density may not be fully converged.')
end
% Renorm and record factors
% TODO: if we still need betam we must fix this
renorms = obj.renormalize();
obj.alfar = renorms(1);
obj.betam = renorms(2);
% Some clean up
warning('on','CMS:maxiter')
% Optional communication
if (verb > 0)
fprintf('Relaxing to desired barotrope...done.\n')
fprintf('Total elapsed time %s\n',lower(seconds2human(ET)))
end
end
function [ET, dJ] = relax_to_HE(obj)
% Call cms to obtain equilibrium shape and gravity.
if (obj.opts.verbosity > 1)
fprintf(' Relaxing to hydrostatic equilibrium...\n')
end
t_rlx = tic;
zvec = obj.ai/obj.ai(1);
dvec = obj.rhoi/obj.rhoi(end);
if isempty(obj.CMS), obj.CMS.JLike = struct(); end
[obj.Js, obj.CMS] = cms(zvec, dvec, obj.qrot,...
'tol',obj.opts.dJtol, 'maxiter',obj.opts.MaxIterHE,...
'xlayers',obj.opts.xlayers, 'J0s',obj.CMS.JLike,...
'prerat', obj.opts.prerat);
ET = toc(t_rlx);
dJ = obj.CMS.dJs;
if (obj.opts.verbosity > 1)
fprintf(' Relaxing to hydrostatic equilibrium...done.\n')
fprintf(' Elapsed time %s\n',lower(seconds2human(ET)))
end
end
function dro = update_densities(obj)
% Set layer densities to match prescribed barotrope.
if isempty(obj.eos)
error('CMSPLANET:noeos','Make sure input barotrope (<obj>.eos) is set.')
end
t_rho = tic;
verb = obj.opts.verbosity;
if (verb > 1)
fprintf(' Updating layer densities...')
end
P = obj.P_mid();
if isscalar(obj.eos)
newro = obj.eos.density(P);
else
newro = repmat(obj.rhoi(1), obj.N, 1);
for k=1:length(newro)
newro(k) = obj.eos(k).density(P(k));
end
end
dro = ((newro - obj.rhoi)./obj.rhoi);
if (verb > 2)
fprintf('done. (%g sec)\n', toc(t_rho))
elseif (verb > 1)
fprintf('done.\n')
end
obj.rhoi = newro;
end
function P_m = P_mid(obj)
% Mid-layer pressure (by interpolation)
P = obj.Pi;
r = obj.ai;
P_m = NaN(size(P));
switch obj.opts.prsmeth
case 'linear'
P_m(1:end-1) = (P(1:end-1) + P(2:end))/2;
P_m(end) = P(end) + ...
(P(end) - P(end-1))/(r(end-1) - r(end))*(r(end)/2);
case 'spline'
otherwise
error('Unknown prsmeth; check cmsset for options.')
end
end
function P_c = P_center(obj)
% Central pressure (by extrapolation)
try
P_c = spline(obj.CMS.lambdas, obj.Pi, 0);
catch
P_c = [];
end
end
function ab = renormalize(obj)
% Match input and calculated mass and equatorial radius.
try
a = obj.radius/obj.a0;
obj.ai = obj.ai*a;
catch
a = [];
end
try
b = obj.mass/obj.M;
obj.rhoi = obj.rhoi*b;
catch
b = [];
end
ab = [a, b];
end
function obj = fix_radius(obj)
% Resize planet to match equatorial radius to observed value.
if isempty(obj.radius) || isempty(obj.a0) || isempty(obj.ai)
warning('CMP:noref','Missing information; no action.')
return
end
obj.ai = obj.ai*obj.radius/obj.a0;
end
function obj = fix_mass(obj)
% Rescale density to match planet mass to observed value.
if isempty(obj.mass) || isempty(obj.rhoi)
warning('CMP:noref','Missing information; no action.')
return
end
obj.rhoi = obj.rhoi*obj.mass/obj.M;
end
function I = NMoI(obj, reduce)
% C/Ma^2, see eq. 5 in Hubbard & Militzer 2016
if nargin < 2 || isempty(reduce), reduce = 'sum'; end
reduce = validatestring(reduce, {'sum', 'csum', 'none'});
if isempty(obj.CMS)
deltas = [obj.rhoi(1); diff(obj.rhoi)];
lambdas = obj.ai/obj.a0;
zetas = ones(obj.N, obj.opts.nangles);
else
deltas = obj.CMS.deltas;
lambdas = obj.CMS.lambdas;
zetas = obj.CMS.zetas;
end
[mus, gws] = gauleg(0, 1, obj.opts.nangles); % Abscissas and weights for Gauss-quad
p2term = 1 - Pn(2, mus);
num(obj.N) = 0;
den = 0;
for j=1:obj.N
fun1 = deltas(j)*((zetas(j,:)*lambdas(j)).^5).*p2term;
fun2 = deltas(j)*((zetas(j,:)*lambdas(j)).^3);
num(j) = fun1*gws';
den = den + fun2*gws';
end
if isequal(reduce, 'none'), I = (2/5)*(num/den); end
if isequal(reduce, 'sum'), I = (2/5)*sum(num)/den; end
if isequal(reduce, 'csum'), I = (2/5)*cumsum(num)/den; end
end
end % End of public methods block
%% Visualizers
methods (Access = public)
function [ah, lh] = plot_rho_of_r(obj, varargin)
% Plot rho(r) where r is equatorial radius.
% Don't bother if uninitialized
if isempty(obj.ai) || isempty(obj.rhoi)
warning('Uninitialized object.')
return
end
% Input parsing
p = inputParser;
p.addParameter('axes', [], @(x)isscalar(x) && isgraphics(x, 'axes'))
p.addParameter('plottype', 'line', @(x)isrow(x) && ischar(x))
p.parse(varargin{:})
pr = p.Results;
% Prepare the canvas
if isempty(pr.axes)
fh = figure;
set(fh, 'defaultTextInterpreter', 'latex')
set(fh, 'defaultLegendInterpreter', 'latex')
ah = axes;
hold(ah, 'on')
else
ah = pr.axes;
hold(ah, 'on')
end
% Prepare the data
x = [obj.ai/obj.a0; 0];
y = [obj.rhoi; obj.rhoi(end)];
% Plot the lines (density in 1000 kg/m^3)
if isequal(lower(pr.plottype), 'stairs')
lh = stairs(x, y/1000);
elseif isequal(lower(pr.plottype), 'line')
lh = line(x, y/1000);
else
error('Unknown value of parameter plottype.') %#ok<*ERTAG>
end
lh.LineWidth = 2;
if isempty(pr.axes)
lh.Color = [0.31, 0.31, 0.31];
end
if isempty(obj.name)
lh.DisplayName = 'CMS model';
else
lh.DisplayName = obj.name;
end
% Style and annotate axes
if isempty(pr.axes)
ah.Box = 'on';
xlabel('Level surface equatorial radius, $a/a_0$', 'fontsize', 12)
ylabel('$\rho$ [1000 kg/m$^3$]', 'fontsize', 12)
else
xlim('auto')
end
end
function [ah, lh] = plot_barotrope(obj, varargin)
% Plot P(rho) of current model and optionally of input barotrope.
% Don't bother if there is no pressure
if isempty(obj.Pi)
warning('Uninitialized object. Remember to set obj.P0?')
return
end
% Input parsing
p = inputParser;
p.addParameter('axes', [],...
@(x)isscalar(x) && isgraphics(x,'axes') && isvalid(x));
p.addParameter('showinput', false,...
@(x)isscalar(x) && islogical(x));
p.addParameter('showscaledinput', false,...
@(x)isscalar(x) && islogical(x));
p.addParameter('includecore', false,...
@(x)isscalar(x) && islogical(x));
p.parse(varargin{:})
pr = p.Results;
% Prepare the canvas
if isempty(pr.axes)
fh = figure;
set(fh, 'defaultTextInterpreter', 'latex')
set(fh, 'defaultLegendInterpreter', 'latex')
ah = axes;
hold(ah, 'on')
else
ah = pr.axes;
hold(ah, 'on')
end
% Prepare the data: model
x_cms = obj.rhoi;
y_cms = obj.P_mid();
% Prepare the data: input
if pr.showinput && ~isempty(obj.eos) && (range(x_cms) > 0)
x_bar = linspace(min(x_cms), max(x_cms));
if isscalar(obj.eos)
y_bar = double(obj.eos.pressure(x_bar));
else
v = 1:length(unique(x_cms));
ind = interp1(unique(x_cms), v, x_bar, 'nearest', 'extrap');
y_bar = nan(size(x_bar));
for k=1:length(x_bar)
y_bar(k) = double(obj.eos(ind(k)).pressure(x_bar(k)));
end
end
else
y_bar = NaN;
end
% Prepare the data: scaled input
if pr.showscaledinput && ~isempty(obj.eos) && (range(x_cms) > 0)
x_bar = linspace(min(x_cms), max(x_cms));
bnorm = obj.betam; % the mass renorm factor
anorm = obj.alfar; % the radius renorm factor
if isempty(bnorm), bnorm = nan; end
if isempty(anorm), anorm = nan; end
if isscalar(obj.eos)
y_bar_scl = double(bnorm/anorm*obj.eos.pressure(x_bar/bnorm));
else
v = 1:length(unique(x_cms));
ind = interp1(unique(x_cms), v, x_bar, 'nearest', 'extrap');
y_bar_scl = nan(size(x_bar));
for k=1:length(x_bar)
y_bar_scl(k) = double(...
bnorm/anorm*obj.eos(ind(k)).pressure(x_bar(k)/bnorm));
end
end
else
y_bar_scl = NaN;
end
% Plot the lines (pressure in GPa)
lh(1) = stairs(x_cms, y_cms/1e9);
lh(1).LineWidth = 2;
if isempty(pr.axes)
lh(1).Color = [0.31, 0.31, 0.31];
end
if isempty(obj.name)
lh(1).DisplayName = 'CMS model';
else
lh(1).DisplayName = obj.name;
end
if pr.showinput && any(isfinite(y_bar))
lh(end+1) = line(x_bar, y_bar/1e9);
lh(end).Color = 'r';
lh(end).LineStyle = '--';
lh(end).DisplayName = 'input barotrope';
end
if pr.showscaledinput && any(isfinite(y_bar_scl))
lh(end+1) = line(x_bar, y_bar_scl/1e9);
lh(end).Color = [0, 0.5, 0];
lh(end).LineStyle = '--';
lh(end).DisplayName = 'input barotrope ($\frac{\beta}{\alpha}$-scaled)';
end
% Style and annotate axes
if isempty(pr.axes)
ah.Box = 'on';
if (range(x_cms) > 0)
xlim([min(x_cms),max(x_cms)])
end
xlabel('$\rho$ [kg/m$^3$]')
ylabel('$P$ [GPa]')
else
xlim('auto')
end
% Legend
legend(ah, 'off')
gh = legend(ah, 'show','location','nw');
gh.FontSize = 11;
end
function [ah, lh] = plot_spheroid_J_contributions(obj, n, varargin)
% Plot relative weights of *spheroids* contribution to Js.
% Input parsing
if nargin < 2
fprintf('Usage:\n\tCMP.plot_spheroid_J_contributions([2,4,...]).\n')
return
end
validateattributes(n,{'numeric'},{'row','nonnegative','integer','even'})
p = inputParser;
p.addParameter('axes',[],@(x)isscalar(x)&&isgraphics(x, 'axes'))
p.addParameter('cumulative',false,@(x)isscalar(x)&&islogical(x))
p.addParameter('noisecancel',false,@(x)isscalar(x)&&islogical(x))
p.parse(varargin{:})
pr = p.Results;
% Prepare the data
x = obj.ai/obj.a0;
y = nan(obj.N, length(n));
for k=1:length(n)
cJi = cumsum(obj.CMS.JLike.fulltilde(:,n(k)+1).*obj.CMS.lambdas.^n(k));
dJi = sdderiv(obj.CMS.lambdas, cJi);
if pr.cumulative
y(:,k) = cJi/cJi(end);
else
if pr.noisecancel
dJi(x > 0.99) = nan;
end
y(:,k) = abs(dJi)/max(abs(dJi));
end
end
% Prepare the canvas
if isempty(pr.axes)
fh = figure;
set(fh, 'defaultTextInterpreter', 'latex')
set(fh, 'defaultLegendInterpreter', 'latex')
ah = axes;
else
ah = pr.axes;
axes(ah)
end
hold(ah, 'on')
% Plot the lines
lh = gobjects(1,length(n));
for k=1:length(n)
lh(k) = plot(obj.CMS.lambdas, y(:,k), 'LineWidth',2);
lh(k).DisplayName = sprintf('$J_{%i}$',n(k));
end
% Style and annotate axes
if isempty(pr.axes)
ah.Box = 'on';
xlabel('Spheroid normalized equatorial radius, $\lambda$', 'fontsize', 12)
if pr.cumulative
ylabel('$|J_n(\lambda>\lambda_i)|$ [normalized]', 'fontsize', 12)
else
ylabel('$|J_n(\lambda_i)|$ [normalized]', 'fontsize', 12)
end
end
% Legend
legend(ah, 'off')
gh = legend(ah, flipud(lh));
if pr.cumulative
gh.Location = 'southwest';
else
gh.Location = 'northwest';
end
gh.FontSize = 11;
end
function [ah, lh] = plot_moi_contribution(obj, varargin)
% Plot relative contribution to MoI by depth.
p = inputParser;
p.addParameter('axes',[],@(x)isscalar(x)&&isgraphics(x, 'axes'))
p.addParameter('cumulative',false,@(x)isscalar(x)&&islogical(x))
p.addParameter('noisecancel',false,@(x)isscalar(x)&&islogical(x))
p.parse(varargin{:})
pr = p.Results;
% Prepare the data
x = obj.ai/obj.a0;
if pr.cumulative
y = obj.NMoI('csum');
y = y/y(end);
else
y = obj.NMoI('none');
if pr.noisecancel
y(x > 0.99) = nan; % the top is always noise
end
y = y/max(abs(y));
end
% Prepare the canvas
if isempty(pr.axes)
fh = figure;
set(fh, 'defaultTextInterpreter', 'latex')
set(fh, 'defaultLegendInterpreter', 'latex')
ah = axes;
else
ah = pr.axes;
axes(ah)
end
hold(ah, 'on')
% Plot the lines
lh = plot(x, y, 'LineWidth',2);
% Style and annotate axes
if isempty(pr.axes)
ah.Box = 'on';
xlabel('Spheroid normalized equatorial radius, $\lambda$', 'fontsize', 12)
if pr.cumulative
ylabel('$I(\lambda>\lambda_i)$ [normalized]', 'fontsize', 12)
else
ylabel('$I(\lambda_i)$ [normalized]', 'fontsize', 12)
end
end
end
end % End of visulaizers block
%% Reporters/exporters
methods (Access = public)
function T = report_card(obj, obs)
% REPORT_CARD Table summary of model's vital statistics.
% Minimal checks
narginchk(1,2);
try
obj.J2;
catch
warning('Uncooked object.') %#ok<*WNTAG>
return
end
% Basic table
vitals = {'Mass [kg]'; 'R_eq [km]'; 'J2'; 'J4'; 'J6'; 'J8'; 'NMoI'};
CMP1 = [obj.M; obj.a0/1e3; obj.J2; obj.J4; obj.J6; obj.J8; obj.NMoI];
T = table(CMP1, 'RowNames', vitals);
if ~isempty(obj.name)
vname = matlab.lang.makeValidName(obj.name);
T.Properties.VariableNames{'CMP1'} = vname;
end
if nargin == 1, return, end
% Optionally compare with something
try
oM = obs.M;
catch
oM = NaN;
end
try
oa0 = obs.a0/1e3;
catch
oa0 = NaN;
end
try
oJ2 = obs.J2;
oJ4 = obs.J4;
oJ6 = obs.J6;
oJ8 = obs.J8;
catch
oJ2 = NaN;
oJ4 = NaN;
oJ6 = NaN;
oJ8 = NaN;
end
try
oNMoI = obs.NMoI;
catch
oNMoI = NaN;
end
try
oname = obs.name;
catch
oname = [];
end
OBS1 = [oM; oa0; oJ2; oJ4; oJ6; oJ8; oNMoI];
OBS1 = double(OBS1);
T = [T table(OBS1)];
if ~isempty(oname)
vname = matlab.lang.makeValidName(obs.name);
try
T.Properties.VariableNames{'OBS1'} = vname;
catch
T.Properties.VariableNames{'OBS1'} = ['x_',vname];
end
end
DIFF = (CMP1 - OBS1)./CMP1;
T = [T table(DIFF, 'VariableNames', {'frac_diff'})];
end
function s = to_struct(obj, rdc, keepjlike)
% Convert object to static struct keeping only essential fields.
if nargin < 2, rdc = 1; end % 0=none, 1=to double, 2=to single, 3=to scalars
if nargin < 3, keepjlike = false; end % keep JLike e.g. to help re-relaxing
s.name = obj.name;
s.M = obj.M;
s.s0 = obj.s0;
s.a0 = obj.a0;
s.rhobar = obj.rhobar;
s.mrot = obj.mrot;
s.qrot = obj.qrot;
s.J2 = obj.J2;
s.J4 = obj.J4;
s.J6 = obj.J6;
s.J8 = obj.J8;
s.J10 = obj.Js(6);
s.J12 = obj.Js(7);
s.J14 = obj.Js(8);
s.NMoI = obj.NMoI;
s.si = obj.si;
s.ai = obj.ai;
s.rhoi = obj.rhoi;
s.Pi = obj.Pi;
s.mi = obj.mi;
if rdc > 0
s = structfun(@double, s, 'UniformOutput', false);
s.name = obj.name;
end
if rdc > 1
s = structfun(@single, s, 'UniformOutput', false);
s.name = obj.name;
end
if rdc > 2
s.si = [];
s.ai = [];
s.rhoi = [];
s.Pi = [];
s.mi = [];
end
try
s.eos = obj.eos.name;
catch
s.eos = '';
end
try
s.bgeos = obj.bgeos.name;
catch
s.bgeos = '';
end
try
s.fgeos = obj.fgeos.name;
catch
s.fgeos = '';
end
if keepjlike
s.CMS.JLike = obj.CMS.JLike;
else
s.CMS.JLike = [];
end
end
function T = to_table(obj)
% Return a table of critical quantities.
T = table;
T.ai = double(obj.ai);
T.si = double(obj.si);
T.rhoi = double(obj.rhoi);
T.Pi = double(obj.Pi);
T.mi = double(obj.mi);
end
function to_ascii(obj, fname)
% Export the state of the model as ascii file.
% File name
if nargin < 2
fprintf('Usage:\n\tcmp.to_ascii(filename)\n')
return
end
validateattributes(fname, {'char'}, {'row'}, '', 'fname', 1)
% Open file
fid = fopen(fname,'wt');
cleanup = onCleanup(@()fclose(fid));
% Write the header
fprintf(fid,'# Rotating fluid planet modeled by Concentric Maclaurin Spheroids.\n');
fprintf(fid,'#\n');
fprintf(fid,'# Model name: %s\n', obj.name);
fprintf(fid,'#\n');
fprintf(fid,'# Scalar quantities:\n');
fprintf(fid,'# N layers = %d\n',obj.N);
fprintf(fid,'# Mass M = %g kg\n', obj.M);
fprintf(fid,'# Mean radius s0 = %0.6e m\n', obj.s0);
fprintf(fid,'# Equatorial radius a0 = %0.6e m\n', obj.a0);
fprintf(fid,'# Rotation period P = %0.6g s\n', obj.period);
fprintf(fid,'# Rotation parameter m = %0.6f\n', obj.mrot);
fprintf(fid,'# Rotation parameter q = %0.6f\n', obj.qrot);
fprintf(fid,'#\n');
fprintf(fid,'# Calculated gravity zonal harmonics (x 10^6):\n');
fprintf(fid,'# J2 = %12.6f\n', obj.J2*1e6);
fprintf(fid,'# J4 = %12.6f\n', obj.J4*1e6);
fprintf(fid,'# J6 = %12.6f\n', obj.J6*1e6);
fprintf(fid,'# J8 = %12.6f\n', obj.J8*1e6);
fprintf(fid,'#\n');
fprintf(fid,'# Column data description (MKS):\n');
fprintf(fid,'# i - level surface index (increasing with depth)\n');
fprintf(fid,'# s_i - mean radius of level surface i\n');
fprintf(fid,'# a_i - equatorial radius of level surface i\n');
fprintf(fid,'# rho_i - density on level surfaces i\n');
fprintf(fid,'# P_i - pressure on level surface i\n');
fprintf(fid,'# m_i - mass below level surface i\n');
fprintf(fid,'#\n');
% Write the data
fprintf(fid,'# Column data:\n');
fprintf(fid,'# %-4s ','i');
fprintf(fid,'%-10s ','s_i','a_i');
fprintf(fid,'%-7s ','rho_i');
fprintf(fid,'%-10s ','P_i','m_i');
fprintf(fid,'\n');
for k=1:obj.N
fprintf(fid,' %-4d ',k);
fprintf(fid,'%10.4e ', obj.si(k));
fprintf(fid,'%10.4e ', obj.ai(k));
fprintf(fid,'%7.1f ', obj.rhoi(k));
fprintf(fid,'%10.4e ', obj.Pi(k), obj.mi(k));
fprintf(fid,'\n');
end
end
end % End of reporters/exporters block
%% Private (or obsolete) methods
methods (Access = private)
function Upu = calc_equipotential_Upu(obj)
% See ./notes/cms.pdf eqs. 49 and 51
Upu = NaN(obj.N,1);
lam = obj.CMS.lambdas;
til = obj.CMS.JLike.tilde;
tilp = obj.CMS.JLike.tildeprime;
tilpp = obj.CMS.JLike.tildeprimeprime;
q = obj.qrot;
zet = obj.CMS.zetas(:,2); % 2 for no reason, could pick any angle
P2k = obj.CMS.Ps.Pnmu(:,2);
n = (0:obj.opts.kmax)';
xind = obj.CMS.xind;
xlam = lam(xind);
nx = length(xind);
for j=1:obj.N
U = 0;
xj = find(xlam <= lam(j), 1);
if isempty(xj), xj = nx + 1; end % if no xplicit layer below jth
for i=xj:nx
U = U + sum((xlam(i)/lam(j)).^n.*til(i,:)'.*zet(j).^-n.*P2k(:));
end
for i=1:xj-1
U = U + sum((lam(j)/xlam(i)).^(n+1).*tilp(i,:)'.*zet(j).^(n+1).*P2k(:));
U = U + (lam(j)/xlam(i))^3*tilpp(i)*zet(j)^3;
end
U = U*(-1/(lam(j)*zet(j)));
U = U + (1/3)*q*lam(j)^2*zet(j)^2*(1 - P2k(3)); % add rotation
Upu(j) = U;
end
end
function val = mzi(obj)
% heavy element mass below level i
z = obj.zi;
if isempty(obj.ai) || isempty(obj.rhoi) || isempty(z)
val = [];
else
val = NaN; %TODO: implement
end
end
function val = zi(obj)
% heavy element mass fraction in layer i
P = obj.Pi;
if isempty(obj.bgeos) || isempty(obj.fgeos) || isempty(P)
val = [];
else
roxy = obj.bgeos.density(P);
roz = obj.fgeos.density(P);
ro = obj.rhoi;
val = (1./ro - 1./roxy)./(1./roz - 1./roxy);
val(~isfinite(val)) = 0;
end
end
function val = M_Z(obj)
try
val = obj.mzi(1);
catch
val = [];
end
end
end
%% Access and pseudo-access methods
methods
function set.name(obj,val)
if ~isempty(val)
validateattributes(val, {'char'}, {'row'})
end
obj.name = val;
end
function set.mass(obj,val)
validateattributes(val,{'numeric'},{'positive','scalar'})
obj.mass = val;
end
function set.radius(obj,val)
validateattributes(val,{'numeric'},{'positive','scalar'})
obj.radius = val;
end
function set.ai(obj, val)
assert(isnumeric(val) && isvector(val) && ~any(val<0),...
'obj.ai must be a nonnegative vector.')
assert(all(diff(val)<=0),'obj.ai must be non-ascending.')
obj.ai = val(:);
end
function set.rhoi(obj, val)
assert(isnumeric(val) && isvector(val),...
'obj.rhoi must be a nonnegative vector.')
if any(val<0)
warning('CMSPLANET:assertion','negative density. Is this on purpose?')
end
obj.rhoi = val(:);
end
function set.eos(obj,val)
if isempty(val)
obj.eos = [];
return
end
if ~isa(val,'barotropes.Barotrope')
error('eos must be a valid instance of class Barotrope')
end
obj.eos = val(:);
end
function set.bgeos(obj,val)
if isempty(val)
obj.bgeos = [];
return
end
if ~isa(val,'barotropes.Barotrope')
error('bgeos must be a valid instance of class Barotrope')
end
obj.bgeos = val;
end
function set.fgeos(obj,val)
if isempty(val)
obj.fgeos = [];
return
end
if ~isa(val,'barotropes.Barotrope')
error('fgeos must be a valid instance of class Barotrope')
end
obj.fgeos = val;
end
function val = get.a0(obj)
if isempty(obj.ai)
val = [];
else
val = obj.ai(1);
end
end
function val = get.N(obj)
if isempty(obj.ai) || isempty(obj.rhoi)
val = 0;
elseif length(obj.ai) == length(obj.rhoi)
val = length(obj.ai);
else
error('length(ai) = %g ~= length(rhoi) = %g',...
length(obj.ai),length(obj.rhoi))
end
end
function val = Ui(obj, ind)
try
val = (obj.G*obj.mass/obj.radius)*obj.calc_equipotential_Upu();
if nargin > 1
val = val(ind);
end
catch
val = [];
end
end
function val = Pi(obj, ind)
try
U = obj.Ui;
rho = obj.rhoi;
val = zeros(obj.N,1);
val(1) = obj.P0;
val(2:end) = val(1) + cumsum(rho(1:end-1).*diff(U));
if nargin > 1
val = val(ind);
end
catch
val = [];
end
end
function val = get.M(obj)
try
drho = [obj.rhoi(1); diff(obj.rhoi)];
if isempty(obj.si)
val = (4*pi/3)*sum(drho.*obj.ai.^3);
else
val = (4*pi/3)*sum(drho.*obj.si.^3);
end
catch
val = [];
end
end
function val = get.mi(obj)
% mass _below_ level i
if isempty(obj.ai) || isempty(obj.rhoi)
val = [];
else
rho = obj.rhoi;
s = obj.si;
n = obj.N;
val(n) = 4*pi/3*rho(n)*s(n)^3;
for k=n-1:-1:1
val(k) = val(k+1) + 4*pi/3*rho(k)*(s(k)^3 - s(k+1)^3);
end
val = val';
end
end
function val = get.rhobar(obj)
if isempty(obj.M) || isempty(obj.si)
val = [];
else
val = obj.M/(4*pi/3*obj.s0^3);
end
end
function val = get.wrot(obj)
val = 2*pi./obj.period;
end
function val = get.qrot(obj)
GM = obj.G*obj.mass;
val = obj.wrot^2.*obj.radius^3./GM;
end
function val = get.mrot(obj)
GM = obj.G*obj.mass;
val = obj.wrot^2.*obj.s0^3./GM;
end
function set.qrot(~,~)
error('CMSPLANET:deprecation',...
'Setting qrot is deprecated; set a reference period instead.')
end
function val = get.si(obj)
try
Vs = NaN(size(obj.CMS.lambdas));
for j=1:obj.N
Vs(j) = obj.CMS.lambdas(j)^3*(obj.CMS.zetas(j,:).^3)*(obj.CMS.gws');
end
val = obj.a0*Vs.^(1/3);
catch
val = [];
end
end
function val = get.s0(obj)
try
val = obj.si(1);
catch
val = [];
end
end
function val = get.J2(obj)
if isempty(obj.Js)
val = 0;
else
val = obj.Js(2);
end
end
function val = get.J4(obj)
if isempty(obj.Js)
val = 0;
else
val = obj.Js(3);
end
end
function val = get.J6(obj)
if isempty(obj.Js)
val = 0;
else
val = obj.Js(4);
end
end
function val = get.J8(obj)
if isempty(obj.Js)
val = 0;
else
val = obj.Js(5);
end
end
end % End of access methods block
%% Static methods
methods (Static)
end % End of static methods block
end % End of classdef
%% Class-related functions
function [x,w] = gauleg(x1,x2,n)
%GAULEG Calculate abscissas and weights for Gauss-Legendre n-point quadrature.
% [x,w] = GAULEG(x1,x2,n) returns the abscissas x and weights w that can be
% used to evaluate the definite integral, I, of a function well approximated
% by an (2n - 1) degree polynomial in the interval [x1,x2] using the
% Gauss-Legendre formula:
%
% I = sum(w.*f(x))
%
% Algorithm
% This function is based on the C++ implementation of a routine with the
% same name in Numerical Recipes, 3rd Edition. But in several places I opt
% for readability over performance, on the assumption that this function is
% most likely to be called in a setup routine rather than in an inner-loop
% computation.
%
% Example
% fun = @(x)sin(x);
% [x,w] = gauleg(0,pi,6);
% I_adaptive = integral(fun,0,pi)
% I_gaussleg = sum(w.*fun(x))
%
% Author: Naor Movshovitz (nmovshov at google dot com)
% Earth and Planetary Sciences, UC Santa Cruz
%
% Reference: William H. Press, Saul A. Teukolsky, William T. Vetterling, and
% Brian P. Flannery. 2007. Numerical Recipes 3rd Edition: The Art of Scientific
% Computing (3 ed.). Cambridge University Press, New York, NY, USA.
% Input parsing and minimal assertions
narginchk(3,3)
nargoutchk(2,2)
validateattributes(x1,{'numeric'},{'scalar','finite','real'},1)
validateattributes(x2,{'numeric'},{'scalar','finite','real'},2)
validateattributes(n,{'numeric'},{'scalar','finite','integer','>=',2},3)
assert(x2 > x1, 'Interval must be positive.');
% Local variables
tol = 1e-14;
m = ceil(n/2);
xmid = (x1 + x2)/2;
dx = (x2 - x1);
x = NaN(1,n);
w = NaN(1,n);
% Main loop
for j=1:m
% Get j-th root of Legendre polynomial Pn, along with Pn' value there.
z = cos(pi*((j - 1) + 0.75)/(n + 0.5)); % initial guess for j-th root
while true
% Calculate Pn(z) and Pn-1(z) and Pn'(z)
p = NaN(1,n+1);
p(1) = 1;
p(2) = z;
for k=2:n
pkm1 = p(k);
pkm2 = p(k-1);
pk = (1/k)*((2*k - 1)*z*pkm1 - (k - 1)*pkm2);
p(k+1) = pk;
end
pn = p(end);
pp = (n*p(end-1) - n*z*p(end))/(1 - z^2);
% And now Newton's method (we are hopefully very near j-th root)
oldz = z;
z = z - pn/pp;
if abs(z - oldz) < tol, break, end
end
% Now use j-th root to get 2 abscissas and weights
x(j) = xmid - z*dx/2; % Scaled abscissa left of center
x(n+1-j) = xmid + z*dx/2; % Scaled abscissa right of center
w(j) = dx/((1 - z^2)*pp^2);
w(n+1-j) = w(j);
end
% Verify and return
assert(all(isfinite(x)))
assert(all(isfinite(w)))
end
function y = Pn(n,x)
% Fast implementation of ordinary Legendre polynomials of low degree.
switch n
case 0
y = ones(size(x));
case 1
y = x;
case 2
y = 0.5*(3*x.^2 - 1);
case 3
y = 0.5*(5*x.^3 - 3*x);
case 4
y = (1/8)*(35*x.^4 - 30*x.^2 + 3);
case 5
y = (1/8)*(63*x.^5 - 70*x.^3 + 15*x);
case 6
y = (1/16)*(231*x.^6 - 315*x.^4 + 105*x.^2 - 5);
case 7
y = (1/16)*(429*x.^7 - 693*x.^5 + 315*x.^3 - 35*x);
case 8
y = (1/128)*(6435*x.^8 - 12012*x.^6 + 6930*x.^4 - 1260*x.^2 + 35);
case 9
y = (1/128)*(12155*x.^9 - 25740*x.^7 + 18018*x.^5 - 4620*x.^3 + 315*x);
case 10
y = (1/256)*(46189*x.^10 - 109395*x.^8 + 90090*x.^6 - 30030*x.^4 + 3465*x.^2 - 63);
case 11
y = (1/256)*(88179*x.^11 - 230945*x.^9 + 218790*x.^7 - 90090*x.^5 + 15015*x.^3 - 693*x);
case 12
y = (1/1024)*(676039*x.^12 - 1939938*x.^10 + 2078505*x.^8 - 1021020*x.^6 + 225225*x.^4 - 18018*x.^2 + 231);
otherwise
assert(isvector(x))
Pnm = legendre(n,x);
y = Pnm(1,:);
if ~isrow(x), y = y'; end
end
end
|
github
|
nmovshov/CMS-planet-master
|
cmsset.m
|
.m
|
CMS-planet-master/cmsset.m
| 2,632 |
utf_8
|
7a832d5c0ef8fe7bcac0c07172e51730
|
function options = cmsset(varargin)
%CMSSET Create options structure used by CMSPlanet class methods.
% OPTIONS = CMSSET('NAME1',VALUE1,'NAME2',VALUE2,...) creates an options
% structure OPTIONS in which the named properties have the specified values.
% Any unspecified properties have default values. Case is ignored for property
% names and unique partials are allowed.
%
% CMSSET with no input or output arguments displays all property names and
% their possible values.
%
%KNOWN PROPERTIES
%
%dJtol - Convergence tolerance for gravity coefficients [ positive real {1e-8} ]
%drhotol - Convergence tolerance for density adjustment [ positive real {1e-6} ]
%MaxIterBar - Number of iterations allowed for relaxation to barotrope [ positive integer {60} ]
%MaxIterHE - Number of iterations allowed for relaxation to equilibrium shape [ positive integer {60} ]
%xlayers - Solve shape functions on xlayers and spline the rest [ integer scalar or vector (-1 to disable) {-1} ]
%verbosity - Level of runtime messages [0 {1} 2 3 4]
%prerat - Precalculate powers of ratios of lambdas (trades memory for speed) [ {true} | false ]
% If no arguments print usage and return.
if (nargin == 0) && (nargout == 0)
print_usage()
return
end
% Define name-value pairs.
p = inputParser;
p.FunctionName = mfilename;
p.addParameter('dJtol',1e-8,@isposscalar)
p.addParameter('drhotol',1e-6,@isposscalar)
p.addParameter('MaxIterBar',60,@isposintscalar)
p.addParameter('MaxIterHE',60,@isposintscalar)
p.addParameter('xlayers',-1,@(x)validateattributes(x,{'numeric'},{'vector','integer'}))
p.addParameter('verbosity',1,@isnonnegintscalar)
p.addParameter('prerat',true,@islogicalscalar)
% undocumented or obsolete options
p.addParameter('prsmeth','linear'); % undocumented pressure interpolation method
p.addParameter('moimeth','midlayerz'); % undocumented moi integral method
p.addParameter('nangles',48,@isposintscalar)% #colatitudes used to define level surface
p.addParameter('kmax',30,@isposintscalar) % degree to carry out gravity mulitpole expansion
p.addParameter('TolX',1e-13,@isposscalar) % termination tolerance for root finding
% Parse name-value pairs and return.
p.parse(varargin{:})
options = p.Results;
end
function isposscalar(x)
validateattributes(x,{'numeric'},{'positive','scalar'})
end
function islogicalscalar(x)
validateattributes(x,{'logical'},{'scalar'})
end
function isposintscalar(x)
validateattributes(x,{'numeric'},{'positive','integer','scalar'})
end
function isnonnegintscalar(x)
validateattributes(x,{'numeric'},{'nonnegative','integer','scalar'})
end
function print_usage()
help(mfilename)
end
|
github
|
nmovshov/CMS-planet-master
|
cms.m
|
.m
|
CMS-planet-master/cms.m
| 17,189 |
utf_8
|
f23b04b52e20488a26fc3db9df9258ec
|
function [Js, out] = cms(zvec, dvec, qrot, varargin)
%CMS Concentric Maclaurin Spheroids equilibrium shape and gravity.
% Js = CMS(zvec, dvec, qrot) returns 1-by-16 vector Js of gravity
% coefficients J0 through J30 of a rotating fluid planet in hydrostatic
% equilibrium. Coefficients are stored in ascending order so that Js(1) is
% J0, Js(2) is J2, Js(3) is J4, etc. The mandatory inputs are a vector of
% equatorial radii zvec, vector of corresponding layer densities dvec, and
% rotation parameter qrot, assumed normalized to the outer layer's equatorial
% radius (to zvec(1)).
%
% [Js, out] = CMS(zvec, dvec, qrot, 'NAME1',VALUE1, 'NAME2',VALUE2,...)
% accepts additional parameters as NAME/VALUE pairs, and also returns an
% output struct holding diagnostic values and additional derived quantities,
% including the full hydrostatic spheroid shapes.
%
% Inputs, required
% ----------------
% zvec : 1d array, positive real
% Equatorial radii of constant density layers, indexed from the outside in,
% i.e., zvec(1)=a0 is the outer radius of the outermost layer, zvec(2) is
% the inner radius of the outermost layer as well as the outer radius of
% the next layer, etc. The innermost layer extends all the way to the
% center, so that zvec(end) is the outer radius of a central spheroid
% layer. Units of zvec are unimportant as values will be normalized to
% outer radius.
% dvec : 1d array, positive real
% Layer densities. The layer lying between zvec(i) and zvec(i+1) has
% constant density dvec(i). Units are unimportant as values will be
% normalized to the mean (bulk) density. The density should be
% monotonically non-increasing with zvec, but this is not enforced. (Note:
% these are layer densities and NOT the density "deltas" of concentric
% spheroids.)
% qrot : scalar, nonnegative
% Dimensionless rotation parameter. Recall q = w^2a0^3/GM.
%
% Inputs, NAME/VALUE pairs
% tol : scalar, positive, (tol=1e-6)
% Convergence tolerance on fractional change in Js in successive iterations.
% maxiter : scalar, positive, integer, (maxiter=60)
% Maximum number of iterations of CMS algorithm.
% xlayers : scalar or vector, nonnegative, integer (xlayers=-1)
% Layers whose shape will be explicitly calculated. The shape functions
% (zetas) will be explicitly calculated for these layers, and
% spline-interpolated in between. This can result in significant speedup
% with minimal loss of precision, if the xlayers are chosen by trial and
% error to fit the required precision and the spacing of density layers. A
% scalar value is interpreted as a number of xlayers to be uniformaly
% distributed among the density layers. For example, a smooth-density
% 1024-layer model can benefit from almost 16x-speedup by specifying
% xlayers=64 while retaining a 10^-6 relative precision on J2. A vector
% value is interpreted as indices of layers to be used as xlayers. (A
% negative value is a shortcut to flag a full calculation instead of
% skip-n-spline.)
% J0s : struct
% J-like values representing initial state. This is not just for speeding
% up convergence. Mostly it's a mechanism to preserve state between calls.
%
% Outputs
% -------
% Js : 1-by-16 vector, real
% Even harmonic gravity coefficients J0 to J30. Typically only J2 to J10
% are helpful. J0 is included as a sanity check and test of convergence.
% out : struct
% A structure holding other quantities calculated in the course of running
% cms. Including out.zetas and out.JLike that together define the converged
% hydrostatic shape.
%
% Algorithm
% ---------
% Concentric Maclaurin Spheroids from Hubbard 2012, 2013 ApJ/ApJL.
%% Input parsing
% Zero inputs case, usage only
if nargin == 0
print_usage()
return
end
narginchk(3,inf);
% Mandatory inputs
validateattributes(zvec,{'numeric'},{'finite','nonnegative','vector'},'','zvec',1)
validateattributes(dvec,{'numeric'},{'finite','nonnegative','vector'},'','dvec',2)
validateattributes(qrot,{'numeric'},{'finite','nonnegative','scalar'},'','qrot',3)
assert(length(zvec) == length(dvec),...
'length(zvec)=%d~=%d=length(dvec)',length(zvec),length(dvec))
[zvec, I] = sort(zvec);
dvec = dvec(I);
zvec = flipud(zvec(:)); % now it's a column for sure
dvec = flipud(dvec(:)); % now it's a column for sure
if zvec(end) == 0, zvec(end) = eps; end
% Optional arguments
opts = parsem(varargin{:});
% Normalize radii and density
dro = [dvec(1); diff(dvec)];
m = sum(dro.*zvec.^3);
robar = m/zvec(1)^3;
zvec = zvec/zvec(1);
dvec = dvec/robar;
%% Define and initialize local variables (in CMS notation)
lambdas = zvec;
deltas = [dvec(1); diff(dvec)];
nlay = length(lambdas);
nangles = opts.nangles;
kmax = opts.kmax;
% Define down-sampled variabels (for skip-n-spline)
if isscalar(opts.xlayers)
if opts.xlayers > 0
sskip = max(fix(nlay/opts.xlayers), 1);
xind = 1:sskip:nlay;
else
xind = 1:nlay;
end
else
warning('CMS:xind','Experimental feature, use with care.')
xind = opts.xlayers;
end
xlambdas = lambdas(xind);
xdvec = dvec(xind);
xdeltas = [xdvec(1); diff(xdvec)];
nxlay = length(xlambdas);
% Initialize spherical layer shapes
zetas = NaN(nlay,nangles);
xzetas = ones(nxlay, nangles);
% Initialize J-like quantities for spherical planet
if isempty(fieldnames(opts.J0s))
Jlike = allocate_spherical_Js(nxlay, kmax, xlambdas, xdeltas);
else
Jlike = opts.J0s;
end
% Abscissas and weights for Gaussian quadrature
[mus, gws] = gauleg(0, 1, nangles);
% Precompute Legendre polynomials for fixed colatitudes (for gauss quad)
Ps.Pnmu(kmax+1,nangles) = 0;
Ps.Pnzero(kmax+1,1) = 0;
for k=0:kmax
Ps.Pnmu(k+1,1:nangles) = Pn(k, mus);
Ps.Pnzero(k+1,1) = Pn(k, 0);
end
% Precompute powers of ratios of lambdas (only for explicit layers)
if opts.prerat
lamratpow = nan(kmax+2,nxlay,nxlay);
for ii=1:nxlay
for jj=1:nxlay
for kk=1:kmax+2
lamratpow(kk,ii,jj) = ...
(xlambdas(ii)/xlambdas(jj))^(kk-1);
end
end
end
else
lamratpow = @(kk,ii,jj)(xlambdas(ii)./xlambdas(jj)).^(kk-1);
end
%% The loop (see Hubbard, 2012 and ./notes/CMS.pdf)
Js = Jlike.Jn;
for iter=1:opts.maxiter
% Update shape with current gravity
new_xzetas = update_zetas(Jlike, Ps, lamratpow, qrot, xzetas);
for alfa = 1:nangles
zetas(:,alfa) = spline(xlambdas, new_xzetas(:,alfa), lambdas);
end
% Update gravity with current shape
new_Jlike = update_Js(lambdas, deltas, zetas, xind, Ps, gws);
new_Js = new_Jlike.Jn;
% Check for convergence of J0-J8 to terminate...
dJs = abs(Js - new_Js)./abs(Js+eps);
dJs(abs(new_Js) < 1e-12) = 0; % a hack for nonrotating planets
if all(dJs(1:5) < opts.tol), break, end
% ... or update to new values and continue
Jlike = new_Jlike;
Js = new_Js;
xzetas = new_xzetas;
end
% It's not always a disaster if maxiter is reached, but we'd like to know
if iter == opts.maxiter
warning('CMS:maxiter','Shape may not be fully converged.')
end
%% Return
Js = new_Js; % may as well use the latest...
out.dJs = dJs;
out.iter = iter;
out.zetas = zetas;
out.lambdas = lambdas;
out.deltas = deltas;
out.JLike = new_Jlike;
out.mus = mus;
out.gws = gws;
out.Ps = Ps;
out.xind = xind;
end
%% Helper functions
function print_usage()
fprintf('Usage:\n\tcms(zvec,dvec,qrot,''name'',value)\n')
fprintf('Name-Value pair arguments:\n')
fprintf('tol - Convergence tolerance for gravity coefficients [ positive real {1e-6} ]\n')
fprintf('maxiter - Number of iterations allowed for relaxation to equilibrium shape [ positive integer {60} ]\n')
fprintf('xlayers - Solve shape functions on xlayers and spline the rest [ integer scalar or vector {-1} ]\n')
fprintf('prerat - Precalculate powers of ratios of lambdas (trades memory for speed) [ {true} | false ]\n')
fprintf('J0s - J-like values representing initial state [ scalar struct {[]} ]\n')
end
function options = parsem(varargin)
p = inputParser;
p.FunctionName = 'cms.m';
p.addParameter('tol',1e-6,@(x)isscalar(x)&&isreal(x)&&x>0)
p.addParameter('maxiter',60,@(x)isscalar(x)&&isreal(x)&&x>0&&mod(x,1)==0)
p.addParameter('xlayers',-1,@(x)validateattributes(x,{'numeric'},{'vector','integer'}))
p.addParameter('prerat',true,@(x)isscalar(x)&&islogical(x))
p.addParameter('J0s',struct(),@(x)isscalar(x)&&isstruct(x))
% undocumented or obsolete options
p.addParameter('nangles',48,@(x)isscalar(x)&&(x>0)&&(mod(x,1)==0)) % colatitudes defining level surface
p.addParameter('kmax',30,@(x)isscalar(x)&&(x>6)&&(mod(x,2)==0)) % degree to cut mulitpole expansion
p.addParameter('TolX',1e-12,@(x)isscalar(x)&&(x>0)) % termination tolerance for root finding
% Parse name-value pairs and return
p.parse(varargin{:})
options = p.Results;
end
function Js = allocate_spherical_Js(nlay,nmom,lambdas,deltas)
Js.tilde = zeros(nlay,(nmom+1));
Js.tildeprime = zeros(nlay,(nmom+1));
Js.tildeprimeprime = zeros(nlay,1);
Js.Jn = zeros(1,nmom/2+1);
den = sum(deltas.*lambdas.^3);
Js.tilde(:,1) = -1*(deltas.*lambdas.^3)/den;
Js.tildeprime(:,1) = -1.5*(deltas.*lambdas.^3)/den;
Js.tildeprimeprime(:) = 0.5*(deltas.*lambdas.^3)/den;
Js.Jn(1) = sum(Js.tilde(:,1));
end
function y = Pn(n,x)
% Fast implementation of ordinary Legendre polynomials of low degree.
switch n
case 0
y = ones(size(x));
case 1
y = x;
case 2
y = 0.5*(3*x.^2 - 1);
case 3
y = 0.5*(5*x.^3 - 3*x);
case 4
y = (1/8)*(35*x.^4 - 30*x.^2 + 3);
case 5
y = (1/8)*(63*x.^5 - 70*x.^3 + 15*x);
case 6
y = (1/16)*(231*x.^6 - 315*x.^4 + 105*x.^2 - 5);
case 7
y = (1/16)*(429*x.^7 - 693*x.^5 + 315*x.^3 - 35*x);
case 8
y = (1/128)*(6435*x.^8 - 12012*x.^6 + 6930*x.^4 - 1260*x.^2 + 35);
case 9
y = (1/128)*(12155*x.^9 - 25740*x.^7 + 18018*x.^5 - 4620*x.^3 + 315*x);
case 10
y = (1/256)*(46189*x.^10 - 109395*x.^8 + 90090*x.^6 - 30030*x.^4 + 3465*x.^2 - 63);
case 11
y = (1/256)*(88179*x.^11 - 230945*x.^9 + 218790*x.^7 - 90090*x.^5 + 15015*x.^3 - 693*x);
case 12
y = (1/1024)*(676039*x.^12 - 1939938*x.^10 + 2078505*x.^8 - 1021020*x.^6 + 225225*x.^4 - 18018*x.^2 + 231);
otherwise
assert(isvector(x))
Pnm = legendre(n,x);
y = Pnm(1,:);
if ~isrow(x), y = y'; end
end
end
function [x,w] = gauleg(x1,x2,n)
%GAULEG Calculate abscissas and weights for Gauss-Legendre n-point quadrature.
% [x,w] = GAULEG(x1,x2,n) returns the abscissas x and weights w that can be
% used to evaluate the definite integral, I, of a function well approximated
% by an (2n - 1) degree polynomial in the interval [x1,x2] using the
% Gauss-Legendre formula:
%
% I = sum(w.*f(x))
%
% Algorithm
% This function is based on the C++ implementation of a routine with the
% same name in Numerical Recipes, 3rd Edition. But in several places I opt
% for readability over performance, on the assumption that this function is
% most likely to be called in a setup routine rather than in an inner-loop
% computation.
%
% Example
% fun = @(x)sin(x);
% [x,w] = gauleg(0,pi,6);
% I_adaptive = integral(fun,0,pi)
% I_gaussleg = sum(w.*fun(x))
%
% Author: Naor Movshovitz (nmovshov at google dot com)
% Earth and Planetary Sciences, UC Santa Cruz
%
% Reference: William H. Press, Saul A. Teukolsky, William T. Vetterling, and
% Brian P. Flannery. 2007. Numerical Recipes 3rd Edition: The Art of Scientific
% Computing (3 ed.). Cambridge University Press, New York, NY, USA.
% Input parsing and minimal assertions
narginchk(3,3)
nargoutchk(2,2)
validateattributes(x1,{'numeric'},{'scalar','finite','real'},1)
validateattributes(x2,{'numeric'},{'scalar','finite','real'},2)
validateattributes(n,{'numeric'},{'scalar','finite','integer','>=',2},3)
assert(x2 > x1, 'Interval must be positive.');
% Local variables
tol = 1e-14;
m = ceil(n/2);
xmid = (x1 + x2)/2;
dx = (x2 - x1);
x = NaN(1,n);
w = NaN(1,n);
% Main loop
for j=1:m
% Get j-th root of Legendre polynomial Pn, along with Pn' value there.
z = cos(pi*((j - 1) + 0.75)/(n + 0.5)); % initial guess for j-th root
while true
% Calculate Pn(z) and Pn-1(z) and Pn'(z)
p = NaN(1,n+1);
p(1) = 1;
p(2) = z;
for k=2:n
pkm1 = p(k);
pkm2 = p(k-1);
pk = (1/k)*((2*k - 1)*z*pkm1 - (k - 1)*pkm2);
p(k+1) = pk;
end
pn = p(end);
pp = (n*p(end-1) - n*z*p(end))/(1 - z^2);
% And now Newton's method (we are hopefully very near j-th root)
oldz = z;
z = z - pn/pp;
if abs(z - oldz) < tol, break, end
end
% Now use j-th root to get 2 abscissas and weights
x(j) = xmid - z*dx/2; % Scaled abscissa left of center
x(n+1-j) = xmid + z*dx/2; % Scaled abscissa right of center
w(j) = dx/((1 - z^2)*pp^2);
w(n+1-j) = w(j);
end
% Verify and return
assert(all(isfinite(x)))
assert(all(isfinite(w)))
end
function newzetas = update_zetas(Js, Ps, lamrats, qrot, oldzetas)
% Update level surfaces using current value of Js.
% Loop over layers (outer) and colatitudes (inner)
nlay = size(oldzetas, 1);
nangles = size(oldzetas, 2);
newzetas = NaN(nlay,nangles);
for j=1:nlay
for alfa=1:nangles
oldzeta = oldzetas(j,alfa);
newzetas(j,alfa) = zeta_j_of_alfa(j, alfa, Js, Ps, lamrats, qrot, oldzeta);
end
end
end
function newJs = update_Js(lambdas, deltas, zetas, xind, Ps, gws)
% Single-pass update of gravitational moments by Gaussian quad.
nlay = length(lambdas);
kmax = length(Ps.Pnzero)-1;
xlambdas = lambdas(xind);
dvec = cumsum(deltas);
xdvec = dvec(xind);
xdeltas = [xdvec(1); diff(xdvec)];
xzetas = zetas(xind, :);
nxlay = length(xlambdas);
% Do common denominator in eqs. (48) (USING FULL DENSITY PROFILE)
denom = 0;
for j=1:nlay
fun = zetas(j,:).^3;
I = gws*fun'; % gauss quad formula
denom = denom + deltas(j)*lambdas(j)^3*I;
end
% Do J tilde, eq. (48a)
new_tilde = zeros(nxlay,kmax+1);
for ii=1:nxlay
for kk=0:kmax
if rem(kk, 2), continue, end
fun = Ps.Pnmu(kk+1,:).*xzetas(ii,:).^(kk+3);
I = gws*fun'; % gauss quad formula
new_tilde(ii,kk+1) = -(3/(kk + 3))*xdeltas(ii)*xlambdas(ii)^3*I/denom;
end
end
% Do J tilde prime, eqs. (48b and 48c)
new_tprime = zeros(nxlay,kmax+1);
for ii=1:nxlay
for kk=0:kmax
if rem(kk, 2), continue, end
if kk == 2 % eq. (48c)
fun = Ps.Pnmu(3,:).*log(xzetas(ii,:));
I = gws*fun'; % gauss quad formula
new_tprime(ii,kk+1) = -3*xdeltas(ii)*xlambdas(ii)^3*I/denom;
else % eq. (48b)
fun = Ps.Pnmu(kk+1,:).*xzetas(ii,:).^(2 - kk);
I = gws*fun'; % gauss quad formula
new_tprime(ii,kk+1) = -(3/(2 - kk))*xdeltas(ii)*xlambdas(ii)^3*I/denom;
end
end
end
% Do J tilde double prime, eq. (48d)
new_tpprime = zeros(nxlay,1);
for ii=1:nxlay
new_tpprime(ii) = 0.5*xdeltas(ii)*xlambdas(ii)^3/denom;
end
% And finally, the external Js deserve full grid resolution
full_tilde = zeros(nlay,kmax+1);
for ii=1:nlay
for kk=0:kmax
if rem(kk, 2), continue, end
fun = Ps.Pnmu(kk+1,:).*zetas(ii,:).^(kk+3);
I = gws*fun'; % gauss quad formula
full_tilde(ii,kk+1) = -(3/(kk + 3))*deltas(ii)*lambdas(ii)^3*I/denom;
end
end
% Return updated Js struct
newJs.tilde = new_tilde;
newJs.tildeprime = new_tprime;
newJs.tildeprimeprime = new_tpprime;
newJs.fulltilde = full_tilde;
n = 0:2:kmax;
for k=1:length(n)
newJs.Jn(k) = dot(full_tilde(:,n(k)+1),lambdas.^n(k));
end
end
function y = zeta_j_of_alfa(j, alfa, Js, Ps, lamrats, qrot, oldzeta)
persistent os
if isempty(os), os = optimset('TolX',1e-12); end
fun = @(x)eq52(x,j,alfa,Js,Ps,lamrats,qrot);
y = fzero(fun, oldzeta, os);
end
function y = eq52(zja, jl, alfa, Js, Ps, lamrats, qrot)
% locals
nlay = size(Js.tilde,1);
kmax = length(Ps.Pnzero)-1;
Jt = Js.tilde;
Jtp = Js.tildeprime;
Jtpp = Js.tildeprimeprime;
lamj3 = lamrats(4,jl,1);
q = qrot;
P0 = Ps.Pnzero;
Pmu = Ps.Pnmu(:,alfa);
zetpow = zja.^(0:kmax+1);
zetipow = zja.^-(0:kmax+1);
% first double sum
y1 = 0;
for ii=jl:nlay
for kk=0:2:kmax
y1 = y1 + lamrats(kk+1,ii,jl)*Jt(ii,kk+1)*zetipow(kk+1)*Pmu(kk+1);
end
end
% second double sum
y2 = 0;
for ii=jl:nlay
for kk=0:2:kmax
y2 = y2 + lamrats(kk+1,ii,jl)*Jt(ii,kk+1)*P0(kk+1);
end
end
% third double sum
y3 = 0;
for ii=1:jl-1
for kk=0:2:kmax
y3 = y3 + lamrats(kk+2,jl,ii)*Jtp(ii,kk+1)*(zetpow(kk+2))*Pmu(kk+1);
end
end
% and forth double sum
y4 = 0;
for ii=1:jl-1
for kk=0:2:kmax
y4 = y4 + lamrats(kk+2,jl,ii)*Jtp(ii,kk+1)*P0(kk+1);
end
end
% a single sum
y5 = 0;
for ii=1:jl-1
y5 = y5 + lamrats(4,jl,ii)*Jtpp(ii,1)*zetpow(4);
end
% another single sum
y6 = 0;
for ii=1:jl-1
y6 = y6 + lamrats(4,jl,ii)*Jtpp(ii,1);
end
% and the rotation term
y7 = -(1/3)*q*lamj3*zja^2*(1 - Pmu(3)) + (1/2)*q*lamj3;
% Now combine
y = (1/zja)*(y1 + y3 + y5) - y2 - y4 - y6 + y7;
end
|
github
|
nmovshov/CMS-planet-master
|
Polytrope.m
|
.m
|
CMS-planet-master/+barotropes/Polytrope.m
| 1,505 |
utf_8
|
5def90a212dafec35c75aed8b89395ce
|
classdef Polytrope < barotropes.Barotrope
%POLYTROPE A barotrope of the form P = K*rho^(1 + 1/n).
%% Properties
properties (SetAccess = private)
K % polytropic constant, dimensions depend on index
n % polytropic index, dimensionless
alpha % 1 + 1/n
end
%% The constructor
methods
function obj = Polytrope(K, n)
if (nargin == 0)
if nargout > 0, return, end
print_usage()
clear obj
return
end
try
narginchk(2,2)
assert(isnumeric(K) && isscalar(K) && double(K) > 0)
assert(isnumeric(n) && isscalar(n) && double(n) > 0)
catch ME
print_usage()
rethrow(ME)
end
obj.K = K;
obj.n = n;
obj.alpha = 1 + 1/n;
end
end
%% Required barotrope methods
methods
function PF = test(~)
PF = true;
end
function P = pressure(obj,rho)
P = obj.K*rho.^(obj.alpha);
end
function rho = density(obj,P)
rho = (P/obj.K).^(1/obj.alpha);
end
end
end
%% Usage message
function print_usage()
fprintf('Usage: barotropes.Polytrope(K, n)\n')
fprintf('positional arguments:\n')
fprintf(' K polytropic constant (positive real scalar)\n')
fprintf(' n polytropic index (positive real scalar)\n')
end
|
github
|
nmovshov/CMS-planet-master
|
Tabular.m
|
.m
|
CMS-planet-master/+barotropes/Tabular.m
| 2,338 |
utf_8
|
3d41e90db26d52cbd5eacc4521c5a1af
|
classdef Tabular < barotropes.Barotrope
%TABULAR A base class for a generic table barotrope.
%% Properties
properties (SetAccess = protected)
P_vals % a vector of pressure values
rho_vals % a vector of density values
end
properties (Access = public)
interpolation_method
extrapolation_method
meta
end
%% The constructor
methods
function obj = Tabular(P, rho, intm, extm)
if (nargin == 0)
if nargout > 0, return, end
print_usage()
clear obj
return
end
try
narginchk(2,4)
if nargin < 4, extm = 'extrap'; end
if nargin < 3, intm = 'linear'; end
assert(isnumeric(P) && isvector(P) && all(double(P) >= 0))
assert(isnumeric(rho) && isvector(rho) && all(double(rho) >= 0))
assert(length(P) == length(rho),...
'len(P) = %i ~= %i = len(rho)', length(P), length(rho))
assert(interp1(P, rho, P(1), intm, extm) == rho(1))
catch ME
print_usage()
rethrow(ME)
end
obj.P_vals = P;
obj.rho_vals = rho;
obj.interpolation_method = intm;
obj.extrapolation_method = extm;
end
end
%% Required barotrope methods
methods
function PF = test(~)
PF = true;
end
function P = pressure(obj,rho)
P = interp1(obj.rho_vals, obj.P_vals, rho, obj.interpolation_method, obj.extrapolation_method);
end
function rho = density(obj,P)
rho = interp1(obj.P_vals, obj.rho_vals, P, obj.interpolation_method, obj.extrapolation_method);
end
end
end
%% Usage message
function print_usage()
fprintf('Usage: barotropes.tabular(P, rho, intm, extm)\n')
fprintf('positional arguments:\n')
fprintf(' P a vector of pressure values [ real nonnegative ]\n')
fprintf(' rho a vector of density values [ real nonnegative ]\n')
fprintf(' intm (optional) interpolation method [ {''linear''} | ''nearest'' | ''spline'' | ''pchip'' ]\n')
fprintf(' extm (optional) extrapolation strategy [ ''extrap'' | scalar value | {NaN} ]\n')
end
|
github
|
mcyeh/aaltd16_fusion-master
|
trainTask1Classifier.m
|
.m
|
aaltd16_fusion-master/trainTask1Classifier.m
| 3,952 |
utf_8
|
4a9e5c59630def2d8883663ac78df516
|
% Train the classifier using the training data
% Chin-Chia Michael Yeh 05/28/2016
%
% trainTask1Classifier(dataPath, classPath, dicNum, spletLen, spletNum)
% Input:
% dataPath: path to the training data (string)
% classPath: path to the output directory (string)
% dicNum: number of dictionary element for sparse coding (scalar)
% spletLen: shaplet length (scalar)
% spletNum: number of shapelet (scalar)
%
function trainTask1Classifier(dataPath, classPath, dicNum, spletLen, spletNum)
%% check if the classifier is already train
fname = sprintf('dn%d__sl%d__sn%d.mat', dicNum, spletLen, spletNum);
fname = fullfile(classPath, fname);
if exist(fname, 'file')
return
end
%% load data
fprintf('Loading data ... ');
tTemp = tic();
info = hdf5info(dataPath);
lab = hdf5read(info.GroupHierarchy.Datasets(1));
dataOrg = hdf5read(info.GroupHierarchy.Datasets(2));
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% organize data
fprintf('Organizing data ... ');
tTemp = tic();
dataDim = size(dataOrg, 1);
dataLen = size(dataOrg, 2);
dataNum = size(dataOrg, 3);
dataOrg2 = cell(dataDim, 1);
for i = 1:dataDim
dataOrg2{i} = squeeze(dataOrg(i, :, :))';
end
dataRS = cell(dataNum, 1);
dataSC = cell(dataNum, 1);
lab = double(lab);
for i = 1:dataNum
dataRS{i} = zeros(dataDim, dataLen);
for k = 1:dataDim
dataRS{i}(k, :) = dataOrg2{k}(i, :);
end
dataSC{i} = normalizeData(dataRS{i});
end
dataRSLen = size(dataRS{1}, 2);
dataSCLen = size(dataSC{1}, 2);
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% extract shapelet
fprintf('Dictionary training ... ');
tTemp = tic();
splet = randSpletSele(dataRS, spletLen, spletNum);
dic = learnDic(dataSC, dicNum);
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% shapelet trainsform
fprintf('Shapelet transform and sparse coding ... ');
tTemp = tic();
featRS = cell(dataNum, 1);
featSC = cell(dataNum, 1);
for i = 1:dataNum
featRS{i} = spletTran(dataRS{i}, splet);
featSC{i} = sparseCodeTran(dataSC{i}, dic);
end
featRS = cell2mat(featRS);
featSC = cell2mat(featSC);
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% normalization
fprintf('Normalize feature ... ');
tTemp = tic();
featMean = mean(featRS, 1);
featStd = std(featRS, 1, 1);
featRS = featRS - repmat(featMean, dataNum, 1);
featRS = featRS ./ repmat(featStd, dataNum, 1);
featRS = full(featRS);
featSC = full(featSC);
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% cv for svm parameter
fprintf('Cross validation for SVM parameter (random shapelet) ...\n');
tTemp = tic();
svmCs = 2 .^ (-5:2:5);
bestAccRS = 0;
bestCRS = 1;
for i = 1:length(svmCs)
svmC = svmCs(i);
svmOption = ['-q -v 5 -t 0 -c ', num2str(svmC)];
accTemp = svmtrain2(lab, featRS, svmOption);
if accTemp >= bestAccRS
bestCRS = svmC;
bestAccRS = accTemp;
end
end
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% cv for svm parameter
fprintf('Cross validation for SVM parameter (sparse coding) ...\n');
tTemp = tic();
svmCs = 2 .^ (-5:2:5);
bestAccSC = 0;
bestCSC = 1;
for i = 1:length(svmCs)
svmC = svmCs(i);
svmOption = ['-q -v 5 -t 0 -c ', num2str(svmC)];
accTemp = svmtrain2(lab, featSC, svmOption);
if accTemp >= bestAccSC
bestCSC = svmC;
bestAccSC = accTemp;
end
end
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% train svm model
fprintf('Train SVM model (random shapelet) ... ');
tTemp = tic();
svmOption = ['-q -t 0 -b 1 -c ', num2str(bestCRS)];
svmModelRS = svmtrain2(lab, featRS, svmOption);
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% train svm model
fprintf('Train SVM model (sparse coding) ... ');
tTemp = tic();
svmOption = ['-q -t 0 -b 1 -c ', num2str(bestCSC)];
svmModelSC = svmtrain2(lab, featSC, svmOption);
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% save model
save(fname, 'dataRSLen', 'dataSCLen', 'splet', 'dic', 'featMean', 'featStd', ...
'svmModelRS', 'svmModelSC', 'bestAccRS', 'bestAccSC');
|
github
|
mcyeh/aaltd16_fusion-master
|
applyTask1Classifier.m
|
.m
|
aaltd16_fusion-master/applyTask1Classifier.m
| 2,991 |
utf_8
|
67f695bc5e1a7e7b708106cc2b1acfdc
|
% Apply the classifier to task 1's test data
% Chin-Chia Michael Yeh 05/28/2016
%
% applyTask1Classifier(dataPath, classPath, dicNum, spletLen, spletNum)
% Input:
% dataPath: path to task 1's test data (string)
% classPath: path to the output directory (string)
% dicNum: number of dictionary element for sparse coding (scalar)
% spletLen: shaplet length (scalar)
% spletNum: number of shapelet (scalar)
%
function applyTask1Classifier(dataPath, classPath, dicNum, spletLen, spletNum)
%% check if the prediction is already done
fnameOut = sprintf('dn%d__sl%d__sn%d__task1.txt', dicNum, spletLen, spletNum);
fnameOut = fullfile(classPath, fnameOut);
if exist(fnameOut, 'file')
return
end
%% load classifier
fnameClass = sprintf('dn%d__sl%d__sn%d.mat', dicNum, spletLen, spletNum);
fnameClass = fullfile(classPath, fnameClass);
load(fnameClass);
%% load data
fprintf('Loading data ... ');
tTemp = tic();
info = hdf5info(dataPath);
dataOrg = hdf5read(info.GroupHierarchy.Datasets(1));
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% organize data
fprintf('Organizing data ... ');
tTemp = tic();
dataDim = size(dataOrg, 1);
dataLen = size(dataOrg, 2);
dataNum = size(dataOrg, 3);
dataOrg2 = cell(dataDim, 1);
for i = 1:dataDim
dataOrg2{i} = squeeze(dataOrg(i, :, :))';
end
dataRS = cell(dataNum, 1);
dataSC = cell(dataNum, 1);
lab = double(ones(dataNum, 1));
for i = 1:dataNum
dataRS{i} = zeros(dataDim, dataLen);
for k = 1:dataDim
dataRS{i}(k, :) = dataOrg2{k}(i, :);
end
dataSC{i} = normalizeData(dataRS{i});
end
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% shapelet trainsform
fprintf('Shapelet transform and sparse coding ... ');
tTemp = tic();
featRS = cell(dataNum, 1);
featSC = cell(dataNum, 1);
for i = 1:dataNum
featRS{i} = spletTran(dataRS{i}, splet);
featSC{i} = sparseCodeTran(dataSC{i}, dic);
end
featRS = cell2mat(featRS);
featSC = cell2mat(featSC);
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% normalization
fprintf('Normalize feature ... ');
tTemp = tic();
featRS = featRS - repmat(featMean, dataNum, 1);
featRS = featRS ./ repmat(featStd, dataNum, 1);
featRS = full(featRS);
featSC = full(featSC);
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% apply svm model
fprintf('Apply SVM model ...\n');
tTemp = tic();
[~, ~, probRSTemp] = svmpredict2(lab, featRS, svmModelRS, '-b 1');
probRS = zeros(size(probRSTemp));
probRS(:, svmModelRS.Label) = probRSTemp;
[~, ~, probSCTemp] = svmpredict2(lab, featSC, svmModelSC, '-b 1');
probSC = zeros(size(probSCTemp));
probSC(:, svmModelSC.Label) = probSCTemp;
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% fuse result
fprintf('Fusion ... ');
tTemp = tic();
probFus = probSC*bestAccSC + probRS*bestAccRS;
labPr = zeros(dataNum, 1);
for j = 1:dataNum
[~, labPr(j)] = max(probFus(j, :));
end
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% output predicted label
fileID = fopen(fnameOut,'w');
fprintf(fileID, '%d\n',labPr);
fclose(fileID);
|
github
|
mcyeh/aaltd16_fusion-master
|
distanceProfile.m
|
.m
|
aaltd16_fusion-master/distanceProfile.m
| 1,179 |
utf_8
|
9014f01902d4f8d1c7e8a7d385eddce7
|
% Compute the distance profile on a given time series with the query
% Modify by Chin-Chia Michael Yeh 05/28/2016
% Original from http://www.cs.unm.edu/~mueen/FastestSimilaritySearch.html
%
% dist = distanceProfile(data, query)
% Output:
% dist: distance profile (vector)
% Input:
% data: the time series (vector)
% query: the query (vector)
%
function dist = distanceProfile(x,y)
%% x is the data, y is the query
if length(x) == size(x, 2)
x = x';
end
if length(y) == size(y, 2)
y = y';
end
%% prepaire data
n = length(x);
meany = mean(y);
sigmay = std(y,1);
m = length(y);
x(n+1:2*n) = 0;
y = y(end:-1:1); %Reverse the query
y(m+1:2*n) = 0;
%% The main trick of getting dot products in O(n log n) time
X = fft(x);
Y = fft(y);
Z = X.*Y;
z = ifft(Z);
%% compute x stats -- O(n)
cum_sumx = cumsum(x);
cum_sumx2 = cumsum(x.^2);
sumx2 = cum_sumx2(m:n)-[0;cum_sumx2(1:n-m)];
sumx = cum_sumx(m:n)-[0;cum_sumx(1:n-m)];
meanx = sumx./m;
sigmax2 = (sumx2./m)-(meanx.^2);
sigmax = sqrt(sigmax2);
%% computing the distances -- O(n) time
dist = 2*m*(1-(z(m:n)-m*meanx*meany)./(m*sigmax*sigmay));
dist = sqrt(dist);
dist = real(dist);
|
github
|
mcyeh/aaltd16_fusion-master
|
randSpletSele.m
|
.m
|
aaltd16_fusion-master/randSpletSele.m
| 1,001 |
utf_8
|
643d94e985a0e1f00e0247ef204aee2c
|
% Randomly select shapelet from the dataset
% Chin-Chia Michael Yeh 06/16/2016
%
% splet = randSpletSele(data, spletLen, spletNum)
% Output:
% splet: shaplet set, each row is a shapelet (matrix)
% Input:
% data: training set, each row is a training data (cell)
% spletLen: shaplet length (scalar)
% spletNum: number of shapelet (scalar)
%
function splet = randSpletSele(data, spletLen, spletNum)
%% extract stat
data = cell2mat(data);
dataNum = size(data, 1);
dataLen = size(data, 2);
%% select shapelet
splet = zeros(spletNum, spletLen);
randIdx = randi([1, dataNum], spletNum, 1);
for i = 1:spletNum
isHozLine = true;
while isHozLine
% generate random length and postiion
randPos = randi([1, dataLen - spletLen + 1]);
% extract shapelet
splet(i, :) = data(randIdx(i), randPos:randPos+spletLen-1);
% check if the selected shapelet is valid
if std(splet(i, :), 1) > eps
isHozLine = false;
end
end
end
|
github
|
mcyeh/aaltd16_fusion-master
|
normalizeData.m
|
.m
|
aaltd16_fusion-master/normalizeData.m
| 674 |
utf_8
|
9f72db7df8188f8f8df2ef7acbd233dc
|
% Perform power normalization (cube root), consecutive frame concatenation, and
% unit-norm normalize to the input data
% Chin-Chia Michael Yeh 05/28/2016
%
% data = normalizeData(data)
% Output:
% data: the normalized multi dimensional time series (matrix)
% Input:
% data: the multi dimensional time series (matrix)
%
function data = normalizeData(data)
dataTemp = nthroot(data, 3);
canNum = 16;
data = zeros(canNum*size(dataTemp, 1), size(dataTemp, 2)-canNum+1);
for i = 1:canNum
data((i-1)*size(dataTemp, 1)+1:i*size(dataTemp, 1), :) = ...
dataTemp(:, i:end-canNum+i);
end
for i = 1:size(data, 2)
data(:, i) = data(:, i) / norm(data(:, i));
end
|
github
|
mcyeh/aaltd16_fusion-master
|
learnDic.m
|
.m
|
aaltd16_fusion-master/learnDic.m
| 622 |
utf_8
|
c4bea5eda0b8d68bd85e4f5ec0781ce0
|
% Learn dictionary from the dataset
% Chin-Chia Michael Yeh 05/30/2016
%
% dic = learnDic(data, dicNum)
% Output:
% dic: dictionary, each column is a dictionary element (matrix)
% Input:
% data: training set, each row is a training data (cell)
% dicNum: number of dictionary element (scalar)
%
function dic = learnDic(data, dicNum)
%% convert to matrix
data = cell2mat(data');
%% extract stat
dataDim = size(data, 1);
dataNum = size(data, 2);
%% learning parameter
param.K = dicNum;
param.lambda = 1/sqrt(dataDim);
param.iter = round(dataNum*10 / 512);
%% dictionary learning
dic = mexTrainDL(data, param);
|
github
|
mcyeh/aaltd16_fusion-master
|
applyTask2Classifier.m
|
.m
|
aaltd16_fusion-master/applyTask2Classifier.m
| 5,954 |
utf_8
|
c02a19b80fb4b73c15134ef9a2df4125
|
% Apply the classifier to task 2's test data
% Chin-Chia Michael Yeh 05/28/2016
%
% applyTask1Classifier(dataPath, classPath, dicNum, spletLen, spletNum)
% Input:
% dataPath: path to task 2's test data (string)
% classPath: path to the output directory (string)
% dicNum: number of dictionary element for sparse coding (scalar)
% spletLen: shaplet length (scalar)
% spletNum: number of shapelet (scalar)
%
function applyTask2Classifier(dataPath, classPath, dicNum, spletLen, spletNum)
%% check if the prediction is already done
fnameOut = sprintf('dn%d__sl%d__sn%d__task2.txt', dicNum, spletLen, spletNum);
fnameOut = fullfile(classPath, fnameOut);
if exist(fnameOut, 'file')
return
end
%% initialization for parfor
fprintf('Initialize for parfor ... \n');
tTemp = tic();
workerNum = 4;
if isempty(which('parpool'))
if matlabpool('size') <= 0 %#ok<*DPOOL>
matlabpool(workerNum);
elseif matlabpool('size')~= workerNum
matlabpool('close');
matlabpool(workerNum);
end
else
parProfile = gcp('nocreate');
if isempty(gcp('nocreate'))
parpool(workerNum);
elseif parProfile.NumWorkers ~= workerNum
delete(gcp('nocreate'));
parpool(workerNum);
end
end
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% load classifier
fnameClass = sprintf('dn%d__sl%d__sn%d.mat', dicNum, spletLen, spletNum);
fnameClass = fullfile(classPath, fnameClass);
dataRSLen = [];
dataSCLen = [];
splet = [];
dic = [];
svmModelRS = [];
svmModelSC = [];
featMean = [];
featStd = [];
load(fnameClass);
%% load data
fprintf('Loading data ... ');
tTemp = tic();
info = hdf5info(dataPath);
dataOrg = hdf5read(info.GroupHierarchy.Datasets(1));
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% organize data
fprintf('Organizing data ... ');
tTemp = tic();
dataDim = size(dataOrg, 1);
dataLen = size(dataOrg, 2);
dataNum = size(dataOrg, 3);
dataOrg2 = cell(dataDim, 1);
for i = 1:dataDim
dataOrg2{i} = squeeze(dataOrg(i, :, :))';
end
dataRS = cell(dataNum, 1);
dataSC = cell(dataNum, 1);
for i = 1:dataNum
dataRS{i} = zeros(dataDim, dataLen);
for k = 1:dataDim
dataRS{i}(k, :) = dataOrg2{k}(i, :);
end
dataRS{i} = [dataRS{i}, repmat(dataRS{i}(:, end), 1, 50)];
dataSC{i} = normalizeData(dataRS{i});
end
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% compute prediction curve
fprintf('Generate prediction curve (random shapelet) ...\n');
tTemp = tic();
probPrRS = cell(dataNum, 1);
parfor i = 1:dataNum
[~, probPrRS{i}] = applySlidingWindowRS(dataRS{i}, dataRSLen, ...
splet, featMean, featStd, svmModelRS);
end
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% compute prediction curve
fprintf('Generate prediction curve (sparse coding) ...\n');
tTemp = tic();
probPrSC = cell(dataNum, 1);
parfor i = 1:dataNum
[~, probPrSC{i}] = applySlidingWindowSC(dataSC{i}, dataSCLen, ...
dic, svmModelSC);
end
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% post process the curve
probPr = cell(size(probPrRS));
labPr = cell(size(probPrRS));
for i = 1:length(probPr)
for j = 1:size(probPrRS{i}, 1)
probPrRS{i}(j, :) = smooth(probPrRS{i}(j, :), 51);
end
probPr{i} = probPrRS{i} * bestAccRS + probPrSC{i} * bestAccSC;
labPr{i} = zeros(1, size(probPr{i}, 2));
for j = 1:size(probPr{i}, 2)
probPr{i}(:, j) = probPr{i}(:, j) / sum(probPr{i}(:, j));
[~, labPr{i}(j)] = max(probPr{i}(:, j));
end
end
%% find start and end
fprintf('Find label, start points, and end points ... ');
tTemp = tic();
stLoc = zeros(dataNum, 6);
edLoc = zeros(dataNum, 6);
lab = zeros(dataNum, 6);
for i = 1:dataNum
for j = 1:6
stLocLeft = max(1, (j-1)*51-24);
stLocRight = min(306, (j-1)*51+26);
lab(i,j) = mode(labPr{i}(stLocLeft:stLocRight));
[~, stLoc(i,j)] = max(probPr{i}(lab(i,j), stLocLeft:stLocRight));
stLoc(i,j) = stLoc(i,j) + stLocLeft - 1;
end
for j = 1:6
edLocLeft = stLoc(i,j);
if j == 6
edLocRight = 306;
else
edLocRight = stLoc(i,j+1)-1;
end
[~, edLoc(i,j)] = min(probPr{i}(lab(i,j), edLocLeft:edLocRight));
edLoc(i,j) = edLoc(i,j) + edLocLeft - 1;
if j < 6 && stLoc(i,j+1)-edLoc(i,j)>20
stLoc(i,j+1) = edLoc(i,j) + 1;
end
end
edLoc(i,6) = 306;
end
tTemp = toc(tTemp);
fprintf('%5.3f s\n', tTemp);
%% output predicted label
fileID = fopen(fnameOut,'w');
for i = 1:dataNum
for j = 1:6
fprintf(fileID, '%d:%d-%d ', lab(i,j), stLoc(i,j)-1, edLoc(i,j)-1);
end
fprintf(fileID, '\n');
end
fclose(fileID);
function [labPr, probPr] = applySlidingWindowRS(data, slLen, splet, ...
featMean, featStd, svmModel)
%% initialization
dataLen = size(data, 2);
slNum = dataLen - slLen + 1;
%% extract subsequences
feat = cell(slNum, 1);
for j = 1:slNum
feat{j} = spletTran(data(:, j:j+slLen-1), splet);
end
feat = cell2mat(feat);
feat = feat - repmat(featMean, slNum, 1);
feat = feat ./ repmat(featStd, slNum, 1);
feat = full(feat);
%% apply svm model
lab = double(ones(slNum, 1));
[labPr, ~, probPr] = svmpredict2(lab, feat, svmModel, '-b 1');
%% reorder svm model
labPr = labPr';
probPrRe = zeros(size(probPr))';
for i = 1:length(svmModel.Label)
probPrRe(svmModel.Label(i), :) = probPr(:, i);
end
probPr = probPrRe;
function [labPr, probPr] = applySlidingWindowSC(data, slLen, dic, svmModel)
%% initialization
dataLen = size(data, 2);
slNum = dataLen - slLen + 1;
%% extract subsequences
feat = cell(slNum, 1);
for j = 1:slNum
feat{j} = sparseCodeTran(data(:, j:j+slLen-1), dic);
end
feat = cell2mat(feat);
feat = full(feat);
%% apply svm model
lab = double(ones(slNum, 1));
[labPr, ~, probPr] = svmpredict2(lab, feat, svmModel, '-b 1');
%% reorder svm model
labPr = labPr';
probPrRe = zeros(size(probPr))';
for i = 1:length(svmModel.Label)
probPrRe(svmModel.Label(i), :) = probPr(:, i);
end
probPr = probPrRe;
|
github
|
mcyeh/aaltd16_fusion-master
|
sparseCodeTran.m
|
.m
|
aaltd16_fusion-master/sparseCodeTran.m
| 663 |
utf_8
|
451fab4e711dc77f697c0f828b7bcb37
|
% Compute the sparse coding of the input data, pool the sparse coding result
% with mean, and power normalized (cube root) the pooled result
% Chin-Chia Michael Yeh 05/28/2016
%
% feat = sparseCodeTran(data, dic)
% Output:
% feat: pooled and normalized sparse coding output (vector)
% Input:
% data: the multi dimensional time series (matrix)
% dic: dictionary, each column is a dictionary element (matrix)
%
function feat = sparseCodeTran(data, dic)
%% sparse coding
param.lambda = 1/sqrt(size(data, 1));
code = mexLasso(data, dic, param);
code = full(code);
%% pooling
code = mean(code, 2);
code = abs(code);
feat = nthroot(code, 3);
feat = feat';
|
github
|
mcyeh/aaltd16_fusion-master
|
spletTran.m
|
.m
|
aaltd16_fusion-master/spletTran.m
| 707 |
utf_8
|
60f253335139479b25221db4188f7d66
|
% Shapelet transform and power normalized (square root) the output
% Chin-Chia Michael Yeh 05/28/2016
%
% dataTran = spletTran(data, splet)
% Output:
% dataTran: shapelet trasformation output (vector)
% Input:
% data: the multi dimensional time series (matrix)
% splet: shaplet set, each row is a shapelet (matrix)
%
function dataTran = spletTran(data, splet)
%% perform the transform
spletNum = size(splet, 1);
dataDim = size(data, 1);
dataTran = cell(1, dataDim);
for i = 1:dataDim
dataTran{i} = zeros(1, spletNum);
for j = 1:spletNum
dist = distanceProfile(data(i, :)', splet(j, :)');
dataTran{i}(j) = nthroot(min(dist), 2);
end
end
dataTran = cell2mat(dataTran);
|
github
|
swag-kaust/ASOFI3D-master
|
write_asofi3D_json.m
|
.m
|
ASOFI3D-master/mfiles/write_asofi3D_json.m
| 1,074 |
utf_8
|
0cc977a208c79a5d7a0c858fd57fa85e
|
function write_asofi3D_json(filename, config)
% writes to json file
%% make all numbers strings
field_list = fieldnames(config);
for field_n = 1:length(field_list)
config.(field_list{field_n}) = ...
num2str(config.(field_list{field_n}));
end
%% encode to json
bb = jsonencode(config);
% replace manually the field names altered by MATLAB
bb = replace(bb, '"REFRECX_REFRECY_REFRECZ"','"REFRECX, REFRECY, REFRECZ"');
bb = replace(bb,'"XREC1_YREC1_ZREC1"','"XREC1,YREC1, ZREC1"');
bb = replace(bb,'"XREC2_YREC2_ZREC2"','"XREC2,YREC2, ZREC2"');
bb = replace(bb, '"SOURCE_ALPHA_SOURCE_BETA"', '"SOURCE_ALPHA, SOURCE_BETA"');
bb = replace(bb, '"AMON_STR_DIP_RAKE"', '"AMON, STR, DIP, RAKE"');
bb = replace(bb, '"AMON_M11_M12_M13_M22_M23_M33"',...
'"AMON, M11, M12, M13, M22, M23, M33"');
bb = replace(bb, '","', ['",', newline,'"']);
bb = sprintf(bb);
bb = replace(bb, '"NDT_NDTSHIFT"', '"NDT, NDTSHIFT"');
filewrite(filename,bb);
end
function filewrite(file_name, text)
fid = fopen(file_name,'wt');
fprintf(fid, '%s', text);
fclose(fid);
end
|
github
|
swag-kaust/ASOFI3D-master
|
plot_2Dslices.m
|
.m
|
ASOFI3D-master/mfiles/plot_2Dslices.m
| 25,867 |
utf_8
|
315f4f842972061d8c2b83ae1947f73f
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---script for the visualization of snapshots gained from the ASOFI simulation
%---most parameters are as specified in ASOFI parameter-file, e.g. sofi3D.json
%---Please note : y denotes the vertical axis!!
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%close all;
clearvars; clc;
addpath('./utils');
% User-defined parameters.
% Directory name with the simulation input and output, relative to this script.
plot_opts.par_folder = '../par';
% % Path to configuration file, relative to par_folder.
plot_opts.config_file='./in_and_out/sofi3D.json';
plot_opts.file_out = [plot_opts.par_folder, '/figures'];
plot_opts.file_ext = '.bin.div';
for phi2=0:15:90
plot_opts.phi2 = phi2;
snap3D_ASOFI_fun(plot_opts);
end
function snap3D_ASOFI_fun(plot_opts)
phi2 = plot_opts.phi2;
par_folder = plot_opts.par_folder;
config_file = plot_opts.config_file;
%% Read from json to opts.
opts = read_asofi3D_json([par_folder, '/', config_file]);
%% merge snapshots if they were not merged before
oldpwd = pwd;
cd(par_folder)
snap_name_full = [opts.SNAP_FILE,'.bin.div'];
dir_Full = dir(snap_name_full);
dir_000 = dir([opts.SNAP_FILE,'.bin.div.0.0.0']);
if ~exist(snap_name_full,'file')
system('../bin/snapmerge ./in_and_out/sofi3D.json');
elseif dir_Full.datenum < dir_000.datenum
system('../bin/snapmerge ./in_and_out/sofi3D.json');
end
cd(oldpwd)
%% prepare colormap
create_colormaps;
%% read parameters from json
nx = str2num(opts.NX);
ny = str2num(opts.NY);
nz = str2num(opts.NZ);
outx = str2num(opts.IDX);
outy = str2num(opts.IDY);
outz = str2num(opts.IDZ);
dh = str2num(opts.DX);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---input, output files
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Choose number of input files (1 or 2), only for 2D plots
% For the 3D plot (image_switch=2) only file_inp1 is used by default
num_switch=2;
% Input file1 (snapshot file1)
file_inp1 = [par_folder,'/snap/test.bin.div'];
% Input file2 (snapshot file2)
file_inp2 = [par_folder,'/snap/test.bin.curl'];
% Model file (for single display or contour plot ontop of snapshot)
file_mod = [par_folder,'/model/test.SOFI3D.rho'];
% Output file
% switch for saving snapshots to picture file 1=yes (jpg) 2= yes (png) other=no
filesave=1;
% base name of picture file output, will be expanded by extension jpg/png
file_out=plot_opts.file_out;
% title strings for each sub-figure
title_inp1='\nabla \cdot u';
title_inp2='S-wave field (curl)';
title_mod='Density model';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---variety of switches
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% switch for contour of model overlaying the model or snapshot file
% 1=yes other=no
cont_switch=1;
% number of contours for the contour-plot
numbOFcont=8;
% Choose model or snapshot plot (model-->1; snapshot-->2)
type_switch=2;
% Choose 2D slice or 3D surf plot
image_switch=2; % 1 = 2D; 2 = 3D;
% Choose slice geometry and postion (for 2-D plots)
slice_switch=1; % horizontal(zx)=1; vertical (yx)=2; vertical (yz)=3;
% slice definition, where to slice trough the 3-D space
nx=nx/outx;ny=ny/outy;nz=nz/outz;
zslice=nz/2; % for xy plane
yslice=ny/2; % for xz plane
xslice=nx/2; % for yz plane in grid points
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---Snapshot definitions
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% time increment for snapshots:
TSNAP1=0.8;
TSNAPINC=0.2;
% firts and last snapshot that is considered for displayin
firstframe=1;
lastframe=3;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---3D definitions: defines two rotating planes (xz, yz plane)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
phi1=0; % a horizontal plane (x-z plane) is rotated by phi1 with respect to the rotpoint
%phi2=90; % a horizontal plane (x-z plane) is rotated by phi2 with respect to the rotpoint
% rotaxis and rotpoint refers to the rotation of the 2D-slices within the 3D volume
% direction rotation axes [0,1,0] rotation of plane around vertical axis
% [1,0,0] rotation of plane around x-axis
rotaxis=[0,1,0];
% defines point for rotation of the 2D slices [x y z] in meter
% values are defined as difference to the center of the model/snaphot
% in case of rotpoint=[0,0,0], this point is excatly the center of the model/snaphot
rotpoint=[0 0 0];
% defines angles of perspective for the 3-D view
% i.e. it rotates the plot to favorable perspective
viewpoint=[1,4,1];
file_out = [file_out, num2str(phi2)];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---axis limits for 2D and 3D visualization
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% colorbar boundaries for cropping the snapshot value range
% only used if type_switch=2
auto_scaling=1; % 1= automatic determination of boundaries, 2= constant values caxis_value , 3= no scaling
caxis_value_1=5e-11;
caxis_value_2=caxis_value_1; % only used if num_switch=2
% use custom axis limits if axisoverwrite==1, otherwise matlab will
% determine axis limits automatically
axisoverwrite=0;
newaxis=[35 65 35 65];
% delay between the display of each snaphot in s
pause4display=0.2;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---end of input parameter definition
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---creation of model vectors and loading file data----
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% plot range and increment
xp1=dh; xp2=nx*dh; yp1=dh; yp2=ny*dh; zp1=dh; zp2=nz*dh;
% Computing range for axis and subscript range for the movie
x=xp1:dh*outx:xp2*outx;
y=yp1:dh*outy:yp2*outy; % vertical axis
z=zp1:dh*outz:zp2*outz;
% if model is plotted, than there is only one snapshot
% loop over # of snapshots will terminate after one iteration
if type_switch==1
firstframe=1
lastframe=1;
num_switch=1;
end
% if 3D snaphot visualization is chosen, the number of snapshots for
% simultaneous display is set to 1
if image_switch==2
num_switch=1;
end
% load model file ; Format ieee-be for Sun or Workstation, -le for PC
if (cont_switch==1) | (type_switch==1)
% opening file and reading
disp(['Loading file ' file_mod]);
fid_mod=fopen(file_mod,'r','ieee-le');
mod_data=fread(fid_mod,'float');
mod_data=reshape(mod_data,ny,nx,nz);
mod_data=permute(mod_data,[2,3,1]);
fclose(fid_mod);
if (slice_switch==1) % z-x-plane (horizontal)
mod_data=squeeze(mod_data(:,:,yslice));
mod_data=permute(mod_data,[2,1]);
end
if (slice_switch==2) % y-x-plane (vertical)
mod_data=squeeze(mod_data(:,zslice,:));
mod_data=permute(mod_data,[2,1]);
end
if (slice_switch==3) % y-z-plane (vertical)
mod_data=squeeze(mod_data(xslice,:,:));
mod_data=permute(mod_data,[2,1]);
end
end
% open snapshot data of 1st input file
disp(['Loading snap shot file ' file_inp1]);
fid_file1=fopen(file_inp1,'r','ieee-le');
if num_switch==2;
% open snapshot data of 2nd input file
disp(['Loading snap shot file ' file_inp2]);
fid_file2=fopen(file_inp2,'r','ieee-le');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---2D display of snapshot data (Slices )
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
disp([' ']);
if image_switch==1
% in case of snapshot files use seismic colormap
if type_switch==2
myMap = colormap(load('./srgb.map'));
end
% creating variables for snapshot content
% file1_data (and file2_data) depending on number of snapshots
% displayed simultaneously (switch num_switch)
% selected slice of y-x-plane (vertical plane)
if(slice_switch==2)
% allocate memory for 1st snapshot file
file1_data=zeros(ny,nx);
if num_switch==2
% allocate memory for 2nd snapshot file
file2_data=zeros(ny,nx);
end
end
% selected slice of z-x-plane (horizontal plane)
if(slice_switch==1)
% allocate memory for 1st snapshot file
file1_data2=zeros(nz,nx);
if num_switch==2
% allocate memory for 2nd snapshot file
file2_data2=zeros(nz,nx);
end
end
% selected slice of y-z-plane (vertical plane)
if(slice_switch==3)
% allocate memory for 1st snapshot file
file1_data2=zeros(ny,nz);
if num_switch==2
% allocate memory for 2nd snapshot file
file2_data2=zeros(ny,nz);
end
end
% determing imaging vectors (for contour and imagesc) and labels
% according to chosen image plane
if slice_switch==1
image_vect1=x;
image_vect2=z;
labelstring1='x in m';
labelstring2='z in m (horizontal)';
end
if slice_switch==2
image_vect1=x;
image_vect2=y;
labelstring1='x in m';
labelstring2='y in m (vertical)';
end
if slice_switch==3
image_vect1=z;
image_vect2=y;
labelstring1='z in m';
labelstring2='y in m (vertical)';
end
h1=figure(1);
% determination of screen size
scrsz = get(0,'ScreenSize');
% determination size of window for plotting
%set(h1,'Position',[1 scrsz(4)*2/3 scrsz(3)*1/4 scrsz(4)*2/3]);
%creating subfigure handles if 2 snapshots are displayed simultaneously
if num_switch==2
ax1=subplot('Position',[0.05 0.3 0.4 0.4]);
ax2=subplot('Position',[0.55 0.3 0.4 0.4]);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---loop over timesteps (2D snapshots)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for i=firstframe:1:lastframe;
%calculating time of snapshot
tsnap=(i-1)*TSNAPINC+TSNAP1;
disp(['Loading snapshot no ',int2str(i),' at time=',num2str(tsnap),' s.']);
% loading data:
if(slice_switch==2) % y-x-plane (vertical)
% since the models are stores as a series of yx-slices
% we just have to seek/jump to the mid-slice and load the data
offset=4*nx*ny*(zslice-1)+4*nx*ny*nz*(i-1);
fseek(fid_file1,offset,-1);
if num_switch==2
fseek(fid_file2,offset,-1);
end
file1_data(:,:)=fread(fid_file1,[ny,nx],'float');
if num_switch==2
file2_data(:,:)=fread(fid_file2,[ny,nx],'float');
end
else
% z-x-plane (horizontal) and %y-z-plane (vertical)
% to display slices in any other plane besides y-x, we have to load
% each single yx slice and extract a single line and put them together
for l=1:nz
file1_data=fread(fid_file1,[ny,nx],'float');
if num_switch==2
file2_data=fread(fid_file2,[ny,nx],'float');
end
if(slice_switch==1) % z-x plane (horizontal)
file1_data2(l,:)=file1_data(yslice,:);
if num_switch==2
file2_data2(l,:)=file2_data(yslice,:);
end
end
if(slice_switch==3) % y-z-plane (vertical)
file1_data2(:,l)=file1_data(:,xslice);
if num_switch==2
file2_data2(:,l)=file2_data(:,xslice);
end
end
end
file1_data=file1_data2;
clear file1_data2;
if num_switch==2
file2_data=file2_data2;
clear file2_data2;
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---plotting 2D slices
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% switch for loading 1 or 2 input files
if num_switch==2
% now switching to secondary subplot
axes(ax2);
imagesc(image_vect1,image_vect2,file2_data);
% determing maximum amplitude of plot
file2max=max(max(abs(file2_data)));
% switch whether model contour should be plotted
if cont_switch==1
hold on
contour(image_vect1,image_vect2,mod_data,numbOFcont,'k-','LineWidth',1);
hold off
end
% formatting the 2nd sub-figure
colorbar
xlabel(labelstring1);
ylabel(labelstring2);
title(title_inp2);
set(gca,'DataAspectRatio',[1 1 1]);
set(get(gca,'title'),'FontSize',12,'FontWeight','bold');
set(get(gca,'Ylabel'),'FontSize',12,'FontWeight','bold');
set(get(gca,'Xlabel'),'FontSize',12,'FontWeight','bold');
set(gca,'FontSize',12,'FontWeight','bold');
set(gca,'Linewidth',1.0);
set(gca,'Box','on');
if axisoverwrite==1
axis(newaxis);
end
% limiting the colorbar to specified range
switch auto_scaling
case 1
caxis([-file2max/10 file2max/10]);
case 2
caxis([-caxis_value_2 caxis_value_2])
otherwise
end
% now switching to primary subplot
axes(ax1);
end
% plot 1st input file
if type_switch==2
% plot snapshot file
imagesc(image_vect1,image_vect2,file1_data);
end
if type_switch==1
% plot model file
imagesc(image_vect1,image_vect2,mod_data);
end
colorbar
if type_switch==2
title(title_inp1);
end
if type_switch==1
title(title_mod);
end
% determing maximum amplitude of plot
file1max=max(max(abs(file1_data)));
% switch whether model contour should be plotted
if cont_switch==1
hold on
contour(image_vect1,image_vect2,mod_data,numbOFcont,'k-','LineWidth',1);
hold off
end
% formating the 1st sub-figure
xlabel(labelstring1);
ylabel(labelstring2);
set(gca,'DataAspectRatio',[1 1 1]);
set(get(gca,'title'),'FontSize',12,'FontWeight','bold');
set(get(gca,'Ylabel'),'FontSize',12,'FontWeight','bold');
set(get(gca,'Xlabel'),'FontSize',12,'FontWeight','bold');
set(gca,'FontSize',12,'FontWeight','bold');
set(gca,'Linewidth',1.0);
set(gca,'Box','on');
if axisoverwrite==1
axis(newaxis);
end
if type_switch==2 % in case of snapshot:
% display maximum amplitude of sub-figures to command window
if num_switch==2
disp([' Maximum amplitude of ',title_inp2,'-snapshot: ', num2str(file2max)]);
end
disp([' Maximum amplitude of ',title_inp1,'-snapshot: ', num2str(file1max)]);
% limiting the colorbar to specified range
switch auto_scaling
case 1
caxis([-file1max/10 file1max/10]);
case 2
caxis([-caxis_value_1 caxis_value_1]);
otherwise
end
% adding text string for timestep of snapshot
set(text(7000,7000,['T2= ',sprintf('%2.4f',tsnap), 's']),'FontSize',14,'FontWeight','bold','Color','black');
end
% delay the display of snapshots by frictions of seconds
pause(pause4display);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---Saving the snapshot to file
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if (filesave~=0)
% generating filename string
if i<10
imagefile=[file_out,'2D_00',int2str(i)];
else if i<100
imagefile=[file_out,'2D_0',int2str(i)];
else
imagefile=[file_out,'2D_',int2str(i)];
end
end
% output as jpg graphic (or eps) via print command
if filesave==1
eval(['print -djpeg100 ' [imagefile,'.jpg']]);
% eval(['print -depsc '[imagefile2,'.eps']]);
end
% output as png graphic via additional matlab function
if filesave==2
savefig([imagefile], 'png', '-rgb', '-c0', '-r250');
end
end
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---3D display of snapshot data (single snapshot only)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if image_switch==2
if type_switch==1
% loading model data
fid=fopen(file_mod,'r','ieee-le');
lastframe=1;
if cont_switch==1
fid_mod=fopen(file_mod,'r','ieee-le');
mod_data=fread(fid_mod,(nx*ny*nz),'float');
mod_data=reshape(mod_data,ny,nx,nz);
mod_data=permute(mod_data,[3,2,1]);
fclose(fid_mod);
end
end
if type_switch==2
% loading snapshot data of input1
fid=fopen(file_inp1,'r','ieee-le');
colormap(load('./srgb.map'));
% adding contour of model to snapshot data
if cont_switch==1
fid_mod=fopen(file_mod,'r','ieee-le');
mod_data=fread(fid_mod,(nx*ny*nz),'float');
mod_data=reshape(mod_data,ny,nx,nz);
mod_data=permute(mod_data,[3,2,1]);
fclose(fid_mod);
end
end
% loop over number of snaphsots
for i=firstframe:lastframe
% calculating time of snapshot
tsnap=(i-1)*TSNAPINC+TSNAP1;
disp(['Loading snapshot no ',int2str(i),' at time=',num2str(tsnap),' s.']);
% loading data (model or snapshot)
% calculate offset in order to jump to specific snapshot within file
offset=4*nx*ny*nz*(i-1);
fseek(fid,offset,-1);
file1_data=fread(fid,(nx*ny*nz),'float');
file1_data=reshape(file1_data,ny,nx,nz);
file1_data=permute(file1_data,[3,2,1]);
D = merge_snapshots(par_folder, plot_opts.file_ext);
file1_data = D(:,:,:,i);
% creating a grid
[X,Z,Y]=meshgrid(x,z,y);
%surface height of horizontal y-x plane
xyplane=zeros(ny,nx);
xyplane(:,:)=(nz*dh*outy)/2+rotpoint(2);
% creating a vertical slice plane in y-x plane
hslice = surf(x,y,xyplane);
% rotate slice plane with, with respect to a point from which rotation is defined
% in case of rotpoint=[0,0,0], this point is the center of the model/snaphot
rotate(hslice,rotaxis,phi1,[mean(x)-rotpoint(1),mean(y)-rotpoint(2),mean(mean(xyplane))-rotpoint(3)]);
% get boundaries of rotated plane
xd = get(hslice,'XData');
yd = get(hslice,'YData');
zd = get(hslice,'ZData');
% remove plane, only limits are further used
delete(hslice);
% creating a horizontal slice plane in z-x plane
hslice2 = surf(x,y,xyplane);
% rotate slice plane with, with respect to a point from which rotation is defined
% in case of rotpoint=[0,0,0], this point is the center of the model/snaphot
rotate(hslice2,rotaxis,phi2,[mean(x)-rotpoint(1),mean(y)-rotpoint(2),mean(mean(xyplane))-rotpoint(3)]);
% get boundaries of rotated plane
xd2 = get(hslice2,'XData');
yd2 = get(hslice2,'YData');
zd2 = get(hslice2,'ZData');
% remove plane, only limits are further used
delete(hslice2);
% display sliced and rotated plane
h1=figure(1);
% determination of screen size
scrsz = get(0,'ScreenSize');
% determination size of window for plotting
%set(h1,'Position',[1 scrsz(4)*2/3 scrsz(3)*1/4 scrsz(4)*2/3]);
axis equal
% strict vertical slice, no rotation applied
% h = slice(Y,X,Z,file1_data,[],yslice*dh*outy-1,[]);
% rotated vertical slice
% !!! note that taking sclices from a homogeneous model is - for some
% reason not working, please use 2-D visualization instead !!!
h = slice(X,Z,Y,file1_data,xd,zd,yd);
set(h,'FaceColor','interp','EdgeColor','none','DiffuseStrength',.8,'FaceAlpha',0.5);
hold on
% strict horizontal, no rotation applied
% h2 = slice(Y,X,Z,file1_data,[],[],zslice*dh*outz); % this is strict vertical, no rotation applied
% rotated horizontal slice
h2 = slice(X,Z,Y,file1_data,xd2,zd2,yd2);
set(h2,'FaceColor','interp','EdgeColor','none','DiffuseStrength',.8,'FaceAlpha',0.5);
axis equal
% vertical slice at model boundary
% h3 = slice(X,Z,Y,file1_data,10,[],[]);
% set(h3,'FaceColor','interp','EdgeColor','none','DiffuseStrength',.8);
% % black outline of the vertical slice
% plot3([0 max(max(xd))],[max(max(zd)) max(max(zd))],[0 0],'-black','LineWidth',2);
% plot3([max(max(xd)) max(max(xd))],[max(max(zd)) max(max(zd))],[0 max(max(yd))],'-black','LineWidth',2);
% plot3([max(max(xd)) 0],[max(max(zd)) max(max(zd))],[max(max(yd)) max(max(yd))],'-black','LineWidth',2);
% plot3([0 0],[max(max(zd)) max(max(zd))],[0 max(max(yd))],'-black','LineWidth',2);
%
% % black outline of the horizontal slice
% plot3([0 max(max(xd2))],[max(max(zd2)) max(max(zd2))],[max(max(yd2)) min(min(yd2))],'-black','LineWidth',2);
% plot3([max(max(xd2)) max(max(xd2))],[max(max(zd2)) 0],[max(max(yd2)) max(max(yd2))],'-black','LineWidth',2);
% plot3([max(max(xd2)) 0],[0 0],[max(max(yd2)) min(min(yd2))],'-black','LineWidth',2);
% plot3([0 0],[0 max(max(zd2))],[max(max(yd2)) max(max(yd2))],'-black','LineWidth',2);
if cont_switch==1
% vertical contour slice
h=contourslice(X,Z,Y,mod_data,xd,zd,yd,numbOFcont);
set(h,'FaceColor','black','EdgeColor','black');
% horizontal contour slice
h2=contourslice(X,Z,Y,mod_data,xd2,zd2,yd2,numbOFcont);
set(h2,'FaceColor','black','EdgeColor','black');
end
hold off
% formating figure
%daspect([1,1,1]);
axis tight
box on
% set viewpoint within 3-D space
% (rotate 3-D figure plot to favorable perspective)
view(viewpoint);
camproj perspective
% lightangle(-45,45);
set(gcf,'Renderer','zbuffer');
%colorbar
xlabel('x in m');
ylabel('y in m');
zlabel('Depth z in m');
% adding title string to plot
if type_switch==2
title(title_inp1);
end
if type_switch==1
title(title_mod);
end
set(get(gca,'title'),'FontSize',13);
set(get(gca,'title'),'FontWeight','bold');
set(get(gca,'Ylabel'),'FontSize',12);
set(get(gca,'Ylabel'),'FontWeight','bold');
set(get(gca,'Xlabel'),'FontSize',12);
set(get(gca,'Xlabel'),'FontWeight','bold');
set(get(gca,'Zlabel'),'FontSize',12);
set(get(gca,'Zlabel'),'FontWeight','bold');
set(gca, 'ZDir', 'reverse')
set(gca, 'YDir','reverse')
set(gca,'FontSize',12);
set(gca,'FontWeight','bold');
set(gca,'Linewidth',1.0);
set(gca,'Box','on');
% determing maximum amplitude of plot
file1max=max(max(max(abs(file1_data))));
% display maximum amplitude to command window
disp([' Maximum amplitude of ',title_inp1,'-snapshot: ', num2str(file1max)]);
% cropping model/snapshot dimensions
ylim([0 nz*dh*outz+0.1])
xlim([0 nx*dh*outx+0.1])
zlim([0 ny*dh*outy+1.1])
% for snapshot input files
if type_switch==2
% limiting the colorbar to specified range
switch auto_scaling
case 1
caxis([-file1max/5 file1max/5]);
case 2
caxis([-caxis_value_1 caxis_value_1]);
otherwise
end
% adding text string for timestep of snapshot
set(text(3500,-1000,['T= ',sprintf('%2.4f',tsnap), 's']),'FontSize',12,'FontWeight','bold','Color','black');
pause(pause4display);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---Saving the snapshot to file
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if (filesave~=0)
% generating filename string
if i<10
imagefile=[file_out,'3D_00',int2str(i)];
else if i<100
imagefile=[file_out,'3D_0',int2str(i)];
else
imagefile=[file_out,'3D_',int2str(i)];
end
end
% output as jpg graphic (or eps) via print command
if filesave==1
eval(['print -djpeg100 ' [imagefile,'.jpg']]);
% eval(['print -depsc '[imagefile2,'.eps']]);
end
% output as png graphic via additional matlab function
if filesave==2
savefig([imagefile], 'png', '-rgb', '-c0', '-r250');
end
end
end
end
end
disp([' ']);
disp(['Script ended...']);
end
|
github
|
swag-kaust/ASOFI3D-master
|
read_asofi3D_json.m
|
.m
|
ASOFI3D-master/mfiles/read_asofi3D_json.m
| 922 |
utf_8
|
572c5ceb8ee2a49a9b110240dbf26c43
|
function config = read_asofi3D_json(filename)
%READ_ASOFI3D_JSON Read configuration file into `struct`.
% json_config = read_asofi3D_json('in_and_out/sofi3D.json') reads file
% 'in_and_out/sofi3D.json' relative to the current directory.
json_text = fileread(filename);
i = find(json_text=='{');
j = find(json_text=='}');
b = json_text(i:j);
config = jsondecode(b);
% ASOFI3D JSON parameter file contains all variables in string datatype.
% We convert numerical parameters to double datatype, this does not over
% parsing
field_list = fieldnames(config);
for field_n = 1:length(field_list)
config.(field_list{field_n}) = ...
str2num_if_num(config.(field_list{field_n}));
end
%write_asofi3D_json([filename, '_snap'], config);
end
function numval = str2num_if_num(strval)
%makes double from string only if it is a number
numval = str2double(strval);
if isnan(numval)
numval = strval;
end
end
|
github
|
swag-kaust/ASOFI3D-master
|
snap3D_ASOFI.m
|
.m
|
ASOFI3D-master/mfiles/snap3D_ASOFI.m
| 16,756 |
utf_8
|
ae3f6d8fa159ce4bda3d5aef9588f3d6
|
function [opts, plot_opts, D] = snap3D_ASOFI(folder_out, diffFlag, config_file)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---script for the visualization of snapshots gained from the ASOFI simulation
%---most parameters are as specified in ASOFI parameter-file, e.g. sofi3D.json
%---Please note : y denotes the vertical axis!!
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clc;
% by default the figures will be saved to the figures folder without any
% subfolder structure
defval('folder_out', '.');
defval('diffFlag', false);
defval('config_file','./in_and_out/asofi3D.json');
%% check MATLAB
MATLAB_MIN_VERSION = 'R2016b';
if verLessThan('matlab', MATLAB_MIN_VERSION)
fprintf('ERROR: The minimal supported version of MATLAB is %s.\n', ...
MATLAB_MIN_VERSION);
return;
end
%%
addpath('./utils');
% User-defined parameters.
% Directory name with the simulation input and output, relative to this script.
plot_opts.par_folder = '../par';
% % Path to configuration file, relative to par_folder.
plot_opts.config_file = config_file;
plot_opts.file_ext = '.bin.curl';
plot_opts.file_out = [plot_opts.par_folder, '/figures/',folder_out,'/'];
mkdir(plot_opts.file_out);
plot_opts.diffFlag = diffFlag;
for phi2=90:90
plot_opts.phi2 = phi2;
[opts, D] = snap3D_asofi3D_func(plot_opts);
end
disp(' ');
disp('Script ended...');
end
function [opts, D] = snap3D_asofi3D_func(plot_opts)
phi2 = plot_opts.phi2;
par_folder = plot_opts.par_folder;
config_file = plot_opts.config_file;
%% Read from json to opts.
opts = read_asofi3D_json([par_folder, '/', config_file]);
%% merge snapshots if they were not merged before
oldpwd = pwd;
cd(par_folder)
snap_name_full = [opts.SNAP_FILE,'.bin.div'];
dir_Full = dir(snap_name_full);
dir_000 = dir([opts.SNAP_FILE,'.bin.div.0.0.0']);
if ~exist(snap_name_full,'file')
system(['../bin/snapmerge ' config_file]);
elseif dir_Full.datenum < dir_000.datenum
system(['../bin/snapmerge ' config_file]);
end
cd(oldpwd)
%% prepare colormap
create_colormaps;
%% read parameters from json
nx = opts.NX;
ny = opts.NY;
nz = opts.NZ;
outx = opts.IDX;
outy = opts.IDY;
outz = opts.IDZ;
% TODO: Why we think that DX is the same as DY or DZ?
dh = opts.DX;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---input, output files
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Input file1 (snapshot file1)
file_inp1 = [par_folder '/' opts.SNAP_FILE '.bin.div'];
% Model file (for single display or contour plot ontop of snapshot)
file_mod = fullfile(par_folder, [opts.MFILE '.SOFI3D.rho']);
% Output file
% switch for saving snapshots to picture file 1=yes (jpg) 2= yes (png) other=no
file_save=1;
% base name of picture file output, will be expanded by extension jpg/png
file_out=plot_opts.file_out;
% title strings for each sub-figure
title_inp1='|\nabla \times u|';
title_mod='Density model';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---variety of switches
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% switch for contour of model overlaying the model or snapshot file
% 1=yes other=no
cont_switch=1;
% number of contours for the contour-plot
numbOFcont=8;
% Choose model or snapshot plot (model-->1; snapshot-->2)
type_switch=2;
% Choose slice geometry and postion (for 2-D plots)
slice_switch=1; % horizontal(zx)=1; vertical (yx)=2; vertical (yz)=3;
% slice definition, where to slice trough the 3-D space
nx=nx/outx;ny=ny/outy;nz=nz/outz;
zslice=nz/2; % for xy plane
yslice=ny/2; % for xz plane
xslice=nx/2; % for yz plane in grid points
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---Snapshot definitions
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% time increment for snapshots.
TSNAP1=opts.TSNAP1;
TSNAP2=opts.TSNAP2;
TIME = opts.TIME;
TSNAPINC=opts.TSNAPINC;
if TSNAP2 > TIME
fprintf(['WARNING: TSNAP2 = %f is larger than TIME = %f. ' ...
'Set TSNAP2 = TIME.\n'], ...
TSNAP2, TIME);
TSNAP2 = TIME;
end
nsnap = 1+floor(10*eps+(TSNAP2 - TSNAP1) / TSNAPINC);
% first and last snapshot that is considered for displayin
firstframe=1;
lastframe=3;
if lastframe > nsnap
fprintf(['WARNING: Number of snapshots (nsnap = %d) is ' ...
'larger than the last snapshot number (lastframe = %d). ' ...
'Set lastframe = nsnap.\n'], ...
nsnap, lastframe);
lastframe = nsnap;
end
if firstframe > nsnap
error('snap3D_ASOFI:incorrectFirstFrame', ...
['ERROR: First snapshot number (firstframe = %d) is ' ...
'larger than the number of snapshots (nsnap = %d).\n'], ...
firstframe, nsnap);
end
if lastframe < firstframe
fprintf(['WARNING: First snapshot number (firstframe = %d) is ' ...
'larger than the last snapshot number (lastframe = %d). ' ...
'Set lastframe = firstframe\n'], ...
firstframe, lastframe);
lastframe = firstframe;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---3D definitions: defines two rotating planes (xz, yz plane)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
phi1=0; % a horizontal plane (x-z plane) is rotated by phi1 with respect to the rotpoint
%phi2=90; % a horizontal plane (x-z plane) is rotated by phi2 with respect to the rotpoint
% rotaxis and rotpoint refers to the rotation of the 2D-slices within the 3D volume
% direction rotation axes [0,1,0] rotation of plane around vertical axis
% [1,0,0] rotation of plane around x-axis
rotaxis=[0,1,0];
% defines point for rotation of the 2D slices [x y z] in meter
% values are defined as difference to the center of the model/snaphot
% in case of rotpoint=[0,0,0], this point is excatly the center of the model/snaphot
rotpoint=[0 0 0];
% defines angles of perspective for the 3-D view
% i.e. it rotates the plot to favorable perspective
viewpoint=0.8*[1,4,1];
file_out = [file_out, num2str(phi2)];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---axis limits for 2D and 3D visualization
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% colorbar boundaries for cropping the snapshot value range
% only used if type_switch=2
auto_scaling=1; % 1= automatic determination of boundaries, 2= constant values caxis_value , 3= no scaling
caxis_value_1=5e-11;
% delay between the display of each snaphot in s
pause4display=0.2;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---end of input parameter definition
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---creation of model vectors and loading file data----
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% plot range and increment
xp1=dh; xp2=nx*dh; yp1=dh; yp2=ny*dh; zp1=dh; zp2=nz*dh;
% Computing range for axis and subscript range for the movie
x=xp1:dh*outx:xp2*outx;
y=yp1:dh*outy:yp2*outy; % vertical axis
z=zp1:dh*outz:zp2*outz;
% if model is plotted, than there is only one snapshot
% loop over # of snapshots will terminate after one iteration
if type_switch==1
firstframe=1;
lastframe=1;
end
% load model file ; Format ieee-be for Sun or Workstation, -le for PC
if (cont_switch==1) || (type_switch==1)
% opening file and reading
disp(['Loading file ' file_mod]);
[fid_mod, err_msg] = fopen(file_mod, 'r', 'ieee-le');
if fid_mod == -1
disp(['Cannot open file ' file_mod]);
disp(['Reason: ' err_msg]);
disp('Now exiting');
return;
end
mod_data=fread(fid_mod,'float');
mod_data=reshape(mod_data,ny,nx,nz);
mod_data=permute(mod_data,[2,3,1]);
fclose(fid_mod);
if (slice_switch==1) % z-x-plane (horizontal)
mod_data=squeeze(mod_data(:,:,yslice));
mod_data=permute(mod_data,[2,1]);
end
if (slice_switch==2) % y-x-plane (vertical)
mod_data=squeeze(mod_data(:,zslice,:));
mod_data=permute(mod_data,[2,1]);
end
if (slice_switch==3) % y-z-plane (vertical)
mod_data=squeeze(mod_data(xslice,:,:));
mod_data=permute(mod_data,[2,1]);
end
end
% open snapshot data of 1st input file
disp(['Loading snapshot file ' file_inp1]);
fid_file1=fopen(file_inp1,'r','ieee-le');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---3D display of snapshot data (single snapshot only)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if type_switch==1
% loading model data
fid=fopen(file_mod,'r','ieee-le');
lastframe=1;
if cont_switch==1
fid_mod=fopen(file_mod,'r','ieee-le');
mod_data=fread(fid_mod,(nx*ny*nz),'float');
mod_data=reshape(mod_data,ny,nx,nz);
mod_data=permute(mod_data,[3,2,1]);
fclose(fid_mod);
end
end
if type_switch==2
% loading snapshot data of input1
fid=fopen(file_inp1,'r','ieee-le');
colormap(rdbuMap());
% adding contour of model to snapshot data
if cont_switch==1
fid_mod=fopen(file_mod,'r','ieee-le');
mod_data=fread(fid_mod,(nx*ny*nz),'float');
mod_data=reshape(mod_data,ny,nx,nz);
mod_data=permute(mod_data,[3,2,1]);
fclose(fid_mod);
end
end
if plot_opts.diffFlag
load('D_hom','D');
D_hom = D;
D = merge_snapshots(plot_opts, opts);
D = D-D_hom;
else
D = merge_snapshots(plot_opts, opts);
end
% loop over number of snaphsots
for i=firstframe:lastframe
% calculating time of snapshot
tsnap=(i-1)*TSNAPINC+TSNAP1;
disp(['Displaying snapshot no ',int2str(i),' at time=',num2str(tsnap),' s.']);
file1_data = D(:,:,:,i);
% creating a grid
[X,Z,Y]=meshgrid(x,z,y);
%surface height of horizontal y-x plane
xyplane=zeros(ny,nx);
xyplane(:,:)=(nz*dh*outy)/2+rotpoint(2);
% creating a vertical slice plane in y-x plane
hslice = surf(x,y,xyplane);
% rotate slice plane with, with respect to a point from which rotation is defined
% in case of rotpoint=[0,0,0], this point is the center of the model/snaphot
rotate(hslice,rotaxis,phi1,[mean(x)-rotpoint(1),mean(y)-rotpoint(2),mean(mean(xyplane))-rotpoint(3)]);
% get boundaries of rotated plane
xd = get(hslice,'XData');
yd = get(hslice,'YData');
zd = get(hslice,'ZData');
% remove plane, only limits are further used
delete(hslice);
% creating a horizontal slice plane in z-x plane
hslice2 = surf(x,y,xyplane);
% rotate slice plane with, with respect to a point from which rotation is defined
% in case of rotpoint=[0,0,0], this point is the center of the model/snaphot
rotate(hslice2,rotaxis,phi2,[mean(x)-rotpoint(1),mean(y)-rotpoint(2),mean(mean(xyplane))-rotpoint(3)]);
% get boundaries of rotated plane
xd2 = get(hslice2,'XData');
yd2 = get(hslice2,'YData');
zd2 = get(hslice2,'ZData');
% remove plane, only limits are further used
delete(hslice2);
% display sliced and rotated plane
figure(1);
% determination of screen size
get(0,'ScreenSize');
% determination size of window for plotting
%set(h1,'Position',[1 scrsz(4)*2/3 scrsz(3)*1/4 scrsz(4)*2/3]);
axis equal
% strict vertical slice, no rotation applied
% h = slice(Y,X,Z,file1_data,[],yslice*dh*outy-1,[]);
% rotated vertical slice
% !!! note that taking sclices from a homogeneous model is - for some
% reason not working, please use 2-D visualization instead !!!
h = slice(X,Z,Y,file1_data,xd,zd,yd);
set(h,'FaceColor','interp','EdgeColor','none','DiffuseStrength',.8,'FaceAlpha',0.5);
hold on
% strict horizontal, no rotation applied
% h2 = slice(Y,X,Z,file1_data,[],[],zslice*dh*outz); % this is strict vertical, no rotation applied
% rotated horizontal slice
h2 = slice(X,Z,Y,file1_data,xd2,zd2,yd2);
set(h2,'FaceColor','interp','EdgeColor','none','DiffuseStrength',.8,'FaceAlpha',0.5);
axis equal
if cont_switch==1
% vertical contour slice
h=contourslice(X,Z,Y,mod_data,xd,zd,yd,numbOFcont);
set(h,'FaceColor','black','EdgeColor','black');
% horizontal contour slice
h2=contourslice(X,Z,Y,mod_data,xd2,zd2,yd2,numbOFcont);
set(h2,'FaceColor','black','EdgeColor','black');
end
%% 3D scatter snapshot visualization
if plot_opts.diffFlag
scatter_plot(file1_data, opts);
end
hold off
%% formating figure
%daspect([1,1,1]);
axis tight
box on
% set viewpoint within 3-D space
% (rotate 3-D figure plot to favorable perspective)
view(viewpoint);
camproj perspective
% lightangle(-45,45);
set(gcf,'Renderer','zbuffer');
%colorbar
xlabel('x(m)');
ylabel('y(m)');
zlabel('z(m)');
% adding title string to plot
if type_switch==2
title(title_inp1);
end
if type_switch==1
title(title_mod);
end
%
set(get(gca,'title'),'FontSize',13);
set(get(gca,'title'),'FontWeight','bold');
set(get(gca,'Ylabel'),'FontSize',12);
set(get(gca,'Ylabel'),'FontWeight','bold');
set(get(gca,'Xlabel'),'FontSize',12);
set(get(gca,'Xlabel'),'FontWeight','bold');
set(get(gca,'Zlabel'),'FontSize',12);
set(get(gca,'Zlabel'),'FontWeight','bold');
set(gca, 'ZDir', 'reverse')
set(gca, 'YDir','reverse')
set(gca,'FontSize',12);
set(gca,'FontWeight','bold');
set(gca,'Linewidth',1.0);
set(gca,'Box','on');
%%
% determing maximum amplitude of plot
file1max=max(max(max(abs(file1_data))));
% display maximum amplitude to command window
disp([' Maximum amplitude of ',title_inp1,'-snapshot: ', num2str(file1max)]);
% cropping model/snapshot dimensions
ylim([0 nz*dh*outz])
xlim([0 nx*dh*outx])
zlim([0 ny*dh*outy])
% for snapshot input files
if type_switch==2
% limiting the colorbar to specified range
switch auto_scaling
case 1
caxis([-file1max/5 file1max/5]);
case 2
caxis([-caxis_value_1 caxis_value_1]);
otherwise
end
% adding text string for timestep of snapshot
%set(text(3500,-1000,['T= ',sprintf('%2.4f',tsnap), 's']),'FontSize',12,'FontWeight','bold','Color','black');
pause(pause4display);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---Saving the snapshot to file
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
save_snap(file_save, file_out, i)
end
end
end
function scatter_plot(last_snap, config)
%%
[Y, X, Z] = ndgrid(0:size(last_snap,1)-1, ...
0:size(last_snap,2)-1, ...
0:size(last_snap,3)-1);
X = config.IDX * config.DX * X;
Y = config.IDY * config.DY * Y;
Z = config.IDZ * config.DZ * Z;
mask_3D = (abs(last_snap(:)) > max(abs(last_snap(:)))*0.2);% .* (Z(:)<(config.DH1-100));
mask_3D = logical(mask_3D);
h = scatter3(X(mask_3D), Y(mask_3D), Z(mask_3D), 100*abs(last_snap(mask_3D))./max(last_snap(:))+eps, last_snap(mask_3D), 'filled');
alpha = 0.5;
set(h, 'MarkerFaceAlpha',alpha, 'MarkerEdgeAlpha', alpha)
end
function save_snap(file_save, file_out, i)
%%
if (file_save~=0)
% generating filename string
if i<10
imagefile=[file_out,'3D_00',int2str(i)];
elseif i<100
imagefile=[file_out,'3D_0',int2str(i)];
else
imagefile=[file_out,'3D_',int2str(i)];
end
% output as jpg graphic (or eps) via print command
if file_save==1
fig2 = gcf;
fig2.PaperPosition = [0 0 8 4.5];
eval(['print -djpeg100 ' [imagefile,'.jpg']]);
%eval(['print -depsc ' [imagefile,'.eps']]);
end
% output as png graphic via additional matlab function
if file_save==2
savefig(imagefile, 'png', '-rgb', '-c0', '-r250');
end
end
end
|
github
|
swag-kaust/ASOFI3D-master
|
run_and_snap.m
|
.m
|
ASOFI3D-master/mfiles/run_and_snap.m
| 3,456 |
utf_8
|
3260c20b15fe9a0556b9050e371101a5
|
function run_and_snap
%% run modeling
% load config
config = read_asofi3D_json('../par/in_and_out/asofi3D_hom.json');
jsonPath = '../par/in_and_out/asofi3D.json';
config.jsonPath = jsonPath;
config_hom = config;
%% generate homogeneous data
config.DH1 = 1e6;
write_asofi3D_json(jsonPath, config);
% run modeling
run_asofi3D(config_hom);
%% show snapshots
[opts, plot_opts, D] = snap3D_ASOFI('hom');
save('D_hom','D','-v7.3');
%%
% isotropic parameters are perturbed by 5%
iso_par = {'VPV2', 'VSV2', 'RHO2'};
for i = 1:length(iso_par)
config = config_hom;
config.(iso_par{i}) = 1.1 * config.(iso_par{i});
config.folderOut = iso_par{i};
last_snap = create_snapshots(config);
last_snap_collection(i, :) = last_snap(:);
end
%% anisotropic deviation parameters are perturbed by 0.1
aniso_par = {'EPSX2', 'EPSY2', 'DELX2', 'DELY2', 'DELXY2', 'GAMX2', 'GAMY2'};
for i = 1:length(aniso_par)
config = config_hom;
% perturb parameter
config.(aniso_par{i}) = 0.1;
% saving figure to folderOut
config.folderOut = aniso_par{i};
last_snap = create_snapshots(config);
last_snap_collection(i+3, :) = last_snap(:);
end
%% SVD
[U, S, V] = svd(last_snap_collection(:,:),'econ');
figure(777);
S = diag(S);
semilogy(S/max(S));
ylim([10^-2 1]);
%semilogy((S(1:end-1)-S(2:end))/max(S));
%imagesc(U);
%% full model
write_asofi3D_json(jsonPath, config_hom);
end
%%
function run_asofi3D(config)
% write config to jsonPath
write_asofi3D_json(config.jsonPath, config);
%% run modeling
% find number of processors NP to run on
NP = config.NPROCX * config.NPROCY * config.NPROCZ;
NP_max = feature('numcores');
if NP > NP_max
error('ASOFI3D:NP_MAX', ...
['Required number of MPI processes %d is larger ' ...
'than available %d'], ...
NP, NP_max);
end
cd ../
system(['./run_asofi3D.sh ', num2str(NP)])
cd mfiles
end
function last_snap = create_snapshots(config)
% runs asofi3D, then generates snapshots
run_asofi3D(config);
[opts, plot_opts, D] = snap3D_ASOFI(config.folderOut, true);
caxis(caxis());
save('D_diff','D','-v7.3');
%%
last_snap = squeeze(D(:,:,:,3));
[M, I] = max(abs(last_snap), [], 3);
[ind_1, ind_2] = ndgrid(1:size(I,1), 1:size(I,2));
I_linear = sub2ind(size(last_snap), ind_1(:), ind_2(:), I(:));
figure; surf(I, reshape(last_snap(I_linear),size(I)));
set(gca,'Zdir','reverse')
colormap(rdbuMap());
title('Max along Z');
%%
M = M(:);
mask_wave = M > max(M)/10;
K = abs(last_snap(I_linear));
figure; scatter3(ind_1(mask_wave), ind_2(mask_wave), I(mask_wave), (K(mask_wave)./max(K))+eps);
set(gca,'Zdir','reverse')
axis equal
xlim([0 size(last_snap,1)])
ylim([0 size(last_snap,2)])
zlim([0 size(last_snap,3)])
%% 3D scatter snapshot visualization
last_snap = squeeze(D(:,:,:,3));
[Y, X, Z] = ndgrid(0:size(last_snap,1)-1, ...
0:size(last_snap,2)-1, ...
0:size(last_snap,3)-1);
X = config.IDX * config.DX * X;
Y = config.IDY * config.DY * Y;
Z = config.IDZ * config.DZ * Z;
mask_3D = (abs(last_snap(:)) > max(abs(last_snap(:)))*0.2);% .* (Z(:)<config.DH1);
mask_3D = logical(mask_3D);
figure; scatter3(X(mask_3D), Y(mask_3D), Z(mask_3D), 100*abs(last_snap(mask_3D))./max(last_snap(:))+eps, last_snap(mask_3D), 'filled');
axis equal
xlim([0 max(X(:))])
xlabel X
ylim([0 max(Y(:))])
ylabel Y
zlim([0 max(Z(:))])
zlabel Z
colormap(rdbuMap());
colorbar
title(config.folderOut);
caxis(caxis()/10);
set(gca,'Zdir','reverse')
end
|
github
|
swag-kaust/ASOFI3D-master
|
savefig.m
|
.m
|
ASOFI3D-master/mfiles/utils/savefig.m
| 13,343 |
utf_8
|
5e55383fee448146f66f14d4c342b027
|
function savefig(fname, varargin)
% Usage: savefig(filename, fighdl, options)
%
% Saves a pdf, eps, png, jpeg, and/or tiff of the contents of the fighandle's (or current) figure.
% It saves an eps of the figure and the uses Ghostscript to convert to the other formats.
% The result is a cropped, clean picture. There are options for using rgb or cmyk colours,
% or grayscale. You can also choose the resolution.
%
% The advantage of savefig is that there is very little empty space around the figure in the
% resulting files, you can export to more than one format at once, and Ghostscript generates
% trouble-free files.
%
% If you find any errors, please let me know! (peder at axensten dot se)
%
% filename: File name without suffix.
%
% fighdl: (default: gcf) Integer handle to figure.
%
% options: (default: '-r300', '-lossless', '-rgb') You can define your own
% defaults in a global variable savefig_defaults, if you want to, i.e.
% savefig_defaults= {'-r200','-gray'};.
% 'eps': Output in Encapsulated Post Script (no preview yet).
% 'pdf': Output in (Adobe) Portable Document Format.
% 'png': Output in Portable Network Graphics.
% 'jpeg': Output in Joint Photographic Experts Group format.
% 'tiff': Output in Tagged Image File Format (no compression: huge files!).
% '-rgb': Output in rgb colours.
% '-cmyk': Output in cmyk colours (not yet 'png' or 'jpeg' -- '-rgb' is used).
% '-gray': Output in grayscale (not yet 'eps' -- '-rgb' is used).
% '-fonts': Include fonts in eps or pdf. Includes only the subset needed.
% '-lossless': Use lossless compression, works on most formats. same as '-c0', below.
% '-c<float>': Set compression for non-indexed bitmaps in PDFs -
% 0: lossless; 0.1: high quality; 0.5: medium; 1: high compression.
% '-r<integer>': Set resolution.
% '-crop': Removes points and line segments outside the viewing area -- permanently.
% Only use this on figures where many points and/or line segments are outside
% the area zoomed in to. This option will result in smaller vector files (has no
% effect on pixel files).
% '-dbg': Displays gs command line(s).
%
% EXAMPLE:
% savefig('nicefig', 'pdf', 'jpeg', '-cmyk', '-c0.1', '-r250');
% Saves the current figure to nicefig.pdf and nicefig.png, both in cmyk and at 250 dpi,
% with high quality lossy compression.
%
% REQUIREMENT: Ghostscript. Version 8.57 works, probably older versions too, but '-dEPSCrop'
% must be supported. I think version 7.32 or newer is ok.
%
% HISTORY:
% Version 1.0, 2006-04-20.
% Version 1.1, 2006-04-27:
% - No 'epstopdf' stuff anymore! Using '-dEPSCrop' option in gs instead!
% Version 1.2, 2006-05-02:
% - Added a '-dbg' option (see options, above).
% - Now looks for a global variable 'savefig_defaults' (see options, above).
% - More detailed Ghostscript options (user will not really notice).
% - Warns when there is no device for a file-type/color-model combination.
% Version 1.3, 2006-06-06:
% - Added a check to see if there actually is a figure handle.
% - Now works in Matlab 6.5.1 (R13SP1) (maybe in 6.5 too).
% - Now compatible with Ghostscript 8.54, released 2006-06-01.
% Version 1.4, 2006-07-20:
% - Added an option '-soft' that enables anti-aliasing on pixel graphics (on by default).
% - Added an option '-hard' that don't do anti-aliasing on pixel graphics.
% Version 1.5, 2006-07-27:
% - Fixed a bug when calling with a figure handle argument.
% Version 1.6, 2006-07-28:
% - Added a crop option, see above.
% Version 1.7, 2007-03-31:
% - Fixed bug: calling print with invalid renderer value '-none'.
% - Removed GhostScript argument '-dUseCIEColor' as it sometimes discoloured things.
% Version 1.8, 2008-01-03:
% - Added MacIntel: 'MACI'.
% - Added 64bit PC (I think, can't test it myself).
% - Added option '-nointerpolate' (use it to prevent blurring of pixelated).
% - Removed '-hard' and '-soft'. Use '-nointerpolate' for '-hard', default for '-soft'.
% - Fixed the gs 8.57 warning on UseCIEColor (it's now set).
% - Added '-gray' for pdf, but gs 8.56 or newer is needed.
% - Added '-gray' and '-cmyk' for eps, but you a fairly recent gs might be needed.
% Version 1.9, 2008-07-27:
% - Added lossless compression, see option '-lossless', above. Works on most formats.
% - Added lossy compression, see options '-c<float>...', above. Works on 'pdf'.
% Thanks to Olly Woodford for idea and implementation!
% - Removed option '-nointerpolate' -- now savefig never interpolates.
% - Fixed a few small bugs and removed some mlint comments.
% Version 2.0, 2008-11-07:
% - Added the possibility to include fonts into eps or pdf.
%
% TO DO: (Need Ghostscript support for these, so don't expect anything soon...)
% - svg output.
% - '-cmyk' for 'jpeg' and 'png'.
% - Preview in 'eps'.
% - Embedded vector fonts, not bitmap, in 'eps'.
%
% Copyright (C) Peder Axensten (peder at axensten dot se), 2006.
% KEYWORDS: eps, pdf, jpg, jpeg, png, tiff, eps2pdf, epstopdf, ghostscript
%
% INSPIRATION: eps2pdf (5782), eps2xxx (6858)
%
% REQUIREMENTS: Works in Matlab 6.5.1 (R13SP1) (maybe in 6.5 too).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
op_dbg= false; % Default value.
% Compression
compr= [' -dUseFlateCompression=true -dLZWEncodePages=true -dCompatibilityLevel=1.6' ...
' -dAutoFilterColorImages=false -dAutoFilterGrayImages=false ' ...
' -dColorImageFilter=%s -dGrayImageFilter=%s']; % Compression.
lossless= sprintf (compr, '/FlateEncode', '/FlateEncode');
lossy= sprintf (compr, '/DCTEncode', '/DCTEncode' );
lossy= [lossy ' -c ".setpdfwrite << /ColorImageDict << /QFactor %g ' ...
'/Blend 1 /HSample [%s] /VSample [%s] >> >> setdistillerparams"'];
% Create gs command.
cmdEnd= ' -sDEVICE=%s -sOutputFile="%s"'; % Essential.
epsCmd= '';
epsCmd= [epsCmd ' -dSubsetFonts=true -dNOPLATFONTS']; % Future support?
epsCmd= [epsCmd ' -dUseCIEColor=true -dColorConversionStrategy=/UseDeviceIndependentColor'];
epsCmd= [epsCmd ' -dProcessColorModel=/%s']; % Color conversion.
pdfCmd= [epsCmd ' -dAntiAliasColorImages=false' cmdEnd];
epsCmd= [epsCmd cmdEnd];
% Get file name.
if((nargin < 1) || isempty(fname) || ~ischar(fname)) % Check file name.
error('No file name specified.');
end
[pathstr, namestr] = fileparts(fname);
if(isempty(pathstr)), fname= fullfile(cd, namestr); end
% Get handle.
fighdl= get(0, 'CurrentFigure'); % See gcf. % Get figure handle.
if((nargin >= 2) && (numel(varargin{1}) == 1) && isnumeric(varargin{1}))
fighdl= varargin{1};
varargin= {varargin{2:end}};
end
if(isempty(fighdl)), error('There is no figure to save!?'); end
set(fighdl, 'Units', 'centimeters') % Set paper stuff.
sz= get(fighdl, 'Position');
sz(1:2)= 0;
set(fighdl, 'PaperUnits', 'centimeters', 'PaperSize', sz(3:4), 'PaperPosition', sz);
% Set up the various devices.
% Those commented out are not yet supported by gs (nor by savefig).
% pdf-cmyk works due to the Matlab '-cmyk' export being carried over from eps to pdf.
device.eps.rgb= sprintf(epsCmd, 'DeviceRGB', 'epswrite', [fname '.eps']);
device.jpeg.rgb= sprintf(cmdEnd, 'jpeg', [fname '.jpeg']);
% device.jpeg.cmyk= sprintf(cmdEnd, 'jpegcmyk', [fname '.jpeg']);
device.jpeg.gray= sprintf(cmdEnd, 'jpeggray', [fname '.jpeg']);
device.pdf.rgb= sprintf(pdfCmd, 'DeviceRGB', 'pdfwrite', [fname '.pdf']);
device.pdf.cmyk= sprintf(pdfCmd, 'DeviceCMYK', 'pdfwrite', [fname '.pdf']);
device.pdf.gray= sprintf(pdfCmd, 'DeviceGray', 'pdfwrite', [fname '.pdf']);
device.png.rgb= sprintf(cmdEnd, 'png16m', [fname '.png']);
% device.png.cmyk= sprintf(cmdEnd, 'png???', [fname '.png']);
device.png.gray= sprintf(cmdEnd, 'pnggray', [fname '.png']);
device.tiff.rgb= sprintf(cmdEnd, 'tiff24nc', [fname '.tiff']);
device.tiff.cmyk= sprintf(cmdEnd, 'tiff32nc', [fname '.tiff']);
device.tiff.gray= sprintf(cmdEnd, 'tiffgray', [fname '.tiff']);
% Get options.
global savefig_defaults; % Add global defaults.
if( iscellstr(savefig_defaults)), varargin= {savefig_defaults{:}, varargin{:}};
elseif(ischar(savefig_defaults)), varargin= {savefig_defaults, varargin{:}};
end
varargin= {'-r300', '-lossless', '-rgb', varargin{:}}; % Add defaults.
res= '';
types= {};
fonts= 'false';
crop= false;
for n= 1:length(varargin) % Read options.
if(ischar(varargin{n}))
switch(lower(varargin{n}))
case {'eps','jpeg','pdf','png','tiff'}, types{end+1}= lower(varargin{n});
case '-rgb', color= 'rgb'; deps= {'-depsc2'};
case '-cmyk', color= 'cmyk'; deps= {'-depsc2', '-cmyk'};
case '-gray', color= 'gray'; deps= {'-deps2'};
case '-fonts', fonts= 'true';
case '-lossless', comp= 0;
case '-crop', crop= true;
case '-dbg', op_dbg= true;
otherwise
if(regexp(varargin{n}, '^\-r[0-9]+$')), res= varargin{n};
elseif(regexp(varargin{n}, '^\-c[0-9.]+$')), comp= str2double(varargin{n}(3:end));
else warning('pax:savefig:inputError', 'Unknown option in argument: ''%s''.', varargin{n});
end
end
else
warning('pax:savefig:inputError', 'Wrong type of argument: ''%s''.', class(varargin{n}));
end
end
types= unique(types);
if(isempty(types)), error('No output format given.'); end
if (comp == 0) % Lossless compression
gsCompr= lossless;
elseif (comp <= 0.1) % High quality lossy
gsCompr= sprintf(lossy, comp, '1 1 1 1', '1 1 1 1');
else % Normal lossy
gsCompr= sprintf(lossy, comp, '2 1 1 2', '2 1 1 2');
end
% Generate the gs command.
switch(computer) % Get gs command.
case {'MAC','MACI'}, gs= '/usr/local/bin/gs';
case {'PCWIN','PCWIN64'}, gs= 'gswin32c.exe';
otherwise, gs= 'gs';
end
gs= [gs ' -q -dNOPAUSE -dBATCH -dEPSCrop']; % Essential.
gs= [gs ' -dPDFSETTINGS=/prepress -dEmbedAllFonts=' fonts]; % Must be first?
gs= [gs ' -dUseFlateCompression=true']; % Useful stuff.
gs= [gs ' -dAutoRotatePages=/None']; % Probably good.
gs= [gs ' -dHaveTrueTypes']; % Probably good.
gs= [gs ' ' res]; % Add resolution to cmd.
if(crop && ismember(types, {'eps', 'pdf'})) % Crop the figure.
fighdl= do_crop(fighdl);
end
% Output eps from Matlab.
renderer= ['-' lower(get(fighdl, 'Renderer'))]; % Use same as in figure.
if(strcmpi(renderer, '-none')), renderer= '-painters'; end % We need a valid renderer.
print(fighdl, deps{:}, '-noui', renderer, res, [fname '-temp']); % Output the eps.
% Convert to other formats.
for n= 1:length(types) % Output them.
if(isfield(device.(types{n}), color))
cmd= device.(types{n}).(color); % Colour model exists.
else
cmd= device.(types{n}).rgb; % Use alternative.
if(~strcmp(types{n}, 'eps')) % It works anyways for eps (VERY SHAKY!).
warning('pax:savefig:deviceError', ...
'No device for %s using %s. Using rgb instead.', types{n}, color);
end
end
cmp= lossless;
if (strcmp(types{n}, 'pdf')), cmp= gsCompr; end % Lossy compr only for pdf.
if (strcmp(types{n}, 'eps')), cmp= ''; end % eps can't use lossless.
cmd= sprintf('%s %s %s -f "%s-temp.eps"', gs, cmd, cmp, fname);% Add up.
status= system(cmd); % Run Ghostscript.
if (op_dbg || status), display (cmd), end
end
delete([fname '-temp.eps']); % Clean up.
end
function fig= do_crop(fig)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Remove line segments that are outside the view.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
haxes= findobj(fig, 'Type', 'axes', '-and', 'Tag', '');
for n=1:length(haxes)
xl= get(haxes(n), 'XLim');
yl= get(haxes(n), 'YLim');
lines= findobj(haxes(n), 'Type', 'line');
for m=1:length(lines)
x= get(lines(m), 'XData');
y= get(lines(m), 'YData');
inx= (xl(1) <= x) & (x <= xl(2)); % Within the x borders.
iny= (yl(1) <= y) & (y <= yl(2)); % Within the y borders.
keep= inx & iny; % Within the box.
if(~strcmp(get(lines(m), 'LineStyle'), 'none'))
crossx= ((x(1:end-1) < xl(1)) & (xl(1) < x(2:end))) ... % Crossing border x1.
| ((x(1:end-1) < xl(2)) & (xl(2) < x(2:end))) ... % Crossing border x2.
| ((x(1:end-1) > xl(1)) & (xl(1) > x(2:end))) ... % Crossing border x1.
| ((x(1:end-1) > xl(2)) & (xl(2) > x(2:end))); % Crossing border x2.
crossy= ((y(1:end-1) < yl(1)) & (yl(1) < y(2:end))) ... % Crossing border y1.
| ((y(1:end-1) < yl(2)) & (yl(2) < y(2:end))) ... % Crossing border y2.
| ((y(1:end-1) > yl(1)) & (yl(1) > y(2:end))) ... % Crossing border y1.
| ((y(1:end-1) > yl(2)) & (yl(2) > y(2:end))); % Crossing border y2.
crossp= [( (crossx & iny(1:end-1) & iny(2:end)) ... % Crossing a x border within y limits.
| (crossy & inx(1:end-1) & inx(2:end)) ... % Crossing a y border within x limits.
| crossx & crossy ... % Crossing a x and a y border (corner).
), false ...
];
crossp(2:end)= crossp(2:end) | crossp(1:end-1); % Add line segment's secont end point.
keep= keep | crossp;
end
set(lines(m), 'XData', x(keep))
set(lines(m), 'YData', y(keep))
end
end
end
|
github
|
swag-kaust/ASOFI3D-master
|
rdbuMap.m
|
.m
|
ASOFI3D-master/mfiles/utils/rdbuMap.m
| 220 |
utf_8
|
a4b9788f7d2378e6472061570a33ad37
|
% creates colormap blue-white-red
% (c) Vladimir Kazei, 2019
function a = rdbuMap()
a = zeros(2001,3);
a(:,:) = NaN;
a(1,:) = [0 0 1];
a(1001,:) = [0.85 0.95 0.85];
a(2001,:) = [1 0 0];
a = fillmissing(a,'linear',1);
end
|
github
|
swag-kaust/ASOFI3D-master
|
qgsls.m
|
.m
|
ASOFI3D-master/mfiles/attenuation_tools/qgsls.m
| 419 |
utf_8
|
ee9ed1e0880ff718c9e25bbb36e746d2
|
function q=qgsls(te,ts,L,w)
% Q fuer den L-fachen standard linear solid:
sumzQ=0;sumnQ=0;
for l=1:L,
d=1.0+w.*w*ts(l)*ts(l);
sumzQ=((1.0+w.*w*te(l)*ts(l))./d)+sumzQ;
sumnQ=(w*(te(l)-ts(l))./d)+sumnQ;
end
% Qualitaetsfaktor als Funktion der Frequenz fuer dem L-fachen SLS
q=(1.0-L+sumzQ)./sumnQ;
|
github
|
swag-kaust/ASOFI3D-master
|
qflt.m
|
.m
|
ASOFI3D-master/mfiles/attenuation_tools/qflt.m
| 698 |
utf_8
|
0fced6eb730fe5fed0ef8e9d9957c78d
|
% This function computes the difference between a
% frequency indepent quality factor and Q as function of
% relaxation frequencies and tau (written for optimization with leastsq).
function delta=qstd(x)
global L w Qf1 Qf2
fl=x(1:L);
t=x(L+1);
% computing telaxation times and relaxation frequencies
ts=1./(2*pi*fl);
% Q for a generalized standard linear solid:
sumzQ=0;sumnQ=0;
for l=1:L,
d=1+w.*w*ts(l)*ts(l);
sumnQ=(w*ts(l)*t./d)+sumnQ;
sumzQ=((w.*w*ts(l)*ts(l)*t)./d)+sumzQ;
end
Qf2=(1+sumzQ)./sumnQ;
delta=(Qf2-Qf1);
|
github
|
swag-kaust/ASOFI3D-master
|
source.m
|
.m
|
ASOFI3D-master/mfiles/old_scripts/source.m
| 1,269 |
utf_8
|
e4b570daa2f489f0dac5304bd6462abc
|
% The function computes amplitude spectrum of a Ricker, Fumue
% or extern source wavelet (central frequency: fs, spectral sampling: df)
function [amp,f]=source(s,fp1,fp2,df,fs)
if ~strcmp(s,'from_file'),
tsour=1/fs;
dt=tsour/256;
k=5;
tbeg=0;
tend=k*tsour;
t=[tbeg:dt:tend];
n=length(t);
lsour=tsour/dt;
if strcmp(s,'fumue'),
% FUCHS-MUELLER-SIGNAL
w1=2*pi/tsour;
ft=sin(w1*(t-tsour))-0.5*sin(2*w1*(t-tsour));
ft(1:(k/2-0.5)*n/k)=0.0;
ft((k/2+0.5)*n/k:n)=0.0;
elseif strcmp(s,'ricker'),
% RICKER-SIGNAL:
t0=tsour*1.5;
T0=tsour*1.5;
tau=pi*(t-t0)/T0;
a=4;
ft=(1-a*tau.*tau).*exp(-2*tau.*tau);
end
else
load wavelet.dat
ft=wavelet(2:size(wavelet,1))';
dt=1/fs;
end
ft=ft-mean(ft);
% Graphische Darstellung der eingelesenen Zeitreihe
figure;
plot(t,ft)
title(['Eingelesene Zeitreihe']);
xlabel('Zeit [s]');
ylabel('Amplitude');
fnyq=1/(2*dt);
nfft=2^nextpow2(length(ft))
%FFT
y=fftshift(fft(ft,nfft));
amp=abs(y);
amp=amp/max(amp);
f=fnyq*(-nfft/2:nfft/2-1)/(nfft/2);
nn1=(nfft/2+1)+(fp1/fnyq)*nfft/2 ;
nn2=(nfft/2+1)+(fp2/fnyq)*nfft/2;
figure;
plot(f(nn1:nn2),amp(nn1:nn2))
title('Amplitudenspektrum')
xlabel('Frequenz [Hz]')
ylabel('Amplitude');
|
github
|
benoitberanger/FunctionalLocalizer-master
|
FunctionalLocalizer_GUI.m
|
.m
|
FunctionalLocalizer-master/FunctionalLocalizer_GUI.m
| 28,972 |
utf_8
|
95aa8b0810a0f3270a2d22ed06e28883
|
function varargout = FunctionalLocalizer_GUI
% global handles
%% Open a singleton figure
% Is the GUI already open ?
figPtr = findall(0,'Tag',mfilename);
if isempty(figPtr) % Create the figure
clc
% Create a figure
figHandle = figure( ...
'HandleVisibility', 'off',... % close all does not close the figure
'MenuBar' , 'none' , ...
'Toolbar' , 'none' , ...
'Name' , mfilename , ...
'NumberTitle' , 'off' , ...
'Units' , 'Normalized' , ...
'Position' , [0.05, 0.05, 0.50, 0.90] , ...
'Tag' , mfilename );
figureBGcolor = [0.9 0.9 0.9]; set(figHandle,'Color',figureBGcolor);
buttonBGcolor = figureBGcolor - 0.1;
editBGcolor = [1.0 1.0 1.0];
% Create GUI handles : pointers to access the graphic objects
handles = guihandles(figHandle);
%% Panel proportions
panelProp.xposP = 0.05; % xposition of panel normalized : from 0 to 1
panelProp.wP = 1 - panelProp.xposP * 2;
panelProp.vect = ...
[1 1 2 2 1 1 2]; % relative proportions of each panel, from bottom to top
panelProp.vectLength = length(panelProp.vect);
panelProp.vectTotal = sum(panelProp.vect);
panelProp.adjustedTotal = panelProp.vectTotal + 1;
panelProp.unitWidth = 1/panelProp.adjustedTotal;
panelProp.interWidth = panelProp.unitWidth/panelProp.vectLength;
panelProp.countP = panelProp.vectLength + 1;
panelProp.yposP = @(countP) panelProp.unitWidth*sum(panelProp.vect(1:countP-1)) + 1*countP*panelProp.interWidth;
%% Panel : Subject & Run
p_sr.x = panelProp.xposP;
p_sr.w = panelProp.wP;
panelProp.countP = panelProp.countP - 1;
p_sr.y = panelProp.yposP(panelProp.countP);
p_sr.h = panelProp.unitWidth*panelProp.vect(panelProp.countP);
handles.uipanel_SubjectRun = uipanel(handles.(mfilename),...
'Title','Subject & Run',...
'Units', 'Normalized',...
'Position',[p_sr.x p_sr.y p_sr.w p_sr.h],...
'BackgroundColor',figureBGcolor);
p_sr.nbO = 3; % Number of objects
p_sr.Ow = 1/(p_sr.nbO + 1); % Object width
p_sr.countO = 0; % Object counter
p_sr.xposO = @(countO) p_sr.Ow/(p_sr.nbO+1)*countO + (countO-1)*p_sr.Ow;
p_sr.yposOmain = 0.1;
p_sr.hOmain = 0.6;
p_sr.yposOhdr = 0.7;
p_sr.hOhdr = 0.2;
% ---------------------------------------------------------------------
% Edit : Subject ID
p_sr.countO = p_sr.countO + 1;
e_sid.x = p_sr.xposO(p_sr.countO);
e_sid.y = p_sr.yposOmain ;
e_sid.w = p_sr.Ow;
e_sid.h = p_sr.hOmain;
handles.edit_SubjectID = uicontrol(handles.uipanel_SubjectRun,...
'Style','edit',...
'Units', 'Normalized',...
'Position',[e_sid.x e_sid.y e_sid.w e_sid.h],...
'BackgroundColor',editBGcolor,...
'String','');
% ---------------------------------------------------------------------
% Text : Subject ID
t_sid.x = p_sr.xposO(p_sr.countO);
t_sid.y = p_sr.yposOhdr ;
t_sid.w = p_sr.Ow;
t_sid.h = p_sr.hOhdr;
handles.text_SubjectID = uicontrol(handles.uipanel_SubjectRun,...
'Style','text',...
'Units', 'Normalized',...
'Position',[t_sid.x t_sid.y t_sid.w t_sid.h],...
'String','Subject ID',...
'BackgroundColor',figureBGcolor);
% ---------------------------------------------------------------------
% Pushbutton : Check SubjectID data
p_sr.countO = p_sr.countO + 1;
b_csidd.x = p_sr.xposO(p_sr.countO);
b_csidd.y = p_sr.yposOmain;
b_csidd.w = p_sr.Ow;
b_csidd.h = p_sr.hOmain;
handles.pushbutton_Check_SubjectID_data = uicontrol(handles.uipanel_SubjectRun,...
'Style','pushbutton',...
'Units', 'Normalized',...
'Position',[b_csidd.x b_csidd.y b_csidd.w b_csidd.h],...
'String','Check SubjectID data',...
'BackgroundColor',buttonBGcolor,...
'TooltipString','Display in Command Window the content of data/(SubjectID)',...
'Callback',@(hObject,eventdata)GUI.Pushbutton_Check_SubjectID_data_Callback(handles.edit_SubjectID,eventdata));
% ---------------------------------------------------------------------
% Text : Last file name annoucer
p_sr.countO = p_sr.countO + 1;
t_lfna.x = p_sr.xposO(p_sr.countO);
t_lfna.y = p_sr.yposOhdr ;
t_lfna.w = p_sr.Ow;
t_lfna.h = p_sr.hOhdr;
handles.text_LastFileNameAnnouncer = uicontrol(handles.uipanel_SubjectRun,...
'Style','text',...
'Units', 'Normalized',...
'Position',[t_lfna.x t_lfna.y t_lfna.w t_lfna.h],...
'String','Last file name',...
'BackgroundColor',figureBGcolor,...
'Visible','Off');
% ---------------------------------------------------------------------
% Text : Last file name
t_lfn.x = p_sr.xposO(p_sr.countO);
t_lfn.y = p_sr.yposOmain ;
t_lfn.w = p_sr.Ow;
t_lfn.h = p_sr.hOmain;
handles.text_LastFileName = uicontrol(handles.uipanel_SubjectRun,...
'Style','text',...
'Units', 'Normalized',...
'Position',[t_lfn.x t_lfn.y t_lfn.w t_lfn.h],...
'String','',...
'BackgroundColor',figureBGcolor,...
'Visible','Off');
%% Panel : Save mode
p_sm.x = panelProp.xposP;
p_sm.w = panelProp.wP;
panelProp.countP = panelProp.countP - 1;
p_sm.y = panelProp.yposP(panelProp.countP);
p_sm.h = panelProp.unitWidth*panelProp.vect(panelProp.countP);
handles.uipanel_SaveMode = uibuttongroup(handles.(mfilename),...
'Title','Save mode',...
'Units', 'Normalized',...
'Position',[p_sm.x p_sm.y p_sm.w p_sm.h],...
'BackgroundColor',figureBGcolor);
p_sm.nbO = 2; % Number of objects
p_sm.Ow = 1/(p_sm.nbO + 1); % Object width
p_sm.countO = 0; % Object counter
p_sm.xposO = @(countO) p_sm.Ow/(p_sm.nbO+1)*countO + (countO-1)*p_sm.Ow;
% ---------------------------------------------------------------------
% RadioButton : Save Data
p_sm.countO = p_sm.countO + 1;
r_sd.x = p_sm.xposO(p_sm.countO);
r_sd.y = 0.1 ;
r_sd.w = p_sm.Ow;
r_sd.h = 0.8;
r_sd.tag = 'radiobutton_SaveData';
handles.(r_sd.tag) = uicontrol(handles.uipanel_SaveMode,...
'Style','radiobutton',...
'Units', 'Normalized',...
'Position',[r_sd.x r_sd.y r_sd.w r_sd.h],...
'String','Save data',...
'TooltipString','Save data to : /data/SubjectID/SubjectID_Task_RunNumber',...
'HorizontalAlignment','Center',...
'Tag',r_sd.tag,...
'BackgroundColor',figureBGcolor);
% ---------------------------------------------------------------------
% RadioButton : No save
p_sm.countO = p_sm.countO + 1;
r_ns.x = p_sm.xposO(p_sm.countO);
r_ns.y = 0.1 ;
r_ns.w = p_sm.Ow;
r_ns.h = 0.8;
r_ns.tag = 'radiobutton_NoSave';
handles.(r_ns.tag) = uicontrol(handles.uipanel_SaveMode,...
'Style','radiobutton',...
'Units', 'Normalized',...
'Position',[r_ns.x r_ns.y r_ns.w r_ns.h],...
'String','No save',...
'TooltipString','In Acquisition mode, Save mode must be engaged',...
'HorizontalAlignment','Center',...
'Tag',r_ns.tag,...
'BackgroundColor',figureBGcolor);
%% Panel : Environement
p_env.x = panelProp.xposP;
p_env.w = panelProp.wP;
panelProp.countP = panelProp.countP - 1;
p_env.y = panelProp.yposP(panelProp.countP);
p_env.h = panelProp.unitWidth*panelProp.vect(panelProp.countP);
handles.uipanel_Environement = uibuttongroup(handles.(mfilename),...
'Title','Environement',...
'Units', 'Normalized',...
'Position',[p_env.x p_env.y p_env.w p_env.h],...
'BackgroundColor',figureBGcolor);
p_env.nbO = 2; % Number of objects
p_env.Ow = 1/(p_env.nbO + 1); % Object width
p_env.countO = 0; % Object counter
p_env.xposO = @(countO) p_env.Ow/(p_env.nbO+1)*countO + (countO-1)*p_env.Ow;
% ---------------------------------------------------------------------
% RadioButton : MRI
p_env.countO = p_env.countO + 1;
r_mri.x = p_env.xposO(p_env.countO);
r_mri.y = 0.1 ;
r_mri.w = p_env.Ow;
r_mri.h = 0.8;
r_mri.tag = 'radiobutton_MRI';
handles.(r_mri.tag) = uicontrol(handles.uipanel_Environement,...
'Style','radiobutton',...
'Units', 'Normalized',...
'Position',[r_mri.x r_mri.y r_mri.w r_mri.h],...
'String','MRI',...
'TooltipString','fMRI task',...
'HorizontalAlignment','Center',...
'Tag',(r_mri.tag),...
'BackgroundColor',figureBGcolor);
% ---------------------------------------------------------------------
% RadioButton : Training
p_env.countO = p_env.countO + 1;
r_tain.x = p_env.xposO(p_env.countO);
r_tain.y = 0.1 ;
r_tain.w = p_env.Ow;
r_tain.h = 0.8;
r_tain.tag = 'radiobutton_Training';
handles.(r_tain.tag) = uicontrol(handles.uipanel_Environement,...
'Style','radiobutton',...
'Units', 'Normalized',...
'Position',[r_tain.x r_tain.y r_tain.w r_tain.h],...
'String','Training',...
'TooltipString','Training inside the MRI, just before the scan',...
'HorizontalAlignment','Center',...
'Tag',(r_tain.tag),...
'BackgroundColor',figureBGcolor);
%% Panel : Eyelink mode
el_shift = 0.30;
p_el.x = panelProp.xposP + el_shift;
p_el.w = panelProp.wP - el_shift ;
panelProp.countP = panelProp.countP - 1;
p_el.y = panelProp.yposP(panelProp.countP);
p_el.h = panelProp.unitWidth*panelProp.vect(panelProp.countP);
handles.uipanel_EyelinkMode = uibuttongroup(handles.(mfilename),...
'Title','Eyelink mode',...
'Units', 'Normalized',...
'Position',[p_el.x p_el.y p_el.w p_el.h],...
'BackgroundColor',figureBGcolor,...
'SelectionChangeFcn',@uipanel_EyelinkMode_SelectionChangeFcn);
% ---------------------------------------------------------------------
% Checkbox : Windowed screen
c_ws.x = panelProp.xposP;
c_ws.w = el_shift - panelProp.xposP;
c_ws.y = panelProp.yposP(panelProp.countP) ;
c_ws.h = p_el.h * 0.3;
handles.checkbox_WindowedScreen = uicontrol(handles.(mfilename),...
'Style','checkbox',...
'Units', 'Normalized',...
'Position',[c_ws.x c_ws.y c_ws.w c_ws.h],...
'String','Windowed screen',...
'HorizontalAlignment','Center',...
'BackgroundColor',figureBGcolor);
% ---------------------------------------------------------------------
% Listbox : Screens
l_sc.x = panelProp.xposP;
l_sc.w = el_shift - panelProp.xposP;
l_sc.y = c_ws.y + c_ws.h ;
l_sc.h = p_el.h * 0.5;
handles.listbox_Screens = uicontrol(handles.(mfilename),...
'Style','listbox',...
'Units', 'Normalized',...
'Position',[l_sc.x l_sc.y l_sc.w l_sc.h],...
'String',{'a' 'b' 'c'},...
'TooltipString','Select the display mode PTB : 0 for extended display (over all screens) , 1 for screen 1 , 2 for screen 2 , etc.',...
'HorizontalAlignment','Center',...
'CreateFcn',@GUI.Listbox_Screens_CreateFcn);
% ---------------------------------------------------------------------
% Text : ScreenMode
t_sm.x = panelProp.xposP;
t_sm.w = el_shift - panelProp.xposP;
t_sm.y = l_sc.y + l_sc.h ;
t_sm.h = p_el.h * 0.15;
handles.text_ScreenMode = uicontrol(handles.(mfilename),...
'Style','text',...
'Units', 'Normalized',...
'Position',[t_sm.x t_sm.y t_sm.w t_sm.h],...
'String','Screen mode selection',...
'TooltipString','Output of Screen(''Screens'') Use ''Screen Screens?'' in Command window for help',...
'HorizontalAlignment','Center',...
'BackgroundColor',figureBGcolor);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
p_el_up.nbO = 6; % Number of objects
p_el_up.Ow = 1/(p_el_up.nbO + 1); % Object width
p_el_up.countO = 0; % Object counter
p_el_up.xposO = @(countO) p_el_up.Ow/(p_el_up.nbO+1)*countO + (countO-1)*p_el_up.Ow;
p_el_up.y = 0.6;
p_el_up.h = 0.3;
% ---------------------------------------------------------------------
% RadioButton : Eyelink ON
p_el_up.countO = p_el_up.countO + 1;
r_elon.x = p_el_up.xposO(p_el_up.countO);
r_elon.y = p_el_up.y ;
r_elon.w = p_el_up.Ow;
r_elon.h = p_el_up.h;
r_elon.tag = 'radiobutton_EyelinkOn';
handles.(r_elon.tag) = uicontrol(handles.uipanel_EyelinkMode,...
'Style','radiobutton',...
'Units', 'Normalized',...
'Position',[r_elon.x r_elon.y r_elon.w r_elon.h],...
'String','On',...
'HorizontalAlignment','Center',...
'Tag',r_elon.tag,...
'BackgroundColor',figureBGcolor);
% ---------------------------------------------------------------------
% RadioButton : Eyelink OFF
p_el_up.countO = p_el_up.countO + 1;
r_eloff.x = p_el_up.xposO(p_el_up.countO);
r_eloff.y = p_el_up.y ;
r_eloff.w = p_el_up.Ow;
r_eloff.h = p_el_up.h;
r_eloff.tag = 'radiobutton_EyelinkOff';
handles.(r_eloff.tag) = uicontrol(handles.uipanel_EyelinkMode,...
'Style','radiobutton',...
'Units', 'Normalized',...
'Position',[r_eloff.x r_eloff.y r_eloff.w r_eloff.h],...
'String','Off',...
'HorizontalAlignment','Center',...
'Tag',r_eloff.tag,...
'BackgroundColor',figureBGcolor);
% ---------------------------------------------------------------------
% Checkbox : Parallel port
p_el_up.countO = p_el_up.countO + 1;
c_pp.x = p_el_up.xposO(p_el_up.countO);
c_pp.y = p_el_up.y ;
c_pp.w = p_el_up.Ow*2;
c_pp.h = p_el_up.h;
handles.checkbox_ParPort = uicontrol(handles.uipanel_EyelinkMode,...
'Style','checkbox',...
'Units', 'Normalized',...
'Position',[c_pp.x c_pp.y c_pp.w c_pp.h],...
'String','Parallel port',...
'HorizontalAlignment','Center',...
'TooltipString','Send messages via parallel port : useful for Eyelink',...
'BackgroundColor',figureBGcolor,...
'Value',1,...
'Callback',@GUI.Checkbox_ParPort_Callback,...
'CreateFcn',@GUI.Checkbox_ParPort_Callback);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
p_el_dw.nbO = 3; % Number of objects
p_el_dw.Ow = 1/(p_el_dw.nbO + 1); % Object width
p_el_dw.countO = 0; % Object counter
p_el_dw.xposO = @(countO) p_el_dw.Ow/(p_el_dw.nbO+1)*countO + (countO-1)*p_el_dw.Ow;
p_el_dw.y = 0.1;
p_el_dw.h = 0.4 ;
% ---------------------------------------------------------------------
% Pushbutton : Eyelink Initialize
p_el_dw.countO = p_el_dw.countO + 1;
b_init.x = p_el_dw.xposO(p_el_dw.countO);
b_init.y = p_el_dw.y ;
b_init.w = p_el_dw.Ow;
b_init.h = p_el_dw.h;
handles.pushbutton_Initialize = uicontrol(handles.uipanel_EyelinkMode,...
'Style','pushbutton',...
'Units', 'Normalized',...
'Position',[b_init.x b_init.y b_init.w b_init.h],...
'String','Initialize',...
'BackgroundColor',buttonBGcolor,...
'Callback','Eyelink.Initialize');
% ---------------------------------------------------------------------
% Pushbutton : Eyelink IsConnected
p_el_dw.countO = p_el_dw.countO + 1;
b_isco.x = p_el_dw.xposO(p_el_dw.countO);
b_isco.y = p_el_dw.y ;
b_isco.w = p_el_dw.Ow;
b_isco.h = p_el_dw.h;
handles.pushbutton_IsConnected = uicontrol(handles.uipanel_EyelinkMode,...
'Style','pushbutton',...
'Units', 'Normalized',...
'Position',[b_isco.x b_isco.y b_isco.w b_isco.h],...
'String','IsConnected',...
'BackgroundColor',buttonBGcolor,...
'Callback','Eyelink.IsConnected');
% ---------------------------------------------------------------------
% Pushbutton : Eyelink Calibration
p_el_dw.countO = p_el_dw.countO + 1;
b_cal.x = p_el_dw.xposO(p_el_dw.countO);
b_cal.y = p_el_dw.y ;
b_cal.w = p_el_dw.Ow;
b_cal.h = p_el_dw.h;
b_cal.tag = 'pushbutton_EyelinkCalibration';
handles.(b_cal.tag) = uicontrol(handles.uipanel_EyelinkMode,...
'Style','pushbutton',...
'Units', 'Normalized',...
'Position',[b_cal.x b_cal.y b_cal.w b_cal.h],...
'String','Calibration',...
'BackgroundColor',buttonBGcolor,...
'Tag',b_cal.tag,...
'Callback',@FunctionalLocalizer_main);
% ---------------------------------------------------------------------
% Pushbutton : Eyelink force shutdown
b_fsd.x = c_pp.x + c_pp.h;
b_fsd.y = p_el_up.y ;
b_fsd.w = p_el_dw.Ow*1.25;
b_fsd.h = p_el_dw.h;
handles.pushbutton_ForceShutDown = uicontrol(handles.uipanel_EyelinkMode,...
'Style','pushbutton',...
'Units', 'Normalized',...
'Position',[b_fsd.x b_fsd.y b_fsd.w b_fsd.h],...
'String','ForceShutDown',...
'BackgroundColor',buttonBGcolor,...
'Callback','Eyelink.ForceShutDown');
%% Panel : Task
p_tk.x = panelProp.xposP;
p_tk.w = panelProp.wP;
panelProp.countP = panelProp.countP - 1;
p_tk.y = panelProp.yposP(panelProp.countP);
p_tk.h = panelProp.unitWidth*panelProp.vect(panelProp.countP);
handles.uipanel_Task = uibuttongroup(handles.(mfilename),...
'Title','Task',...
'Units', 'Normalized',...
'Position',[p_tk.x p_tk.y p_tk.w p_tk.h],...
'BackgroundColor',figureBGcolor);
p_tk.nbO = 4; % Number of objects
p_tk.Ow = 1/(p_tk.nbO + 1); % Object width
p_tk.countO = 0; % Object counter
p_tk.xposO = @(countO) p_tk.Ow/(p_tk.nbO+1)*countO + (countO-1)*p_tk.Ow;
buttun_y = 0.20;
buttun_h = 0.60;
% ---------------------------------------------------------------------
% Pushbutton : Calibration
p_tk.countO = p_tk.countO + 1;
b_cal.x = p_tk.xposO(p_tk.countO);
b_cal.y = buttun_y;
b_cal.w = p_tk.Ow;
b_cal.h = buttun_h;
b_cal.tag = 'pushbutton_Calibration';
handles.(b_cal.tag) = uicontrol(handles.uipanel_Task,...
'Style','pushbutton',...
'Units', 'Normalized',...
'Position',[b_cal.x b_cal.y b_cal.w b_cal.h],...
'String','Calibration',...
'BackgroundColor',buttonBGcolor,...
'Tag',b_cal.tag,...
'Callback',@FunctionalLocalizer_main);
% ---------------------------------------------------------------------
% Pushbutton : Instructions
p_tk.countO = p_tk.countO + 1;
b_inst.x = p_tk.xposO(p_tk.countO);
b_inst.y = buttun_y;
b_inst.w = p_tk.Ow;
b_inst.h = buttun_h;
b_inst.tag = 'pushbutton_Instructions';
handles.(b_inst.tag) = uicontrol(handles.uipanel_Task,...
'Style','pushbutton',...
'Units', 'Normalized',...
'Position',[b_inst.x b_inst.y b_inst.w b_inst.h],...
'String','Instructions',...
'BackgroundColor',buttonBGcolor,...
'Tag',b_inst.tag,...
'Callback',@FunctionalLocalizer_main);
% ---------------------------------------------------------------------
% Pushbutton : Session
p_tk.countO = p_tk.countO + 1;
b_sess.x = p_tk.xposO(p_tk.countO);
b_sess.y = buttun_y;
b_sess.w = p_tk.Ow*2;
b_sess.h = buttun_h;
b_sess.tag = 'pushbutton_Session';
handles.(b_sess.tag) = uicontrol(handles.uipanel_Task,...
'Style','pushbutton',...
'Units', 'Normalized',...
'Position',[b_sess.x b_sess.y b_sess.w b_sess.h],...
'String','Session',...
'BackgroundColor',buttonBGcolor,...
'Tag',b_sess.tag,...
'Callback',@FunctionalLocalizer_main);
%% Panel : Record video
p_rv.x = panelProp.xposP;
p_rv.w = panelProp.wP;
panelProp.countP = panelProp.countP - 1;
p_rv.y = panelProp.yposP(panelProp.countP);
p_rv.h = panelProp.unitWidth*panelProp.vect(panelProp.countP);
handles.uipanel_RecordVideo = uibuttongroup(handles.(mfilename),...
'Title','Record mode',...
'Units', 'Normalized',...
'Position',[p_rv.x p_rv.y p_rv.w p_rv.h],...
'BackgroundColor',figureBGcolor,...
'SelectionChangeFcn',@uipanel_RecordVideo_SelectionChangeFcn,...
'Visible','Off');
p_rv.nbO = 3; % Number of objects
p_rv.Ow = 1/(p_rv.nbO + 1); % Object width
p_rv.countO = 0; % Object counter
p_rv.xposO = @(countO) p_rv.Ow/(p_rv.nbO+1)*countO + (countO-1)*p_rv.Ow;
% ---------------------------------------------------------------------
% RadioButton : Record video OFF
p_rv.countO = p_rv.countO + 1;
r_rvoff.x = p_rv.xposO(p_rv.countO);
r_rvoff.y = 0.1 ;
r_rvoff.w = p_rv.Ow;
r_rvoff.h = 0.8;
r_rvoff.tag = 'radiobutton_RecordOff';
handles.(r_rvoff.tag) = uicontrol(handles.uipanel_RecordVideo,...
'Style','radiobutton',...
'Units', 'Normalized',...
'Position',[r_rvoff.x r_rvoff.y r_rvoff.w r_rvoff.h],...
'String','Off',...
'HorizontalAlignment','Center',...
'Tag',r_rvoff.tag,...
'BackgroundColor',figureBGcolor);
% ---------------------------------------------------------------------
% RadioButton : Record video ON
p_rv.countO = p_rv.countO + 1;
r_rvon.x = p_rv.xposO(p_rv.countO);
r_rvon.y = 0.1 ;
r_rvon.w = p_rv.Ow;
r_rvon.h = 0.8;
r_rvon.tag = 'radiobutton_RecordOn';
handles.(r_rvon.tag) = uicontrol(handles.uipanel_RecordVideo,...
'Style','radiobutton',...
'Units', 'Normalized',...
'Position',[r_rvon.x r_rvon.y r_rvon.w r_rvon.h],...
'String','On',...
'HorizontalAlignment','Center',...
'Tag',r_rvon.tag,...
'BackgroundColor',figureBGcolor);
% ---------------------------------------------------------------------
% Text : File name
t_fn.x = p_rv.xposO(p_rv.countO) + p_rv.Ow/2;
t_fn.y = 0.2 ;
t_fn.w = p_rv.Ow;
t_fn.h = 0.4;
handles.text_RecordName = uicontrol(handles.uipanel_RecordVideo,...
'Style','text',...
'Units', 'Normalized',...
'Position',[t_fn.x t_fn.y t_fn.w t_fn.h],...
'String','File name : ',...
'HorizontalAlignment','Center',...
'Visible','Off',...
'BackgroundColor',figureBGcolor);
% ---------------------------------------------------------------------
% Edit : File name
p_rv.countO = p_rv.countO + 1;
e_fn.x = p_rv.xposO(p_rv.countO);
e_fn.y = 0.1 ;
e_fn.w = p_rv.Ow;
e_fn.h = 0.8;
handles.edit_RecordName = uicontrol(handles.uipanel_RecordVideo,...
'Style','edit',...
'Units', 'Normalized',...
'Position',[e_fn.x e_fn.y e_fn.w e_fn.h],...
'String','',...
'Visible','Off',...
'BackgroundColor',editBGcolor,...
'HorizontalAlignment','Center');
%% Panel : Operation mode
p_op.x = panelProp.xposP;
p_op.w = panelProp.wP;
panelProp.countP = panelProp.countP - 1;
p_op.y = panelProp.yposP(panelProp.countP);
p_op.h = panelProp.unitWidth*panelProp.vect(panelProp.countP);
handles.uipanel_OperationMode = uibuttongroup(handles.(mfilename),...
'Title','Operation mode',...
'Units', 'Normalized',...
'Position',[p_op.x p_op.y p_op.w p_op.h],...
'BackgroundColor',figureBGcolor);
p_op.nbO = 3; % Number of objects
p_op.Ow = 1/(p_op.nbO + 1); % Object width
p_op.countO = 0; % Object counter
p_op.xposO = @(countO) p_op.Ow/(p_op.nbO+1)*countO + (countO-1)*p_op.Ow;
% ---------------------------------------------------------------------
% RadioButton : Acquisition
p_op.countO = p_op.countO + 1;
r_aq.x = p_op.xposO(p_op.countO);
r_aq.y = 0.1 ;
r_aq.w = p_op.Ow;
r_aq.h = 0.8;
r_aq.tag = 'radiobutton_Acquisition';
handles.(r_aq.tag) = uicontrol(handles.uipanel_OperationMode,...
'Style','radiobutton',...
'Units', 'Normalized',...
'Position',[r_aq.x r_aq.y r_aq.w r_aq.h],...
'String','Acquisition',...
'TooltipString','Should be used for all the environements',...
'HorizontalAlignment','Center',...
'Tag',r_aq.tag,...
'BackgroundColor',figureBGcolor);
% ---------------------------------------------------------------------
% RadioButton : FastDebug
p_op.countO = p_op.countO + 1;
r_fd.x = p_op.xposO(p_op.countO);
r_fd.y = 0.1 ;
r_fd.w = p_op.Ow;
r_fd.h = 0.8;
r_fd.tag = 'radiobutton_FastDebug';
handles.radiobutton_FastDebug = uicontrol(handles.uipanel_OperationMode,...
'Style','radiobutton',...
'Units', 'Normalized',...
'Position',[r_fd.x r_fd.y r_fd.w r_fd.h],...
'String','FastDebug',...
'TooltipString','Only to work on the scripts',...
'HorizontalAlignment','Center',...
'Tag',r_fd.tag,...
'BackgroundColor',figureBGcolor);
% ---------------------------------------------------------------------
% RadioButton : RealisticDebug
p_op.countO = p_op.countO + 1;
r_rd.x = p_op.xposO(p_op.countO);
r_rd.y = 0.1 ;
r_rd.w = p_op.Ow;
r_rd.h = 0.8;
r_rd.tag = 'radiobutton_RealisticDebug';
handles.(r_rd.tag) = uicontrol(handles.uipanel_OperationMode,...
'Style','radiobutton',...
'Units', 'Normalized',...
'Position',[r_rd.x r_rd.y r_rd.w r_rd.h],...
'String','RealisticDebug',...
'TooltipString','Only to work on the scripts',...
'HorizontalAlignment','Center',...
'Tag',r_rd.tag,...
'BackgroundColor',figureBGcolor);
%% End of opening
% IMPORTANT
guidata(figHandle,handles)
% After creating the figure, dont forget the line
% guidata(figHandle,handles) . It allows smart retrive like
% handles=guidata(hObject)
% assignin('base','handles',handles)
% disp(handles)
figPtr = figHandle;
else % Figure exists so brings it to the focus
figure(figPtr);
% close(figPtr);
% FunctionalLocalizer_GUI;
end
if nargout > 0
varargout{1} = guidata(figPtr);
end
end % function
%% GUI Functions
% -------------------------------------------------------------------------
function uipanel_RecordVideo_SelectionChangeFcn(hObject, eventdata)
handles = guidata(hObject);
switch get(eventdata.NewValue,'Tag') % Get Tag of selected object.
case 'radiobutton_RecordOn'
set(handles.text_RecordName,'Visible','On')
set(handles.edit_RecordName,'Visible','On')
case 'radiobutton_RecordOff'
set(handles.text_RecordName,'Visible','off')
set(handles.edit_RecordName,'Visible','off')
end
end % function
% -------------------------------------------------------------------------
function edit_SessionNumber_Callback(hObject, ~)
block = str2double(get(hObject,'String'));
if block ~= round(block) || block < 1 || block > 4
set(hObject,'String','1');
error('Session number must be from 1 to 4')
end
end % function
% -------------------------------------------------------------------------
function uipanel_EyelinkMode_SelectionChangeFcn(hObject, eventdata)
handles = guidata(hObject);
switch get(eventdata.NewValue,'Tag') % Get Tag of selected object.
case 'radiobutton_EyelinkOff'
set(handles.pushbutton_EyelinkCalibration,'Visible','off')
set(handles.pushbutton_IsConnected ,'Visible','off')
set(handles.pushbutton_ForceShutDown ,'Visible','off')
set(handles.pushbutton_Initialize ,'Visible','off')
case 'radiobutton_EyelinkOn'
set(handles.pushbutton_EyelinkCalibration,'Visible','on')
set(handles.pushbutton_IsConnected ,'Visible','on')
set(handles.pushbutton_ForceShutDown ,'Visible','on')
set(handles.pushbutton_Initialize ,'Visible','on')
end
end % function
|
github
|
thomaspingel/mackskill-matlab-master
|
mackskill.m
|
.m
|
mackskill-matlab-master/mackskill.m
| 11,461 |
utf_8
|
db972920708c5fc02fcf0833526bf16f
|
% The Mack-Skillings Statistical Test
% A nonparametric two-way ANOVA used for unbalanced incomplete block
% designs, when the number of observations in each treatment/block pair is
% one or greater. The test is equivalent to the Friedman test when
% balanced and there are no missing observations.
%
% Syntax:
% [p stats] = mackskill(response, treatments, blocks)
% [p stats] = mackskill(M, reps)
% This version assumes M is structured similar to a table in the function
% friedman(), where columns are treatments (k) (the variable of interest),
% and rows are blocks (n) (a nuisance variable). Reps (c)
% corresponds to the maximum number of repeated observations.
% The M matrix should therefore have k columns and n*c rows.
%
% Example:
%
% The friedman() function uses popcorn data originally published by Hogg
% (1987). From the text of that function:
% "The columns of the matrix popcorn are brands (Gourmet, National,
% and Generic). The rows are popper type (Oil and Air). The study popped
% a batch of each brand three times with each popper. The values are the
% yield in cups of popped popcorn." Given that the data is structured this
% way, we must be asking "Which brand of popcorn pops the best, apart from
% the type of popper it is popped in." k=3,n=2,c=3.
%
% load popcorn
% [p table stats] = friedman(popcorn,3);
% % chisq = 13.76
% % p = 0.001
%
% If there are missing observations, use the Mack-Skilling test:
%
% popcorn(5) = NaN;
% [p stats] = mackskill(popcorn,3);
% stats.T = 8.5856
% stats.p = 0.0137
%
%
% Example:
%
% Hollander and Wolfe (1999) describe the Mack-Skillings test in their
% example 7.9 "Determination of Niacin in Bran Flakes" on page 331, from
% data originally published in Campbell & Pelletier (1962).
%
% % Table 7.20 Amount of Niacin in Enriched Bran Flakes
% %
% % Enrichment (mg/100g of Bran Flakes)
% % 1 4 8
% M = [7.58 11.63 15.00; ... % lab 1
% 7.87 11.87 15.92; ...
% 7.71 11.40 15.58; ...
% 8.00 12.20 16.60; ... % lab 2
% 8.27 11.70 16.40; ...
% 8.00 11.80 15.90; ...
% 7.60 11.04 15.87; ... % lab 3
% 7.30 11.50 15.91; ...
% 7.82 11.49 16.28; ...
% 8.03 11.50 15.10; ... % lab 4
% 7.35 10.10 14.80; ...
% 7.66 11.70 15.70];
%
%
% However, we want to know whether the _labs_ are different, apart from the
% amount of niacin enrichment, or as Hollander and Wolfe put it, "Of
% primary interest here is the precision of the laboratory procedure for
% determining niacin content in bran flakes." Our table is therefore not
% formatted correctly to call mackskill(M,3), as this would tell us whether
% our columns were notably different, apart from rows.
%
% We can reformat our data this way:
%
% [row enrichment niacin] = find(sparse(M));
% lab = ceil(row/3); % 3, since there are 3 repetitions
% response = niacin; treatment = lab; block = enrichment;
%
% [p stats] = mackskill(response,treatment,block)
%
% stats.T = 12.9274
% stats.df = 3
% stats.p = 0.0048
%
% These results are equivalent to the test statistic in Hollander & Wolfe
% (1999), MS = 12.93 (page 332).
%
%
% The algorithm uses the chi-squared approximation for the p-value, which
% should not be used when there are very few observations. Please refer to
% the original text for a complete description.
%
% References:
% Hollander, M., & Wolfe, D. A. (1999). Nonparametric statistical methods (2nd ed.). New York: Wiley.
% Mack, G. A., & Skillings, J. H. (1980). A Friedman-Type Rank Test for Main Effects in a 2-Factor Anova. Journal of the American Statistical Association, 75(372), 947-951.
% Skillings, J. H., & Mack, G. A. (1981). On the Use of a Friedman-Type Statistic in Balanced and Unbalanced Block Designs. Technometrics, 23(2), 171-177.
%
% The code was tested against several published datasets. For a copy of
% these datasets and other code written by the author, please see:
% http://www.geog.ucsb.edu/~pingel/matlabCode/index.html
%
% Use of this code for any non-commercial purpose is granted under the GNU
% Public License.
%
% Author:
% Thomas J. Pingel
% Department of Geography
% University of California, Santa Barbara
% 11 November 2010
function [p stats rankedobs] = mackskill(M, treatments,blocks)
% load popcorn
% M = popcorn;
% reps = 3;
if nargin<3
% This section reformats matrix M into:
% X (observations in the matrix M)
% treatments (columns of M, and the variable of interest)
% blocks (rows of M, and the nuisance variable)
% Second argument is assumed to be reps
% Pick apart the observations
reps = treatments;
X = reshape(M,numel(M),1);
% Since input is a matrix, define the levels.
treatmentlevels = [1:size(M,2)]; % Columns
blocklevels = size(M,1)/reps; % Rows
blocklevels = [1:blocklevels];
% treatmentlevels = ([1:size(M,2)]'); % Columns
% blocklevels = ([1:size(M,1)]'); % Rows
k = length(treatmentlevels);
n = length(blocklevels);
% Create a vector of treatment and observation levels.
treatmentsMatrix = repmat(treatmentlevels,n*reps,1);
treatments = reshape(treatmentsMatrix,numel(X),1);
blocksMatrix = repmat(reshape(repmat(blocklevels,reps,1),n*reps,1),1,k);
blocks = reshape(blocksMatrix,numel(X),1);
% blocks = reshape(repmat(reshape(repmat([1:n],k,1),n*reps,1),1,k),numel(M),1);
stats.treatmentsMatrix = treatmentsMatrix;
stats.blocksMatrix = blocksMatrix;
%% For testing, keep these handy in double form
tr = treatments;
br = blocks;
% Get rid of extraneous information, as this will be redefined in the next
% section anyway.
clear treatmentlevels blocklevels k n;
end
%%
% This section applies to if the preferred format is supplied
% skillmack(X,treatments,blocks) where X is a vector (double) and
% treatments and blocks are cell arrays.
% X is now the first argument, input as M
if nargin==3
X = M;
end
% First, convert to a cell array from matrix if necessary
if ~iscell(treatments)
treatments2 = cell(size(treatments));
for i=1:length(treatments)
treatments2{i,1} = treatments(i);
end
treatments = treatments2;
clear treatments2 i;
end
if ~iscell(blocks)
blocks2 = cell(size(blocks));
for i=1:length(blocks)
blocks2{i,1} = blocks(i);
end
blocks = blocks2;
clear blocks2 i;
end
%%
% Change to cell array of strings, for standardization.
for i=1:length(blocks)
blocks{i,1} = num2str(blocks{i});
treatments{i,1} = num2str(treatments{i});
end
clear i;
%% Remove NaNs from cell array
indx = find(isnan(X));
X(indx) = [];
blocks(indx) = [];
treatments(indx) = [];
%%
% Determine unique levels
treatmentlevels = unique(treatments);
blocklevels = unique(blocks);
%%
% Check to see if any block has only one observation. If so, for now just
% issue a warning. Technically, this block should be removed.
% [this isn't the case for mack-skill, but leave in for later
% errorchecking]
% for i=1:length(blocklevels)
% indx = find(strcmp(blocks,blocklevels{i}));
% if length(indx) <= 1
% disp(['Block ',num2str(blocklevels{i}),' has an insufficient number of observations.']);
% end
% end
% clear i indx;
%% Balance the observations
% See if the results improve if 'unbalanced' setups are replaced with NaNs
%%
% Create a vector to hold ranked observations
rankedobs = nan(size(X));
% disp(num2str(size(X)));
% disp(num2str(length(blocklevels)));
for i=1:length(blocklevels)
% Step II
% Within each block, rank the observations from 1 to ki, where ki is the
% number of treatments present in block i. If ties occur, use average
% ranks.
% Grab the blocks at level i
indx = find(strcmp(blocks,blocklevels{i}));
% r holds the ranks for that block. NaNs in empty values.
r = tiedrank(X(indx));
for j=1:length(indx)
rankedobs(indx(j)) = r(j);
end
end
clear i j indx indx2 r replacementr
% disp(num2str(rankedobs));
stats.rankedobs = rankedobs;
% stats.rankedobs2 = reshape(rankedobs,size(M));
%% Sum the ranks for each treatment and block
I = length(blocklevels);
J = length(treatmentlevels);
rankblock = nan(I,J);
for i=1:I % rows, blocks
for j=1:J % treatments, columns
indx = intersect(find(strcmp(treatments,treatmentlevels{j})),find(strcmp(blocks,blocklevels{i})));
rankblock(i,j) = sum(rankedobs(indx));
end
end
clear i j indx;
stats.rankblock = rankblock;
% stats.rankblock2 = reshape(rankblock,size(M));
%% Modify these by treatment
I = length(blocklevels);
J = length(treatmentlevels);
Rj = nan(J,1);
for j=1:J % column, treatment
s = 0;
for i=1:I % row, blocks
s = s + (rankblock(i,j) / length(find(strcmp(blocks,blocklevels{i}))));
end
Rj(j) = s;
clear s;
end
R = Rj - mean(Rj);
clear s j i
%% Calculate sigma
I = length(blocklevels);
J = length(treatmentlevels);
sigma = nan(J,J);
for t=1:J % treatments, columns
for j=1:J % also treatments, columns
s = 0;
for i = 1:I % blocks, rows
nij = length(intersect(find(strcmp(treatments,treatmentlevels{j})),find(strcmp(blocks,blocklevels{i}))));
nit = length(intersect(find(strcmp(treatments,treatmentlevels{t})),find(strcmp(blocks,blocklevels{i}))));
ni = length(find(strcmp(blocks,blocklevels{i})));
s = s + ((nij*nit*(ni+1))/(12*(ni^2)));
% clear nij nit ni;
end
sigma(t,j) = -s;
% clear s
end
end
clear t j i
for i=1:J
sigma(i,i) = 0;
sigma(i,i) = abs(sum(sigma(i,:)));
end
%% Let's try step 4: Calculating weights.
% A = nan(length(treatmentlevels),1);
% maxrank = nan(size(rankedobs));
% frontweight = nan(size(rankedobs));
% backweight = nan(size(rankedobs));
% totalweight = nan(size(rankedobs));
% % Calculate front and back weights
% for i=1:numel(X)
% maxrank(i) = max(rankedobs(find(strcmp(blocks,blocks{i}))));
% frontweight(i) = sqrt(12/(maxrank(i)+1));
% backweight(i) = rankedobs(i) - ((maxrank(i) + 1)/2);
% end
%
% % Multiply them together to get total weights
% totalweight = frontweight.*backweight;
% % Sum each treatment.
% for i=1:length(A)
% indx = find(strcmp(treatments,treatmentlevels{i}));
% A(i) = sum(totalweight(indx));
% end
% clear i totalweight frontweight backweight maxrank indx;
% % disp(num2str(A));
%
% %% Create sigma matrix
% sigma = nan(length(treatmentlevels),length(treatmentlevels));
% k = length(treatmentlevels);
% for i=1:k % row
% for j=1:k % column
% indxi = intersect(find(strcmp(treatments,treatmentlevels{i})),find(isfinite(X)==1));
% indxj = intersect(find(strcmp(treatments,treatmentlevels{j})),find(isfinite(X)==1));
% % indxk = intersect(indxi,indxj);
% sigma(i,j) = -length(intersect([blocks{indxi}],[blocks{indxj}]));
% end
% end
% for i=1:length(treatmentlevels)
% j = setdiff([1:length(treatmentlevels)],i);
% sigma(i,i) = sum(abs(sigma(i,j)));
% end
%
%% Calculate the final statistic.
T = R' * pinv(sigma) * R;
df = length(treatmentlevels)-1;
p = 1 - chi2cdf(T,df);
stats.T = T;
stats.df = df;
stats.p = p;
stats.labels = treatmentlevels;
stats.meanranks = Rj';
stats.source = 'mackskill';
stats.n = df;
stats.rankblock = rankblock;
stats.X = X;
stats.blocks = blocks;
stats.treatments = treatments;
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
imagesAlign.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/videos/imagesAlign.m
| 8,167 |
utf_8
|
d125eb5beb502d940be5bd145521f34b
|
function [H,Ip] = imagesAlign( I, Iref, varargin )
% Fast and robust estimation of homography relating two images.
%
% The algorithm for image alignment is a simple but effective variant of
% the inverse compositional algorithm. For a thorough overview, see:
% "Lucas-kanade 20 years on A unifying framework,"
% S. Baker and I. Matthews. IJCV 2004.
% The implementation is optimized and can easily run at 20-30 fps.
%
% type may take on the following values:
% 'translation' - translation only
% 'rigid' - translation and rotation
% 'similarity' - translation, rotation and scale
% 'affine' - 6 parameter affine transform
% 'rotation' - pure rotation (about x, y and z)
% 'projective' - full 8 parameter homography
% Alternatively, type may be a vector of ids between 1 and 8, specifying
% exactly the types of transforms allowed. The ids correspond, to: 1:
% translate-x, 2: translate-y, 3: uniform scale, 4: shear, 5: non-uniform
% scale, 6: rotate-z, 7: rotate-x, 8: rotate-y. For example, to specify
% translation use type=[1,2]. If the transforms don't form a group, the
% returned homography may have more degrees of freedom than expected.
%
% Parameters (in rough order of importance): [resample] controls image
% downsampling prior to computing H. Runtime is proportional to area, so
% using resample<1 can dramatically speed up alignment, and in general not
% degrade performance much. [sig] controls image smoothing, sig=2 gives
% good performance, setting sig too low causes loss of information and too
% high will violate the linearity assumption. [epsilon] defines the
% stopping criteria, use to adjust performance versus speed tradeoff.
% [lambda] is a regularization term that causes small transforms to be
% favored, in general any small non-zero setting of lambda works well.
% [outThr] is a threshold beyond which pixels are considered outliers, be
% careful not to set too low. [minArea] determines coarsest scale beyond
% which the image is not downsampled (should not be set too low). [H0] can
% be used to specify an initial alignment. Use [show] to display results.
%
% USAGE
% [H,Ip] = imagesAlign( I, Iref, varargin )
%
% INPUTS
% I - transformed version of I
% Iref - reference grayscale double image
% varargin - additional params (struct or name/value pairs)
% .type - ['projective'] see above for options
% .resample - [1] image resampling prior to homography estimation
% .sig - [2] amount of Gaussian spatial smoothing to apply
% .epsilon - [1e-3] stopping criteria (min change in error)
% .lambda - [1e-6] regularization term favoring small transforms
% .outThr - [inf] outlier threshold
% .minArea - [4096] minimum image area in coarse to fine search
% .H0 - [eye(3)] optional initial homography estimate
% .show - [0] optionally display results in figure show
%
% OUTPUTS
% H - estimated homography to transform I into Iref
% Ip - tranformed version of I (slow to compute)
%
% EXAMPLE
% Iref = double(imread('cameraman.tif'))/255;
% H0 = [eye(2)+randn(2)*.1 randn(2,1)*10; randn(1,2)*1e-3 1];
% I = imtransform2(Iref,H0^-1,'pad','replicate');
% o=50; P=ones(o)*1; I(150:149+o,150:149+o)=P;
% prmAlign={'outThr',.1,'resample',.5,'type',1:8,'show'};
% [H,Ip]=imagesAlign(I,Iref,prmAlign{:},1);
% tic, for i=1:30, H=imagesAlign(I,Iref,prmAlign{:},0); end;
% t=toc; fprintf('average fps: %f\n',30/t)
%
% See also imTransform2
%
% Piotr's Computer Vision Matlab Toolbox Version 2.61
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
% get parameters
dfs={'type','projective','resample',1,'sig',2,'epsilon',1e-3,...
'lambda',1e-6,'outThr',inf,'minArea',4096,'H0',eye(3),'show',0};
[type,resample,sig,epsilon,lambda,outThr,minArea,H0,show] = ...
getPrmDflt(varargin,dfs,1);
filt = filterGauss(2*ceil(sig*2.5)+1,[],sig^2);
% determine type of transformation to recover
if(isnumeric(type)), assert(length(type)<=8); else
id=find(strcmpi(type,{'translation','rigid','similarity','affine',...
'rotation','projective'})); msgId='piotr:imagesAlign';
if(isempty(id)), error(msgId,'unknown type: %s',type); end
type={1:2,[1:2 6],[1:3 6],1:6,6:8,1:8}; type=type{id};
end; keep=zeros(1,8); keep(type)=1; keep=keep>0;
% compute image alignment (optionally resample first)
prm={keep,filt,epsilon,H0,minArea,outThr,lambda};
if( resample==1 ), H=imagesAlign1(I,Iref,prm); else
S=eye(3); S([1 5])=resample; H0=S*H0*S^-1; prm{4}=H0;
I1=imResample(I,resample); Iref1=imResample(Iref,resample);
H=imagesAlign1(I1,Iref1,prm); H=S^-1*H*S;
end
% optionally rectify I and display results (can be expensive)
if(nargout==1 && show==0), return; end
Ip = imtransform2(I,H,'pad','replicate');
if(show), figure(show); clf; s=@(i) subplot(2,3,i);
Is=[I Iref Ip]; ri=[min(Is(:)) max(Is(:))];
D0=abs(I-Iref); D1=abs(Ip-Iref); Ds=[D0 D1]; di=[min(Ds(:)) max(Ds(:))];
s(1); im(I,ri,0); s(2); im(Iref,ri,0); s(3); im(D0,di,0);
s(4); im(Ip,ri,0); s(5); im(Iref,ri,0); s(6); im(D1,di,0);
s(3); title('|I-Iref|'); s(6); title('|Ip-Iref|');
end
end
function H = imagesAlign1( I, Iref, prm )
% apply recursively if image large
[keep,filt,epsilon,H0,minArea,outThr,lambda]=deal(prm{:});
[h,w]=size(I); hc=mod(h,2); wc=mod(w,2);
if( w*h<minArea ), H=H0; else
I1=imResample(I(1:(h-hc),1:(w-wc)),.5);
Iref1=imResample(Iref(1:(h-hc),1:(w-wc)),.5);
S=eye(3); S([1 5])=2; H0=S^-1*H0*S; prm{4}=H0;
H=imagesAlign1(I1,Iref1,prm); H=S*H*S^-1;
end
% smooth images (pad first so dimensions unchanged)
O=ones(1,(length(filt)-1)/2); hs=[O 1:h h*O]; ws=[O 1:w w*O];
Iref=conv2(conv2(Iref(hs,ws),filt','valid'),filt,'valid');
I=conv2(conv2(I(hs,ws),filt','valid'),filt,'valid');
% pad images with nan so later can determine valid regions
hs=[1 1 1:h h h]; ws=[1 1 1:w w w]; I=I(hs,ws); Iref=Iref(hs,ws);
hs=[1:2 h+3:h+4]; I(hs,:)=nan; Iref(hs,:)=nan;
ws=[1:2 w+3:w+4]; I(:,ws)=nan; Iref(:,ws)=nan;
% convert weights hardcoded for 128x128 image to given image dims
wts=[1 1 1.0204 .03125 1.0313 0.0204 .00055516 .00055516];
s=sqrt(numel(Iref))/128;
wts=[wts(1:2) wts(3)^(1/s) wts(4)/s wts(5)^(1/s) wts(6)/s wts(7:8)/(s*s)];
% prepare subspace around Iref
[~,Hs]=ds2H(-ones(1,8),wts); Hs=Hs(:,:,keep); K=size(Hs,3);
[h,w]=size(Iref); Ts=zeros(h,w,K); k=0;
if(keep(1)), k=k+1; Ts(:,1:end-1,k)=Iref(:,2:end); end
if(keep(2)), k=k+1; Ts(1:end-1,:,k)=Iref(2:end,:); end
pTransf={'method','bilinear','pad','none','useCache'};
for i=k+1:K, Ts(:,:,i)=imtransform2(Iref,Hs(:,:,i),pTransf{:},1); end
Ds=Ts-Iref(:,:,ones(1,K)); Mref = ~any(isnan(Ds),3);
if(0), figure(10); montage2(Ds); end
Ds = reshape(Ds,[],size(Ds,3));
% iteratively project Ip onto subspace, storing transformation
lambda=lambda*w*h*eye(K); ds=zeros(1,8); err=inf;
for i=1:100
s=svd(H); if(s(3)<=1e-4*s(1)), H=eye(3); return; end
Ip=imtransform2(I,H,pTransf{:},0); dI=Ip-Iref; dI0=abs(dI);
M=Mref & ~isnan(Ip); M0=M; if(outThr<inf), M=M & dI0<outThr; end
M1=find(M); D=Ds(M1,:); ds1=(D'*D + lambda)^(-1)*(D'*dI(M1));
if(any(isnan(ds1))), ds1=zeros(K,1); end
ds(keep)=ds1; H1=ds2H(ds,wts); H=H*H1; H=H/H(9);
err0=err; err=dI0; err(~M0)=0; err=mean2(err); del=err0-err;
if(0), fprintf('i=%03i err=%e del=%e\n',i,err,del); end
if( del<epsilon ), break; end
end
end
function [H,Hs] = ds2H( ds, wts )
% compute homography from offsets ds
Hs=eye(3); Hs=Hs(:,:,ones(1,8));
Hs(2,3,1)=wts(1)*ds(1); % 1 x translation
Hs(1,3,2)=wts(2)*ds(2); % 2 y translation
Hs(1:2,1:2,3)=eye(2)*wts(3)^ds(3); % 3 scale
Hs(2,1,4)=wts(4)*ds(4); % 4 shear
Hs(1,1,5)=wts(5)^ds(5); % 5 scale non-uniform
ct=cos(wts(6)*ds(6)); st=sin(wts(6)*ds(6));
Hs(1:2,1:2,6)=[ct -st; st ct]; % 6 rotation about z
ct=cos(wts(7)*ds(7)); st=sin(wts(7)*ds(7));
Hs([1 3],[1 3],7)=[ct -st; st ct]; % 7 rotation about x
ct=cos(wts(8)*ds(8)); st=sin(wts(8)*ds(8));
Hs(2:3,2:3,8)=[ct -st; st ct]; % 8 rotation about y
H=eye(3); for i=1:8, H=Hs(:,:,i)*H; end
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
opticalFlow.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/videos/opticalFlow.m
| 7,385 |
utf_8
|
0fdca13d3caa4421fc488d0031e7838c
|
function [Vx,Vy,reliab] = opticalFlow( I1, I2, varargin )
% Coarse-to-fine optical flow using Lucas&Kanade or Horn&Schunck.
%
% Implemented 'type' of optical flow estimation:
% LK: http://en.wikipedia.org/wiki/Lucas-Kanade_method
% HS: http://en.wikipedia.org/wiki/Horn-Schunck_method
% SD: Simple block-based sum of absolute differences flow
% LK is a local, fast method (the implementation is fully vectorized).
% HS is a global, slower method (an SSE implementation is provided).
% SD is a simple but potentially expensive approach.
%
% Common parameters: 'smooth' determines smoothing prior to computing flow
% and can make flow estimation more robust. 'filt' determines amount of
% median filtering of the computed flow field which improves results but is
% costly. 'minScale' and 'maxScale' control image scales in the pyramid.
% Setting 'maxScale'<1 results in faster but lower quality results, e.g.
% maxScale=.5 makes flow computation about 4x faster. Method specific
% parameters: 'radius' controls window size (and smoothness of flow) for LK
% and SD. 'nBlock' determines number of blocks tested in each direction for
% SD, computation time is O(nBlock^2). For HS, 'alpha' controls tradeoff
% between data and smoothness term (and smoothness of flow) and 'nIter'
% determines number of gradient decent steps.
%
% USAGE
% [Vx,Vy,reliab] = opticalFlow( I1, I2, pFlow )
%
% INPUTS
% I1, I2 - input images to calculate flow between
% pFlow - parameters (struct or name/value pairs)
% .type - ['LK'] may be 'LK', 'HS' or 'SD'
% .smooth - [1] smoothing radius for triangle filter (may be 0)
% .filt - [0] median filtering radius for smoothing flow field
% .minScale - [1/64] minimum pyramid scale (must be a power of 2)
% .maxScale - [1] maximum pyramid scale (must be a power of 2)
% .radius - [10] integration radius for weighted window [LK/SD only]
% .nBlock - [5] number of tested blocks [SD only]
% .alpha - [1] smoothness constraint [HS only]
% .nIter - [250] number of iterations [HS only]
%
% OUTPUTS
% Vx, Vy - x,y components of flow [Vx>0->right, Vy>0->down]
% reliab - reliability of flow in given window
%
% EXAMPLE - compute LK flow on test images
% load opticalFlowTest;
% [Vx,Vy]=opticalFlow(I1,I2,'smooth',1,'radius',10,'type','LK');
% figure(1); im(I1); figure(2); im(I2);
% figure(3); im([Vx Vy]); colormap jet;
%
% EXAMPLE - rectify I1 to I2 using computed flow
% load opticalFlowTest;
% [Vx,Vy]=opticalFlow(I1,I2,'smooth',1,'radius',10,'type','LK');
% I1=imtransform2(I1,[],'vs',-Vx,'us',-Vy,'pad','replicate');
% figure(1); im(I1); figure(2); im(I2);
%
% EXAMPLE - compare LK/HS/SD flows
% load opticalFlowTest;
% prm={'smooth',1,'radius',10,'alpha',20,'nIter',250,'type'};
% tic, [Vx1,Vy1]=opticalFlow(I1,I2,prm{:},'LK'); toc
% tic, [Vx2,Vy2]=opticalFlow(I1,I2,prm{:},'HS'); toc
% tic, [Vx3,Vy3]=opticalFlow(I1,I2,prm{:},'SD','minScale',1); toc
% figure(1); im([Vx1 Vy1; Vx2 Vy2; Vx3 Vy3]); colormap jet;
%
% See also convTri, imtransform2, medfilt2
%
% Piotr's Computer Vision Matlab Toolbox Version NEW
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
% get default parameters and do error checking
dfs={ 'type','LK', 'smooth',1, 'filt',0, 'minScale',1/64, ...
'maxScale',1, 'radius',10, 'nBlock',5, 'alpha',1, 'nIter',250 };
[type,smooth,filt,minScale,maxScale,radius,nBlock,alpha,nIter] = ...
getPrmDflt(varargin,dfs,1);
assert(any(strcmp(type,{'LK','HS','SD'})));
if( ~ismatrix(I1) || ~ismatrix(I2) || any(size(I1)~=size(I2)) )
error('Input images must be 2D and have same dimensions.'); end
% run optical flow in coarse to fine fashion
if(~isa(I1,'single')), I1=single(I1); I2=single(I2); end
[h,w]=size(I1); nScales=max(1,floor(log2(min([h w 1/minScale])))+1);
for s=1:max(1,nScales + round(log2(maxScale)))
% get current scale and I1s and I2s at given scale
scale=2^(nScales-s); h1=round(h/scale); w1=round(w/scale);
if( scale==1 ), I1s=I1; I2s=I2; else
I1s=imResample(I1,[h1 w1]); I2s=imResample(I2,[h1 w1]); end
% initialize Vx,Vy or upsample from previous scale
if(s==1), Vx=zeros(h1,w1,'single'); Vy=Vx; else r=sqrt(h1*w1/numel(Vx));
Vx=imResample(Vx,[h1 w1])*r; Vy=imResample(Vy,[h1 w1])*r; end
% transform I2s according to current estimate of Vx and Vy
if(s>1), I2s=imtransform2(I2s,[],'pad','replciate','vs',Vx,'us',Vy); end
% smooth images
I1s=convTri(I1s,smooth); I2s=convTri(I2s,smooth);
% run optical flow on current scale
switch type
case 'LK', [Vx1,Vy1,reliab]=opticalFlowLk(I1s,I2s,radius);
case 'HS', [Vx1,Vy1,reliab]=opticalFlowHs(I1s,I2s,alpha,nIter);
case 'SD', [Vx1,Vy1,reliab]=opticalFlowSd(I1s,I2s,radius,nBlock,1);
end
Vx=Vx+Vx1; Vy=Vy+Vy1;
% finally median filter the resulting flow field
if(filt), Vx=medfilt2(Vx,[filt filt],'symmetric'); end
if(filt), Vy=medfilt2(Vy,[filt filt],'symmetric'); end
end
r=sqrt(h*w/numel(Vx));
if(r~=1), Vx=imResample(Vx,[h w])*r; Vy=imResample(Vy,[h w])*r; end
if(r~=1 && nargout==3), reliab=imResample(reliab,[h w]); end
end
function [Vx,Vy,reliab] = opticalFlowLk( I1, I2, radius )
% Compute elements of A'A and also of A'b
radius=min(radius,floor(min(size(I1,1),size(I1,2))/2)-1);
[Ix,Iy]=gradient2(I1); It=I2-I1; AAxy=convTri(Ix.*Iy,radius);
AAxx=convTri(Ix.^2,radius)+1e-5; ABxt=convTri(-Ix.*It,radius);
AAyy=convTri(Iy.^2,radius)+1e-5; AByt=convTri(-Iy.*It,radius);
% Find determinant and trace of A'A
AAdet=AAxx.*AAyy-AAxy.^2; AAdeti=1./AAdet;
AAdeti(isinf(AAdeti))=0; AAtr=AAxx+AAyy;
% Compute components of velocity vectors (A'A)^-1 * A'b
Vx = AAdeti .* ( AAyy.*ABxt - AAxy.*AByt);
Vy = AAdeti .* (-AAxy.*ABxt + AAxx.*AByt);
% Check for ill conditioned second moment matrices
reliab = 0.5*AAtr - 0.5*sqrt(AAtr.^2-4*AAdet);
end
function [Vx,Vy,reliab] = opticalFlowHs( I1, I2, alpha, nIter )
% compute derivatives (averaging over 2x2 neighborhoods)
pad = @(I,p) imPad(I,p,'replicate');
crop = @(I,c) I(1+c:end-c,1+c:end-c);
Ex = I1(:,2:end)-I1(:,1:end-1) + I2(:,2:end)-I2(:,1:end-1);
Ey = I1(2:end,:)-I1(1:end-1,:) + I2(2:end,:)-I2(1:end-1,:);
Ex = Ex/4; Ey = Ey/4; Et = (I2-I1)/4;
Ex = pad(Ex,[1 1 1 2]) + pad(Ex,[0 2 1 2]);
Ey = pad(Ey,[1 2 1 1]) + pad(Ey,[1 2 0 2]);
Et=pad(Et,[0 2 1 1])+pad(Et,[1 1 1 1])+pad(Et,[1 1 0 2])+pad(Et,[0 2 0 2]);
Z=1./(alpha*alpha + Ex.*Ex + Ey.*Ey); reliab=crop(Z,1);
% iterate updating Ux and Vx in each iter
if( 1 )
[Vx,Vy]=opticalFlowHsMex(Ex,Ey,Et,Z,nIter);
Vx=crop(Vx,1); Vy=crop(Vy,1);
else
Ex=crop(Ex,1); Ey=crop(Ey,1); Et=crop(Et,1); Z=crop(Z,1);
Vx=zeros(size(I1),'single'); Vy=Vx;
f=single([0 1 0; 1 0 1; 0 1 0])/4;
for i = 1:nIter
Mx=conv2(Vx,f,'same'); My=conv2(Vy,f,'same');
m=(Ex.*Mx+Ey.*My+Et).*Z; Vx=Mx-Ex.*m; Vy=My-Ey.*m;
end
end
end
function [Vx,Vy,reliab] = opticalFlowSd( I1, I2, radius, nBlock, step )
% simple block-based sum of absolute differences flow
[h,w]=size(I1); k=2*nBlock+1; k=k*k; D=zeros(h,w,k,'single'); k=1;
rng = @(x,w) max(1+x*step,1):min(w+x*step,w);
for x=-nBlock:nBlock, xs0=rng(x,w); xs1=rng(-x,w);
for y=-nBlock:nBlock, ys0=rng(y,h); ys1=rng(-y,h);
D(ys0,xs0,k)=abs(I1(ys0,xs0)-I2(ys1,xs1)); k=k+1;
end
end
D=convTri(D,radius); [reliab,D]=min(D,[],3);
k=2*nBlock+1; Vy=mod(D-1,k)+1; Vx=(D-Vy)/k+1;
Vy=(nBlock+1-Vy)*step; Vx=(nBlock+1-Vx)*step;
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
seqWriterPlugin.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/videos/seqWriterPlugin.m
| 8,280 |
utf_8
|
597792f79fff08b8bb709313267c3860
|
function varargout = seqWriterPlugin( cmd, h, varargin )
% Plugin for seqIo and videoIO to allow writing of seq files.
%
% Do not call directly, use as plugin for seqIo or videoIO instead.
% The following is a list of commands available (swp=seqWriterPlugin):
% h=swp('open',h,fName,info) % Open a seq file for writing (h ignored).
% h=swp('close',h) % Close seq file (output h is -1).
% swp('addframe',h,I,[ts]) % Writes video frame (and timestamp).
% swp('addframeb',h,I,[ts]) % Writes video frame with no encoding.
% info = swp('getinfo',h) % Return struct with info about video.
%
% The following params must be specified in struct 'info' upon opening:
% width - frame width
% height - frame height
% fps - frames per second
% quality - [80] compression quality (0 to 100)
% codec - string representing codec, options include:
% 'monoraw'/'imageFormat100' - black/white uncompressed
% 'raw'/'imageFormat200' - color (BGR) uncompressed
% 'monojpg'/'imageFormat102' - black/white jpg compressed
% 'jpg'/'imageFormat201' - color jpg compressed
% 'monopng'/'imageFormat001' - black/white png compressed
% 'png'/'imageFormat002' - color png compressed
%
% USAGE
% varargout = seqWriterPlugin( cmd, h, varargin )
%
% INPUTS
% cmd - string indicating operation to perform
% h - unique identifier for open seq file
% varargin - additional options (vary according to cmd)
%
% OUTPUTS
% varargout - output (varies according to cmd)
%
% EXAMPLE
%
% See also SEQIO, SEQREADERPLUGIN
%
% Piotr's Computer Vision Matlab Toolbox Version 2.66
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
% persistent variables to keep track of all loaded .seq files
persistent h1 hs fids infos tNms;
if(isempty(h1)), h1=int32(now); hs=int32([]); infos={}; tNms={}; end
nIn=nargin-2; in=varargin; o1=[]; cmd=lower(cmd);
% open seq file
if(strcmp(cmd,'open'))
chk(nIn,2); h=length(hs)+1; hs(h)=h1; varargout={h1}; h1=h1+1;
[pth,name]=fileparts(in{1}); if(isempty(pth)), pth='.'; end
fName=[pth filesep name];
[infos{h},fids(h),tNms{h}]=open(fName,in{2}); return;
end
% Get the handle for this instance
[v,h]=ismember(h,hs); if(~v), error('Invalid load plugin handle'); end
fid=fids(h); info=infos{h}; tNm=tNms{h};
% close seq file
if(strcmp(cmd,'close'))
writeHeader(fid,info);
chk(nIn,0); varargout={-1}; fclose(fid); kp=[1:h-1 h+1:length(hs)];
hs=hs(kp); fids=fids(kp); infos=infos(kp);
tNms=tNms(kp); if(exist(tNm,'file')), delete(tNm); end; return;
end
% perform appropriate operation
switch( cmd )
case 'addframe', chk(nIn,1,2); info=addFrame(fid,info,tNm,1,in{:});
case 'addframeb', chk(nIn,1,2); info=addFrame(fid,info,tNm,0,in{:});
case 'getinfo', chk(nIn,0); o1=info;
otherwise, error(['Unrecognized command: "' cmd '"']);
end
infos{h}=info; varargout={o1};
end
function chk(nIn,nMin,nMax)
if(nargin<3), nMax=nMin; end
if(nIn>0 && nMin==0 && nMax==0), error(['"' cmd '" takes no args.']); end
if(nIn<nMin||nIn>nMax), error(['Incorrect num args for "' cmd '".']); end
end
function success = getImgFile( fName )
% create local copy of fName which is in a imagesci/private
fName = [fName '.' mexext]; s = filesep; success = 1;
sName = [fileparts(which('imread.m')) s 'private' s fName];
tName = [fileparts(mfilename('fullpath')) s 'private' s fName];
if(~exist(tName,'file')), success=copyfile(sName,tName); end
end
function [info, fid, tNm] = open( fName, info )
% open video for writing, create space for header
t=[fName '.seq']; if(exist(t,'file')), delete(t); end
t=[fName '-seek.mat']; if(exist(t,'file')), delete(t); end
fid=fopen([fName '.seq'],'w','l'); assert(fid~=-1);
fwrite(fid,zeros(1,1024),'uint8');
% initialize info struct (w all fields necessary for writeHeader)
assert(isfield2(info,{'width','height','fps','codec'},1));
switch(info.codec)
case {'monoraw', 'imageFormat100'}, frmt=100; nCh=1; ext='raw';
case {'raw', 'imageFormat200'}, frmt=200; nCh=3; ext='raw';
case {'monojpg', 'imageFormat102'}, frmt=102; nCh=1; ext='jpg';
case {'jpg', 'imageFormat201'}, frmt=201; nCh=3; ext='jpg';
case {'monopng', 'imageFormat001'}, frmt=001; nCh=1; ext='png';
case {'png', 'imageFormat002'}, frmt=002; nCh=3; ext='png';
otherwise, error('unknown format');
end; s=1;
if(strcmp(ext,'jpg')), s=getImgFile('wjpg8c'); end
if(strcmp(ext,'png')), s=getImgFile('png');
if(s), info.writeImg=@(p) png('write',p{:}); end; end
if(strcmp(ext,'png') && ~s), s=getImgFile('pngwritec');
if(s), info.writeImg=@(p) pngwritec(p{:}); end; end
if(~s), error('Cannot find Matlab''s source image writer'); end
info.imageFormat=frmt; info.ext=ext;
if(any(strcmp(ext,{'jpg','png'}))), info.seek=1024; info.seekNm=t; end
if(~isfield2(info,'quality')), info.quality=80; end
info.imageBitDepth=8*nCh; info.imageBitDepthReal=8;
nByte=info.width*info.height*nCh; info.imageSizeBytes=nByte;
info.numFrames=0; info.trueImageSize=nByte+6+512-mod(nByte+6,512);
% generate unique temporary name
[~,tNm]=fileparts(fName); t=clock; t=mod(t(end),1);
tNm=sprintf('tmp_%s_%15i.%s',tNm,round((t+rand)/2*1e15),ext);
end
function info = addFrame( fid, info, tNm, encode, I, ts )
% write frame
nCh=info.imageBitDepth/8; ext=info.ext; c=info.numFrames+1;
if( encode )
siz = [info.height info.width nCh];
assert(size(I,1)==siz(1) && size(I,2)==siz(2) && size(I,3)==siz(3));
end
switch ext
case 'raw'
% write an uncompressed image (assume imageBitDepthReal==8)
if( ~encode ), assert(numel(I)==info.imageSizeBytes); else
if(nCh==3), t=I(:,:,3); I(:,:,3)=I(:,:,1); I(:,:,1)=t; end
if(nCh==1), I=I'; else I=permute(I,[3,2,1]); end
end
fwrite(fid,I(:),'uint8'); pad=info.trueImageSize-info.imageSizeBytes-6;
case 'jpg'
if( encode )
% write/read to/from temporary .jpg (not that much overhead)
p=struct('quality',info.quality,'comment',{{}},'mode','lossy');
for t=0:99, try wjpg8c(I,tNm,p); fr=fopen(tNm,'r'); assert(fr>0);
break; catch, pause(.01); fr=-1; end; end %#ok<CTCH>
if(fr<0), error(['write fail: ' tNm]); end; I=fread(fr); fclose(fr);
end
assert(I(1)==255 && I(2)==216 && I(end-1)==255 && I(end)==217); % JPG
fwrite(fid,numel(I)+4,'uint32'); fwrite(fid,I); pad=10;
case 'png'
if( encode )
% write/read to/from temporary .png (not that much overhead)
p=cell(1,17); if(nCh==1), p{4}=0; else p{4}=2; end
p{1}=I; p{3}=tNm; p{5}=8; p{8}='none'; p{16}=cell(0,2);
for t=0:99, try info.writeImg(p); fr=fopen(tNm,'r'); assert(fr>0);
break; catch, pause(.01); fr=-1; end; end %#ok<CTCH>
if(fr<0), error(['write fail: ' tNm]); end; I=fread(fr); fclose(fr);
end
fwrite(fid,numel(I)+4,'uint32'); fwrite(fid,I); pad=10;
otherwise, assert(false);
end
% store seek info
if(any(strcmp(ext,{'jpg','png'})))
if(length(info.seek)<c+1), info.seek=[info.seek; zeros(c,1)]; end
info.seek(c+1)=info.seek(c)+numel(I)+10+pad;
end
% write timestamp
if(nargin<6),ts=(c-1)/info.fps; end; s=floor(ts); ms=round(mod(ts,1)*1000);
fwrite(fid,s,'int32'); fwrite(fid,ms,'uint16'); info.numFrames=c;
% pad with zeros
if(pad>0), fwrite(fid,zeros(1,pad),'uint8'); end
end
function writeHeader( fid, info )
fseek(fid,0,'bof');
% first 4 bytes store OxFEED, next 24 store 'Norpix seq '
fwrite(fid,hex2dec('FEED'),'uint32');
fwrite(fid,['Norpix seq' 0 0],'uint16');
% next 8 bytes for version (3) and header size (1024), then 512 for descr
fwrite(fid,[3 1024],'int32');
if(isfield(info,'descr')), d=info.descr(:); else d=('No Description')'; end
d=[d(1:min(256,end)); zeros(256-length(d),1)]; fwrite(fid,d,'uint16');
% write remaining info
vals=[info.width info.height info.imageBitDepth info.imageBitDepthReal ...
info.imageSizeBytes info.imageFormat info.numFrames 0 ...
info.trueImageSize];
fwrite(fid,vals,'uint32');
% store frame rate and pad with 0's
fwrite(fid,info.fps,'float64'); fwrite(fid,zeros(1,432),'uint8');
% write seek info for compressed images to disk
if(any(strcmp(info.ext,{'jpg','png'})))
seek=info.seek(1:info.numFrames); %#ok<NASGU>
try save(info.seekNm,'seek'); catch; end %#ok<CTCH>
end
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
kernelTracker.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/videos/kernelTracker.m
| 9,315 |
utf_8
|
4a7d0235f1e518ab5f1c9f1b5450b3f0
|
function [allRct, allSim, allIc] = kernelTracker( I, prm )
% Kernel Tracker from Comaniciu, Ramesh and Meer PAMI 2003.
%
% Implements the algorithm described in "Kernel-Based Object Tracking" by
% Dorin Comaniciu, Visvanathan Ramesh and Peter Meer, PAMI 25, 564-577,
% 2003. This is a fast tracking algorithm that utilizes a histogram
% representation of an object (in this implementation we use color
% histograms, as in the original work). The idea is given a histogram q in
% frame t, find histogram p in frame t+1 that is most similar to q. It
% turns out that this can be formulated as a mean shift problem. Here, the
% kernel is fixed to the Epanechnikov kernel.
%
% This implementation uses mex files to optimize speed, it is significantly
% faster than real time for a single object on a 2GHz standard laptop (as
% of 2007).
%
% If I==[], toy data is created. If rctS==0, the user is queried to
% specify the first rectangle. rctE, denoting the object location in the
% last frame, can optionally be specified. If rctE is given, the model
% histogram at fraction r of the video is (1-r)*histS+r*histE where histS
% and histE are the model histograms from the first and last frame. If
% rctE==0 rectangle in final frame is queried, if rectE==-1 it is not used.
%
% Let T denote the length of the video. Returned values are of length t,
% where t==T if the object was tracked through the whole sequence (ie sim
% does not fall below simThr), otherwise t<=T is equal to the last frame in
% which obj was found. You can test if the object was tracked using:
% success = (size(allRct,1)==size(I,4));
%
% USAGE
% [allRct, allIc, allSim] = kernelTracker( [I], [prm] )
%
% INPUTS
% I - MxNx3xT input video
% [prm]
% .rctS - [0] rectangle denoting initial object location
% .rctE - [-1] rectangle denoting final object location
% .dispFlag - [1] show interactive display
% .scaleSrch - [1] if true search over scale
% .nBit - [4] n=2^nBit, color histograms are [n x n x n]
% .simThr - [.7] sim thr for when obj is considered lost
% .scaleDel - [.9] multiplicative diff between consecutive scales
%
% OUTPUTS
% allRct - [t x 4] array of t locations [x,y,wd,ht]
% allSim - [1 x t] array of similarity measures during tracking
% allIc - [1 x t] cell array of cropped windows containing obj
%
% EXAMPLE
% disp('Select a rectangular region for tracking');
% [allRct,allSim,allIc] = kernelTracker();
% figure(2); clf; plot(allRct);
% figure(3); clf; montage2(allIc,struct('hasChn',true));
%
% See also
%
% Piotr's Computer Vision Matlab Toolbox Version 3.22
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
%%% get parameters (set defaults)
if( nargin<1 ); I=[]; end;
if( nargin<2 ); prm=struct(); end;
dfs = {'scaleSrch',1, 'nBit',4, 'simThr',.7, ...
'dispFlag',1, 'scaleDel',.9, 'rctS',0, 'rctE',-1 };
prm = getPrmDflt( prm, dfs );
scaleSrch=prm.scaleSrch; nBit=prm.nBit; simThr=prm.simThr;
dispFlag=prm.dispFlag; scaleDel=prm.scaleDel;
rctS=prm.rctS; rctE=prm.rctE;
if(isempty(I)); I=toyData(100,1); end;
%%% get rctS and rectE if necessary
rctProp = {'EdgeColor','g','Curvature',[1 1],'LineWidth',2};
if(rctS==0); figure(1); clf; imshow(I(:,:,:,1)); rctS=getrect; end
if(rctE==0); figure(1); clf; imshow(I(:,:,:,end)); rctE=getrect; end
%%% precompute kernels for all relevant scales
rctS=round(rctS); rctS(3:4)=rctS(3:4)-mod(rctS(3:4),2);
pos1 = rctS(1:2)+rctS(3:4)/2; wd=rctS(3); ht=rctS(4);
[mRows,nCols,~,nFrame] = size(I);
nScaleSm = max(1,floor(log(max(10/wd,10/ht))/log(scaleDel)));
nScaleLr = max(1,floor(-log(min(nCols/wd,mRows/ht)/2)/log(scaleDel)));
nScale = nScaleSm+nScaleLr+1; scale = nScaleSm+1;
kernel = repmat( buildKernel(wd,ht), [1 nScale] );
for s=1:nScale
r = power(scaleDel,s-1-nScaleSm);
kernel(s) = buildKernel( wd/r, ht/r );
end
%%% build model histogram for rctS
[Ic,Qc] = cropWindow( I(:,:,:,1), nBit, pos1, wd, ht );
qS = buildHist( Qc, kernel(scale), nBit );
%%% optionally build model histogram for rctE
if(length(rctE)==4);
rctE=round(rctE); rctE(3:4)=rctE(3:4)-mod(rctE(3:4),2);
posE = rctE(1:2)+rctE(3:4)/2; wdE=rctE(3); htE=rctE(4);
kernelE = buildKernel(wdE,htE);
[Ic,Qc] = cropWindow( I(:,:,:,end), nBit, posE, wdE, htE ); %end
qE = buildHist( Qc, kernelE, nBit );
else
qE = qS;
end
%%% setup display
if( dispFlag )
figure(1); clf; hImg=imshow(I(:,:,:,1));
hR = rectangle('Position', rctS, rctProp{:} );
pause(.1);
end
%%% main loop
pos = pos1;
allRct = zeros(nFrame,4); allRct(1,:)=rctS;
allIc = cell(1,nFrame); allIc{1}=Ic;
allSim = zeros(1,nFrame);
for frm = 1:nFrame
Icur = I(:,:,:,frm);
% current model (linearly interpolate)
r=(frm-1)/nFrame; q = qS*(1-r) + qE*r;
if( scaleSrch )
% search over scale
best={}; bestSim=-1; pos1=pos;
for s=max(1,scale-1):min(nScale,scale+1)
[p,pos,Ic,sim]=kernelTracker1(Icur,q,pos1,kernel(s),nBit);
if( sim>bestSim ); best={p,pos,Ic,s}; bestSim=sim; end;
end
[~,pos,Ic,scale]=deal(best{:});
wd=kernel(scale).wd; ht=kernel(scale).ht;
else
% otherwise just do meanshift once
[~,pos,Ic,bestSim]=kernelTracker1(Icur,q,pos,kernel(scale),nBit);
end
% record results
if( bestSim<simThr ); break; end;
rctC=[pos(1)-wd/2 pos(2)-ht/2 wd, ht ];
allIc{frm}=Ic; allRct(frm,:)=rctC;
allSim(frm)=bestSim;
% display
if( dispFlag )
set(hImg,'CData',Icur); title(['bestSim=' num2str(bestSim)]);
delete(hR); hR=rectangle('Position', rctC, rctProp{:} );
if(0); waitforbuttonpress; else drawnow; end
end
end
%%% finalize & display
if( bestSim<simThr ); frm=frm-1; end;
allIc=allIc(1:frm); allRct=allRct(1:frm,:); allSim=allSim(1:frm);
if( dispFlag )
if( bestSim<simThr ); disp('lost target'); end
disp( ['final sim = ' num2str(bestSim) ] );
end
end
function [p,pos,Ic,sim] = kernelTracker1( I, q, pos, kernel, nBit )
mRows=size(I,1); nCols=size(I,2);
wd=kernel.wd; wd2=wd/2;
ht=kernel.ht; ht2=ht/2;
xs=kernel.xs; ys=kernel.ys;
for iter=1:1000
posPrev = pos;
% check if pos in bounds
rct = [pos(1)-wd/2 pos(2)-ht/2 wd, ht ];
if( rct(1)<1 || rct(2)<1 || (rct(1)+wd)>nCols || (rct(2)+ht)>mRows )
pos=posPrev; p=[]; Ic=[]; sim=eps; return;
end
% crop window / compute histogram
[Ic,Qc] = cropWindow( I, nBit, pos, wd, ht );
p = buildHist( Qc, kernel, nBit );
if( iter==20 ); break; end;
% compute meanshift step
w = ktComputeW_c( Qc, q, p, nBit );
posDel = [sum(xs.*w)*wd2, sum(ys.*w)*ht2] / (sum(w)+eps);
posDel = round(posDel+.1);
if(all(posDel==0)); break; end;
pos = pos + posDel;
end
locs=p>0; sim=sum( sqrt(q(locs).*p(locs)) );
end
function kernel = buildKernel( wd, ht )
wd = round(wd/2)*2; xs = linspace(-1,1,wd);
ht = round(ht/2)*2; ys = linspace(-1,1,ht);
[ys,xs] = ndgrid(ys,xs); xs=xs(:); ys=ys(:);
xMag = ys.*ys + xs.*xs; xMag(xMag>1) = 1;
K = 2/pi * (1-xMag); sumK=sum(K);
kernel = struct( 'K',K, 'sumK',sumK, 'xs',xs, 'ys',ys, 'wd',wd, 'ht',ht );
end
function p = buildHist( Qc, kernel, nBit )
p = ktHistcRgb_c( Qc, kernel.K, nBit ) / kernel.sumK;
if(0); p=gaussSmooth(p,.5,'same',2); p=p*(1/sum(p(:))); end;
end
function [Ic,Qc] = cropWindow( I, nBit, pos, wd, ht )
row = pos(2)-ht/2; col = pos(1)-wd/2;
Ic = I(row:row+ht-1,col:col+wd-1,:);
if(nargout==2); Qc=bitshift(reshape(Ic,[],3),nBit-8); end;
end
function I = toyData( n, sigma )
I1 = imresize(imread('peppers.png'),[256 256],'bilinear');
I=ones(512,512,3,n,'uint8')*100;
pos = round(gaussSmooth(randn(2,n)*80,[0 4]))+128;
for i=1:n
I((1:256)+pos(1,i),(1:256)+pos(2,i),:,i)=I1;
I1 = uint8(double(I1) + randn(size(I1))*sigma);
end;
I=I((1:256)+128,(1:256)+128,:,:);
end
% % debugging code
% if( debug )
% figure(1);
% subplot(2,3,2); image( Ic ); subplot(2,3,1); image(Icur);
% rectangle('Position', posToRct(pos0,wd,ht), rctProp{:} );
% subplot(2,3,3); imagesc( reshape(w,wd,ht), [0 5] ); colormap gray;
% subplot(2,3,4); montage2( q ); subplot(2,3,5); montage2( p1 );
% waitforbuttonpress;
% end
% % search over 9 locations (with fixed scale)
% if( locSrch )
% best={}; bestSim=0.0; pos1=pos;
% for lr=-1:1
% for ud=-1:1
% posSt = pos1 + [wd*lr ht*ud];
% [p,pos,Ic,sim] = kernelTracker1(Icur,q,posSt,kernel(scale),nBit);
% if( sim>bestSim ); best={p,pos,Ic}; bestSim=sim; end;
% end
% end
% [p,pos,Ic]=deal(best{:});
% end
%%% background histogram -- seems kind of useless, removed
% if( 0 )
% bgSiz = 3; bgImp = 2;
% rctBgStr = max([1 1],rctS(1:2)-rctS(3:4)*(bgSiz/2-.5));
% rctBgEnd = min([nCols mRows],rctS(1:2)+rctS(3:4)*(bgSiz/2+.5));
% rctBg = [rctBgStr rctBgEnd-rctBgStr+1];
% posBg = rctBg(1:2)+rctBg(3:4)/2; wdBg=rctBg(3); htBg=rctBg(4);
% [IcBg,QcBg] = cropWindow( I(:,:,:,1), nBit, posBg, wdBg, htBg );
% wtBg = double( reshape(kernel.K,ht,wd)==0 );
% pre=rctS(1:2)-rctBg(1:2); pst=rctBg(3:4)-rctS(3:4)-pre;
% wtBg = padarray( wtBg, fliplr(pre), 1, 'pre' );
% wtBg = padarray( wtBg, fliplr(pst), 1, 'post' );
% pBg = buildHist( QcBg, wtBg, [], nBit );
% pWts = min( 1, max(pBg(:))/bgImp./pBg );
% if(0); montage2(pWts); impixelinfo; return; end
% else
% pWts=[];
% end;
% if(~isempty(pWts)); p = p .* pWts; end; % in buildHistogram
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
seqIo.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/videos/seqIo.m
| 17,019 |
utf_8
|
9c631b324bb527372ec3eed3416c5dcc
|
function out = seqIo( fName, action, varargin )
% Utilities for reading and writing seq files.
%
% A seq file is a series of concatentated image frames with a fixed size
% header. It is essentially the same as merging a directory of images into
% a single file. seq files are convenient for storing videos because: (1)
% no video codec is required, (2) seek is instant and exact, (3) seq files
% can be read on any operating system. The main drawback is that each frame
% is encoded independently, resulting in increased file size. The advantage
% over storing as a directory of images is that a single large file is
% created. Currently, either uncompressed, jpg or png compressed frames
% are supported. The seq file format is modeled after the Norpix seq format
% (in fact this reader can be used to read some Norpix seq files). The
% actual work of reading/writing seq files is done by seqReaderPlugin and
% seqWriterPlugin (there is no need to call those functions directly).
%
% seqIo contains a number of utility functions for working with seq files.
% The format for accessing the various utility functions is:
% out = seqIo( fName, 'action', inputs );
% The list of functions and help for each is given below. Also, help on
% individual subfunctions can be accessed by: "help seqIo>action".
%
% Create interface sr for reading seq files.
% sr = seqIo( fName, 'reader', [cache] )
% Create interface sw for writing seq files.
% sw = seqIo( fName, 'writer', info )
% Get info about seq file.
% info = seqIo( fName, 'getInfo' )
% Crop sub-sequence from seq file.
% seqIo( fName, 'crop', tName, frames )
% Extract images from seq file to target directory or array.
% Is = seqIo( fName, 'toImgs', [tDir], [skip], [f0], [f1], [ext] )
% Create seq file from an array or directory of images or from an AVI file.
% seqIo( fName, 'frImgs', info, varargin )
% Convert seq file by applying imgFun(I) to each frame I.
% seqIo( fName, 'convert', tName, imgFun, varargin )
% Replace header of seq file with provided info.
% seqIo( fName, 'newHeader', info )
% Create interface sr for reading dual seq files.
% sr = seqIo( fNames, 'readerDual', [cache] )
%
% USAGE
% out = seqIo( fName, action, varargin )
%
% INPUTS
% fName - seq file to open
% action - controls action (see above)
% varargin - additional inputs (see above)
%
% OUTPUTS
% out - depends on action (see above)
%
% EXAMPLE
%
% See also seqIo>reader, seqIo>writer, seqIo>getInfo, seqIo>crop,
% seqIo>toImgs, seqIo>frImgs, seqIo>convert, seqIo>newHeader,
% seqIo>readerDual, seqPlayer, seqReaderPlugin, seqWriterPlugin
%
% Piotr's Computer Vision Matlab Toolbox Version 2.61
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
switch lower(action)
case {'reader','r'}, out = reader( fName, varargin{:} );
case {'writer','w'}, out = writer( fName, varargin{:} );
case 'getinfo', out = getInfo( fName );
case 'crop', crop( fName, varargin{:} ); out=1;
case 'toimgs', out = toImgs( fName, varargin{:} );
case 'frimgs', frImgs( fName, varargin{:} ); out=1;
case 'convert', convert( fName, varargin{:} ); out=1;
case 'newheader', newHeader( fName, varargin{:} ); out=1;
case {'readerdual','rdual'}, out=readerDual(fName,varargin{:});
otherwise, error('seqIo unknown action: ''%s''',action);
end
end
function sr = reader( fName, cache )
% Create interface sr for reading seq files.
%
% Create interface sr to seq file with the following commands:
% sr.close(); % Close seq file (sr is useless after).
% [I,ts]=sr.getframe(); % Get current frame (returns [] if invalid).
% [I,ts]=sr.getframeb(); % Get current frame with no decoding.
% ts = sr.getts(); % Return timestamps for all frames.
% info = sr.getinfo(); % Return struct with info about video.
% [I,ts]=sr.getnext(); % Shortcut for next() followed by getframe().
% out = sr.next(); % Go to next frame (out=0 on fail).
% out = sr.seek(frame); % Go to specified frame (out=0 on fail).
% out = sr.step(delta); % Go to current frame+delta (out=0 on fail).
%
% If cache>0, reader() will cache frames in memory, so that calls to
% getframe() can avoid disk IO for cached frames (note that only frames
% returned by getframe() are cached). This is useful if the same frames are
% accessed repeatedly. When the cache is full, the frame in the cache
% accessed least recently is discarded. Memory requirements are
% proportional to cache size.
%
% USAGE
% sr = seqIo( fName, 'reader', [cache] )
%
% INPUTS
% fName - seq file name
% cache - [0] size of cache
%
% OUTPUTS
% sr - interface for reading seq file
%
% EXAMPLE
%
% See also seqIo, seqReaderPlugin
if(nargin<2 || isempty(cache)), cache=0; end
if( cache>0 ), [as, fs, Is, ts, inds]=deal([]); end
r=@seqReaderPlugin; s=r('open',int32(-1),fName);
sr = struct( 'close',@() r('close',s), 'getframe',@getframe, ...
'getframeb',@() r('getframeb',s), 'getts',@() r('getts',s), ...
'getinfo',@() r('getinfo',s), 'getnext',@() r('getnext',s), ...
'next',@() r('next',s), 'seek',@(f) r('seek',s,f), ...
'step',@(d) r('step',s,d));
function [I,t] = getframe()
% if not using cache simply call 'getframe' and done
if(cache<=0), [I,t]=r('getframe',s); return; end
% if cache initialized and frame in cache perform lookup
f=r('getinfo',s); f=f.curFrame; i=find(f==fs,1);
if(i), as=as+1; as(i)=0; t=ts(i); I=Is(inds{:},i); return; end
% if image not in cache add (and possibly initialize)
[I,t]=r('getframe',s); if(0), fprintf('reading frame %i\n',f); end
if(isempty(Is)), Is=zeros([size(I) cache],class(I));
as=ones(1,cache); fs=-as; ts=as; inds=repmat({':'},1,ndims(I)); end
[~,i]=max(as); as(i)=0; fs(i)=f; ts(i)=t; Is(inds{:},i)=I;
end
end
function sw = writer( fName, info )
% Create interface sw for writing seq files.
%
% Create interface sw to seq file with the following commands:
% sw.close(); % Close seq file (sw is useless after).
% sw.addframe(I,[ts]); % Writes video frame (and timestamp)
% sw.addframeb(bytes); % Writes video frame with no encoding.
% info = sw.getinfo(); % Return struct with info about video.
%
% The following params must be specified in struct 'info' upon opening:
% width - frame width
% height - frame height
% fps - frames per second
% quality - [80] compression quality (0 to 100)
% codec - string representing codec, options include:
% 'monoraw'/'imageFormat100' - black/white uncompressed
% 'raw'/'imageFormat200' - color (BGR) uncompressed
% 'monojpg'/'imageFormat102' - black/white jpg compressed
% 'jpg'/'imageFormat201' - color jpg compressed
% 'monopng'/'imageFormat001' - black/white png compressed
% 'png'/'imageFormat002' - color png compressed
%
% USAGE
% sw = seqIo( fName, 'writer', info )
%
% INPUTS
% fName - seq file name
% info - see above
%
% OUTPUTS
% sw - interface for writing seq file
%
% EXAMPLE
%
% See also seqIo, seqWriterPlugin
w=@seqWriterPlugin; s=w('open',int32(-1),fName,info);
sw = struct( 'close',@() w('close',s), 'getinfo',@() w('getinfo',s), ...
'addframe',@(varargin) w('addframe',s,varargin{:}), ...
'addframeb',@(varargin) w('addframeb',s,varargin{:}) );
end
function info = getInfo( fName )
% Get info about seq file.
%
% USAGE
% info = seqIo( fName, 'getInfo' )
%
% INPUTS
% fName - seq file name
%
% OUTPUTS
% info - information struct
%
% EXAMPLE
%
% See also seqIo
sr=reader(fName); info=sr.getinfo(); sr.close();
end
function crop( fName, tName, frames )
% Crop sub-sequence from seq file.
%
% Frame indices are 0 indexed. frames need not be consecutive and can
% contain duplicates. An index of -1 indicates a blank (all 0) frame. If
% contiguous subset of frames is cropped timestamps are preserved.
%
% USAGE
% seqIo( fName, 'crop', tName, frames )
%
% INPUTS
% fName - seq file name
% tName - cropped seq file name
% frames - frame indices (0 indexed)
%
% OUTPUTS
%
% EXAMPLE
%
% See also seqIo
sr=reader(fName); info=sr.getinfo(); sw=writer(tName,info);
frames=frames(:)'; pad=sr.getnext(); pad(:)=0;
kp=frames>=0 & frames<info.numFrames; if(~all(kp)), frames=frames(kp);
warning('piotr:seqIo:crop','%i out of bounds frames',sum(~kp)); end
ordered=all(frames(2:end)==frames(1:end-1)+1);
n=length(frames); k=0; tid=ticStatus;
for f=frames
if(f<0), sw.addframe(pad); continue; end
sr.seek(f); [I,ts]=sr.getframeb(); k=k+1; tocStatus(tid,k/n);
if(ordered), sw.addframeb(I,ts); else sw.addframeb(I); end
end; sw.close(); sr.close();
end
function Is = toImgs( fName, tDir, skip, f0, f1, ext )
% Extract images from seq file to target directory or array.
%
% USAGE
% Is = seqIo( fName, 'toImgs', [tDir], [skip], [f0], [f1], [ext] )
%
% INPUTS
% fName - seq file name
% tDir - [] target directory (if empty extract images to array)
% skip - [1] skip between written frames
% f0 - [0] first frame to write
% f1 - [numFrames-1] last frame to write
% ext - [] optionally save as given type (slow, reconverts)
%
% OUTPUTS
% Is - if isempty(tDir) outputs image array (else Is=[])
%
% EXAMPLE
%
% See also seqIo
if(nargin<2 || isempty(tDir)), tDir=[]; end
if(nargin<3 || isempty(skip)), skip=1; end
if(nargin<4 || isempty(f0)), f0=0; end
if(nargin<5 || isempty(f1)), f1=inf; end
if(nargin<6 || isempty(ext)), ext=''; end
sr=reader(fName); info=sr.getinfo(); f1=min(f1,info.numFrames-1);
frames=f0:skip:f1; n=length(frames); tid=ticStatus; k=0;
% output images to array
if(isempty(tDir))
I=sr.getnext(); d=ndims(I); assert(d==2 || d==3);
try Is=zeros([size(I) n],class(I)); catch e; sr.close(); throw(e); end
for k=1:n, sr.seek(frames(k)); I=sr.getframe(); tocStatus(tid,k/n);
if(d==2), Is(:,:,k)=I; else Is(:,:,:,k)=I; end; end
sr.close(); return;
end
% output images to directory
if(~exist(tDir,'dir')), mkdir(tDir); end; Is=[];
for frame=frames
f=[tDir '/I' int2str2(frame,5) '.']; sr.seek(frame);
if(~isempty(ext)), I=sr.getframe(); imwrite(I,[f ext]); else
I=sr.getframeb(); f=fopen([f info.ext],'w');
if(f<=0), sr.close(); assert(false); end
fwrite(f,I); fclose(f);
end; k=k+1; tocStatus(tid,k/n);
end; sr.close();
end
function frImgs( fName, info, varargin )
% Create seq file from an array or directory of images or from an AVI file.
%
% For info, if converting from array, only codec (e.g., 'jpg') and fps must
% be specified while width and height and determined automatically. If
% converting from AVI, fps is also determined automatically.
%
% USAGE
% seqIo( fName, 'frImgs', info, varargin )
%
% INPUTS
% fName - seq file name
% info - defines codec, etc, see seqIo>writer
% varargin - additional params (struct or name/value pairs)
% .aviName - [] if specified create seq from avi file
% .Is - [] if specified create seq from image array
% .sDir - [] source directory
% .skip - [1] skip between frames
% .name - ['I'] base name of images
% .nDigits - [5] number of digits for filename index
% .f0 - [0] first frame to read
% .f1 - [10^6] last frame to read
%
% OUTPUTS
%
% EXAMPLE
%
% See also seqIo, seqIo>writer
dfs={'aviName','','Is',[],'sDir',[],'skip',1,'name','I',...
'nDigits',5,'f0',0,'f1',10^6};
[aviName,Is,sDir,skip,name,nDigits,f0,f1] ...
= getPrmDflt(varargin,dfs,1);
if(~isempty(aviName))
if(exist('mmread.m','file')==2) % use external mmread function
% mmread requires full pathname, which is obtained via 'which'. But,
% 'which' can fail (maltab bug), so best to just pass in full pathname
t=which(aviName); if(~isempty(t)), aviName=t; end
V=mmread(aviName); n=V.nrFramesTotal;
info.height=V.height; info.width=V.width; info.fps=V.rate;
sw=writer(fName,info); tid=ticStatus('creating seq from avi');
for f=1:n, sw.addframe(V.frames(f).cdata); tocStatus(tid,f/n); end
sw.close();
else % use matlab mmreader function
emsg=['mmreader.m failed to load video. In general mmreader.m is ' ...
'known to have many issues, especially on Linux. I suggest ' ...
'installing the similarly named mmread toolbox from Micah ' ...
'Richert, available at Matlab Central. If mmread is installed, ' ...
'seqIo will automatically use mmread instead of mmreader.'];
try V=mmreader(aviName); catch %#ok<DMMR,CTCH>
error('piotr:seqIo:frImgs',emsg); end; n=V.NumberOfFrames;
info.height=V.Height; info.width=V.Width; info.fps=V.FrameRate;
sw=writer(fName,info); tid=ticStatus('creating seq from avi');
for f=1:n, sw.addframe(read(V,f)); tocStatus(tid,f/n); end
sw.close();
end
elseif( isempty(Is) )
assert(exist(sDir,'dir')==7); sw=writer(fName,info); info=sw.getinfo();
frmStr=sprintf('%s/%s%%0%ii.%s',sDir,name,nDigits,info.ext);
for frame = f0:skip:f1
f=sprintf(frmStr,frame); if(~exist(f,'file')), break; end
f=fopen(f,'r'); if(f<=0), sw.close(); assert(false); end
I=fread(f); fclose(f); sw.addframeb(I);
end; sw.close();
if(frame==f0), warning('No images found.'); end %#ok<WNTAG>
else
nd=ndims(Is); if(nd==2), nd=3; end; assert(nd<=4); nFrm=size(Is,nd);
info.height=size(Is,1); info.width=size(Is,2); sw=writer(fName,info);
if(nd==3), for f=1:nFrm, sw.addframe(Is(:,:,f)); end; end
if(nd==4), for f=1:nFrm, sw.addframe(Is(:,:,:,f)); end; end
sw.close();
end
end
function convert( fName, tName, imgFun, varargin )
% Convert seq file by applying imgFun(I) to each frame I.
%
% USAGE
% seqIo( fName, 'convert', tName, imgFun, varargin )
%
% INPUTS
% fName - seq file name
% tName - converted seq file name
% imgFun - function to apply to each image
% varargin - additional params (struct or name/value pairs)
% .info - [] info for target seq file
% .skip - [1] skip between frames
% .f0 - [0] first frame to read
% .f1 - [inf] last frame to read
%
% OUTPUTS
%
% EXAMPLE
%
% See also seqIo
dfs={'info',[],'skip',1,'f0',0,'f1',inf};
[info,skip,f0,f1]=getPrmDflt(varargin,dfs,1);
assert(~strcmp(tName,fName)); sr=reader(fName); infor=sr.getinfo();
if(isempty(info)), info=infor; end; n=infor.numFrames; f1=min(f1,n-1);
I=sr.getnext(); I=imgFun(I); info.width=size(I,2); info.height=size(I,1);
sw=writer(tName,info); tid=ticStatus('converting seq');
frames=f0:skip:f1; n=length(frames); k=0;
for f=frames, sr.seek(f); [I,ts]=sr.getframe(); I=imgFun(I);
if(skip==1), sw.addframe(I,ts); else sw.addframe(I); end
k=k+1; tocStatus(tid,k/n);
end; sw.close(); sr.close();
end
function newHeader( fName, info )
% Replace header of seq file with provided info.
%
% Can be used if the file fName has a corrupt header. Automatically tries
% to compute number of frames in fName. No guarantees that it will work.
%
% USAGE
% seqIo( fName, 'newHeader', info )
%
% INPUTS
% fName - seq file name
% info - info for target seq file
%
% OUTPUTS
%
% EXAMPLE
%
% See also seqIo
[d,n]=fileparts(fName); if(isempty(d)), d='.'; end
fName=[d '/' n]; tName=[fName '-new' datestr(now,30)];
if(exist([fName '-seek.mat'],'file')); delete([fName '-seek.mat']); end
srp=@seqReaderPlugin; hr=srp('open',int32(-1),fName,info); tid=ticStatus;
info=srp('getinfo',hr); sw=writer(tName,info); n=info.numFrames;
for f=1:n, srp('next',hr); [I,ts]=srp('getframeb',hr);
sw.addframeb(I,ts); tocStatus(tid,f/n); end
srp('close',hr); sw.close();
end
function sr = readerDual( fNames, cache )
% Create interface sr for reading dual seq files.
%
% Wrapper for two seq files of the same image dims and roughly the same
% frame counts that are treated as a single reader object. getframe()
% returns the concatentation of the two frames. For videos of different
% frame counts, the first video serves as the "dominant" video and the
% frame count of the second video is adjusted accordingly. Same general
% usage as in reader, but the only supported operations are: close(),
% getframe(), getinfo(), and seek().
%
% USAGE
% sr = seqIo( fNames, 'readerDual', [cache] )
%
% INPUTS
% fNames - two seq file names
% cache - [0] size of cache (see seqIo>reader)
%
% OUTPUTS
% sr - interface for reading seq file
%
% EXAMPLE
%
% See also seqIo, seqIo>reader
if(nargin<2 || isempty(cache)), cache=0; end
s1=reader(fNames{1}, cache); i1=s1.getinfo();
s2=reader(fNames{2}, cache); i2=s2.getinfo();
info=i1; info.width=i1.width+i2.width;
if( i1.width~=i2.width || i1.height~=i2.height )
s1.close(); s2.close(); error('Mismatched videos'); end
if( i1.numFrames~=i2.numFrames )
warning('seq files of different lengths'); end %#ok<WNTAG>
frame2=@(f) round(f/(i1.numFrames-1)*(i2.numFrames-1));
sr=struct('close',@() min(s1.close(),s2.close()), ...
'getframe',@getframe, 'getinfo',@() info, ...
'seek',@(f) s1.seek(f) & s2.seek(frame2(f)) );
function [I,t] = getframe()
[I1,t]=s1.getframe(); I2=s2.getframe(); I=[I1 I2]; end
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
seqReaderPlugin.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/videos/seqReaderPlugin.m
| 9,617 |
utf_8
|
ad8f912634cafe13df6fc7d67aeff05a
|
function varargout = seqReaderPlugin( cmd, h, varargin )
% Plugin for seqIo and videoIO to allow reading of seq files.
%
% Do not call directly, use as plugin for seqIo or videoIO instead.
% The following is a list of commands available (srp=seqReaderPlugin):
% h = srp('open',h,fName) % Open a seq file for reading (h ignored).
% h = srp('close',h); % Close seq file (output h is -1).
% [I,ts] =srp('getframe',h) % Get current frame (returns [] if invalid).
% [I,ts] =srp('getframeb',h) % Get current frame with no decoding.
% ts = srp('getts',h) % Return timestamps for all frames.
% info = srp('getinfo',h) % Return struct with info about video.
% [I,ts] =srp('getnext',h) % Shortcut for 'next' followed by 'getframe'.
% out = srp('next',h) % Go to next frame (out=0 on fail).
% out = srp('seek',h,frame) % Go to specified frame (out=0 on fail).
% out = srp('step',h,delta) % Go to current frame+delta (out=0 on fail).
%
% USAGE
% varargout = seqReaderPlugin( cmd, h, varargin )
%
% INPUTS
% cmd - string indicating operation to perform
% h - unique identifier for open seq file
% varargin - additional options (vary according to cmd)
%
% OUTPUTS
% varargout - output (varies according to cmd)
%
% EXAMPLE
%
% See also SEQIO, SEQWRITERPLUGIN
%
% Piotr's Computer Vision Matlab Toolbox Version 3.10
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
% persistent variables to keep track of all loaded .seq files
persistent h1 hs cs fids infos tNms;
if(isempty(h1)), h1=int32(now); hs=int32([]); infos={}; tNms={}; end
nIn=nargin-2; in=varargin; o2=[]; cmd=lower(cmd);
% open seq file
if(strcmp(cmd,'open'))
chk(nIn,1,2); h=length(hs)+1; hs(h)=h1; varargout={h1}; h1=h1+1;
[pth,name]=fileparts(in{1}); if(isempty(pth)), pth='.'; end
if(nIn==1), info=[]; else info=in{2}; end
fName=[pth filesep name]; cs(h)=-1;
[infos{h},fids(h),tNms{h}]=open(fName,info); return;
end
% Get the handle for this instance
[v,h]=ismember(h,hs); if(~v), error('Invalid load plugin handle'); end
c=cs(h); fid=fids(h); info=infos{h}; tNm=tNms{h};
% close seq file
if(strcmp(cmd,'close'))
chk(nIn,0); varargout={-1}; fclose(fid); kp=[1:h-1 h+1:length(hs)];
hs=hs(kp); cs=cs(kp); fids=fids(kp); infos=infos(kp);
tNms=tNms(kp); if(exist(tNm,'file')), delete(tNm); end; return;
end
% perform appropriate operation
switch( cmd )
case 'getframe', chk(nIn,0); [o1,o2]=getFrame(c,fid,info,tNm,1);
case 'getframeb', chk(nIn,0); [o1,o2]=getFrame(c,fid,info,tNm,0);
case 'getts', chk(nIn,0); o1=getTs(0:info.numFrames-1,fid,info);
case 'getinfo', chk(nIn,0); o1=info; o1.curFrame=c;
case 'getnext', chk(nIn,0); c=c+1; [o1,o2]=getFrame(c,fid,info,tNm,1);
case 'next', chk(nIn,0); [c,o1]=valid(c+1,info);
case 'seek', chk(nIn,1); [c,o1]=valid(in{1},info);
case 'step', chk(nIn,1); [c,o1]=valid(c+in{1},info);
otherwise, error(['Unrecognized command: "' cmd '"']);
end
cs(h)=c; varargout={o1,o2};
end
function chk(nIn,nMin,nMax)
if(nargin<3), nMax=nMin; end
if(nIn>0 && nMin==0 && nMax==0), error(['"' cmd '" takes no args.']); end
if(nIn<nMin||nIn>nMax), error(['Incorrect num args for "' cmd '".']); end
end
function success = getImgFile( fName )
% create local copy of fName which is in a imagesci/private
fName = [fName '.' mexext]; s = filesep; success = 1;
sName = [fileparts(which('imread.m')) s 'private' s fName];
tName = [fileparts(mfilename('fullpath')) s 'private' s fName];
if(~exist(tName,'file')), success=copyfile(sName,tName); end
end
function [info, fid, tNm] = open( fName, info )
% open video for reading, get header
if(exist([fName '.seq'],'file')==0)
error('seq file not found: %s.seq',fName); end
fid=fopen([fName '.seq'],'r','l');
if(isempty(info)), info=readHeader(fid); else
info.numFrames=0; fseek(fid,1024,'bof'); end
switch(info.imageFormat)
case {100,200}, ext='raw';
case {101 }, ext='brgb8';
case {102,201}, ext='jpg';
case {103 }, ext ='jbrgb';
case {001,002}, ext='png';
otherwise, error('unknown format');
end; info.ext=ext; s=1;
if(any(strcmp(ext,{'jpg','jbrgb'}))), s=getImgFile('rjpg8c'); end
if(strcmp(ext,'png')), s=getImgFile('png');
if(s), info.readImg=@(nm) png('read',nm,[]); end; end
if(strcmp(ext,'png') && ~s), s=getImgFile('pngreadc');
if(s), info.readImg=@(nm) pngreadc(nm,[],false); end; end
if(~s), error('Cannot find Matlab''s source image reader'); end
% generate unique temporary name
[~,tNm]=fileparts(fName); t=clock; t=mod(t(end),1);
tNm=sprintf('tmp_%s_%15i.%s',tNm,round((t+rand)/2*1e15),ext);
% compute seek info for compressed images
if(any(strcmp(ext,{'raw','brgb8'}))), assert(info.numFrames>0); else
oName=[fName '-seek.mat']; n=info.numFrames; if(n==0), n=10^7; end
if(exist(oName,'file')==2), load(oName); info.seek=seek; else %#ok<NODEF>
tid=ticStatus('loading seek info',.1,5); seek=zeros(n,1); seek(1)=1024;
extra=8; % extra bytes after image data (8 for ts, then 0 or 8 empty)
for i=2:n
s=seek(i-1)+fread(fid,1,'uint32')+extra; valid=fseek(fid,s,'bof')==0;
if(i==2 && valid), if(fread(fid,1,'uint32')~=0), fseek(fid,-4,'cof');
else extra=extra+8; s=s+8; valid=fseek(fid,s,'bof')==0; end; end
if(valid), seek(i)=s; tocStatus(tid,i/n);
else n=i-1; seek=seek(1:n); tocStatus(tid,1); break; end
end; if(info.numFrames==0), info.numFrames=n; end
try save(oName,'seek'); catch; end; info.seek=seek; %#ok<CTCH>
end
end
% compute frame rate from timestamps as stored fps may be incorrect
n=min(100,info.numFrames); if(n==1), return; end
ts = getTs( 0:(n-1), fid, info );
ds=ts(2:end)-ts(1:end-1); ds=ds(abs(ds-median(ds))<.005);
if(~isempty(ds)), info.fps=1/mean(ds); end
end
function [frame,v] = valid( frame, info )
v=(frame>=0 && frame<info.numFrames);
end
function [I,ts] = getFrame( frame, fid, info, tNm, decode )
% get frame image (I) and timestamp (ts) at which frame was recorded
nCh=info.imageBitDepth/8; ext=info.ext;
if(frame<0 || frame>=info.numFrames), I=[]; ts=[]; return; end
switch ext
case {'raw','brgb8'}
% read in an uncompressed image (assume imageBitDepthReal==8)
fseek(fid,1024+frame*info.trueImageSize,'bof');
I = fread(fid,info.imageSizeBytes,'*uint8');
if( decode )
% reshape appropriately for mxn or mxnx3 RGB image
siz = [info.height info.width nCh];
if(nCh==1), I=reshape(I,siz(2),siz(1))'; else
I = permute(reshape(I,siz(3),siz(2),siz(1)),[3,2,1]);
end
if(nCh==3), t=I(:,:,3); I(:,:,3)=I(:,:,1); I(:,:,1)=t; end
if(strcmp(ext,'brgb8')), I=demosaic(I,'bggr'); end
end
case {'jpg','jbrgb'}
fseek(fid,info.seek(frame+1),'bof'); nBytes=fread(fid,1,'uint32');
I = fread(fid,nBytes-4,'*uint8');
if( decode )
% write/read to/from temporary .jpg (not that much overhead)
assert(I(1)==255 && I(2)==216 && I(end-1)==255 && I(end)==217); % JPG
for t=0:99, fw=fopen(tNm,'w'); if(fw>=0), break; end; pause(.01); end
if(fw==-1), error(['unable to write: ' tNm]); end
fwrite(fw,I); fclose(fw); I=rjpg8c(tNm);
if(strcmp(ext,'jbrgb')), I=demosaic(I,'bggr'); end
end
case 'png'
fseek(fid,info.seek(frame+1),'bof'); nBytes=fread(fid,1,'uint32');
I = fread(fid,nBytes-4,'*uint8');
if( decode )
% write/read to/from temporary .png (not that much overhead)
for t=0:99, fw=fopen(tNm,'w'); if(fw>=0), break; end; pause(.01); end
if(fw==-1), error(['unable to write: ' tNm]); end
fwrite(fw,I); fclose(fw); I=info.readImg(tNm);
I=permute(I,ndims(I):-1:1);
end
otherwise, assert(false);
end
if(nargout==2), ts=fread(fid,1,'uint32')+fread(fid,1,'uint16')/1000; end
end
function ts = getTs( frames, fid, info )
% get timestamps (ts) at which frames were recorded
n=length(frames); ts=nan(1,n);
for i=1:n, frame=frames(i);
if(frame<0 || frame>=info.numFrames), continue; end
switch info.ext
case {'raw','brgb8'} % uncompressed
fseek(fid,1024+frame*info.trueImageSize+info.imageSizeBytes,'bof');
case {'jpg','png','jbrgb'} % compressed
fseek(fid,info.seek(frame+1),'bof');
fseek(fid,fread(fid,1,'uint32')-4,'cof');
otherwise, assert(false);
end
ts(i)=fread(fid,1,'uint32')+fread(fid,1,'uint16')/1000;
end
end
function info = readHeader( fid )
% see streampix manual for info on header
fseek(fid,0,'bof');
% check that header is not all 0's (a common error)
[tmp,n]=fread(fid,1024); if(n<1024), error('no header'); end
if(all(tmp==0)), error('fully empty header'); end; fseek(fid,0,'bof');
% first 4 bytes store OxFEED, next 24 store 'Norpix seq '
if( ~strcmp(sprintf('%X',fread(fid,1,'uint32')),'FEED') || ...
~strcmp(char(fread(fid,10,'uint16'))','Norpix seq') ) %#ok<FREAD>
error('invalid header');
end; fseek(fid,4,'cof');
% next 8 bytes for version and header size (1024), then 512 for descr
version=fread(fid,1,'int32'); assert(fread(fid,1,'uint32')==1024);
descr=char(fread(fid,256,'uint16'))'; %#ok<FREAD>
% read in more info
tmp=fread(fid,9,'uint32'); assert(tmp(8)==0);
fps = fread(fid,1,'float64'); codec=['imageFormat' int2str2(tmp(6),3)];
% store information in info struct
info=struct( 'width',tmp(1), 'height',tmp(2), 'imageBitDepth',tmp(3), ...
'imageBitDepthReal',tmp(4), 'imageSizeBytes',tmp(5), ...
'imageFormat',tmp(6), 'numFrames',tmp(7), 'trueImageSize', tmp(9),...
'fps',fps, 'seqVersion',version, 'codec',codec, 'descr',descr, ...
'nHiddenFinalFrames',0 );
assert(info.imageBitDepthReal==8);
% seek to end of header
fseek(fid,432,'cof');
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
pcaApply.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/classify/pcaApply.m
| 3,320 |
utf_8
|
a06fc0e54d85930cbc0536c874ac63b7
|
function varargout = pcaApply( X, U, mu, k )
% Companion function to pca.
%
% Use pca.m to retrieve the principal components U and the mean mu from a
% set of vectors x, then use pcaApply to get the first k coefficients of
% x in the space spanned by the columns of U. See pca for general usage.
%
% If x is large, pcaApply first splits and processes x in parts. This
% allows pcaApply to work even for very large arrays.
%
% This may prove useful:
% siz=size(X); k=100; Uim=reshape(U(:,1:k),[siz(1:end-1) k ]);
%
% USAGE
% [ Yk, Xhat, avsq ] = pcaApply( X, U, mu, k )
%
% INPUTS
% X - data for which to get PCA coefficients
% U - returned by pca.m
% mu - returned by pca.m
% k - number of principal coordinates to approximate X with
%
% OUTPUTS
% Yk - first k coordinates of X in column space of U
% Xhat - approximation of X corresponding to Yk
% avsq - measure of squared error normalized to fall between [0,1]
%
% EXAMPLE
%
% See also PCA, PCAVISUALIZE
%
% Piotr's Computer Vision Matlab Toolbox Version 2.0
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
% sizes / dimensions
siz = size(X); nd = ndims(X); [D,r] = size(U);
if(D==prod(siz) && ~(nd==2 && siz(2)==1)); siz=[siz, 1]; nd=nd+1; end
n = siz(end);
% some error checking
if(prod(siz(1:end-1))~=D); error('incorrect size for X or U'); end
if(isa(X,'uint8')); X = double(X); end
if(k>r); warning(['k set to ' int2str(r)]); k=r; end; %#ok<WNTAG>
% If X is small simply call pcaApply1 once.
% OW break up X and call pcaApply1 multiple times and recombine.
maxWidth = ceil( (10^7) / D );
if( maxWidth > n )
varargout = cell(1,nargout);
[varargout{:}] = pcaApply1( X, U, mu, k );
else
inds = {':'}; inds = inds(:,ones(1,nd-1));
Yk = zeros( k, n ); Xhat = zeros( siz );
avsq = 0; avsqOrig = 0; last = 0;
if( nargout==3 ); out=cell(1,4); else out=cell(1,nargout); end;
while(last < n)
first=last+1; last=min(first+maxWidth-1,n);
Xi = X(inds{:}, first:last);
[out{:}] = pcaApply1( Xi, U, mu, k );
Yk(:,first:last) = out{1};
if( nargout>=2 ); Xhat(inds{:},first:last)=out{2}; end;
if( nargout>=3 ); avsq=avsq+out{3}; avsqOrig=avsqOrig+out{4}; end;
end;
varargout = {Yk, Xhat, avsq/avsqOrig};
end
function [ Yk, Xhat, avsq, avsqOrig ] = pcaApply1( X, U, mu, k )
% sizes / dimensions
siz = size(X); nd = ndims(X); [D,r] = size(U);
if(D==prod(siz) && ~(nd==2 && siz(2)==1)); siz=[siz, 1]; nd=nd+1; end
n = siz(end);
% subtract mean, then flatten X
Xorig = X;
muRep = repmat(mu, [ones(1,nd-1), n ] );
X = X - muRep;
X = reshape( X, D, n );
% Find Yk, the first k coefficients of X in the new basis
if( r<=k ); Uk=U; else Uk=U(:,1:k); end;
Yk = Uk' * X;
% calculate Xhat - the approx of X using the first k princ components
if( nargout>1 )
Xhat = Uk * Yk;
Xhat = reshape( Xhat, siz );
Xhat = Xhat + muRep;
end
% caclulate average value of (Xhat-Xorig).^2 compared to average value
% of X.^2, where X is Xorig without the mean. This is equivalent to
% what fraction of the variance is captured by Xhat.
if( nargout>2 )
avsq = Xhat - Xorig;
avsq = dot(avsq(:),avsq(:));
avsqOrig = dot(X(:),X(:));
if( nargout==3 ); avsq=avsq/avsqOrig; end
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
forestTrain.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/classify/forestTrain.m
| 6,138 |
utf_8
|
de534e2a010f452a7b13167dbf9df239
|
function forest = forestTrain( data, hs, varargin )
% Train random forest classifier.
%
% Dimensions:
% M - number trees
% F - number features
% N - number input vectors
% H - number classes
%
% USAGE
% forest = forestTrain( data, hs, [varargin] )
%
% INPUTS
% data - [NxF] N length F feature vectors
% hs - [Nx1] or {Nx1} target output labels in [1,H]
% varargin - additional params (struct or name/value pairs)
% .M - [1] number of trees to train
% .H - [max(hs)] number of classes
% .N1 - [5*N/M] number of data points for training each tree
% .F1 - [sqrt(F)] number features to sample for each node split
% .split - ['gini'] options include 'gini', 'entropy' and 'twoing'
% .minCount - [1] minimum number of data points to allow split
% .minChild - [1] minimum number of data points allowed at child nodes
% .maxDepth - [64] maximum depth of tree
% .dWts - [] weights used for sampling and weighing each data point
% .fWts - [] weights used for sampling features
% .discretize - [] optional function mapping structured to class labels
% format: [hsClass,hBest] = discretize(hsStructured,H);
%
% OUTPUTS
% forest - learned forest model struct array w the following fields
% .fids - [Kx1] feature ids for each node
% .thrs - [Kx1] threshold corresponding to each fid
% .child - [Kx1] index of child for each node
% .distr - [KxH] prob distribution at each node
% .hs - [Kx1] or {Kx1} most likely label at each node
% .count - [Kx1] number of data points at each node
% .depth - [Kx1] depth of each node
%
% EXAMPLE
% N=10000; H=5; d=2; [xs0,hs0,xs1,hs1]=demoGenData(N,N,H,d,1,1);
% xs0=single(xs0); xs1=single(xs1);
% pTrain={'maxDepth',50,'F1',2,'M',150,'minChild',5};
% tic, forest=forestTrain(xs0,hs0,pTrain{:}); toc
% hsPr0 = forestApply(xs0,forest);
% hsPr1 = forestApply(xs1,forest);
% e0=mean(hsPr0~=hs0); e1=mean(hsPr1~=hs1);
% fprintf('errors trn=%f tst=%f\n',e0,e1); figure(1);
% subplot(2,2,1); visualizeData(xs0,2,hs0);
% subplot(2,2,2); visualizeData(xs0,2,hsPr0);
% subplot(2,2,3); visualizeData(xs1,2,hs1);
% subplot(2,2,4); visualizeData(xs1,2,hsPr1);
%
% See also forestApply, fernsClfTrain
%
% Piotr's Computer Vision Matlab Toolbox Version 3.24
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
% get additional parameters and fill in remaining parameters
dfs={ 'M',1, 'H',[], 'N1',[], 'F1',[], 'split','gini', 'minCount',1, ...
'minChild',1, 'maxDepth',64, 'dWts',[], 'fWts',[], 'discretize','' };
[M,H,N1,F1,splitStr,minCount,minChild,maxDepth,dWts,fWts,discretize] = ...
getPrmDflt(varargin,dfs,1);
[N,F]=size(data); assert(length(hs)==N); discr=~isempty(discretize);
minChild=max(1,minChild); minCount=max([1 minCount minChild]);
if(isempty(H)), H=max(hs); end; assert(discr || all(hs>0 & hs<=H));
if(isempty(N1)), N1=round(5*N/M); end; N1=min(N,N1);
if(isempty(F1)), F1=round(sqrt(F)); end; F1=min(F,F1);
if(isempty(dWts)), dWts=ones(1,N,'single'); end; dWts=dWts/sum(dWts);
if(isempty(fWts)), fWts=ones(1,F,'single'); end; fWts=fWts/sum(fWts);
split=find(strcmpi(splitStr,{'gini','entropy','twoing'}))-1;
if(isempty(split)), error('unknown splitting criteria: %s',splitStr); end
% make sure data has correct types
if(~isa(data,'single')), data=single(data); end
if(~isa(hs,'uint32') && ~discr), hs=uint32(hs); end
if(~isa(fWts,'single')), fWts=single(fWts); end
if(~isa(dWts,'single')), dWts=single(dWts); end
% train M random trees on different subsets of data
prmTree = {H,F1,minCount,minChild,maxDepth,fWts,split,discretize};
for i=1:M
if(N==N1), data1=data; hs1=hs; dWts1=dWts; else
d=wswor(dWts,N1,4); data1=data(d,:); hs1=hs(d);
dWts1=dWts(d); dWts1=dWts1/sum(dWts1);
end
tree = treeTrain(data1,hs1,dWts1,prmTree);
if(i==1), forest=tree(ones(M,1)); else forest(i)=tree; end
end
end
function tree = treeTrain( data, hs, dWts, prmTree )
% Train single random tree.
[H,F1,minCount,minChild,maxDepth,fWts,split,discretize]=deal(prmTree{:});
N=size(data,1); K=2*N-1; discr=~isempty(discretize);
thrs=zeros(K,1,'single'); distr=zeros(K,H,'single');
fids=zeros(K,1,'uint32'); child=fids; count=fids; depth=fids;
hsn=cell(K,1); dids=cell(K,1); dids{1}=uint32(1:N); k=1; K=2;
while( k < K )
% get node data and store distribution
dids1=dids{k}; dids{k}=[]; hs1=hs(dids1); n1=length(hs1); count(k)=n1;
if(discr), [hs1,hsn{k}]=feval(discretize,hs1,H); hs1=uint32(hs1); end
if(discr), assert(all(hs1>0 & hs1<=H)); end; pure=all(hs1(1)==hs1);
if(~discr), if(pure), distr(k,hs1(1))=1; hsn{k}=hs1(1); else
distr(k,:)=histc(hs1,1:H)/n1; [~,hsn{k}]=max(distr(k,:)); end; end
% if pure node or insufficient data don't train split
if( pure || n1<=minCount || depth(k)>maxDepth ), k=k+1; continue; end
% train split and continue
fids1=wswor(fWts,F1,4); data1=data(dids1,fids1);
[~,order1]=sort(data1); order1=uint32(order1-1);
[fid,thr,gain]=forestFindThr(data1,hs1,dWts(dids1),order1,H,split);
fid=fids1(fid); left=data(dids1,fid)<thr; count0=nnz(left);
if( gain>1e-10 && count0>=minChild && (n1-count0)>=minChild )
child(k)=K; fids(k)=fid-1; thrs(k)=thr;
dids{K}=dids1(left); dids{K+1}=dids1(~left);
depth(K:K+1)=depth(k)+1; K=K+2;
end; k=k+1;
end
% create output model struct
K=1:K-1; if(discr), hsn={hsn(K)}; else hsn=[hsn{K}]'; end
tree=struct('fids',fids(K),'thrs',thrs(K),'child',child(K),...
'distr',distr(K,:),'hs',hsn,'count',count(K),'depth',depth(K));
end
function ids = wswor( prob, N, trials )
% Fast weighted sample without replacement. Alternative to:
% ids=datasample(1:length(prob),N,'weights',prob,'replace',false);
M=length(prob); assert(N<=M); if(N==M), ids=1:N; return; end
if(all(prob(1)==prob)), ids=randperm(M,N); return; end
cumprob=min([0 cumsum(prob)],1); assert(abs(cumprob(end)-1)<.01);
cumprob(end)=1; [~,ids]=histc(rand(N*trials,1),cumprob);
[s,ord]=sort(ids); K(ord)=[1; diff(s)]~=0; ids=ids(K);
if(length(ids)<N), ids=wswor(cumprob,N,trials*2); end
ids=ids(1:N)';
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
fernsRegTrain.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/classify/fernsRegTrain.m
| 5,914 |
utf_8
|
b9ed2d87a22cb9cbb1e2632495ddaf1d
|
function [ferns,ysPr] = fernsRegTrain( data, ys, varargin )
% Train boosted fern regressor.
%
% Boosted regression using random ferns as the weak regressor. See "Greedy
% function approximation: A gradient boosting machine", Friedman, Annals of
% Statistics 2001, for more details on boosted regression.
%
% A few notes on the parameters: 'type' should in general be set to 'res'
% (the 'ave' version is an undocumented variant that only performs well
% under limited conditions). 'loss' determines the loss function being
% optimized, in general the 'L2' version is the most robust and effective.
% 'reg' is a regularization term for the ferns, a low value such as .01 can
% improve results. Setting the learning rate 'eta' is crucial in order to
% achieve good performance, especially on noisy data. In general, eta
% should decreased as M is increased.
%
% Dimensions:
% M - number ferns
% R - number repeats
% S - fern depth
% N - number samples
% F - number features
%
% USAGE
% [ferns,ysPr] = fernsRegTrain( data, hs, [varargin] )
%
% INPUTS
% data - [NxF] N length F feature vectors
% ys - [Nx1] target output values
% varargin - additional params (struct or name/value pairs)
% .type - ['res'] options include {'res','ave'}
% .loss - ['L2'] options include {'L1','L2','exp'}
% .S - [2] fern depth (ferns are exponential in S)
% .M - [50] number ferns (same as number phases)
% .R - [10] number repetitions per fern
% .thrr - [0 1] range for randomly generated thresholds
% .reg - [0.01] fern regularization term in [0,1]
% .eta - [1] learning rate in [0,1] (not used if type='ave')
% .verbose - [0] if true output info to display
%
% OUTPUTS
% ferns - learned fern model w the following fields
% .fids - [MxS] feature ids for each fern for each depth
% .thrs - [MxS] threshold corresponding to each fid
% .ysFern - [2^SxM] stored values at fern leaves
% .loss - loss(ys,ysGt) computes loss of ys relateive to ysGt
% ysPr - [Nx1] predicted output values
%
% EXAMPLE
% %% generate toy data
% N=1000; sig=.5; f=@(x) cos(x*pi*4)+(x+1).^2;
% xs0=rand(N,1); ys0=f(xs0)+randn(N,1)*sig;
% xs1=rand(N,1); ys1=f(xs1)+randn(N,1)*sig;
% %% train and apply fern regressor
% prm=struct('type','res','loss','L2','eta',.05,...
% 'thrr',[-1 1],'reg',.01,'S',2,'M',1000,'R',3,'verbose',0);
% tic, [ferns,ysPr0] = fernsRegTrain(xs0,ys0,prm); toc
% tic, ysPr1 = fernsRegApply( xs1, ferns ); toc
% fprintf('errors train=%f test=%f\n',...
% ferns.loss(ysPr0,ys0),ferns.loss(ysPr1,ys1));
% %% visualize results
% figure(1); clf; hold on; plot(xs0,ys0,'.b'); plot(xs0,ysPr0,'.r');
% figure(2); clf; hold on; plot(xs1,ys1,'.b'); plot(xs1,ysPr1,'.r');
%
% See also fernsRegApply, fernsInds
%
% Piotr's Computer Vision Matlab Toolbox Version 2.50
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
% get/check parameters
dfs={'type','res','loss','L2','S',2,'M',50,'R',10,'thrr',[0 1],...
'reg',0.01,'eta',1,'verbose',0};
[type,loss,S,M,R,thrr,reg,eta,verbose]=getPrmDflt(varargin,dfs,1);
type=type(1:3); assert(any(strcmp(type,{'res','ave'})));
assert(any(strcmp(loss,{'L1','L2','exp'}))); N=length(ys);
if(strcmp(type,'ave')), eta=1; end
% train stagewise regressor (residual or average)
fids=zeros(M,S,'uint32'); thrs=zeros(M,S);
ysSum=zeros(N,1); ysFern=zeros(2^S,M);
for m=1:M
% train R random ferns using different losses, keep best
if(strcmp(type,'ave')), d=m; else d=1; end
ysTar=d*ys-ysSum; best={};
if(strcmp(loss,'L1')), e=sum(abs(ysTar));
for r=1:R
[fids1,thrs1,ysFern1,ys1]=trainFern(data,sign(ysTar),S,thrr,reg);
a=medianw(ysTar./ys1,abs(ys1)); ysFern1=ysFern1*a; ys1=ys1*a;
e1=sum(abs(ysTar-ys1));
if(e1<=e), e=e1; best={fids1,thrs1,ysFern1,ys1}; end
end
elseif(strcmp(loss,'L2')), e=sum(ysTar.^2);
for r=1:R
[fids1,thrs1,ysFern1,ys1]=trainFern(data,ysTar,S,thrr,reg);
e1=sum((ysTar-ys1).^2);
if(e1<=e), e=e1; best={fids1,thrs1,ysFern1,ys1}; end
end
elseif(strcmp(loss,'exp')), e=sum(exp(ysTar/d)+exp(-ysTar/d));
ysDeriv=exp(ysTar/d)-exp(-ysTar/d);
for r=1:R
[fids1,thrs1,ysFern1,ys1]=trainFern(data,ysDeriv,S,thrr,reg);
e1=inf; if(m==1), aBst=1; end; aMin=aBst/5; aMax=aBst*5;
for phase=1:3, aDel=(aMax-aMin)/10;
for a=aMin:aDel:aMax
eTmp=sum(exp((ysTar-a*ys1)/d)+exp((a*ys1-ysTar)/d));
if(eTmp<e1), a1=a; e1=eTmp; end
end; aMin=a1-aDel; aMax=a1+aDel;
end; ysFern1=ysFern1*a1; ys1=ys1*a1;
if(e1<=e), e=e1; aBst=a1; best={fids1,thrs1,ysFern1,ys1}; end
end
end
% store results and update sums
assert(~isempty(best)); [fids1,thrs1,ysFern1,ys1]=deal(best{:});
fids(m,:)=fids1; thrs(m,:)=thrs1;
ysFern(:,m)=ysFern1*eta; ysSum=ysSum+ys1*eta;
if(verbose), fprintf('phase=%i error=%f\n',m,e); end
end
% create output struct
if(strcmp(type,'ave')), d=M; else d=1; end; clear data;
ferns=struct('fids',fids,'thrs',thrs,'ysFern',ysFern/d); ysPr=ysSum/d;
switch loss
case 'L1', ferns.loss=@(ys,ysGt) mean(abs(ys-ysGt));
case 'L2', ferns.loss=@(ys,ysGt) mean((ys-ysGt).^2);
case 'exp', ferns.loss=@(ys,ysGt) mean(exp(ys-ysGt)+exp(ysGt-ys))-2;
end
end
function [fids,thrs,ysFern,ysPr] = trainFern( data, ys, S, thrr, reg )
% Train single random fern regressor.
[N,F]=size(data); mu=sum(ys)/N; ys=ys-mu;
fids = uint32(floor(rand(1,S)*F+1));
thrs = rand(1,S)*(thrr(2)-thrr(1))+thrr(1);
inds = fernsInds(data,fids,thrs);
ysFern=zeros(2^S,1); cnts=zeros(2^S,1);
for n=1:N, ind=inds(n);
ysFern(ind)=ysFern(ind)+ys(n);
cnts(ind)=cnts(ind)+1;
end
ysFern = ysFern ./ max(cnts+reg*N,eps) + mu;
ysPr = ysFern(inds);
end
function m = medianw(x,w)
% Compute weighted median of x.
[x,ord]=sort(x(:)); w=w(ord);
[~,ind]=max(cumsum(w)>=sum(w)/2);
m = x(ind);
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
rbfDemo.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/classify/rbfDemo.m
| 2,929 |
utf_8
|
14cc64fb77bcac3edec51cf6b84ab681
|
function rbfDemo( dataType, noiseSig, scale, k, cluster, show )
% Demonstration of rbf networks for regression.
%
% See rbfComputeBasis for discussion of rbfs.
%
% USAGE
% rbfDemo( dataType, noiseSig, scale, k, cluster, show )
%
% INPUTS
% dataType - 0: 1D sinusoid
% 1: 2D sinusoid
% 2: 2D stretched sinusoid
% noiseSig - std of idd gaussian noise
% scale - see rbfComputeBasis
% k - see rbfComputeBasis
% cluster - see rbfComputeBasis
% show - figure to use for display (no display if == 0)
%
% OUTPUTS
%
% EXAMPLE
% rbfDemo( 0, .2, 2, 5, 0, 1 );
% rbfDemo( 1, .2, 2, 50, 0, 3 );
% rbfDemo( 2, .2, 5, 50, 0, 5 );
%
% See also RBFCOMPUTEBASIS, RBFCOMPUTEFTRS
%
% Piotr's Computer Vision Matlab Toolbox Version 2.0
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
%%% generate trn/tst data
if( 1 )
[Xtrn,ytrn] = rbfToyData( 500, noiseSig, dataType );
[Xtst,ytst] = rbfToyData( 100, noiseSig, dataType );
end;
%%% trn/apply rbfs
rbfBasis = rbfComputeBasis( Xtrn, k, cluster, scale, show );
rbfWeight = rbfComputeFtrs(Xtrn,rbfBasis) \ ytrn;
yTrnRes = rbfComputeFtrs(Xtrn,rbfBasis) * rbfWeight;
yTstRes = rbfComputeFtrs(Xtst,rbfBasis) * rbfWeight;
%%% get relative errors
fracErrorTrn = sum((ytrn-yTrnRes).^2) / sum(ytrn.^2);
fracErrorTst = sum((ytst-yTstRes).^2) / sum(ytst.^2);
%%% display output
display(fracErrorTst);
display(fracErrorTrn);
display(rbfBasis);
%%% visualize surface
minX = min([Xtrn; Xtst],[],1); maxX = max([Xtrn; Xtst],[],1);
if( size(Xtrn,2)==1 )
xs = linspace( minX, maxX, 1000 )';
ys = rbfComputeFtrs(xs,rbfBasis) * rbfWeight;
figure(show+1); clf; hold on; plot( xs, ys );
plot( Xtrn, ytrn, '.b' ); plot( Xtst, ytst, '.r' );
elseif( size(Xtrn,2)==2 )
xs1 = linspace(minX(1),maxX(1),25);
xs2 = linspace(minX(2),maxX(2),25);
[xs1,xs2] = ndgrid( xs1, xs2 );
ys = rbfComputeFtrs([xs1(:) xs2(:)],rbfBasis) * rbfWeight;
figure(show+1); clf; surf( xs1, xs2, reshape(ys,size(xs1)) ); hold on;
plot3( Xtrn(:,1), Xtrn(:,2), ytrn, '.b' );
plot3( Xtst(:,1), Xtst(:,2), ytst, '.r' );
end
function [X,y] = rbfToyData( N, noiseSig, dataType )
% Toy data for rbfDemo.
%
% USAGE
% [X,y] = rbfToyData( N, noiseSig, dataType )
%
% INPUTS
% N - number of points
% dataType - 0: 1D sinusoid
% 1: 2D sinusoid
% 2: 2D stretched sinusoid
% noiseSig - std of idd gaussian noise
%
% OUTPUTS
% X - [N x d] N points of d dimensions each
% y - [1 x N] value at example i
%%% generate data
if( dataType==0 )
X = rand( N, 1 ) * 10;
y = sin( X );
elseif( dataType==1 )
X = rand( N, 2 ) * 10;
y = sin( X(:,1)+X(:,2) );
elseif( dataType==2 )
X = rand( N, 2 ) * 10;
y = sin( X(:,1)+X(:,2) );
X(:,2) = X(:,2) * 5;
else
error('unknown dataType');
end
y = y + randn(size(y))*noiseSig;
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
pdist2.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/classify/pdist2.m
| 5,162 |
utf_8
|
768ff9e8818251f756c8325368ee7d90
|
function D = pdist2( X, Y, metric )
% Calculates the distance between sets of vectors.
%
% Let X be an m-by-p matrix representing m points in p-dimensional space
% and Y be an n-by-p matrix representing another set of points in the same
% space. This function computes the m-by-n distance matrix D where D(i,j)
% is the distance between X(i,:) and Y(j,:). This function has been
% optimized where possible, with most of the distance computations
% requiring few or no loops.
%
% The metric can be one of the following:
%
% 'euclidean' / 'sqeuclidean':
% Euclidean / SQUARED Euclidean distance. Note that 'sqeuclidean'
% is significantly faster.
%
% 'chisq'
% The chi-squared distance between two vectors is defined as:
% d(x,y) = sum( (xi-yi)^2 / (xi+yi) ) / 2;
% The chi-squared distance is useful when comparing histograms.
%
% 'cosine'
% Distance is defined as the cosine of the angle between two vectors.
%
% 'emd'
% Earth Mover's Distance (EMD) between positive vectors (histograms).
% Note for 1D, with all histograms having equal weight, there is a simple
% closed form for the calculation of the EMD. The EMD between histograms
% x and y is given by the sum(abs(cdf(x)-cdf(y))), where cdf is the
% cumulative distribution function (computed simply by cumsum).
%
% 'L1'
% The L1 distance between two vectors is defined as: sum(abs(x-y));
%
%
% USAGE
% D = pdist2( X, Y, [metric] )
%
% INPUTS
% X - [m x p] matrix of m p-dimensional vectors
% Y - [n x p] matrix of n p-dimensional vectors
% metric - ['sqeuclidean'], 'chisq', 'cosine', 'emd', 'euclidean', 'L1'
%
% OUTPUTS
% D - [m x n] distance matrix
%
% EXAMPLE
% % simple example where points cluster well
% [X,IDX] = demoGenData(100,0,5,4,10,2,0);
% D = pdist2( X, X, 'sqeuclidean' );
% distMatrixShow( D, IDX );
% % comparison to pdist
% n=500; d=200; r=100; X=rand(n,d);
% tic, for i=1:r, D1 = pdist( X, 'euclidean' ); end, toc
% tic, for i=1:r, D2 = pdist2( X, X, 'euclidean' ); end, toc
% D1=squareform(D1); del=D1-D2; sum(abs(del(:)))
%
% See also pdist, distMatrixShow
%
% Piotr's Computer Vision Matlab Toolbox Version 2.52
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
if( nargin<3 || isempty(metric) ); metric=0; end;
switch metric
case {0,'sqeuclidean'}
D = distEucSq( X, Y );
case 'euclidean'
D = sqrt(distEucSq( X, Y ));
case 'L1'
D = distL1( X, Y );
case 'cosine'
D = distCosine( X, Y );
case 'emd'
D = distEmd( X, Y );
case 'chisq'
D = distChiSq( X, Y );
otherwise
error(['pdist2 - unknown metric: ' metric]);
end
D = max(0,D);
end
function D = distL1( X, Y )
m = size(X,1); n = size(Y,1);
mOnes = ones(1,m); D = zeros(m,n);
for i=1:n
yi = Y(i,:); yi = yi( mOnes, : );
D(:,i) = sum( abs( X-yi),2 );
end
end
function D = distCosine( X, Y )
p=size(X,2);
XX = sqrt(sum(X.*X,2)); X = X ./ XX(:,ones(1,p));
YY = sqrt(sum(Y.*Y,2)); Y = Y ./ YY(:,ones(1,p));
D = 1 - X*Y';
end
function D = distEmd( X, Y )
Xcdf = cumsum(X,2);
Ycdf = cumsum(Y,2);
m = size(X,1); n = size(Y,1);
mOnes = ones(1,m); D = zeros(m,n);
for i=1:n
ycdf = Ycdf(i,:);
ycdfRep = ycdf( mOnes, : );
D(:,i) = sum(abs(Xcdf - ycdfRep),2);
end
end
function D = distChiSq( X, Y )
% note: supposedly it's possible to implement this without a loop!
m = size(X,1); n = size(Y,1);
mOnes = ones(1,m); D = zeros(m,n);
for i=1:n
yi = Y(i,:); yiRep = yi( mOnes, : );
s = yiRep + X; d = yiRep - X;
D(:,i) = sum( d.^2 ./ (s+eps), 2 );
end
D = D/2;
end
function D = distEucSq( X, Y )
Yt = Y';
XX = sum(X.*X,2);
YY = sum(Yt.*Yt,1);
D = bsxfun(@plus,XX,YY)-2*X*Yt;
end
%%%% code from Charles Elkan with variables renamed
% function D = distEucSq( X, Y )
% m = size(X,1); n = size(Y,1);
% D = sum(X.^2, 2) * ones(1,n) + ones(m,1) * sum(Y.^2, 2)' - 2.*X*Y';
% end
%%% LOOP METHOD - SLOW
% [m p] = size(X);
% [n p] = size(Y);
% D = zeros(m,n);
% onesM = ones(m,1);
% for i=1:n
% y = Y(i,:);
% d = X - y(onesM,:);
% D(:,i) = sum( d.*d, 2 );
% end
%%% PARALLEL METHOD THAT IS SUPER SLOW (slower than loop)!
% % From "MATLAB array manipulation tips and tricks" by Peter J. Acklam
% Xb = permute(X, [1 3 2]);
% Yb = permute(Y, [3 1 2]);
% D = sum( (Xb(:,ones(1,n),:) - Yb(ones(1,m),:,:)).^2, 3);
%%% USELESS FOR EVEN VERY LARGE ARRAYS X=16000x1000!! and Y=100x1000
% call recursively to save memory
% if( (m+n)*p > 10^5 && (m>1 || n>1))
% if( m>n )
% X1 = X(1:floor(end/2),:);
% X2 = X((floor(end/2)+1):end,:);
% D1 = distEucSq( X1, Y );
% D2 = distEucSq( X2, Y );
% D = cat( 1, D1, D2 );
% else
% Y1 = Y(1:floor(end/2),:);
% Y2 = Y((floor(end/2)+1):end,:);
% D1 = distEucSq( X, Y1 );
% D2 = distEucSq( X, Y2 );
% D = cat( 2, D1, D2 );
% end
% return;
% end
%%% L1 COMPUTATION WITH LOOP OVER p, FAST FOR SMALL p.
% function D = distL1( X, Y )
%
% m = size(X,1); n = size(Y,1); p = size(X,2);
% mOnes = ones(1,m); nOnes = ones(1,n); D = zeros(m,n);
% for i=1:p
% yi = Y(:,i); yi = yi( :, mOnes );
% xi = X(:,i); xi = xi( :, nOnes );
% D = D + abs( xi-yi' );
% end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
pca.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/classify/pca.m
| 3,244 |
utf_8
|
848f2eb05c18a6e448e9d22af27b9422
|
function [U,mu,vars] = pca( X )
% Principal components analysis (alternative to princomp).
%
% A simple linear dimensionality reduction technique. Use to create an
% orthonormal basis for the points in R^d such that the coordinates of a
% vector x in this basis are of decreasing importance. Instead of using all
% d basis vectors to specify the location of x, using only the first k<d
% still gives a vector xhat that is close to x.
%
% This function operates on arrays of arbitrary dimension, by first
% converting the arrays to vectors. If X is m+1 dimensional, say of size
% [d1 x d2 x...x dm x n], then the first m dimensions of X are combined. X
% is flattened to be 2 dimensional: [dxn], with d=prod(di). Once X is
% converted to 2 dimensions of size dxn, each column represents a single
% observation, and each row is a different variable. Note that this is the
% opposite of many matlab functions such as princomp. If X is MxNxn, then
% X(:,:,i) represents the ith observation (useful for stack of n images),
% likewise for n videos X is MxNxKxn. If X is very large, it is sampled
% before running PCA. Use this function to retrieve the basis U. Use
% pcaApply to retrieve that basis coefficients for a novel vector x. Use
% pcaVisualize(X,...) for visualization of approximated X.
%
% To calculate residuals:
% residuals = cumsum(vars/sum(vars)); plot(residuals,'-.')
%
% USAGE
% [U,mu,vars] = pca( X )
%
% INPUTS
% X - [d1 x ... x dm x n], treated as n [d1 x ... x dm] elements
%
% OUTPUTS
% U - [d x r], d=prod(di), each column is a principal component
% mu - [d1 x ... x dm] mean of X
% vars - sorted eigenvalues corresponding to eigenvectors in U
%
% EXAMPLE
% load pcaData;
% [U,mu,vars] = pca( I3D1(:,:,1:12) );
% [Y,Xhat,avsq] = pcaApply( I3D1(:,:,1), U, mu, 5 );
% pcaVisualize( U, mu, vars, I3D1, 13, [0:12], [], 1 );
% Xr = pcaRandVec( U, mu, vars, 1, 25, 0, 3 );
%
% See also princomp, pcaApply, pcaVisualize, pcaRandVec, visualizeData
%
% Piotr's Computer Vision Matlab Toolbox Version 3.24
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
% set X to be zero mean, then flatten
d=size(X); n=d(end); d=prod(d(1:end-1));
if(~isa(X,'double')), X=double(X); end
if(n==1); mu=X; U=zeros(d,1); vars=0; return; end
mu = mean( X, ndims(X) );
X = bsxfun(@minus,X,mu)/sqrt(n-1);
X = reshape( X, d, n );
% make sure X not too large or SVD slow O(min(d,n)^2.5)
m=2500; if( min(d,n)>m ), X=X(:,randperm(n,m)); n=m; end
% get principal components using the SVD of X: X=U*S*V'
if( 0 )
[U,S]=svd(X,'econ'); vars=diag(S).^2;
elseif( d>n )
[~,SS,V]=robustSvd(X'*X); vars=diag(SS);
U = X * V * diag(1./sqrt(vars));
else
[~,SS,U]=robustSvd(X*X'); vars=diag(SS);
end
% discard low variance prinicipal components
K=vars>1e-30; vars=vars(K); U=U(:,K);
end
function [U,S,V] = robustSvd( X, trials )
% Robust version of SVD more likely to always converge.
% [Converge issues only seem to appear on Matlab 2013a in Windows.]
if(nargin<2), trials=100; end
try [U,S,V] = svd(X); catch
if(trials<=0), error('svd did not converge'); end
n=numel(X); j=randi(n); X(j)=X(j)+eps;
[U,S,V]=robustSvd(X,trials-1);
end
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
kmeans2.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/classify/kmeans2.m
| 5,251 |
utf_8
|
f941053f03c3e9eda40389a4cc64ee00
|
function [ IDX, C, d ] = kmeans2( X, k, varargin )
% Fast version of kmeans clustering.
%
% Cluster the N x p matrix X into k clusters using the kmeans algorithm. It
% returns the cluster memberships for each data point in the N x 1 vector
% IDX and the K x p matrix of cluster means in C.
%
% This function is in some ways less general than Matlab's kmeans.m (for
% example it only uses euclidian distance), but it has some options that
% the Matlab version does not (for example, it has a notion of outliers and
% min-cluster size). It is also many times faster than matlab's kmeans.
% General kmeans help can be found in help for the matlab implementation of
% kmeans. Note that the although the names and conventions for this
% algorithm are taken from Matlab's implementation, there are slight
% alterations (for example, IDX==-1 is used to indicate outliers).
%
% IDX is a n-by-1 vector used to indicated cluster membership. Let X be a
% set of n points. Then the ID of X - or IDX is a column vector of length
% n, where each element is an integer indicating the cluster membership of
% the corresponding element in X. IDX(i)=c indicates that the ith point in
% X belongs to cluster c. Cluster labels range from 1 to k, and thus
% k=max(IDX) is typically the number of clusters IDX divides X into. The
% cluster label "-1" is reserved for outliers. IDX(i)==-1 indicates that
% the given point does not belong to any of the discovered clusters. Note
% that matlab's version of kmeans does not have outliers.
%
% USAGE
% [ IDX, C, d ] = kmeans2( X, k, [varargin] )
%
% INPUTS
% X - [n x p] matrix of n p-dim vectors.
% k - maximum nuber of clusters (actual number may be smaller)
% prm - additional params (struct or name/value pairs)
% .k - [] alternate way of specifying k (if not given above)
% .nTrial - [1] number random restarts
% .maxIter - [100] max number of iterations
% .display - [0] Whether or not to display algorithm status
% .rndSeed - [] random seed for kmeans; useful for replicability
% .outFrac - [0] max frac points that can be treated as outliers
% .minCl - [1] min cluster size (smaller clusters get eliminated)
% .metric - [] metric for pdist2
% .C0 - [] initial cluster centers for first trial
%
% OUTPUTS
% IDX - [n x 1] cluster membership (see above)
% C - [k x p] matrix of centroid locations C(j,:) = mean(X(IDX==j,:))
% d - [1 x k] d(j) is sum of distances from X(IDX==j,:) to C(j,:)
% sum(d) is a typical measure of the quality of a clustering
%
% EXAMPLE
%
% See also DEMOCLUSTER
%
% Piotr's Computer Vision Matlab Toolbox Version 3.24
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
% get input args
dfs = {'nTrial',1, 'maxIter',100, 'display',0, 'rndSeed',[],...
'outFrac',0, 'minCl',1, 'metric',[], 'C0',[],'k',k };
[nTrial,maxt,dsp,rndSeed,outFrac,minCl,metric,C0,k] = ...
getPrmDflt(varargin,dfs); assert(~isempty(k) && k>0);
% error checking
if(k<1); error('k must be greater than 1'); end
if(~ismatrix(X) || any(size(X)==0)); error('Illegal X'); end
if(outFrac<0 || outFrac>=1), error('outFrac must be in [0,1)'); end
nOut = floor( size(X,1)*outFrac );
% initialize random seed if specified
if(~isempty(rndSeed)); rand('state',rndSeed); end; %#ok<RAND>
% run kmeans2main nTrial times
bd=inf; t0=clock;
for i=1:nTrial, t1=clock; if(i>1), C0=[]; end
if(dsp), fprintf('kmeans2 iter %i/%i step: ',i,nTrial); end
[IDX,C,d]=kmeans2main(X,k,nOut,minCl,maxt,dsp,metric,C0);
if(sum(d)<sum(bd)), bIDX=IDX; bC=C; bd=d; end
if(dsp), fprintf(' d=%f t=%fs\n',sum(d),etime(clock,t1)); end
end
IDX=bIDX; C=bC; d=bd; k=max(IDX);
if(dsp), fprintf('k=%i d=%f t=%fs\n',k,sum(d),etime(clock,t0)); end
% sort IDX to have biggest clusters have lower indicies
cnts = zeros(1,k); for i=1:k; cnts(i) = sum( IDX==i ); end
[~,order] = sort( -cnts ); C = C(order,:); d = d(order);
IDX2=IDX; for i=1:k; IDX2(IDX==order(i))=i; end; IDX = IDX2;
end
function [IDX,C,d] = kmeans2main( X, k, nOut, minCl, maxt, dsp, metric, C )
% initialize cluster centers to be k random X points
[N,p] = size(X); k = min(k,N); t=0;
IDX = ones(N,1); oldIDX = zeros(N,1);
if(isempty(C)), C = X(randperm(N,k),:)+randn(k,p)/1e5; end
% MAIN LOOP: loop until the cluster assigments do not change
if(dsp), nDg=ceil(log10(maxt-1)); fprintf(int2str2(0,nDg)); end
while( any(oldIDX~=IDX) && t<maxt )
% assign each point to closest cluster center
oldIDX=IDX; D=pdist2(X,C,metric); [mind,IDX]=min(D,[],2);
% do not use most distant nOut elements in computation of centers
mind1=sort(mind); thr=mind1(end-nOut); IDX(mind>thr)=-1;
% Recalculate means based on new assignment, discard small clusters
k0=0; C=zeros(k,p);
for IDx=1:k
ids=find(IDX==IDx); nCl=size(ids,1);
if( nCl<minCl ), IDX(ids)=-1; continue; end
k0=k0+1; IDX(ids)=k0; C(k0,:)=sum(X(ids,:),1)/nCl;
end
if(k0>0), k=k0; C=C(1:k,:); else k=1; C=X(randint2(1,1,[1 N]),:); end
t=t+1; if(dsp), fprintf([repmat('\b',[1 nDg]) int2str2(t,nDg)]); end
end
% record within-cluster sums of point-to-centroid distances
d=zeros(1,k); for i=1:k, d(i)=sum(mind(IDX==i)); end
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
acfModify.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/detector/acfModify.m
| 4,202 |
utf_8
|
7a49406d51e7a9431b8fd472be0476e8
|
function detector = acfModify( detector, varargin )
% Modify aggregate channel features object detector.
%
% Takes an object detector trained by acfTrain() and modifies it. Only
% certain modifications are allowed to the detector and the detector should
% never be modified directly (this may cause the detector to be invalid and
% cause segmentation faults). Any valid modification to a detector after it
% is trained should be performed using acfModify().
%
% The parameters 'nPerOct', 'nOctUp', 'nApprox', 'lambdas', 'pad', 'minDs'
% modify the channel feature pyramid created (see help of chnsPyramid.m for
% more details) and primarily control the scales used. The parameters
% 'pNms', 'stride', 'cascThr' and 'cascCal' modify the detector behavior
% (see help of acfTrain.m for more details). Finally, 'rescale' can be
% used to rescale the trained detector (this change is irreversible).
%
% USAGE
% detector = acfModify( detector, pModify )
%
% INPUTS
% detector - detector trained via acfTrain
% pModify - parameters (struct or name/value pairs)
% .nPerOct - [] number of scales per octave
% .nOctUp - [] number of upsampled octaves to compute
% .nApprox - [] number of approx. scales to use
% .lambdas - [] coefficients for power law scaling (see BMVC10)
% .pad - [] amount to pad channels (along T/B and L/R)
% .minDs - [] minimum image size for channel computation
% .pNms - [] params for non-maximal suppression (see bbNms.m)
% .stride - [] spatial stride between detection windows
% .cascThr - [] constant cascade threshold (affects speed/accuracy)
% .cascCal - [] cascade calibration (affects speed/accuracy)
% .rescale - [] rescale entire detector by given ratio
%
% OUTPUTS
% detector - modified object detector
%
% EXAMPLE
%
% See also chnsPyramid, bbNms, acfTrain, acfDetect
%
% Piotr's Computer Vision Matlab Toolbox Version 3.20
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
% get parameters (and copy to detector and pPyramid structs)
opts=detector.opts; p=opts.pPyramid;
dfs={ 'nPerOct',p.nPerOct, 'nOctUp',p.nOctUp, 'nApprox',p.nApprox, ...
'lambdas',p.lambdas, 'pad',p.pad, 'minDs',p.minDs, 'pNms',opts.pNms, ...
'stride',opts.stride,'cascThr',opts.cascThr,'cascCal',0,'rescale',1 };
[p.nPerOct,p.nOctUp,p.nApprox,p.lambdas,p.pad,p.minDs,opts.pNms,...
opts.stride,opts.cascThr,cascCal,rescale] = getPrmDflt(varargin,dfs,1);
% finalize pPyramid and opts
p.complete=0; p.pChns.complete=0; p=chnsPyramid([],p); p=p.pPyramid;
p.complete=1; p.pChns.complete=1; shrink=p.pChns.shrink;
opts.stride=max(1,round(opts.stride/shrink))*shrink;
opts.pPyramid=p; detector.opts=opts;
% calibrate and rescale detector
detector.clf.hs = detector.clf.hs+cascCal;
if(rescale~=1), detector=detectorRescale(detector,rescale); end
end
function detector = detectorRescale( detector, rescale )
% Rescale detector by ratio rescale.
opts=detector.opts; shrink=opts.pPyramid.pChns.shrink;
bh=opts.modelDsPad(1)/shrink; bw=opts.modelDsPad(2)/shrink;
opts.stride=max(1,round(opts.stride*rescale/shrink))*shrink;
modelDsPad=round(opts.modelDsPad*rescale/shrink)*shrink;
rescale=modelDsPad./opts.modelDsPad; opts.modelDsPad=modelDsPad;
opts.modelDs=round(opts.modelDs.*rescale); detector.opts=opts;
bh1=opts.modelDsPad(1)/shrink; bw1=opts.modelDsPad(2)/shrink;
% move 0-indexed (x,y) location of each lookup feature
clf=detector.clf; fids=clf.fids; is=find(clf.child>0);
fids=double(fids(is)); n=length(fids); loc=zeros(n,3);
loc(:,3)=floor(fids/bh/bw); fids=fids-loc(:,3)*bh*bw;
loc(:,2)=floor(fids/bh); fids=fids-loc(:,2)*bh; loc(:,1)=fids;
loc(:,1)=min(bh1-1,round(loc(:,1)*rescale(1)));
loc(:,2)=min(bw1-1,round(loc(:,2)*rescale(2)));
fids = loc(:,3)*bh1*bw1 + loc(:,2)*bh1 + loc(:,1);
clf.fids(is)=int32(fids);
% rescale thrs for all features (fpdw trick)
nChns=[detector.info.nChns]; assert(max(loc(:,3))<sum(nChns));
k=[]; for i=1:length(nChns), k=[k ones(1,nChns(i))*i]; end %#ok<AGROW>
lambdas=opts.pPyramid.lambdas; lambdas=sqrt(prod(rescale)).^-lambdas(k);
clf.thrs(is)=clf.thrs(is).*lambdas(loc(:,3)+1)'; detector.clf=clf;
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
acfDetect.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/detector/acfDetect.m
| 3,659 |
utf_8
|
cf1384311b16371be6fa4715140e5c81
|
function bbs = acfDetect( I, detector, fileName )
% Run aggregate channel features object detector on given image(s).
%
% The input 'I' can either be a single image (or filename) or a cell array
% of images (or filenames). In the first case, the return is a set of bbs
% where each row has the format [x y w h score] and score is the confidence
% of detection. If the input is a cell array, the output is a cell array
% where each element is a set of bbs in the form above (in this case a
% parfor loop is used to speed execution). If 'fileName' is specified, the
% bbs are saved to a comma separated text file and the output is set to
% bbs=1. If saving detections for multiple images the output is stored in
% the format [imgId x y w h score] and imgId is a one-indexed image id.
%
% A cell of detectors trained with the same channels can be specified,
% detected bbs from each detector are concatenated. If using multiple
% detectors and opts.pNms.separate=1 then each bb has a sixth element
% bbType=j, where j is the j-th detector, see bbNms.m for details.
%
% USAGE
% bbs = acfDetect( I, detector, [fileName] )
%
% INPUTS
% I - input image(s) of filename(s) of input image(s)
% detector - detector(s) trained via acfTrain
% fileName - [] target filename (if specified return is 1)
%
% OUTPUTS
% bbs - [nx5] array of bounding boxes or cell array of bbs
%
% EXAMPLE
%
% See also acfTrain, acfModify, bbGt>loadAll, bbNms
%
% Piotr's Computer Vision Matlab Toolbox Version 3.40
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
% run detector on every image
if(nargin<3), fileName=''; end; multiple=iscell(I);
if(~isempty(fileName) && exist(fileName,'file')), bbs=1; return; end
if(~multiple), bbs=acfDetectImg(I,detector); else
n=length(I); bbs=cell(n,1);
parfor i=1:n, bbs{i}=acfDetectImg(I{i},detector); end
end
% write results to disk if fileName specified
if(isempty(fileName)), return; end
d=fileparts(fileName); if(~isempty(d)&&~exist(d,'dir')), mkdir(d); end
if( multiple ) % add image index to each bb and flatten result
for i=1:n, bbs{i}=[ones(size(bbs{i},1),1)*i bbs{i}]; end
bbs=cell2mat(bbs);
end
dlmwrite(fileName,bbs); bbs=1;
end
function bbs = acfDetectImg( I, detector )
% Run trained sliding-window object detector on given image.
Ds=detector; if(~iscell(Ds)), Ds={Ds}; end; nDs=length(Ds);
opts=Ds{1}.opts; pPyramid=opts.pPyramid; pNms=opts.pNms;
imreadf=opts.imreadf; imreadp=opts.imreadp;
shrink=pPyramid.pChns.shrink; pad=pPyramid.pad;
separate=nDs>1 && isfield(pNms,'separate') && pNms.separate;
% read image and compute features (including optionally applying filters)
if(all(ischar(I))), I=feval(imreadf,I,imreadp{:}); end
P=chnsPyramid(I,pPyramid); bbs=cell(P.nScales,nDs);
if(isfield(opts,'filters') && ~isempty(opts.filters)), shrink=shrink*2;
for i=1:P.nScales, fs=opts.filters; C=repmat(P.data{i},[1 1 size(fs,4)]);
for j=1:size(C,3), C(:,:,j)=conv2(C(:,:,j),fs(:,:,j),'same'); end
P.data{i}=imResample(C,.5);
end
end
% apply sliding window classifiers
for i=1:P.nScales
for j=1:nDs, opts=Ds{j}.opts;
modelDsPad=opts.modelDsPad; modelDs=opts.modelDs;
bb = acfDetect1(P.data{i},Ds{j}.clf,shrink,...
modelDsPad(1),modelDsPad(2),opts.stride,opts.cascThr);
shift=(modelDsPad-modelDs)/2-pad;
bb(:,1)=(bb(:,1)+shift(2))/P.scaleshw(i,2);
bb(:,2)=(bb(:,2)+shift(1))/P.scaleshw(i,1);
bb(:,3)=modelDs(2)/P.scales(i);
bb(:,4)=modelDs(1)/P.scales(i);
if(separate), bb(:,6)=j; end; bbs{i,j}=bb;
end
end; bbs=cat(1,bbs{:});
if(~isempty(pNms)), bbs=bbNms(bbs,pNms); end
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
acfSweeps.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/detector/acfSweeps.m
| 10,730 |
utf_8
|
78d640ed4b5b62600dd5164118a15408
|
function acfSweeps
% Parameter sweeps for ACF pedestrian detector.
%
% Running the parameter sweeps requires altering internal flags.
% The sweeps are not well documented, use at your own discretion.
%
% Piotr's Computer Vision Matlab Toolbox Version NEW
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
% specify type and location of cluster (see fevalDistr.m)
rtDir=[fileparts(fileparts(fileparts(mfilename('fullpath')))) '/data/'];
pDistr={'type','parfor'}; if(0), matlabpool('open',11); end
% define all parameter sweeps
expNms = {'FtrsColorSpace','FtrsChnTypes','FtrsGradColorChn',...
'FtrsGradNormRad','FtrsGradNormConst','FtrsGradOrients',...
'FtrsGradSoftBins','FtrsSmoothIm','FtrsSmoothChns','FtrsShrink',...
'DetModelDs','DetModelDsPad','DetStride','DetNumOctaves',...
'DetNumApprox','DetLambda','DetCascThr','DetCascCal','DetNmsThr',...
'TrnNumWeak','TrnNumBoot','TrnDepth','TrnNumBins','TrnFracFtrs',...
'DataNumPos','DataNumNeg','DataNumNegAcc','DataNumNegPer',...
'DataNumPosStump','DataJitterTran','DataJitterRot'};
expNms=expNms(:); T = 10;
[opts,lgd,lbl]=createExp(rtDir,expNms);
% run training and testing jobs
[jobsTrn,jobsTst] = createJobs( rtDir, opts, T ); N=length(expNms);
fprintf('nTrain = %i; nTest = %i\n',length(jobsTrn),length(jobsTst));
tic, s=fevalDistr('acfTrain',jobsTrn,pDistr); assert(s==1); toc
tic, s=fevalDistr('acfTest',jobsTst,pDistr); assert(s==1); toc
% create plots for all experiments
for e=1:N, plotExps(rtDir,expNms{e},opts{e},lgd{e},lbl{e},T); end
end
function plotExps( rtDir, expNm, opts, lgd, lbl, T )
% data location and parameters for plotting
plDir=[rtDir 'sweeps/plots/']; if(~exist(plDir,'dir')), mkdir(plDir); end
diary([plDir 'sweeps.txt']); disp([expNm ' [' lbl ']']); N=length(lgd);
pLoad=struct('squarify',{{3,.41}},'hRng',[0 inf]);
pTest=struct('name','', 'imgDir',[rtDir 'Inria/test/pos'],...
'gtDir',[rtDir 'Inria/test/posGt'], 'pLoad',pLoad);
pTest=repmat(pTest,N,T); for e=1:N, for t=1:T,
pTest(e,t).name=[opts(e).name 'T' int2str2(t,2)]; end; end
% get all miss rates and display error
miss=zeros(N,T); parfor e=1:N*T, miss(e)=acfTest(pTest(e)); end
stds=std(miss,0,2); R=mean(miss,2); msg=' %.2f +/- %.2f [%s]\n';
for e=1:N, fprintf(msg,R(e)*100,stds(e)*100,lgd{e}); end
% plot sweeps
figPrp = {'Units','Pixels','Position',[800 600 800 400]};
figure(1); clf; set(1,figPrp{:}); set(gca,'FontSize',24); clr=[0 .69 .94];
pPl1={'LineWidth',3,'MarkerSize',15,'Color',clr,'MarkerFaceColor',clr};
pPl2=pPl1; clr=[1 .75 0]; pPl2{6}=clr; pPl2{8}=clr;
for e=1:N, if(lgd{e}(end)=='*'), def=e; end; end; lgd{def}(end)=[];
plot(R,'-d',pPl1{:}); hold on; plot(def,R(def),'d',pPl2{:}); e=.001;
ylabel('MR'); axis([.5 N+.5 min([R; .15]) max([R; .3])+e]);
if(isempty(lbl)), imLabel(lgd,'bottom',30,{'FontSize',24}); lgd=[]; end
xlabel(lbl); set(gca,'XTick',1:N,'XTickLabel',lgd);
% save plot
fFig=[plDir expNm]; diary('off');
for t=1:25, try savefig(fFig,1,'png'); break; catch, pause(1), end; end
end
function [jobsTrn,jobsTst] = createJobs( rtDir, opts, T )
% Prepare all jobs (one train and one test job per set of opts).
opts=[opts{:}]; N=length(opts); NT=N*T;
opts=repmat(opts,1,T); nms=cell(1,NT);
jobsTrn=cell(1,NT); doneTrn=zeros(1,NT);
jobsTst=cell(1,NT); doneTst=zeros(1,NT);
pLoad=struct('squarify',{{3,.41}},'hRng',[0 inf]);
pTest=struct('name','', 'imgDir',[rtDir 'Inria/test/pos'],...
'gtDir',[rtDir 'Inria/test/posGt'], 'pLoad',pLoad);
for e=1:NT
t=ceil(e/N); opts(e).seed=(t-1)*100000+1;
nm=[opts(e).name 'T' int2str2(t,2)];
opts(e).name=nm; pTest.name=nm; nms{e}=nm;
doneTrn(e)=exist([nm 'Detector.mat'],'file')==2; jobsTrn{e}={opts(e)};
doneTst(e)=exist([nm 'Dets.txt'],'file')==2; jobsTst{e}={pTest};
end
[~,kp]=unique(nms,'stable');
doneTrn=doneTrn(kp); jobsTrn=jobsTrn(kp); jobsTrn=jobsTrn(~doneTrn);
doneTst=doneTst(kp); jobsTst=jobsTst(kp); jobsTst=jobsTst(~doneTst);
end
function [opts,lgd,lbl] = createExp( rtDir, expNm )
% if expNm is a cell, call recursively and return
if( iscell(expNm) )
N=length(expNm); opts=cell(1,N); lgd=cell(1,N); lbl=lgd;
for e=1:N, [opts{e},lgd{e},lbl{e}]=createExp(rtDir,expNm{e}); end; return
end
% default params for detectorTrain.m
dataDir=[rtDir 'Inria/'];
opts=acfTrain(); opts.modelDs=[100 41]; opts.modelDsPad=[128 64];
opts.posGtDir=[dataDir 'train/posGt']; opts.nWeak=[32 128 512 2048];
opts.posImgDir=[dataDir 'train/pos']; opts.pJitter=struct('flip',1);
opts.negImgDir=[dataDir 'train/neg']; opts.pBoost.pTree.fracFtrs=1/16;
if(~exist([rtDir 'sweeps/res/'],'dir')), mkdir([rtDir 'sweeps/res/']); end
opts.pBoost.pTree.nThreads=1;
% setup experiments (N sets of params)
optsDefault=opts; N=100; lgd=cell(1,N); ss=lgd; lbl=''; O=ones(1,N);
pChns=opts.pPyramid.pChns(O); pPyramid=opts.pPyramid(O); opts=opts(O);
switch expNm
case 'FtrsColorSpace'
N=8; clrs={'Gray','rgb','hsv','luv'};
for e=1:N, pChns(e).pColor.colorSpace=clrs{mod(e-1,4)+1}; end
for e=5:N, pChns(e).pGradMag.enabled=0; end
for e=5:N, pChns(e).pGradHist.enabled=0; end
ss=[clrs clrs]; for e=1:4, ss{e}=[ss{e} '+G+H']; end
ss=upper(ss); lgd=ss;
case 'FtrsChnTypes'
nms={'LUV+','G+','H+'}; N=7;
for e=1:N
en=false(1,3); for i=1:3, en(i)=bitget(uint8(e),i); end
pChns(e).pColor.enabled=en(1); pChns(e).pGradMag.enabled=en(2);
pChns(e).pGradHist.enabled=en(3);
nm=[nms{en}]; nm=nm(1:end-1); lgd{e}=nm; ss{e}=nm;
end
case 'FtrsGradColorChn'
lbl='gradient color channel';
N=4; ss={'Max','L','U','V'}; lgd=ss;
for e=1:N, pChns(e).pGradMag.colorChn=e-1; end
case 'FtrsGradNormRad'
lbl='norm radius';
vs=[0 1 2 5 10]; N=length(vs);
for e=1:N, pChns(e).pGradMag.normRad=vs(e); end
case 'FtrsGradNormConst'
lbl='norm constant x 10^3';
vs=[1 2 5 10 20 50 100]; N=length(vs);
for e=1:N, pChns(e).pGradMag.normConst=vs(e)/1000; end
case 'FtrsGradOrients'
lbl='# orientations';
vs=[2 4 6 8 10 12]; N=length(vs);
for e=1:N, pChns(e).pGradHist.nOrients=vs(e); end
case 'FtrsGradSoftBins'
lbl='use soft bins';
vs=[0 1]; N=length(vs);
for e=1:N, pChns(e).pGradHist.softBin=vs(e); end
case 'FtrsSmoothIm'
lbl='image smooth radius';
vs=[0 50 100 200]; N=length(vs);
for e=1:N, pChns(e).pColor.smooth=vs(e)/100; end
for e=1:N, lgd{e}=num2str(vs(e)/100); end
case 'FtrsSmoothChns'
lbl='channel smooth radius';
vs=[0 50 100 200]; N=length(vs);
for e=1:N, pPyramid(e).smooth=vs(e)/100; end
for e=1:N, lgd{e}=num2str(vs(e)/100); end
case 'FtrsShrink'
lbl='channel shrink';
vs=2.^(1:4); N=length(vs);
for e=1:N, pChns(e).shrink=vs(e); end
case 'DetModelDs'
lbl='model height';
rs=1.1.^(-2:2); vs=round(100*rs); ws=round(41*rs); N=length(vs);
for e=1:N, opts(e).modelDs=[vs(e) ws(e)]; end
for e=1:N, opts(e).modelDsPad=opts(e).modelDs+[28 23]; end
case 'DetModelDsPad'
lbl='padded model height';
rs=1.1.^(-2:2); vs=round(128*rs); ws=round(64*rs); N=length(vs);
for e=1:N, opts(e).modelDsPad=[vs(e) ws(e)]; end
case 'DetStride'
lbl='detector stride';
vs=4:4:16; N=length(vs);
for e=1:N, opts(e).stride=vs(e); end
case 'DetNumOctaves'
lbl='# scales per octave';
vs=2.^(0:5); N=length(vs);
for e=1:N, pPyramid(e).nPerOct=vs(e); pPyramid(e).nApprox=vs(e)-1; end
case 'DetNumApprox'
lbl='# approx scales';
vs=2.^(0:5)-1; N=length(vs);
for e=1:N, pPyramid(e).nApprox=vs(e); end
case 'DetLambda'
lbl='lambda x 100';
vs=-45:15:70; N=length(vs);
for e=[1:4 6:N], pPyramid(e).lambdas=[0 vs(e) vs(e)]/100; end
for e=1:N, lgd{e}=int2str(vs(e)); end; vs=vs+100;
case 'DetCascThr'
lbl='cascade threshold';
vs=[-.5 -1 -2 -5 -10]; N=length(vs);
for e=1:N, opts(e).cascThr=vs(e); end
for e=1:N, lgd{e}=num2str(vs(e)); end; vs=vs*-10;
case 'DetCascCal'
lbl='cascade offset x 10^4';
vs=[5 10 20 50 100 200 500]; N=length(vs);
for e=1:N, opts(e).cascCal=vs(e)/1e4; end
case 'DetNmsThr'
lbl='nms overlap';
vs=25:10:95; N=length(vs);
for e=1:N, opts(e).pNms.overlap=vs(e)/1e2; end
for e=1:N, lgd{e}=['.' num2str(vs(e))]; end
case 'TrnNumWeak'
lbl='# decision trees / x';
vs=2.^(0:3); N=length(vs);
for e=1:N, opts(e).nWeak=opts(e).nWeak/vs(e); end
case 'TrnNumBoot'
lbl='bootstrap schedule';
vs={5:1:11,5:2:11,3:1:11,3:2:11}; N=length(vs);
ss={'5-1-11','5-2-11','3-1-11','3-2-11'}; lgd=ss;
for e=1:N, opts(e).nWeak=2.^vs{e}; end
case 'TrnDepth'
lbl='tree depth';
vs=1:5; N=length(vs);
for e=1:N, opts(e).pBoost.pTree.maxDepth=vs(e); end
case 'TrnNumBins'
lbl='# bins';
vs=2.^(4:8); N=length(vs);
for e=1:N, opts(e).pBoost.pTree.nBins=vs(e); end
case 'TrnFracFtrs'
lbl='fraction features';
vs=2.^(1:8); N=length(vs);
for e=1:N, opts(e).pBoost.pTree.fracFtrs=1/vs(e); end
case 'DataNumPos'
lbl='# pos examples';
vs=[2.^(6:9) inf]; N=length(vs);
for e=1:N-1, opts(e).nPos=vs(e); end
case 'DataNumNeg'
lbl='# neg examples';
vs=[5 10 25 50 100 250]*100; N=length(vs);
for e=1:N, opts(e).nNeg=vs(e); end
case 'DataNumNegAcc'
lbl='# neg examples total';
vs=[25 50 100 250 500]*100; N=length(vs);
for e=1:N, opts(e).nAccNeg=vs(e); end
case 'DataNumNegPer'
lbl='# neg example / image';
vs=[5 10 25 50 100]; N=length(vs);
for e=1:N, opts(e).nPerNeg=vs(e); end
case 'DataNumPosStump'
lbl='# pos examples (stumps)';
vs=[2.^(6:9) 1237 1237]; N=length(vs); lgd{N}='1237*';
for e=1:N-1, opts(e).nPos=vs(e); opts(e).pBoost.pTree.maxDepth=1; end
case 'DataJitterTran'
lbl='translational jitter';
vs=[0 1 2 4]; N=length(vs); opts(1).pJitter=struct('flip',1);
for e=2:N, opts(e).pJitter=struct('flip',1,'nTrn',3,'mTrn',vs(e)); end
for e=1:N, lgd{e}=['+/-' int2str(vs(e))]; end
case 'DataJitterRot'
lbl='rotational jitter';
vs=[0 2 4 8]; N=length(vs);
for e=2:N, opts(e).pJitter=struct('flip',1,'nPhi',3,'mPhi',vs(e)); end
for e=1:N, lgd{e}=['+/-' int2str(vs(e))]; end
otherwise, error('invalid exp: %s',expNm);
end
% produce final set of opts and find default opts
for e=1:N, if(isempty(lgd{e})), lgd{e}=int2str(vs(e)); end; end
for e=1:N, if(isempty(ss{e})), ss{e}=int2str2(vs(e),5); end; end
O=1:N; opts=opts(O); lgd=lgd(O); ss=ss(O); d=0;
for e=1:N, pPyramid(e).pChns=pChns(e); opts(e).pPyramid=pPyramid(e); end
for e=1:N, if(isequal(optsDefault,opts(e))), d=e; break; end; end
if(d==0), disp(expNm); assert(false); end
for e=1:N, opts(e).name=[rtDir 'sweeps/res/' expNm ss{e}]; end
lgd{d}=[lgd{d} '*']; opts(d).name=[rtDir 'sweeps/res/Default'];
if(0), disp([ss' lgd']'); end
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
bbGt.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/detector/bbGt.m
| 34,046 |
utf_8
|
69e66c9a0cc143fb9a794fbc9233246e
|
function varargout = bbGt( action, varargin )
% Bounding box (bb) annotations struct, evaluation and sampling routines.
%
% bbGt gives access to two types of routines:
% (1) Data structure for storing bb image annotations.
% (2) Routines for evaluating the Pascal criteria for object detection.
%
% The bb annotation stores bb for objects of interest with additional
% information per object, such as occlusion information. The underlying
% data structure is simply a Matlab stuct array, one struct per object.
% This annotation format is an alternative to the annotation format used
% for the PASCAL object challenges (in addition routines for loading PASCAL
% format data are provided, see bbLoad()).
%
% Each object struct has the following fields:
% lbl - a string label describing object type (eg: 'pedestrian')
% bb - [l t w h]: bb indicating predicted object extent
% occ - 0/1 value indicating if bb is occluded
% bbv - [l t w h]: bb indicating visible region (may be [0 0 0 0])
% ign - 0/1 value indicating bb was marked as ignore
% ang - [0-360] orientation of bb in degrees
%
% Note: although orientation (angle) is stored for each bb, for now it is
% not being used during evaluation or sampling.
%
% bbGt contains a number of utility functions, accessed using:
% outputs = bbGt( 'action', inputs );
% The list of functions and help for each is given below. Also, help on
% individual subfunctions can be accessed by: "help bbGt>action".
%
%%% (1) Data structure for storing bb image annotations.
% Create annotation of n empty objects.
% objs = bbGt( 'create', [n] );
% Save bb annotation to text file.
% objs = bbGt( 'bbSave', objs, fName )
% Load bb annotation from text file and filter.
% [objs,bbs] = bbGt( 'bbLoad', fName, [pLoad] )
% Get object property 'name' (in a standard array).
% vals = bbGt( 'get', objs, name )
% Set object property 'name' (with a standard array).
% objs = bbGt( 'set', objs, name, vals )
% Draw an ellipse for each labeled object.
% hs = draw( objs, pDraw )
%
%%% (2) Routines for evaluating the Pascal criteria for object detection.
% Get all corresponding files in given directories.
% [fs,fs0] = bbGt('getFiles', dirs, [f0], [f1] )
% Copy corresponding files into given directories.
% fs = bbGt( 'copyFiles', fs, dirs )
% Load all ground truth and detection bbs in given directories.
% [gt0,dt0] = bbGt( 'loadAll', gtDir, [dtDir], [pLoad] )
% Evaluates detections against ground truth data.
% [gt,dt] = bbGt( 'evalRes', gt0, dt0, [thr], [mul] )
% Display evaluation results for given image.
% [hs,hImg] = bbGt( 'showRes' I, gt, dt, varargin )
% Compute ROC or PR based on outputs of evalRes on multiple images.
% [xs,ys,ref] = bbGt( 'compRoc', gt, dt, roc, ref )
% Extract true or false positives or negatives for visualization.
% [Is,scores,imgIds] = bbGt( 'cropRes', gt, dt, imFs, varargin )
% Computes (modified) overlap area between pairs of bbs.
% oa = bbGt( 'compOas', dt, gt, [ig] )
% Optimized version of compOas for a single pair of bbs.
% oa = bbGt( 'compOa', dt, gt, ig )
%
% USAGE
% varargout = bbGt( action, varargin );
%
% INPUTS
% action - string specifying action
% varargin - depends on action, see above
%
% OUTPUTS
% varargout - depends on action, see above
%
% EXAMPLE
%
% See also bbApply, bbLabeler, bbGt>create, bbGt>bbSave, bbGt>bbLoad,
% bbGt>get, bbGt>set, bbGt>draw, bbGt>getFiles, bbGt>copyFiles,
% bbGt>loadAll, bbGt>evalRes, bbGt>showRes, bbGt>compRoc, bbGt>cropRes,
% bbGt>compOas, bbGt>compOa
%
% Piotr's Computer Vision Matlab Toolbox Version 3.26
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
%#ok<*DEFNU>
varargout = cell(1,max(1,nargout));
[varargout{:}] = feval(action,varargin{:});
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function objs = create( n )
% Create annotation of n empty objects.
%
% USAGE
% objs = bbGt( 'create', [n] )
%
% INPUTS
% n - [1] number of objects to create
%
% OUTPUTS
% objs - annotation of n 'empty' objects
%
% EXAMPLE
% objs = bbGt('create')
%
% See also bbGt
o=struct('lbl','','bb',[0 0 0 0],'occ',0,'bbv',[0 0 0 0],'ign',0,'ang',0);
if(nargin<1 || n==1), objs=o; return; end; objs=o(ones(n,1));
end
function objs = bbSave( objs, fName )
% Save bb annotation to text file.
%
% USAGE
% objs = bbGt( 'bbSave', objs, fName )
%
% INPUTS
% objs - objects to save
% fName - name of text file
%
% OUTPUTS
% objs - objects to save
%
% EXAMPLE
%
% See also bbGt, bbGt>bbLoad
vers=3; fid=fopen(fName,'w'); assert(fid>0);
fprintf(fid,'%% bbGt version=%i\n',vers);
objs=set(objs,'bb',round(get(objs,'bb')));
objs=set(objs,'bbv',round(get(objs,'bbv')));
objs=set(objs,'ang',round(get(objs,'ang')));
for i=1:length(objs)
o=objs(i); bb=o.bb; bbv=o.bbv;
fprintf(fid,['%s' repmat(' %i',1,11) '\n'],o.lbl,...
bb,o.occ,bbv,o.ign,o.ang);
end
fclose(fid);
end
function [objs,bbs] = bbLoad( fName, varargin )
% Load bb annotation from text file and filter.
%
% FORMAT: Specify 'format' to indicate the format of the ground truth.
% format=0 is the default format (created by bbSave/bbLabeler). format=1 is
% the PASCAL VOC format. Loading ground truth in this format requires
% 'VOCcode/' to be in directory path. It's part of VOCdevkit available from
% the PASCAL VOC: http://pascallin.ecs.soton.ac.uk/challenges/VOC/. Objects
% labeled as either 'truncated' or 'occluded' using the PASCAL definitions
% have the 'occ' flag set to true. Objects labeled as 'difficult' have the
% 'ign' flag set to true. 'class' is used for 'lbl'. format=2 is the
% ImageNet detection format and requires the ImageNet Dev Kit.
%
% FILTERING: After loading, the objects can be filtered. First, only
% objects with lbl in lbls or ilbls or returned. For each object, obj.ign
% is set to 1 if it was already at 1, if its label was in ilbls, or if any
% object property is outside of the specified range. The ignore flag is
% used during training and testing so that objects with certain properties
% (such as very small or heavily occluded objects) are excluded. The range
% for each property is a two element vector, [0 inf] by default; a property
% value v is inside the range if v>=rng(1) && v<=rng(2). Tested properties
% include height (h), width (w), area (a), aspect ratio (ar), orientation
% (o), extent x-coordinate (x), extent y-coordinate (y), and fraction
% visible (v). The last property is computed as the visible object area
% divided by the total area, except if o.occ==0, in which case v=1, or
% all(o.bbv==o.bb), which indicates the object may be barely visible, in
% which case v=0 (note that v~=1 in this case).
%
% RETURN: In addition to outputting the objs, bbLoad() can return the
% corresponding bounding boxes (bbs) in an [nx5] array where each row is of
% the form [x y w h ignore], [x y w h] is the bb and ignore=obj.ign. For
% oriented bbs, the extent of the bb is returned, where the extent is the
% smallest axis aligned bb containing the oriented bb. If the oriented bb
% was labeled as a rectangle as opposed to an ellipse, the tightest bb will
% usually increase slightly in size due to the corners of the rectangle
% sticking out beyond the ellipse bounds. The 'ellipse' flag controls how
% an oriented bb is converted to a regular bb. Specifically, set ellipse=1
% if an ellipse tightly delineates the object and 0 if a rectangle does.
% Finally, if 'squarify' is not empty the (non-ignore) bbs are converted to
% a fixed aspect ratio using bbs=bbApply('squarify',bbs,squarify{:}).
%
% USAGE
% [objs,bbs] = bbGt( 'bbLoad', fName, [pLoad] )
%
% INPUTS
% fName - name of text file
% pLoad - parameters (struct or name/value pairs)
% .format - [0] gt format 0:default, 1:PASCAL, 2:ImageNet
% .ellipse - [1] controls how oriented bb is converted to regular bb
% .squarify - [] controls optional reshaping of bbs to fixed aspect ratio
% .lbls - [] return objs with these labels (or [] to return all)
% .ilbls - [] return objs with these labels but set to ignore
% .hRng - [] range of acceptable obj heights
% .wRng - [] range of acceptable obj widths
% .aRng - [] range of acceptable obj areas
% .arRng - [] range of acceptable obj aspect ratios
% .oRng - [] range of acceptable obj orientations (angles)
% .xRng - [] range of x coordinates of bb extent
% .yRng - [] range of y coordinates of bb extent
% .vRng - [] range of acceptable obj occlusion levels
%
% OUTPUTS
% objs - loaded objects
% bbs - [nx5] array containg ground truth bbs [x y w h ignore]
%
% EXAMPLE
%
% See also bbGt, bbGt>bbSave
% get parameters
df={'format',0,'ellipse',1,'squarify',[],'lbls',[],'ilbls',[],'hRng',[],...
'wRng',[],'aRng',[],'arRng',[],'oRng',[],'xRng',[],'yRng',[],'vRng',[]};
[format,ellipse,sqr,lbls,ilbls,hRng,wRng,aRng,arRng,oRng,xRng,yRng,vRng]...
= getPrmDflt(varargin,df,1);
% load objs
if( format==0 )
% load objs stored in default format
fId=fopen(fName);
if(fId==-1), error(['unable to open file: ' fName]); end; v=0;
try v=textscan(fId,'%% bbGt version=%d'); v=v{1}; catch, end %#ok<CTCH>
if(isempty(v)), v=0; end
% read in annotation (m is number of fields for given version v)
if(all(v~=[0 1 2 3])), error('Unknown version %i.',v); end
frmt='%s %d %d %d %d %d %d %d %d %d %d %d';
ms=[10 10 11 12]; m=ms(v+1); frmt=frmt(1:2+(m-1)*3);
in=textscan(fId,frmt); for i=2:m, in{i}=double(in{i}); end; fclose(fId);
% create objs struct from read in fields
n=length(in{1}); objs=create(n);
for i=1:n, objs(i).lbl=in{1}{i}; objs(i).occ=in{6}(i); end
bb=[in{2} in{3} in{4} in{5}]; bbv=[in{7} in{8} in{9} in{10}];
for i=1:n, objs(i).bb=bb(i,:); objs(i).bbv=bbv(i,:); end
if(m>=11), for i=1:n, objs(i).ign=in{11}(i); end; end
if(m>=12), for i=1:n, objs(i).ang=in{12}(i); end; end
elseif( format==1 )
% load objs stored in PASCAL VOC format
if(exist('PASreadrecord.m','file')~=2)
error('bbLoad() requires the PASCAL VOC code.'); end
os=PASreadrecord(fName); os=os.objects;
n=length(os); objs=create(n);
if(~isfield(os,'occluded')), for i=1:n, os(i).occluded=0; end; end
for i=1:n
bb=os(i).bbox; bb(3)=bb(3)-bb(1); bb(4)=bb(4)-bb(2); objs(i).bb=bb;
objs(i).lbl=os(i).class; objs(i).ign=os(i).difficult;
objs(i).occ=os(i).occluded || os(i).truncated;
if(objs(i).occ), objs(i).bbv=bb; end
end
elseif( format==2 )
if(exist('VOCreadxml.m','file')~=2)
error('bbLoad() requires the ImageNet dev code.'); end
os=VOCreadxml(fName); os=os.annotation;
if(isfield(os,'object')), os=os.object; else os=[]; end
n=length(os); objs=create(n);
for i=1:n
bb=os(i).bndbox; bb=str2double({bb.xmin bb.ymin bb.xmax bb.ymax});
bb(3)=bb(3)-bb(1); bb(4)=bb(4)-bb(2); objs(i).bb=bb;
objs(i).lbl=os(i).name;
end
else error('bbLoad() unknown format: %i',format);
end
% only keep objects whose lbl is in lbls or ilbls
if(~isempty(lbls) || ~isempty(ilbls)), K=true(n,1);
for i=1:n, K(i)=any(strcmp(objs(i).lbl,[lbls ilbls])); end
objs=objs(K); n=length(objs);
end
% filter objs (set ignore flags)
for i=1:n, objs(i).ang=mod(objs(i).ang,360); end
if(~isempty(ilbls)), for i=1:n, v=objs(i).lbl;
objs(i).ign = objs(i).ign || any(strcmp(v,ilbls)); end; end
if(~isempty(xRng)), for i=1:n, v=objs(i).bb(1);
objs(i).ign = objs(i).ign || v<xRng(1) || v>xRng(2); end; end
if(~isempty(xRng)), for i=1:n, v=objs(i).bb(1)+objs(i).bb(3);
objs(i).ign = objs(i).ign || v<xRng(1) || v>xRng(2); end; end
if(~isempty(yRng)), for i=1:n, v=objs(i).bb(2);
objs(i).ign = objs(i).ign || v<yRng(1) || v>yRng(2); end; end
if(~isempty(yRng)), for i=1:n, v=objs(i).bb(2)+objs(i).bb(4);
objs(i).ign = objs(i).ign || v<yRng(1) || v>yRng(2); end; end
if(~isempty(wRng)), for i=1:n, v=objs(i).bb(3);
objs(i).ign = objs(i).ign || v<wRng(1) || v>wRng(2); end; end
if(~isempty(hRng)), for i=1:n, v=objs(i).bb(4);
objs(i).ign = objs(i).ign || v<hRng(1) || v>hRng(2); end; end
if(~isempty(oRng)), for i=1:n, v=objs(i).ang; if(v>180), v=v-360; end
objs(i).ign = objs(i).ign || v<oRng(1) || v>oRng(2); end; end
if(~isempty(aRng)), for i=1:n, v=objs(i).bb(3)*objs(i).bb(4);
objs(i).ign = objs(i).ign || v<aRng(1) || v>aRng(2); end; end
if(~isempty(arRng)), for i=1:n, v=objs(i).bb(3)/objs(i).bb(4);
objs(i).ign = objs(i).ign || v<arRng(1) || v>arRng(2); end; end
if(~isempty(vRng)), for i=1:n, o=objs(i); bb=o.bb; bbv=o.bbv; %#ok<ALIGN>
if(~o.occ || all(bbv==0)), v=1; elseif(all(bbv==bb)), v=0; else
v=(bbv(3)*bbv(4))/(bb(3)*bb(4)); end
objs(i).ign = objs(i).ign || v<vRng(1) || v>vRng(2); end
end
% finally get extent of each bounding box (not trivial if ang~=0)
if(nargout<=1), return; end; if(n==0), bbs=zeros(0,5); return; end
bbs=double([reshape([objs.bb],4,[]); [objs.ign]]'); ign=bbs(:,5)==1;
for i=1:n, bbs(i,1:4)=bbExtent(bbs(i,1:4),objs(i).ang,ellipse); end
if(~isempty(sqr)), bbs(~ign,:)=bbApply('squarify',bbs(~ign,:),sqr{:}); end
function bb = bbExtent( bb, ang, ellipse )
% get bb that fully contains given oriented bb
if(~ang), return; end
if( ellipse ) % get bb that encompases ellipse (tighter)
x=bbApply('getCenter',bb); a=bb(4)/2; b=bb(3)/2; ang=ang-90;
rx=(a*cosd(ang))^2+(b*sind(ang))^2; rx=abs(rx/sqrt(rx));
ry=(a*sind(ang))^2+(b*cosd(ang))^2; ry=abs(ry/sqrt(ry));
bb=[x(1)-rx x(2)-ry 2*rx 2*ry];
else % get bb that encompases rectangle (looser)
c=cosd(ang); s=sind(ang); R=[c -s; s c]; rs=bb(3:4)/2;
x0=-rs(1); x1=rs(1); y0=-rs(2); y1=rs(2); pc=bb(1:2)+rs;
p=[x0 y0; x1 y0; x1 y1; x0 y1]*R'+pc(ones(4,1),:);
x0=min(p(:,1)); x1=max(p(:,1)); y0=min(p(:,2)); y1=max(p(:,2));
bb=[x0 y0 x1-x0 y1-y0];
end
end
end
function vals = get( objs, name )
% Get object property 'name' (in a standard array).
%
% USAGE
% vals = bbGt( 'get', objs, name )
%
% INPUTS
% objs - [nx1] struct array of objects
% name - property name ('lbl','bb','occ',etc.)
%
% OUTPUTS
% vals - [nxk] array of n values (k=1 or 4)
%
% EXAMPLE
%
% See also bbGt, bbGt>set
nObj=length(objs); if(nObj==0), vals=[]; return; end
switch name
case 'lbl', vals={objs.lbl}';
case 'bb', vals=reshape([objs.bb]',4,[])';
case 'occ', vals=[objs.occ]';
case 'bbv', vals=reshape([objs.bbv]',4,[])';
case 'ign', vals=[objs.ign]';
case 'ang', vals=[objs.ang]';
otherwise, error('unkown type %s',name);
end
end
function objs = set( objs, name, vals )
% Set object property 'name' (with a standard array).
%
% USAGE
% objs = bbGt( 'set', objs, name, vals )
%
% INPUTS
% objs - [nx1] struct array of objects
% name - property name ('lbl','bb','occ',etc.)
% vals - [nxk] array of n values (k=1 or 4)
%
% OUTPUTS
% objs - [nx1] struct array of updated objects
%
% EXAMPLE
%
% See also bbGt, bbGt>get
nObj=length(objs);
switch name
case 'lbl', for i=1:nObj, objs(i).lbl=vals{i}; end
case 'bb', for i=1:nObj, objs(i).bb=vals(i,:); end
case 'occ', for i=1:nObj, objs(i).occ=vals(i); end
case 'bbv', for i=1:nObj, objs(i).bbv=vals(i,:); end
case 'ign', for i=1:nObj, objs(i).ign=vals(i); end
case 'ang', for i=1:nObj, objs(i).ang=vals(i); end
otherwise, error('unkown type %s',name);
end
end
function hs = draw( objs, varargin )
% Draw an ellipse for each labeled object.
%
% USAGE
% hs = bbGt( 'draw', objs, pDraw )
%
% INPUTS
% objs - [nx1] struct array of objects
% pDraw - parameters (struct or name/value pairs)
% .col - ['g'] color or [nx1] array of colors
% .lw - [2] line width
% .ls - ['-'] line style
%
% OUTPUTS
% hs - [nx1] handles to drawn graphic objects
%
% EXAMPLE
%
% See also bbGt
dfs={'col',[],'lw',2,'ls','-'};
[col,lw,ls]=getPrmDflt(varargin,dfs,1);
n=length(objs); hold on; hs=zeros(n,4);
if(isempty(col)), if(n==1), col='g'; else col=hsv(n); end; end
tProp={'FontSize',10,'color','w','FontWeight','bold',...
'VerticalAlignment','bottom'};
for i=1:n
bb=objs(i).bb; ci=col(i,:);
hs(i,1)=text(bb(1),bb(2),objs(i).lbl,tProp{:});
x=bbApply('getCenter',bb); r=bb(3:4)/2; a=objs(i).ang/180*pi-pi/2;
[hs(i,2),hs(i,3),hs(i,4)]=plotEllipse(x(2),x(1),r(2),r(1),a,ci,[],lw,ls);
end; hold off;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [fs,fs0] = getFiles( dirs, f0, f1 )
% Get all corresponding files in given directories.
%
% The first dir in 'dirs' serves as the baseline dir. getFiles() returns
% all files in the baseline dir and all corresponding files in the
% remaining dirs to the files in the baseline dir, in the same order. Two
% files are in correspondence if they have the same base name (regardless
% of extension). For example, given a file named "name.jpg", a
% corresponding file may be named "name.txt" or "name.jpg.txt". Every file
% in the baseline dir must have a matching file in the remaining dirs.
%
% USAGE
% [fs,fs0] = bbGt('getFiles', dirs, [f0], [f1] )
%
% INPUTS
% dirs - {1xm} list of m directories
% f0 - [1] index of first file in baseline dir to use
% f1 - [inf] index of last file in baseline dir to use
%
% OUTPUTS
% fs - {mxn} list of full file names in each dir
% fs0 - {1xn} list of file names without path or extensions
%
% EXAMPLE
%
% See also bbGt
if(nargin<2 || isempty(f0)), f0=1; end
if(nargin<3 || isempty(f1)), f1=inf; end
m=length(dirs); assert(m>0); sep=filesep;
for d=1:m, dir1=dirs{d}; dir1(dir1=='\')=sep; dir1(dir1=='/')=sep;
if(dir1(end)==sep), dir1(end)=[]; end; dirs{d}=dir1; end
[fs0,fs1] = getFiles0(dirs{1},f0,f1,sep);
n1=length(fs0); fs=cell(m,n1); fs(1,:)=fs1;
for d=2:m, fs(d,:)=getFiles1(dirs{d},fs0,sep); end
function [fs0,fs1] = getFiles0( dir1, f0, f1, sep )
% get fs1 in dir1 (and fs0 without path or extension)
fs1=dir([dir1 sep '*']); fs1={fs1.name}; fs1=fs1(3:end);
fs1=fs1(f0:min(f1,end)); fs0=fs1; n=length(fs0);
if(n==0), error('No files found in baseline dir %s.',dir1); end
for i=1:n, fs1{i}=[dir1 sep fs0{i}]; end
n=length(fs0); for i=1:n, f=fs0{i};
f(find(f=='.',1,'first'):end)=[]; fs0{i}=f; end
end
function fs1 = getFiles1( dir1, fs0, sep )
% get fs1 in dir1 corresponding to fs0
n=length(fs0); fs1=cell(1,n); i2=0; i1=0;
fs2=dir(dir1); fs2={fs2.name}; n2=length(fs2);
eMsg='''%s'' has no corresponding file in %s.';
for i0=1:n, r=length(fs0{i0}); match=0;
while(i2<n2), i2=i2+1; if(strcmpi(fs0{i0},fs2{i2}(1:min(end,r))))
i1=i1+1; fs1{i1}=fs2{i2}; match=1; break; end; end
if(~match), error(eMsg,fs0{i0},dir1); end
end
for i1=1:n, fs1{i1}=[dir1 sep fs1{i1}]; end
end
end
function fs = copyFiles( fs, dirs )
% Copy corresponding files into given directories.
%
% Useful for splitting data into training, validation and testing sets.
% See also bbGt>getFiles for obtaining a set of corresponding files.
%
% USAGE
% fs = bbGt( 'copyFiles', fs, dirs )
%
% INPUTS
% fs - {mxn} list of full file names in each dir
% dirs - {1xm} list of m target directories
%
% OUTPUTS
% fs - {mxn} list of full file names of copied files
%
% EXAMPLE
%
% See also bbGt, bbGt>getFiles
[m,n]=size(fs); assert(numel(dirs)==m); if(n==0), return; end
for d=1:m
if(~exist(dirs{d},'dir')), mkdir(dirs{d}); end
for i=1:n, f=fs{d,i}; j=[0 find(f=='/' | f=='\')]; j=j(end);
fs{d,i}=[dirs{d} '/' f(j+1:end)]; copyfile(f,fs{d,i}); end
end
end
function [gt0,dt0] = loadAll( gtDir, dtDir, pLoad )
% Load all ground truth and detection bbs in given directories.
%
% Loads each ground truth (gt) annotation in gtDir and the corresponding
% detection (dt) in dtDir. gt and dt files must correspond according to
% getFiles(). Alternatively, dtDir may be a filename of a single text file
% that contains the detection results across all images.
%
% Each dt should be a text file where each row contains 5 numbers
% representing a bb (left/top/width/height/score). If dtDir is a text file,
% it should contain the detection results across the full set of images. In
% this case each row in the text file should have an extra leading column
% specifying the image id: (imgId/left/top/width/height/score).
%
% The output of this function can be used in bbGt>evalRes().
%
% USAGE
% [gt0,dt0] = bbGt( 'loadAll', gtDir, [dtDir], [pLoad] )
%
% INPUTS
% gtDir - location of ground truth
% dtDir - [] optional location of detections
% pLoad - {} params for bbGt>bbLoad() (determine format/filtering)
%
% OUTPUTS
% gt0 - {1xn} loaded ground truth bbs (each is a mx5 array of bbs)
% dt0 - {1xn} loaded detections (each is a mx5 array of bbs)
%
% EXAMPLE
%
% See also bbGt, bbGt>getFiles, bbGt>evalRes
% get list of files
if(nargin<2), dtDir=[]; end
if(nargin<3), pLoad={}; end
if(isempty(dtDir)), fs=getFiles({gtDir}); gtFs=fs(1,:); else
dtFile=length(dtDir)>4 && strcmp(dtDir(end-3:end),'.txt');
if(dtFile), dirs={gtDir}; else dirs={gtDir,dtDir}; end
fs=getFiles(dirs); gtFs=fs(1,:);
if(dtFile), dtFs=dtDir; else dtFs=fs(2,:); end
end
% load ground truth
persistent keyPrv gtPrv; key={gtDir,pLoad}; n=length(gtFs);
if(isequal(key,keyPrv)), gt0=gtPrv; else gt0=cell(1,n);
for i=1:n, [~,gt0{i}]=bbLoad(gtFs{i},pLoad); end
gtPrv=gt0; keyPrv=key;
end
% load detections
if(isempty(dtDir) || nargout<=1), dt0=cell(0); return; end
if(iscell(dtFs)), dt0=cell(1,n);
for i=1:n, dt1=load(dtFs{i},'-ascii');
if(numel(dt1)==0), dt1=zeros(0,5); end; dt0{i}=dt1(:,1:5); end
else
dt1=load(dtFs,'-ascii'); if(numel(dt1)==0), dt1=zeros(0,6); end
ids=dt1(:,1); assert(max(ids)<=n);
dt0=cell(1,n); for i=1:n, dt0{i}=dt1(ids==i,2:6); end
end
end
function [gt,dt] = evalRes( gt0, dt0, thr, mul )
% Evaluates detections against ground truth data.
%
% Uses modified Pascal criteria that allows for "ignore" regions. The
% Pascal criteria states that a ground truth bounding box (gtBb) and a
% detected bounding box (dtBb) match if their overlap area (oa):
% oa(gtBb,dtBb) = area(intersect(gtBb,dtBb)) / area(union(gtBb,dtBb))
% is over a sufficient threshold (typically .5). In the modified criteria,
% the dtBb can match any subregion of a gtBb set to "ignore". Choosing
% gtBb' in gtBb that most closely matches dtBb can be done by using
% gtBb'=intersect(dtBb,gtBb). Computing oa(gtBb',dtBb) is equivalent to
% oa'(gtBb,dtBb) = area(intersect(gtBb,dtBb)) / area(dtBb)
% For gtBb set to ignore the above formula for oa is used.
%
% Highest scoring detections are matched first. Matches to standard,
% (non-ignore) gtBb are preferred. Each dtBb and gtBb may be matched at
% most once, except for ignore-gtBb which can be matched multiple times.
% Unmatched dtBb are false-positives, unmatched gtBb are false-negatives.
% Each match between a dtBb and gtBb is a true-positive, except matches
% between dtBb and ignore-gtBb which do not affect the evaluation criteria.
%
% In addition to taking gt/dt results on a single image, evalRes() can take
% cell arrays of gt/dt bbs, in which case evaluation proceeds on each
% element. Use bbGt>loadAll() to load gt/dt for multiple images.
%
% Each gt/dt output row has a flag match that is either -1/0/1:
% for gt: -1=ignore, 0=fn [unmatched], 1=tp [matched]
% for dt: -1=ignore, 0=fp [unmatched], 1=tp [matched]
%
% USAGE
% [gt, dt] = bbGt( 'evalRes', gt0, dt0, [thr], [mul] )
%
% INPUTS
% gt0 - [mx5] ground truth array with rows [x y w h ignore]
% dt0 - [nx5] detection results array with rows [x y w h score]
% thr - [.5] the threshold on oa for comparing two bbs
% mul - [0] if true allow multiple matches to each gt
%
% OUTPUTS
% gt - [mx5] ground truth results [x y w h match]
% dt - [nx6] detection results [x y w h score match]
%
% EXAMPLE
%
% See also bbGt, bbGt>compOas, bbGt>loadAll
% get parameters
if(nargin<3 || isempty(thr)), thr=.5; end
if(nargin<4 || isempty(mul)), mul=0; end
% if gt0 and dt0 are cell arrays run on each element in turn
if( iscell(gt0) && iscell(dt0) ), n=length(gt0);
assert(length(dt0)==n); gt=cell(1,n); dt=gt;
for i=1:n, [gt{i},dt{i}] = evalRes(gt0{i},dt0{i},thr,mul); end; return;
end
% check inputs
if(isempty(gt0)), gt0=zeros(0,5); end
if(isempty(dt0)), dt0=zeros(0,5); end
assert( size(dt0,2)==5 ); nd=size(dt0,1);
assert( size(gt0,2)==5 ); ng=size(gt0,1);
% sort dt highest score first, sort gt ignore last
[~,ord]=sort(dt0(:,5),'descend'); dt0=dt0(ord,:);
[~,ord]=sort(gt0(:,5),'ascend'); gt0=gt0(ord,:);
gt=gt0; gt(:,5)=-gt(:,5); dt=dt0; dt=[dt zeros(nd,1)];
% Attempt to match each (sorted) dt to each (sorted) gt
oa = compOas( dt(:,1:4), gt(:,1:4), gt(:,5)==-1 );
for d=1:nd
bstOa=thr; bstg=0; bstm=0; % info about best match so far
for g=1:ng
% if this gt already matched, continue to next gt
m=gt(g,5); if( m==1 && ~mul ), continue; end
% if dt already matched, and on ignore gt, nothing more to do
if( bstm~=0 && m==-1 ), break; end
% compute overlap area, continue to next gt unless better match made
if(oa(d,g)<bstOa), continue; end
% match successful and best so far, store appropriately
bstOa=oa(d,g); bstg=g; if(m==0), bstm=1; else bstm=-1; end
end; g=bstg; m=bstm;
% store type of match for both dt and gt
if(m==-1), dt(d,6)=m; elseif(m==1), gt(g,5)=m; dt(d,6)=m; end
end
end
function [hs,hImg] = showRes( I, gt, dt, varargin )
% Display evaluation results for given image.
%
% USAGE
% [hs,hImg] = bbGt( 'showRes', I, gt, dt, varargin )
%
% INPUTS
% I - image to display, image filename, or []
% gt - first output of evalRes()
% dt - second output of evalRes()
% varargin - additional parameters (struct or name/value pairs)
% .evShow - [1] if true show results of evaluation
% .gtShow - [1] if true show ground truth
% .dtShow - [1] if true show detections
% .cols - ['krg'] colors for ignore/mistake/correct
% .gtLs - ['-'] line style for gt bbs
% .dtLs - ['--'] line style for dt bbs
% .lw - [3] line width
%
% OUTPUTS
% hs - handles to bbs and text labels
% hImg - handle for image graphics object
%
% EXAMPLE
%
% See also bbGt, bbGt>evalRes
dfs={'evShow',1,'gtShow',1,'dtShow',1,'cols','krg',...
'gtLs','-','dtLs','--','lw',3};
[evShow,gtShow,dtShow,cols,gtLs,dtLs,lw]=getPrmDflt(varargin,dfs,1);
% optionally display image
if(ischar(I)), I=imread(I); end
if(~isempty(I)), hImg=im(I,[],0); title(''); end
% display bbs with or w/o color coding based on output of evalRes
hold on; hs=cell(1,1000); k=0;
if( evShow )
if(gtShow), for i=1:size(gt,1), k=k+1;
hs{k}=bbApply('draw',gt(i,1:4),cols(gt(i,5)+2),lw,gtLs); end; end
if(dtShow), for i=1:size(dt,1), k=k+1;
hs{k}=bbApply('draw',dt(i,1:5),cols(dt(i,6)+2),lw,dtLs); end; end
else
if(gtShow), k=k+1; hs{k}=bbApply('draw',gt(:,1:4),cols(3),lw,gtLs); end
if(dtShow), k=k+1; hs{k}=bbApply('draw',dt(:,1:5),cols(3),lw,dtLs); end
end
hs=[hs{:}]; hold off;
end
function [xs,ys,score,ref] = compRoc( gt, dt, roc, ref )
% Compute ROC or PR based on outputs of evalRes on multiple images.
%
% ROC="Receiver operating characteristic"; PR="Precision Recall"
% Also computes result at reference points (ref):
% which for ROC curves is the *detection* rate at reference *FPPI*
% which for PR curves is the *precision* at reference *recall*
% Note, FPPI="false positive per image"
%
% USAGE
% [xs,ys,score,ref] = bbGt( 'compRoc', gt, dt, roc, ref )
%
% INPUTS
% gt - {1xn} first output of evalRes() for each image
% dt - {1xn} second output of evalRes() for each image
% roc - [1] if 1 compue ROC else compute PR
% ref - [] reference points for ROC or PR curve
%
% OUTPUTS
% xs - x coords for curve: ROC->FPPI; PR->recall
% ys - y coords for curve: ROC->TP; PR->precision
% score - detection scores corresponding to each (x,y)
% ref - recall or precision at each reference point
%
% EXAMPLE
%
% See also bbGt, bbGt>evalRes
% get additional parameters
if(nargin<3 || isempty(roc)), roc=1; end
if(nargin<4 || isempty(ref)), ref=[]; end
% convert to single matrix, discard ignore bbs
nImg=length(gt); assert(length(dt)==nImg);
gt=cat(1,gt{:}); gt=gt(gt(:,5)~=-1,:);
dt=cat(1,dt{:}); dt=dt(dt(:,6)~=-1,:);
% compute results
if(size(dt,1)==0), xs=0; ys=0; score=0; ref=ref*0; return; end
m=length(ref); np=size(gt,1); score=dt(:,5); tp=dt(:,6);
[score,order]=sort(score,'descend'); tp=tp(order);
fp=double(tp~=1); fp=cumsum(fp); tp=cumsum(tp);
if( roc )
xs=fp/nImg; ys=tp/np; xs1=[-inf; xs]; ys1=[0; ys];
for i=1:m, j=find(xs1<=ref(i)); ref(i)=ys1(j(end)); end
else
xs=tp/np; ys=tp./(fp+tp); xs1=[xs; inf]; ys1=[ys; 0];
for i=1:m, j=find(xs1>=ref(i)); ref(i)=ys1(j(1)); end
end
end
function [Is,scores,imgIds] = cropRes( gt, dt, imFs, varargin )
% Extract true or false positives or negatives for visualization.
%
% USAGE
% [Is,scores,imgIds] = bbGt( 'cropRes', gt, dt, imFs, varargin )
%
% INPUTS
% gt - {1xN} first output of evalRes() for each image
% dt - {1xN} second output of evalRes() for each image
% imFs - {1xN} name of each image
% varargin - additional parameters (struct or name/value pairs)
% .dims - ['REQ'] target dimensions for extracted windows
% .pad - [0] padding amount for cropping
% .type - ['fp'] one of: 'fp', 'fn', 'tp', 'dt'
% .n - [100] max number of windows to extract
% .show - [1] figure for displaying results (or 0)
% .fStr - ['%0.1f'] label{i}=num2str(score(i),fStr)
% .embed - [0] if true embed dt/gt bbs into cropped windows
%
% OUTPUTS
% Is - [dimsxn] extracted image windows
% scores - [1xn] detection score for each bb unless 'fn'
% imgIds - [1xn] image id for each cropped window
%
% EXAMPLE
%
% See also bbGt, bbGt>evalRes
dfs={'dims','REQ','pad',0,'type','fp','n',100,...
'show',1,'fStr','%0.1f','embed',0};
[dims,pad,type,n,show,fStr,embed]=getPrmDflt(varargin,dfs,1);
N=length(imFs); assert(length(gt)==N && length(dt)==N);
% crop patches either in gt or dt according to type
switch type
case 'fn', bbs=gt; keep=@(bbs) bbs(:,5)==0;
case 'fp', bbs=dt; keep=@(bbs) bbs(:,6)==0;
case 'tp', bbs=dt; keep=@(bbs) bbs(:,6)==1;
case 'dt', bbs=dt; keep=@(bbs) bbs(:,6)>=0;
otherwise, error('unknown type: %s',type);
end
% create ids that will map each bb to correct name
ms=zeros(1,N); for i=1:N, ms(i)=size(bbs{i},1); end; cms=[0 cumsum(ms)];
ids=zeros(1,sum(ms)); for i=1:N, ids(cms(i)+1:cms(i+1))=i; end
% flatten bbs and keep relevent subset
bbs=cat(1,bbs{:}); K=keep(bbs); bbs=bbs(K,:); ids=ids(K); n=min(n,sum(K));
% reorder bbs appropriately
if(~strcmp(type,'fn')), [~,ord]=sort(bbs(:,5),'descend'); else
if(size(bbs,1)<n), ord=randperm(size(bbs,1)); else ord=1:n; end; end
bbs=bbs(ord(1:n),:); ids=ids(ord(1:n));
% extract patches from each image
if(n==0), Is=[]; scores=[]; imgIds=[]; return; end;
Is=cell(1,n); scores=zeros(1,n); imgIds=zeros(1,n);
if(any(pad>0)), dims1=dims.*(1+pad); rs=dims1./dims; dims=dims1; end
if(any(pad>0)), bbs=bbApply('resize',bbs,rs(1),rs(2)); end
for i=1:N
locs=find(ids==i); if(isempty(locs)), continue; end; I=imread(imFs{i});
if( embed )
if(any(strcmp(type,{'fp','dt'}))), bbs1=gt{i};
else bbs1=dt{i}(:,[1:4 6]); end
I=bbApply('embed',I,bbs1(bbs1(:,5)==0,1:4),'col',[255 0 0]);
I=bbApply('embed',I,bbs1(bbs1(:,5)==1,1:4),'col',[0 255 0]);
end
Is1=bbApply('crop',I,bbs(locs,1:4),'replicate',dims);
for j=1:length(locs), Is{locs(j)}=Is1{j}; end;
scores(locs)=bbs(locs,5); imgIds(locs)=i;
end; Is=cell2array(Is);
% optionally display
if(~show), return; end; figure(show); pMnt={'hasChn',size(Is1{1},3)>1};
if(isempty(fStr)), montage2(Is,pMnt); title(type); return; end
ls=cell(1,n); for i=1:n, ls{i}=int2str2(imgIds(i)); end
if(~strcmp(type,'fn'))
for i=1:n, ls{i}=[ls{i} '/' num2str(scores(i),fStr)]; end; end
montage2(Is,[pMnt 'labels' {ls}]); title(type);
end
function oa = compOas( dt, gt, ig )
% Computes (modified) overlap area between pairs of bbs.
%
% Uses modified Pascal criteria with "ignore" regions. The overlap area
% (oa) of a ground truth (gt) and detected (dt) bb is defined as:
% oa(gt,dt) = area(intersect(dt,dt)) / area(union(gt,dt))
% In the modified criteria, a gt bb may be marked as "ignore", in which
% case the dt bb can can match any subregion of the gt bb. Choosing gt' in
% gt that most closely matches dt can be done using gt'=intersect(dt,gt).
% Computing oa(gt',dt) is equivalent to:
% oa'(gt,dt) = area(intersect(gt,dt)) / area(dt)
%
% USAGE
% oa = bbGt( 'compOas', dt, gt, [ig] )
%
% INPUTS
% dt - [mx4] detected bbs
% gt - [nx4] gt bbs
% ig - [nx1] 0/1 ignore flags (0 by default)
%
% OUTPUTS
% oas - [m x n] overlap area between each gt and each dt bb
%
% EXAMPLE
% dt=[0 0 10 10]; gt=[0 0 20 20];
% oa0 = bbGt('compOas',dt,gt,0)
% oa1 = bbGt('compOas',dt,gt,1)
%
% See also bbGt, bbGt>evalRes
m=size(dt,1); n=size(gt,1); oa=zeros(m,n);
if(nargin<3), ig=zeros(n,1); end
de=dt(:,[1 2])+dt(:,[3 4]); da=dt(:,3).*dt(:,4);
ge=gt(:,[1 2])+gt(:,[3 4]); ga=gt(:,3).*gt(:,4);
for i=1:m
for j=1:n
w=min(de(i,1),ge(j,1))-max(dt(i,1),gt(j,1)); if(w<=0), continue; end
h=min(de(i,2),ge(j,2))-max(dt(i,2),gt(j,2)); if(h<=0), continue; end
t=w*h; if(ig(j)), u=da(i); else u=da(i)+ga(j)-t; end; oa(i,j)=t/u;
end
end
end
function oa = compOa( dt, gt, ig )
% Optimized version of compOas for a single pair of bbs.
%
% USAGE
% oa = bbGt( 'compOa', dt, gt, ig )
%
% INPUTS
% dt - [1x4] detected bb
% gt - [1x4] gt bb
% ig - 0/1 ignore flag
%
% OUTPUTS
% oa - overlap area between gt and dt bb
%
% EXAMPLE
% dt=[0 0 10 10]; gt=[0 0 20 20];
% oa0 = bbGt('compOa',dt,gt,0)
% oa1 = bbGt('compOa',dt,gt,1)
%
% See also bbGt, bbGt>compOas
w=min(dt(3)+dt(1),gt(3)+gt(1))-max(dt(1),gt(1)); if(w<=0),oa=0; return; end
h=min(dt(4)+dt(2),gt(4)+gt(2))-max(dt(2),gt(2)); if(h<=0),oa=0; return; end
i=w*h; if(ig),u=dt(3)*dt(4); else u=dt(3)*dt(4)+gt(3)*gt(4)-i; end; oa=i/u;
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
bbApply.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/detector/bbApply.m
| 21,195 |
utf_8
|
8c02a6999a84bfb5fcbf2274b8b91a97
|
function varargout = bbApply( action, varargin )
% Functions for manipulating bounding boxes (bb).
%
% A bounding box (bb) is also known as a position vector or a rectangle
% object. It is a four element vector with the fields: [x y w h]. A set of
% n bbs can be stores as an [nx4] array, most funcitons below can handle
% either a single or multiple bbs. In addtion, typically [nxm] inputs with
% m>4 are ok (with the additional columns ignored/copied to the output).
%
% bbApply contains a number of utility functions for working with bbs. The
% format for accessing the various utility functions is:
% outputs = bbApply( 'action', inputs );
% The list of functions and help for each is given below. Also, help on
% individual subfunctions can be accessed by: "help bbApply>action".
%
% Compute area of bbs.
% bb = bbApply( 'area', bb )
% Shift center of bbs.
% bb = bbApply( 'shift', bb, xdel, ydel )
% Get center of bbs.
% cen = bbApply( 'getCenter', bb )
% Get bb at intersection of bb1 and bb2 (may be empty).
% bb = bbApply( 'intersect', bb1, bb2 )
% Get bb that is union of bb1 and bb2 (smallest bb containing both).
% bb = bbApply( 'union', bb1, bb2 )
% Resize the bbs (without moving their centers).
% bb = bbApply( 'resize', bb, hr, wr, [ar] )
% Fix bb aspect ratios (without moving the bb centers).
% bbr = bbApply( 'squarify', bb, flag, [ar] )
% Draw single or multiple bbs to image (calls rectangle()).
% hs = bbApply( 'draw', bb, [col], [lw], [ls], [prop], [ids] )
% Embed single or multiple bbs directly into image.
% I = bbApply( 'embed', I, bb, [varargin] )
% Crop image regions from I encompassed by bbs.
% [patches, bbs] = bbApply('crop',I,bb,[padEl],[dims])
% Convert bb relative to absolute coordinates and vice-versa.
% bb = bbApply( 'convert', bb, bbRef, isAbs )
% Randomly generate bbs that fall in a specified region.
% bbs = bbApply( 'random', pRandom )
% Convert weighted mask to bbs.
% bbs = bbApply('frMask',M,bbw,bbh,[thr])
% Create weighted mask encoding bb centers (or extent).
% M = bbApply('toMask',bbs,w,h,[fill],[bgrd])
%
% USAGE
% varargout = bbApply( action, varargin );
%
% INPUTS
% action - string specifying action
% varargin - depends on action, see above
%
% OUTPUTS
% varargout - depends on action, see above
%
% EXAMPLE
%
% See also bbApply>area bbApply>shift bbApply>getCenter bbApply>intersect
% bbApply>union bbApply>resize bbApply>squarify bbApply>draw bbApply>crop
% bbApply>convert bbApply>random bbApply>frMask bbApply>toMask
%
% Piotr's Computer Vision Matlab Toolbox Version 3.30
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
%#ok<*DEFNU>
varargout = cell(1,max(1,nargout));
[varargout{:}] = feval(action,varargin{:});
end
function a = area( bb )
% Compute area of bbs.
%
% USAGE
% bb = bbApply( 'area', bb )
%
% INPUTS
% bb - [nx4] original bbs
%
% OUTPUTS
% a - [nx1] area of each bb
%
% EXAMPLE
% a = bbApply('area', [0 0 10 10])
%
% See also bbApply
a=prod(bb(:,3:4),2);
end
function bb = shift( bb, xdel, ydel )
% Shift center of bbs.
%
% USAGE
% bb = bbApply( 'shift', bb, xdel, ydel )
%
% INPUTS
% bb - [nx4] original bbs
% xdel - amount to shift x coord of each bb left
% ydel - amount to shift y coord of each bb up
%
% OUTPUTS
% bb - [nx4] shifted bbs
%
% EXAMPLE
% bb = bbApply('shift', [0 0 10 10], 1, 2)
%
% See also bbApply
bb(:,1)=bb(:,1)-xdel; bb(:,2)=bb(:,2)-ydel;
end
function cen = getCenter( bb )
% Get center of bbs.
%
% USAGE
% cen = bbApply( 'getCenter', bb )
%
% INPUTS
% bb - [nx4] original bbs
%
% OUTPUTS
% cen - [nx1] centers of bbs
%
% EXAMPLE
% cen = bbApply('getCenter', [0 0 10 10])
%
% See also bbApply
cen=bb(:,1:2)+bb(:,3:4)/2;
end
function bb = intersect( bb1, bb2 )
% Get bb at intersection of bb1 and bb2 (may be empty).
%
% USAGE
% bb = bbApply( 'intersect', bb1, bb2 )
%
% INPUTS
% bb1 - [nx4] first set of bbs
% bb2 - [nx4] second set of bbs
%
% OUTPUTS
% bb - [nx4] intersection of bbs
%
% EXAMPLE
% bb = bbApply('intersect', [0 0 10 10], [5 5 10 10])
%
% See also bbApply bbApply>union
n1=size(bb1,1); n2=size(bb2,1);
if(n1==0 || n2==0), bb=zeros(0,4); return, end
if(n1==1 && n2>1), bb1=repmat(bb1,n2,1); n1=n2; end
if(n2==1 && n1>1), bb2=repmat(bb2,n1,1); n2=n1; end
assert(n1==n2);
lcsE=min(bb1(:,1:2)+bb1(:,3:4),bb2(:,1:2)+bb2(:,3:4));
lcsS=max(bb1(:,1:2),bb2(:,1:2)); empty=any(lcsE<lcsS,2);
bb=[lcsS lcsE-lcsS]; bb(empty,:)=0;
end
function bb = union( bb1, bb2 )
% Get bb that is union of bb1 and bb2 (smallest bb containing both).
%
% USAGE
% bb = bbApply( 'union', bb1, bb2 )
%
% INPUTS
% bb1 - [nx4] first set of bbs
% bb2 - [nx4] second set of bbs
%
% OUTPUTS
% bb - [nx4] intersection of bbs
%
% EXAMPLE
% bb = bbApply('union', [0 0 10 10], [5 5 10 10])
%
% See also bbApply bbApply>intersect
n1=size(bb1,1); n2=size(bb2,1);
if(n1==0 || n2==0), bb=zeros(0,4); return, end
if(n1==1 && n2>1), bb1=repmat(bb1,n2,1); n1=n2; end
if(n2==1 && n1>1), bb2=repmat(bb2,n1,1); n2=n1; end
assert(n1==n2);
lcsE=max(bb1(:,1:2)+bb1(:,3:4),bb2(:,1:2)+bb2(:,3:4));
lcsS=min(bb1(:,1:2),bb2(:,1:2));
bb=[lcsS lcsE-lcsS];
end
function bb = resize( bb, hr, wr, ar )
% Resize the bbs (without moving their centers).
%
% If wr>0 or hr>0, the w/h of each bb is adjusted in the following order:
% if(hr~=0), h=h*hr; end
% if(wr~=0), w=w*wr; end
% if(hr==0), h=w/ar; end
% if(wr==0), w=h*ar; end
% Only one of hr/wr may be set to 0, and then only if ar>0. If, however,
% hr=wr=0 and ar>0 then resizes bbs such that areas and centers are
% preserved but aspect ratio becomes ar.
%
% USAGE
% bb = bbApply( 'resize', bb, hr, wr, [ar] )
%
% INPUTS
% bb - [nx4] original bbs
% hr - ratio by which to multiply height (or 0)
% wr - ratio by which to multiply width (or 0)
% ar - [0] target aspect ratio (used only if hr=0 or wr=0)
%
% OUTPUT
% bb - [nx4] the output resized bbs
%
% EXAMPLE
% bb = bbApply('resize',[0 0 1 1],1.2,0,.5) % h'=1.2*h; w'=h'/2;
%
% See also bbApply, bbApply>squarify
if(nargin<4), ar=0; end; assert(size(bb,2)>=4);
assert((hr>0&&wr>0)||ar>0);
% preserve area and center, set aspect ratio
if(hr==0 && wr==0), a=sqrt(bb(:,3).*bb(:,4)); ar=sqrt(ar);
d=a*ar-bb(:,3); bb(:,1)=bb(:,1)-d/2; bb(:,3)=bb(:,3)+d;
d=a/ar-bb(:,4); bb(:,2)=bb(:,2)-d/2; bb(:,4)=bb(:,4)+d; return;
end
% possibly adjust h/w based on hr/wr
if(hr~=0), d=(hr-1)*bb(:,4); bb(:,2)=bb(:,2)-d/2; bb(:,4)=bb(:,4)+d; end
if(wr~=0), d=(wr-1)*bb(:,3); bb(:,1)=bb(:,1)-d/2; bb(:,3)=bb(:,3)+d; end
% possibly adjust h/w based on ar and NEW h/w
if(~hr), d=bb(:,3)/ar-bb(:,4); bb(:,2)=bb(:,2)-d/2; bb(:,4)=bb(:,4)+d; end
if(~wr), d=bb(:,4)*ar-bb(:,3); bb(:,1)=bb(:,1)-d/2; bb(:,3)=bb(:,3)+d; end
end
function bbr = squarify( bb, flag, ar )
% Fix bb aspect ratios (without moving the bb centers).
%
% The w or h of each bb is adjusted so that w/h=ar.
% The parameter flag controls whether w or h should change:
% flag==0: expand bb to given ar
% flag==1: shrink bb to given ar
% flag==2: use original w, alter h
% flag==3: use original h, alter w
% flag==4: preserve area, alter w and h
% If ar==1 (the default), always converts bb to a square, hence the name.
%
% USAGE
% bbr = bbApply( 'squarify', bb, flag, [ar] )
%
% INPUTS
% bb - [nx4] original bbs
% flag - controls whether w or h should change
% ar - [1] desired aspect ratio
%
% OUTPUT
% bbr - the output 'squarified' bbs
%
% EXAMPLE
% bbr = bbApply('squarify',[0 0 1 2],0)
%
% See also bbApply, bbApply>resize
if(nargin<3 || isempty(ar)), ar=1; end; bbr=bb;
if(flag==4), bbr=resize(bb,0,0,ar); return; end
for i=1:size(bb,1), p=bb(i,1:4);
usew = (flag==0 && p(3)>p(4)*ar) || (flag==1 && p(3)<p(4)*ar) || flag==2;
if(usew), p=resize(p,0,1,ar); else p=resize(p,1,0,ar); end; bbr(i,1:4)=p;
end
end
function hs = draw( bb, col, lw, ls, prop, ids )
% Draw single or multiple bbs to image (calls rectangle()).
%
% To draw bbs aligned with pixel boundaries, subtract .5 from the x and y
% coordinates (since pixel centers are located at integer locations).
%
% USAGE
% hs = bbApply( 'draw', bb, [col], [lw], [ls], [prop], [ids] )
%
% INPUTS
% bb - [nx4] standard bbs or [nx5] weighted bbs
% col - ['g'] color or [kx1] array of colors
% lw - [2] LineWidth for rectangle
% ls - ['-'] LineStyle for rectangle
% prop - [] other properties for rectangle
% ids - [ones(1,n)] id in [1,k] for each bb into colors array
%
% OUTPUT
% hs - [nx1] handles to drawn rectangles (and labels)
%
% EXAMPLE
% im(rand(3)); bbApply('draw',[1.5 1.5 1 1 .5],'g');
%
% See also bbApply, bbApply>embed, rectangle
[n,m]=size(bb); if(n==0), hs=[]; return; end
if(nargin<2 || isempty(col)), col=[]; end
if(nargin<3 || isempty(lw)), lw=2; end
if(nargin<4 || isempty(ls)), ls='-'; end
if(nargin<5 || isempty(prop)), prop={}; end
if(nargin<6 || isempty(ids)), ids=ones(1,n); end
% prepare display properties
prop=['LineWidth' lw 'LineStyle' ls prop 'EdgeColor'];
tProp={'FontSize',10,'color','w','FontWeight','bold',...
'VerticalAlignment','bottom'}; k=max(ids);
if(isempty(col)), if(k==1), col='g'; else col=hsv(k); end; end
if(size(col,1)<k), ids=ones(1,n); end; hs=zeros(1,n);
% draw rectangles and optionally labels
for b=1:n, hs(b)=rectangle('Position',bb(b,1:4),prop{:},col(ids(b),:)); end
if(m==4), return; end; hs=[hs zeros(1,n)]; bb=double(bb);
for b=1:n, hs(b+n)=text(bb(b,1),bb(b,2),num2str(bb(b,5),4),tProp{:}); end
end
function I = embed( I, bb, varargin )
% Embed single or multiple bbs directly into image.
%
% USAGE
% I = bbApply( 'embed', I, bb, varargin )
%
% INPUTS
% I - input image
% bb - [nx4] or [nx5] input bbs
% varargin - additional params (struct or name/value pairs)
% .col - [0 255 0] color for rectangle or nx3 array of colors
% .lw - [3] width for rectangle in pixels
% .fh - [35] font height (if displaying weight), may be 0
% .fcol - [255 0 0] font color or nx3 array of colors
%
% OUTPUT
% I - output image
%
% EXAMPLE
% I=imResample(imread('cameraman.tif'),2); bb=[200 70 70 90 0.25];
% J=bbApply('embed',I,bb,'col',[0 0 255],'lw',8,'fh',30); figure(1); im(J)
% K=bbApply('embed',J,bb,'col',[0 255 0],'lw',2,'fh',30); figure(2); im(K)
%
% See also bbApply, bbApply>draw, char2img
% get additional parameters
dfs={'col',[0 255 0],'lw',3,'fh',35,'fcol',[255 0 0]};
[col,lw,fh,fcol]=getPrmDflt(varargin,dfs,1);
n=size(bb,1); bb(:,1:4)=round(bb(:,1:4));
if(size(col,1)==1), col=col(ones(1,n),:); end
if(size(fcol,1)==1), fcol=fcol(ones(1,n),:); end
if( ismatrix(I) ), I=I(:,:,[1 1 1]); end
% embed each bb
x0=bb(:,1); x1=x0+bb(:,3)-1; y0=bb(:,2); y1=y0+bb(:,4)-1;
j0=floor((lw-1)/2); j1=ceil((lw-1)/2); h=size(I,1); w=size(I,2);
x00=max(1,x0-j0); x01=min(x0+j1,w); x10=max(1,x1-j0); x11=min(x1+j1,w);
y00=max(1,y0-j0); y01=min(y0+j1,h); y10=max(1,y1-j0); y11=min(y1+j1,h);
for b=1:n
for c=1:3, I([y00(b):y01(b) y10(b):y11(b)],x00(b):x11(b),c)=col(b,c); end
for c=1:3, I(y00(b):y11(b),[x00(b):x01(b) x10(b):x11(b)],c)=col(b,c); end
end
% embed text displaying bb score (inside upper-left bb corner)
if(size(bb,2)<5 || fh==0), return; end
bb(:,1:4)=intersect(bb(:,1:4),[1 1 w h]);
for b=1:n
M=char2img(sprintf('%.4g',bb(b,5)),fh); M=M{1}==0; [h,w]=size(M);
y0=bb(b,2); y1=y0+h-1; x0=bb(b,1); x1=x0+w-1;
if( x0>=1 && y0>=1 && x1<=size(I,2) && y1<=size(I,1))
Ir=I(y0:y1,x0:x1,1); Ig=I(y0:y1,x0:x1,2); Ib=I(y0:y1,x0:x1,3);
Ir(M)=fcol(b,1); Ig(M)=fcol(b,2); Ib(M)=fcol(b,3);
I(y0:y1,x0:x1,:)=cat(3,Ir,Ig,Ib);
end
end
end
function [patches, bbs] = crop( I, bbs, padEl, dims )
% Crop image regions from I encompassed by bbs.
%
% The only subtlety is that a pixel centered at location (i,j) would have a
% bb of [j-1/2,i-1/2,1,1]. The -1/2 is because pixels are located at
% integer locations. This is a Matlab convention, to confirm use:
% im(rand(3)); bbApply('draw',[1.5 1.5 1 1],'g')
% If bb contains all integer entries cropping is straightforward. If
% entries are not integers, x=round(x+.499) is used, eg 1.2 actually goes
% to 2 (since it is closer to 1.5 then .5), and likewise for y.
%
% If ~isempty(padEl), image is padded so can extract full bb region (no
% actual padding is done, this is fast). Otherwise bb is intersected with
% the image bb prior to cropping. If padEl is a string ('circular',
% 'replicate', or 'symmetric'), uses padarray to do actual padding (slow).
%
% USAGE
% [patches, bbs] = bbApply('crop',I,bb,[padEl],[dims])
%
% INPUTS
% I - image from which to crop patches
% bbs - bbs that indicate regions to crop
% padEl - [0] value to pad I or [] to indicate no padding (see above)
% dims - [] if specified resize each cropped patch to [w h]
%
% OUTPUTS
% patches - [1xn] cell of cropped image regions
% bbs - actual integer-valued bbs used to crop
%
% EXAMPLE
% I=imread('cameraman.tif'); bb=[-10 -10 100 100];
% p1=bbApply('crop',I,bb); p2=bbApply('crop',I,bb,'replicate');
% figure(1); im(I); figure(2); im(p1{1}); figure(3); im(p2{1});
%
% See also bbApply, ARRAYCROP, PADARRAY, IMRESAMPLE
% get padEl, bound bb to visible region if empty
if( nargin<3 ), padEl=0; end; h=size(I,1); w=size(I,2);
if( nargin<4 ), dims=[]; end;
if(isempty(padEl)), bbs=intersect([.5 .5 w h],bbs); end
% crop each patch in turn
n=size(bbs,1); patches=cell(1,n);
for i=1:n, [patches{i},bbs(i,1:4)]=crop1(bbs(i,1:4)); end
function [patch, bb] = crop1( bb )
% crop single patch (use arrayCrop only if necessary)
lcsS=round(bb([2 1])+.5-.001); lcsE=lcsS+round(bb([4 3]))-1;
if( any(lcsS<1) || lcsE(1)>h || lcsE(2)>w )
if( ischar(padEl) )
pt=max(0,1-lcsS(1)); pb=max(0,lcsE(1)-h);
pl=max(0,1-lcsS(2)); pr=max(0,lcsE(2)-w);
lcsS1=max(1,lcsS); lcsE1=min(lcsE,[h w]);
patch = I(lcsS1(1):lcsE1(1),lcsS1(2):lcsE1(2),:);
patch = padarray(patch,[pt pl],padEl,'pre');
patch = padarray(patch,[pb pr],padEl,'post');
else
if(ndims(I)==3); lcsS=[lcsS 1]; lcsE=[lcsE 3]; end
patch = arrayCrop(I,lcsS,lcsE,padEl);
end
else
patch = I(lcsS(1):lcsE(1),lcsS(2):lcsE(2),:);
end
bb = [lcsS([2 1]) lcsE([2 1])-lcsS([2 1])+1];
if(~isempty(dims)), patch=imResample(patch,[dims(2),dims(1)]); end
end
end
function bb = convert( bb, bbRef, isAbs )
% Convert bb relative to absolute coordinates and vice-versa.
%
% If isAbs==1, bb is assumed to be given in absolute coords, and the output
% is given in coords relative to bbRef. Otherwise, if isAbs==0, bb is
% assumed to be given in coords relative to bbRef and the output is given
% in absolute coords.
%
% USAGE
% bb = bbApply( 'convert', bb, bbRef, isAbs )
%
% INPUTS
% bb - original bb, either in abs or rel coords
% bbRef - reference bb
% isAbs - 1: bb is in abs coords, 0: bb is in rel coords
%
% OUTPUTS
% bb - converted bb
%
% EXAMPLE
% bbRef=[5 5 15 15]; bba=[10 10 5 5];
% bbr = bbApply( 'convert', bba, bbRef, 1 )
% bba2 = bbApply( 'convert', bbr, bbRef, 0 )
%
% See also bbApply
if( isAbs )
bb(1:2)=bb(1:2)-bbRef(1:2);
bb=bb./bbRef([3 4 3 4]);
else
bb=bb.*bbRef([3 4 3 4]);
bb(1:2)=bb(1:2)+bbRef(1:2);
end
end
function bbs = random( varargin )
% Randomly generate bbs that fall in a specified region.
%
% The vector dims defines the region in which bbs are generated. Specify
% dims=[height width] to generate bbs=[x y w h] such that: 1<=x<=width,
% 1<=y<=height, x+w-1<=width, y+h-1<=height. The biggest bb generated can
% be bb=[1 1 width height]. If dims is a three element vector the third
% coordinate is the depth, in this case bbs=[x y w h d] where 1<=d<=depth.
%
% A number of constraints can be specified that control the size and other
% characteristics of the generated bbs. Note that if incompatible
% constraints are specified (e.g. if the maximum width and height are both
% 5 while the minimum area is 100) no bbs will be generated. More
% generally, if fewer than n bbs are generated a warning is displayed.
%
% USAGE
% bbs = bbApply( 'random', pRandom )
%
% INPUTS
% pRandom - parameters (struct or name/value pairs)
% .n - ['REQ'] number of bbs to generate
% .dims - ['REQ'] region in which to generate bbs [height,width]
% .wRng - [1 inf] range for width of bbs (or scalar value)
% .hRng - [1 inf] range for height of bbs (or scalar value)
% .aRng - [1 inf] range for area of bbs
% .arRng - [0 inf] range for aspect ratio (width/height) of bbs
% .unique - [1] if true generate unique bbs
% .maxOverlap - [1] max overlap (intersection/union) between bbs
% .maxIter - [100] max iterations to go w/o changes before giving up
% .show - [0] if true show sample generated bbs
%
% OUTPUTS
% bbs - [nx4] array of randomly generated integer bbs
%
% EXAMPLE
% bbs=bbApply('random','n',50,'dims',[20 20],'arRng',[.5 .5],'show',1);
%
% See also bbApply
% get parameters
rng=[1 inf]; dfs={ 'n','REQ', 'dims','REQ', 'wRng',rng, 'hRng',rng, ...
'aRng',rng, 'arRng',[0 inf], 'unique',1, 'maxOverlap',1, ...
'maxIter',100, 'show',0 };
[n,dims,wRng,hRng,aRng,arRng,uniqueOnly,maxOverlap,maxIter,show] ...
= getPrmDflt(varargin,dfs,1);
if(length(hRng)==1), hRng=[hRng hRng]; end
if(length(wRng)==1), wRng=[wRng wRng]; end
if(length(dims)==3), d=5; else d=4; end
% generate random bbs satisfying constraints
bbs=zeros(0,d); ids=zeros(0,1); n1=min(n*10,1000);
M=max(dims)+1; M=M.^(0:d-1); iter=0; k=0;
tid=ticStatus('generating random bbs',1,2);
while( k<n && iter<maxIter )
ys=1+floor(rand(2,n1)*dims(1)); ys0=min(ys); ys1=max(ys); hs=ys1-ys0+1;
xs=1+floor(rand(2,n1)*dims(2)); xs0=min(xs); xs1=max(xs); ws=xs1-xs0+1;
if(d==5), ds=1+floor(rand(1,n1)*dims(3)); else ds=zeros(0,n1); end
if(arRng(1)==arRng(2)), ws=hs.*arRng(1); end
ars=ws./hs; ws=round(ws); xs1=xs0+ws-1; as=ws.*hs;
kp = ys0>0 & xs0>0 & ys1<=dims(1) & xs1<=dims(2) & ...
hs>=hRng(1) & hs<=hRng(2) & ws>=wRng(1) & ws<=wRng(2) & ...
as>=aRng(1) & as<=aRng(2) & ars>=arRng(1) & ars<=arRng(2);
bbs1=[xs0' ys0' ws' hs' ds']; bbs1=bbs1(kp,:);
k0=k; bbs=[bbs; bbs1]; k=size(bbs,1); %#ok<AGROW>
if( maxOverlap<1 && k ), bbs=bbs(1:k0,:);
for j=1:size(bbs1,1), bbs0=bbs; bb=bbs1(j,:);
if(d==5), bbs=bbs(bbs(:,5)==bb(5),:); end
if(isempty(bbs)), bbs=[bbs0; bb]; continue; end
ws1=min(bbs(:,1)+bbs(:,3),bb(1)+bb(3))-max(bbs(:,1),bb(1));
hs1=min(bbs(:,2)+bbs(:,4),bb(2)+bb(4))-max(bbs(:,2),bb(2));
o=max(0,ws1).*max(0,hs1); o=o./(bbs(:,3).*bbs(:,4)+bb(3).*bb(4)-o);
if(max(o)<=maxOverlap), bbs=[bbs0; bb]; else bbs=bbs0; end
end
elseif( uniqueOnly && k )
ids=[ids; sum(bbs1.*M(ones(1,size(bbs1,1)),:),2)]; %#ok<AGROW>
[ids,o]=sort(ids); bbs=bbs(o,:); kp=[ids(1:end-1)~=ids(2:end); true];
bbs=bbs(kp,:); ids=ids(kp,:);
end
k=size(bbs,1); if(k0==k), iter=iter+1; else iter=0; end
if(k>n), bbs=bbs(randSample(k,n),:); k=n; end;
tocStatus(tid,max(k/n,iter/maxIter));
end
if( k<n ), warning('only generated %i of %i bbs',k,n); n=k; end %#ok<WNTAG>
% optionally display a few bbs
if( show )
k=8; figure(show); im(zeros(dims)); cs=uniqueColors(1,k,0,0);
if(n>k), bbs1=bbs(randsample(n,k),:); else bbs1=bbs; end
bbs1(:,1:2)=bbs1(:,1:2)-.5;
for i=1:min(k,n), rectangle('Position',bbs1(i,:),...
'EdgeColor',cs(i,:),'LineStyle','--'); end
end
end
function bbs = frMask( M, bbw, bbh, thr )
% Convert weighted mask to bbs.
%
% Pixels in mask above given threshold (thr) indicate bb centers.
%
% USAGE
% bbs = bbApply('frMask',M,bbw,bbh,[thr])
%
% INPUTS
% M - mask
% bbw - bb target width
% bbh - bb target height
% thr - [0] mask threshold
%
% OUTPUTS
% bbs - bounding boxes
%
% EXAMPLE
% w=20; h=10; bbw=5; bbh=8; M=double(rand(h,w)); M(M<.95)=0;
% bbs=bbApply('frMask',M,bbw,bbh); M2=bbApply('toMask',bbs,w,h);
% sum(abs(M(:)-M2(:)))
%
% See also bbApply, bbApply>toMask
if(nargin<4), thr=0; end
ids=find(M>thr); ids=ids(:); h=size(M,1);
if(isempty(ids)), bbs=zeros(0,5); return; end
xs=floor((ids-1)/h); ys=ids-xs*h; xs=xs+1;
bbs=[xs-floor(bbw/2) ys-floor(bbh/2)];
bbs(:,3)=bbw; bbs(:,4)=bbh; bbs(:,5)=M(ids);
end
function M = toMask( bbs, w, h, fill, bgrd )
% Create weighted mask encoding bb centers (or extent).
%
% USAGE
% M = bbApply('toMask',bbs,w,h,[fill],[bgrd])
%
% INPUTS
% bbs - bounding boxes
% w - mask target width
% h - mask target height
% fill - [0] if 1 encodes extent of bbs
% bgrd - [0] default value for background pixels
%
% OUTPUTS
% M - hxw mask
%
% EXAMPLE
%
% See also bbApply, bbApply>frMask
if(nargin<4||isempty(fill)), fill=0; end
if(nargin<5||isempty(bgrd)), bgrd=0; end
if(size(bbs,2)==4), bbs(:,5)=1; end
M=zeros(h,w); B=true(h,w); n=size(bbs,1);
if( fill==0 )
p=floor(getCenter(bbs)); p=sub2ind([h w],p(:,2),p(:,1));
for i=1:n, M(p(i))=M(p(i))+bbs(i,5); end
if(bgrd~=0), B(p)=0; end
else
bbs=[intersect(round(bbs),[1 1 w h]) bbs(:,5)]; n=size(bbs,1);
x0=bbs(:,1); x1=x0+bbs(:,3)-1; y0=bbs(:,2); y1=y0+bbs(:,4)-1;
for i=1:n, y=y0(i):y1(i); x=x0(i):x1(i);
M(y,x)=M(y,x)+bbs(i,5); B(y,x)=0; end
end
if(bgrd~=0), M(B)=bgrd; end
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
imwrite2.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/images/imwrite2.m
| 5,086 |
utf_8
|
c98d66c2cddd9ec90beb9b1bbde31fe0
|
function I = imwrite2( I, mulFlag, imagei, path, ...
name, ext, nDigits, nSplits, spliti, varargin )
% Similar to imwrite, except follows a strict naming convention.
%
% Wrapper for imwrite that writes file to the filename:
% fName = [path name int2str2(i,nDigits) '.' ext];
% Using imwrite:
% imwrite( I, fName, writePrms )
% If I represents a stack of images, the ith image is written to:
% fNamei = [path name int2str2(i+imagei-1,nDigits) '.' ext];
% If I=[], then imwrite2 will attempt to read images from disk instead.
% If dir spec. by 'path' does not exist, imwrite2 attempts to create it.
%
% mulFlag controls how I is interpreted. If mulFlag==0, then I is
% intrepreted as a single image, otherwise I is interpreted as a stack of
% images, where I(:,:,...,j) represents the jth image (see fevalArrays for
% more info).
%
% If nSplits>1, writes/reads images into/from multiple directories. This is
% useful since certain OS handle very large directories (of say >20K
% images) rather poorly (I'm talking to you Bill). Thus, can take 100K
% images, and write into 5 separate dirs, then read them back in.
%
% USAGE
% I = imwrite2( I, mulFlag, imagei, path, ...
% [name], [ext], [nDigits], [nSplits], [spliti], [varargin] )
%
% INPUTS
% I - image or array or cell of images (if [] reads else writes)
% mulFlag - set to 1 if I represents a stack of images
% imagei - first image number
% path - directory where images are
% name - ['I'] base name of images
% ext - ['png'] extension of image
% nDigits - [5] number of digits for filename index
% nSplits - [1] number of dirs to break data into
% spliti - [0] first split (dir) number
% writePrms - [varargin] parameters to imwrite
%
% OUTPUTS
% I - image or images (read from disk if input I=[])
%
% EXAMPLE
% load images; I=images(:,:,1:10); clear IDXi IDXv t video videos images;
% imwrite2( I(:,:,1), 0, 0, 'rats/', 'rats', 'png', 5 ); % write 1
% imwrite2( I, 1, 0, 'rats/', 'rats', 'png', 5 ); % write 5
% I2 = imwrite2( [], 1, 0, 'rats/', 'rats', 'png', 5 ); % read 5
% I3 = fevalImages(@(x) x,{},'rats/','rats','png',0,4,5); % read 5
%
% EXAMPLE - multiple splits
% load images; I=images(:,:,1:10); clear IDXi IDXv t video videos images;
% imwrite2( I, 1, 0, 'rats', 'rats', 'png', 5, 2, 0 ); % write 10
% I2=imwrite2( [], 1, 0, 'rats', 'rats', 'png', 5, 2, 0 ); % read 10
%
% See also FEVALIMAGES, FEVALARRAYS
%
% Piotr's Computer Vision Matlab Toolbox Version 2.30
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
if( nargin<5 || isempty(name) ); name='I'; end;
if( nargin<6 || isempty(ext) ); ext='png'; end;
if( nargin<7 || isempty(nDigits) ); nDigits=5; end;
if( nargin<8 || isempty(nSplits) ); nSplits=1; end;
if( nargin<9 || isempty(spliti) ); spliti=0; end;
n = size(I,3); if(isempty(I)); n=0; end
% multiple splits -- call imwrite2 recursively
if( nSplits>1 )
write2inp = [ {name, ext, nDigits, 1, 0} varargin ];
if(n>0); nSplits=min(n,nSplits); end;
for s=1:nSplits
pathS = [path int2str2(s-1+spliti,2)];
if( n>0 ) % write
nPerDir = ceil( n / nSplits );
ISplit = I(:,:,1:min(end,nPerDir));
imwrite2( ISplit, nPerDir>1, 0, pathS, write2inp{:} );
if( s~=nSplits ); I = I(:,:,(nPerDir+1):end); end
else % read
ISplit = imwrite2( [], 1, 0, pathS, write2inp{:} );
I = cat(3,I,ISplit);
end
end
return;
end
% if I is empty read from disk
if( n==0 )
I = fevalImages( @(x) x, {}, path, name, ext, imagei, [], nDigits );
return;
end
% Check if path exists (create if not) and add '/' at end if needed
if( ~isempty(path) )
if(~exist(path,'dir'))
warning( ['creating directory: ' path] ); %#ok<WNTAG>
mkdir( path );
end;
if( path(end)~='\' && path(end)~='/' ); path(end+1) = '/'; end
end
% Write images using one of the two subfunctions
params = varargin;
if( mulFlag )
imwrite2m( [], 'init', imagei, path, name, ext, nDigits, params );
if( ~iscell(I) )
fevalArrays( I, @imwrite2m, 'write' );
else
fevalArrays( I, @(x) imwrite2m(x{1},'write') );
end
else
if( ~iscell(I) )
imwrite2s( I, imagei, path, name, ext, nDigits, params );
else
imwrite2s( I{1}, imagei, path, name, ext, nDigits, params );
end;
end
function varargout = imwrite2m( I, type, varargin )
% helper for writing multiple images (passed to fevalArrays)
persistent imagei path name ext nDigits params
switch type
case 'init'
narginchk(8,8);
[nstart, path, name, ext, nDigits, params] = deal(varargin{:});
if(isempty(nstart)); imagei=0; else imagei=nstart; end
varargout = {[]};
case 'write'
narginchk(2,2);
imwrite2s( I, imagei, path, name, ext, nDigits, params );
imagei = imagei+1;
varargout = {[]};
end
function imwrite2s( I, imagei, path, name, ext, nDigits, params )
% helper for writing a single image
fullname = [path name int2str2(imagei,nDigits) '.' ext];
imwrite( I, fullname, params{:} );
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
convnFast.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/images/convnFast.m
| 9,102 |
utf_8
|
03d05e74bb7ae2ecb0afd0ac115fda39
|
function C = convnFast( A, B, shape )
% Fast convolution, replacement for both conv2 and convn.
%
% See conv2 or convn for more information on convolution in general.
%
% This works as a replacement for both conv2 and convn. Basically,
% performs convolution in either the frequency or spatial domain, depending
% on which it thinks will be faster (see below). In general, if A is much
% bigger then B then spatial convolution will be faster, but if B is of
% similar size to A and both are fairly big (such as in the case of
% correlation), convolution as multiplication in the frequency domain will
% tend to be faster.
%
% The shape flag can take on 1 additional value which is 'smooth'. This
% flag is intended for use with smoothing kernels. The returned matrix C
% is the same size as A with boundary effects handled in a special manner.
% That is instead of A being zero padded before being convolved with B;
% near the boundaries a cropped version of the matrix B is used, and the
% results is scaled by the fraction of the weight found in the cropped
% version of B. In this case each dimension of B must be odd, and all
% elements of B must be positive. There are other restrictions on when
% this flag can be used, and in general it is only useful for smoothing
% kernels. For 2D filtering it does not have much overhead, for 3D it has
% more and for higher dimensions much much more.
%
% For optimal performance some timing constants must be set to choose
% between doing convolution in the spatial and frequency domains, for more
% info see timeConv below.
%
% USAGE
% C = convnFast( A, B, [shape] )
%
% INPUTS
% A - d dimensional input matrix
% B - d dimensional matrix to convolve with A
% shape - ['full'] 'valid', 'full', 'same', or 'smooth'
%
% OUTPUTS
% C - result of convolution
%
% EXAMPLE
%
% See also CONV2, CONVN
%
% Piotr's Computer Vision Matlab Toolbox Version 2.61
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
if( nargin<3 || isempty(shape)); shape='full'; end
if( ~any(strcmp(shape,{'same', 'valid', 'full', 'smooth'})) )
error( 'convnFast: unknown shape flag' ); end
shapeorig = shape;
smoothFlag = (strcmp(shape,'smooth'));
if( smoothFlag ); shape = 'same'; end;
% get dimensions of A and B
ndA = ndims(A); ndB = ndims(B); nd = max(ndA,ndB);
sizA = size(A); sizB = size(B);
if (ndA>ndB); sizB = [sizB ones(1,ndA-ndB)]; end
if (ndA<ndB); sizA = [sizA ones(1,ndB-ndA)]; end
% ERROR CHECK if smoothflag
if( smoothFlag )
if( ~all( mod(sizB,2)==1 ) )
error('If flag==''smooth'' then must have odd sized mask');
end;
if( ~all( B>0 ) )
error('If flag==''smooth'' then mask must have >0 values.');
end;
if( any( (sizB-1)/2>sizA ) )
error('B is more then twice as big as A, cannot use flag==''smooth''');
end;
end
% OPTIMIZATION for 3D conv when B is actually 2D - calls (spatial) conv2
% repeatedly on 2D slices of A. Note that may need to rearange A and B
% first and use recursion. The benefits carry over to convnBound
% (which is faster for 2D arrays).
if( ndA==3 && ndB==3 && (sizB(1)==1 || sizB(2)==1) )
if (sizB(1)==1)
A = permute( A, [2 3 1]); B = permute( B, [2 3 1]);
C = convnFast( A, B, shapeorig );
C = permute( C, [3 1 2] );
elseif (sizB(2)==1)
A = permute( A, [3 1 2]); B = permute( B, [3 1 2]);
C = convnFast( A, B, shapeorig );
C = permute( C, [2 3 1] );
end
return;
elseif( ndA==3 && ndB==2 )
C1 = conv2( A(:,:,1), B, shape );
C = zeros( [size(C1), sizA(3)] ); C(:,:,1) = C1;
for i=2:sizA(3); C(:,:,i) = conv2( A(:,:,i), B, shape ); end
if (smoothFlag)
for i=1:sizA(3)
C(:,:,i) = convnBound(A(:,:,i),B,C(:,:,i),sizA(1:2),sizB(1:2));
end
end
return;
end
% get predicted time of convolution in frequency and spatial domain
% constants taken from timeConv
sizfft = 2.^ceil(real(log2(sizA+sizB-1))); psizfft=prod(sizfft);
frequenPt = 3 * 1e-7 * psizfft * log(psizfft);
if (nd==2)
spatialPt = 5e-9 * sizA(1) * sizA(2) * sizB(1) * sizB(2);
else
spatialPt = 5e-8 * prod(sizA) * prod(sizB);
end
% perform convolution
if ( spatialPt < frequenPt )
if (nd==2)
C = conv2( A, B, shape );
else
C = convn( A, B, shape );
end
else
C = convnFreq( A, B, sizA, sizB, shape );
end;
% now correct boundary effects (if shape=='smooth')
if( ~smoothFlag ); return; end;
C = convnBound( A, B, C, sizA, sizB );
function C = convnBound( A, B, C, sizA, sizB )
% calculate boundary values for C in spatial domain
nd = length(sizA);
radii = (sizB-1)/2;
% flip B appropriately (conv flips B)
for d=1:nd; B = flipdim(B,d); end
% accelerated case for 1D mask B
if( nd==2 && sizB(1)==1 )
sumB=sum(B(:)); r=radii(2); O=ones(1,sizA(1));
for i=1:r
Ai=A(:,1:r+i); Bi=B(r+2-i:end);
C(:,i)=sum(Ai.*Bi(O,:),2)/sum(Bi)*sumB;
Ai=A(:,end+1-r-i:end); Bi=B(1:(end-r+i-1));
C(:,end-i+1)=sum(Ai.*Bi(O,:),2)/sum(Bi)*sumB;
end; return;
elseif( nd==2 && sizB(2)==1 )
sumB=sum(B(:)); r=radii(1); O=ones(1,sizA(2));
for i=1:r
Ai=A(1:r+i,:); Bi=B(r+2-i:end);
C(i,:)=sum(Ai.*Bi(:,O),1)/sum(Bi)*sumB;
Ai=A(end+1-r-i:end,:); Bi=B(1:(end-r+i-1));
C(end-i+1,:)=sum(Ai.*Bi(:,O),1)/sum(Bi)*sumB;
end; return;
end
% get location that need to be updated
inds = {':'}; inds = inds(:,ones(1,nd));
Dind = zeros( sizA );
for d=1:nd
inds1 = inds; inds1{ d } = 1:radii(d);
inds2 = inds; inds2{ d } = sizA(d)-radii(d)+1:sizA(d);
Dind(inds1{:}) = 1; Dind(inds2{:}) = 1;
end
Dind = find( Dind );
Dndx = ind2sub2( sizA, Dind );
nlocs = length(Dind);
% get cuboid dimensions for all the boundary regions
sizeArep = repmat( sizA, [nlocs,1] );
radiiRep = repmat( radii, [nlocs,1] );
Astarts = max(1,Dndx-radiiRep);
Aends = min( sizeArep, Dndx+radiiRep);
Bstarts = Astarts + (1-Dndx+radiiRep);
Bends = Bstarts + (Aends-Astarts);
% now update these locations
vs = zeros( 1, nlocs );
if( nd==2 )
for i=1:nlocs % accelerated for 2D arrays
Apart = A( Astarts(i,1):Aends(i,1), Astarts(i,2):Aends(i,2) );
Bpart = B( Bstarts(i,1):Bends(i,1), Bstarts(i,2):Bends(i,2) );
v = (Apart.*Bpart); vs(i) = sum(v(:)) ./ sum(Bpart(:));
end
elseif( nd==3 ) % accelerated for 3D arrays
for i=1:nlocs
Apart = A( Astarts(i,1):Aends(i,1), Astarts(i,2):Aends(i,2), ...
Astarts(i,3):Aends(i,3) );
Bpart = B( Bstarts(i,1):Bends(i,1), Bstarts(i,2):Bends(i,2), ...
Bstarts(i,3):Bends(i,3) );
za = sum(sum(sum(Apart.*Bpart))); zb=sum(sum(sum(Bpart)));
vs(1,i) = za./zb;
end
else % general case [slow]
extract=cell(1,nd);
for i=1:nlocs
for d=1:nd; extract{d} = Astarts(i,d):Aends(i,d); end
Apart = A( extract{:} );
for d=1:nd; extract{d} = Bstarts(i,d):Bends(i,d); end
Bpart = B( extract{:} );
v = (Apart.*Bpart); vs(i) = sum(v(:)) ./ sum(Bpart(:));
end
end
C( Dind ) = vs * sum(B(:));
function C = convnFreq( A, B, sizA, sizB, shape )
% Convolution as multiplication in the frequency domain
siz = sizA + sizB - 1;
% calculate correlation in frequency domain
Fa = fftn(A,siz);
Fb = fftn(B,siz);
C = ifftn(Fa .* Fb);
% make sure output is real if inputs were both real
if(isreal(A) && isreal(B)); C = real(C); end
% crop to size
if(strcmp(shape,'valid'))
C = arrayToDims( C, max(0,sizA-sizB+1 ) );
elseif(strcmp(shape,'same'))
C = arrayToDims( C, sizA );
elseif(~strcmp(shape,'full'))
error('unknown shape');
end
function K = timeConv() %#ok<DEFNU>
% Function used to calculate constants for prediction of convolution in the
% frequency and spatial domains. Method taken from normxcorr2.m
% May need to reset K's if placing this on a new machine, however, their
% ratio should be about the same..
mintime = 4;
switch 3
case 1 % conv2 [[empirically K = 5e-9]]
% convolution time = K*prod(size(a))*prod(size(b))
siza = 30; sizb = 200;
a = ones(siza); b = ones(sizb);
t1 = cputime; t2 = t1; k = 0;
while (t2-t1)<mintime;
disc = conv2(a,b); k = k + 1; t2 = cputime; %#ok<NASGU>
end
K = (t2-t1)/k/siza^2/sizb^2;
case 2 % convn [[empirically K = 5e-8]]
% convolution time = K*prod(size(a))*prod(size(b))
siza = [10 10 10]; sizb = [30 30 10];
a = ones(siza); b = ones(sizb);
t1 = cputime; t2 = t1; k = 0;
while (t2-t1)<mintime;
disc = convn(a,b); k = k + 1; t2 = cputime; %#ok<NASGU>
end
K = (t2-t1)/k/prod(siza)/prod(sizb);
case 3 % fft (one dimensional) [[empirically K = 1e-7]]
% fft time = K * n log(n) [if n is power of 2]
% Works fastest for powers of 2. (so always zero pad until have
% size of power of 2?). 2 dimensional fft has to apply single
% dimensional fft to each column, and then signle dimensional fft
% to each resulting row. time = K * (mn)log(mn). Likewise for
% highter dimensions. convnFreq requires 3 such ffts.
n = 2^nextpow2(2^15);
vec = complex(rand(n,1),rand(n,1));
t1 = cputime; t2 = t1; k = 0;
while (t2-t1) < mintime;
disc = fft(vec); k = k + 1; t2 = cputime; %#ok<NASGU>
end
K = (t2-t1) / k / n / log(n);
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
imMlGauss.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/images/imMlGauss.m
| 5,674 |
utf_8
|
56ead1b25fbe356f7912993d46468d02
|
function varargout = imMlGauss( G, symmFlag, show )
% Calculates max likelihood params of Gaussian that gave rise to image G.
%
% Suppose G contains an image of a gaussian distribution. One way to
% recover the parameters of the gaussian is to threshold the image, and
% then estimate the mean/covariance based on the coordinates of the
% thresholded points. A better method is to do no thresholding and instead
% use all the coordinates, weighted by their value. This function does the
% latter, except in a very efficient manner since all computations are done
% in parallel over the entire image.
%
% This function works over 2D or 3D images. It makes most sense when G in
% fact contains an image of a single gaussian, but a result will be
% returned regardless. All operations are performed on abs(G) in case it
% contains negative or complex values.
%
% symmFlag is an optional flag that if set to 1 then imMlGauss recovers
% the maximum likelihood symmetric gaussian. That is the variance in each
% direction is equal, and all covariance terms are 0. If symmFlag is set
% to 2 and G is 3D, imMlGauss recovers the ML guassian with equal
% variance in the 1st 2 dimensions (row and col) and all covariance terms
% equal to 0, but a possibly different variance in the 3rd (z or t)
% dimension.
%
% USAGE
% varargout = imMlGauss( G, [symmFlag], [show] )
%
% INPUTS
% G - image of a gaussian (weighted pixels)
% symmFlag - [0] see above
% show - [0] figure to use for optional display
%
% OUTPUTS
% mu - 2 or 3 element vector specifying the mean [row,col,z]
% C - 2x2 or 3x3 covariance matrix [row,col,z]
% GR - image of the recovered gaussian (faster if omitted)
% logl - log likelihood of G given recov. gaussian (faster if omitted)
%
% EXAMPLE - 2D
% R = rotationMatrix( pi/6 ); C=R'*[10^2 0; 0 20^2]*R;
% G = filterGauss( [200, 300], [150,100], C, 0 );
% [mu,C,GR,logl] = imMlGauss( G, 0, 1 );
% mask = maskEllipse( size(G,1), size(G,2), mu, C );
% figure(2); im(mask)
%
% EXAMPLE - 3D
% R = rotationMatrix( [1,1,0], pi/4 );
% C = R'*[5^2 0 0; 0 2^2 0; 0 0 4^2]*R;
% G = filterGauss( [50,50,50], [25,25,25], C, 0 );
% [mu,C,GR,logl] = imMlGauss( G, 0, 1 );
%
% See also GAUSS2ELLIPSE, PLOTGAUSSELLIPSES, MASKELLIPSE
%
% Piotr's Computer Vision Matlab Toolbox Version 2.0
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
if( nargin<2 || isempty(symmFlag) ); symmFlag=0; end;
if( nargin<3 || isempty(show) ); show=0; end;
varargout = cell(1,max(nargout,2));
nd = ndims(G); G = abs(G);
if( nd==2 )
[varargout{:}] = imMlGauss2D( G, symmFlag, show );
elseif( nd==3 )
[varargout{:}] = imMlGauss3D( G, symmFlag, show );
else
error( 'Unsupported dimension for G. G must be 2D or 3D.' );
end
function [mu,C,GR,logl] = imMlGauss2D( G, symmFlag, show )
% to be used throughout calculations
[ gridCols, gridRows ] = meshgrid( 1:size(G,2), 1:size(G,1) );
sumG = sum(G(:)); if(sumG==0); sumG=1; end;
% recover mean
muCol = (gridCols .* G); muCol = sum( muCol(:) ) / sumG;
muRow = (gridRows .* G); muRow = sum( muRow(:) ) / sumG;
mu = [muRow, muCol];
% recover sigma
distCols = (gridCols - muCol);
distRows = (gridRows - muRow);
if( symmFlag==0 )
Ccc = (distCols .^ 2) .* G; Ccc = sum(Ccc(:)) / sumG;
Crr = (distRows .^ 2) .* G; Crr = sum(Crr(:)) / sumG;
Crc = (distCols .* distRows) .* G; Crc = sum(Crc(:)) / sumG;
C = [Crr Crc; Crc Ccc];
elseif( symmFlag==1 )
sigSq = (distCols.^2 + distRows.^2) .* G;
sigSq = 1/2 * sum(sigSq(:)) / sumG;
C = sigSq*eye(2);
else
error(['Illegal value for symmFlag: ' num2str(symmFlag)]);
end
% get the log likelihood of the data
if (nargout>2)
GR = filterGauss( size(G), mu, C );
probs = GR; probs( probs<realmin ) = realmin;
logl = G .* log( probs );
logl = sum( logl(:) );
end
% plot ellipses
if (show)
figure(show); im(G);
hold('on'); plotGaussEllipses( mu, C, 2 ); hold('off');
end
function [mu,C,GR,logl] = imMlGauss3D( G, symmFlag, show )
% to be used throughout calculations
[gridCols,gridRows,gridZs]=meshgrid(1:size(G,2),1:size(G,1),1:size(G,3));
sumG = sum(G(:));
% recover mean
muCol = (gridCols .* G); muCol = sum( muCol(:) ) / sumG;
muRow = (gridRows .* G); muRow = sum( muRow(:) ) / sumG;
muZ = (gridZs .* G); muZ = sum( muZ(:) ) / sumG;
mu = [muRow, muCol, muZ];
% recover C
distCols = (gridCols - muCol);
distRows = (gridRows - muRow);
distZs = (gridZs - muZ);
if( symmFlag==0 )
distColsG = distCols .* G; distRowsG = distRows .* G;
Ccc = distCols .* distColsG; Ccc = sum(Ccc(:));
Crc = distRows .* distColsG; Crc = sum(Crc(:));
Czc = distZs .* distColsG; Czc = sum(Czc(:));
Crr = distRows .* distRowsG; Crr = sum(Crr(:));
Czr = distZs .* distRowsG; Czr = sum(Czr(:));
Czz = distZs .* distZs .* G; Czz = sum(Czz(:));
C = [Crr Crc Czr; Crc Ccc Czc; Czr Czc Czz] / sumG;
elseif( symmFlag==1 )
sigSq = (distCols.^2 + distRows.^2 + distZs .^ 2) .* G;
sigSq = 1/3 * sum(sigSq(:));
C = [sigSq 0 0; 0 sigSq 0; 0 0 sigSq] / sumG;
elseif( symmFlag==2 )
sigSq = (distCols.^2 + distRows.^2) .* G; sigSq = 1/2 * sum(sigSq(:));
tauSq = (distZs .^ 2) .* G; tauSq = sum(tauSq(:));
C = [sigSq 0 0; 0 sigSq 0; 0 0 tauSq] / sumG;
else
error(['Illegal value for symmFlag: ' num2str(symmFlag)])
end
% get the log likelihood of the data
if( nargout>2 || (show) )
GR = filterGauss( size(G), mu, C );
probs = GR; probs( probs<realmin ) = realmin;
logl = G .* log( probs );
logl = sum( logl(:) );
end
% plot G and GR
if( show )
figure(show); montage2(G);
figure(show+1); montage2(GR);
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
montage2.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/images/montage2.m
| 7,484 |
utf_8
|
828f57d7b1f67d36eeb6056f06568ebf
|
function varargout = montage2( IS, prm )
% Used to display collections of images and videos.
%
% Improved version of montage, with more control over display.
% NOTE: Can convert between MxNxT and MxNx3xT image stack via:
% I = repmat( I, [1,1,1,3] ); I = permute(I, [1,2,4,3] );
%
% USAGE
% varargout = montage2( IS, [prm] )
%
% INPUTS
% IS - MxNxTxR or MxNxCxTxR, where C==1 or C==3, and R may be 1
% or cell vector of MxNxT or MxNxCxT matrices
% prm
% .showLines - [1] whether to show lines separating the various frames
% .extraInfo - [0] if 1 then a colorbar is shown as well as impixelinfo
% .cLim - [] cLim = [clow chigh] optional scaling of data
% .mm - [] #images/col per montage
% .nn - [] #images/row per montage
% .labels - [] cell array of labels (strings) (T if R==1 else R)
% .perRow - [0] only if R>1 and not cell, alternative displays method
% .hasChn - [0] if true assumes IS is MxNxCxTxR else MxNxTxR
% .padAmt - [0] only if perRow, amount to pad when in row mode
% .padEl - [] pad element, defaults to min value in IS
%
% OUTPUTS
% h - image handle
% m - #images/col
% nn - #images/row
%
% EXAMPLE - [3D] show a montage of images
% load( 'images.mat' ); clf; montage2( images );
%
% EXAMPLE - [3D] show a montage of images with labels
% load( 'images.mat' );
% for i=1:50; labels{i}=['I-' int2str2(i,2)]; end
% prm = struct('extraInfo',1,'perRow',0,'labels',{labels});
% clf; montage2( images(:,:,1:50), prm );
%
% EXAMPLE - [3D] show a montage of images with color boundaries
% load( 'images.mat' );
% I3 = repmat(permute(images,[1 2 4 3]),[1,1,3,1]); % add color chnls
% prm = struct('padAmt',4,'padEl',[50 180 50],'hasChn',1,'showLines',0);
% clf; montage2( I3(:,:,:,1:48), prm )
%
% EXAMPLE - [4D] show a montage of several groups of images
% for i=1:25; labels{i}=['V-' int2str2(i,2)]; end
% prm = struct('labels',{labels});
% load( 'images.mat' ); clf; montage2( videos(:,:,:,1:25), prm );
%
% EXAMPLE - [4D] show using 'row' format
% load( 'images.mat' );
% prm = struct('perRow',1, 'padAmt',6, 'padEl',255 );
% figure(1); clf; montage2( videos(:,:,:,1:10), prm );
%
% See also MONTAGE, PLAYMOVIE, FILMSTRIP
%
% Piotr's Computer Vision Matlab Toolbox Version 2.0
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
if( nargin<2 ); prm=struct(); end
varargout = cell(1,nargout);
%%% get parameters (set defaults)
dfs = {'showLines',1, 'extraInfo',0, 'cLim',[], 'mm',[], 'nn',[],...
'labels',[], 'perRow',false, 'padAmt',0, 'padEl',[], 'hasChn',false };
prm = getPrmDflt( prm, dfs );
extraInfo=prm.extraInfo; labels=prm.labels; perRow=prm.perRow;
hasChn=prm.hasChn;
%%% If IS is not a cell convert to MxNxCxTxR array
if( iscell(IS) && numel(IS)==1 ); IS=IS{1}; end;
if( ~iscell(IS) && ~ismatrix(IS) )
siz=size(IS);
if( ~hasChn );
IS=reshape(IS,[siz(1:2),1,siz(3:end)]);
prm.hasChn = true;
end;
if(ndims(IS)>5); error('montage2: input too large'); end;
end
if( ~iscell(IS) && size(IS,5)==1 ) %%% special case call subMontage once
[varargout{:}] = subMontage(IS,prm);
title(inputname(1));
elseif( perRow ) %%% display each montage in row format
if(iscell(IS)); error('montage2: IS cannot be a cell if perRow'); end;
siz = size(IS);
IS=reshape(permute(IS,[1 2 4 3 5]),siz(1),[],siz(3),siz(5));
if( nargout ); varargout{1}=IS; end
prm.perRow = false; prm.hasChn=true;
[varargout{2:end}] = subMontage( IS, prm );
title(inputname(1));
else %%% display each montage using subMontage
% convert to cell array
if( iscell(IS) )
nMontages = numel(IS);
else
nMontages = size(IS,5);
IS = squeeze(mat2cell2( IS, [1 1 1 1 nMontages] ));
end
% draw each montage
clf;
nn = ceil( sqrt(nMontages) ); mm = ceil(nMontages/nn);
for i=1:nMontages
subplot(mm,nn,i);
prmSub=prm; prmSub.extraInfo=0; prmSub.labels=[];
if( ~isempty(IS{i}) )
subMontage( IS{i}, prmSub );
else
set(gca,'XTick',[]); set(gca,'YTick',[]);
end
if(~isempty(labels)); title(labels{i}); end
end
if( extraInfo ); impixelinfo; end;
end
function varargout = subMontage( IS, prm )
% this function is a generalized version of Matlab's montage.m
% get parameters (set defaults)
dfs = {'showLines',1, 'extraInfo',0, 'cLim',[], 'mm',[], 'nn',[], ...
'labels',[], 'perRow',false, 'hasChn',false, 'padAmt',0, 'padEl',[] };
prm = getPrmDflt( prm, dfs );
showLines=prm.showLines; extraInfo=prm.extraInfo; cLim=prm.cLim;
mm=prm.mm; nn=prm.nn; labels=prm.labels; hasChn=prm.hasChn;
padAmt=prm.padAmt; padEl=prm.padEl;
if( prm.perRow ); mm=1; end;
% get/test image format info and parameters
if( hasChn )
if( ndims(IS)>4 || ~any(size(IS,3)==[1 3]) )
error('montage2: unsupported dimension of IS'); end
else
if( ndims(IS)>3 );
error('montage2: unsupported dimension of IS'); end
IS = permute(IS, [1 2 4 3] );
end
siz = size(IS); nCh=size(IS,3); nIm = size(IS,4); sizPad=siz+padAmt;
if( ~isempty(labels) && nIm~=length(labels) )
error('montage2: incorrect number of labels');
end
% set up the padEl correctly (must have same type / nCh as IS)
if(isempty(padEl))
if(isempty(cLim)); padEl=min(IS(:)); else padEl=cLim(1); end; end
if(length(padEl)==1); padEl=repmat(padEl,[1 nCh]); end;
if(length(padEl)~=nCh); error( 'invalid padEl' ); end;
padEl = feval( class(IS), padEl );
padEl = reshape( padEl, 1, 1, [] );
padAmt = floor(padAmt/2 + .5)*2;
% get layout of images (mm=#images/row, nn=#images/col)
if( isempty(mm) || isempty(nn))
if( isempty(mm) && isempty(nn))
nn = min( ceil(sqrt(sizPad(1)*nIm/sizPad(2))), nIm );
mm = ceil( nIm/nn );
elseif( isempty(mm) )
nn = min( nn, nIm );
mm = ceil(nIm/nn);
else
mm = min( mm, nIm );
nn = ceil(nIm/mm);
end
% often can shrink dimension further
while((mm-1)*nn>=nIm); mm=mm-1; end;
while((nn-1)*mm>=nIm); nn=nn-1; end;
end
% Calculate I (M*mm x N*nn size image)
I = repmat(padEl, [mm*sizPad(1), nn*sizPad(2), 1]);
rows = 1:siz(1); cols = 1:siz(2);
for k=1:nIm
rowsK = rows + floor((k-1)/nn)*sizPad(1)+padAmt/2;
colsK = cols + mod(k-1,nn)*sizPad(2)+padAmt/2;
I(rowsK,colsK,:) = IS(:,:,:,k);
end
% display I
if( ~isempty(cLim)); h=imagesc(I,cLim); else h=imagesc(I); end
colormap(gray); axis('image');
if( extraInfo )
colorbar; impixelinfo;
else
set(gca,'Visible','off')
end
% draw lines separating frames
if( showLines )
montageWd = nn * sizPad(2) + .5;
montageHt = mm * sizPad(1) + .5;
for i=1:mm-1
ht = i*sizPad(1) +.5; line([.5,montageWd],[ht,ht]);
end
for i=1:nn-1
wd = i*sizPad(2) +.5; line([wd,wd],[.5,montageHt]);
end
end
% plot text labels
textalign = { 'VerticalAlignment','bottom','HorizontalAlignment','left'};
if( ~isempty(labels) )
count=1;
for i=1:mm;
for j=1:nn
if( count<=nIm )
rStr = i*sizPad(1)-padAmt/2;
cStr =(j-1+.1)*sizPad(2)+padAmt/2;
text(cStr,rStr,labels{count},'color','r',textalign{:});
count = count+1;
end
end
end
end
% cross out unused frames
[nns,mms] = ind2sub( [nn,mm], nIm+1 );
for i=mms-1:mm-1
for j=nns-1:nn-1,
rStr = i*sizPad(1)+.5+padAmt/2; rs = [rStr,rStr+siz(1)];
cStr = j*sizPad(2)+.5+padAmt/2; cs = [cStr,cStr+siz(2)];
line( cs, rs ); line( fliplr(cs), rs );
end
end
% optional output
if( nargout>0 ); varargout={h,mm,nn}; end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
jitterImage.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/images/jitterImage.m
| 5,252 |
utf_8
|
3310f8412af00fd504c6f94b8c48992c
|
function IJ = jitterImage( I, varargin )
% Creates multiple, slightly jittered versions of an image.
%
% Takes an image I, and generates a number of images that are copies of the
% original image with slight translation, rotation and scaling applied. If
% the input image is actually an MxNxK stack of images then applies op to
% each image. Rotations and translations are specified by giving a range
% and a max value for each. For example, if mPhi=10 and nPhi=5, then the
% actual rotations applied are linspace(-mPhi,mPhi,nPhi)=[-10 -5 0 5 10].
% Likewise if mTrn=3 and nTrn=3 then the translations are [-3 0 3]. Each
% tran is applied in the x direction as well as the y direction. Each
% combination of rotation, tran in x, tran in y and scale is used (for
% example phi=5, transx=-3, transy=0), so the total number of images
% generated is R=nTrn*nTrn*nPhi*nScl. Finally, jsiz controls the size of
% the cropped images. If jsiz gives a size that's sufficiently smaller than
% I then all data in the the final set will come from I. Otherwise, I must
% be padded first (by calling padarray with the 'replicate' option).
%
% USAGE
% function IJ = jitterImage( I, varargin )
%
% INPUTS
% I - image (MxN) or set of K images (MxNxK)
% varargin - additional params (struct or name/value pairs)
% .maxn - [inf] maximum jitters to generate (prior to flip)
% .nPhi - [0] number of rotations
% .mPhi - [0] max value for rotation
% .nTrn - [0] number of translations
% .mTrn - [0] max value for translation
% .flip - [0] if true then also adds reflection of each image
% .jsiz - [] Final size of each image in IJ
% .scls - [1 1] nScl x 2 array of vert/horiz scalings
% .method - ['linear'] interpolation method for imtransform2
% .hasChn - [0] if true I is MxNxC or MxNxCxK
%
% OUTPUTS
% IJ - MxNxKxR or MxNxCxKxR set of images, R=(nTrn^2*nPhi*nScl)
%
% EXAMPLE
% load trees; I=imresize(ind2gray(X,map),[41 41]); clear X caption map
% % creates 10 (of 7^2*2) images of slight trans
% IJ = jitterImage(I,'nTrn',7,'mTrn',3,'maxn',10); montage2(IJ)
% % creates 5 images of slight rotations w reflection
% IJ = jitterImage(I,'nPhi',5,'mPhi',25,'flip',1); montage2(IJ)
% % creates 45 images of both rot and slight trans
% IJ = jitterImage(I,'nPhi',5,'mPhi',10,'nTrn',3,'mTrn',2); montage2(IJ)
% % additionally create multiple scaled versions
% IJ = jitterImage(I,'scls',[1 1; 2 1; 1 2; 2 2]); montage2(IJ)
% % example on color image (5 images of slight rotations)
% I=imResample(imread('peppers.png'),[100,100]);
% IJ=jitterImage(I,'nPhi',5,'mPhi',25,'hasChn',1);
% montage2(uint8(IJ),{'hasChn',1})
%
% See also imtransform2
%
% Piotr's Computer Vision Matlab Toolbox Version 2.65
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
% get additional parameters
siz=size(I);
dfs={'maxn',inf, 'nPhi',0, 'mPhi',0, 'nTrn',0, 'mTrn',0, 'flip',0, ...
'jsiz',siz(1:2), 'scls',[1 1], 'method','linear', 'hasChn',0};
[maxn,nPhi,mPhi,nTrn,mTrn,flip,jsiz,scls,method,hasChn] = ...
getPrmDflt(varargin,dfs,1);
if(nPhi<1), mPhi=0; nPhi=1; end; if(nTrn<1), mTrn=0; nTrn=1; end
% I must be big enough to support given ops so grow I if necessary
trn=linspace(-mTrn,mTrn,nTrn); [dX,dY]=meshgrid(trn,trn);
dY=dY(:)'; dX=dX(:)'; phis=linspace(-mPhi,mPhi,nPhi)/180*pi;
siz1=jsiz+2*max(dX); if(nPhi>1), siz1=sqrt(2)*siz1+1; end
siz1=[siz1(1)*max(scls(:,1)) siz1(2)*max(scls(:,2))];
pad=(siz1-siz(1:2))/2; pad=max([ceil(pad) 0],0);
if(any(pad>0)), I=padarray(I,pad,'replicate','both'); end
% jitter each image
nScl=size(scls,1); nTrn=length(dX); nPhi=length(phis);
nOps=min(maxn,nTrn*nPhi*nScl); if(flip), nOps=nOps*2; end
if(hasChn), nd=3; jsiz=[jsiz siz(3)]; else nd=2; end
n=size(I,nd+1); IJ=zeros([jsiz nOps n],class(I));
is=repmat({':'},1,nd); prm={method,maxn,jsiz,phis,dX,dY,scls,flip};
for i=1:n, IJ(is{:},:,i)=jitterImage1(I(is{:},i),prm{:}); end
end
function IJ = jitterImage1( I,method,maxn,jsiz,phis,dX,dY,scls,flip )
% generate list of transformations (HS)
nScl=size(scls,1); nTrn=length(dX); nPhi=length(phis);
nOps=nTrn*nPhi*nScl; HS=zeros(3,3,nOps); k=0;
for s=1:nScl, S=[scls(s,1) 0; 0 scls(s,2)];
for p=1:nPhi, R=rotationMatrix(phis(p));
for t=1:nTrn, k=k+1; HS(:,:,k)=[S*R [dX(t); dY(t)]; 0 0 1]; end
end
end
% apply each transformation HS(:,:,i) to image I
if(nOps>maxn), HS=HS(:,:,randSample(nOps,maxn)); nOps=maxn; end
siz=size(I); nd=ndims(I); nCh=size(I,3);
I1=I; p=(siz-jsiz)/2; IJ=zeros([jsiz nOps],class(I));
for i=1:nOps, H=HS(:,:,i); d=H(1:2,3)';
if( all(all(H(1:2,1:2)==eye(2))) && all(mod(d,1)==0) )
% handle transformation that's just an integer translation
s=max(1-d,1); e=min(siz(1:2)-d,siz(1:2)); s1=2-min(1-d,1); e1=e-s+s1;
I1(s1(1):e1(1),s1(2):e1(2),:) = I(s(1):e(1),s(2):e(2),:);
else % handle general transformations
for j=1:nCh, I1(:,:,j)=imtransform2(I(:,:,j),H,'method',method); end
end
% crop and store result
I2 = I1(p(1)+1:end-p(1),p(2)+1:end-p(2),:);
if(nd==2), IJ(:,:,i)=I2; else IJ(:,:,:,i)=I2; end
end
% finally flip each resulting image
if(flip), IJ=cat(nd+1,IJ,IJ(:,end:-1:1,:,:)); end
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
movieToImages.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/images/movieToImages.m
| 889 |
utf_8
|
28c71798642af276951ee27e2d332540
|
function I = movieToImages( M )
% Creates a stack of images from a matlab movie M.
%
% Repeatedly calls frame2im. Useful for playback with playMovie.
%
% USAGE
% I = movieToImages( M )
%
% INPUTS
% M - a matlab movie
%
% OUTPUTS
% I - MxNxT array (of images)
%
% EXAMPLE
% load( 'images.mat' ); [X,map]=gray2ind(video(:,:,1));
% M = fevalArrays( video, @(x) im2frame(gray2ind(x),map) );
% I = movieToImages(M); playMovie(I);
%
% See also PLAYMOVIE
%
% Piotr's Computer Vision Matlab Toolbox Version 2.0
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
I = fevalArrays( M, @frame2Ii );
function I = frame2Ii( F )
[I,map] = frame2im( F );
if( isempty(map) )
if( size(I,3)==3 )
classname = class( I );
I = sum(I,3)/3;
I = feval( classname, I );
end
else
I = ind2gray( I, map );
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
toolboxUpdateHeader.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/toolboxUpdateHeader.m
| 2,255 |
utf_8
|
7a5b75e586be48da97c84d20b59887ff
|
function toolboxUpdateHeader
% Update the headers of all the files.
%
% USAGE
% toolboxUpdateHeader
%
% INPUTS
%
% OUTPUTS
%
% EXAMPLE
%
% See also
%
% Piotr's Computer Vision Matlab Toolbox Version 3.40
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
header={
'Piotr''s Computer Vision Matlab Toolbox Version 3.40'; ...
'Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]'; ...
'Licensed under the Simplified BSD License [see external/bsd.txt]'};
root=fileparts(fileparts(mfilename('fullpath')));
ds=dir(root); ds=ds([ds.isdir]); ds={ds.name};
ds=ds(3:end); ds=setdiff(ds,{'.git','doc'});
subds = { '/', '/private/' };
exts = {'m','c','cpp','h','hpp'};
omit = {'Contents.m','fibheap.h','fibheap.cpp'};
for i=1:length(ds)
for j=1:length(subds)
for k=1:length(exts)
d=[root '/' ds{i} subds{j}];
if(k==1), comment='%'; else comment='*'; end
fs=dir([d '*.' exts{k}]); fs={fs.name}; fs=setdiff(fs,omit);
n=length(fs); for f=1:n, fs{f}=[d fs{f}]; end
for f=1:n, toolboxUpdateHeader1(fs{f},header,comment); end
end
end
end
end
function toolboxUpdateHeader1( fName, header, comment )
% set appropriate comment symbol in header
m=length(header); for i=1:m, header{i}=[comment ' ' header{i}]; end
% read in file and find header
disp(fName); lines=readFile(fName);
loc = find(not(cellfun('isempty',strfind(lines,header{1}(1:40)))));
if(isempty(loc)), error('NO HEADER: %s\n',fName); end; loc=loc(1);
% check that header is properly formed, return if up to date
for i=1:m; assert(isequal(lines{loc+i-1}(1:10),header{i}(1:10))); end
if(~any(strfind(lines{loc},'NEW'))); return; end
% update copyright year and overwrite rest of header
lines{loc+1}(13:16)=header{2}(13:16);
for i=[1 3:m]; lines{loc+i-1}=header{i}; end
writeFile( fName, lines );
end
function lines = readFile( fName )
fid = fopen( fName, 'rt' ); assert(fid~=-1);
lines=cell(10000,1); n=0;
while( 1 )
n=n+1; lines{n}=fgetl(fid);
if( ~ischar(lines{n}) ), break; end
end
fclose(fid); n=n-1; lines=lines(1:n);
end
function writeFile( fName, lines )
fid = fopen( fName, 'w' );
for i=1:length(lines); fprintf( fid, '%s\n', lines{i} ); end
fclose(fid);
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
toolboxGenDoc.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/toolboxGenDoc.m
| 3,639 |
utf_8
|
4c21fb34fa9b6002a1a98a28ab40c270
|
function toolboxGenDoc
% Generate documentation, must run from dir toolbox.
%
% 1) Make sure to update and run toolboxUpdateHeader.m
% 2) Update history.txt appropriately, including w current version
% 3) Update overview.html file with the version/date/link to zip:
% edit external/m2html/templates/frame-piotr/overview.html
%
% USAGE
% toolboxGenDoc
%
% INPUTS
%
% OUTPUTS
%
% EXAMPLE
%
% See also
%
% Piotr's Computer Vision Matlab Toolbox Version 3.40
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
% Requires external/m2html to be in path.
cd(fileparts(mfilename('fullpath'))); cd('../');
addpath([pwd '/external/m2html']);
% delete temporary files that should not be part of release
fs={'pngreadc','pngwritec','rjpg8c','wjpg8c','png'};
for i=1:length(fs), delete(['videos/private/' fs{i} '.*']); end
delete('detector/models/*Dets.txt');
% delete old doc and run m2html
if(exist('doc/','dir')), rmdir('doc/','s'); end
dirs={'channels','classify','detector',...
'images','filters','matlab','videos'};
m2html('mfiles',dirs,'htmldir','doc','recursive','on','source','off',...
'template','frame-piotr','index','menu','global','on');
% copy custom menu.html and history file
sDir='external/m2html/templates/';
copyfile([sDir 'menu-for-frame-piotr.html'],'doc/menu.html');
copyfile('external/history.txt','doc/history.txt');
% remove links to private/ in the menu.html files and remove private/ dirs
for i=1:length(dirs)
name = ['doc/' dirs{i} '/menu.html'];
fid=fopen(name,'r'); c=fread(fid,'*char')'; fclose(fid);
c=regexprep(c,'<li>([^<]*[<]?[^<]*)private([^<]*[<]?[^<]*)</li>','');
fid=fopen(name,'w'); fwrite(fid,c); fclose(fid);
name = ['doc/' dirs{i} '/private/'];
if(exist(name,'dir')), rmdir(name,'s'); end
end
% postprocess each html file
for d=1:length(dirs)
fs=dir(['doc/' dirs{d} '/*.html']); fs={fs.name};
for j=1:length(fs), postProcess(['doc/' dirs{d} '/' fs{j}]); end
end
end
function postProcess( fName )
lines=readFile(fName);
assert(strcmp(lines{end-1},'</body>') && strcmp(lines{end},'</html>'));
% remove m2html datestamp (if present)
assert(strcmp(lines{end-2}(1:22),'<hr><address>Generated'));
if( strcmp(lines{end-2}(1:25),'<hr><address>Generated on'))
lines{end-2}=regexprep(lines{end-2}, ...
'<hr><address>Generated on .* by','<hr><address>Generated by');
end
% remove crossreference information
is=find(strcmp('<!-- crossreference -->',lines));
if(~isempty(is)), assert(length(is)==2); lines(is(1):is(2))=[]; end
% insert Google Analytics snippet to end of file
ga={ '';
'<!-- Start of Google Analytics Code -->';
'<script type="text/javascript">';
'var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www.");';
'document.write(unescape("%3Cscript src=''" + gaJsHost + "google-analytics.com/ga.js'' type=''text/javascript''%3E%3C/script%3E"));';
'</script>';
'<script type="text/javascript">';
'var pageTracker = _gat._getTracker("UA-4884268-1");';
'pageTracker._initData();';
'pageTracker._trackPageview();';
'</script>';
'<!-- end of Google Analytics Code -->';
'' };
lines = [lines(1:end-3); ga; lines(end-2:end)];
% write file
writeFile( fName, lines );
end
function lines = readFile( fName )
fid = fopen( fName, 'rt' ); assert(fid~=-1);
lines=cell(10000,1); n=0;
while( 1 )
n=n+1; lines{n}=fgetl(fid);
if( ~ischar(lines{n}) ), break; end
end
fclose(fid); n=n-1; lines=lines(1:n);
end
function writeFile( fName, lines )
fid = fopen( fName, 'w' );
for i=1:length(lines); fprintf( fid, '%s\r\n', lines{i} ); end
fclose(fid);
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
toolboxHeader.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/toolboxHeader.m
| 2,391 |
utf_8
|
30c24a94fb54ca82622719adcab17903
|
function [y1,y2] = toolboxHeader( x1, x2, x3, prm )
% One line description of function (will appear in file summary).
%
% General commments explaining purpose of function [width is 75
% characters]. There may be multiple paragraphs. In special cases some or
% all of these guidelines may need to be broken.
%
% Next come a series of sections, including USAGE, INPUTS, OUTPUTS,
% EXAMPLE, and "See also". Each of these fields should always appear, even
% if nothing follows (for example no inputs). USAGE should usually be a
% copy of the first line of code (which begins with "function"), minus the
% word "function". Optional parameters are surrounded by brackets.
% Occasionally, there may be more than 1 distinct usage, in this case list
% additional usages. In general try to avoid this. INPUTS/OUTPUTS are
% self explanatory, however if there are multiple usages can be subdivided
% as below. EXAMPLE should list 1 or more useful examples. Main comment
% should all appear as one contiguous block. Next a blank comment line,
% and then a short comment that includes the toolbox version.
%
% USAGE
% xsum = toolboxHeader( x1, x2, [x3], [prm] )
% [xprod, xdiff] = toolboxHeader( x1, x2, [x3], [prm] )
%
% INPUTS
% x1 - descr. of variable 1,
% x2 - descr. of variable 2, keep spacing like this
% if descr. spans multiple lines do this
% x3 - [0] indicates an optional variable, put def val in []
% prm - [] param struct
% .p1 parameter 1 descr
% .p2 parameter 2 descr
%
% OUTPUTS - and whatever after the dash
% xsum - sum of xs
%
% OUTPUTS - usage 2
% xprod - prod of xs
% xdiff - negative sum of xs
%
% EXAMPLE - and whatever after the dash
% y = toolboxHeader( 1, 2 );
%
% EXAMPLE - example 2
% y = toolboxHeader( 2, 3 );
%
% See also GETPRMDFLT
%
% Piotr's Computer Vision Matlab Toolbox Version 2.10
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see external/bsd.txt]
% optional arguments x3 and prm
if( nargin<3 || isempty(x3) ), x3=0; end
if( nargin<4 || isempty(prm) ), prm=[]; end %#ok<NASGU>
% indents should be set with Matlab's "smart indent" (with 2 spaces)
if( nargout==1 )
y1 = add(x1,x2) + x3;
else
y1 = x1 * x2 * x3;
y2 = - x1 - x2 - x3;
end
function s=add(x,y)
% optional sub function comment
s=x+y;
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
mdot.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/m2html/mdot.m
| 2,516 |
utf_8
|
34a14428c433e118d1810e23f5a6caf5
|
function mdot(mmat, dotfile,f)
%MDOT - Export a dependency graph into DOT language
% MDOT(MMAT, DOTFILE) loads a .mat file generated by M2HTML using option
% ('save','on') and writes an ascii file using the DOT language that can
% be drawn using <dot> or <neato> .
% MDOT(MMAT, DOTFILE,F) builds the graph containing M-file F and its
% neighbors only.
% See the following page for more details:
% <http://www.graphviz.org/>
%
% Example:
% mdot('m2html.mat','m2html.dot');
% !dot -Tps m2html.dot -o m2html.ps
% !neato -Tps m2html.dot -o m2html.ps
%
% See also M2HTML
% Copyright (C) 2004 Guillaume Flandin <[email protected]>
% $Revision: 1.1 $Date: 2004/05/05 17:14:09 $
error(nargchk(2,3,nargin));
if ischar(mmat)
load(mmat);
elseif iscell(mmat)
hrefs = mmat{1};
names = mmat{2};
options = mmat{3};
if nargin == 3, mfiles = mmat{4}; end
mdirs = cell(size(names));
[mdirs{:}] = deal('');
if nargin == 2 & length(mmat) > 3,
mdirs = mmat{4};
end;
else
error('[mdot] Invalid argument: mmat.');
end
fid = fopen(dotfile,'wt');
if fid == -1, error(sprintf('[mdot] Cannot open %s.',dotfile)); end
fprintf(fid,'/* Created by mdot for Matlab */\n');
fprintf(fid,'digraph m2html {\n');
% if 'names' contains '.' then they should be surrounded by '"'
if nargin == 2
for i=1:size(hrefs,1)
n = find(hrefs(i,:) == 1);
m{i} = n;
for j=1:length(n)
fprintf(fid,[' ' names{i} ' -> ' names{n(j)} ';\n']);
end
end
%m = unique([m{:}]);
fprintf(fid,'\n');
for i=1:size(hrefs,1)
fprintf(fid,[' ' names{i} ' [URL="' ...
fullurl(mdirs{i},[names{i} options.extension]) '"];\n']);
end
else
i = find(strcmp(f,mfiles));
if length(i) ~= 1
error(sprintf('[mdot] Cannot find %s.',f));
end
n = find(hrefs(i,:) == 1);
for j=1:length(n)
fprintf(fid,[' ' names{i} ' -> ' names{n(j)} ';\n']);
end
m = find(hrefs(:,i) == 1);
for j=1:length(m)
if n(j) ~= i
fprintf(fid,[' ' names{m(j)} ' -> ' names{i} ';\n']);
end
end
n = unique([n(:)' m(:)']);
fprintf(fid,'\n');
for i=1:length(n)
fprintf(fid,[' ' names{n(i)} ' [URL="' fullurl(mdirs{i}, ...
[names{n(i)} options.extension]) '"];\n']);
end
end
fprintf(fid,'}');
fid = fclose(fid);
if fid == -1, error(sprintf('[mdot] Cannot close %s.',dotfile)); end
%===========================================================================
function f = fullurl(varargin)
%- Build full url from parts (using '/' and not filesep)
f = strrep(fullfile(varargin{:}),'\','/');
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
m2html.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/m2html/m2html.m
| 49,063 |
utf_8
|
472047b4c36a4f8b162012840e31b59b
|
function m2html(varargin)
%M2HTML - Documentation Generator for Matlab M-files and Toolboxes in HTML
% M2HTML by itself generates an HTML documentation of the Matlab M-files found
% in the direct subdirectories of the current directory. HTML files are
% written in a 'doc' directory (created if necessary). All the others options
% are set to default (in brackets in the following).
% M2HTML('PropertyName1',PropertyValue1,'PropertyName2',PropertyValue2,...)
% sets multiple option values. The list of option names and default values is:
% o mFiles - Cell array of strings or character array containing the
% list of M-files and/or directories of M-files for which an HTML
% documentation will be built (use relative paths without backtracking).
% Launch M2HTML one directory above the directory your wanting to
% generate documentation for [ <all direct subdirectories> ]
% o htmlDir - Top level directory for generated HTML files [ 'doc' ]
% o recursive - Process subdirectories recursively [ on | {off} ]
% o source - Include Matlab source code in the generated documentation
% [ {on} | off ]
% o download - Add a link to download each M-file separately [ on | {off} ]
% o syntaxHighlighting - Source Code Syntax Highlighting [ {on} | off ]
% o tabs - Replace '\t' (horizontal tab) in source code by n white space
% characters [ 0 ... {4} ... n ]
% o globalHypertextLinks - Hypertext links among separate Matlab
% directories [ on | {off} ]
% o todo - Create a TODO list in each directory summarizing all the
% '% TODO %' lines found in Matlab code [ on | {off}]
% o graph - Compute a dependency graph using GraphViz [ on | {off}]
% 'dot' required, see <http://www.graphviz.org/>
% o indexFile - Basename of the HTML index file [ 'index' ]
% o extension - Extension of generated HTML files [ '.html' ]
% o template - HTML template name to use [ {'blue'} | 'frame' | ... ]
% o search - Add a PHP search engine [ on | {off}] - beta version!
% o save - Save current state after M-files parsing in 'm2html.mat'
% in directory htmlDir [ on | {off}]
% o load - Load a previously saved '.mat' M2HTML state to generate HTML
% files once again with possibly other options [ <none> ]
% o verbose - Verbose mode [ {on} | off ]
%
% For more information, please read the M2HTML tutorial and FAQ at:
% <http://www.artefact.tk/software/matlab/m2html/>
%
% Examples:
% >> m2html('mfiles','matlab', 'htmldir','doc');
% >> m2html('mfiles',{'matlab/signal' 'matlab/image'}, 'htmldir','doc');
% >> m2html('mfiles','matlab', 'htmldir','doc', 'recursive','on');
% >> m2html('mfiles','mytoolbox', 'htmldir','doc', 'source','off');
% >> m2html('mfiles','matlab', 'htmldir','doc', 'global','on');
% >> m2html( ... , 'template','frame', 'index','menu');
%
% See also MWIZARD, MDOT, TEMPLATE.
% Copyright (C) 2005 Guillaume Flandin <[email protected]>
% $Revision: 1.5 $Date: 2005/04/29 16:04:17 $
% This program is free software; you can redistribute it and/or
% modify it under the terms of the GNU General Public License
% as published by the Free Software Foundation; either version 2
% of the License, or any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program; if not, write to the Free Software
% Foundation Inc, 59 Temple Pl. - Suite 330, Boston, MA 02111-1307, USA.
% Suggestions for improvement and fixes are always welcome, although no
% guarantee is made whether and when they will be implemented.
% Send requests to [email protected]
% For tips on how to write Matlab code, see:
% * MATLAB Programming Style Guidelines, by R. Johnson:
% <http://www.datatool.com/prod02.htm>
% * For tips on creating help for your m-files 'type help.m'.
% * Matlab documentation on M-file Programming:
% <http://www.mathworks.com/access/helpdesk/help/techdoc/matlab_prog/ch_funh8.html>
% This function uses the Template class so that you can fully customize
% the output. You can modify .tpl files in templates/blue/ or create new
% templates in a new directory.
% See the template class documentation for more details.
% <http://www.artefact.tk/software/matlab/template/>
% Latest information on M2HTML is available on the web through:
% <http://www.artefact.tk/software/matlab/m2html/>
% Other Matlab to HTML converters available on the web:
% 1/ mat2html.pl, J.C. Kantor, in Perl, 1995:
% <http://fresh.t-systems-sfr.com/unix/src/www/mat2html>
% 2/ htmltools, B. Alsberg, in Matlab, 1997:
% <http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=175>
% 3/ mtree2html2001, H. Pohlheim, in Perl, 1996, 2001:
% <http://www.pohlheim.com/perl_main.html#matlabdocu>
% 4/ MatlabToHTML, T. Kristjansson, binary, 2001:
% <http://www.psi.utoronto.ca/~trausti/MatlabToHTML/MatlabToHTML.html>
% 5/ Highlight, G. Flandin, in Matlab, 2003:
% <http://www.artefact.tk/software/matlab/highlight/>
% 6/ mdoc, P. Brinkmann, in Matlab, 2003:
% <http://www.math.uiuc.edu/~brinkman/software/mdoc/>
% 7/ Ocamaweb, Miriad Technologies, in Ocaml, 2002:
% <http://ocamaweb.sourceforge.net/>
% 8/ Matdoc, M. Kaminsky, in Perl, 2003:
% <http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=3498>
% 9/ Matlab itself, The Mathworks Inc, with HELPWIN, DOC and PUBLISH (R14)
%-------------------------------------------------------------------------------
%- Set up options and default parameters
%-------------------------------------------------------------------------------
t0 = clock; % for statistics
msgInvalidPair = 'Bad value for argument: ''%s''';
options = struct('verbose', 1,...
'mFiles', {{'.'}},...
'htmlDir', 'doc',...
'recursive', 0,...
'source', 1,...
'download',0,...
'syntaxHighlighting', 1,...
'tabs', 4,...
'globalHypertextLinks', 0,...
'graph', 0,...
'todo', 0,...
'load', 0,...
'save', 0,...
'search', 0,...
'helptocxml', 0,...
'indexFile', 'index',...
'extension', '.html',...
'template', 'blue',...
'rootdir', pwd,...
'language', 'english');
if nargin == 1 & isstruct(varargin{1})
paramlist = [ fieldnames(varargin{1}) ...
struct2cell(varargin{1}) ]';
paramlist = { paramlist{:} };
else
if mod(nargin,2)
error('Invalid parameter/value pair arguments.');
end
paramlist = varargin;
end
optionsnames = lower(fieldnames(options));
for i=1:2:length(paramlist)
pname = paramlist{i};
pvalue = paramlist{i+1};
ind = strmatch(lower(pname),optionsnames);
if isempty(ind)
error(['Invalid parameter: ''' pname '''.']);
elseif length(ind) > 1
error(['Ambiguous parameter: ''' pname '''.']);
end
switch(optionsnames{ind})
case 'verbose'
if strcmpi(pvalue,'on')
options.verbose = 1;
elseif strcmpi(pvalue,'off')
options.verbose = 0;
else
error(sprintf(msgInvalidPair,pname));
end
case 'mfiles'
if iscellstr(pvalue)
options.mFiles = pvalue;
elseif ischar(pvalue)
options.mFiles = cellstr(pvalue);
else
error(sprintf(msgInvalidPair,pname));
end
options.load = 0;
case 'htmldir'
if ischar(pvalue)
if isempty(pvalue),
options.htmlDir = '.';
else
options.htmlDir = pvalue;
end
else
error(sprintf(msgInvalidPair,pname));
end
case 'recursive'
if strcmpi(pvalue,'on')
options.recursive = 1;
elseif strcmpi(pvalue,'off')
options.recursive = 0;
else
error(sprintf(msgInvalidPair,pname));
end
options.load = 0;
case 'source'
if strcmpi(pvalue,'on')
options.source = 1;
elseif strcmpi(pvalue,'off')
options.source = 0;
else
error(sprintf(msgInvalidPair,pname));
end
case 'download'
if strcmpi(pvalue,'on')
options.download = 1;
elseif strcmpi(pvalue,'off')
options.download = 0;
else
error(sprintf(msgInvalidPair,pname));
end
case 'syntaxhighlighting'
if strcmpi(pvalue,'on')
options.syntaxHighlighting = 1;
elseif strcmpi(pvalue,'off')
options.syntaxHighlighting = 0;
else
error(sprintf(msgInvalidPair,pname));
end
case 'tabs'
if pvalue >= 0
options.tabs = pvalue;
else
error(sprintf(msgInvalidPair,pname));
end
case 'globalhypertextlinks'
if strcmpi(pvalue,'on')
options.globalHypertextLinks = 1;
elseif strcmpi(pvalue,'off')
options.globalHypertextLinks = 0;
else
error(sprintf(msgInvalidPair,pname));
end
options.load = 0;
case 'graph'
if strcmpi(pvalue,'on')
options.graph = 1;
elseif strcmpi(pvalue,'off')
options.graph = 0;
else
error(sprintf(msgInvalidPair,pname));
end
case 'todo'
if strcmpi(pvalue,'on')
options.todo = 1;
elseif strcmpi(pvalue,'off')
options.todo = 0;
else
error(sprintf(msgInvalidPair,pname));
end
case 'load'
if ischar(pvalue)
if exist(pvalue) == 7 % directory provided
pvalue = fullfile(pvalue,'m2html.mat');
end
try
load(pvalue);
catch
error(sprintf('Unable to load %s.', pvalue));
end
options.load = 1;
[dummy options.template] = fileparts(options.template);
else
error(sprintf(msgInvalidPair,pname));
end
case 'save'
if strcmpi(pvalue,'on')
options.save = 1;
elseif strcmpi(pvalue,'off')
options.save = 0;
else
error(sprintf(msgInvalidPair,pname));
end
case 'search'
if strcmpi(pvalue,'on')
options.search = 1;
elseif strcmpi(pvalue,'off')
options.search = 0;
else
error(sprintf(msgInvalidPair,pname));
end
case 'helptocxml'
if strcmpi(pvalue,'on')
options.helptocxml = 1;
elseif strcmpi(pvalue,'off')
options.helptocxml = 0;
else
error(sprintf(msgInvalidPair,pname));
end
case 'indexfile'
if ischar(pvalue)
options.indexFile = pvalue;
else
error(sprintf(msgInvalidPair,pname));
end
case 'extension'
if ischar(pvalue) & pvalue(1) == '.'
options.extension = pvalue;
else
error(sprintf(msgInvalidPair,pname));
end
case 'template'
if ischar(pvalue)
options.template = pvalue;
else
error(sprintf(msgInvalidPair,pname));
end
case 'language'
if ischar(pvalue)
options.language = pvalue;
else
error(sprintf(msgInvalidPair,pname));
end
otherwise
error(['Invalid parameter: ''' pname '''.']);
end
end
%-------------------------------------------------------------------------------
%- Get template files location
%-------------------------------------------------------------------------------
s = fileparts(which(mfilename));
options.template = fullfile(s,'templates',options.template);
if exist(options.template) ~= 7
error('[Template] Unknown template.');
end
%-------------------------------------------------------------------------------
%- Get list of M-files
%-------------------------------------------------------------------------------
if ~options.load
if strcmp(options.mFiles,'.')
d = dir(pwd); d = {d([d.isdir]).name};
options.mFiles = {d{~ismember(d,{'.' '..'})}};
end
mfiles = getmfiles(options.mFiles,{},options.recursive);
if ~length(mfiles), fprintf('Nothing to be done.\n'); return; end
if options.verbose,
fprintf('Found %d M-files.\n',length(mfiles));
end
mfiles = sort(mfiles); % sort list of M-files in dictionary order
end
%-------------------------------------------------------------------------------
%- Get list of (unique) directories and (unique) names
%-------------------------------------------------------------------------------
if ~options.load
mdirs = {};
names = {};
for i=1:length(mfiles)
[mdirs{i}, names{i}] = fileparts(mfiles{i});
if isempty(mdirs{i}), mdirs{i} = '.'; end
end
mdir = unique(mdirs);
if options.verbose,
fprintf('Found %d unique Matlab directories.\n',length(mdir));
end
name = names;
%name = unique(names); % output is sorted
%if options.verbose,
% fprintf('Found %d unique Matlab files.\n',length(name));
%end
end
%-------------------------------------------------------------------------------
%- Create output directory, if necessary
%-------------------------------------------------------------------------------
if isempty(dir(options.htmlDir))
%- Create the top level output directory
if options.verbose
fprintf('Creating directory %s...\n',options.htmlDir);
end
if options.htmlDir(end) == filesep,
options.htmlDir(end) = [];
end
[pathdir, namedir] = fileparts(options.htmlDir);
if isempty(pathdir)
[status, msg] = mkdir(escapeblank(namedir));
else
[status, msg] = mkdir(escapeblank(pathdir), escapeblank(namedir));
end
if ~status, error(msg); end
end
%-------------------------------------------------------------------------------
%- Get synopsis, H1 line, script/function, subroutines, cross-references, todo
%-------------------------------------------------------------------------------
if ~options.load
synopsis = cell(size(mfiles));
h1line = cell(size(mfiles));
subroutine = cell(size(mfiles));
hrefs = sparse(length(mfiles), length(mfiles));
todo = struct('mfile',[], 'line',[], 'comment',{{}});
ismex = zeros(length(mfiles), length(mexexts));
statlist = {};
statinfo = sparse(1,length(mfiles));
kw = cell(size(mfiles));
freq = cell(size(mfiles));
for i=1:length(mfiles)
if options.verbose
fprintf('Processing file %s...',mfiles{i});
end
s = mfileparse(mfiles{i}, mdirs, names, options);
synopsis{i} = s.synopsis;
h1line{i} = s.h1line;
subroutine{i} = s.subroutine;
hrefs(i,:) = s.hrefs;
todo.mfile = [todo.mfile repmat(i,1,length(s.todo.line))];
todo.line = [todo.line s.todo.line];
todo.comment = {todo.comment{:} s.todo.comment{:}};
ismex(i,:) = s.ismex;
if options.search
if options.verbose, fprintf('search...'); end
[kw{i}, freq{i}] = searchindex(mfiles{i});
statlist = union(statlist, kw{i});
end
if options.verbose, fprintf('\n'); end
end
hrefs = hrefs > 0;
if options.search
if options.verbose
fprintf('Creating the search index...');
end
statinfo = sparse(length(statlist),length(mfiles));
for i=1:length(mfiles)
i1 = find(ismember(statlist, kw{i}));
i2 = repmat(i,1,length(i1));
if ~isempty(i1)
statinfo(sub2ind(size(statinfo),i1,i2)) = freq{i};
end
if options.verbose, fprintf('.'); end
end
clear kw freq;
if options.verbose, fprintf('\n'); end
end
end
%-------------------------------------------------------------------------------
%- Save M-filenames and cross-references for further analysis
%-------------------------------------------------------------------------------
matfilesave = 'm2html.mat';
if options.save
if options.verbose
fprintf('Saving MAT file %s...\n',matfilesave);
end
save(fullfile(options.htmlDir,matfilesave), ...
'mfiles', 'names', 'mdirs', 'name', 'mdir', 'options', ...
'hrefs', 'synopsis', 'h1line', 'subroutine', 'todo', 'ismex', ...
'statlist', 'statinfo');
end
%-------------------------------------------------------------------------------
%- Setup the output directories
%-------------------------------------------------------------------------------
for i=1:length(mdir)
if exist(fullfile(options.htmlDir,mdir{i})) ~= 7
ldir = splitpath(mdir{i});
for j=1:length(ldir)
if exist(fullfile(options.htmlDir,ldir{1:j})) ~= 7
%- Create the output directory
if options.verbose
fprintf('Creating directory %s...\n',...
fullfile(options.htmlDir,ldir{1:j}));
end
if j == 1
[status, msg] = mkdir(escapeblank(options.htmlDir), ...
escapeblank(ldir{1}));
else
[status, msg] = mkdir(escapeblank(options.htmlDir), ...
escapeblank(fullfile(ldir{1:j})));
end
error(msg);
end
end
end
end
%-------------------------------------------------------------------------------
%- Write the master index file
%-------------------------------------------------------------------------------
tpl_master = 'master.tpl';
tpl_master_identifier_nbyline = 4;
php_search = 'search.php';
dotbase = 'graph';
%- Create the HTML template
tpl = template(options.template,'remove');
tpl = set(tpl,'file','TPL_MASTER',tpl_master);
tpl = set(tpl,'block','TPL_MASTER','rowdir','rowdirs');
tpl = set(tpl,'block','TPL_MASTER','idrow','idrows');
tpl = set(tpl,'block','idrow','idcolumn','idcolumns');
tpl = set(tpl,'block','TPL_MASTER','search','searchs');
tpl = set(tpl,'block','TPL_MASTER','graph','graphs');
%- Open for writing the HTML master index file
curfile = fullfile(options.htmlDir,[options.indexFile options.extension]);
if options.verbose
fprintf('Creating HTML file %s...\n',curfile);
end
fid = openfile(curfile,'w');
%- Set some template variables
tpl = set(tpl,'var','DATE',[datestr(now,8) ' ' datestr(now,1) ' ' ...
datestr(now,13)]);
tpl = set(tpl,'var','MASTERPATH', './');
tpl = set(tpl,'var','DIRS', sprintf('%s ',mdir{:}));
%- Print list of unique directories
for i=1:length(mdir)
tpl = set(tpl,'var','L_DIR',...
fullurl(mdir{i},[options.indexFile options.extension]));
tpl = set(tpl,'var','DIR',mdir{i});
tpl = parse(tpl,'rowdirs','rowdir',1);
end
%- Print full list of M-files (sorted by column)
[sortnames, ind] = sort(names);
m_mod = mod(length(sortnames), tpl_master_identifier_nbyline);
ind = [ind zeros(1,tpl_master_identifier_nbyline-m_mod)];
m_floor = floor(length(ind) / tpl_master_identifier_nbyline);
ind = reshape(ind,m_floor,tpl_master_identifier_nbyline)';
for i=1:prod(size(ind))
if ind(i)
tpl = set(tpl,'var','L_IDNAME',...
fullurl(mdirs{ind(i)},[names{ind(i)} options.extension]));
tpl = set(tpl,'var','T_IDNAME',mdirs{ind(i)});
tpl = set(tpl,'var','IDNAME',names{ind(i)});
tpl = parse(tpl,'idcolumns','idcolumn',1);
else
tpl = set(tpl,'var','L_IDNAME','');
tpl = set(tpl,'var','T_IDNAME','');
tpl = set(tpl,'var','IDNAME','');
tpl = parse(tpl,'idcolumns','idcolumn',1);
end
if mod(i,tpl_master_identifier_nbyline) == 0
tpl = parse(tpl,'idrows','idrow',1);
tpl = set(tpl,'var','idcolumns','');
end
end
%- Add a search form if necessary
tpl = set(tpl,'var','searchs','');
if options.search
tpl = set(tpl,'var','PHPFILE',php_search);
tpl = parse(tpl,'searchs','search',1);
end
%- Link to a full dependency graph, if necessary
tpl = set(tpl,'var','graphs','');
if options.graph & options.globalHypertextLinks & length(mdir) > 1
tpl = set(tpl,'var','LGRAPH',[dotbase options.extension]);
tpl = parse(tpl,'graphs','graph',1);
end
%- Print the template in the HTML file
tpl = parse(tpl,'OUT','TPL_MASTER');
fprintf(fid,'%s',get(tpl,'OUT'));
fclose(fid);
%-------------------------------------------------------------------------------
%- Copy template files (CSS, images, ...)
%-------------------------------------------------------------------------------
% Get list of files
d = dir(options.template);
d = {d(~[d.isdir]).name};
% Copy files
for i=1:length(d)
[p, n, ext] = fileparts(d{i});
if ~strcmp(ext,'.tpl') ... % do not copy .tpl files
& ~strcmp([n ext],'Thumbs.db') % do not copy this Windows generated file
if isempty(dir(fullfile(options.htmlDir,d{i})))
if options.verbose
fprintf('Copying template file %s...\n',d{i});
end
%- there is a bug with <copyfile> in Matlab 6.5 :
% http://www.mathworks.com/support/solutions/data/1-1B5JY.html
%- and <copyfile> does not overwrite files even if newer...
[status, errmsg] = copyfile(fullfile(options.template,d{i}),...
options.htmlDir);
%- If you encounter this bug, please uncomment one of the following lines
% eval(['!cp -rf ' fullfile(options.template,d{i}) ' ' options.htmlDir]);
% eval(['!copy ' fullfile(options.template,d{i}) ' ' options.htmlDir]);
% status = 1;
if ~status
if ~isempty(errmsg)
error(errmsg)
else
warning(sprintf(['<copyfile> failed to do its job...\n' ...
'This is a known bug in Matlab 6.5 (R13).\n' ...
'See http://www.mathworks.com/support/solutions/data/1-1B5JY.html']));
end
end
end
end
end
%-------------------------------------------------------------------------------
%- Search engine (index file and PHP script)
%-------------------------------------------------------------------------------
tpl_search = 'search.tpl';
idx_search = 'search.idx';
% TODO % improving the fill in of 'statlist' and 'statinfo'
% TODO % improving the search template file and update the CSS file
if options.search
%- Write the search index file in output directory
if options.verbose
fprintf('Creating Search Index file %s...\n', idx_search);
end
docinfo = cell(length(mfiles),2);
for i=1:length(mfiles)
docinfo{i,1} = h1line{i};
docinfo{i,2} = fullurl(mdirs{i}, [names{i} options.extension]);
end
doxywrite(fullfile(options.htmlDir,idx_search),statlist,statinfo,docinfo);
%- Create the PHP template
tpl = template(options.template,'remove');
tpl = set(tpl,'file','TPL_SEARCH',tpl_search);
%- Open for writing the PHP search script
curfile = fullfile(options.htmlDir, php_search);
if options.verbose
fprintf('Creating PHP script %s...\n',curfile);
end
fid = openfile(curfile,'w');
%- Set template fields
tpl = set(tpl,'var','INDEX',[options.indexFile options.extension]);
tpl = set(tpl,'var','MASTERPATH','./');
tpl = set(tpl,'var','DATE',[datestr(now,8) ' ' datestr(now,1) ' ' ...
datestr(now,13)]);
tpl = set(tpl,'var','IDXFILE',idx_search);
tpl = set(tpl,'var','PHPFILE',php_search);
%- Print the template in the HTML file
tpl = parse(tpl,'OUT','TPL_SEARCH');
fprintf(fid,'%s',get(tpl,'OUT'));
fclose(fid);
end
%-------------------------------------------------------------------------------
%- Create <helptoc.xml> needed to display hierarchical entries in Contents panel
%-------------------------------------------------------------------------------
% See http://www.mathworks.com/access/helpdesk/help/techdoc/matlab_env/guiref16.html
% and http://www.mathworks.com/support/solutions/data/1-18U6Q.html?solution=1-18U6Q
% TODO % display directories in TOC hierarchically instead of linearly
if options.helptocxml
curfile = fullfile(options.htmlDir, 'helptoc.xml');
if options.verbose
fprintf('Creating XML Table-Of-Content %s...\n',curfile);
end
fid = openfile(curfile,'w');
fprintf(fid,'<?xml version=''1.0'' encoding=''ISO-8859-1'' ?>\n');
fprintf(fid,'<!-- $Date: %s $ -->\n\n', datestr(now,31));
fprintf(fid,'<toc version="1.0">\n\n');
fprintf(fid,['<tocitem target="%s" ',...
'image="$toolbox/matlab/icons/book_mat.gif">%s\n'], ...
[options.indexFile options.extension],'Toolbox');
for i=1:length(mdir)
fprintf(fid,['<tocitem target="%s" ',...
'image="$toolbox/matlab/icons/reficon.gif">%s\n'], ...
fullfile(mdir{i}, ...
[options.indexFile options.extension]),mdir{i});
if options.graph
fprintf(fid,['\t<tocitem target="%s" ',...
'image="$toolbox/matlab/icons/simulinkicon.gif">%s</tocitem>\n'], ...
fullfile(mdir{i},...
[dotbase options.extension]),'Dependency Graph');
end
if options.todo
if ~isempty(intersect(find(strcmp(mdir{i},mdirs)),todo.mfile))
fprintf(fid,['\t<tocitem target="%s" ',...
'image="$toolbox/matlab/icons/demoicon.gif">%s</tocitem>\n'], ...
fullfile(mdir{i},...
['todo' options.extension]),'Todo list');
end
end
for j=1:length(mdirs)
if strcmp(mdirs{j},mdir{i})
curfile = fullfile(mdir{i},...
[names{j} options.extension]);
fprintf(fid,'\t<tocitem target="%s">%s</tocitem>\n', ...
curfile,names{j});
end
end
fprintf(fid,'</tocitem>\n');
end
fprintf(fid,'</tocitem>\n');
fprintf(fid,'\n</toc>\n');
fclose(fid);
end
%-------------------------------------------------------------------------------
%- Write an index for each output directory
%-------------------------------------------------------------------------------
tpl_mdir = 'mdir.tpl';
tpl_mdir_link = '<a href="%s">%s</a>';
%dotbase defined earlier
%- Create the HTML template
tpl = template(options.template,'remove');
tpl = set(tpl,'file','TPL_MDIR',tpl_mdir);
tpl = set(tpl,'block','TPL_MDIR','row-m','rows-m');
tpl = set(tpl,'block','row-m','mexfile','mex');
tpl = set(tpl,'block','TPL_MDIR','othermatlab','other');
tpl = set(tpl,'block','othermatlab','row-other','rows-other');
tpl = set(tpl,'block','TPL_MDIR','subfolder','subfold');
tpl = set(tpl,'block','subfolder','subdir','subdirs');
tpl = set(tpl,'block','TPL_MDIR','todolist','todolists');
tpl = set(tpl,'block','TPL_MDIR','graph','graphs');
tpl = set(tpl,'var','DATE',[datestr(now,8) ' ' datestr(now,1) ' ' ...
datestr(now,13)]);
for i=1:length(mdir)
%- Open for writing each output directory index file
curfile = fullfile(options.htmlDir,mdir{i},...
[options.indexFile options.extension]);
if options.verbose
fprintf('Creating HTML file %s...\n',curfile);
end
fid = openfile(curfile,'w');
%- Set template fields
tpl = set(tpl,'var','INDEX', [options.indexFile options.extension]);
tpl = set(tpl,'var','MASTERPATH',backtomaster(mdir{i}));
tpl = set(tpl,'var','MDIR', mdir{i});
%- Display Matlab m-files, their H1 line and their Mex status
tpl = set(tpl,'var','rows-m','');
for j=1:length(mdirs)
if strcmp(mdirs{j},mdir{i})
tpl = set(tpl,'var','L_NAME', [names{j} options.extension]);
tpl = set(tpl,'var','NAME', names{j});
tpl = set(tpl,'var','H1LINE', h1line{j});
if any(ismex(j,:))
tpl = parse(tpl,'mex','mexfile');
else
tpl = set(tpl,'var','mex','');
end
tpl = parse(tpl,'rows-m','row-m',1);
end
end
%- Display other Matlab-specific files (.mat,.mdl,.p)
tpl = set(tpl,'var','other','');
tpl = set(tpl,'var','rows-other','');
w = what(mdir{i}); w = w(1);
w = {w.mat{:} w.mdl{:} w.p{:}};
for j=1:length(w)
tpl = set(tpl,'var','OTHERFILE',w{j});
tpl = parse(tpl,'rows-other','row-other',1);
end
if ~isempty(w)
tpl = parse(tpl,'other','othermatlab');
end
%- Display subsequent directories and classes
tpl = set(tpl,'var','subdirs','');
tpl = set(tpl,'var','subfold','');
d = dir(mdir{i});
d = {d([d.isdir]).name};
d = {d{~ismember(d,{'.' '..'})}};
for j=1:length(d)
if ismember(fullfile(mdir{i},d{j}),mdir)
tpl = set(tpl,'var','SUBDIRECTORY',...
sprintf(tpl_mdir_link,...
fullurl(d{j},[options.indexFile options.extension]),d{j}));
else
tpl = set(tpl,'var','SUBDIRECTORY',d{j});
end
tpl = parse(tpl,'subdirs','subdir',1);
end
if ~isempty(d)
tpl = parse(tpl,'subfold','subfolder');
end
%- Link to the TODO list if necessary
tpl = set(tpl,'var','todolists','');
if options.todo
if ~isempty(intersect(find(strcmp(mdir{i},mdirs)),todo.mfile))
tpl = set(tpl,'var','LTODOLIST',['todo' options.extension]);
tpl = parse(tpl,'todolists','todolist',1);
end
end
%- Link to the dependency graph if necessary
tpl = set(tpl,'var','graphs','');
if options.graph
tpl = set(tpl,'var','LGRAPH',[dotbase options.extension]);
tpl = parse(tpl,'graphs','graph',1);
end
%- Print the template in the HTML file
tpl = parse(tpl,'OUT','TPL_MDIR');
fprintf(fid,'%s',get(tpl,'OUT'));
fclose(fid);
end
%-------------------------------------------------------------------------------
%- Write a TODO list file for each output directory, if necessary
%-------------------------------------------------------------------------------
tpl_todo = 'todo.tpl';
if options.todo
%- Create the HTML template
tpl = template(options.template,'remove');
tpl = set(tpl,'file','TPL_TODO',tpl_todo);
tpl = set(tpl,'block','TPL_TODO','filelist','filelists');
tpl = set(tpl,'block','filelist','row','rows');
tpl = set(tpl,'var','DATE',[datestr(now,8) ' ' datestr(now,1) ' ' ...
datestr(now,13)]);
for i=1:length(mdir)
mfilestodo = intersect(find(strcmp(mdir{i},mdirs)),todo.mfile);
if ~isempty(mfilestodo)
%- Open for writing each TODO list file
curfile = fullfile(options.htmlDir,mdir{i},...
['todo' options.extension]);
if options.verbose
fprintf('Creating HTML file %s...\n',curfile);
end
fid = openfile(curfile,'w');
%- Set template fields
tpl = set(tpl,'var','INDEX',[options.indexFile options.extension]);
tpl = set(tpl,'var','MASTERPATH', backtomaster(mdir{i}));
tpl = set(tpl,'var','MDIR', mdir{i});
tpl = set(tpl,'var','filelists', '');
for k=1:length(mfilestodo)
tpl = set(tpl,'var','MFILE',names{mfilestodo(k)});
tpl = set(tpl,'var','rows','');
nbtodo = find(todo.mfile == mfilestodo(k));
for l=1:length(nbtodo)
tpl = set(tpl,'var','L_NBLINE',...
[names{mfilestodo(k)} ...
options.extension ...
'#l' num2str(todo.line(nbtodo(l)))]);
tpl = set(tpl,'var','NBLINE',num2str(todo.line(nbtodo(l))));
tpl = set(tpl,'var','COMMENT',todo.comment{nbtodo(l)});
tpl = parse(tpl,'rows','row',1);
end
tpl = parse(tpl,'filelists','filelist',1);
end
%- Print the template in the HTML file
tpl = parse(tpl,'OUT','TPL_TODO');
fprintf(fid,'%s',get(tpl,'OUT'));
fclose(fid);
end
end
end
%-------------------------------------------------------------------------------
%- Create dependency graphs using GraphViz, if requested
%-------------------------------------------------------------------------------
tpl_graph = 'graph.tpl';
% You may have to modify the following line with Matlab7 (R14) to specify
% the full path to where GraphViz is installed
dot_exec = 'dot';
%dotbase defined earlier
if options.graph
%- Create the HTML template
tpl = template(options.template,'remove');
tpl = set(tpl,'file','TPL_GRAPH',tpl_graph);
tpl = set(tpl,'var','DATE',[datestr(now,8) ' ' datestr(now,1) ' ' ...
datestr(now,13)]);
%- Create a full dependency graph for all directories if possible
if options.globalHypertextLinks & length(mdir) > 1
mdotfile = fullfile(options.htmlDir,[dotbase '.dot']);
if options.verbose
fprintf('Creating full dependency graph %s...',mdotfile);
end
mdot({hrefs, names, options, mdirs}, mdotfile); %mfiles
calldot(dot_exec, mdotfile, ...
fullfile(options.htmlDir,[dotbase '.map']), ...
fullfile(options.htmlDir,[dotbase '.png']));
if options.verbose, fprintf('\n'); end
fid = openfile(fullfile(options.htmlDir, [dotbase options.extension]),'w');
tpl = set(tpl,'var','INDEX',[options.indexFile options.extension]);
tpl = set(tpl,'var','MASTERPATH', './');
tpl = set(tpl,'var','MDIR', 'the whole toolbox');
tpl = set(tpl,'var','GRAPH_IMG', [dotbase '.png']);
try % if <dot> failed...
fmap = openfile(fullfile(options.htmlDir,[dotbase '.map']),'r');
tpl = set(tpl,'var','GRAPH_MAP', fscanf(fmap,'%c'));
fclose(fmap);
end
tpl = parse(tpl,'OUT','TPL_GRAPH');
fprintf(fid,'%s', get(tpl,'OUT'));
fclose(fid);
end
%- Create a dependency graph for each output directory
for i=1:length(mdir)
mdotfile = fullfile(options.htmlDir,mdir{i},[dotbase '.dot']);
if options.verbose
fprintf('Creating dependency graph %s...',mdotfile);
end
ind = find(strcmp(mdirs,mdir{i}));
href1 = zeros(length(ind),length(hrefs));
for j=1:length(hrefs), href1(:,j) = hrefs(ind,j); end
href2 = zeros(length(ind));
for j=1:length(ind), href2(j,:) = href1(j,ind); end
mdot({href2, {names{ind}}, options}, mdotfile); %{mfiles{ind}}
calldot(dot_exec, mdotfile, ...
fullfile(options.htmlDir,mdir{i},[dotbase '.map']), ...
fullfile(options.htmlDir,mdir{i},[dotbase '.png']));
if options.verbose, fprintf('\n'); end
fid = openfile(fullfile(options.htmlDir,mdir{i},...
[dotbase options.extension]),'w');
tpl = set(tpl,'var','INDEX',[options.indexFile options.extension]);
tpl = set(tpl,'var','MASTERPATH', backtomaster(mdir{i}));
tpl = set(tpl,'var','MDIR', mdir{i});
tpl = set(tpl,'var','GRAPH_IMG', [dotbase '.png']);
try % if <dot> failed, no '.map' file has been created
fmap = openfile(fullfile(options.htmlDir,mdir{i},[dotbase '.map']),'r');
tpl = set(tpl,'var','GRAPH_MAP', fscanf(fmap,'%c'));
fclose(fmap);
end
tpl = parse(tpl,'OUT','TPL_GRAPH');
fprintf(fid,'%s', get(tpl,'OUT'));
fclose(fid);
end
end
%-------------------------------------------------------------------------------
%- Write an HTML file for each M-file
%-------------------------------------------------------------------------------
%- List of Matlab keywords (output from iskeyword)
matlabKeywords = {'break', 'case', 'catch', 'continue', 'elseif', 'else', ...
'end', 'for', 'function', 'global', 'if', 'otherwise', ...
'persistent', 'return', 'switch', 'try', 'while'};
%'keyboard', 'pause', 'eps', 'NaN', 'Inf'
tpl_mfile = 'mfile.tpl';
tpl_mfile_code = '<a href="%s" class="code" title="%s">%s</a>';
tpl_mfile_keyword = '<span class="keyword">%s</span>';
tpl_mfile_comment = '<span class="comment">%s</span>';
tpl_mfile_string = '<span class="string">%s</span>';
tpl_mfile_aname = '<a name="%s" href="#_subfunctions" class="code">%s</a>';
tpl_mfile_line = '%04d %s\n';
%- Delimiters used in strtok: some of them may be useless (% " .), removed '.'
strtok_delim = sprintf(' \t\n\r(){}[]<>+-*~!|\\@&/,:;="''%%');
%- Create the HTML template
tpl = template(options.template,'remove');
tpl = set(tpl,'file','TPL_MFILE',tpl_mfile);
tpl = set(tpl,'block','TPL_MFILE','pathline','pl');
tpl = set(tpl,'block','TPL_MFILE','mexfile','mex');
tpl = set(tpl,'block','TPL_MFILE','script','scriptfile');
tpl = set(tpl,'block','TPL_MFILE','crossrefcall','crossrefcalls');
tpl = set(tpl,'block','TPL_MFILE','crossrefcalled','crossrefcalleds');
tpl = set(tpl,'block','TPL_MFILE','subfunction','subf');
tpl = set(tpl,'block','subfunction','onesubfunction','onesubf');
tpl = set(tpl,'block','TPL_MFILE','source','thesource');
tpl = set(tpl,'block','TPL_MFILE','download','downloads');
tpl = set(tpl,'var','DATE',[datestr(now,8) ' ' datestr(now,1) ' ' ...
datestr(now,13)]);
nblinetot = 0;
for i=1:length(mdir)
for j=1:length(mdirs)
if strcmp(mdirs{j},mdir{i})
curfile = fullfile(options.htmlDir,mdir{i},...
[names{j} options.extension]);
%- Copy M-file for download, if necessary
if options.download
if options.verbose
fprintf('Copying M-file %s.m to %s...\n',names{j},...
fullfile(options.htmlDir,mdir{i}));
end
[status, errmsg] = copyfile(mfiles{j},...
fullfile(options.htmlDir,mdir{i}));
error(errmsg);
end
%- Open for writing the HTML file
if options.verbose
fprintf('Creating HTML file %s...\n',curfile);
end
fid = openfile(curfile,'w');
if strcmp(names{j},options.indexFile)
fprintf(['Warning: HTML index file %s will be ' ...
'overwritten by Matlab function %s.\n'], ...
[options.indexFile options.extension], mfiles{j});
end
%- Open for reading the M-file
fid2 = openfile(mfiles{j},'r');
%- Set some template fields
tpl = set(tpl,'var','INDEX', [options.indexFile options.extension]);
tpl = set(tpl,'var','MASTERPATH', backtomaster(mdir{i}));
tpl = set(tpl,'var','MDIR', mdirs{j});
tpl = set(tpl,'var','NAME', names{j});
tpl = set(tpl,'var','H1LINE', entity(h1line{j}));
tpl = set(tpl,'var','scriptfile', '');
if isempty(synopsis{j})
tpl = set(tpl,'var','SYNOPSIS',get(tpl,'var','script'));
else
tpl = set(tpl,'var','SYNOPSIS', synopsis{j});
end
s = splitpath(mdir{i});
tpl = set(tpl,'var','pl','');
for k=1:length(s)
c = cell(1,k); for l=1:k, c{l} = filesep; end
cpath = {s{1:k};c{:}}; cpath = [cpath{:}];
if ~isempty(cpath), cpath = cpath(1:end-1); end
if ismember(cpath,mdir)
tpl = set(tpl,'var','LPATHDIR',[repmat('../',...
1,length(s)-k) options.indexFile options.extension]);
else
tpl = set(tpl,'var','LPATHDIR','#');
end
tpl = set(tpl,'var','PATHDIR',s{k});
tpl = parse(tpl,'pl','pathline',1);
end
%- Handle mex files
tpl = set(tpl,'var','mex', '');
samename = dir(fullfile(mdir{i},[names{j} '.*']));
samename = {samename.name};
tpl = set(tpl,'var','MEXTYPE', 'mex');
for k=1:length(samename)
[dummy, dummy, ext] = fileparts(samename{k});
switch ext
case '.c'
tpl = set(tpl,'var','MEXTYPE', 'c');
case {'.cpp' '.c++' '.cxx' '.C'}
tpl = set(tpl,'var','MEXTYPE', 'c++');
case {'.for' '.f' '.FOR' '.F'}
tpl = set(tpl,'var','MEXTYPE', 'fortran');
otherwise
%- Unknown mex file source
end
end
[exts, platform] = mexexts;
mexplatforms = sprintf('%s, ',platform{find(ismex(j,:))});
if ~isempty(mexplatforms)
tpl = set(tpl,'var','PLATFORMS', mexplatforms(1:end-2));
tpl = parse(tpl,'mex','mexfile');
end
%- Set description template field
descr = '';
flagsynopcont = 0;
flag_seealso = 0;
while 1
tline = fgets(fid2);
if ~ischar(tline), break, end
tline = entity(fliplr(deblank(fliplr(tline))));
%- Synopsis line
if ~isempty(strmatch('function',tline))
if ~isempty(strmatch('...',fliplr(deblank(tline))))
flagsynopcont = 1;
end
%- H1 line and description
elseif ~isempty(strmatch('%',tline))
%- Hypertext links on the "See also" line
ind = findstr(lower(tline),'see also');
if ~isempty(ind) | flag_seealso
%- "See also" only in files in the same directory
indsamedir = find(strcmp(mdirs{j},mdirs));
hrefnames = {names{indsamedir}};
r = deblank(tline);
flag_seealso = 1; %(r(end) == ',');
tline = '';
while 1
[t,r,q] = strtok(r,sprintf(' \t\n\r.,;%%'));
tline = [tline q];
if isempty(t), break, end;
ii = strcmpi(hrefnames,t);
if any(ii)
jj = find(ii);
tline = [tline sprintf(tpl_mfile_code,...
[hrefnames{jj(1)} options.extension],...
synopsis{indsamedir(jj(1))},t)];
else
tline = [tline t];
end
end
tline = sprintf('%s\n',tline);
end
descr = [descr tline(2:end)];
elseif isempty(tline)
if ~isempty(descr), break, end;
else
if flagsynopcont
if isempty(strmatch('...',fliplr(deblank(tline))))
flagsynopcont = 0;
end
else
break;
end
end
end
tpl = set(tpl,'var','DESCRIPTION',...
horztab(descr,options.tabs));
%- Set cross-references template fields:
% Function called
ind = find(hrefs(j,:) == 1);
tpl = set(tpl,'var','crossrefcalls','');
for k=1:length(ind)
if strcmp(mdirs{j},mdirs{ind(k)})
tpl = set(tpl,'var','L_NAME_CALL', ...
[names{ind(k)} options.extension]);
else
tpl = set(tpl,'var','L_NAME_CALL', ...
fullurl(backtomaster(mdirs{j}), ...
mdirs{ind(k)}, ...
[names{ind(k)} options.extension]));
end
tpl = set(tpl,'var','SYNOP_CALL', synopsis{ind(k)});
tpl = set(tpl,'var','NAME_CALL', names{ind(k)});
tpl = set(tpl,'var','H1LINE_CALL', h1line{ind(k)});
tpl = parse(tpl,'crossrefcalls','crossrefcall',1);
end
% Callers
ind = find(hrefs(:,j) == 1);
tpl = set(tpl,'var','crossrefcalleds','');
for k=1:length(ind)
if strcmp(mdirs{j},mdirs{ind(k)})
tpl = set(tpl,'var','L_NAME_CALLED', ...
[names{ind(k)} options.extension]);
else
tpl = set(tpl,'var','L_NAME_CALLED', ...
fullurl(backtomaster(mdirs{j}),...
mdirs{ind(k)}, ...
[names{ind(k)} options.extension]));
end
tpl = set(tpl,'var','SYNOP_CALLED', synopsis{ind(k)});
tpl = set(tpl,'var','NAME_CALLED', names{ind(k)});
tpl = set(tpl,'var','H1LINE_CALLED', h1line{ind(k)});
tpl = parse(tpl,'crossrefcalleds','crossrefcalled',1);
end
%- Set subfunction template field
tpl = set(tpl,'var',{'subf' 'onesubf'},{'' ''});
if ~isempty(subroutine{j}) & options.source
for k=1:length(subroutine{j})
tpl = set(tpl, 'var', 'L_SUB', ['#_sub' num2str(k)]);
tpl = set(tpl, 'var', 'SUB', subroutine{j}{k});
tpl = parse(tpl, 'onesubf', 'onesubfunction',1);
end
tpl = parse(tpl,'subf','subfunction');
end
subname = extractname(subroutine{j});
%- Link to M-file (for download)
tpl = set(tpl,'var','downloads','');
if options.download
tpl = parse(tpl,'downloads','download',1);
end
%- Display source code with cross-references
if options.source & ~strcmpi(names{j},'contents')
fseek(fid2,0,-1);
it = 1;
matlabsource = '';
nbsubroutine = 1;
%- Get href function names of this file
indhrefnames = find(hrefs(j,:) == 1);
hrefnames = {names{indhrefnames}};
%- Loop over lines
while 1
tline = fgetl(fid2);
if ~ischar(tline), break, end
myline = '';
splitc = splitcode(entity(tline));
for k=1:length(splitc)
if isempty(splitc{k})
elseif ~isempty(strmatch('function',splitc{k}))
%- Subfunctions definition
myline = [myline ...
sprintf(tpl_mfile_aname,...
['_sub' num2str(nbsubroutine-1)],splitc{k})];
nbsubroutine = nbsubroutine + 1;
elseif splitc{k}(1) == ''''
myline = [myline ...
sprintf(tpl_mfile_string,splitc{k})];
elseif splitc{k}(1) == '%'
myline = [myline ...
sprintf(tpl_mfile_comment,deblank(splitc{k}))];
elseif ~isempty(strmatch('...',splitc{k}))
myline = [myline sprintf(tpl_mfile_keyword,'...')];
if ~isempty(splitc{k}(4:end))
myline = [myline ...
sprintf(tpl_mfile_comment,splitc{k}(4:end))];
end
else
%- Look for keywords
r = splitc{k};
while 1
[t,r,q] = strtok(r,strtok_delim);
myline = [myline q];
if isempty(t), break, end;
%- Highlight Matlab keywords &
% cross-references on known functions
if options.syntaxHighlighting & ...
any(strcmp(matlabKeywords,t))
if strcmp('end',t)
rr = fliplr(deblank(fliplr(r)));
icomma = strmatch(',',rr);
isemicolon = strmatch(';',rr);
if ~(isempty(rr) | ~isempty([icomma isemicolon]))
myline = [myline t];
else
myline = [myline sprintf(tpl_mfile_keyword,t)];
end
else
myline = [myline sprintf(tpl_mfile_keyword,t)];
end
elseif any(strcmp(hrefnames,t))
indt = indhrefnames(logical(strcmp(hrefnames,t)));
flink = [t options.extension];
ii = ismember({mdirs{indt}},mdirs{j});
if ~any(ii)
% take the first one...
flink = fullurl(backtomaster(mdirs{j}),...
mdirs{indt(1)}, flink);
else
indt = indt(logical(ii));
end
myline = [myline sprintf(tpl_mfile_code,...
flink, synopsis{indt(1)}, t)];
elseif any(strcmp(subname,t))
ii = find(strcmp(subname,t));
myline = [myline sprintf(tpl_mfile_code,...
['#_sub' num2str(ii)],...
['sub' subroutine{j}{ii}],t)];
else
myline = [myline t];
end
end
end
end
matlabsource = [matlabsource sprintf(tpl_mfile_line,it,myline)];
it = it + 1;
end
nblinetot = nblinetot + it - 1;
tpl = set(tpl,'var','SOURCECODE',...
horztab(matlabsource,options.tabs));
tpl = parse(tpl,'thesource','source');
else
tpl = set(tpl,'var','thesource','');
end
tpl = parse(tpl,'OUT','TPL_MFILE');
fprintf(fid,'%s',get(tpl,'OUT'));
fclose(fid2);
fclose(fid);
end
end
end
%-------------------------------------------------------------------------------
%- Display Statistics
%-------------------------------------------------------------------------------
if options.verbose
prnbline = '';
if options.source
prnbline = sprintf('(%d lines) ', nblinetot);
end
fprintf('Stats: %d M-files %sin %d directories documented in %d s.\n', ...
length(mfiles), prnbline, length(mdir), round(etime(clock,t0)));
end
%===============================================================================
function mfiles = getmfiles(mdirs, mfiles, recursive)
%- Extract M-files from a list of directories and/or M-files
for i=1:length(mdirs)
currentdir = fullfile(pwd, mdirs{i});
if exist(currentdir) == 2 % M-file
mfiles{end+1} = mdirs{i};
elseif exist(currentdir) == 7 % Directory
d = dir(fullfile(currentdir, '*.m'));
d = {d(~[d.isdir]).name};
for j=1:length(d)
%- don't take care of files containing ','
% probably a sccs file...
if isempty(findstr(',',d{j}))
mfiles{end+1} = fullfile(mdirs{i}, d{j});
end
end
if recursive
d = dir(currentdir);
d = {d([d.isdir]).name};
d = {d{~ismember(d,{'.' '..'})}};
for j=1:length(d)
mfiles = getmfiles(cellstr(fullfile(mdirs{i},d{j})), ...
mfiles, recursive);
end
end
else
fprintf('Warning: Unprocessed file %s.\n',mdirs{i});
if ~isempty(strmatch('/',mdirs{i})) | findstr(':',mdirs{i})
fprintf(' Use relative paths in ''mfiles'' option\n');
end
end
end
%===============================================================================
function calldot(dotexec, mdotfile, mapfile, pngfile, opt)
%- Draw a dependency graph in a PNG image using <dot> from GraphViz
if nargin == 4, opt = ''; end
try
%- See <http://www.graphviz.org/>
% <dot> must be in your system path, see M2HTML FAQ:
% <http://www.artefact.tk/software/matlab/m2html/faq.php>
eval(['!"' dotexec '" ' opt ' -Tcmap -Tpng "' mdotfile ...
'" -o "' mapfile ...
'" -o "' pngfile '"']);
% use '!' rather than 'system' for backward compability with Matlab 5.3
catch % use of '!' prevents errors to be catched...
fprintf('<dot> failed.');
end
%===============================================================================
function s = backtomaster(mdir)
%- Provide filesystem path to go back to the root folder
ldir = splitpath(mdir);
s = repmat('../',1,length(ldir));
%===============================================================================
function ldir = splitpath(p)
%- Split a filesystem path into parts using filesep as separator
ldir = {};
p = deblank(p);
while 1
[t,p] = strtok(p,filesep);
if isempty(t), break; end
if ~strcmp(t,'.')
ldir{end+1} = t;
end
end
if isempty(ldir)
ldir{1} = '.'; % should be removed
end
%===============================================================================
function name = extractname(synopsis)
%- Extract function name in a synopsis
if ischar(synopsis), synopsis = {synopsis}; end
name = cell(size(synopsis));
for i=1:length(synopsis)
ind = findstr(synopsis{i},'=');
if isempty(ind)
ind = findstr(synopsis{i},'function');
s = synopsis{i}(ind(1)+8:end);
else
s = synopsis{i}(ind(1)+1:end);
end
name{i} = strtok(s,[9:13 32 40]); % white space characters and '('
end
if length(name) == 1, name = name{1}; end
%===============================================================================
function f = fullurl(varargin)
%- Build full url from parts (using '/' and not filesep)
f = strrep(fullfile(varargin{:}),'\','/');
%===============================================================================
function str = escapeblank(str)
%- Escape white spaces using '\'
str = deblank(fliplr(deblank(fliplr(str))));
str = strrep(str,' ','\ ');
%===============================================================================
function str = entity(str)
%- Escape HTML special characters
%- See http://www.w3.org/TR/html4/charset.html#h-5.3.2
str = strrep(str,'&','&');
str = strrep(str,'<','<');
str = strrep(str,'>','>');
str = strrep(str,'"','"');
%===============================================================================
function str = horztab(str,n)
%- For browsers, the horizontal tab character is the smallest non-zero
%- number of spaces necessary to line characters up along tab stops that are
%- every 8 characters: behaviour obtained when n = 0.
if n > 0
str = strrep(str,sprintf('\t'),blanks(n));
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
doxysearch.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/m2html/private/doxysearch.m
| 7,724 |
utf_8
|
8331cde8495f34b86aef8c18656b37f2
|
function result = doxysearch(query,filename)
%DOXYSEARCH Search a query in a 'search.idx' file
% RESULT = DOXYSEARCH(QUERY,FILENAME) looks for request QUERY
% in FILENAME (Doxygen search.idx format) and returns a list of
% files responding to the request in RESULT.
%
% See also DOXYREAD, DOXYWRITE
% Copyright (C) 2004 Guillaume Flandin <[email protected]>
% $Revision: 1.1 $Date: 2004/05/05 14:33:55 $
% This program is free software; you can redistribute it and/or
% modify it under the terms of the GNU General Public License
% as published by the Free Software Foundation; either version 2
% of the License, or any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program; if not, write to the Free Software
% Foundation Inc, 59 Temple Pl. - Suite 330, Boston, MA 02111-1307, USA.
% Suggestions for improvement and fixes are always welcome, although no
% guarantee is made whether and when they will be implemented.
% Send requests to <[email protected]>
% See <http://www.doxygen.org/> for more details.
error(nargchk(1,2,nargin));
if nargin == 1,
filename = 'search.idx';
end
%- Open the search index file
[fid, errmsg] = fopen(filename,'r','ieee-be');
if fid == -1, error(errmsg); end
%- 4 byte header (DOXS)
header = char(fread(fid,4,'uchar'))';
if ~all(header == 'DOXS')
error('[doxysearch] Header of index file is invalid!');
end
%- many thanks to <doxyread.m> and <doxysearch.php>
r = query;
requiredWords = {};
forbiddenWords = {};
foundWords = {};
res = {};
while 1
% extract each word of the query
[t,r] = strtok(r);
if isempty(t), break, end;
if t(1) == '+'
t = t(2:end); requiredWords{end+1} = t;
elseif t(1) == '-'
t = t(2:end); forbiddenWords{end+1} = t;
end
if ~ismember(t,foundWords)
foundWords{end+1} = t;
res = searchAgain(fid,t,res);
end
end
%- Filter and sort results
docs = combineResults(res);
filtdocs = filterResults(docs,requiredWords,forbiddenWords);
filtdocs = normalizeResults(filtdocs);
res = sortResults(filtdocs);
%-
if nargout
result = res;
else
for i=1:size(res,1)
fprintf(' %d. %s - %s\n ',i,res{i,1},res{i,2});
for j=1:size(res{i,4},1)
fprintf('%s ',res{i,4}{j,1});
end
fprintf('\n');
end
end
%- Close the search index file
fclose(fid);
%===========================================================================
function res = searchAgain(fid, word,res)
i = computeIndex(word);
if i > 0
fseek(fid,i*4+4,'bof'); % 4 bytes per entry, skip header
start = size(res,1);
idx = readInt(fid);
if idx > 0
fseek(fid,idx,'bof');
statw = readString(fid);
while ~isempty(statw)
statidx = readInt(fid);
if length(statw) >= length(word) & ...
strcmp(statw(1:length(word)),word)
res{end+1,1} = statw; % word
res{end,2} = word; % match
res{end,3} = statidx; % index
res{end,4} = (length(statw) == length(word)); % full
res{end,5} = {}; % doc
end
statw = readString(fid);
end
totalfreq = 0;
for j=start+1:size(res,1)
fseek(fid,res{j,3},'bof');
numdoc = readInt(fid);
docinfo = {};
for m=1:numdoc
docinfo{m,1} = readInt(fid); % idx
docinfo{m,2} = readInt(fid); % freq
docinfo{m,3} = 0; % rank
totalfreq = totalfreq + docinfo{m,2};
if res{j,2},
totalfreq = totalfreq + docinfo{m,2};
end;
end
for m=1:numdoc
fseek(fid, docinfo{m,1}, 'bof');
docinfo{m,4} = readString(fid); % name
docinfo{m,5} = readString(fid); % url
end
res{j,5} = docinfo;
end
for j=start+1:size(res,1)
for m=1:size(res{j,5},1)
res{j,5}{m,3} = res{j,5}{m,2} / totalfreq;
end
end
end % if idx > 0
end % if i > 0
%===========================================================================
function docs = combineResults(result)
docs = {};
for i=1:size(result,1)
for j=1:size(result{i,5},1)
key = result{i,5}{j,5};
rank = result{i,5}{j,3};
if ~isempty(docs) & ismember(key,{docs{:,1}})
l = find(ismember({docs{:,1}},key));
docs{l,3} = docs{l,3} + rank;
docs{l,3} = 2 * docs{l,3};
else
l = size(docs,1)+1;
docs{l,1} = key; % key
docs{l,2} = result{i,5}{j,4}; % name
docs{l,3} = rank; % rank
docs{l,4} = {}; %words
end
n = size(docs{l,4},1);
docs{l,4}{n+1,1} = result{i,1}; % word
docs{l,4}{n+1,2} = result{i,2}; % match
docs{l,4}{n+1,3} = result{i,5}{j,2}; % freq
end
end
%===========================================================================
function filtdocs = filterResults(docs,requiredWords,forbiddenWords)
filtdocs = {};
for i=1:size(docs,1)
words = docs{i,4};
c = 1;
j = size(words,1);
% check required
if ~isempty(requiredWords)
found = 0;
for k=1:j
if ismember(words{k,1},requiredWords)
found = 1;
break;
end
end
if ~found, c = 0; end
end
% check forbidden
if ~isempty(forbiddenWords)
for k=1:j
if ismember(words{k,1},forbiddenWords)
c = 0;
break;
end
end
end
% keep it or not
if c,
l = size(filtdocs,1)+1;
filtdocs{l,1} = docs{i,1};
filtdocs{l,2} = docs{i,2};
filtdocs{l,3} = docs{i,3};
filtdocs{l,4} = docs{i,4};
end;
end
%===========================================================================
function docs = normalizeResults(docs);
m = max([docs{:,3}]);
for i=1:size(docs,1)
docs{i,3} = 100 * docs{i,3} / m;
end
%===========================================================================
function result = sortResults(docs);
[y, ind] = sort([docs{:,3}]);
result = {};
ind = fliplr(ind);
for i=1:size(docs,1)
result{i,1} = docs{ind(i),1};
result{i,2} = docs{ind(i),2};
result{i,3} = docs{ind(i),3};
result{i,4} = docs{ind(i),4};
end
%===========================================================================
function i = computeIndex(word)
if length(word) < 2,
i = -1;
else
i = double(word(1)) * 256 + double(word(2));
end
%===========================================================================
function s = readString(fid)
s = '';
while 1
w = fread(fid,1,'uchar');
if w == 0, break; end
s(end+1) = char(w);
end
%===========================================================================
function i = readInt(fid)
i = fread(fid,1,'uint32');
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
doxywrite.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/m2html/private/doxywrite.m
| 3,584 |
utf_8
|
3255d8f824957ebc173dde374d0f78af
|
function doxywrite(filename, kw, statinfo, docinfo)
%DOXYWRITE Write a 'search.idx' file compatible with DOXYGEN
% DOXYWRITE(FILENAME, KW, STATINFO, DOCINFO) writes file FILENAME
% (Doxygen search.idx. format) using the cell array KW containing the
% word list, the sparse matrix (nbword x nbfile) with non-null values
% in (i,j) indicating the frequency of occurence of word i in file j
% and the cell array (nbfile x 2) containing the list of urls and names
% of each file.
%
% See also DOXYREAD
% Copyright (C) 2003 Guillaume Flandin <[email protected]>
% $Revision: 1.0 $Date: 2003/23/10 15:52:56 $
% This program is free software; you can redistribute it and/or
% modify it under the terms of the GNU General Public License
% as published by the Free Software Foundation; either version 2
% of the License, or any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program; if not, write to the Free Software
% Foundation Inc, 59 Temple Pl. - Suite 330, Boston, MA 02111-1307, USA.
% Suggestions for improvement and fixes are always welcome, although no
% guarantee is made whether and when they will be implemented.
% Send requests to <[email protected]>
% See <http://www.doxygen.org/> for more details.
error(nargchk(4,4,nargin));
%- Open the search index file
[fid, errmsg] = fopen(filename,'w','ieee-be');
if fid == -1, error(errmsg); end
%- Write 4 byte header (DOXS)
fwrite(fid,'DOXS','uchar');
pos = ftell(fid);
%- Write 256 * 256 header
idx = zeros(256);
writeInt(fid, idx);
%- Write word lists
i = 1;
idx2 = zeros(1,length(kw));
while 1
s = kw{i}(1:2);
idx(double(s(2)+1), double(s(1)+1)) = ftell(fid);
while i <= length(kw) & strmatch(s, kw{i})
writeString(fid,kw{i});
idx2(i) = ftell(fid);
writeInt(fid,0);
i = i + 1;
end
fwrite(fid, 0, 'int8');
if i > length(kw), break; end
end
%- Write extra padding bytes
pad = mod(4 - mod(ftell(fid),4), 4);
for i=1:pad, fwrite(fid,0,'int8'); end
pos2 = ftell(fid);
%- Write 256*256 header again
fseek(fid, pos, 'bof');
writeInt(fid, idx);
% Write word statistics
fseek(fid,pos2,'bof');
idx3 = zeros(1,length(kw));
for i=1:length(kw)
idx3(i) = ftell(fid);
[ia, ib, v] = find(statinfo(i,:));
counter = length(ia); % counter
writeInt(fid,counter);
for j=1:counter
writeInt(fid,ib(j)); % index
writeInt(fid,v(j)); % freq
end
end
pos3 = ftell(fid);
%- Set correct handles to keywords
for i=1:length(kw)
fseek(fid,idx2(i),'bof');
writeInt(fid,idx3(i));
end
% Write urls
fseek(fid,pos3,'bof');
idx4 = zeros(1,length(docinfo));
for i=1:length(docinfo)
idx4(i) = ftell(fid);
writeString(fid, docinfo{i,1}); % name
writeString(fid, docinfo{i,2}); % url
end
%- Set corrext handles to word statistics
fseek(fid,pos2,'bof');
for i=1:length(kw)
[ia, ib, v] = find(statinfo(i,:));
counter = length(ia);
fseek(fid,4,'cof'); % counter
for m=1:counter
writeInt(fid,idx4(ib(m)));% index
fseek(fid,4,'cof'); % freq
end
end
%- Close the search index file
fclose(fid);
%===========================================================================
function writeString(fid, s)
fwrite(fid,s,'uchar');
fwrite(fid,0,'int8');
%===========================================================================
function writeInt(fid, i)
fwrite(fid,i,'uint32');
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
doxyread.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/m2html/private/doxyread.m
| 3,093 |
utf_8
|
3152e7d26bf7ac64118be56f72832a20
|
function [statlist, docinfo] = doxyread(filename)
%DOXYREAD Read a 'search.idx' file generated by DOXYGEN
% STATLIST = DOXYREAD(FILENAME) reads FILENAME (Doxygen search.idx
% format) and returns the list of keywords STATLIST as a cell array.
% [STATLIST, DOCINFO] = DOXYREAD(FILENAME) also returns a cell array
% containing details for each keyword (frequency in each file where it
% appears and the URL).
%
% See also DOXYSEARCH, DOXYWRITE
% Copyright (C) 2003 Guillaume Flandin <[email protected]>
% $Revision: 1.0 $Date: 2003/05/10 17:41:21 $
% This program is free software; you can redistribute it and/or
% modify it under the terms of the GNU General Public License
% as published by the Free Software Foundation; either version 2
% of the License, or any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program; if not, write to the Free Software
% Foundation Inc, 59 Temple Pl. - Suite 330, Boston, MA 02111-1307, USA.
% Suggestions for improvement and fixes are always welcome, although no
% guarantee is made whether and when they will be implemented.
% Send requests to <[email protected]>
% See <http://www.doxygen.org/> for more details.
error(nargchk(0,1,nargin));
if nargin == 0,
filename = 'search.idx';
end
%- Open the search index file
[fid, errmsg] = fopen(filename,'r','ieee-be');
if fid == -1, error(errmsg); end
%- 4 byte header (DOXS)
header = char(fread(fid,4,'uchar'))';
%- 256*256*4 byte index
idx = fread(fid,256*256,'uint32');
idx = reshape(idx,256,256);
%- Extract list of words
i = find(idx);
statlist = cell(0,2);
for j=1:length(i)
fseek(fid, idx(i(j)), 'bof');
statw = readString(fid);
while ~isempty(statw)
statidx = readInt(fid);
statlist{end+1,1} = statw; % word
statlist{end,2} = statidx; % index
statw = readString(fid);
end
end
%- Extract occurence frequency of each word and docs info (name and url)
docinfo = cell(size(statlist,1),1);
for k=1:size(statlist,1)
fseek(fid, statlist{k,2}, 'bof');
numdoc = readInt(fid);
docinfo{k} = cell(numdoc,4);
for m=1:numdoc
docinfo{k}{m,1} = readInt(fid); % idx
docinfo{k}{m,2} = readInt(fid); % freq
end
for m=1:numdoc
fseek(fid, docinfo{k}{m,1}, 'bof');
docinfo{k}{m,3} = readString(fid); % name
docinfo{k}{m,4} = readString(fid); % url
end
docinfo{k} = reshape({docinfo{k}{:,2:4}},numdoc,[]);
end
%- Close the search index file
fclose(fid);
%- Remove indexes
statlist = {statlist{:,1}}';
%===========================================================================
function s = readString(fid)
s = '';
while 1
w = fread(fid,1,'uchar');
if w == 0, break; end
s(end+1) = char(w);
end
%===========================================================================
function i = readInt(fid)
i = fread(fid,1,'uint32');
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
imwrite2split.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/deprecated/imwrite2split.m
| 1,617 |
utf_8
|
4222fd45df123e6dec9ef40ae793004f
|
% Writes/reads a large set of images into/from multiple directories.
%
% This is useful since certain OS handle very large directories (of say
% >20K images) rather poorly (I'm talking to you Bill). Thus, can take
% 100K images, and write into 5 separate directories, then read them back
% in.
%
% USAGE
% I = imwrite2split( I, nSplits, spliti, path, [varargin] )
%
% INPUTS
% I - image or images (if [] reads else writes)
% nSplits - number of directories to split data into
% spliti - first split number
% path - directory where images are
% writePrms - [varargin] parameters to imwrite2
%
% OUTPUTS
% I - image or images (read from disk if input I=[])
%
% EXAMPLE
% load images; clear IDXi IDXv t video videos;
% imwrite2split( images(:,:,1:10), 2, 0, 'rats', 'rats', 'png', 5 );
% images2=imwrite2split( [], 2, 0, 'rats', 'rats', 'png', 5 );
%
% See also IMWRITE2
% Piotr's Image&Video Toolbox Version NEW
% Written and maintained by Piotr Dollar pdollar-at-cs.ucsd.edu
% Please email me if you find bugs, or have suggestions or questions!
function I = imwrite2split( I, nSplits, spliti, path, varargin )
n = size(I,3); if( isempty(I) ); n=0; end
nSplits = min(n,nSplits);
for s=1:nSplits
pathSplit = [path int2str2(s-1+spliti,2)];
if( n>0 ) % write
nPerDir = ceil( n / nSplits );
ISplit = I(:,:,1:min(end,nPerDir));
imwrite2( ISplit, nPerDir>1, 0, pathSplit, varargin{:} );
if( s~=nSplits ); I = I(:,:,(nPerDir+1):end); end
else % read
ISplit = imwrite2( [], 1, 0, pathSplit, varargin{:} );
I = cat(3,I,ISplit);
end
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
playmovies.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/deprecated/playmovies.m
| 1,935 |
utf_8
|
ef2eaad8a130936a1a281f1277ca0ea1
|
% [4D] shows R videos simultaneously as a movie.
%
% Plays a movie.
%
% USAGE
% playmovies( I, [fps], [loop] )
%
% INPUTS
% I - MxNxTxR or MxNx1xTxR or MxNx3xTxR array (if MxNxT calls
% playmovie)
% fps - [100] maximum number of frames to display per second use
% fps==0 to introduce no pause and have the movie play as
% fast as possible
% loop - [0] number of time to loop video (may be inf),
% if neg plays video forward then backward then forward etc.
%
% OUTPUTS
%
% EXAMPLE
% load( 'images.mat' );
% playmovies( videos );
%
% See also MONTAGES, PLAYMOVIE, MAKEMOVIES
% Piotr's Image&Video Toolbox Version 1.5
% Written and maintained by Piotr Dollar pdollar-at-cs.ucsd.edu
% Please email me if you find bugs, or have suggestions or questions!
function playmovies( I, fps, loop )
wid = sprintf('Images:%s:obsoleteFunction',mfilename);
warning(wid,[ '%s is obsolete in Piotr''s toolbox.\n PLAYMOVIE is its '...
'recommended replacement.'],upper(mfilename));
if( nargin<2 || isempty(fps)); fps = 100; end
if( nargin<3 || isempty(loop)); loop = 1; end
playmovie( I, fps, loop )
%
% nd=ndims(I); siz=size(I); nframes=siz(end-1);
% if( nd==3 ); playmovie( I, fps, loop ); return; end
% if( iscell(I) ); error('cell arrays not supported.'); end
% if( ~(nd==4 || (nd==5 && any(size(I,3)==[1 3]))) )
% error('unsupported dimension of I'); end
% inds={':'}; inds=inds(:,ones(1,nd-2));
% clim = [min(I(:)),max(I(:))];
%
% h=gcf; colormap gray; figure(h); % bring to focus
% for nplayed = 1 : abs(loop)
% if( loop<0 && mod(nplayed,2)==1 )
% order = nframes:-1:1;
% else
% order = 1:nframes;
% end
% for i=order
% tic; try disc=get(h); catch return; end %#ok<NASGU>
% montage2(squeeze(I(inds{:},i,:)),1,[],clim);
% title(sprintf('frame %d of %d',i,nframes));
% if(fps>0); pause(1/fps - toc); else pause(eps); end
% end
% end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
pca_apply_large.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/deprecated/pca_apply_large.m
| 2,062 |
utf_8
|
af84a2179b9d8042519bc6b378736a88
|
% Wrapper for pca_apply that allows for application to large X.
%
% Wrapper for pca_apply that splits and processes X in parts, this may be
% useful if processing cannot be done fully in parallel because of memory
% constraints. See pca_apply for usage.
%
% USAGE
% same as pca_apply
%
% INPUTS
% same as pca_apply
%
% OUTPUTS
% same as pca_apply
%
% EXAMPLE
%
% See also PCA, PCA_APPLY, PCA_VISUALIZE
% Piotr's Image&Video Toolbox Version 1.5
% Written and maintained by Piotr Dollar pdollar-at-cs.ucsd.edu
% Please email me if you find bugs, or have suggestions or questions!
function [ Yk, Xhat, avsq ] = pca_apply_large( X, U, mu, vars, k )
siz = size(X); nd = ndims(X); [N,r] = size(U);
if(N==prod(siz) && ~(nd==2 && siz(2)==1)); siz=[siz, 1]; nd=nd+1; end
inds = {':'}; inds = inds(:,ones(1,nd-1));
d= prod(siz(1:end-1));
% some error checking
if(d~=N); error('incorrect size for X or U'); end
if(isa(X,'uint8')); X = double(X); end
if( k>r )
warning(['Only ' int2str(r) '<k comp. available.']); %#ok<WNTAG>
k=r;
end
% Will run out of memory if X has too many elements. Hence, run
% pca_apply on parts of X and recombine.
maxwidth = ceil( (10^7) / d );
if(maxwidth > siz(end))
if (nargout==1)
Yk = pca_apply( X, U, mu, vars, k );
elseif (nargout==2)
[Yk, Xhat] = pca_apply( X, U, mu, vars, k );
else
[ Yk, Xhat, avsq ] = pca_apply( X, U, mu, vars, k );
end
else
Yk = zeros( k, siz(end) ); Xhat = zeros( siz );
avsq = 0; avsqOrig = 0; last = 0;
while(last < siz(end))
first=last+1; last=min(first+maxwidth-1,siz(end));
Xi = X(inds{:}, first:last);
if( nargout==1 )
Yki = pca_apply( Xi, U, mu, vars, k );
else
if( nargout==2 )
[Yki,Xhati] = pca_apply( Xi, U, mu, vars, k );
else
[Yki,Xhati,avsqi,avsqOrigi] = pca_apply( Xi, U, mu, vars, k );
avsq = avsq + avsqi; avsqOrig = avsqOrig + avsqOrigi;
end;
Xhat(inds{:}, first:last ) = Xhati;
end
Yk( :, first:last ) = Yki;
end;
if( nargout==3); avsq = avsq / avsqOrig; end
end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
montages2.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/deprecated/montages2.m
| 2,269 |
utf_8
|
505e2be915d65fff8bfef8473875cc98
|
% MONTAGES2 [4D] Used to display R sets of T images each.
%
% Displays one montage (see montage2) per row. Each of the R image sets is
% flattened to a single long image by concatenating the T images in the
% set. Alternative to montages.
%
% USAGE
% varargout = montages2( IS, [montage2prms], [padSiz] )
%
% INPUTS
% IS - MxNxTxR or MxNx1xTxR or MxNx3xTxR array
% montage2prms - [] params for montage2; ex: {showLns,extraInf}
% padSiz - [4] total amount of vertical or horizontal padding
%
% OUTPUTS
% I - 3D or 4D array of flattened images, disp with montage2
% mm - #montages/row
% nn - #montages/col
%
% EXAMPLE
% load( 'images.mat' );
% imageclusters = clustermontage( images, IDXi, 16, 1 );
% montages2( imageclusters );
%
% See also MONTAGES, MAKEMOVIES, MONTAGE2, CLUSTERMONTAGE
% Piotr's Image&Video Toolbox Version 1.5
% Written and maintained by Piotr Dollar pdollar-at-cs.ucsd.edu
% Please email me if you find bugs, or have suggestions or questions!
function varargout = montages2( IS, montage2prms, padSiz )
if( nargin<2 || isempty(montage2prms) ); montage2prms = {}; end
if( nargin<3 || isempty(padSiz) ); padSiz = 4; end
[padSiz,er] = checknumericargs( padSiz,[1 1], 0, 1 ); error(er);
% get/test image format info
nd = ndims(IS); siz = size(IS);
if( nd==5 ) %MxNx1xTxR or MxNx3xTxR
nch = size(IS,3);
if( nch~=1 && nch~=3 ); error('illegal image stack format'); end
if( nch==1 ); IS = squeeze(IS); nd=4; siz=size(IS); end
end
if ~any(nd==3:5)
error('unsupported dimension of IS');
end
% reshape IS so that each 3D element is concatenated to a 2D image, adding
% padding
padEl = max(IS(:));
IS=arraycrop2dims(IS, [siz(1)+padSiz siz(2:end)], padEl ); %UD pad
siz=size(IS);
if(nd==3) % reshape bw single
IS=squeeze( reshape( IS, siz(1), [] ) );
elseif(nd==4) % reshape bw
IS=squeeze( reshape( IS, siz(1), [], siz(4) ) );
else % reshape color
IS=squeeze( reshape(permute(IS,[1 2 4 3 5]),siz(1),[],siz(3),siz(5)));
end; siz = size(IS);
IS=arraycrop2dims(IS, [siz(1) siz(2)+padSiz siz(3:end)], padEl);
% show using montage2
varargout = cell(1,nargout);
if( nargout); varargout{1}=IS; end;
[varargout{2:end}] = montage2( IS, montage2prms{:} );
title(inputname(1));
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
filter_gauss_1D.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/deprecated/filter_gauss_1D.m
| 1,137 |
utf_8
|
94a453b82dcdeba67bd886e042d552d9
|
% 1D Gaussian filter.
%
% Equivalent to (but faster then):
% f = fspecial('Gaussian',[2*r+1,1],sigma);
% f = filter_gauss_nD( 2*r+1, r+1, sigma^2 );
%
% USAGE
% f = filter_gauss_1D( r, sigma, [show] )
%
% INPUTS
% r - filter size=2r+1, if r=[] -> r=ceil(2.25*sigma)
% sigma - standard deviation of filter
% show - [0] figure to use for optional display
%
% OUTPUTS
% f - 1D Gaussian filter
%
% EXAMPLE
% f1 = filter_gauss_1D( 10, 2, 1 );
% f2 = filter_gauss_nD( 21, [], 2^2, 2);
%
% See also FILTER_BINOMIAL_1D, FILTER_GAUSS_ND, FSPECIAL
% Piotr's Image&Video Toolbox Version 1.5
% Written and maintained by Piotr Dollar pdollar-at-cs.ucsd.edu
% Please email me if you find bugs, or have suggestions or questions!
function f = filter_gauss_1D( r, sigma, show )
if( nargin<3 || isempty(show) ); show=0; end
if( isempty(r) ); r = ceil(sigma*2.25); end
if( mod(r,1)~=0 ); error( 'r must be an integer'); end
% compute filter
x = -r:r;
f = exp(-(x.*x)/(2*sigma*sigma))';
f(f<eps*max(f(:))*10) = 0;
sumf = sum(f(:)); if(sumf~=0); f = f/sumf; end
% display
if(show); filter_visualize_1D( f, show ); end
|
github
|
GYZHikari/Semantic-Cosegmentation-master
|
clfEcoc.m
|
.m
|
Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/deprecated/clfEcoc.m
| 1,493 |
utf_8
|
e77e1b4fd5469ed39f47dd6ed15f130f
|
function clf = clfEcoc(p,clfInit,clfparams,nclasses,use01targets)
% Wrapper for ecoc that makes ecoc compatible with nfoldxval.
%
% Requires the SVM toolbox by Anton Schwaighofer.
%
% USAGE
% clf = clfEcoc(p,clfInit,clfparams,nclasses,use01targets)
%
% INPUTS
% p - data dimension
% clfInit - binary classifier init (see nfoldxval)
% clfparams - binary classifier parameters (see nfoldxval)
% nclasses - num of classes (currently 3<=nclasses<=7 suppored)
% use01targets - see ecoc
%
% OUTPUTS
% clf - see ecoc
%
% EXAMPLE
%
% See also ECOC, NFOLDXVAL, CLFECOCCODE
%
% Piotr's Image&Video Toolbox Version 2.0
% Copyright 2008 Piotr Dollar. [pdollar-at-caltech.edu]
% Please email me if you find bugs, or have suggestions or questions!
% Licensed under the Lesser GPL [see external/lgpl.txt]
if( nclasses<3 || nclasses>7 )
error( 'currently only works if 3<=nclasses<=7'); end;
if( nargin<5 || isempty(use01targets)); use01targets=0; end;
% create code (limited for now)
[C,nbits] = clfEcocCode( nclasses );
clf = ecoc(nclasses, nbits, C, use01targets ); % didn't use to pass use01?
clf.verbosity = 0; % don't diplay output
% initialize and temporarily store binary learner
clf.templearner = feval( clfInit, p, clfparams{:} );
% ecoctrain2 is custom version of ecoctrain
clf.funTrain = @clfEcocTrain;
clf.funFwd = @ecocfwd;
function clf = clfEcocTrain( clf, varargin )
clf = ecoctrain( clf, clf.templearner, varargin{:} );
|
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