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github
|
shangjingbo1226/DPPred-master
|
mrelnet.m
|
.m
|
DPPred-master/glmnet_matlab/mrelnet.m
| 2,585 |
utf_8
|
2f946c92aba76c5b8ef746baed5e437c
|
function fit = mrelnet(x,is_sparse,irs,pcs,y,weights,offset,parm,nobs,nvars,...
jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,jsd,intr,maxit,family)
nr = size(y, 2);
wym = wtmean(y, weights);
nulldev = sum(wtmean(bsxfun(@minus,y,wym).^2,weights)*sum(weights));
if isempty(offset)
offset = y * 0;
is_offset = false;
else
if (size(offset) ~= size(y))
error('Offset must match dimension of y');
end
is_offset = true;
end
if is_sparse
task = 30;
[lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr] = glmnetMex(task,parm,x,y-offset,jd,vp,ne,nx,nlam,flmin,ulam,thresh,isd,weights,cl,intr,maxit,irs,pcs,jsd);
else
task = 31;
[lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr] = glmnetMex(task,parm,x,y-offset,jd,vp,ne,nx,nlam,flmin,ulam,thresh,isd,weights,cl,intr,maxit,jsd);
end
if (jerr ~= 0)
errmsg = err(jerr,maxit,nx,family);
if (errmsg.fatal)
error(errmsg.msg);
else
warning(errmsg.msg);
end
end
ninmax = max(nin);
lam = alm;
if (ulam == 0.0)
lam = fix_lam(lam); % first lambda is infinity; changed to entry point
end
if (nr > 1)
beta_list = {};
% a0 = a0 - repmat(mean(a0), nr, 1); do not center for mrelnet!
dfmat=a0;
dd=[nvars, lmu];
if ninmax > 0
ca = reshape(ca, nx, nr, lmu);
ca = ca(1:ninmax,:,:);
ja = ia(1:ninmax);
[ja1,oja] = sort(ja);
df = any(abs(ca) > 0, 2);
df = sum(df, 1);
df = df(:);
for k=1:nr
ca1 = reshape(ca(:,k,:), ninmax, lmu);
cak = ca1(oja,:);
dfmat(k,:) = sum(abs(cak) > 0, 1);
beta = zeros(nvars, lmu);
beta(ja1,:) = cak;
beta_list{k} = beta;
end
else
for k = 1:nr
dfmat(k,:) = zeros(1,lmu);
beta_list{k} = zeros(nvars, lmu);
end
df = zeros(1,lmu);
end
fit.beta = beta_list;
fit.dfmat = dfmat;
else
dd=[nvars, lmu];
if ninmax > 0
ca = ca(1:ninmax,:);
df = sum(abs(ca) > 0, 1);
ja = ia(1:ninmax);
[ja1,oja] = sort(ja);
beta = zeros(nvars, lmu);
beta (ja1, :) = ca(oja,:);
else
beta = zeros(nvars,lmu);
df = zeros(1,lmu);
end
fit.beta = beta;
end
fit.a0 = a0;
fit.dev = rsq;
fit.nulldev = nulldev;
fit.df = df';
fit.lambda = lam;
fit.npasses = nlp;
fit.jerr = jerr;
fit.dim = dd;
fit.offset = is_offset;
fit.class = 'mrelnet';
function new_lam = fix_lam(lam)
new_lam = lam;
if (length(lam) > 2)
llam=log(lam);
new_lam(1)=exp(2*llam(2)-llam(3));
end
|
github
|
shangjingbo1226/DPPred-master
|
coxnet.m
|
.m
|
DPPred-master/glmnet_matlab/coxnet.m
| 1,563 |
utf_8
|
895747785766daa2c82975f3d019dd18
|
function fit = coxnet(x,is_sparse,irs,pcs,y,weights,offset,parm,nobs,nvars,...
jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,maxit,family)
% Internal glmnet function. See also glmnet, cvglmnet.
%time --- column 1
%status --- column 2
ty = y(:,1);
tevent = y(:,2);
if (any(ty <= 0))
error('negative event times encountered; not permitted for Cox family');
end
if isempty(offset)
offset = ty * 0;
is_offset = false;
else
is_offset = true;
end
if (is_sparse)
error('Cox model not implemented for sparse x in glmnet');
else
task = 41;
[lmu,ca,ia,nin,dev,alm,nlp,jerr,dev0,ot] = glmnetMex(task,parm,x,ty,jd,vp,ne,nx,nlam,flmin,ulam,thresh,isd,weights,tevent,cl,maxit,offset);
end
if (jerr ~= 0)
errmsg = err(jerr,maxit,nx,family);
if (errmsg.fatal)
error(errmsg.msg);
else
warning(errmsg.msg);
end
end
ninmax = max(nin);
lam = alm;
if (ulam == 0.0)
lam = fix_lam(lam); % first lambda is infinity; changed to entry point
end
dd=[nvars, lmu];
if ninmax > 0
ca = ca(1:ninmax,:);
df = sum(abs(ca) > 0, 1);
ja = ia(1:ninmax);
[ja1,oja] = sort(ja);
beta = zeros(nvars, lmu);
beta (ja1,:) = ca(oja,:);
else
beta = zeros(nvars,lmu);
df = zeros(1,lmu);
end
fit.beta = beta;
fit.dev = dev;
fit.nulldev = dev0;
fit.df = df';
fit.lambda = lam;
fit.npasses = nlp;
fit.jerr = jerr;
fit.dim = dd;
fit.offset = is_offset;
fit.class = 'coxnet';
function new_lam = fix_lam(lam)
new_lam = lam;
if (length(lam) > 2)
llam=log(lam);
new_lam(1)=exp(2*llam(2)-llam(3));
end
|
github
|
shangjingbo1226/DPPred-master
|
err.m
|
.m
|
DPPred-master/glmnet_matlab/err.m
| 3,764 |
utf_8
|
eeab9274fcf914b604c55eb4ec2a867a
|
function output = err(n,maxit,pmax,family)
if n==0
output.n=0;
output.fatal=false;
output.msg='';
else
switch family
case 'gaussian'
output = err_elnet(n,maxit,pmax);
case 'binomial'
output = err_lognet(n,maxit,pmax);
case 'multinomial'
output = err_lognet(n,maxit,pmax);
case 'poisson'
output = err_fishnet(n,maxit,pmax);
case 'cox'
output = err_coxnet(n,maxit,pmax);
case 'mrelnet'
output = err_mrelnet(n,maxit,pmax);
end
output.msg = sprintf('from glmnet Fortran code (error code %d); %s', n, output.msg);
end
%------------------------------------------------------------------
% End private function err
%------------------------------------------------------------------
function output = err_elnet(n,maxit,pmax)
if (n > 0) %fatal error
if (n < 7777)
msg = 'Memory allocation error; contact package maintainer';
elseif (n == 7777)
msg = 'All used predictors have zero variance';
elseif (n == 10000)
msg = 'All penalty factors are <= 0';
else
msg = 'Unknown error';
end
output.n = n;
output.fatal = true;
output.msg = msg;
elseif (n < 0) %non-fatal error
if (n > -10000)
msg = sprintf('Convergence for %dth lambda value not reached after maxit=%d iterations; solutions for larger lambdas returned',-n,maxit);
end
if (n < -10000)
msg = sprintf('Number of nonzero coefficients along the path exceeds pmax=%d at %dth lambda value; solutions for larger lambdas returned',pmax,-n-10000);
end
output.n = n;
output.fatal = false;
output.msg = msg;
end
function output = err_lognet(n,maxit,pmax)
output = err_elnet(n,maxit,pmax);
if (n < -20000)
output.msg = sprintf('Max(p(1-p),1.0e-6 at %dth value of lambda; solutions for larger values of lambda returned',-n-20000);
end
if ~strcmp(output.msg, 'Unknown error')
return;
end
if (8000 < n) && (n < 9000)
msg = sprintf('Null probability for class%d< 1.0e-5', n-8000);
elseif (9000 < n) && (n < 10000)
msg = sprintf('Null probability for class%d> 1.0 - 1.0e-5',n-9000);
else
msg = 'Unknown error';
end
output.n = n;
output.fatal = true;
output.msg = msg;
function output = err_fishnet(n,maxit,pmax)
output = err_elnet(n,maxit,pmax);
if ~strcmp(output.msg, 'Unknown error')
return;
end
if (n == 8888)
msg = 'Negative response values - should be counts';
elseif (n == 9999)
msg = 'No positive observation weights';
else
msg = 'Unknown error';
end
output.n = n;
output.fatal = true;
output.msg = msg;
function output = err_coxnet(n,maxit,pmax)
if (n > 0) %fatal error
output = err_elnet(n,maxit,pmax);
if ~strcmp(msg, 'Unknown error')
return;
end
if (n == 8888)
msg = 'All observations censored - cannot proceed';
elseif (n == 9999)
msg = 'No positive observation weights';
elseif (n == 20000) || (n == 30000)
msg = 'Inititialization numerical error; probably too many censored observations';
else
msg = 'Unknown error';
end
output.n = n;
output.fatal = true;
output.msg = msg;
elseif (n < 0)
if (n <= -30000)
msg = sprintf('Numerical error at %dth lambda value; solutions for larger values of lambda returned',-n-30000);
output.n = n;
output.fatal = false;
output.msg = msg;
else
output = err_elnet(n,maxit,pmax);
end
end
function output = err_mrelnet(n,maxit,pmax)
if (n == 90000)
msg = 'Newton stepping for bounded multivariate response did not converge';
output.n = n;
output.fatal = false;
output.msg = msg;
else
output = err_elnet(n,maxit,pmax);
end
|
github
|
shangjingbo1226/DPPred-master
|
glmnetPlot.m
|
.m
|
DPPred-master/glmnet_matlab/glmnetPlot.m
| 7,609 |
utf_8
|
12fcbd8fb5cafce7d419403fcfab5649
|
function glmnetPlot( x, xvar, label, type, varargin )
%--------------------------------------------------------------------------
% glmnetPlot.m: plot coefficients from a "glmnet" object
%--------------------------------------------------------------------------
%
% DESCRIPTION:
% Produces a coefficient profile plot fo the coefficient paths for a
% fitted "glmnet" object.
%
% USAGE:
% glmnetPlot(fit);
% glmnetPlot(fit, xvar);
% glmnetPlot(fit, xvar, label);
% glmnetPlot(fit, xvar, label, type);
% glmnetPlot(fit, xvar, label, type, ...);
% (Use empty matrix [] to apply the default value, eg. glmnetPlot(fit,
% [], [], type).)
%
% INPUT ARGUMENTS:
% x fitted "glmnet" model.
% xvar What is on the X-axis. 'norm' plots against the L1-norm of
% the coefficients, 'lambda' against the log-lambda sequence,
% and 'dev' against the percent deviance explained.
% label If true, label the curves with variable sequence numbers.
% type If type='2norm' then a single curve per variable, else
% if type='coef', a coefficient plot per response.
% varargin Other graphical parameters to plot.
%
% DETAILS:
% A coefficient profile plot is produced. If x is a multinomial model, a
% coefficient plot is produced for each class.
%
% LICENSE: GPL-2
%
% DATE: 30 Aug 2013
%
% AUTHORS:
% Algorithm was designed by Jerome Friedman, Trevor Hastie and Rob Tibshirani
% Fortran code was written by Jerome Friedman
% R wrapper (from which the MATLAB wrapper was adapted) was written by Trevor Hasite
% The original MATLAB wrapper was written by Hui Jiang (14 Jul 2009),
% and was updated and maintained by Junyang Qian (30 Aug 2013) [email protected],
% Department of Statistics, Stanford University, Stanford, California, USA.
%
% REFERENCES:
% Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent,
% http://www.jstatsoft.org/v33/i01/
% Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010
%
% Simon, N., Friedman, J., Hastie, T., Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent,
% http://www.jstatsoft.org/v39/i05/
% Journal of Statistical Software, Vol. 39(5) 1-13
%
% Tibshirani, Robert., Bien, J., Friedman, J.,Hastie, T.,Simon, N.,Taylor, J. and Tibshirani, Ryan. (2010) Strong Rules for Discarding Predictors in Lasso-type Problems,
% http://www-stat.stanford.edu/~tibs/ftp/strong.pdf
% Stanford Statistics Technical Report
%
% SEE ALSO:
% glmnet, glmnetSet, glmnetPrint, glmnetPredict and glmnetCoef.
%
% EXAMPLES:
% x=randn(100,20);
% y=randn(100,1);
% g2=randsample(2,100,true);
% g4=randsample(4,100,true);
% fit1=glmnet(x,y);
% glmnetPlot(fit1);
% glmnetPlot(fit1, 'lambda', true);
% fit3=glmnet(x,g4,'multinomial');
% glmnetPlot(fit3);
%
% DEVELOPMENT:
% 14 Jul 2009: Original version of glmnet.m written.
% 30 Aug 2013: Updated glmnet.m with more options and more models
% (multi-response Gaussian, cox and Poisson models) supported.
if nargin < 2 || isempty(xvar)
xvar = 'norm';
end
if nargin < 3 || isempty(label)
label = false;
end
if nargin < 4 || isempty(type)
type = 'coef';
end
xvarbase = {'norm','lambda','dev'};
xvarind = find(strncmp(xvar,xvarbase,length(xvar)),1);
if isempty(xvarind)
error('xvar should be one of ''norm'', ''lambda'', ''dev''');
else
xvar = xvarbase{xvarind};
end
typebase = {'coef','2norm'};
typeind = find(strncmp(type,typebase,length(type)),1);
if isempty(typeind)
error('type should be one of ''coef'', ''2norm''');
else
type = typebase{typeind};
end
if any(strcmp(x.class,{'elnet','lognet','coxnet','fishnet'}))
plotCoef(x.beta,[],x.lambda,x.df,x.dev,label,xvar,'','Coefficients',varargin{:});
end
if strcmp(x.class,'multnet') || strcmp(x.class,'mrelnet')
beta = x.beta;
if strcmp(xvar,'norm')
norm = 0;
nzbeta = beta;
for i=1:length(beta)
which = nonzeroCoef(beta{i});
nzbeta{i} = beta{i}(which,:);
norm = norm + sum(abs(nzbeta{i}),1);
end
else
norm = 0;
end
if strcmp(type,'coef')
ncl = size(x.dfmat,1);
if strcmp(x.class,'multnet')
for i = 1:ncl
plotCoef(beta{i},norm,x.lambda,x.dfmat(i,:),x.dev,label,xvar,'',sprintf('Coefficients: Class %d', i),varargin{:});
end
else
for i = 1:ncl
plotCoef(beta{i},norm,x.lambda,x.dfmat(i,:),x.dev,label,xvar,'',sprintf('Coefficients: Response %d', i),varargin{:});
end
end
else
dfseq = round(mean(x.dfmat,1));
coefnorm = beta{1}*0;
for i=1:length(beta)
coefnorm = coefnorm + abs(beta{i}).^2;
end
coefnorm = sqrt(coefnorm);
if strcmp(x.class,'multnet')
plotCoef(coefnorm,norm,x.lambda,dfseq,x.dev,label,xvar,'',sprintf('Coefficient 2Norms'),varargin{:});
else
plotCoef(coefnorm,norm,x.lambda,x.dfmat(1,:),x.dev,label,xvar,'',sprintf('Coefficient 2Norms'),varargin{:});
end
end
end
%----------------------------------------------------------------
% End function glmnetPlot
%----------------------------------------------------------------
function plotCoef(beta,norm,lambda,df,dev,label,xvar,xlab,ylab,varargin)
which = nonzeroCoef(beta);
idwhich = find(which); %row indices
nwhich = length(idwhich);
if nwhich == 0
error('No plot produced since all coefficients zero')
end
if nwhich == 1
warning('1 or less nonzero coefficients; glmnet plot is not meaningful');
end
beta = beta(which,:);
if strcmp(xvar, 'norm')
if isempty(norm)
index = sum(abs(beta),1);
else
index = norm;
end
iname = 'L1 Norm';
elseif strcmp(xvar, 'lambda')
index=log(lambda);
iname='Log Lambda';
elseif strcmp(xvar, 'dev')
index=dev;
iname='Fraction Deviance Explained';
end
if isempty(xlab)
xlab = iname;
end
%Prepare for the figure (especially for the df labels)
clf;
beta = transpose(beta);
plot(index,beta,varargin{:});
axes1 = gca;
axes;
axes2 = gca;
xlim1 = get(axes1,'XLim');
ylim1 = get(axes1,'YLim');
%idxrange = range(index);
%atdf = linspace(min(index)+idxrange/12, max(index)-idxrange/12, 6);
atdf = get(axes1,'XTick');
indat = ones(size(atdf));
if (index(end) >= index(1))
for j = length(index):-1:1
indat(atdf <= index(j)) = j;
end
else
for j = 1:length(index)
indat(atdf <= index(j)) = j;
end
end
prettydf = df(indat);
prettydf(end) = df(end);
set(axes1,'box','off','XAxisLocation','bottom','YAxisLocation','left');
set(axes2,'XAxisLocation','top','YAxisLocation','right',...
'XLim',[min(index),max(index)],'XTick',atdf,'XTickLabel',prettydf,...
'YTick',[],'YTickLabel',[],'TickDir','out');
xlabel(axes2,'Degrees of Freedom')
axes(axes1);
line(xlim1,[ylim1(2),ylim1(2)],'Color','k');
line([xlim1(2),xlim1(2)],ylim1,'Color','k');
xlabel(xlab);
ylabel(ylab);
if (label)
xpos = max(index);
adjpos = 2;
if strcmp(xvar,'lambda')
xpos = min(index);
adjpos = 1;
end
bsize = size(beta);
for i = 1: bsize(2)
text(1/2*xpos+1/2*xlim1(adjpos),beta(bsize(1),i),num2str(idwhich(i)));
end
end
linkaxes([axes1 axes2],'x');
%----------------------------------------------------------------
% End private function plotCoef
%----------------------------------------------------------------
|
github
|
shangjingbo1226/DPPred-master
|
glmnetPredict.m
|
.m
|
DPPred-master/glmnet_matlab/glmnetPredict.m
| 16,486 |
utf_8
|
58bccedbf1d9b31e6c7b0e05d2058f3d
|
function result = glmnetPredict(object, newx, s, type, exact, offset)
%--------------------------------------------------------------------------
% glmnetPredict.m: make predictions from a "glmnet" object.
%--------------------------------------------------------------------------
%
% DESCRIPTION:
% Similar to other predict methods, this functions predicts fitted
% values, logits, coefficients and more from a fitted "glmnet" object.
%
% USAGE:
% glmnetPredict(object, newx, s, type, exact, offset)
%
% Fewer input arguments(more often) are allowed in the call, but must
% come in the order listed above. To set default values on the way, use
% empty matrix [].
% For example, pred=glmnetPredict(fit,[],[],'coefficients').
%
% To make EXACT prediction, the input arguments originally passed to
% "glmnet" MUST be VARIABLES (instead of expressions, or fields
% extracted from some struct objects). Alternatively, users should
% manually revise the "call" field in "object" (expressions or variable
% names) to match the original call to glmnet in the parent environment.
%
% INPUT ARGUMENTS:
% object Fitted "glmnet" model object.
% s Value(s) of the penalty parameter lambda at which predictions
% are required. Default is the entire sequence used to create
% the model.
% newx Matrix of new values for x at which predictions are to be
% made. Must be a matrix; can be sparse. This argument is not
% used for type='coefficients' or type='nonzero'.
% type Type of prediction required. Type 'link' gives the linear
% predictors for 'binomial', 'multinomial', 'poisson' or 'cox'
% models; for 'gaussian' models it gives the fitted values.
% Type 'response' gives the fitted probabilities for 'binomial'
% or 'multinomial', fitted mean for 'poisson' and the fitted
% relative-risk for 'cox'; for 'gaussian' type 'response' is
% equivalent to type 'link'. Type 'coefficients' computes the
% coefficients at the requested values for s. Note that for
% 'binomial' models, results are returned only for the class
% corresponding to the second level of the factor response.
% Type 'class' applies only to 'binomial' or 'multinomial'
% models, and produces the class label corresponding to the
% maximum probability. Type 'nonzero' returns a matrix of
% logical values with each column for each value of s,
% indicating if the corresponding coefficient is nonzero or not.
% exact If exact=true, and predictions are to made at values of s not
% included in the original fit, these values of s are merged
% with object.lambda, and the model is refit before predictions
% are made. If exact=false (default), then the predict function
% uses linear interpolation to make predictions for values of s
% that do not coincide with those used in the fitting
% algorithm. Note that exact=true is fragile when used inside a
% nested sequence of function calls. glmnetPredict() needs to
% update the model, and expects the data used to create it in
% the parent environment.
% offset If an offset is used in the fit, then one must be supplied
% for making predictions (except for type='coefficients' or
% type='nonzero')
%
% DETAILS:
% The shape of the objects returned are different for "multinomial"
% objects. glmnetCoef(fit, ...) is equivalent to
% glmnetPredict(fit,[],[],'coefficients").
%
% LICENSE: GPL-2
%
% DATE: 30 Aug 2013
%
% AUTHORS:
% Algorithm was designed by Jerome Friedman, Trevor Hastie and Rob Tibshirani
% Fortran code was written by Jerome Friedman
% R wrapper (from which the MATLAB wrapper was adapted) was written by Trevor Hasite
% The original MATLAB wrapper was written by Hui Jiang (14 Jul 2009),
% and was updated and maintained by Junyang Qian (30 Aug 2013) [email protected],
% Department of Statistics, Stanford University, Stanford, California, USA.
%
% REFERENCES:
% Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent,
% http://www.jstatsoft.org/v33/i01/
% Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010
%
% Simon, N., Friedman, J., Hastie, T., Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent,
% http://www.jstatsoft.org/v39/i05/
% Journal of Statistical Software, Vol. 39(5) 1-13
%
% Tibshirani, Robert., Bien, J., Friedman, J.,Hastie, T.,Simon, N.,Taylor, J. and Tibshirani, Ryan. (2010) Strong Rules for Discarding Predictors in Lasso-type Problems,
% http://www-stat.stanford.edu/~tibs/ftp/strong.pdf
% Stanford Statistics Technical Report
%
% SEE ALSO:
% glmnet, glmnetPrint, glmnetCoef, and cvglmnet.
%
% EXAMPLES:
% x=randn(100,20);
% y=randn(100,1);
% g2=randsample(2,100,true);
% g4=randsample(4,100,true);
% fit1=glmnet(x,y);
% glmnetPredict(fit1,x(1:5,:),[0.01,0.005]') % make predictions
% glmnetPredict(fit1,[],[],'coefficients')
% fit2=glmnet(x,g2,'binomial');
% glmnetPredict(fit2, x(2:5,:),[], 'response')
% glmnetPredict(fit2, [], [], 'nonzero')
% fit3=glmnet(x,g4,'multinomial');
% glmnetPredict(fit3, x(1:3,:), 0.01, 'response')
%
% DEVELOPMENT:
% 14 Jul 2009: Original version of glmnet.m written.
% 30 Aug 2013: Updated glmnet.m with more options and more models
% (multi-response Gaussian, cox and Poisson models) supported.
% OLDER UPDATES:
% 20 Oct 2009: Fixed a bug in bionomial response, pointed out by Ramon
% Casanov from Wake Forest University.
% 26 Jan 2010: Fixed a bug in multinomial link and class, pointed out by
% Peter Rijnbeek from Erasmus University.
% 23 Jun 2010: Fixed a bug in multinomial with s, pointed out by
% Robert Jacobsen from Aalborg University.
if nargin < 2 || isempty(newx)
newx = [];
end
if nargin < 3
s = [];
end
if nargin < 4 || isempty(type)
type = 'link';
end
if nargin < 5 || isempty(exact)
exact = false;
end
if nargin < 6
offset = [];
end
typebase = {'link','response','coefficients','nonzero','class'};
typeind = find(strncmp(type,typebase,length(type)),1);
type = typebase{typeind};
if isempty(newx)
if ~strcmp(type, 'coefficients') && ~strcmp(type, 'nonzero')
error('You need to supply a value for ''newx''');
end
end
%exact case: need to execute statements back in the parent environment
if (exact && ~isempty(s))
which = ismember(s,object.lambda);
if ~all(which)
lambda = unique([object.lambda;reshape(s,length(s),1)]);
%-----create a new variable in the parent environment
vname = 'newlam';
expr = sprintf('any(strcmp(''%s'', who))',vname);
newname = vname;
i = 0;
while (evalin('caller',expr))
i = i + 1;
newname = [vname,num2str(i)];
expr = sprintf('any(strcmp(who,''%s''))',newname);
end
parlam = newname;
%-----
assignin('caller', parlam, lambda);
vname = 'temp_opt';
expr = sprintf('any(strcmp(''%s'', who))',vname);
newname = vname;
i = 0;
while (evalin('caller',expr))
i = i + 1;
newname = [vname,num2str(i)];
expr = sprintf('any(strcmp(who,''%s''))',newname);
end
paropt = newname;
if strcmp('[]',object.call{3})
famcall = object.call{3};
else
famcall = sprintf('''%s''',object.call{3});
end
if ~strcmp('[]', object.call{4})
evalin('caller', strcat(paropt,'=',object.call{4},';'));
evalin('caller', strcat(paropt,'.lambda = ',parlam,';'));
newcall = sprintf('glmnet(%s, %s, %s, %s)', ...
object.call{1}, object.call{2}, famcall, paropt);
object = evalin('caller', newcall);
else
evalin('caller', strcat(paropt,'.lambda = ',parlam,';'));
newcall = sprintf('glmnet(%s, %s, %s, %s)', ...
object.call{1}, object.call{2}, famcall, paropt);
object = evalin('caller', newcall);
end
evalin('caller', sprintf('clearvars %s %s;',parlam,paropt));
end
end
if strcmp(object.class,'elnet')
a0 = transpose(object.a0);
nbeta=[a0; object.beta];
if (~isempty(s))
lambda=object.lambda;
lamlist=lambda_interp(lambda,s);
nbeta=nbeta(:,lamlist.left).*repmat(lamlist.frac',size(nbeta,1),1) +nbeta(:,lamlist.right).*(1-repmat(lamlist.frac',size(nbeta,1),1));
end
if strcmp(type, 'coefficients')
result = nbeta;
return;
end
if strcmp(type, 'nonzero')
result = nonzeroCoef(nbeta(2:size(nbeta,1),:), true);
return;
end
result = [ones(size(newx,1),1), newx] * nbeta;
if (object.offset)
if isempty(offset)
error('No offset provided for prediction, yet used in fit of glmnet');
end
if (size(offset,2)==2)
offset = offset(:,2);
end
result = result + repmat(offset, 1, size(result, 2));
end
end
if strcmp(object.class,'fishnet')
a0 = transpose(object.a0);
nbeta=[a0; object.beta];
if (~isempty(s))
lambda=object.lambda;
lamlist=lambda_interp(lambda,s);
nbeta=nbeta(:,lamlist.left).*repmat(lamlist.frac',size(nbeta,1),1) +nbeta(:,lamlist.right).*(1-repmat(lamlist.frac',size(nbeta,1),1));
end
if strcmp(type, 'coefficients')
result = nbeta;
return;
end
if strcmp(type, 'nonzero')
result = nonzeroCoef(nbeta(2:size(nbeta,1),:), true);
return;
end
result = [ones(size(newx,1),1), newx] * nbeta;
if (object.offset)
if isempty(offset)
error('No offset provided for prediction, yet used in fit of glmnet');
end
if (size(offset,2) == 2)
offset = offset(:, 2);
end
result = result + repmat(offset, 1, size(result,2));
end
if strcmp(type, 'response')
result = exp(result);
end
end
if strcmp(object.class, 'lognet')
a0 = object.a0;
nbeta=[a0; object.beta];
if (~isempty(s))
lambda=object.lambda;
lamlist=lambda_interp(lambda,s);
nbeta=nbeta(:,lamlist.left).*repmat(lamlist.frac',size(nbeta,1),1) +nbeta(:,lamlist.right).*(1-repmat(lamlist.frac',size(nbeta,1),1));
end
if strcmp(type, 'coefficients')
result = nbeta;
return;
end
if strcmp(type, 'nonzero')
result = nonzeroCoef(nbeta(2:size(nbeta,1),:), true);
return;
end
result = [ones(size(newx,1),1), newx] * nbeta;
if (object.offset)
if isempty(offset)
error('No offset provided for prediction, yet used in fit of glmnet');
end
if (size(offset,2)==2)
offset = offset(:,2);
end
result = result + repmat(offset, 1, size(result, 2));
end
switch type
case 'response'
pp = exp(-result);
result = 1./ (1+pp);
case 'class'
result = (result > 0) * 2 + (result <= 0) * 1;
result = object.label(result);
end
end
if strcmp(object.class, 'multnet') || strcmp(object.class,'mrelnet')
if strcmp(object.class,'mrelnet')
if strcmp(type, 'response')
type = 'link';
end
object.grouped = true;
end
a0=object.a0;
nbeta=object.beta;
nclass=size(a0,1);
nlambda=length(s);
if (~isempty(s))
lambda=object.lambda;
lamlist=lambda_interp(lambda,s);
for i=1:nclass
kbeta=[a0(i,:); nbeta{i}];
kbeta=kbeta(:,lamlist.left).*repmat(lamlist.frac',size(kbeta,1),1)+kbeta(:,lamlist.right).*(1-repmat(lamlist.frac',size(kbeta,1),1));
nbeta{i}=kbeta;
end
else
for i=1:nclass
nbeta{i} = [a0(i,:);nbeta{i}];
end
nlambda = length(object.lambda);
end
if strcmp(type, 'coefficients')
result = nbeta;
return;
end
if strcmp(type, 'nonzero')
if (object.grouped)
result{1} = nonzeroCoef(nbeta{1}(2:size(nbeta{1},1),:),true);
else
for i=1:nclass
result{i}=nonzeroCoef(nbeta{i}(2:size(nbeta{i},1),:),true);
end
end
return;
end
npred=size(newx,1);
dp = zeros(nclass,nlambda,npred);
for i=1:nclass
fitk = [ones(size(newx,1),1), newx] * nbeta{i};
dp(i,:,:)=dp(i,:,:)+reshape(transpose(fitk),1,nlambda,npred);
end
if (object.offset)
if (isempty(offset))
error('No offset provided for prediction, yet used in fit of glmnet');
end
if (size(offset,2) ~= nclass)
error('Offset should be dimension%dx%d',npred,nclass)
end
toff = transpose(offset);
for i = 1:nlambda
dp(:,i,:) = dp(:,i,:) + toff;
end
end
switch type
case 'response'
pp = exp(dp);
psum = sum(pp,1);
result = permute(pp./repmat(psum,nclass,1),[3,1,2]);
case 'link'
result=permute(dp,[3,1,2]);
case 'class'
dp=permute(dp,[3,1,2]);
result = [];
for i=1:size(dp,3)
result = [result, object.label(softmax(dp(:,:,i)))];
end
end
end
if strcmp(object.class,'coxnet')
nbeta = object.beta;
if (~isempty(s))
lambda=object.lambda;
lamlist=lambda_interp(lambda,s);
nbeta=nbeta(:,lamlist.left).*repmat(lamlist.frac',size(nbeta,1),1) +nbeta(:,lamlist.right).*(1-repmat(lamlist.frac',size(nbeta,1),1));
end
if strcmp(type, 'coefficients')
result = nbeta;
return;
end
if strcmp(type, 'nonzero')
result = nonzeroCoef(nbeta, true);
return;
end
result = newx * nbeta;
if (object.offset)
if isempty(offset)
error('No offset provided for prediction, yet used in fit of glmnet');
end
result = result + repmat(offset, 1, size(result, 2));
end
if strcmp(type, 'response')
result = exp(result);
end
end
%-------------------------------------------------------------
% End private function glmnetPredict
%-------------------------------------------------------------
function result = lambda_interp(lambda,s)
% lambda is the index sequence that is produced by the model
% s is the new vector at which evaluations are required.
% the value is a vector of left and right indices, and a vector of fractions.
% the new values are interpolated bewteen the two using the fraction
% Note: lambda decreases. you take:
% sfrac*left+(1-sfrac*right)
if length(lambda)==1 % degenerate case of only one lambda
nums=length(s);
left=ones(nums,1);
right=left;
sfrac=ones(nums,1);
else
s(s > max(lambda)) = max(lambda);
s(s < min(lambda)) = min(lambda);
k=length(lambda);
sfrac =(lambda(1)-s)/(lambda(1) - lambda(k));
lambda = (lambda(1) - lambda)/(lambda(1) - lambda(k));
coord = interp1(lambda, 1:length(lambda), sfrac);
left = floor(coord);
right = ceil(coord);
sfrac=(sfrac-lambda(right))./(lambda(left) - lambda(right));
sfrac(left==right)=1;
end
result.left = left;
result.right = right;
result.frac = sfrac;
%-------------------------------------------------------------
% End private function lambda_interp
%-------------------------------------------------------------
function result = softmax(x, gap)
if nargin < 2
gap = false;
end
d = size(x);
maxdist = x(:, 1);
pclass = repmat(1, d(1), 1);
for i =2:d(2)
l = x(:, i) > maxdist;
pclass(l) = i;
maxdist(l) = x(l, i);
end
if gap
x = abs(maxdist - x);
x(1:d(1), pclass) = x * repmat(1, d(2));
gaps = pmin(x);
end
if gap
result = {pclass, gaps};
else
result = pclass;
end
%-------------------------------------------------------------
% End private function softmax
%-------------------------------------------------------------
|
github
|
shangjingbo1226/DPPred-master
|
fishnet.m
|
.m
|
DPPred-master/glmnet_matlab/fishnet.m
| 1,567 |
utf_8
|
a38133450377adad1e403b48017b5d9e
|
function fit = fishnet(x,is_sparse,irs,pcs,y,weights,offset,parm,nobs,nvars,...
jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,maxit,family)
if any(y < 0)
error('negative responses encountered; not permitted for Poisson family');
end
if isempty(offset)
offset = y * 0;
is_offset = false;
else
is_offset = true;
end
if (is_sparse)
task = 50;
[lmu,a0,ca,ia,nin,dev,alm,nlp,jerr,dev0,ot] = glmnetMex(task,parm,x,y,jd,vp,ne,nx,nlam,flmin,ulam,thresh,isd,weights,cl,intr,maxit,offset,irs,pcs);
else
task = 51;
[lmu,a0,ca,ia,nin,dev,alm,nlp,jerr,dev0,ot] = glmnetMex(task,parm,x,y,jd,vp,ne,nx,nlam,flmin,ulam,thresh,isd,weights,cl,intr,maxit,offset);
end
if (jerr ~= 0)
errmsg = err(jerr,maxit,nx,family);
if (errmsg.fatal)
error(errmsg.msg);
else
warning(errmsg.msg);
end
end
ninmax = max(nin);
lam = alm;
if (ulam == 0.0)
lam = fix_lam(lam); % first lambda is infinity; changed to entry point
end
dd=[nvars, lmu];
if ninmax > 0
ca = ca(1:ninmax,:);
df = sum(abs(ca) > 0, 1);
ja = ia(1:ninmax);
[ja1,oja] = sort(ja);
beta = zeros(nvars, lmu);
beta (ja1, :) = ca(oja,:);
else
beta = zeros(nvars,lmu);
df = zeros(1,lmu);
end
fit.a0 = a0;
fit.beta = beta;
fit.dev = dev;
fit.nulldev = dev0;
fit.df = df';
fit.lambda = lam;
fit.npasses = nlp;
fit.jerr = jerr;
fit.dim = dd;
fit.offset = is_offset;
fit.class = 'fishnet';
function new_lam = fix_lam(lam)
new_lam = lam;
if (length(lam) > 2)
llam=log(lam);
new_lam(1)=exp(2*llam(2)-llam(3));
end
|
github
|
shangjingbo1226/DPPred-master
|
cvmrelnet.m
|
.m
|
DPPred-master/glmnet_matlab/cvmrelnet.m
| 2,101 |
utf_8
|
d369fd63346be95c8ff0d36501321ea1
|
function result = cvmrelnet(object, lambda, x, y, weights, offset, foldid, ...
type, grouped, keep)
if nargin < 10 || isempty(keep)
keep = false;
end
typenames = struct('deviance','Mean-Squared Error','mse','Mean-Squared Error',...
'mae','Mean Absolute Error');
if strcmp(type,'default')
type = 'mse';
end
if ~any(strcmp(type,{'mse','mae','deviance'}))
warning('Only ''mse'',''deviance'' or ''mae'' available for multiresponse Gaussian models; ''mse'' used');
type = 'mse';
end
[nobs, nc] = size(y);
if ~isempty(offset)
y = y - offset;
end
predmat = NaN(nobs,nc,length(lambda));
nfolds = max(foldid);
nlams = nfolds;
for i = 1:nfolds
which = foldid == i;
fitobj = object{i};
fitobj.offset = false;
preds = glmnetPredict(fitobj,x(which,:));
nlami = length(object{i}.lambda);
predmat(which,:,1:nlami) = preds;
nlams(i) = nlami;
end
N = nobs - reshape(sum(isnan(predmat(:,1,:)),1),1,[]);
bigY = repmat(y, [1,1,length(lambda)]);
switch type
case 'mse'
cvraw = squeeze(sum((bigY - predmat).^2, 2));
case 'mae'
cvraw = squeeze(sum(abs(bigY - predmat), 2));
end
if (nobs/nfolds < 3) && grouped
warning('Option grouped=false enforced in cv.glmnet, since < 3 observations per fold');
grouped = false;
end
if (grouped)
cvob = cvcompute(cvraw, weights, foldid, nlams);
cvraw = cvob.cvraw;
weights = cvob.weights;
N = cvob.N;
end
% end
cvm = wtmean(cvraw,weights);
sqccv = (bsxfun(@minus,cvraw,cvm)).^2;
cvsd = sqrt(wtmean(sqccv,weights)./(N-1));
result.cvm = cvm; result.cvsd = cvsd; result.name = typenames.(type);
if (keep)
result.fit_preval = predmat;
end
function result = glmnet_softmax(x)
d = size(x);
nas = any(isnan(x),2);
if any(nas)
pclass = NaN(d(1),1);
if (sum(nas) < d(1))
pclass2 = glmnet_softmax(x(~nas,:));
pclass(~nas) = pclass2;
result = pclass;
end
else
maxdist = x(:,1);
pclass = ones(d(1),1);
for i = 2:d(2)
l = x(:,i)>maxdist;
pclass(l) = i;
maxdist(l) = x(l,i);
end
result = pclass;
end
|
github
|
shangjingbo1226/DPPred-master
|
cvmultnet.m
|
.m
|
DPPred-master/glmnet_matlab/cvmultnet.m
| 2,980 |
utf_8
|
245d373006161331bd149a8cc0756fcd
|
function result = cvmultnet(object, lambda, x, y, weights, offset, foldid, ...
type, grouped, keep)
if nargin < 10 || isempty(keep)
keep = false;
end
typenames = struct('mse','Mean-Squared Error','mae','Mean Absolute Error',...
'deviance','Multinomial Deviance','class','Misclassification Error');
if strcmp(type,'default')
type = 'deviance';
end
if ~any(strcmp(type,{'mse','mae','deviance','class'}))
warning('Only ''deviance'', ''class'', ''mse'' or ''mae'' available for multinomial models; ''deviance'' used');
type = 'deviance';
end
prob_min = 1e-5; prob_max = 1 - prob_min;
nc = size(y);
if nc(2) == 1
[classes,~,sy] = unique(y);
nc = length(classes);
indexes = eye(nc);
y = indexes(sy,:);
else
nc = nc(2);
end
is_offset = ~isempty(offset);
predmat = NaN(size(y,1),nc,length(lambda));
nfolds = max(foldid);
nlams = zeros(nfolds,1);
for i = 1:nfolds
which = foldid==i;
fitobj = object{i};
if (is_offset)
off_sub = offset(which,:);
else
off_sub = [];
end
preds = glmnetPredict(fitobj,x(which,:),[],'response',[],off_sub);
nlami = length(object{i}.lambda);
predmat(which,:,1:nlami) = preds;
nlams(i) = nlami;
end
ywt = sum(y, 2);
y = y ./ repmat(ywt,1,size(y,2));
weights = weights .* ywt;
N = size(y,1) - sum(isnan(predmat(:,1,:)),1);
bigY = repmat(y, [1,1,length(lambda)]);
switch type
case 'mse'
cvraw = squeeze(sum((bigY - predmat).^2, 2));
case 'mae'
cvraw = squeeze(sum(abs(bigY - predmat), 2));
case 'deviance'
predmat = min(max(predmat,prob_min),prob_max);
lp = bigY .* log(predmat);
ly = bigY .* log(bigY);
ly(bigY == 0) = 0;
cvraw = squeeze(sum(2 * (ly - lp), 2));
case 'class'
classid = NaN(size(y,1),length(lambda));
for i = 1:length(lambda)
classid(:,i) = glmnet_softmax(predmat(:,:,i));
end
classid = reshape(classid,[],1);
yperm = reshape(permute(bigY, [1,3,2]),[],nc);
idx = sub2ind(size(yperm), 1:length(classid), classid');
cvraw = reshape(1 - yperm(idx), [], length(lambda));
end
if (grouped)
cvob = cvcompute(cvraw, weights, foldid, nlams);
cvraw = cvob.cvraw;
weights = cvob.weights;
N = cvob.N;
end
cvm = wtmean(cvraw,weights);
sqccv = (bsxfun(@minus,cvraw,cvm)).^2;
cvsd = sqrt(wtmean(sqccv,weights)./(N-1));
result.cvm = cvm; result.cvsd = cvsd; result.name = typenames.(type);
if (keep)
result.fit_preval = predmat;
end
function result = glmnet_softmax(x)
d = size(x);
nas = any(isnan(x),2);
if any(nas)
pclass = NaN(d(1),1);
if (sum(nas) < d(1))
pclass2 = glmnet_softmax(x(~nas,:));
pclass(~nas) = pclass2;
result = pclass;
end
else
maxdist = x(:,1);
pclass = ones(d(1),1);
for i = 2:d(2)
l = x(:,i)>maxdist;
pclass(l) = i;
maxdist(l) = x(l,i);
end
result = pclass;
end
|
github
|
shangjingbo1226/DPPred-master
|
lognet.m
|
.m
|
DPPred-master/glmnet_matlab/lognet.m
| 4,177 |
utf_8
|
c749bb59e945862ae7b1962afc11fbfc
|
function fit = lognet(x,is_sparse,irs,pcs,y,weights,offset,parm,nobs,nvars,...
jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,maxit,kopt,family)
[noo,nc] = size(y);
if noo ~= nobs
error('x and y have different number of rows in call to glmnet');
end
if nc == 1
[classes,~,sy] = unique(y);
nc = length(classes);
indexes = eye(nc);
y = indexes(sy,:);
else
classes = 1: nc;
end
if strcmp(family, 'binomial')
if nc > 2
error ('More than two classes; use multinomial family instead');
end
nc = 1; % for calling binomial
y = y(:,[2,1]);
end
o = [];
if ~isempty(weights)
% check if any are zero
o = weights > 0;
if ~all(o) %subset the data
y = y(o,:);
x = x(o,:);
weights = weights(o);
nobs = sum(o);
else
o = [];
end
[my,ny] = size(y);
y = y .* repmat(weights,1,ny);
end
if isempty(offset)
offset = y * 0;
is_offset = false;
else
if ~isempty(o)
offset = offset(o,:);
end
do = size(offset);
if (do(1) ~= nobs)
error('offset should have the same number of values as observations in binomial/multinomial call to glmnet');
end
if (nc == 1)
if (do(2) == 1)
offset = cat(2,offset,-offset);
end
if (do(2) > 2)
error('offset should have 1 or 2 columns in binomial call to glmnet');
end
end
if strcmp(family,'multinomial') && (do(2) ~= nc)
error('offset should have same shape as y in multinomial call to glmnet');
end
is_offset = true;
end
if (is_sparse)
task = 20;
[lmu,a0,ca,ia,nin,dev,alm,nlp,jerr,dev0,ot] = glmnetMex(task,parm,x,y,jd,vp,ne,nx,nlam,flmin,ulam,thresh,isd,cl,intr,maxit,nc,kopt,offset,irs,pcs);
else
task = 21;
[lmu,a0,ca,ia,nin,dev,alm,nlp,jerr,dev0,ot] = glmnetMex(task,parm,x,y,jd,vp,ne,nx,nlam,flmin,ulam,thresh,isd,cl,intr,maxit,nc,kopt,offset);
end
if (jerr ~= 0)
errmsg = err(jerr,maxit,nx,family);
if (errmsg.fatal)
error(errmsg.msg);
else
warning(errmsg.msg);
end
end
ninmax = max(nin);
lam = alm;
if (ulam == 0.0)
lam = fix_lam(lam); % first lambda is infinity; changed to entry point
end
if strcmp(family, 'multinomial')
beta_list = {};
a0 = a0 - repmat(mean(a0), nc, 1); %multinomial: center the coefficients
dfmat=a0;
dd=[nvars, lmu];
if ninmax > 0
ca = reshape(ca, nx, nc, lmu);
ca = ca(1:ninmax,:,:);
ja = ia(1:ninmax);
[ja1,oja] = sort(ja);
df = any(abs(ca) > 0, 2);
df = sum(df, 1);
df = df(:)';
for k=1:nc
ca1 = reshape(ca(:,k,:), ninmax, lmu);
cak = ca1(oja,:);
dfmat(k,:) = sum(abs(cak) > 0, 1);
beta = zeros(nvars, lmu);
beta(ja1,:) = cak;
beta_list{k} = beta;
end
else
for k = 1:nc
dfmat(k,:) = zeros(1,lmu);
beta_list{k} = zeros(nvars, lmu);
end
df = zeros(1,lmu);
end
fit.a0 = a0;
fit.label = classes;
fit.beta = beta_list;
fit.dev = dev;
fit.nulldev = dev0;
fit.dfmat = dfmat;
fit.df = df';
fit.lambda = lam;
fit.npasses = nlp;
fit.jerr = jerr;
fit.dim = dd;
if (kopt == 2)
grouped = true;
else
grouped = false;
end
fit.grouped = grouped;
fit.offset = is_offset;
fit.class = 'multnet';
else
dd=[nvars, lmu];
if ninmax > 0
ca = ca(1:ninmax,:);
df = sum(abs(ca) > 0, 1);
ja = ia(1:ninmax);
[ja1,oja] = sort(ja);
beta = zeros(nvars, lmu);
beta (ja1, :) = ca(oja,:);
else
beta = zeros(nvars,lmu);
df = zeros(1,lmu);
end
fit.a0 = a0;
fit.label = classes;
fit.beta = beta; %sign flips make 2 arget class
fit.dev = dev;
fit.nulldev = dev0;
fit.df = df';
fit.lambda = lam;
fit.npasses = nlp;
fit.jerr = jerr;
fit.dim = dd;
fit.offset = is_offset;
fit.class = 'lognet';
end
function new_lam = fix_lam(lam)
new_lam = lam;
if (length(lam) > 2)
llam=log(lam);
new_lam(1)=exp(2*llam(2)-llam(3));
end
|
github
|
shangjingbo1226/DPPred-master
|
elnet.m
|
.m
|
DPPred-master/glmnet_matlab/elnet.m
| 1,671 |
utf_8
|
5e93be0d78726b154d27eed1f02e36be
|
function fit = elnet(x, is_sparse, irs, pcs, y, weights, offset, gtype, ...
parm, lempty, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, ...
isd, intr, maxit, family)
ybar = y' * weights/ sum(weights);
nulldev = (y' - ybar).^2 * weights;
ka = find(strncmp(gtype,{'covariance','naive'},length(gtype)),1);
if isempty(ka)
error('unrecognized type');
end
if isempty(offset)
offset = y * 0;
is_offset = false;
else
is_offset = true;
end
if is_sparse
task = 10;
[lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr] = glmnetMex(task,parm,x,y-offset,jd,vp,ne,nx,nlam,flmin,ulam,thresh,isd,weights,ka,cl,intr,maxit,irs,pcs);
else
task = 11;
[lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr] = glmnetMex(task,parm,x,y-offset,jd,vp,ne,nx,nlam,flmin,ulam,thresh,isd,weights,ka,cl,intr,maxit);
end
if (jerr ~= 0)
errmsg = err(jerr,maxit,nx,family);
if (errmsg.fatal)
error(errmsg.msg);
else
warning(errmsg.msg);
end
end
ninmax = max(nin);
lam = alm;
if lempty
lam = fix_lam(lam); % first lambda is infinity; changed to entry point
end
dd=[nvars, lmu];
if ninmax > 0
ca = ca(1:ninmax,:);
df = sum(abs(ca) > 0, 1);
ja = ia(1:ninmax);
[ja1,oja] = sort(ja);
beta = zeros(nvars, lmu);
beta (ja1, :) = ca(oja,:);
else
beta = zeros(nvars,lmu);
df = zeros(1,lmu);
end
fit.a0 = a0;
fit.beta = beta;
fit.dev = rsq;
fit.nulldev = nulldev;
fit.df = df';
fit.lambda = lam;
fit.npasses = nlp;
fit.jerr = jerr;
fit.dim = dd;
fit.offset = is_offset;
fit.class = 'elnet';
function new_lam = fix_lam(lam)
new_lam = lam;
if (length(lam) > 2)
llam=log(lam);
new_lam(1)=exp(2*llam(2)-llam(3));
end
|
github
|
shangjingbo1226/DPPred-master
|
cvcoxnet.m
|
.m
|
DPPred-master/glmnet_matlab/cvcoxnet.m
| 2,338 |
utf_8
|
eed15cafcfb188d8f0abde603f2b3ee9
|
function result = cvcoxnet(object, lambda, x, y, weights, offset, foldid, ...
type, grouped, keep)
% Internal glmnet function. See also cvglmnet.
if nargin < 10 || isempty(keep)
keep = false;
end
typenames = struct('deviance','Partial Likelihood Deviance');
if strcmp(type, 'default')
type = 'deviance';
end
if ~any(strcmp(type, {'deviance'}))
warning('Only ''deviance'' available for Cox models; changed to type=''deviance''');
type = 'deviance';
end
if isempty(offset)
offset = zeros(size(x,1),1);
end
nfolds = max(foldid);
if (length(weights)/nfolds < 10) && ~grouped
warning('Option grouped=true enforced for cv.coxnet, since < 3 observations per fold');
grouped = true;
end
cvraw = NaN(nfolds,length(lambda));
for i = 1:nfolds
which = foldid == i;
fitobj = object{i};
coefmat = glmnetPredict(fitobj,[],[],'coefficients');
if (grouped)
plfull = cox_deviance([],y,x,offset,weights,coefmat);
plminusk = cox_deviance([],y(~which,:),x(~which,:),offset(~which),...
weights(~which),coefmat);
cvraw(i,1:length(plfull)) = plfull - plminusk;
else
plk = cox_deviance([],y(which,:),x(which,:),offset(which),...
weights(which),coefmat);
cvraw(i,1:length(plk)) = plk;
end
end
status = y(:,2);
N = nfolds - sum(isnan(cvraw),1);
weights = accumarray(reshape(foldid,[],1),weights.*status);
cvraw = bsxfun(@rdivide,cvraw,weights); %even some weight = 0 does matter because of adjustment in wtmean!
cvm = wtmean(cvraw,weights);
sqccv = (bsxfun(@minus,cvraw,cvm)).^2;
cvsd = sqrt(wtmean(sqccv,weights)./(N-1));
result.cvm = cvm; result.cvsd = cvsd; result.name = typenames.(type);
if (keep)
warning('keep=TRUE not implemented for coxnet');
end
function result = cox_deviance(pred, y, x, offset, weights, beta)
ty = y(:,1);
tevent = y(:,2);
nobs = length(ty); nvars = size(x,2);
if isempty(weights)
weights = ones(nobs,1);
end
if isempty(offset)
offset = zeros(nobs,1);
end
if isempty(beta)
beta = []; nvec = 1; nvars = 0;
else
nvec = size(beta,2);
end
task = 42;
[flog, jerr] = glmnetMex(task,x,ty,tevent,offset,weights,nvec,beta);
if (jerr ~= 0)
errmsg = err(jerr,0,0,'cox');
if (errmsg.fatal)
error(errmsg.msg);
else
warning(errmsg.msg);
end
end
result = -2 * flog;
|
github
|
shangjingbo1226/DPPred-master
|
cvfishnet.m
|
.m
|
DPPred-master/glmnet_matlab/cvfishnet.m
| 1,872 |
utf_8
|
d7cb4820ff676ff3db1adcb0418e402f
|
function result = cvfishnet(object,lambda,x,y,weights,offset,foldid,type,grouped,keep)
% Internal glmnet function. See also cvglmnet.
if nargin < 10 || isempty(keep)
keep = false;
end
typenames = struct('mse','Mean-Squared Error','mae','Mean Absolute Error','deviance','Poisson Deviance');
if strcmp(type, 'default')
type = 'deviance';
end
if ~any(strcmp(type, {'mse','mae','deviance'}))
warning('Only ''mse'', ''deviance'' or ''mae'' available for Poisson models; ''deviance'' used');
type = 'deviance';
end
is_offset = ~isempty(offset);
predmat = NaN(length(y),length(lambda));
nfolds = max(foldid);
nlams = nfolds;
for i = 1:nfolds
which = foldid == i;
fitobj = object{i};
if (is_offset)
off_sub = offset(which);
else
off_sub = [];
end
preds = glmnetPredict(fitobj,x(which,:),[],[],[],off_sub);
nlami = length(object{i}.lambda);
predmat(which,1:nlami) = preds;
nlams(i) = nlami;
end
N = size(y,1) - sum(isnan(predmat),1);
yy = repmat(y, 1, length(lambda));
switch type
case 'mse'
cvraw = (yy - predmat).^2;
case 'mae'
cvraw = abs(yy - predmat);
case 'deviance'
cvraw = devi(yy, predmat);
end
if (length(y)/nfolds < 3) && grouped
warning('Option grouped=false enforced in cv.glmnet, since < 3 observations per fold');
grouped = false;
end
if (grouped)
cvob = cvcompute(cvraw,weights,foldid,nlams);
cvraw = cvob.cvraw;
weights = cvob.weights;
N = cvob.N;
end
cvm = wtmean(cvraw,weights);
sqccv = (bsxfun(@minus,cvraw,cvm)).^2;
cvsd = sqrt(wtmean(sqccv,weights)./(N-1));
result.cvm = cvm; result.cvsd = cvsd; result.name = typenames.(type);
if (keep)
result.fit_preval = predmat;
end
function result = devi(yy, eta)
deveta = yy .* eta - exp(eta);
devy = yy .* log(yy) - yy;
devy(yy == 0) = 0;
result = 2 * (devy - deveta);
|
github
|
shangjingbo1226/DPPred-master
|
cvlognet.m
|
.m
|
DPPred-master/glmnet_matlab/cvlognet.m
| 4,184 |
utf_8
|
265205120a633a060736651a5eee583d
|
function result = cvlognet(object, lambda, x, y, weights, offset, foldid, ...
type, grouped, keep)
if nargin < 10 || isempty(keep)
keep = false;
end
typenames = struct('mse','Mean-Squared Error','mae','Mean Absolute Error',...
'deviance','Binomial Deviance','auc','AUC','class','Misclassification Error');
if strcmp(type,'default')
type = 'deviance';
end
if ~any(strcmp(type,{'mse','mae','deviance','auc','class'}))
warning('Only ''deviance'', ''class'', ''auc'', ''mse'' or ''mae'' available for binomial models; ''deviance'' used');
type = 'deviance';
end
prob_min = 1e-5; prob_max = 1 - prob_min;
nc = size(y);
if nc(2) == 1
[classes,~,sy] = unique(y);
nc = length(classes);
indexes = eye(nc);
y = indexes(sy,:);
end
N = size(y,1);
nfolds = max(foldid);
if (N/nfolds < 10) && strcmp(type,'auc')
warning(strcat('Too few (< 10) observations per fold for type.measure=''auc'' in cv.lognet; ',...
'changed to type.measure=''deviance''. Alternatively, use smaller value for nfolds'));
type = 'deviance';
end
if (N/nfolds < 3) && grouped
warning(strcat('Option grouped=FALSE enforced in cv.glmnet, ',...
'since < 3 observations per fold'));
grouped = false;
end
is_offset = ~isempty(offset);
predmat = NaN(size(y,1),length(lambda));
nlams = zeros(nfolds,1);
for i = 1:nfolds
which = foldid==i;
fitobj = object{i};
if (is_offset)
off_sub = offset(which,:);
else
off_sub = []; %a bit different from that in R
end
preds = glmnetPredict(fitobj,x(which,:),[],'response',[],off_sub);
nlami = length(object{i}.lambda);
predmat(which,1:nlami) = preds;
nlams(i) = nlami;
end
if strcmp(type,'auc')
cvraw = NaN(nfolds, length(lambda));
good = zeros(nfolds, length(lambda));
for i = 1:nfolds
good(i,1:nlams(i)) = 1;
which = foldid == i;
for j = 1:nlams(i)
cvraw(i,j) = auc_mat(y(which,:), predmat(which,j), weights(which));
end
end
N = sum(good,1);
sweights = zeros(nfolds, 1);
for i = 1:nfolds
sweights(i) = sum(weights(foldid==i));
end
weights = sweights;
else
ywt = sum(y, 2);
y = y ./ repmat(ywt,1,size(y,2));
weights = weights .* ywt;
N = size(y,1) - sum(isnan(predmat),1);
yy1 = repmat(y(:,1),1,length(lambda));
yy2 = repmat(y(:,2),1,length(lambda));
switch type
case 'mse'
cvraw = (yy1 - (1 - predmat)).^2 + (yy2 - (1 - predmat)).^2;
case 'mae'
cvraw = abs(yy1 - (1 - predmat)) + abs(yy2 - (1 - predmat));
case 'deviance'
predmat = min(max(predmat,prob_min),prob_max);
lp = yy1.*log(1-predmat) + yy2.*log(predmat);
ly = log(y);
ly(y == 0) = 0;
ly = (y.*ly) * [1;1];
cvraw = 2 * (repmat(ly,1,length(lambda)) - lp);
case 'class'
cvraw = yy1.*(predmat > 0.5) + yy2.*(predmat <= 0.5);
end
if (grouped)
cvob = cvcompute(cvraw, weights, foldid, nlams);
cvraw = cvob.cvraw;
weights = cvob.weights;
N = cvob.N;
end
end
cvm = wtmean(cvraw,weights);
sqccv = (bsxfun(@minus,cvraw,cvm)).^2;
cvsd = sqrt(wtmean(sqccv,weights)./(N-1));
result.cvm = cvm; result.cvsd = cvsd; result.name = typenames.(type);
if (keep)
result.fit_preval = predmat;
end
function result = auc_mat(y, prob, weights)
if nargin < 3 || isempty(weights)
weights = ones(size(y,1),1);
end
Weights = bsxfun(@times,weights,y);
Weights = Weights(:)';
ny = size(y,1);
Y = [zeros(ny,1);ones(ny,1)];
Prob = [prob; prob];
result = auc(Y, Prob, Weights);
function result = auc(y, prob, w)
if isempty(w)
mindiff = min(diff(unique(prob)));
pert = unifrnd(0,mindiff/3,size(prob));
[~,~,rprob] = unique(prob+pert);
n1 = sum(y); n0 = length(y) - n1;
u = sum(rprob(y == 1)) - n1*(n1+1)/2;
result = u / (n1*n0);
else
[~,op] = sort(prob);
y = y(op); w = w(op);
cw = cumsum(w);
w1 = w(y == 1);
cw1 = cumsum(w1);
wauc = sum(w1.*(cw(y==1)-cw1));
sumw = cw1(length(cw1));
sumw = sumw * (cw(length(cw)) - sumw);
result = wauc / sumw;
end
|
github
|
FelixWinterstein/FPGA-shared-mem-master
|
generate_data_points.m
|
.m
|
FPGA-shared-mem-master/examples/filtering_algorithm/host/data/generate_data_points.m
| 2,172 |
utf_8
|
deb2eed5cac280eaf68b63d34bd507ca
|
%**********************************************************************
% Felix Winterstein, Imperial College London, 2016
%
% File: generate_data_points
%
% Revision 1.01
% Additional Comments: distributed under an Apache-2.0 license, see LICENSE
%
%**********************************************************************
function generate_data_points
clear;
clc;
%% config
N=2^20;
D=3;
K=128;
Knew =K;
std_dev = 0.10;
M=1;
fractional_bits = 10;
gbl_seed_offset = 0;
for file_idx=1:M
%% generate data points
%rng(16221+gbl_seed_offset);
rand('seed',16221+gbl_seed_offset+file_idx);
centres = 5*(rand(K,D)-0.5);
points=zeros(N,D);
for I=1:K
for II=1:D
%rng(4567+gbl_seed_offset+10*I+II);
randn('seed',4567+gbl_seed_offset+10*I+II+file_idx);
points((I-1)*N/K+1:N/K*I,II) = centres(I,II)+std_dev*randn(N/K,1);
end
end
tmp=max([abs(min(points(:,1))),abs(max(points(:,1))),abs(min(points(:,2))),abs(max(points(:,2)))]);
points=points/tmp;
centres = centres/tmp;
points=round(points*2^fractional_bits);
centres=round(centres*2^fractional_bits);
%% save data points to file
tmp_points=reshape(points,D*N,1); % append 2nd dim after 1st dim
fid=fopen(['./data_points','_N',num2str(N),'_K',num2str(K),'_D',num2str(D),'_s',num2str(std_dev,'%.2f'),'.mat'],'w');
for I=1:D*N
fprintf(fid,'%d\n',tmp_points(I));
end
fclose(fid);
%% generate new random centres (pick K data points randomly) and save to file
new_centres = zeros(Knew,D);
%rng(4567+gbl_seed_offset+10000+II);
rand('seed', 4567+gbl_seed_offset+10000+file_idx);
new_centres_idx= round(rand(N,1)*N);
new_centres_idx = new_centres_idx(1:Knew);
%new_centres = points(new_centres_idx,:);
%tmp_new_centres=reshape(new_centres,D*Knew,1); % append 2nd dim after 1st dim
fid=fopen(['./initial_centers','_N',num2str(N),'_K',num2str(Knew),'_D',num2str(D),'_s',num2str(std_dev,'%.2f'),'_',num2str(file_idx),'.mat'],'w');
for I=1:Knew
fprintf(fid,'%d\n',new_centres_idx(I));
end
fclose(fid);
end
end
|
github
|
FelixWinterstein/FPGA-shared-mem-master
|
generate_data_points.m
|
.m
|
FPGA-shared-mem-master/examples/filtering_algorithm_no_svm/host/data/generate_data_points.m
| 2,172 |
utf_8
|
68f8ffaf55695f8a76b472d1df424125
|
%**********************************************************************
% Felix Winterstein, Imperial College London, 2016
%
% File: generate_data_points
%
% Revision 1.01
% Additional Comments: distributed under an Apache-2.0 license, see LICENSE
%
%**********************************************************************
function generate_data_points
clear;
clc;
%% config
N=2^10;
D=3;
K=128;
Knew =K;
std_dev = 0.10;
M=1;
fractional_bits = 10;
gbl_seed_offset = 0;
for file_idx=1:M
%% generate data points
%rng(16221+gbl_seed_offset);
rand('seed',16221+gbl_seed_offset+file_idx);
centres = 5*(rand(K,D)-0.5);
points=zeros(N,D);
for I=1:K
for II=1:D
%rng(4567+gbl_seed_offset+10*I+II);
randn('seed',4567+gbl_seed_offset+10*I+II+file_idx);
points((I-1)*N/K+1:N/K*I,II) = centres(I,II)+std_dev*randn(N/K,1);
end
end
tmp=max([abs(min(points(:,1))),abs(max(points(:,1))),abs(min(points(:,2))),abs(max(points(:,2)))]);
points=points/tmp;
centres = centres/tmp;
points=round(points*2^fractional_bits);
centres=round(centres*2^fractional_bits);
%% save data points to file
tmp_points=reshape(points,D*N,1); % append 2nd dim after 1st dim
fid=fopen(['./data_points','_N',num2str(N),'_K',num2str(K),'_D',num2str(D),'_s',num2str(std_dev,'%.2f'),'.mat'],'w');
for I=1:D*N
fprintf(fid,'%d\n',tmp_points(I));
end
fclose(fid);
%% generate new random centres (pick K data points randomly) and save to file
new_centres = zeros(Knew,D);
%rng(4567+gbl_seed_offset+10000+II);
rand('seed', 4567+gbl_seed_offset+10000+file_idx);
new_centres_idx= round(rand(N,1)*N);
new_centres_idx = new_centres_idx(1:Knew);
%new_centres = points(new_centres_idx,:);
%tmp_new_centres=reshape(new_centres,D*Knew,1); % append 2nd dim after 1st dim
fid=fopen(['./initial_centers','_N',num2str(N),'_K',num2str(Knew),'_D',num2str(D),'_s',num2str(std_dev,'%.2f'),'_',num2str(file_idx),'.mat'],'w');
for I=1:Knew
fprintf(fid,'%d\n',new_centres_idx(I));
end
fclose(fid);
end
end
|
github
|
ewine-project/Flexible-GFDM-PHY-master
|
read_ini_file.m
|
.m
|
Flexible-GFDM-PHY-master/Host/Reader/read_ini_file.m
| 2,463 |
utf_8
|
e3cf1e3ecd343ec9b56afbd0fa088639
|
function [ini_struct] = read_ini_file(filename)
ini_struct = struct();
if (~exist('filename', 'var'))
filename = '';
end
if isempty(filename)
[filename, pathname] = uigetfile('*.ini', 'Select a ini file');
if isequal(filename,0)
error('User selected Cancel!')
else
disp(['User selected ', fullfile(pathname, filename)])
filename = fullfile(pathname, filename);
end
end
fid = fopen(filename);
cnt = 0;
while (1)
tline = fgetl(fid);
cnt = cnt + 1;
if (~ischar(tline))
%End of file
break;
end
if (strfind(tline, '#'))
%skip line because of a comment
continue;
end
if (~strfind(tline, '='))
%skip line because there is nothing to read
continue;
end
%Remove whitespace
tline = strrep(tline, ' ', '');
%Split line in variable and value
strings = strtrim(strsplit(tline,'='));
if length(strings) ~= 2
%something is wrong with the line
disp(['Something is missing in line ' num2str(cnt) ': ' tline]);
continue;
end
%Split if array is present
values = strtrim(strsplit(strings{2},';'));
%Is the first character a string?
if (isstrprop(values{1,1}, 'alpha'))
[zeilen, spalten] = size(values);
if (spalten > 1)
ini_struct.(strings{1}) = values;
else
ini_struct.(strings{1}) = values{1};
end
else
ini_struct.(strings{1}) = string_to_number(values);
end
end
fclose(fid);
end
function number = string_to_number(str)
number = [];
[zeilen, spalten] = size(str);
for i = 1:spalten
value = strtrim(str{:,i});
%Convert complex number in a similar writing style
value = strrep(value, 'I', 'i');
value = strrep(value, 'J', 'j');
%+i5 doesnt work it has to be +i*5
value = strrep(value, '+i*', '+i');
value = strrep(value, '+i', '+i*');
number = [number str2double(value)];
end
end
|
github
|
ewine-project/Flexible-GFDM-PHY-master
|
write_ini_file.m
|
.m
|
Flexible-GFDM-PHY-master/Host/Reader/write_ini_file.m
| 2,907 |
utf_8
|
1db51ea5f88734a1e7ea4742f0afe1f1
|
function [file_string] = write_ini_file(filename, ini_struct)
if (~exist('filename', 'var'))
filename = '';
end
if (~exist('ini_struct', 'var'))
error('No data to be saved!')
end
if isempty(filename)
[filename, pathname] = uiputfile('*.ini', 'Select a ini file');
if isequal(filename,0)
error('User selected Cancel!')
else
disp(['User selected ', fullfile(pathname, filename)])
filename = fullfile(pathname, filename);
end
end
%This string is then written into the file
file_string = {};
if (exist(filename, 'file'))
%% Update existing file
fid = fopen(filename);
cnt = 0;
while (1)
tline = fgetl(fid);
cnt = cnt + 1;
if (~ischar(tline))
%End of file
break;
end
if (strfind(tline, '#'))
file_string = {file_string{:} tline};
%skip line because of a comment
continue;
end
if (~strfind(tline, '='))
file_string = {file_string{:} tline};
%skip line because there is nothing to read
continue;
end
%Remove whitespace
tline = strrep(tline, ' ', '');
%Split line in variable and value
strings = strtrim(strsplit(tline,'='));
%this variable exist in our struct?
if isfield(ini_struct, strings{1})
value = ini_struct.(strings{1});
tline = [strings{1} '=' value_to_string(value)];
end
file_string = {file_string{:} tline};
end
fclose(fid);
else
%% Create a new file
fields = fieldnames(ini_struct);
for line = 1:numel(fields)
value = ini_struct.(fields{line});
file_string = {file_string{:} [fields{line} '=' value_to_string(value)]};
end
end
%% (Over-)write string into file
fileID = fopen(filename, 'w');
[zeilen, spalten] = size(file_string);
for i = 1:spalten
fprintf(fileID,'%s\r\n', file_string{i});
end
fclose(fileID);
end
function str = value_to_string(value)
[zeilen,spalten] = size(value);
str = [];
if isnumeric(value)
for i = 1:numel(value)
str = [str num2str(value(i))];
if i ~= numel(value)
str = [str ';'];
end
end
elseif iscellstr(value)
for i = 1:length(value)
str = [str value{i}];
if i ~= numel(value)
str = [str ';'];
end
end
else
str = value;
end
end
|
github
|
tangzhenyu/Scene-Text-Understanding-master
|
predict_depth.m
|
.m
|
Scene-Text-Understanding-master/SynthText_Chinese/prep_scripts/predict_depth.m
| 3,416 |
utf_8
|
f03754d767f958f0bea597bd0c3564cc
|
% MATLAB script to regress a depth mask for an image.
% uses: (1) https://bitbucket.org/fayao/dcnf-fcsp/
% (2) vlfeat
% (3) matconvnet
% Author: Ankush Gupta
function predict_depth()
% setup vlfeat
%run( '../libs/vlfeat-0.9.18/toolbox/vl_setup');
run( '/home/yuz/lijiahui/fayao-dcnf-fcsp/libs/vlfeat-0.9.18/toolbox/vl_setup');
% setup matconvnet
% dir_matConvNet='../libs/matconvnet/matlab/';
dir_matConvNet='/home/yuz/lijiahui/fayao-dcnf-fcsp/libs/matconvnet_20141015/matlab/';
addpath(genpath(dir_matConvNet));
run([dir_matConvNet 'vl_setupnn.m']);
opts=[];
opts.useGpu=false;
opts.inpaint = true;
opts.normalize_depth = false; % limit depth to [0,1]
%opts.imdir = '/path/to/image/dir';
opts.imdir = '/home/yuz/lijiahui/syntheticdata/SynthText/img_dir';
%opts.out_h5 = '/path/to/save/output/depth.h5';
opts.out_h5 = '/home/yuz/lijiahui/syntheticdata/depth.h5';
% these should point to the pre-trained models from:
% https://bitbucket.org/fayao/dcnf-fcsp/
opts.model_file.indoor = '/home/yuz/lijiahui/fayao-dcnf-fcsp/model_trained/model_dcnf-fcsp_NYUD2.mat';
opts.model_file.outdoor = '/home/yuz/lijiahui/fayao-dcnf-fcsp/model_trained/model_dcnf-fcsp_Make3D.mat';
fprintf('\nloading trained model...\n\n');
mdl = load(opts.model_file.indoor);
model.indoor = mdl.data_obj;
mdl = load(opts.model_file.outdoor);
model.outdoor = mdl.data_obj;
%if gpuDeviceCount==0
% fprintf(' ** No GPU found. Using CPU...\n');
% opts.useGpu=false;
%end
imnames = dir(fullfile(opts.imdir),'*');
imnames = {imnames.name};
N = numel(imnames);
for i = 1:N
fprintf('%d of %d\n',i,N);
imname = imnames{i};
imtype = 'outdoor';
img = read_img_rgb(fullfile(opts.imdir,imname));
if strcmp(imtype, 'outdoor')
opts.sp_size=16;
opts.max_edge=600;
elseif strcmp(imtype, 'indoor')
opts.sp_size=20;
opts.max_edge=640;
end
depth = get_depth(img,model.(imtype),opts);
save_depth(imname,depth,opts);
end
end
function save_depth(imname,depth,opts)
dset_name = ['/',imname];
h5create(opts.out_h5, dset_name, size(depth), 'Datatype', 'single');
h5write(opts.out_h5, dset_name, depth);
end
function depth = get_depth(im_rgb,model,opts)
% limit the maximum edge size of the image:
if ~isempty(opts.max_edge)
sz = size(im_rgb);
[~,max_dim] = max(sz(1:2));
osz = NaN*ones(1,2);
osz(max_dim) = opts.max_edge;
im_rgb = imresize(im_rgb, osz);
end
% do super-pixels:
fprintf(' > super-pix\n');
supix = gen_supperpixel_info(im_rgb, opts.sp_size);
pinfo = gen_feature_info_pairwise(im_rgb, supix);
% build "data-set":
ds=[];
ds.img_idxes = 1;
ds.img_data = im_rgb;
ds.sp_info{1} = supix;
ds.pws_info = pinfo;
ds.sp_num_imgs = supix.sp_num;
% run cnn:
fprintf(' > CNN\n');
depth = do_model_evaluate(model, ds, opts);
if opts.inpaint
fprintf(' > inpaint\n');
depth = do_inpainting(depth, im_rgb, supix);
end
if opts.normalize_depth
d_min = min(depth(:));
d_max = max(depth(:));
depth = (depth-d_min) / (d_max-d_min);
depth(depth<0) = 0;
depth(depth>1) = 1;
end
end
predict_depth()
|
github
|
tangzhenyu/Scene-Text-Understanding-master
|
classification_demo.m
|
.m
|
Scene-Text-Understanding-master/ctpn_crnn_ocr/CTPN/caffe/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
|
karthik-kk/Autoware-master
|
velCapture.m
|
.m
|
Autoware-master/ros/src/system/gazebo/catvehicle/matlab files/velCapture.m
| 1,047 |
utf_8
|
994f8bb886b3e3ebef61b89d3c9c00a9
|
% Function to capture catvehicle velocity and plotting live graph
function velCapture(ROS_IP, roboname)
%If number of argument is not two, flag message and exit.
if nargin < 2
disp('Uage: velocityProfiler(192.168.0.32, catvehicle)');
return;
end
close all;
%rosshutdown;
modelname = strcat('/',roboname);
%Connect to ROS master
master_uri= strcat('http://',ROS_IP);
master_uri = strcat(master_uri,':11311');
%rosinit(master_uri);
%get handle for /catvehicle/vel topic for subscribing to the data
speedsub = rossubscriber(strcat(modelname,'/vel'));
dt = datestr(now,'mmmm-dd-yyyy-HH-MM-SS');
sprintf('Velocity capture starts at %s',dt)
t = 0:0.05:50;
output = zeros(length(t),1);
figure;
grid on;
title('Velocity [m/s]');
for i = 1:length(t)
speedata = receive(speedsub,10);
output(i) = speedata.Linear.X;
plot([max(i-1,1),i], output([max(i-1,1),i]),'b-');
hold on;
drawnow;
end
dt = datestr(now,'mmmm-dd-yyyy-HH-MM-SS');
file = strcat(dt,'.mat');
save(file, 'output');
grid on;
title('Velocity [m/s]');
end
|
github
|
karthik-kk/Autoware-master
|
profileByMatrix.m
|
.m
|
Autoware-master/ros/src/system/gazebo/catvehicle/matlab files/profileByMatrix.m
| 1,840 |
utf_8
|
f81181e46cb60adc3466c4779083ce0d
|
%Implementation of follower algorithm
%Developed by Rahul Kumar Bhadani <[email protected]>
%ROS_IP = IP Address of ROS Master
%lead = name of the model of leader AV Car
%follower = name of the model of follower car
function profileByMatrix(ROS_IP, roboname, vel_input, time_input, tire_angle)
%If number of argument is not two, flag message and exit.
if nargin < 4
sprintf('Uage: velocityProfiler(192.168.0.32, catvehicle, velmatfile, timematfile)');
return;
end
if nargin < 5
tire_angle = 0.0;
end
rosshutdown;
close all;
modelname = strcat('/',roboname);
%Connect to ROS master
master_uri= strcat('http://',ROS_IP);
master_uri = strcat(master_uri,':11311');
rosinit(master_uri);
%get handle for /catvehicle/cmd_vel topic for publishing the data
velpub = rospublisher(strcat(modelname,'/cmd_vel'),rostype.geometry_msgs_Twist);
%get handle for /catvehicle/vel topic for subscribing to the data
speedsub = rossubscriber(strcat(modelname,'/vel'));
vmat = load(vel_input);
tmat = load(time_input);
t = tmat.t;
%Velocity profile
input = vmat.Vel;
%Velocity profile will be sine
%input = abs(2*sin(t));
%Variable to store output velocity
output = zeros(length(t),1);
%handle for rosmessage object for velpub topic
velMsgs = rosmessage(velpub);
for i=1:length(t)
velMsgs.Linear.X = input(i);
velMsgs.Angular.Z = tire_angle;
%Publish on the topic /catvehicle/cmd_vel
send(velpub, velMsgs);
%Read from the topic /catvehicle/speed
speedata = receive(speedsub,10);
output(i) = speedata.Linear.X;
pause(0.1);
if i == 3000
break;
end
end
%Plot the input and output velocity profile
[n, p] = size(output);
T = 1:n;
plot(T, input');
hold on;
plot(T, output);
title('Original Data');
legend('Input function', 'Output response');
grid on;
save input.mat input output
|
github
|
karthik-kk/Autoware-master
|
velocityProfiler.m
|
.m
|
Autoware-master/ros/src/system/gazebo/catvehicle/matlab files/velocityProfiler.m
| 1,758 |
utf_8
|
d12bb47043d421e21fc4fd4fa8ce4b02
|
%Matlab scripto to publish velocity on /catvehicle/cmd_vel topic and
%subscribe to catvehicle/speed topic
%Developed by Rahul Kumar Bhadani <[email protected]>
%ROS_IP = IP Address of ROS Master
%roboname = name of the model
function velocityProfiler(ROS_IP, roboname, tire_angle)
%If number of argument is not two, flag message and exit.
if nargin < 2
sprintf('Uage: velocityProfiler(192.168.0.32, catvehicle)');
return;
end
if nargin < 3
tire_angle = 0.0;
end
rosshutdown;
close all;
modelname = strcat('/',roboname);
%Connect to ROS master
master_uri= strcat('http://',ROS_IP);
master_uri = strcat(master_uri,':11311');
rosinit(master_uri);
%get handle for /catvehicle/cmd_vel topic for publishing the data
velpub = rospublisher(strcat(modelname,'/cmd_vel'),rostype.geometry_msgs_Twist);
%get handle for /catvehicle/vel topic for subscribing to the data
speedsub = rossubscriber(strcat(modelname,'/vel'));
%Discretize timestamp
t = 0:0.01:150;
v1 = 3;
v2 = 6;
v3 = 0;
%Velocity profile
input = v1.*(t<50) + v2.*(t>=50).*(t<100) + v3.*(t>= 100);
%Velocity profile will be sine
%input = abs(2*sin(t));
%Variable to store output velocity
output = zeros(length(t),1);
%handle for rosmessage object for velpub topic
velMsgs = rosmessage(velpub);
for i=1:length(t)
velMsgs.Linear.X = input(i);
velMsgs.Angular.Z = tire_angle;
%Publish on the topic /catvehicle/cmd_vel
send(velpub, velMsgs);
%Read from the topic /catvehicle/speed
speedata = receive(speedsub,10);
output(i) = speedata.Linear.X;
end
%Plot the input and output velocity profile
[n, p] = size(output);
T = 1:n;
plot(T, input');
hold on;
plot(T, output);
title('Original Data');
legend('Input function', 'Output response');
grid on;
|
github
|
karthik-kk/Autoware-master
|
follower_profile.m
|
.m
|
Autoware-master/ros/src/system/gazebo/catvehicle/matlab files/follower_profile.m
| 2,381 |
utf_8
|
ea7d00e67f5e7709a2cd7287216af4af
|
%Implementation of follower algorithm
%Developed by Rahul Kumar Bhadani <[email protected]>
%ROS_IP = IP Address of ROS Master
%lead = name of the model of leader AV Car
%follower = name of the model of follower car
function follower_profile(ROS_IP, lead, follower)
%If number of argument is not two, flag message and exit.
if nargin < 2
sprintf('Usage: velocityProfiler(192.168.0.32, catvehicle)');
return;
end
rosshutdown;
close all;
modelname1 = strcat('/',lead);
modelname2 = strcat('/',follower);
%Connect to ROS master
master_uri= strcat('http://',ROS_IP);
master_uri = strcat(master_uri,':11311');
rosinit(master_uri);
%get handle for cmd_vel topic for publishing the data
velpub1 = rospublisher(strcat(modelname1,'/cmd_vel'),rostype.geometry_msgs_Twist);
velpub2 = rospublisher(strcat(modelname2,'/cmd_vel'),rostype.geometry_msgs_Twist);
%get handle for speed topic for subscribing to the data
speedsub1 = rossubscriber(strcat(modelname1,'/vel'));
speedsub2 = rossubscriber(strcat(modelname2,'/vel'));
%get handle for /DistanceEstimator
distanceEstimaterSub = rossubscriber('/DistanceEstimator/dist');
%Discretize timestamp
t = 0:0.05:150;
v1 = 3;
v2 = 6;
v3 = 0;
%Velocity profile
input = v1.*(t<50) + v2.*(t>=50).*(t<100) + v3.*(t>= 100);
%Velocity profile will be sine
%input = abs(2*sin(t));
%Variable to store output velocity
output1 = zeros(length(t),1);
output2 = zeros(length(t),1);
%handle for rosmessage object for velpub topic
velMsgs1 = rosmessage(velpub1);
velMsgs2 = rosmessage(velpub2);
for i=1:length(t)
velMsgs1.Linear.X = input(i);
velMsgs1.Angular.Z = 0.0;
%Publish on the topic /catvehicle/cmd_vel
send(velpub1, velMsgs1);
%Read from the topic /catvehicle/speed
speedata1 = receive(speedsub1,10);
distance = receive(distanceEstimaterSub,10);
x = distance.Data;
%Follower control rule
velMsgs2.Linear.X = (1/30.*x + 2/3).*speedata1.Linear.X;
velMsgs2.Angular.Z = 0.0;
send(velpub2, velMsgs2);
speedata2 = receive(speedsub2,10);
output1(i) = speedata1.Linear.X;
output2(i) = speedata2.Linear.X;
end
%Plot the input and output velocity profile
[n, p] = size(output1);
T = 1:n;
plot(T, input');
hold on;
plot(T, output1);
plot(T, output2);
title('Original Data');
legend('Input function', 'Output response of lead','Output response of follower');
grid on;
|
github
|
karthik-kk/Autoware-master
|
plotDisvout.m
|
.m
|
Autoware-master/ros/src/system/gazebo/catvehicle/simulink/plotDisvout.m
| 241 |
utf_8
|
241fa676f6444e00a60b8c69a5e19efd
|
% Author: Jonathan Sprinkle
% plots the distance outputs from a data file
function plotData( timeseries )
% this timeseries is what we have
figure
hold on
plot(timeseries.Data);
plot(timeseries.uVelOut);
legend({'Distance','VelOut'});
end
|
github
|
karthik-kk/Autoware-master
|
plotData.m
|
.m
|
Autoware-master/ros/src/system/gazebo/catvehicle/simulink/plotData.m
| 350 |
utf_8
|
17951edcd31fa9c02deeb1c49a2e0d7b
|
% Author: Jonathan Sprinkle
% plots the distance outputs from a data file
function plotData( timeseries )
% this timeseries is what we have
figure
hold on
plot(timeseries.dist);
plot(timeseries.velConverted);
plot(timeseries.vdot);
plot(timeseries.vout);
plot(timeseries.uTireAngle);
legend({'dist','velConverted','vdot','vout','uTireAngle'});
end
|
github
|
karthik-kk/Autoware-master
|
plotDistances.m
|
.m
|
Autoware-master/ros/src/system/gazebo/catvehicle/simulink/plotDistances.m
| 288 |
utf_8
|
c0779b1561faff69c733c5ec8a3a9ac7
|
% Author: Jonathan Sprinkle
% plots the distance outputs from a data file
function plotDistances
load distances.mat
% this timeseries is what we have
figure
hold on
plot(DistanceEstimator.Data__signal_1_);
plot(DistanceEstimator.Data__signal_2_);
legend({'Distance','Angle (rad)'});
end
|
github
|
rohinkumarreddy/Iris-Detection-master
|
hamdist.m
|
.m
|
Iris-Detection-master/hamdist.m
| 710 |
utf_8
|
bc94aff9c7917be49e3bb10e78a5e3e4
|
function hd = hamdist(template1, template2, scales)
template1 = logical(template1);
template2 = logical(template2);
hd = NaN;
% shift template left and right, use the lowest Hamming distance
for shifts=-8:8
template1s = shiftbits(template1, shifts,scales);
totalbits = (size(template1s,1)*size(template1s,2));
C = xor(template1s,template2);
bitsdiff = sum(sum(C==1));
if totalbits == 0
hd = NaN;
else
hd1 = bitsdiff / totalbits;
if hd1 < hd || isnan(hd)
hd = hd1;
end
end
end
|
github
|
YasaraPeiris/MNIST_Clusters-master
|
predict.m
|
.m
|
MNIST_Clusters-master/TestMNIST/MatlabCodes/predict.m
| 1,720 |
utf_8
|
b06e9988934fa4b78b42512d26371e90
|
function predict(layerset, dataSize)
global weights numLayers layers;
layers = [784, layerset, 10];
[~, numLayers] = size(layers);
images = loadTrainImages();
labels = loadTrainLabels();
selected = find(labels == 5 | labels == 1);
labels = labels(selected);
images = images(:, selected);
[~, c] = size(images);
dataSize = min(c, dataSize);
iterations = dataSize;
testLabels = [];
clusters = [];
results = cell(numLayers);
loadWeights();
p = 0.3;
unclassified = 0;
for r = 1 : iterations
results{1} = normc(mat2gray(images(:, r)));
%results{1} = sigmf(mat2gray(images(:, r)), [5.0 0.5]);
for k = 1 : numLayers - 1
results{k + 1} = normc(weights{k} * results{k});
%results{k + 1} = sigmf(weights{k} * results{k}, [5.0 0.5]);
end
[m, i] = max(results{numLayers});
if(m >= p)
testLabels = [testLabels; labels(r)];
clusters = [clusters; i];
else
unclassified = unclassified + 1;
end
%{
temp = [temp, [results{numLayers} ; ones(3, 1)]];
c = c + 1;
if mod(c, 100) == 0
disp(c);
picMap = [picMap ; temp];
temp = [];
c = 0;
end
%}
end
plotPerformance([1 : iterations]', [], testLabels, clusters, [2, 3]);
disp(['Unclassified: ', int2str(unclassified), ' out of ', int2str(dataSize)]);
function loadWeights()
global weights layers;
fileName = sprintf('%d_', layers);
fileName = strcat(fileName(1 : end - 1), '.mat');
fileName = fullfile(fileparts(which(mfilename)), '..\WeightDatabase\Temp', fileName);
if exist(fileName, 'file') == 2
load(fileName, 'weights');
else
disp('Trained network not available');
exit;
end
|
github
|
YasaraPeiris/MNIST_Clusters-master
|
trainModel_yas.m
|
.m
|
MNIST_Clusters-master/TestMNIST/MatlabCodes/trainModel_yas.m
| 1,924 |
utf_8
|
6c4950f15dc3468e591c1a2c5aab6d28
|
function trainModel_yas(layerset, dataSize) % Using Oja's rule
trainingRatio = 0.8;
p = 0;
images = loadTrainImages();
labels = loadTrainLabels();
selected = find(labels == 0 | labels == 1 );
labels = labels(selected);
images = images(:, selected');
[~, c] = size(images);
dataSize = min(c, dataSize);
iterations = dataSize;
image_batch = 10;
newIterations = fix(iterations/image_batch);
testLabels = [];
clusters = [];
trainingSize = floor(double(dataSize) * trainingRatio)/image_batch;
unclassified = 0;
norms = [];
updateTime = 0.0;
net = Network_new([784, layerset, 2]);
numLayers = net.numLayers;
tempW = net.feedforwardConnections;
%tempW = net.lateralConnections;
temp = net.feedforwardConnections;
for r= 1:newIterations
images_new = [];
for k=1:image_batch
image_id = image_batch*(r-1)+k;
images_new = [images_new mat2gray(images(:, image_id))];
end
results = net.getOutput(images_new,r);
if(r > trainingSize)
for u=1:image_batch
image_id = image_batch*(r-1)+u;
[m, i] = max(results{numLayers}(:,u));
testLabels = [testLabels; labels(image_id)];
clusters = [clusters; i];
end
end
time = tic;
net.STDP_update(results,r);
updateTime = updateTime + toc(time);
for u = 1 : image_batch
norms = [norms; zeros(1, numLayers - 1)];
weights = net.feedforwardConnections;
for k = 1 : numLayers - 1
norms(end, k) = norm(weights{k}(:,u) - tempW{k}(:,u),'fro') / numel(weights{k}(:,u));
tempW{k}(:,u) = weights{k}(:,u);
end
end
end
plotPerformance([1 : iterations]', norms, testLabels, clusters, [1, 2, 3]);
showFinalImage(abs(weights{1} - temp{1}));
|
github
|
danielemarinazzo/GC_SS_PNAS-master
|
InstModelfilter.m
|
.m
|
GC_SS_PNAS-master/InstModelfilter.m
| 2,343 |
utf_8
|
9ec58f552064a96d14d59ba96f833baf
|
%% realization of the instantaneous model : U = L*W
%%% OUTPUT
% U: N*M matrix of filtered noises
% INPUT
% N data length
% C: input covariance matrix (may be interpreted as Su or Sw, see above)
% B0: M*M matrix of instantaneous effects (when relevant)
% when flag='StrictlyCausal':
% given Su, applies Cholesky decomposition to find L and Sw
% then generates U = L*W, for a realization of gaussian W of variance Sw
% when flag='ExtendedGauss':
% given Sw and B(0), computes L=[I-B(0)]^(-1)
% then generates U = L*W, for a realization of gaussian W of variance Sw
% when flag='ExtendedNonGauss':
% given Swand B(0), computes L=[I-B(0)]^(-1)
% then generates U = L*W, for a realization of nongaussian W of variance Sw
function U=InstModelfilter(N,C,flag,B0)
error(nargchk(3,4,nargin));%min and max input arguments
M=size(C,1);
switch flag
case {'StrictlyCausal'} % C is Su
[L,Sw]=choldiag(C);
W = randn(M,N); % W independent and gaussian
for m=1:M % This normalizes W to have the appropriate variance (and zero mean)
W(m,:)=sqrt(Sw(m,m))*(W(m,:)-mean(W(m,:)))/std(W(m,:));
end
U=L*W;
case {'ExtendedGauss'} % C is Sw
invL=eye(M)-B0;
if det(invL)==0, error('B0 is not invertible, ill-conditioned problem!'), end;
L=inv(invL);
W = randn(M,N); % W independent and gaussian
for m=1:M % This normalizes W to have the appropriate variance (and zero mean)
W(m,:)=sqrt(C(m,m))*(W(m,:)-mean(W(m,:)))/std(W(m,:));
end
U=L*W;
case {'ExtendedNonGauss'} % C is Sw
invL=eye(M)-B0;
if det(invL)==0, error('B0 is not invertible, ill-conditioned problem!'), end;
L=inv(invL);
%note: here we generate W independent but non-Gaussian
% Nonlinearity exponent, selected to lie in [0.5, 0.8] or [1.2, 2.0]. (<1 gives subgaussian, >1 gives supergaussian)
q = rand(M,1)*1.1+0.5;
ind = find(q>0.8);
q(ind) = q(ind)+0.4;
% This generates the disturbance variables, which are mutually independent, and non-gaussian
W = randn(M,N);
W = sign(W).*(abs(W).^(q*ones(1,N)));
% This normalizes the disturbance variables to have the appropriate scales
W = W./( ( sqrt(mean((W').^2)') ./ sqrt(diag(C)) )*ones(1,N) );
U=L*W;
end
|
github
|
danielemarinazzo/GC_SS_PNAS-master
|
MVARfilter.m
|
.m
|
GC_SS_PNAS-master/MVARfilter.m
| 614 |
utf_8
|
0290e5ff7f6435096eed5fecafbf75e9
|
%% FILTER A VECTOR NOISE WITH A SPECIFIED STRICTLY CAUSAL MVAR MODEL: Y(n)=A(1)Y(n-1)+...+A(p)Y(n-p)+U(n)
%%% INPUT
% A=[A(1)...A(p)]: M*pM matrix of the MVAR model coefficients (strictly causal model)
% U: M*N matrix of innovations
%%% OUTPUT
% Y: M*N matrix of simulated time series
function [Y]=MVARfilter(A,U)
N=length(U);
M=size(A,1);
p=size(A,2)/M;
% Y(n)=A(1)Y(n-1)+...+A(p)Y(n-p)+U(n)
Y=zeros(M,N);
for n=1:N
for k=1:p
if n-k<=0, break; end; % if n<=p, stop when k>=n
Y(:,n)=Y(:,n) + ( A(:,(k-1)*M+(1:M)) * Y(:,n-k) );
end
Y(:,n)=Y(:,n)+U(:,n);
end
|
github
|
danielemarinazzo/GC_SS_PNAS-master
|
varma2iss.m
|
.m
|
GC_SS_PNAS-master/varma2iss.m
| 1,206 |
utf_8
|
200c8d913b3e3fe6f30cf6228c4453c9
|
%% VARMA with B0 term to (Innovations form) State Space parameters
% computes innovations form parameters for a state space model from VARMA
% parameters using Aoki's method - this version allows for zero-lag MA coefficients
function [A,C,K,R,lambda0] = varma2iss(Am,Bm,V,B0)
% INPUT: VARMA parameters Am, Bm, V=cov(U)
% OUTPUT: innovations form SS parameters A, C, K, R
%%%%% internal test
%variables to be passed are Am, Bm, B0, V=Su
% clear; close all; clc;
% Am=[0.9 0 0 0.5; 0 0.6 0.2 0];
% Bm=[0.5 0; 0 0.5]; B0=Bm./5;
% V=eye(2);
%
M = size(Am,1); %dimension of observed process
p=floor(size(Am,2)/M); %number of AR lags
q=floor(size(Bm,2)/M); %number of MA lags
L=M*(p+q); % dimension of state process (SS order)
C=[Am Bm];
R=B0*V*B0';
Ip=eye(M*p);
Iq=eye(M*q);
A11=[Am;Ip(1:end-M,:)];
if q==0
A=A11;
K=[eye(M); zeros(M*(p-1),M)];
else
A12=[Bm;zeros(M*(p-1),M*q)];
A21=zeros(M*q,M*p);
A22=[zeros(M,M*q); Iq(1:end-M,:)];
A=[A11 A12; A21 A22];
K=[eye(M); zeros(M*(p-1),M); inv(B0); zeros(M*(q-1),M)];
end
% determine the variance of the process lambda0=E[Yn Yn']
O=dlyap(A,K*R*K');
lambda0=C*O*C'+R;
end
|
github
|
danielemarinazzo/GC_SS_PNAS-master
|
idMVAR.m
|
.m
|
GC_SS_PNAS-master/idMVAR.m
| 1,446 |
utf_8
|
02db6f116c8164266a38371b50da5231
|
%% IDENTIFICATION OF STRICTLY CAUSAL MVAR MODEL: Y(n)=A(1)Y(n-1)+...+A(p)Y(n-p)+U(n)
% makes use of autocovariance method (vector least squares)
%%% input:
% Y, M*N matrix of time series (each time series is in a row)
% p, model order
% Mode, determines estimation algorithm (0:builtin least squares, else other methods [see mvar.m from biosig package])
%%% output:
% Am=[A(1)...A(p)], M*pM matrix of the estimated MVAR model coefficients
% S, estimated M*M input covariance matrix
% Yp, estimated time series
% Up, estimated residuals
% Z, observation matrix (often optional, useful e.g. for resampling)
function [Am,S,Yp,Up,Z,Yb]=idMVAR(Y,p,Mode)
% error(nargchk(1,3,nargin));
% if nargin < 3, Mode=0; end % default use least squares estimate
% if nargin < 2, p=10; end % default model order
[M,N]=size(Y);
%% IDENTIFICATION
Z=NaN*ones(p*M,N-p); % observation matrix
for j=1:p
for i=1:M
Z((j-1)*M+i,1:N-p)=Y(i, p+1-j:N-j);
end
end
if Mode==0
Yb=NaN*ones(M,N-p); % Ybar
for i=1:M
Yb(i,1:N-p)=Y(i,p+1:N);
end
Am=Yb/Z; % least squares!
% fprintf('using least squares\n');
else
Am = mvar(Y', p, Mode); % estimates from biosig code
% fprintf(['using biosig ' int2str(Mode) ' mode\n']);
end
Yp=Am*Z;
Yp=[NaN*ones(M,p) Yp]; % Vector of predicted data
Up=Y-Yp; Up=Up(:,p+1:N); % residuals of strictly causal model
S=cov(Up');
|
github
|
danielemarinazzo/GC_SS_PNAS-master
|
block_fdMVAR.m
|
.m
|
GC_SS_PNAS-master/block_fdMVAR.m
| 5,321 |
utf_8
|
c0626e923c90c31b6156e254a5e1785f
|
%% FREQUENCY DOMAIN BLOCK MVAR ANALYSIS
% References:
% L.Faes and G. Nollo, "Measuring Frequency Domain Granger Causality for Multiple Blocks of Interacting Time Series", Biological Cybernetics 2013. DOI: 10.1007/s00422-013-0547-5
% L.Faes, S. Erla and G. Nollo, "Block Partial Directed Coherence: a New Tool for the Structural Analysis of Brain Networks", International Journal of Bioelectromagnetism, Vol. 14, No. 4, pp. 162 - 166, 2012
%%% inputs:
% Am=[A(1)...A(p)]: Q*pQ matrix of the MVAR model coefficients (strictly causal model)
% Su: Q*Q covariance matrix of the input noises
% Mv: number of series in each block
% N= number of points for calculation of the spectral functions (nfft)
% Fs= sampling frequency
%%% outputs:
% bDC= block Directed Coherence (Eq. 14a)
% bPDC= block Partial Directed Coherence (Eq. 14b)
% mF= multivariate total causality (Eq. 13a)
% mG= multivariate direct causality (Eq. 13b)
% bS= block spectral density matrix (Eq. 12a)
% bP= inverse block spectral density matrix (Eq. 12b)
% bH= block transfer matrix
% bAf= block spectral coefficient matrix
% f= vector of frequencies
function [bDC,bPDC,mF,mG,bS,bP,bH,bAf,f] = block_fdMVAR(Am,Su,Mv,N,Fs)
% clear; close all; clc;
% [Bm,B0,Sw]=simuMVARcoeff(1);
% Am=Bm;Su=Sw;
% Mv=[2 1 1]'; % vector of Mi (dimension of each block)
% N=512;
% Fs=1;
% f = (0:N-1)*(Fs/(2*N));
%%
Q= size(Am,1); % Am has dim Q*pQ
M=length(Mv);
p = size(Am,2)/Q; % p is the order of the MVAR model
if nargin<5, Fs= 1; end;
if nargin<4, N = 512; end;
if all(size(N)==1), %if N is scalar
f = (0:N-1)*(Fs/(2*N)); % frequency axis
else % if N is a vector, we assume that it is the vector of the frequencies
f = N; N = length(N);
end;
s = exp(sqrt(-1)*2*pi*f/Fs); % vector of complex exponentials
z = sqrt(-1)*2*pi/Fs;
%% Initializations: spectral matrices have M rows, M columns and are calculated at each of the N frequencies
A = [eye(Q) -Am]; % matrix from which M*M blocks are selected to calculate spectral functions
invSu=inv(Su);
% i-j block of Su and Su^(-1)
bSu=cell(M,M); binvSu=cell(M,M);
for i=1:M
for j=1:M
i1=sum(Mv(1:i)); i0=i1-Mv(i)+1;
j1=sum(Mv(1:j)); j0=j1-Mv(j)+1;
bSu{i,j}=Su(i0:i1,j0:j1);
binvSu{i,j}=invSu(i0:i1,j0:j1);
end
end
% whole QxQ matrices
Af=zeros(Q,Q,N); % Coefficient Matrix in the frequency domain (it is Abar(w))
H=zeros(Q,Q,N); % Transfer Matrix H(w)
S=zeros(Q,Q,N); % Spectral Matrix S(w)
P=zeros(Q,Q,N); % Inverse Spectral Matrix P(w)
% corresponding cell array of matrices (blocks of size Mi x Mj)
bAf=cell(M,M,N);
bH=cell(M,M,N);
bS=cell(M,M,N);
bP=cell(M,M,N);
% causality functions
bDC=zeros(M,M,N); % block directed coherence
bPDC=zeros(M,M,N); % block partial directed coherence
mF=NaN*ones(M,M,N); % multivariate logarithmic causality f
mG=NaN*ones(M,M,N); % multivariate logarithmic direct causality g
%%% computation of spectral functions
for n=1:N, % at each frequency
%%% Coefficient matrix in the frequency domain
As = zeros(Q,Q); % matrix As(z)=I-sum(A(k))
for k = 1:p+1,
As = As + A(:,k*Q+(1-Q:0))*exp(-z*(k-1)*f(n)); %indicization (:,k*Q+(1-M:0)) extracts the k-th Q*Q block from the matrix B (A(1) is in the second block, and so on)
end;
Af(:,:,n) = As; %%% Coefficient matrix
H(:,:,n) = inv(As); %%% Transfer matrix
S(:,:,n) = H(:,:,n)*Su*H(:,:,n)'; %%% Spectral matrix - ' stands for Hermitian transpose
P(:,:,n) = inv(S(:,:,n)); %%% Inverse Spectral matrix P(:,:,n) = As'*invSu*As;
%%% extraction of blocks indexes
for i=1:M
for j=1:M
%indexes of the i-j block
i1=sum(Mv(1:i)); i0=i1-Mv(i)+1;
j1=sum(Mv(1:j)); j0=j1-Mv(j)+1;
% i-j block of all matrices of interest
bAf{i,j,n}=Af(i0:i1,j0:j1,n);
bH{i,j,n}=H(i0:i1,j0:j1,n);
bS{i,j,n}=S(i0:i1,j0:j1,n);
bP{i,j,n}=P(i0:i1,j0:j1,n);
end
end
% computation of causality measures at frequency n
for i=1:M
for j=1:M
% if det(bP{j,j,n}) < -0.000001, error('determinante negativo!'); end
% if det(bS{i,i,n}) < -0.000001, error('determinante negativo!'); end
% if det(bP{j,j,n} - bAf{i,j,n}'*binvSu{i,i}*bAf{i,j,n}) < -0.000001, error('determinante negativo!'); end
% if det(bS{i,i,n} - bH{i,j,n}*bSu{j,j}*bH{i,j,n}') < -0.000001, error('determinante negativo!'); end
bDC(i,j,n) = 1 - abs(det(bS{i,i,n} - bH{i,j,n}*bSu{j,j}*bH{i,j,n}')) / abs(det(bS{i,i,n}));
bPDC(i,j,n) = 1 - abs(det(bP{j,j,n} - bAf{i,j,n}'*binvSu{i,i}*bAf{i,j,n})) / abs(det(bP{j,j,n}));
if i~=j
mF(i,j,n) = log( abs(det(bS{i,i,n})) / abs(det(bS{i,i,n} - bH{i,j,n}*bSu{j,j}*bH{i,j,n}')) );
mG(i,j,n) = log( abs(det(bP{j,j,n})) / abs(det(bP{j,j,n} - bAf{i,j,n}'*binvSu{i,i}*bAf{i,j,n})) );
end
end
end
end;
|
github
|
bill-codes/netalign-master
|
bipartite_matching.m
|
.m
|
netalign-master/experiments/misc/gaimc/bipartite_matching.m
| 6,580 |
utf_8
|
bd3212ac06f51f9037ca7a7d80b45981
|
function [val m1 m2 mi]=bipartite_matching(varargin)
% BIPARTITE_MATCHING Solve a maximum weight bipartite matching problem
%
% [val m1 m2]=bipartite_matching(A) for a rectangular matrix A
% [val m1 m2 mi]=bipartite_matching(x,ei,ej,n,m) for a matrix stored
% in triplet format. This call also returns a matching indicator mi so
% that val = x'*mi.
%
% The maximum weight bipartite matching problem tries to pick out elements
% from A such that each row and column get only a single non-zero but the
% sum of all the chosen elements is as large as possible.
%
% This function is slightly atypical for a graph library, because it will
% be primarily used on rectangular inputs. However, these rectangular
% inputs model bipartite graphs and we take advantage of that stucture in
% this code. The underlying graph adjency matrix is
% G = spaugment(A,0);
% where A is the rectangular input to the bipartite_matching function.
%
% Matlab already has the dmperm function that computes a maximum
% cardinality matching between the rows and the columns. This function
% gives us the maximum weight matching instead. For unweighted graphs, the
% two functions are equivalent.
%
% Note: If ei and ej contain duplicate edges, the results of this function
% are incorrect.
%
% See also DMPERM
%
% Example:
% A = rand(10,8); % bipartite matching between random data
% [val mi mj] = bipartite_matching(A);
% val
% David F. Gleich and Ying Wang
% Copyright, Stanford University, 2008-2009
% Computational Approaches to Digital Stewardship
% 2008-04-24: Initial coding (copy from Ying Wang matching_sparse_mex.cpp)
% 2008-11-15: Added triplet input/output
% 2009-04-30: Modified for gaimc library
% 2009-05-15: Fixed error with empty inputs and triple added example.
[rp ci ai tripi n m] = bipartite_matching_setup(varargin{:});
if isempty(tripi)
error(nargoutchk(0,3,nargout,'struct'));
else
error(nargoutchk(0,4,nargout,'struct'));
end
if ~isempty(tripi) && nargout>3
[val m1 m2 mi] = bipartite_matching_primal_dual(rp, ci, ai, tripi, n, m);
else
[val m1 m2] = bipartite_matching_primal_dual(rp, ci, ai, tripi, n, m);
end
function [rp ci ai tripi n m]= bipartite_matching_setup(A,ei,ej,n,m)
% convert the input
if nargin == 1
if isstruct(A)
[nzi nzj nzv]=csr_to_sparse(A.rp,A.ci,A.ai);
else
[nzi nzj nzv]=find(A);
end
[n m]=size(A);
triplet = 0;
elseif nargin >= 3 && nargin <= 5
nzi = ei;
nzj = ej;
nzv = A;
if ~exist('n','var') || isempty(n), n = max(nzi); end
if ~exist('m','var') || isempty(m), m = max(nzj); end
triplet = 1;
else
error(nargchk(3,5,nargin,'struct'));
end
nedges = length(nzi);
rp = ones(n+1,1); % csr matrix with extra edges
ci = zeros(nedges+n,1);
ai = zeros(nedges+n,1);
if triplet, tripi = zeros(nedges+n,1); % triplet index
else tripi = [];
end
%
% 1. build csr representation with a set of extra edges from vertex i to
% vertex m+i
%
rp(1)=0;
for i=1:nedges
rp(nzi(i)+1)=rp(nzi(i)+1)+1;
end
rp=cumsum(rp);
for i=1:nedges
if triplet, tripi(rp(nzi(i))+1)=i; end % triplet index
ai(rp(nzi(i))+1)=nzv(i);
ci(rp(nzi(i))+1)=nzj(i);
rp(nzi(i))=rp(nzi(i))+1;
end
for i=1:n % add the extra edges
if triplet, tripi(rp(i)+1)=-1; end % triplet index
ai(rp(i)+1)=0;
ci(rp(i)+1)=m+i;
rp(i)=rp(i)+1;
end
% restore the row pointer array
for i=n:-1:1
rp(i+1)=rp(i);
end
rp(1)=0;
rp=rp+1;
%
% 1a. check for duplicates in the data
%
colind = false(m+n,1);
for i=1:n
for rpi=rp(i):rp(i+1)-1
if colind(ci(rpi)), error('bipartite_matching:duplicateEdge',...
'duplicate edge detected (%i,%i)',i,ci(rpi));
end
colind(ci(rpi))=1;
end
for rpi=rp(i):rp(i+1)-1, colind(ci(rpi))=0; end % reset indicator
end
function [val m1 m2 mi]=bipartite_matching_primal_dual(...
rp, ci, ai, tripi, n, m)
% BIPARTITE_MATCHING_PRIMAL_DUAL
alpha=zeros(n,1); % variables used for the primal-dual algorithm
beta=zeros(n+m,1);
queue=zeros(n,1);
t=zeros(n+m,1);
match1=zeros(n,1);
match2=zeros(n+m,1);
tmod = zeros(n+m,1);
ntmod=0;
%
% initialize the primal and dual variables
%
for i=1:n
for rpi=rp(i):rp(i+1)-1
if ai(rpi) > alpha(i), alpha(i)=ai(rpi); end
end
end
% dual variables (beta) are initialized to 0 already
% match1 and match2 are both 0, which indicates no matches
i=1;
while i<=n
% repeat the problem for n stages
% clear t(j)
for j=1:ntmod, t(tmod(j))=0; end
ntmod=0;
% add i to the stack
head=1; tail=1;
queue(head)=i; % add i to the head of the queue
while head <= tail && match1(i)==0
k=queue(head);
for rpi=rp(k):rp(k+1)-1
j = ci(rpi);
if ai(rpi) < alpha(k)+beta(j) - 1e-8, continue; end % skip if tight
if t(j)==0,
tail=tail+1; queue(tail)=match2(j);
t(j)=k;
ntmod=ntmod+1; tmod(ntmod)=j;
if match2(j)<1,
while j>0,
match2(j)=t(j);
k=t(j);
temp=match1(k);
match1(k)=j;
j=temp;
end
break; % we found an alternating path
end
end
end
head=head+1;
end
if match1(i) < 1, % still not matched, so update primal, dual and repeat
theta=inf;
for j=1:head-1
t1=queue(j);
for rpi=rp(t1):rp(t1+1)-1
t2=ci(rpi);
if t(t2) == 0 && alpha(t1) + beta(t2) - ai(rpi) < theta,
theta = alpha(t1) + beta(t2) - ai(rpi);
end
end
end
for j=1:head-1, alpha(queue(j)) = alpha(queue(j)) - theta; end
for j=1:ntmod, beta(tmod(j)) = beta(tmod(j)) + theta; end
continue;
end
i=i+1; % increment i
end
val=0;
for i=1:n
for rpi=rp(i):rp(i+1)-1
if ci(rpi)==match1(i), val=val+ai(rpi); end
end
end
noute = 0; % count number of output edges
for i=1:n
if match1(i)<=m, noute=noute+1; end
end
m1=zeros(noute,1); m2=m1; % copy over the 0 array
noute=1;
for i=1:n
if match1(i)<=m, m1(noute)=i; m2(noute)=match1(i);noute=noute+1; end
end
if nargout>3
mi= false(length(tripi)-n,1);
for i=1:n
for rpi=rp(i):rp(i+1)-1
if match1(i)<=m && ci(rpi)==match1(i), mi(tripi(rpi))=1; end
end
end
end
|
github
|
bill-codes/netalign-master
|
graph_draw.m
|
.m
|
netalign-master/experiments/misc/gaimc/graph_draw.m
| 23,504 |
utf_8
|
83adf66de4bc94aea62f934d2e1e3da0
|
function h = graph_draw(adj, xy, varargin)
% GRAPH_DRAW Draw a picture of a graph when the coordinates are known
%
% graph_draw(A, xy) draws a picture of graph A where node i is placed
% at x = xy(i,1), y = xy(i,2). In the drawing, shaded nodes have
% self loops.
%
% Some of the parameters of the drawing are controlled by specifying
% optional parameters in the call graph_draw(A, xy, key, value). The keys
% and default values are
% 'linestyle' - default '-'
% 'linewidth' - default .5
% 'linecolor' - default Black
% 'fontsize' - fontsize for labels, default 8
% 'labels' - Cell array containing labels <Default : '1':'N'>
% 'shapes' - 1 if node is a box, 0 if oval <Default : zeros>
%
% h = graph_draw(A,xy,...) returns a handle for each object. h(i,1) is
% the text handle for vertex i, and h(i,2) is the circle handle for
% vertex i.
%
% Originally written by Erik A. Johnson, Ali Taylan Cemgil, and Leon Peskin
% Modified by David F. Gleich for gaimc package.
%
% See also GPLOT
%
% Example:
% load_gaimc_graph('dfs_example');
% graph_draw(A,xy);
% 2009-02-26 interface modified by David Gleich <[email protected]>
% to remove automatic layout
% 2009-05-15: Added example
% 24 Feb 2004 cleaned up, optimized and corrected by Leon Peshkin pesha @ ai.mit.edu
% Apr-2000 draw_graph Ali Taylan Cemgil <[email protected]>
% 1995-1997 arrow Erik A. Johnson <[email protected]>
linestyle = '-'; % -- -.
linewidth = .5; % 2
linecolor = 'Black'; % Red
fontsize = 8;
N = size(adj,1);
color = ones(N, 3); % colors of elipses around text
labels = cellstr(int2str((1:N)')); % labels = cellstr(char(zeros(N,1)+double('+')));
node_t = zeros(N,1); %
for i = 1:2:length(varargin) % get optional args
switch varargin{i}
case 'linestyle', linestyle = varargin{i+1};
case 'linewidth', linewidth = varargin{i+1};
case 'linecolor', linecolor = varargin{i+1};
case 'labels', labels = varargin{i+1};
case 'fontsize', fontsize = varargin{i+1};
case 'shapes', node_t = varargin{i+1}; node_t = node_t(:);
end
end
x = xy(:,1);
x = x - min(x);
y = xy(:,2);
y = y - min(y);
% scale the graph so it's between 0 and 1
xrange = max(x);
yrange = max(y);
scalefactor = max(xrange,yrange);
x = x/scalefactor;
y = y/scalefactor;
lp_ndx = find(diag(adj)); % recover from self-loops = diagonal ones
color(lp_ndx,:) = repmat([.8 .8 .8],length(lp_ndx),1); % makes self-looped nodes blue
adj = adj - diag(diag(adj)); % clean up the diagonal
axis([-0.1 1.1 -0.1 1.1]);
axis off;
set(gcf,'Color',[1 1 1]);
set(gca,'XTick',[], 'YTick',[], 'box','on'); % axis('square'); %colormap(flipud(gray));
idx1 = find(node_t == 0); wd1 = []; % Draw nodes
if ~isempty(idx1),
[h1 wd1] = textoval(x(idx1), y(idx1), labels(idx1), fontsize, color);
end;
idx2 = find(node_t ~= 0); wd2 = [];
if ~isempty(idx2),
[h2 wd2] = textbox(x(idx2), y(idx2), labels(idx2), color);
end;
wd = zeros(size(wd1,1) + size(wd2,1),2);
if ~isempty(idx1), wd(idx1, :) = wd1; end;
if ~isempty(idx2), wd(idx2, :) = wd2; end;
for node = 1:N % Draw edges
edges = find(adj(node,:) == 1);
for node2 = edges
sign = 1;
if ((x(node2) - x(node)) == 0)
if (y(node) > y(node2)), alpha = -pi/2; else alpha = pi/2; end;
else
alpha = atan((y(node2)-y(node))/(x(node2)-x(node)));
if (x(node2) <= x(node)), sign = -1; end;
end;
dy1 = sign.*wd(node,2).*sin(alpha); dx1 = sign.*wd(node,1).*cos(alpha);
dy2 = sign.*wd(node2,2).*sin(alpha); dx2 = sign.*wd(node2,1).*cos(alpha);
if (adj(node2,node) == 0) % if directed edge
my_arrow([x(node)+dx1 y(node)+dy1], [x(node2)-dx2 y(node2)-dy2]);
else
line([x(node)+dx1 x(node2)-dx2], [y(node)+dy1 y(node2)-dy2], ...
'Color', linecolor, 'LineStyle', linestyle, 'LineWidth', linewidth);
adj(node2,node) = -1; % Prevent drawing lines twice
end;
end;
end;
if nargout > 2
h = zeros(length(wd),2);
if ~isempty(idx1), h(idx1,:) = h1; end;
if ~isempty(idx2), h(idx2,:) = h2; end;
end;
function [t, wd] = textoval(x, y, str, fontsize, c)
% [t, wd] = textoval(x, y, str, fontsize) Draws an oval around text objects
% INPUT: x, y - Coordinates
% str - Strings
% c - colors
% OUTPUT: t - Object Handles
% width - x and y width of ovals
if ~isa(str,'cell'), str = cellstr(str); end;
N = length(str);
wd = zeros(N,2);
temp = zeros(N,2);
for i = 1:N,
tx = text(x(i),y(i),str{i},'HorizontalAlignment','center','VerticalAlign','middle','FontSize', fontsize);
sz = get(tx, 'Extent');
wy = sz(4);
wx = max(2/3*sz(3), wy);
wx = 0.9 * wx; % might want to play with this .9 and .5 coefficients
wy = 0.5 * wy;
ptc = ellipse(x(i), y(i), wx, wy, c(i,:));
set(ptc, 'FaceColor', c(i,:)); % 'w'
wd(i,:) = [wx wy];
delete(tx);
tx = text(x(i),y(i),str{i},'HorizontalAlignment','center','VerticalAlign','middle', 'FontSize', fontsize);
temp(i,:) = [tx ptc];
end;
t = temp;
function [p] = ellipse(x, y, rx, ry, c)
% [p] = ellipse(x, y, rx, ry) Draws Ellipse shaped patch objects
% INPUT: x,y - N x 1 vectors of x and y coordinates
% Rx, Ry - Radii
% C - colors
% OUTPUT: p - Handles of Ellipse shaped path objects
if length(rx)== 1, rx = ones(size(x)).*rx; end;
if length(ry)== 1, ry = ones(size(x)).*ry; end;
N = length(x);
p = zeros(size(x));
t = 0:pi/30:2*pi;
for i = 1:N
px = rx(i) * cos(t) + x(i); py = ry(i) * sin(t) + y(i);
p(i) = patch(px, py, c(i,:));
end;
function [h, wd] = textbox(x,y,str,c)
% [h, wd] = textbox(x,y,str) draws a box around the text
% INPUT: x, y - Coordinates
% str - Strings
% OUTPUT: h - Object Handles
% wd - x and y Width of boxes
if ~isa(str,'cell'), str=cellstr(str); end
N = length(str);
wd = zeros(N,2);
h = zeros(N,2);
for i = 1:N,
tx = text(x(i),y(i),str{i},'HorizontalAlignment','center','VerticalAlign','middle');
sz = get(tx, 'Extent');
wy = 2/3 * sz(4); wyB = y(i) - wy; wyT = y(i) + wy;
wx = max(2/3 * sz(3), wy); wxL = x(i) - wx; wxR = x(i) + wx;
ptc = patch([wxL wxR wxR wxL], [wyT wyT wyB wyB], c(i,:));
set(ptc, 'FaceColor', c(i,:)); % 'w'
wd(i,:) = [wx wy];
delete(tx);
tx = text(x(i),y(i),str{i},'HorizontalAlignment','center','VerticalAlign','middle');
h(i,:) = [tx ptc];
end;
function [h,yy,zz] = my_arrow(varargin)
% [h,yy,zz] = my_arrow(varargin) Draw a line with an arrowhead.
% A lot of the original code is removed and most of the remaining can probably go too
% since it comes from a general use function only being called inone context. - Leon Peshkin
% Copyright 1997, Erik A. Johnson <[email protected]>, 8/14/97
ax = []; % set values to empty matrices
deflen = 12; % 16
defbaseangle = 45; % 90
deftipangle = 16;
defwid = 0; defpage = 0; defends = 1;
ArrowTag = 'Arrow'; % The 'Tag' we'll put on our arrows
start = varargin{1}; % fill empty arguments
stop = varargin{2};
crossdir = [NaN NaN NaN];
len = NaN; baseangle = NaN; tipangle = NaN; wid = NaN;
page = 0; ends = NaN;
start = [start NaN]; stop = [stop NaN];
o = 1; % expand single-column arguments
ax = gca;
% set up the UserData data (here so not corrupted by log10's and such)
ud = [start stop len baseangle tipangle wid page crossdir ends];
% Get axes limits, range, min; correct for aspect ratio and log scale
axm = zeros(3,1); axr = axm; axrev = axm; ap = zeros(2,1);
xyzlog = axm; limmin = ap; limrange = ap; oldaxlims = zeros(1,7);
oneax = 1; % all(ax==ax(1)); LPM
if (oneax),
T = zeros(4,4); invT = zeros(4,4);
else
T = zeros(16,1); invT = zeros(16,1);
end
axnotdone = 1; % logical(ones(size(ax))); LPM
while (any(axnotdone))
ii = 1; % LPM min(find(axnotdone));
curax = ax(ii);
curpage = page(ii);
% get axes limits and aspect ratio
axl = [get(curax,'XLim'); get(curax,'YLim'); get(curax,'ZLim')];
oldaxlims(find(oldaxlims(:,1)==0, 1),:) = [curax reshape(axl',1,6)];
% get axes size in pixels (points)
u = get(curax,'Units');
axposoldunits = get(curax,'Position');
really_curpage = curpage & strcmp(u,'normalized');
if (really_curpage)
curfig = get(curax,'Parent'); pu = get(curfig,'PaperUnits');
set(curfig,'PaperUnits','points'); pp = get(curfig,'PaperPosition');
set(curfig,'PaperUnits',pu); set(curax,'Units','pixels');
curapscreen = get(curax,'Position'); set(curax,'Units','normalized');
curap = pp.*get(curax,'Position');
else
set(curax,'Units','pixels');
curapscreen = get(curax,'Position');
curap = curapscreen;
end
set(curax,'Units',u); set(curax,'Position',axposoldunits);
% handle non-stretched axes position
str_stretch = {'DataAspectRatioMode'; 'PlotBoxAspectRatioMode' ; 'CameraViewAngleMode' };
str_camera = {'CameraPositionMode' ; 'CameraTargetMode' ; ...
'CameraViewAngleMode' ; 'CameraUpVectorMode'};
notstretched = strcmp(get(curax,str_stretch),'manual');
manualcamera = strcmp(get(curax,str_camera),'manual');
if ~arrow_WarpToFill(notstretched,manualcamera,curax)
% find the true pixel size of the actual axes
texttmp = text(axl(1,[1 2 2 1 1 2 2 1]), ...
axl(2,[1 1 2 2 1 1 2 2]), axl(3,[1 1 1 1 2 2 2 2]),'');
set(texttmp,'Units','points');
textpos = get(texttmp,'Position');
delete(texttmp);
textpos = cat(1,textpos{:});
textpos = max(textpos(:,1:2)) - min(textpos(:,1:2));
% adjust the axes position
if (really_curpage) % adjust to printed size
textpos = textpos * min(curap(3:4)./textpos);
curap = [curap(1:2)+(curap(3:4)-textpos)/2 textpos];
else % adjust for pixel roundoff
textpos = textpos * min(curapscreen(3:4)./textpos);
curap = [curap(1:2)+(curap(3:4)-textpos)/2 textpos];
end
end
% adjust limits for log scale on axes
curxyzlog = [strcmp(get(curax,'XScale'),'log'); ...
strcmp(get(curax,'YScale'),'log'); strcmp(get(curax,'ZScale'),'log')];
if (any(curxyzlog))
ii = find([curxyzlog;curxyzlog]);
if (any(axl(ii)<=0))
error([upper(mfilename) ' does not support non-positive limits on log-scaled axes.']);
else
axl(ii) = log10(axl(ii));
end
end
% correct for 'reverse' direction on axes;
curreverse = [strcmp(get(curax,'XDir'),'reverse'); ...
strcmp(get(curax,'YDir'),'reverse'); strcmp(get(curax,'ZDir'),'reverse')];
ii = find(curreverse);
if ~isempty(ii)
axl(ii,[1 2])=-axl(ii,[2 1]);
end
% compute the range of 2-D values
curT = get(curax,'Xform');
lim = curT*[0 1 0 1 0 1 0 1;0 0 1 1 0 0 1 1;0 0 0 0 1 1 1 1;1 1 1 1 1 1 1 1];
lim = lim(1:2,:)./([1;1]*lim(4,:));
curlimmin = min(lim,[],2);
curlimrange = max(lim,[],2) - curlimmin;
curinvT = inv(curT);
if ~oneax
curT = curT.'; curinvT = curinvT.'; curT = curT(:); curinvT = curinvT(:);
end
% check which arrows to which cur corresponds
ii = find((ax==curax)&(page==curpage));
oo = ones(1,length(ii)); axr(:,ii) = diff(axl,1,2) * oo;
axm(:,ii) = axl(:,1) * oo; axrev(:,ii) = curreverse * oo;
ap(:,ii) = curap(3:4)' * oo; xyzlog(:,ii) = curxyzlog * oo;
limmin(:,ii) = curlimmin * oo; limrange(:,ii) = curlimrange * oo;
if (oneax),
T = curT; invT = curinvT;
else
T(:,ii) = curT * oo; invT(:,ii) = curinvT * oo;
end;
axnotdone(ii) = zeros(1,length(ii));
end;
oldaxlims(oldaxlims(:,1)==0,:) = [];
% correct for log scales
curxyzlog = xyzlog.'; ii = find(curxyzlog(:));
if ~isempty(ii)
start(ii) = real(log10(start(ii))); stop(ii) = real(log10(stop(ii)));
if (all(imag(crossdir)==0)) % pulled (ii) subscript on crossdir, 12/5/96 eaj
crossdir(ii) = real(log10(crossdir(ii)));
end
end
ii = find(axrev.'); % correct for reverse directions
if ~isempty(ii)
start(ii) = -start(ii); stop(ii) = -stop(ii); crossdir(ii) = -crossdir(ii);
end
start = start.'; stop = stop.'; % transpose start/stop values
% take care of defaults, page was done above
ii = find(isnan(start(:))); if ~isempty(ii), start(ii) = axm(ii)+axr(ii)/2; end;
ii = find(isnan(stop(:))); if ~isempty(ii), stop(ii) = axm(ii)+axr(ii)/2; end;
ii = find(isnan(crossdir(:))); if ~isempty(ii), crossdir(ii) = zeros(length(ii),1); end;
ii = find(isnan(len)); if ~isempty(ii), len(ii) = ones(length(ii),1)*deflen; end;
baseangle(ii) = ones(length(ii),1)*defbaseangle; tipangle(ii) = ones(length(ii),1)*deftipangle;
wid(ii) = ones(length(ii),1) * defwid; ends(ii) = ones(length(ii),1) * defends;
% transpose rest of values
len = len.'; baseangle = baseangle.'; tipangle = tipangle.'; wid = wid.';
page = page.'; crossdir = crossdir.'; ends = ends.'; ax = ax.';
% for all points with start==stop, start=stop-(verysmallvalue)*(up-direction);
ii = find(all(start==stop));
if ~isempty(ii)
% find an arrowdir vertical on screen and perpendicular to viewer
% transform to 2-D
tmp1 = [(stop(:,ii)-axm(:,ii))./axr(:,ii);ones(1,length(ii))];
if (oneax), twoD=T*tmp1;
else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=T(:,ii).*tmp1;
tmp2=zeros(4,4*length(ii)); tmp2(:)=tmp1(:);
twoD=zeros(4,length(ii)); twoD(:)=sum(tmp2)';
end
twoD=twoD./(ones(4,1)*twoD(4,:));
% move the start point down just slightly
tmp1 = twoD + [0;-1/1000;0;0]*(limrange(2,ii)./ap(2,ii));
% transform back to 3-D
if (oneax), threeD=invT*tmp1;
else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=invT(:,ii).*tmp1;
tmp2=zeros(4,4*length(ii)); tmp2(:)=tmp1(:);
threeD=zeros(4,length(ii)); threeD(:)=sum(tmp2)';
end
start(:,ii) = (threeD(1:3,:)./(ones(3,1)*threeD(4,:))).*axr(:,ii)+axm(:,ii);
end;
% compute along-arrow points
% transform Start points
tmp1 = [(start-axm)./axr; 1];
if (oneax), X0=T*tmp1;
else tmp1 = [tmp1;tmp1;tmp1;tmp1]; tmp1=T.*tmp1;
tmp2 = zeros(4,4); tmp2(:)=tmp1(:);
X0=zeros(4,1); X0(:)=sum(tmp2)';
end
X0=X0./(ones(4,1)*X0(4,:));
% transform Stop points
tmp1=[(stop-axm)./axr; 1];
if (oneax), Xf=T*tmp1;
else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=T.*tmp1;
tmp2=zeros(4,4); tmp2(:)=tmp1(:);
Xf=zeros(4,1); Xf(:)=sum(tmp2)';
end
Xf=Xf./(ones(4,1)*Xf(4,:));
% compute pixel distance between points
D = sqrt(sum(((Xf(1:2,:)-X0(1:2,:)).*(ap./limrange)).^2));
% compute and modify along-arrow distances
len1 = len;
len2 = len - (len.*tan(tipangle/180*pi)-wid/2).*tan((90-baseangle)/180*pi);
slen0 = 0; slen1 = len1 .* ((ends==2)|(ends==3));
slen2 = len2 .* ((ends==2)|(ends==3));
len0 = 0; len1 = len1 .* ((ends==1)|(ends==3));
len2 = len2 .* ((ends==1)|(ends==3));
ii = find((ends==1)&(D<len2)); % for no start arrowhead
if ~isempty(ii),
slen0(ii) = D(ii)-len2(ii);
end;
ii = find((ends==2)&(D<slen2)); % for no end arrowhead
if ~isempty(ii),
len0(ii) = D(ii)-slen2(ii);
end;
len1 = len1 + len0; len2 = len2 + len0;
slen1 = slen1 + slen0; slen2 = slen2 + slen0;
% note: the division by D below will probably not be accurate if both
% of the following are true:
% 1. the ratio of the line length to the arrowhead
% length is large
% 2. the view is highly perspective.
% compute stoppoints
tmp1 = X0.*(ones(4,1)*(len0./D))+Xf.*(ones(4,1)*(1-len0./D));
if (oneax), tmp3 = invT*tmp1;
else tmp1 = [tmp1;tmp1;tmp1;tmp1]; tmp1 = invT.*tmp1;
tmp2 = zeros(4,4); tmp2(:) = tmp1(:);
tmp3 = zeros(4,1); tmp3(:) = sum(tmp2)';
end
stoppoint = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)).*axr+axm;
% compute tippoints
tmp1=X0.*(ones(4,1)*(len1./D))+Xf.*(ones(4,1)*(1-len1./D));
if (oneax), tmp3=invT*tmp1;
else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=invT.*tmp1;
tmp2=zeros(4,4); tmp2(:)=tmp1(:);
tmp3=zeros(4,1); tmp3(:)=sum(tmp2)';
end
tippoint = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)).*axr+axm;
% compute basepoints
tmp1=X0.*(ones(4,1)*(len2./D))+Xf.*(ones(4,1)*(1-len2./D));
if (oneax), tmp3=invT*tmp1;
else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=invT.*tmp1;
tmp2=zeros(4,4); tmp2(:)=tmp1(:);
tmp3=zeros(4,1); tmp3(:)=sum(tmp2)';
end
basepoint = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)).*axr+axm;
% compute startpoints
tmp1=X0.*(ones(4,1)*(1-slen0./D))+Xf.*(ones(4,1)*(slen0./D));
if (oneax), tmp3=invT*tmp1;
else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=invT.*tmp1;
tmp2=zeros(4,4); tmp2(:) = tmp1(:);
tmp3=zeros(4,1); tmp3(:) = sum(tmp2)';
end
startpoint = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)).*axr+axm;
% compute stippoints
tmp1=X0.*(ones(4,1)*(1-slen1./D))+Xf.*(ones(4,1)*(slen1./D));
if (oneax), tmp3=invT*tmp1;
else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1 = invT.*tmp1;
tmp2=zeros(4,4); tmp2(:)=tmp1(:);
tmp3=zeros(4,1); tmp3(:)=sum(tmp2)';
end
stippoint = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)).*axr+axm;
% compute sbasepoints
tmp1=X0.*(ones(4,1)*(1-slen2./D))+Xf.*(ones(4,1)*(slen2./D));
if (oneax), tmp3=invT*tmp1;
else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=invT.*tmp1;
tmp2=zeros(4,4); tmp2(:)=tmp1(:);
tmp3=zeros(4,1); tmp3(:)=sum(tmp2)';
end
sbasepoint = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)).*axr+axm;
% compute cross-arrow directions for arrows with NormalDir specified
if (any(imag(crossdir(:))~=0)),
ii = find(any(imag(crossdir)~=0));
crossdir(:,ii) = cross((stop(:,ii)-start(:,ii))./axr(:,ii), ...
imag(crossdir(:,ii))).*axr(:,ii);
end;
basecross = crossdir + basepoint; % compute cross-arrow directions
tipcross = crossdir + tippoint; sbasecross = crossdir + sbasepoint;
stipcross = crossdir + stippoint;
ii = find(all(crossdir==0)|any(isnan(crossdir)));
if ~isempty(ii),
numii = length(ii);
% transform start points
tmp1 = [basepoint(:,ii) tippoint(:,ii) sbasepoint(:,ii) stippoint(:,ii)];
tmp1 = (tmp1-axm(:,[ii ii ii ii])) ./ axr(:,[ii ii ii ii]);
tmp1 = [tmp1; ones(1,4*numii)];
if (oneax), X0=T*tmp1;
else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=T(:,[ii ii ii ii]).*tmp1;
tmp2=zeros(4,16*numii); tmp2(:)=tmp1(:);
X0=zeros(4,4*numii); X0(:)=sum(tmp2)';
end
X0=X0./(ones(4,1)*X0(4,:));
% transform stop points
tmp1 = [(2*stop(:,ii)-start(:,ii)-axm(:,ii))./axr(:,ii);ones(1,numii)];
tmp1 = [tmp1 tmp1 tmp1 tmp1];
if (oneax) Xf=T*tmp1;
else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=T(:,[ii ii ii ii]).*tmp1;
tmp2=zeros(4,16*numii); tmp2(:)=tmp1(:);
Xf=zeros(4,4*numii); Xf(:)=sum(tmp2)';
end
Xf=Xf./(ones(4,1)*Xf(4,:));
% compute perpendicular directions
pixfact = ((limrange(1,ii)./limrange(2,ii)).*(ap(2,ii)./ap(1,ii))).^2;
pixfact = [pixfact pixfact pixfact pixfact];
pixfact = [pixfact;1./pixfact];
[dummyval,jj] = max(abs(Xf(1:2,:)-X0(1:2,:)));
jj1 = ((1:4)'*ones(1,length(jj))==ones(4,1)*jj);
jj2 = ((1:4)'*ones(1,length(jj))==ones(4,1)*(3-jj));
jj3 = jj1(1:2,:);
Xp = X0;
Xp(jj2) = X0(jj2) + ones(sum(jj2(:)),1);
Xp(jj1) = X0(jj1) - (Xf(jj2)-X0(jj2))./(Xf(jj1)-X0(jj1)) .* pixfact(jj3);
% inverse transform the cross points
if (oneax), Xp=invT*Xp;
else, tmp1=[Xp;Xp;Xp;Xp]; tmp1=invT(:,[ii ii ii ii]).*tmp1;
tmp2=zeros(4,16*numii); tmp2(:)=tmp1(:);
Xp=zeros(4,4*numii); Xp(:)=sum(tmp2)'; end;
Xp=(Xp(1:3,:)./(ones(3,1)*Xp(4,:))).*axr(:,[ii ii ii ii])+axm(:,[ii ii ii ii]);
basecross(:,ii) = Xp(:,0*numii+(1:numii));
tipcross(:,ii) = Xp(:,1*numii+(1:numii));
sbasecross(:,ii) = Xp(:,2*numii+(1:numii));
stipcross(:,ii) = Xp(:,3*numii+(1:numii));
end;
% compute all points
% compute start points
axm11 = [axm axm axm axm axm axm axm axm axm axm axm];
axr11 = [axr axr axr axr axr axr axr axr axr axr axr];
st = [stoppoint tippoint basepoint sbasepoint stippoint startpoint stippoint sbasepoint basepoint tippoint stoppoint];
tmp1 = (st - axm11) ./ axr11;
tmp1 = [tmp1; ones(1,size(tmp1,2))];
if (oneax), X0=T*tmp1;
else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=[T T T T T T T T T T T].*tmp1;
tmp2=zeros(4,44); tmp2(:)=tmp1(:);
X0=zeros(4,11); X0(:)=sum(tmp2)';
end
X0=X0./(ones(4,1)*X0(4,:));
% compute stop points
tmp1 = ([start tipcross basecross sbasecross stipcross stop stipcross sbasecross basecross tipcross start] ...
- axm11) ./ axr11;
tmp1 = [tmp1; ones(1,size(tmp1,2))];
if (oneax), Xf=T*tmp1;
else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=[T T T T T T T T T T T].*tmp1;
tmp2=zeros(4,44); tmp2(:)=tmp1(:);
Xf=zeros(4,11); Xf(:)=sum(tmp2)';
end
Xf=Xf./(ones(4,1)*Xf(4,:));
% compute lengths
len0 = len.*((ends==1)|(ends==3)).*tan(tipangle/180*pi);
slen0 = len.*((ends==2)|(ends==3)).*tan(tipangle/180*pi);
le = [0 len0 wid/2 wid/2 slen0 0 -slen0 -wid/2 -wid/2 -len0 0];
aprange = ap./limrange;
aprange = [aprange aprange aprange aprange aprange aprange aprange aprange aprange aprange aprange];
D = sqrt(sum(((Xf(1:2,:)-X0(1:2,:)).*aprange).^2));
Dii=find(D==0); if ~isempty(Dii), D=D+(D==0); le(Dii)=zeros(1,length(Dii)); end;
tmp1 = X0.*(ones(4,1)*(1-le./D)) + Xf.*(ones(4,1)*(le./D));
% inverse transform
if (oneax), tmp3=invT*tmp1;
else tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=[invT invT invT invT invT invT invT invT invT invT invT].*tmp1;
tmp2=zeros(4,44); tmp2(:)=tmp1(:);
tmp3=zeros(4,11); tmp3(:)=sum(tmp2)';
end
pts = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)) .* axr11 + axm11;
% correct for ones where the crossdir was specified
ii = find(~(all(crossdir==0)|any(isnan(crossdir))));
if ~isempty(ii),
D1 = [pts(:,1+ii)-pts(:,9+ii) pts(:,2+ii)-pts(:,8+ii) ...
pts(:,3+ii)-pts(:,7+ii) pts(:,4+ii)-pts(:,6+ii) ...
pts(:,6+ii)-pts(:,4+ii) pts(:,7+ii)-pts(:,3+ii) ...
pts(:,8+ii)-pts(:,2+ii) pts(:,9+ii)-pts(:,1+ii)]/2;
ii = ii'*ones(1,8) + ones(length(ii),1)*[1:4 6:9]; ii = ii(:)';
pts(:,ii) = st(:,ii) + D1;
end;
% readjust for reverse directions
iicols = (1:1)'; iicols = iicols(:,ones(1,11)); iicols = iicols(:).';
tmp1 = axrev(:,iicols);
ii = find(tmp1(:)); if ~isempty(ii), pts(ii)=-pts(ii); end;
% readjust for log scale on axes
tmp1 = xyzlog(:,iicols);
ii = find(tmp1(:)); if ~isempty(ii), pts(ii)=10.^pts(ii); end;
% compute the x,y,z coordinates of the patches;
ii = (0:10)' + ones(11,1);
ii = ii(:)';
x = zeros(11,1); y = x; z = x;
x(:) = pts(1,ii)'; y(:) = pts(2,ii)'; z(:) = pts(3,ii)';
% do the output
% % create or modify the patches
H = 0;
% % make or modify the arrows
if arrow_is2DXY(ax(1)), zz=[]; else zz=z(:,1); end;
xyz = {'XData',x(:,1),'YData',y(:,1),'ZData',zz,'Tag',ArrowTag};
H(1) = patch(xyz{:});
% % additional properties
set(H,'Clipping','off');
set(H,{'UserData'},num2cell(ud,2));
% make sure the axis limits did not change
function [out,is2D] = arrow_is2DXY(ax)
% check if axes are 2-D X-Y plots, may not work for modified camera angles, etc.
out = zeros(size(ax)); % 2-D X-Y plots
is2D = out; % any 2-D plots
views = get(ax(:),{'View'});
views = cat(1,views{:});
out(:) = abs(views(:,2))==90;
is2D(:) = out(:) | all(rem(views',90)==0)';
function out = arrow_WarpToFill(notstretched,manualcamera,curax) %#ok<INUSL>
% check if we are in "WarpToFill" mode.
out = strcmp(get(curax,'WarpToFill'),'on');
% 'WarpToFill' is undocumented, so may need to replace this by
% out = ~( any(notstretched) & any(manualcamera) );
|
github
|
bill-codes/netalign-master
|
netalignmr2.m
|
.m
|
netalign-master/experiments/crystalize_mr/netalignmr2.m
| 6,168 |
utf_8
|
4973769c6f25dc607067664b4d9444ae
|
function [xbest,status,hist] = netalignmr(S,w,a,b,li,lj,gamma,stepm,rtype,maxiter,verbose)
% NETALIGNMR Compute the matching relaxation heuristic for network alignment
%
% Given a network alignment problem, the matching heuristic solves a
% sequence of matching problems to generate good upper and lower bounds on
% the solutions.
%
% [xbest,status,hist] = netalignmr(S,w,a,b,li,lj,stepm,rtype,
% maxiter,verbose)
% fully specifies all inputs and outputs.
%
% Input
% -----
% S : the complete set of squares
% w : the matching weights for all edges in the link graph L
% a : the value of alpha in the netalign objective
% b : the value of beta in the netalign objective
% li : the start point of each edge in L (a vertex number from graph A)
% lj : the end point of each edge in L (a vertex number from graph B)
% gamma : the starting step value (default = 0.5)
% stepm : number of non-decreasing iterations adjusting the step length
% (default = 100)
% rtype : the rounding type (default = 1)
% rtype = 1 : only consider the current matching
% rtype = 2 : try enriching the matching with info from other squares
% maxiter : maximum number of iterations to take (default = 1000)
% verbose : output verbose information at each iteration (default = true)
%
% Output
% ------
% xbest : the best heuristic solution, it may or may not be a matching
% status : three values to describe the status of the solution
% status(1) = 1 if the problem is solved to optimality, 0 otherwise
% status(2) = best lower bound
% status(3) = best upper bound
% hist : the history of properties of each iteration
% hist(k,) =
% hist(k,) = best lower bound at iteration k
% hist(k,) = best upper bound at iteration k
% hist(k,) = current value at iteration k
% hist(k,) = matching weight at iteration k
% hist(k,) = matching cardinality at iteration k
% hist(k,) = overlap at iteration k
%
% Example:
% load('../data/natalie_graphs');
% netalignmr(S,w,0,1,li,lj);
% David F. Gleich, Ying Wang, and Mohsen Bayati
% Copyright, Stanford University, 2008-2009
% Computational Approaches to Digital Stewardship
% 2009-06-11: Initial coding (David and Ying)
% 2009-06-15: Cleanup and optimization (David)
if ~exist('a','var') || isempty(a), a=1; end
if ~exist('b','var') || isempty(b), b=1; end
if ~exist('stepm','var') || isempty(stepm), stepm=25; end
if ~exist('rtype','var') || isempty(rtype), rtype=1; end
if ~exist('maxiter', 'var') || isempty(maxiter), maxiter=1000; end
if ~exist('verbose', 'var') || isempty(verbose), verbose=1; end
if ~exist('gamma', 'var') || isempty(gamma), gamma = 0.4; end
m = max(li);
n = max(lj);
[rp ci ai tripi matn matm] = bipartite_matching_setup(w,li,lj,m,n);
mperm = tripi(tripi>0); % a permutation for the matching problem
S = double(S);
U = sparse(size(S,1),size(S,2));
xbest = w;
xbest(:) = 0;
flower = 0; % best lower bound on the solution
fupper = Inf; % best upper bound on the solution
next_reduction_iteration = stepm; % reduce the step
crystalize_phase = 0; % a variable to indicate we are in the crystalization phase
crystalize_gamma = 1e-20;
crystalize_stepm = 5;
hist = zeros(maxiter,7);
if verbose % print the header
fprintf('%5s %4s %8s %7s %7s %7s %7s %7s %7s %7s\n', ...
'best', 'iter', 'norm-u', 'lower','upper', 'cur', 'obj', 'weight', 'card', 'overlap');
end
for iter = 1:maxiter
[q,SM] = maxrowmatch((b/2)*S + U-U',li,lj,m,n);
x = a*w + q;
%[val ma mb mi]= bipartite_matching(nw,li,lj,m,n);
ai=zeros(length(tripi),1); ai(tripi>0)=x(mperm);
[val ma mb mi]= bipartite_matching_primal_dual(rp,ci,ai,tripi,matn,matm);
% compute statistics
matchval = mi'*w;
overlap = mi'*S*mi/2;
card = length(ma);
f = a*matchval + b*overlap;
if val<fupper
fupper=val;
if ~crystalize_phase
next_reduction_iteration = iter+stepm;
end
end
if f>flower
flower=f;
itermark = '*';
xbest = mi;
else
itermark = ' ';
end
if rtype==1
% no work
elseif rtype==2
mw = S*x;
mw = a*w + b/2*mw;
ai=zeros(length(tripi),1); ai(tripi>0)=mw(mperm);
[val ma mb mx] = bipartite_matching_primal_dual(rp,ci,ai,tripi,matn,matm);
card = length(ma);
matchval = mx'*w;
overlap = mx'*S*mx/2;
f = a*matchval + b*overlap;
if f>flower
flower=f;
itermark = '**';
mi = mx;
xbest = mw;
end
end
% report on current iter
hist(iter,1:end) = [norm(nonzeros(U),1), flower, fupper, f, matchval, card, overlap];
if verbose
fprintf('%5s %4i %8.1e %7g %7g %7g %7g %7g %7i %7i\n', ...
itermark, iter, norm(nonzeros(U),1), ...
flower, fupper, val, ...
f, matchval, card, overlap);
end
if iter==next_reduction_iteration
gamma = gamma*0.5;
if verbose
fprintf('%5s %4s reducing step to %g\n', '', '', gamma);
end
if gamma < 1e-24, break; end
if crystalize_phase
next_reduction_iteration = iter+crystalize_stepm;
else
next_reduction_iteration = iter+stepm;
end
end
if (fupper-flower)<1e-2
break;
end
% check if we need to enter the crystalization phase
num_crystalize_iters = crystalize_stepm*(log(crystalize_gamma)-log(gamma))/log(0.5);
if ~crystalize_phase && num_crystalize_iters > maxiter - iter
crystalize_phase = 1;
next_reduction_iteration = iter+crystalize_stepm;
fprintf('%5s %4s switching to crystalization phase\n', '', '');
end
U = U - diag(sparse(gamma*mi))*triu(SM) + tril(SM)'*diag(sparse(gamma*mi));
U = bound(U, -.5, .5);
end
hist = hist(1:iter,:);
status = zeros(1,3);
status(1) = (fupper-flower)<1e-2;
status(2) = flower;
status(3) = fupper;
function S=bound(S,a,b)
S=spfun(@(x) min(max(x,a),b), S);
|
github
|
bill-codes/netalign-master
|
netalignbp_y.m
|
.m
|
netalign-master/experiments/old/rounding/netalignbp_y.m
| 6,447 |
utf_8
|
60b9cf0f094a4f2449195b5ec82a82d9
|
function [mbest hista histb] = netalignbp(S,w,a,b,li,lj,gamma,maxiter,verbose)
% NETALIGNBP Solve the network alignment problem with Belief Propagation
%
%
% David F. Gleich, Ying Wang, and Mohsen Bayati
% Copyright, Stanford University, 2007-2008
% Computational Approaches to Digital Stewardship
if ~exist('a','var') || isempty(a), a=1; end
if ~exist('b','var') || isempty(b), b=1; end
if ~exist('gamma','var') || isempty(gamma), gamma=0.85; end
if ~exist('maxiter', 'var') || isempty(maxiter), maxiter=100; end
if ~exist('verbose', 'var') || isempty(verbose), verbose=1; end
nedges = length(li);
nsquares = nnz(S)/2;
m = max(li);
n = max(lj);
% compute a vector that allows us to transpose data between squares.
% Recall the BP algorithm requires edge(i,j) to send messages to edge(r,s)
% when (i,j) and (r,s) form a square. However, edge(i,j) needs information
% the information that (r,s) sent it from the previous iteraton.
% If we imagine that each non-zero of S has a value, we want to tranpose
% these values.
[sui suj] = find(triu(S,1)); % only consider each square once.
SI = sparse(sui,suj,1:length(sui),size(S,1),size(S,2)); % assign indices
SI = SI + SI'; % SI now has symmetric indices
[si sj sind] = find(SI);
SP = sparse(si,sind,true,size(S,1),nsquares); % each column in SP has 2 nz
[sij sijrs] = find(SP);
sind = (1:nnz(SP))';
spair = sind; spair(1:2:end) = sind(2:2:end); spair(2:2:end) = sind(1:2:end);
% sij, sijrs maps between rows and squares
% spair is now an indexing vector that accomplishes what we need
% Initialize the messages
ma = zeros(nedges,1);
mb = ma;
ms = zeros(nnz(S),1);
damping = gamma;
curdamp = 1;
iter = 1;
alpha = a;
beta = b;
% Initialize history
hista = zeros(maxiter,4); % history of messages from ei->a vertices
histb = zeros(maxiter,4); % history of messages from ei->b vertices
fbest = 0; fbestiter = 0;
if verbose % print the header
fprintf('%4s %4s %7s %7s %7s %7s %7s %7s %7s %7s\n', ...
'best', 'iter', 'obj_ma', 'wght_ma', 'card_ma', 'over_ma', ...
'obj_mb', 'wght_mb', 'card_mb', 'over_mb');
end
% setup the matching problem once
[rp ci ai tripi matn matm] = bipartite_matching_setup(...
w,li,lj,m,n);
clear ai;
while iter<=maxiter
prevma = ma;
prevmb = mb;
prevms = ms;
curdamp = damping*curdamp;
omaxb = othermaxplus(2,li,lj,mb,m,n);
omaxa = othermaxplus(1,li,lj,ma,m,n);
msflip = ms(spair); % swap ij->ijrs to rs->ijrs
mymsflip = msflip+beta;
mymsflip = min(beta,mymsflip);
mymsflip = max(0,mymsflip);
sums = accumarray(sij,mymsflip,[nedges 1],@sum,0);
ma = alpha*w - omaxb + sums;
mb = alpha*w - omaxa + sums;
ms = alpha*w(sij)-(omaxb(sij) + omaxa(sij));
ms = ms + othersum(sij,sijrs,mymsflip,nedges,nsquares);
ma = curdamp*(ma) + (1-curdamp)*(prevma);
mb = curdamp*(mb) + (1-curdamp)*(prevmb);
ms = curdamp*(ms) + (1-curdamp)*(prevms);
% now compute the matchings
sums1 = accumarray(sij,mymsflip,[nedges 1],@sum,0);
sums2 = accumarray(sij,mymsflip,[nedges 1],@sum,0);
hista(iter,:) = round_messages(alpha*w + beta*sums1,S,w,alpha,beta,rp,ci,tripi,matn,matm);
histb(iter,:) = round_messages(alpha*w + beta*sums2,S,w,alpha,beta,rp,ci,tripi,matn,matm);
if hista(iter,1)>fbest
fbestiter=iter; mbest=sums1; fbest=hista(iter,1);
end
if histb(iter,1)>fbest
fbestiter=-iter; mbest=sums2; fbest=histb(iter,1);
end
if verbose
if fbestiter==iter, bestchar='*a';
elseif fbestiter==-iter, bestchar='*b';
else bestchar='';
end
fprintf('%4s %4i %7g %7g %7i %7i %7g %7g %7i %7i\n', ...
bestchar, iter, hista(iter,:), histb(iter,:));
end
iter=iter+1;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function info=round_messages(messages,S,w,alpha,beta,rp,ci,tripi,n,m)
ai=zeros(length(tripi),1); ai(tripi>0)=messages;
[val ma mb mi]= bipartite_matching_primal_dual(rp,ci,ai,tripi,n,m);
matchweight = sum(w(mi)); cardinality = sum(mi); overlap = (mi'*(S*mi))/2;
f = alpha*matchweight + beta*overlap;
info = [f matchweight cardinality overlap];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function omp=othermaxplus(dim,li,lj,lw,m,n)
% OTHERMAXPLUS Apply the other-max-plus operator to a sparse matrix
%
% The other-max-plus operator applies the max-plus aggegration function
% over the rows or columns of the matrix, where, for
% each non-zeros, the non-zero itself cannot be the maximum. Consequently,
% Hence, it's the max-plus of all the "other" elements in the row or column
% (which is controlled by dim).
%
% omp=othermaxplus(dim,li,lj,lw,m,n) applies the other-max-plus operator
% to the matrix sparse(li, lj, lw, m, n). The return value omp gives
% the other-max-plus value for each non-zero element, e.g. the matrix
% is sparse(li, lj, omp, m, n).
if dim==1
% max-plus over cols
i1 = lj;
i2 = li;
N = n;
else
% max-plus over rows
i1 = li;
i2 = lj;
N = m;
end
% the output of the other-max-plus is either the maximum element
% in the row or column (if the element itself isn't the maximum) or
% the second largest element in the row or column (if the element itself
% IS the maximum).
dimmax1 =0*ones(N,1); % largest value
dimmax2 = 0*ones(N,1); % second largest value,
% this is correct because of the definition of the max-plus function.
dimmaxind = zeros(N,1); % index of largest value
nedges = length(li);
for i=1:nedges
if lw(i) > dimmax2(i1(i))
if lw(i) > dimmax1(i1(i))
dimmax2(i1(i)) = dimmax1(i1(i));
dimmax1(i1(i)) = lw(i);
dimmaxind(i1(i)) = i2(i);
else
dimmax2(i1(i)) = lw(i);
end
end
end
omp = zeros(size(lw));
for i=1:nedges
if i2(i) == dimmaxind(i1(i))
omp(i) = dimmax2(i1(i));
else
omp(i) = dimmax1(i1(i));
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function os=othersum(si,sj,s,m,n) %#ok<INUSD,INUSL>
% OTHERSUM Compute the sum of each column of the matrix, without each
% individual entry. This corresponds to a matrix where each entry is the
% sum of each column but with each entry subtracted.
rowsum=accumarray(si,s,[m,1]);
os=rowsum(si)-s;
|
github
|
bill-codes/netalign-master
|
netalignbp_yz.m
|
.m
|
netalign-master/experiments/old/rounding/netalignbp_yz.m
| 6,465 |
utf_8
|
3bd7d5f146af758410916577c9f26650
|
function [mbest hista histb] = netalignbp(S,w,a,b,li,lj,gamma,maxiter,verbose)
% NETALIGNBP Solve the network alignment problem with Belief Propagation
%
%
% David F. Gleich, Ying Wang, and Mohsen Bayati
% Copyright, Stanford University, 2007-2008
% Computational Approaches to Digital Stewardship
if ~exist('a','var') || isempty(a), a=1; end
if ~exist('b','var') || isempty(b), b=1; end
if ~exist('gamma','var') || isempty(gamma), gamma=0.85; end
if ~exist('maxiter', 'var') || isempty(maxiter), maxiter=100; end
if ~exist('verbose', 'var') || isempty(verbose), verbose=1; end
nedges = length(li);
nsquares = nnz(S)/2;
m = max(li);
n = max(lj);
% compute a vector that allows us to transpose data between squares.
% Recall the BP algorithm requires edge(i,j) to send messages to edge(r,s)
% when (i,j) and (r,s) form a square. However, edge(i,j) needs information
% the information that (r,s) sent it from the previous iteraton.
% If we imagine that each non-zero of S has a value, we want to tranpose
% these values.
[sui suj] = find(triu(S,1)); % only consider each square once.
SI = sparse(sui,suj,1:length(sui),size(S,1),size(S,2)); % assign indices
SI = SI + SI'; % SI now has symmetric indices
[si sj sind] = find(SI);
SP = sparse(si,sind,true,size(S,1),nsquares); % each column in SP has 2 nz
[sij sijrs] = find(SP);
sind = (1:nnz(SP))';
spair = sind; spair(1:2:end) = sind(2:2:end); spair(2:2:end) = sind(1:2:end);
% sij, sijrs maps between rows and squares
% spair is now an indexing vector that accomplishes what we need
% Initialize the messages
ma = zeros(nedges,1);
mb = ma;
ms = zeros(nnz(S),1);
damping = gamma;
curdamp = 1;
iter = 1;
alpha = a;
beta = b;
% Initialize history
hista = zeros(maxiter,4); % history of messages from ei->a vertices
histb = zeros(maxiter,4); % history of messages from ei->b vertices
fbest = 0; fbestiter = 0;
if verbose % print the header
fprintf('%4s %4s %7s %7s %7s %7s %7s %7s %7s %7s\n', ...
'best', 'iter', 'obj_ma', 'wght_ma', 'card_ma', 'over_ma', ...
'obj_mb', 'wght_mb', 'card_mb', 'over_mb');
end
% setup the matching problem once
[rp ci ai tripi matn matm] = bipartite_matching_setup(...
w,li,lj,m,n);
clear ai;
while iter<=maxiter
prevma = ma;
prevmb = mb;
prevms = ms;
curdamp = damping*curdamp;
omaxb = othermaxplus(2,li,lj,mb,m,n);
omaxa = othermaxplus(1,li,lj,ma,m,n);
msflip = ms(spair); % swap ij->ijrs to rs->ijrs
mymsflip = msflip+beta;
mymsflip = min(beta,mymsflip);
mymsflip = max(0,mymsflip);
sums = accumarray(sij,mymsflip,[nedges 1],@sum,0);
ma = alpha*w - omaxb + sums;
mb = alpha*w - omaxa + sums;
ms = alpha*w(sij)-(omaxb(sij) + omaxa(sij));
ms = ms + othersum(sij,sijrs,mymsflip,nedges,nsquares);
ma = curdamp*(ma) + (1-curdamp)*(prevma);
mb = curdamp*(mb) + (1-curdamp)*(prevmb);
ms = curdamp*(ms) + (1-curdamp)*(prevms);
% now compute the matchings
sums1 = accumarray(sij,mymsflip.*ma(sij),[nedges 1],@sum,0);
sums2 = accumarray(sij,mymsflip.*ma(sij),[nedges 1],@sum,0);
hista(iter,:) = round_messages(alpha*w + beta*sums1,S,w,alpha,beta,rp,ci,tripi,matn,matm);
histb(iter,:) = round_messages(alpha*w + beta*sums2,S,w,alpha,beta,rp,ci,tripi,matn,matm);
if hista(iter,1)>fbest
fbestiter=iter; mbest=sums1; fbest=hista(iter,1);
end
if histb(iter,1)>fbest
fbestiter=-iter; mbest=sums2; fbest=histb(iter,1);
end
if verbose
if fbestiter==iter, bestchar='*a';
elseif fbestiter==-iter, bestchar='*b';
else bestchar='';
end
fprintf('%4s %4i %7g %7g %7i %7i %7g %7g %7i %7i\n', ...
bestchar, iter, hista(iter,:), histb(iter,:));
end
iter=iter+1;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function info=round_messages(messages,S,w,alpha,beta,rp,ci,tripi,n,m)
ai=zeros(length(tripi),1); ai(tripi>0)=messages;
[val ma mb mi]= bipartite_matching_primal_dual(rp,ci,ai,tripi,n,m);
matchweight = sum(w(mi)); cardinality = sum(mi); overlap = (mi'*(S*mi))/2;
f = alpha*matchweight + beta*overlap;
info = [f matchweight cardinality overlap];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function omp=othermaxplus(dim,li,lj,lw,m,n)
% OTHERMAXPLUS Apply the other-max-plus operator to a sparse matrix
%
% The other-max-plus operator applies the max-plus aggegration function
% over the rows or columns of the matrix, where, for
% each non-zeros, the non-zero itself cannot be the maximum. Consequently,
% Hence, it's the max-plus of all the "other" elements in the row or column
% (which is controlled by dim).
%
% omp=othermaxplus(dim,li,lj,lw,m,n) applies the other-max-plus operator
% to the matrix sparse(li, lj, lw, m, n). The return value omp gives
% the other-max-plus value for each non-zero element, e.g. the matrix
% is sparse(li, lj, omp, m, n).
if dim==1
% max-plus over cols
i1 = lj;
i2 = li;
N = n;
else
% max-plus over rows
i1 = li;
i2 = lj;
N = m;
end
% the output of the other-max-plus is either the maximum element
% in the row or column (if the element itself isn't the maximum) or
% the second largest element in the row or column (if the element itself
% IS the maximum).
dimmax1 =0*ones(N,1); % largest value
dimmax2 = 0*ones(N,1); % second largest value,
% this is correct because of the definition of the max-plus function.
dimmaxind = zeros(N,1); % index of largest value
nedges = length(li);
for i=1:nedges
if lw(i) > dimmax2(i1(i))
if lw(i) > dimmax1(i1(i))
dimmax2(i1(i)) = dimmax1(i1(i));
dimmax1(i1(i)) = lw(i);
dimmaxind(i1(i)) = i2(i);
else
dimmax2(i1(i)) = lw(i);
end
end
end
omp = zeros(size(lw));
for i=1:nedges
if i2(i) == dimmaxind(i1(i))
omp(i) = dimmax2(i1(i));
else
omp(i) = dimmax1(i1(i));
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function os=othersum(si,sj,s,m,n) %#ok<INUSD,INUSL>
% OTHERSUM Compute the sum of each column of the matrix, without each
% individual entry. This corresponds to a matrix where each entry is the
% sum of each column but with each entry subtracted.
rowsum=accumarray(si,s,[m,1]);
os=rowsum(si)-s;
|
github
|
bill-codes/netalign-master
|
netalignmr.m
|
.m
|
netalign-master/matlab/netalignmr.m
| 5,472 |
utf_8
|
3bba17d17e4d620f961d10dc0048c844
|
function [xbest,status,hist] = netalignmr(S,w,a,b,li,lj,gamma,stepm,rtype,maxiter,verbose)
% NETALIGNMR Compute the matching relaxation heuristic for network alignment
%
% Given a network alignment problem, the matching heuristic solves a
% sequence of matching problems to generate good upper and lower bounds on
% the solutions.
%
% [xbest,status,hist] = netalignmr(S,w,a,b,li,lj,stepm,rtype,
% maxiter,verbose)
% fully specifies all inputs and outputs.
%
% Input
% -----
% S : the complete set of squares
% w : the matching weights for all edges in the link graph L
% a : the value of alpha in the netalign objective
% b : the value of beta in the netalign objective
% li : the start point of each edge in L (a vertex number from graph A)
% lj : the end point of each edge in L (a vertex number from graph B)
% gamma : the starting step value (default = 0.5)
% stepm : number of non-decreasing iterations adjusting the step length
% (default = 100)
% rtype : the rounding type (default = 1)
% rtype = 1 : only consider the current matching
% rtype = 2 : try enriching the matching with info from other squares
% maxiter : maximum number of iterations to take (default = 1000)
% verbose : output verbose information at each iteration (default = true)
%
% Output
% ------
% xbest : the best heuristic solution, it may or may not be a matching
% status : three values to describe the status of the solution
% status(1) = 1 if the problem is solved to optimality, 0 otherwise
% status(2) = best lower bound
% status(3) = best upper bound
% hist : the history of properties of each iteration
% hist(k,) =
% hist(k,) = best lower bound at iteration k
% hist(k,) = best upper bound at iteration k
% hist(k,) = current value at iteration k
% hist(k,) = matching weight at iteration k
% hist(k,) = matching cardinality at iteration k
% hist(k,) = overlap at iteration k
%
% Example:
% load('../data/natalie_graphs');
% netalignmr(S,w,0,1,li,lj);
% David F. Gleich, Ying Wang, and Mohsen Bayati
% Copyright, Stanford University, 2008-2009
% Computational Approaches to Digital Stewardship
% 2009-06-11: Initial coding (David and Ying)
% 2009-06-15: Cleanup and optimization (David)
if ~exist('a','var') || isempty(a), a=1; end
if ~exist('b','var') || isempty(b), b=1; end
if ~exist('stepm','var') || isempty(stepm), stepm=25; end
if ~exist('rtype','var') || isempty(rtype), rtype=1; end
if ~exist('maxiter', 'var') || isempty(maxiter), maxiter=1000; end
if ~exist('verbose', 'var') || isempty(verbose), verbose=1; end
if ~exist('gamma', 'var') || isempty(gamma), gamma = 0.4; end
m = max(li);
n = max(lj);
[rp ci ai tripi matn matm] = bipartite_matching_setup(w,li,lj,m,n);
mperm = tripi(tripi>0); % a permutation for the matching problem
S = double(S);
U = sparse(size(S,1),size(S,2));
xbest = w;
xbest(:) = 0;
flower = 0; % best lower bound on the solution
fupper = Inf; % best upper bound on the solution
next_reduction_iteration = stepm; % reduce the step
hist = zeros(maxiter,7);
if verbose % print the header
fprintf('%5s %4s %8s %7s %7s %7s %7s %7s %7s %7s\n', ...
'best', 'iter', 'norm-u', 'lower','upper', 'cur', 'obj', 'weight', 'card', 'overlap');
end
for iter = 1:maxiter
[q,SM] = maxrowmatch((b/2)*S + U-U',li,lj,m,n);
x = a*w + q;
%[val ma mb mi]= bipartite_matching(nw,li,lj,m,n);
ai=zeros(length(tripi),1); ai(tripi>0)=x(mperm);
[val ma mb mi]= bipartite_matching_primal_dual(rp,ci,ai,tripi,matn,matm);
% compute statistics
matchval = mi'*w;
overlap = mi'*S*mi/2;
card = length(ma);
f = a*matchval + b*overlap;
if val<fupper
fupper=val;
next_reduction_iteration = iter+stepm;
end
if f>flower
flower=f;
itermark = '*';
xbest = mi;
else
itermark = ' ';
end
if rtype==1
% no work
elseif rtype==2
mw = S*x;
mw = a*w + b/2*mw;
ai=zeros(length(tripi),1); ai(tripi>0)=mw(mperm);
[val ma mb mx] = bipartite_matching_primal_dual(rp,ci,ai,tripi,matn,matm);
card = length(ma);
matchval = mx'*w;
overlap = mx'*S*mx/2;
f = a*matchval + b*overlap;
if f>flower
flower=f;
itermark = '**';
mi = mx;
xbest = mw;
end
end
% report on current iter
hist(iter,1:end) = [norm(nonzeros(U),1), flower, fupper, f, matchval, card, overlap];
if verbose
fprintf('%5s %4i %8.1e %7g %7g %7g %7g %7g %7i %7i\n', ...
itermark, iter, norm(nonzeros(U),1), ...
flower, fupper, val, ...
f, matchval, card, overlap);
end
if iter==next_reduction_iteration
gamma = gamma*0.5;
if verbose
fprintf('%5s %4s reducing step to %g\n', '', '', gamma);
end
if gamma < 1e-24, break; end
next_reduction_iteration = iter+stepm;
end
if (fupper-flower)<1e-2
break;
end
U = U - diag(sparse(gamma*mi))*triu(SM) + tril(SM)'*diag(sparse(gamma*mi));
U = bound(U, -.5, .5);
end
hist = hist(1:iter,:);
status = zeros(1,3);
status(1) = (fupper-flower)<1e-2;
status(2) = flower;
status(3) = fupper;
function S=bound(S,a,b)
S=spfun(@(x) min(max(x,a),b), S);
|
github
|
bill-codes/netalign-master
|
netalignbp.m
|
.m
|
netalign-master/matlab/netalignbp.m
| 7,067 |
utf_8
|
366425d10c4e61e2ec0d1782c9ac8c30
|
function [mbest hista histb] = netalignbp(S,w,a,b,li,lj,gamma,dtype,maxiter,verbose)
% NETALIGNBP Solve the network alignment problem with Belief Propagation
%
%
% David F. Gleich, Ying Wang, and Mohsen Bayati
% Copyright, Stanford University, 2007-2009
% Computational Approaches to Digital Stewardship
% History
% 2009-06-02: Implemented Mohsen's new updates to get a higher overlap
if ~exist('a','var') || isempty(a), a=1; end
if ~exist('b','var') || isempty(b), b=1; end
if ~exist('gamma','var') || isempty(gamma), gamma=0.99; end
if ~exist('dtype', 'var') || isempty(dtype), dtype=2; end
if ~exist('maxiter', 'var') || isempty(maxiter), maxiter=100; end
if ~exist('verbose', 'var') || isempty(verbose), verbose=1; end
nedges = length(li);
nsquares = nnz(S)/2;
m = max(li);
n = max(lj);
% compute a vector that allows us to transpose data between squares.
% Recall the BP algorithm requires edge(i,j) to send messages to edge(r,s)
% when (i,j) and (r,s) form a square. However, edge(i,j) needs information
% the information that (r,s) sent it from the previous iteraton.
% If we imagine that each non-zero of S has a value, we want to tranpose
% these values.
[sui suj] = find(triu(S,1)); % only consider each square once.
SI = sparse(sui,suj,1:length(sui),size(S,1),size(S,2)); % assign indices
SI = SI + SI'; % SI now has symmetric indices
[si sj sind] = find(SI);
SP = sparse(si,sind,true,size(S,1),nsquares); % each column in SP has 2 nz
[sij sijrs] = find(SP);
sind = (1:nnz(SP))';
spair = sind; spair(1:2:end) = sind(2:2:end); spair(2:2:end) = sind(1:2:end);
% sij, sijrs maps between rows and squares
% spair is now an indexing vector that accomplishes what we need
% Initialize the messages
ma = zeros(nedges,1);
mb = ma;
ms = zeros(nnz(S),1);
sums = zeros(nedges,1);
damping = gamma;
curdamp = 1;
iter = 1;
alpha = a;
beta = b;
% Initialize history
hista = zeros(maxiter,4); % history of messages from ei->a vertices
histb = zeros(maxiter,4); % history of messages from ei->b vertices
fbest = 0; fbestiter = 0;
if verbose % print the header
fprintf('%4s %4s %7s %7s %7s %7s %7s %7s %7s %7s\n', ...
'best', 'iter', 'obj_ma', 'wght_ma', 'card_ma', 'over_ma', ...
'obj_mb', 'wght_mb', 'card_mb', 'over_mb');
end
% setup the matching problem once
[rp ci ai tripi matn matm] = bipartite_matching_setup(...
w,li,lj,m,n);
mperm = tripi(tripi>0); % a permutation for the matching problem
clear ai;
while iter<=maxiter
prevma = ma;
prevmb = mb;
prevms = ms;
prevsums = sums;
curdamp = damping*curdamp;
omaxb = max(othermaxplus(2,li,lj,mb,m,n),0);
omaxa = max(othermaxplus(1,li,lj,ma,m,n),0);
msflip = ms(spair); % swap ij->ijrs to rs->ijrs
mymsflip = msflip+beta;
mymsflip = min(beta,mymsflip);
mymsflip = max(0,mymsflip);
sums = accumarray(sij,mymsflip,[nedges 1],@sum,0);
ma = alpha*w - omaxb + sums;
mb = alpha*w - omaxa + sums;
ms = alpha*w(sij)-(omaxb(sij) + omaxa(sij));
ms = ms + othersum(sij,sijrs,mymsflip,nedges,nsquares);
% Original updates
if dtype==1
ma = curdamp*(ma) + (1-curdamp)*(prevma);
mb = curdamp*(mb) + (1-curdamp)*(prevmb);
ms = curdamp*(ms) + (1-curdamp)*(prevms);
elseif dtype==2
ma = ma + (1-curdamp)*(prevma+prevmb-alpha*w+prevsums);
mb = mb + (1-curdamp)*(prevmb+prevma-alpha*w+prevsums);
ms = ms + (1-curdamp)*(prevms+prevms(spair)-beta);
elseif dtype==3
ma = curdamp*ma + (1-curdamp)*(prevma+prevmb-alpha*w+prevsums);
mb = curdamp*mb + (1-curdamp)*(prevmb+prevma-alpha*w+prevsums);
ms = curdamp*ms + (1-curdamp)*(prevms+prevms(spair)-beta);
end
% now compute the matchings
hista(iter,:) = round_messages(ma,S,w,alpha,beta,rp,ci,tripi,matn,matm,mperm);
histb(iter,:) = round_messages(mb,S,w,alpha,beta,rp,ci,tripi,matn,matm,mperm);
if hista(iter,1)>fbest
fbestiter=iter; mbest=ma; fbest=hista(iter,1);
end
if histb(iter,1)>fbest
fbestiter=-iter; mbest=mb; fbest=histb(iter,1);
end
if verbose
if fbestiter==iter, bestchar='*a';
elseif fbestiter==-iter, bestchar='*b';
else bestchar='';
end
fprintf('%4s %4i %7g %7g %7i %7i %7g %7g %7i %7i\n', ...
bestchar, iter, hista(iter,:), histb(iter,:));
end
iter=iter+1;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function info=round_messages(messages,S,w,alpha,beta,rp,ci,tripi,n,m,perm)
ai=zeros(length(tripi),1); ai(tripi>0)=messages(perm);
[val ma mb mi]= bipartite_matching_primal_dual(rp,ci,ai,tripi,n,m);
matchweight = sum(w(mi)); cardinality = sum(mi); overlap = (mi'*(S*double(mi)))/2;
f = alpha*matchweight + beta*overlap;
info = [f matchweight cardinality overlap];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function omp=othermaxplus(dim,li,lj,lw,m,n)
% OTHERMAXPLUS Apply the other-max-plus operator to a sparse matrix
%
% The other-max-plus operator applies the max-plus aggegration function
% over the rows or columns of the matrix, where, for
% each non-zeros, the non-zero itself cannot be the maximum. Consequently,
% Hence, it's the max-plus of all the "other" elements in the row or column
% (which is controlled by dim).
%
% omp=othermaxplus(dim,li,lj,lw,m,n) applies the other-max-plus operator
% to the matrix sparse(li, lj, lw, m, n). The return value omp gives
% the other-max-plus value for each non-zero element, e.g. the matrix
% is sparse(li, lj, omp, m, n).
if dim==1
% max-plus over cols
i1 = lj;
i2 = li;
N = n;
else
% max-plus over rows
i1 = li;
i2 = lj;
N = m;
end
% the output of the other-max-plus is either the maximum element
% in the row or column (if the element itself isn't the maximum) or
% the second largest element in the row or column (if the element itself
% IS the maximum).
dimmax1 =0*ones(N,1); % largest value
dimmax2 = 0*ones(N,1); % second largest value,
% this is correct because of the definition of the max-plus function.
dimmaxind = zeros(N,1); % index of largest value
nedges = length(li);
for i=1:nedges
if lw(i) > dimmax2(i1(i))
if lw(i) > dimmax1(i1(i))
dimmax2(i1(i)) = dimmax1(i1(i));
dimmax1(i1(i)) = lw(i);
dimmaxind(i1(i)) = i2(i);
else
dimmax2(i1(i)) = lw(i);
end
end
end
omp = zeros(size(lw));
for i=1:nedges
if i2(i) == dimmaxind(i1(i))
omp(i) = dimmax2(i1(i));
else
omp(i) = dimmax1(i1(i));
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function os=othersum(si,sj,s,m,n) %#ok<INUSD,INUSL>
% OTHERSUM Compute the sum of each column of the matrix, without each
% individual entry. This corresponds to a matrix where each entry is the
% sum of each column but with each entry subtracted.
rowsum=accumarray(si,s,[m,1]);
os=rowsum(si)-s;
|
github
|
bill-codes/netalign-master
|
evaluate_alignment.m
|
.m
|
netalign-master/matlab/evaluate_alignment.m
| 4,797 |
utf_8
|
11f4dfb2cdec144b885fe302325fedc0
|
function [s Mcc]=evaluate_alignment(A,B,mi,li,lj)
% EVALUATE_ALIGNMENT Report properties of the graph alignment
%
% evaluate_alignment(A,B,mi,li,lj) returns properties of an alignment
% between graphs A and B.
% evaluate_alignment(A,B,ma,mb) returns properties of an alignment
% between graphs A and B.
%
% Edges - number of edges (2-cliques) preserved by the mapping
% Triangles - number of triangles (3-cliques) preserved by the mapping
% Largest Component - largest connected component with the mapping
%
% History
% 2009-07-01: Initial coding
if nargin==4
ma = mi;
mb = li;
% given as pairs
X = sparse(ma,mb,1,size(A,1),size(B,1));
else
% given as an indicator
% make sure x is binary
if any(logical(mi)~=mi)
error('netalign:invalidMatching',...
'the matching indicator mi is not binary')
end
X = sparse(li,lj,mi,size(A,1),size(B,1));
end
% make sure x is a matching
s1 = sum(X,1);
s2 = sum(X,2);
if any(s1~=logical(s1)) || any(s2~=logical(s2))
error('netalign:invalidMatching',...
'the given alignment is not a matching');
end
[rpA ciA] = sparse_to_csr(A);
[rpB ciB] = sparse_to_csr(B);
As = struct('rp',rpA,'ci',ciA);
Bs = struct('rp',rpB,'ci',ciB);
nA = length(rpA)-1;
nB = length(rpB)-1;
[ma mb] = find(X);
% the overlapped graph
[nedges,OA,OB]=count_overlap(As,Bs,[ma mb]);
G = [OA X; X' OB];
[ci,sizes] = scomponents(G);
[ma mb] = find(X);
% create the output as a structure
s = [];
s.size = length(ma);
[s.largest_component,max_ci] = max(sizes);
s.edges = count_overlap(As,Bs,[ma mb]);
s.triangles = count_triangle_overlap(As,Bs,[ma mb]);
s.largest_component_A = sum(ci(1:nA)==max_ci);
s.largest_component_B = sum(ci(nA+1:end)==max_ci);
Mcc = sparse(find(ci(1:nA)==max_ci), find(ci(nA+1:end)==max_ci), 1, nA, nB);
% display output unless requested
if nargout == 0
fprintf('%25s %i\n', 'Size', s.size);
fprintf('%25s %i\n', 'Edge Overlap', s.edges);
fprintf('%25s %i\n', 'Triangle Overlap', s.triangles);
fprintf('%25s %i\n', 'Largest Component', s.largest_component);
fprintf('%25s %i\n', 'Largest Component (A)', s.largest_component_A);
fprintf('%25s %i\n', 'Largest Component (B)', s.largest_component_B);
end
function [ntris]=count_triangle_overlap(A,B,m)
if ~isstruct(A)
[rpA ciA] = sparse_to_csr(A);
else
rpA = A.rp;
ciA = A.ci;
end
if ~isstruct(B)
[rpB ciB] = sparse_to_csr(B);
else
rpB = B.rp;
ciB = B.ci;
end
nA = length(rpA)-1;
nB = length(rpB)-1;
% index the matches in a to b
mAB = zeros(nA,1);
mAB(m(:,1)) = m(:,2);
mBA = zeros(nB,1);
mBA(m(:,2)) = m(:,1);
ntris = 0;
nmatches = size(m,1);
aindex = zeros(nA,1);
bindex = zeros(nB,1); % bindex is a work vector
for i=1:nmatches
% count the number of triangles from this match
va = m(i,1);
vb = m(i,2);
% index the neighbors in A
for riA=rpA(va):rpA(va+1)-1
if ciA(riA)==va, continue; end % skip self loops
aindex(ciA(riA))=1;
end
% index the edges in B
for riB=rpB(vb):rpB(vb+1)-1
if ciB(riB)==vb, continue; end % skip self loops
bindex(ciB(riB))=1;
end
% for all the edges in A, see if there is an edge in B too
for riA=rpA(va):rpA(va+1)-1
% va2 is the neighbor of va
va2 = ciA(riA);
% skip self-loops
if va2 == va, continue; end
va2image = mAB(va2); % get the image
if va2image
if bindex(va2image)
% let's see if we can complete a triangle in A from
% this edge
for riA2=rpA(va2):rpA(va2+1)-1
va3 = ciA(riA2);
if va3 == va2, continue; end % skip self-loops
if aindex(va3)
% va,va2,va3 is a triangle and va,va2 is in b
% so check if (va,va3) and (va2,va3) are
% preserved by the mapping
va3image = mAB(va3);
if va3image && bindex(va3image)
% (va,va3) is present, check (va2,va3)
for riB=rpB(va2image):rpB(va2image+1)-1
b3 = ciB(riB);
if mBA(b3)==va3
ntris = ntris + 1;
end
end
end
end
end
end
end
end
% clear the index of edges in A
for riA=rpA(va):rpA(va+1)-1
aindex(ciA(riA))=0;
end
% clear the index of edges in B
for riB=rpB(vb):rpB(vb+1)-1
bindex(ciB(riB))=0;
end
end
ntris = ntris/6;
|
github
|
bill-codes/netalign-master
|
netalign_lagrange.m
|
.m
|
netalign-master/matlab/netalign_lagrange.m
| 3,194 |
utf_8
|
c002a2f87068662095111cbc700b4541
|
function [xbest fupper hist] = netalign_lagrange(S,w,a,b,li,lj,stepm,rtype,maxiter,verbose)
% NETALIGN_LAGRANGE Solve the network alignment problem
% with Lagrangean relaxation
%
% David F. Gleich, Ying Wang, and Mohsen Bayati
% Copyright, Stanford University, 2008-2009
% Computational Approaches to Digital Stewardship
if ~exist('a','var') || isempty(a), a=1; end
if ~exist('b','var') || isempty(b), b=1; end
if ~exist('stepm','var') || isempty(stepm), stepm=100; end
if ~exist('rtype','var') || isempty(rtype), rtype=1; end
if ~exist('maxiter', 'var') || isempty(maxiter), maxiter=1000; end
if ~exist('verbose', 'var') || isempty(verbose), verbose=1; end
m = max(li);
n = max(lj);
[rp ci ai tripi matn matm] = bipartite_matching_setup(w,li,lj,m,n);
mperm = tripi(tripi>0); % a permutation for the matching problem
S = triu(S);
S = double(S);
U = sparse(size(S,1),size(S,2));
xbest = w;
xbest(:) = 0;
flower = 0;
fupper = Inf;
next_reduction_iteration = stepm; % reduce the step
gamma = 1;
hist = zeros(maxiter,7);
if verbose % print the header
fprintf('%5s %4s %8s %7s %7s %7s %7s %7s %7s\n', ...
'best', 'iter', 'norm-u', 'lower','upper', 'obj', 'weight', 'card', 'overlap');
end
for iter = 1:maxiter
nw = a * w + b * (sum(max(0, .5 * S + U), 2) + sum(max(0, .5 * S - U),1)');
%[val ma mb mi]= bipartite_matching(nw,li,lj,m,n);
ai=zeros(length(tripi),1); ai(tripi>0)=nw(mperm);
[val ma mb mi]= bipartite_matching_primal_dual(rp,ci,ai,tripi,matn,matm);
% compute statistics
matchval = mi'*w;
overlap = mi'*S*mi; % no divide by 2 is okay, because S = triu(S) :-).
card = length(ma);
f = a*matchval + b*overlap;
if val<fupper
fupper=val;
next_reduction_iteration = iter+stepm;
end
if f>flower
flower=f;
itermark = '*';
xbest = mi;
else
itermark = ' ';
end
if rtype==1
% no work
elseif rtype==2
mw = S*mi;
mw = mw + S'*mi;
mw = a*w + b/2*mw;
ai=zeros(length(tripi),1); ai(tripi>0)=mw(mperm);
[val ma mb mx] = bipartite_matching_primal_dual(rp,ci,ai,tripi,matn,matm);
card = length(ma);
matchval = mx'*w;
overlap = mx'*S*mx;
f = a*matchval + b*overlap;
if f>flower
flower=f;
itermark = '**';
mi = mx;
xbest = mw;
end
end
% report on current iter
hist(iter,1:end) = [norm(nonzeros(U),1), flower, fupper, f, matchval, card, overlap];
if verbose
fprintf('%5s %4i %8.1e %7g %7g %7g %7g %7i %7i\n', ...
itermark, iter, norm(nonzeros(U),1), ...
flower, fupper, ...
f, matchval, card, overlap);
end
if iter==next_reduction_iteration
gamma = gamma*0.5;
if verbose
fprintf('%5s %4s reducing step to %g\n', '', '', gamma);
end
next_reduction_iteration = iter+stepm;
end
U = bound(U - (diag(sparse(mi)) * S - S * diag(sparse(mi))) * gamma, -.5, .5);
end
function S=bound(S,a,b)
S=spfun(@(x) min(max(x,a),b), S);
|
github
|
bill-codes/netalign-master
|
netalignbpmr.m
|
.m
|
netalign-master/matlab/netalignbpmr.m
| 4,762 |
utf_8
|
7286539d79a120310bfc2d322d442ccc
|
function [mbest hista histb] = netalignbpmr(S,w,a,b,li,lj,gamma,dtype,maxiter,verbose)
% NETALIGNMBP Solve the network alignment problem with Belief Propagation
%
% This version of network alignment uses the matrix formulation of the
% algorithm.
% David F. Gleich, Ying Wang, and Mohsen Bayati
% Copyright, Stanford University, 2007-2009
% Computational Approaches to Digital Stewardship
if ~exist('a','var') || isempty(a), a=1; end
if ~exist('b','var') || isempty(b), b=1; end
if ~exist('gamma','var') || isempty(gamma), gamma=0.99; end
if ~exist('dtype', 'var') || isempty(dtype), dtype=2; end
if ~exist('maxiter', 'var') || isempty(maxiter), maxiter=100; end
if ~exist('verbose', 'var') || isempty(verbose), verbose=1; end
nedges = length(li); nsquares = nnz(S)/2; m = max(li); n = max(lj);
% the following is elegant, but inefficient :-(
%Ar = sparse(li,1:nedges,1,m,nedges); [ari arj arv]=find(Ar'*Ar-speye(nedges));
%Ac = sparse(lj,1:nedges,1,n,nedges); [aci acj acv]=find(Ac'*Ac-speye(nedges));
% Initialize the messages
y = zeros(nedges,1); z = y; Sk = 0*S;
if dtype>1, d = y; end % needed for damping scheme
% Initialize a few parameters
damping = gamma; curdamp = 1; iter = 1;
% Initialize history
hista = zeros(maxiter,4); % history of messages from ei->a vertices
histb = zeros(maxiter,4); % history of messages from ei->b vertices
fbest = 0; fbestiter = 0;
if verbose % print the header
fprintf('%4s %4s %7s %7s %7s %7s %7s %7s %7s %7s\n', ...
'best', 'iter', 'obj_ma', 'wght_ma', 'card_ma', 'over_ma', ...
'obj_mb', 'wght_mb', 'card_mb', 'over_mb');
end
% setup the matching problem once
[rp ci ai tripi matn matm] = bipartite_matching_setup(...
w,li,lj,m,n);
mperm = tripi(tripi>0);
while iter<=maxiter
curdamp = damping*curdamp;
%Sknew = bound(Sk' + b*S,0,b);
if dtype>1, dold=d; end
[d,SM] = maxrowmatch(Sk' + b*S,li,lj,matn,matm);
SM = bound(SM,0,b);
ynew = a*w - max(0,implicit_maxprod(m,li,z)) + d;
znew = a*w - max(0,implicit_maxprod(n,lj,y)) + d;
% NOTE in comparison netalignbp, othersum isn't needed because othersum
% of Sknew = d*S - Sknew
Skt = diag(sparse(ynew+znew-a*w-d))*S-SM;
if dtype==1
Sk = curdamp*Skt+ (1-curdamp)*Sk;
y = curdamp*ynew+(1-curdamp)*y;
z = curdamp*znew+(1-curdamp)*z;
elseif dtype==2
prev = (y+z-a*w+dold);
y = ynew+(1-curdamp)*prev;
z = znew+(1-curdamp)*prev;
clear prev;
Sk = Skt + (1-curdamp)*(Sk+Sk'-b*S);
elseif dtype==3
prev = (y+z-a*w+dold);
y = curdamp*ynew+(1-curdamp)*prev;
z = curdamp*znew+(1-curdamp)*prev;
clear prev;
Sk = curdamp*Skt + (1-curdamp)*(Sk+Sk'-b*S);
end
hista(iter,:) = round_messages(y,S,w,a,b,rp,ci,tripi,matn,matm,mperm);
histb(iter,:) = round_messages(z,S,w,a,b,rp,ci,tripi,matn,matm,mperm);
if hista(iter,1)>fbest
fbestiter=iter; mbest=y; fbest=hista(iter,1);
end
if histb(iter,1)>fbest
fbestiter=-iter; mbest=z; fbest=histb(iter,1);
end
if verbose
if fbestiter==iter, bestchar='*a';
elseif fbestiter==-iter, bestchar='*b';
else bestchar='';
end
fprintf('%4s %4i %7g %7g %7i %7i %7g %7g %7i %7i\n', ...
bestchar, iter, hista(iter,:), histb(iter,:));
end
iter=iter+1;
end
end
function S=bound(S,a,b)
S=spfun(@(x) min(b,max(x,a)),S);
end
function y=maxprod(ai,aj,av,m,x)
y=-inf*ones(m,1);
for i=1:numel(ai), y(ai(i)) = max(y(ai(i)),av(i)*x(aj(i))); end
end
function y=implicit_maxprod(n,ai,x)
% implicitly compute
% Ai=sparse(ai,1:length(ai),1,n,length(z))
% A=Ar'*Ar - speye(length(z))
% maxprod(A,x)
% which has a very nice structure if you look at the actual summation
% definition. It is just the maximum value
N = length(ai);
y=-inf*ones(N,1);
max1 = -inf*ones(n,1);
max2 = -inf*ones(n,1);
max1ind = zeros(n,1);
for i=1:N
if x(i)>max2(ai(i))
if x(i)>max1(ai(i))
max2(ai(i)) = max1(ai(i));
max1(ai(i)) = x(i);
max1ind(ai(i)) = i;
else
max2(ai(i)) = x(i);
end
end
end
for i=1:N
if i==max1ind(ai(i))
y(i)=max2(ai(i));
else
y(i)=max1(ai(i));
end
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function info=round_messages(messages,S,w,alpha,beta,rp,ci,tripi,n,m,perm)
ai=zeros(length(tripi),1); ai(tripi>0)=messages(perm);
[val ma mb mi]= bipartite_matching_primal_dual(rp,ci,ai,tripi,n,m);
matchweight = sum(w(mi)); cardinality = sum(mi); overlap = (mi'*(S*double(mi)))/2;
f = alpha*matchweight + beta*overlap;
info = [f matchweight cardinality overlap];
end
|
github
|
bill-codes/netalign-master
|
netalignscbp.m
|
.m
|
netalign-master/matlab/netalignscbp.m
| 7,240 |
utf_8
|
4d86fc6727c6040b65240c5e0079a468
|
function [mbest hista histb Sk] = netalignscbp(S,w,a,b,li,lj,gamma,dtype,maxiter,verbose)
% NETALIGNMPSC Solve the network alignment problem with message passing
%
% This version of network alignment uses the matrix formulation of the
% algorithm.
% David F. Gleich, Ying Wang, and Mohsen Bayati
% Copyright, Stanford University, 2010
% Computational Approaches to Digital Stewardship
if ~exist('a','var') || isempty(a), a=1; end
if ~exist('b','var') || isempty(b), b=1; end
if ~exist('gamma','var') || isempty(gamma), gamma=0.99; end
if ~exist('dtype', 'var') || isempty(dtype), dtype=2; end
if ~exist('maxiter', 'var') || isempty(maxiter), maxiter=100; end
if ~exist('verbose', 'var') || isempty(verbose), verbose=1; end
nedges = length(li); nsquares = nnz(S); m = max(li); n = max(lj);
% Initialize the messages
y = zeros(nedges,1);
z = y; % copy z as the same size
Sk = zeros(nsquares, 1); % messages and their "reversed/transposed" copies
Skt = zeros(nsquares, 1);
Sw = b * ones(nsquares, 1);
MSc = b * ones(nsquares, 4); MScnew = zeros(nsquares, 4); MP = zeros(nsquares, 4);
if dtype>1, d = y; end % needed for damping scheme
% Initialize a few parameters
damping = gamma; curdamp = 1; iter = 1;
% Initialize history
hista = zeros(maxiter,4); % history of messages from ei->a vertices
histb = zeros(maxiter,4); % history of messages from ei->b vertices
fbest = 0; fbestiter = 0;
if verbose % print the header
fprintf('%4s %4s %7s %7s %7s %7s %7s %7s %7s %7s\n', ...
'best', 'iter', 'obj_ma', 'wght_ma', 'card_ma', 'over_ma', ...
'obj_mb', 'wght_mb', 'card_mb', 'over_mb');
end
% setup the matching problem once
[rp ci ai tripi matn matm] = bipartite_matching_setup(...
w,li,lj,m,n);
mperm = tripi(tripi>0);
[rpS ciS] = sparse_to_csr(S);
riS = zeros(nsquares, 1);
for i=1:nedges
for j=rpS(i):rpS(i+1)-1
riS(j) = i;
end
end
Stmp = sparse(riS, ciS, [1:nsquares]');
[ti tj transmap] = find(Stmp);
SC = findSC(S, li, lj);
r = 1;
while iter<=maxiter
curdamp = damping*curdamp;
if dtype>1, dold=d; end
x = Sw;
% updates for M_{ii'->f_i} and M_{ii'->g_{i'}}, y, z
d = zeros(nedges, 1);
Sk_tran = Sk(transmap);
for i=1:nedges
for j=rpS(i):rpS(i+1)-1
% d(i) = d(i) + max(0, b + Sk_tran(j)) - max(0, Sk_tran(j));
d(i) = d(i) + max(0, (r + 1) * Sw(j) + Sk_tran(j)) - max(0, r * Sw(j) + Sk_tran(j));
end
end
ynew = a*w - max(0,implicit_maxprod(m,li,z)) + d;
znew = a*w - max(0,implicit_maxprod(n,lj,y)) + d;
% updates for M_{ii'->h_{ii'jj'}}, Sk
t = 0;
for i=1:nedges
t = ynew(i) + znew(i) - a*w(i) - d(i);
for j=rpS(i):rpS(i+1)-1
% Skt(j) = t - max(0, b + Sk_tran(j)) + max(0, Sk_tran(j));
Skt(j) = t - max(0, (r + 1) * Sw(j) + Sk_tran(j)) - max(0, r * Sw(j) + Sk_tran(j));
end
end
St = Sk + Sk(transmap) + Sw;
%updates for M_{ii'jj'->h_{ii'jj'}}, Sw
for i=1:4
MP(:,i) = max(0, implicit_maxprod(max(SC(:,i)), SC(:,i), MSc(:, i)));
end
Sw = -(MP(:,1) + MP(:,2) + MP(:,3) + MP(:,4)) + b;
%updates for M_{ii'jj'->d_{ii'j}}, MSc
tmp = Sk + Sk(transmap);
for i=1:4
MSc(:, i) = Sw + MP(:, i) + min(tmp, min(Sk, Sk(transmap)));;
end
if dtype==1
Sk = curdamp*Skt+ (1-curdamp)*Sk;
y = curdamp*ynew+(1-curdamp)*y;
z = curdamp*znew+(1-curdamp)*z;
elseif dtype==2
prev = (y+z-a*w+dold);
y = ynew+(1-curdamp)*prev;
z = znew+(1-curdamp)*prev;
clear prev;
Sk = Skt + (1-curdamp)*St;
Sw = Sw + (1-curdamp)*St;
for i=1:4
MSc(:, i) = MSc(:, i) + (1-curdamp)*St;
end
elseif dtype==3
prev = (y+z-a*w+dold);
y = curdamp*ynew+(1-curdamp)*prev;
z = curdamp*znew+(1-curdamp)*prev;
clear prev;
Sk = curdamp * Skt + (1-curdamp)*St;
Sw = curdamp * Sw + (1-curdamp)*St;
for i=1:4
MSc(:, i) = curdamp * MSc(:, i) + (1-curdamp)*St;
end
end
%tot = roundbyS(Sk, li, lj, riS, ciS, m, n);
%tot2 = roundbyyz(S, y, z, li, lj, m, n);
iter=iter+1;
hista(iter,:) = round_messages(y,S,w,a,b,rp,ci,tripi,matn,matm,mperm);
histb(iter,:) = round_messages(z,S,w,a,b,rp,ci,tripi,matn,matm,mperm);
if hista(iter,1)>fbest
fbestiter=iter; mbest=y; fbest=hista(iter,1);
end
if histb(iter,1)>fbest
fbestiter=-iter; mbest=z; fbest=histb(iter,1);
end
%fbest = max(fbest, tot);
%fbest = max(fbest, tot2);
if verbose
if fbestiter==iter, bestchar='*a';
elseif fbestiter==-iter, bestchar='*b';
else bestchar='';
end
fprintf('%4s %4i %7g %7g %7i %7i %7g %7g %7i %7i\n', ...
bestchar, iter, hista(iter,:), histb(iter,:));
end
end
end
function S=bound(S,a,b)
S=spfun(@(x) max(b+x,a)-max(x,a),S);
end
function y=maxprod(ai,aj,av,m,x)
y=-inf*ones(m,1);
for i=1:numel(ai), y(ai(i)) = max(y(ai(i)),av(i)*x(aj(i))); end
end
function y=implicit_maxprod(n,ai,x)
% implicitly compute
% Ai=sparse(ai,1:length(ai),1,n,length(z))
% A=Ar'*Ar - speye(length(z))
% maxprod(A,x)
% which has a very nice structure if you look at the actual summation
% definition. It is just the maximum value
N = length(ai);
y=-inf*ones(N,1);
max1 = -inf*ones(n,1);
max2 = -inf*ones(n,1);
max1ind = zeros(n,1);
for i=1:N
if x(i)>max2(ai(i))
if x(i)>max1(ai(i))
max2(ai(i)) = max1(ai(i));
max1(ai(i)) = x(i);
max1ind(ai(i)) = i;
else
max2(ai(i)) = x(i);
end
end
end
for i=1:N
if i==max1ind(ai(i))
y(i)=max2(ai(i));
else
y(i)=max1(ai(i));
end
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function info=round_messages(messages,S,w,alpha,beta,rp,ci,tripi,n,m,perm)
ai=zeros(length(tripi),1); ai(tripi>0)=messages(perm);
[val ma mb mi]= bipartite_matching_primal_dual(rp,ci,ai,tripi,n,m);
matchweight = sum(w(mi)); cardinality = sum(mi); overlap = (mi'*(S*double(mi)))/2;
f = alpha*matchweight + beta*overlap;
info = [f matchweight cardinality overlap];
end
function tot=roundbyS(Sk, li, lj, ri, ci, m, n)
[v ind] = sort(Sk, 'descend');
markA = zeros(m, 1);
markB = zeros(n, 1);
chosen = zeros(size(li, 1), 1);
tot = 0;
for i=1:size(Sk, 1)
v1 = ri(ind(i));
v2 = ci(ind(i));
if (v1 > v2)
continue;
end
if ((~chosen(v1) && (markA(li(v1)) || markB(lj(v1)))) || (~chosen(v2) && (markA(li(v2)) || markB(lj(v2)))))
continue;
end
tot = tot + 1;
chosen(v1) = 1;
chosen(v2) = 1;
markA(li(v1)) = 1;
markA(li(v2)) = 1;
markB(lj(v1)) = 1;
markB(lj(v2)) = 1;
end
end
function tot=roundbyyz(S, y, z, li, lj, m, n)
[v ind] = sort(y+z, 'descend');
markA = zeros(m, 1);
markB = zeros(n, 1);
x = zeros(size(y));
for i=1:size(ind,1)
if (~markA(li(ind(i))) && ~markB(lj(ind(i))))
x(ind(i)) = 1;
markA(li(ind(i))) = 1;
markB(lj(ind(i))) = 1;
end
end
tot = x' * S * x / 2;
end
|
github
|
bill-codes/netalign-master
|
expand_match.m
|
.m
|
netalign-master/matlab/expand_match.m
| 2,500 |
utf_8
|
f69e78b513c268f81a604be27667dd91
|
function L = expand_match(A,B,L)
% EXPAND_MATCH Expand a set of possible matchings with breadth first search
%
% L2 = expand_match(A,B,L) returns a new sparse match matrix L2 for the
% network alignment problem where L2 includes the neighbors of all existing
% matchings.
%
%
% David F. Gleich and Ying Wang
% Copyright, Stanford University, 2009
% TODO Example
% History
% 2009-05-29 Initial coding, based on old c code
nA = size(A,1);
[rpB ciB vB] = sparse_to_csr(B);
[lai laj lav] = compute_expanded_match(nA,rpB,ciB,vB,L);
clear rpB ciB vB
nB = size(B,1);
[rpA ciA vA] = sparse_to_csr(A);
[lbj lbi lbv] = compute_expanded_match(nB,rpA,ciA,vA,L');
clear rpA ciA vA
lai = [lai; lbi];
laj = [laj; lbj];
lav = [lav; lbv];
clear lbi lbj lbv
L = sparse(lai, laj, lav, nA, nB);
function [l2i l2j l2v]=compute_expanded_match(nA,rpB,ciB,vB,L)
% just do one step of bfs and expand the current match
% allocate edges
l2i = zeros(nnz(L),1);
l2j = l2i;
l2v = l2i;
curedge = 0;
% convert to CSR
[rpAB ciAB vAB] = sparse_to_csr(L);
nB = length(rpB)-1;
% allocate temp arrays
work = zeros(nB,1);
work_used = zeros(nB,1);
dwork = zeros(nB,1);
for i=1:nA
num_adj = 0;
for ri=rpAB(i):rpAB(i+1)-1
j = ciAB(ri);
deg_in_B = rpB(j+1)-rpB(j);
for ri2=rpB(j):rpB(j+1)-1
k = ciB(ri2);
if work(k) == 0
dwork(num_adj+1) = vAB(ri)/deg_in_B;
work_used(num_adj+1) = k;
work(k) = num_adj+1;
num_adj = num_adj + 1;
else
dwork(work(k)) = max( dwork(work(k)), vAB(ri)/deg_in_B );
end
end
% make sure we include j
if work(j) == 0
dwork(num_adj+1) = vAB(ri);
work_used(num_adj+1) = j;
work(j) = num_adj+1;
num_adj = num_adj + 1;
else
dwork(work(k)) = max( dwork(work(k)), vAB(ri) );
end
end
% copy the data and reset the arrays
% first check if there is enough space
while curedge + num_adj > length(l2i)
% double the arrays
l2i = [l2i; l2i]; l2j = [l2j; l2j]; l2v = [l2v; l2v]; %#ok<AGROW>
end
for j=1:num_adj
curedge = curedge + 1;
l2i(curedge) = i;
l2j(curedge) = work_used(j);
l2v(curedge) = dwork(j);
work(work_used(j)) = 0;
work_used(j) = 0;
dwork(j) = 0;
end
end
% resize at the end
l2i = l2i(1:curedge);
l2j = l2j(1:curedge);
l2v = l2v(1:curedge);
|
github
|
bill-codes/netalign-master
|
netalignmbp.m
|
.m
|
netalign-master/matlab/netalignmbp.m
| 4,716 |
utf_8
|
e294c5801bc3cd3fa0ee4deed6698ebf
|
function [mbest hista histb] = netalignmbp(S,w,a,b,li,lj,gamma,dtype,maxiter,verbose)
% NETALIGNMBP Solve the network alignment problem with Belief Propagation
%
% This version of network alignment uses the matrix formulation of the
% algorithm.
% David F. Gleich, Ying Wang, and Mohsen Bayati
% Copyright, Stanford University, 2007-2009
% Computational Approaches to Digital Stewardship
if ~exist('a','var') || isempty(a), a=1; end
if ~exist('b','var') || isempty(b), b=1; end
if ~exist('gamma','var') || isempty(gamma), gamma=0.99; end
if ~exist('dtype', 'var') || isempty(dtype), dtype=2; end
if ~exist('maxiter', 'var') || isempty(maxiter), maxiter=100; end
if ~exist('verbose', 'var') || isempty(verbose), verbose=1; end
nedges = length(li); nsquares = nnz(S)/2; m = max(li); n = max(lj);
% the following is elegant, but inefficient :-(
%Ar = sparse(li,1:nedges,1,m,nedges); [ari arj arv]=find(Ar'*Ar-speye(nedges));
%Ac = sparse(lj,1:nedges,1,n,nedges); [aci acj acv]=find(Ac'*Ac-speye(nedges));
% Initialize the messages
y = zeros(nedges,1); z = y; Sk = 0*S;
if dtype>1, d = y; end % needed for damping scheme
% Initialize a few parameters
damping = gamma; curdamp = 1; iter = 1;
% Initialize history
hista = zeros(maxiter,4); % history of messages from ei->a vertices
histb = zeros(maxiter,4); % history of messages from ei->b vertices
fbest = 0; fbestiter = 0;
if verbose % print the header
fprintf('%4s %4s %7s %7s %7s %7s %7s %7s %7s %7s\n', ...
'best', 'iter', 'obj_ma', 'wght_ma', 'card_ma', 'over_ma', ...
'obj_mb', 'wght_mb', 'card_mb', 'over_mb');
end
% setup the matching problem once
[rp ci ai tripi matn matm] = bipartite_matching_setup(...
w,li,lj,m,n);
mperm = tripi(tripi>0);
while iter<=maxiter
curdamp = damping*curdamp;
Sknew = bound(Sk' + b*S,0,b);
if dtype>1, dold=d; end
d = sum(Sknew,2);
ynew = a*w - max(0,implicit_maxprod(n,lj,z)) + d;
znew = a*w - max(0,implicit_maxprod(m,li,y)) + d;
% NOTE in comparison netalignbp, othersum isn't needed because othersum
% of Sknew = d*S - Sknew
Skt = diag(sparse(ynew+znew-a*w-d))*S-Sknew;
if dtype==1
Sk = curdamp*Skt+ (1-curdamp)*Sk;
y = curdamp*ynew+(1-curdamp)*y;
z = curdamp*znew+(1-curdamp)*z;
elseif dtype==2
prev = (y+z-a*w+dold);
y = ynew+(1-curdamp)*prev;
z = znew+(1-curdamp)*prev;
clear prev;
Sk = Skt + (1-curdamp)*(Sk+Sk'-b*S);
elseif dtype==3
prev = (y+z-a*w+dold);
y = curdamp*ynew+(1-curdamp)*prev;
z = curdamp*znew+(1-curdamp)*prev;
clear prev;
Sk = curdamp*Skt + (1-curdamp)*(Sk+Sk'-b*S);
end
hista(iter,:) = round_messages(y,S,w,a,b,rp,ci,tripi,matn,matm,mperm);
histb(iter,:) = round_messages(z,S,w,a,b,rp,ci,tripi,matn,matm,mperm);
if hista(iter,1)>fbest
fbestiter=iter; mbest=y; fbest=hista(iter,1);
end
if histb(iter,1)>fbest
fbestiter=-iter; mbest=z; fbest=histb(iter,1);
end
if verbose
if fbestiter==iter, bestchar='*a';
elseif fbestiter==-iter, bestchar='*b';
else bestchar='';
end
fprintf('%4s %4i %7g %7g %7i %7i %7g %7g %7i %7i\n', ...
bestchar, iter, hista(iter,:), histb(iter,:));
end
iter=iter+1;
end
end
function S=bound(S,a,b)
S=spfun(@(x) max(x+b,0) - max(x+a,0),S);
end
function y=maxprod(ai,aj,av,m,x)
y=-inf*ones(m,1);
for i=1:numel(ai), y(ai(i)) = max(y(ai(i)),av(i)*x(aj(i))); end
end
function y=implicit_maxprod(n,ai,x)
% implicitly compute
% Ai=sparse(ai,1:length(ai),1,n,length(z))
% A=Ar'*Ar - speye(length(z))
% maxprod(A,x)
% which has a very nice structure if you look at the actual summation
% definition. It is just the maximum value
N = length(ai);
y=-inf*ones(N,1);
max1 = -inf*ones(n,1);
max2 = -inf*ones(n,1);
max1ind = zeros(n,1);
for i=1:N
if x(i)>max2(ai(i))
if x(i)>max1(ai(i))
max2(ai(i)) = max1(ai(i));
max1(ai(i)) = x(i);
max1ind(ai(i)) = i;
else
max2(ai(i)) = x(i);
end
end
end
for i=1:N
if i==max1ind(ai(i))
y(i)=max2(ai(i));
else
y(i)=max1(ai(i));
end
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function info=round_messages(messages,S,w,alpha,beta,rp,ci,tripi,n,m,perm)
ai=zeros(length(tripi),1); ai(tripi>0)=messages(perm);
[val ma mb mi]= bipartite_matching_primal_dual(rp,ci,ai,tripi,n,m);
matchweight = sum(w(mi)); cardinality = sum(mi); overlap = (mi'*(S*double(mi)))/2;
f = alpha*matchweight + beta*overlap;
info = [f matchweight cardinality overlap];
end
|
github
|
snipsco/kaldi-master
|
Generate_mcTrainData_cut.m
|
.m
|
kaldi-master/egs/reverb/s5/local/Generate_mcTrainData_cut.m
| 7,311 |
utf_8
|
f59dd892f0f8da04a515a2c58ff50a69
|
function Generate_mcTrainData_cut(WSJ_dir_name, save_dir)
%
% Input variables:
% WSJ_dir_name: string name of user's clean wsjcam0 corpus directory
% (*Directory structure for wsjcam0 corpushas to be kept as it is after obtaining it from LDC.
% Otherwise this script does not work.)
%
% This function generates multi-condition traiing data
% based on the following items:
% 1. wsjcam0 corpus (distributed from the LDC)
% 2. room impulse responses (ones under ./RIR/)
% 3. noise (ones under ./NOISE/).
% Generated data has the same directory structure as original wsjcam0 corpus.
%
if nargin<2
error('Usage: Generate_mcTrainData(WSJCAM0_data_path, save_dir) *Note that the input variable WSJCAM0_data_path should indicate the directory name of your clean WSJCAM0 corpus. ');
end
if exist([WSJ_dir_name,'/data/'])==0
error(['Could not find wsjcam0 corpus : Please confirm if ',WSJ_dir_name,' is a correct path to your clean WSJCAM0 corpus']);
end
if ~exist('save_dir', 'var')
error('You have to set the save_dir variable in the code before running this script!')
end
display(['Name of directory for original wsjcam0: ',WSJ_dir_name])
display(['Name of directory to save generated multi-condition training data: ',save_dir])
unix(['chmod u+x sphere_to_wave.csh']);
unix(['chmod u+x bin/*']);
% Parameters related to acoustic conditions
SNRdB=20;
% List of WSJ speech data
flist1='etc/audio_si_tr.lst';
%
% List of RIRs
%
num_RIRvar=24;
RIR_sim1='./RIR/RIR_SmallRoom1_near_AnglA.wav';
RIR_sim2='./RIR/RIR_SmallRoom1_near_AnglB.wav';
RIR_sim3='./RIR/RIR_SmallRoom1_far_AnglA.wav';
RIR_sim4='./RIR/RIR_SmallRoom1_far_AnglB.wav';
RIR_sim5='./RIR/RIR_MediumRoom1_near_AnglA.wav';
RIR_sim6='./RIR/RIR_MediumRoom1_near_AnglB.wav';
RIR_sim7='./RIR/RIR_MediumRoom1_far_AnglA.wav';
RIR_sim8='./RIR/RIR_MediumRoom1_far_AnglB.wav';
RIR_sim9='./RIR/RIR_LargeRoom1_near_AnglA.wav';
RIR_sim10='./RIR/RIR_LargeRoom1_near_AnglB.wav';
RIR_sim11='./RIR/RIR_LargeRoom1_far_AnglA.wav';
RIR_sim12='./RIR/RIR_LargeRoom1_far_AnglB.wav';
RIR_sim13='./RIR/RIR_SmallRoom2_near_AnglA.wav';
RIR_sim14='./RIR/RIR_SmallRoom2_near_AnglB.wav';
RIR_sim15='./RIR/RIR_SmallRoom2_far_AnglA.wav';
RIR_sim16='./RIR/RIR_SmallRoom2_far_AnglB.wav';
RIR_sim17='./RIR/RIR_MediumRoom2_near_AnglA.wav';
RIR_sim18='./RIR/RIR_MediumRoom2_near_AnglB.wav';
RIR_sim19='./RIR/RIR_MediumRoom2_far_AnglA.wav';
RIR_sim20='./RIR/RIR_MediumRoom2_far_AnglB.wav';
RIR_sim21='./RIR/RIR_LargeRoom2_near_AnglA.wav';
RIR_sim22='./RIR/RIR_LargeRoom2_near_AnglB.wav';
RIR_sim23='./RIR/RIR_LargeRoom2_far_AnglA.wav';
RIR_sim24='./RIR/RIR_LargeRoom2_far_AnglB.wav';
%
% List of noise
%
num_NOISEvar=6;
noise_sim1='./NOISE/Noise_SmallRoom1';
noise_sim2='./NOISE/Noise_MediumRoom1';
noise_sim3='./NOISE/Noise_LargeRoom1';
noise_sim4='./NOISE/Noise_SmallRoom2';
noise_sim5='./NOISE/Noise_MediumRoom2';
noise_sim6='./NOISE/Noise_LargeRoom2';
%
% Start generating noisy reverberant data with creating new directories
%
fcount=1;
rcount=1;
ncount=1;
if save_dir(end)=='/';
save_dir_tr=[save_dir,'data/mc_train/'];
else
save_dir_tr=[save_dir,'/data/mc_train/'];
end
mkdir([save_dir_tr]);
%mkdir([save_dir,'/taskfiles/'])
mic_idx=['A';'B';'C';'D';'E';'F';'G';'H'];
prev_fname='dummy';
for nlist=1:1
% Open file list
eval(['fid=fopen(flist',num2str(nlist),',''r'');']);
while 1
% Set data file name
fname=fgetl(fid);
if ~ischar(fname);
break;
end
idx1=find(fname=='/');
% Make directory if there isn't any
if ~strcmp(prev_fname,fname(1:idx1(end)))
mkdir([save_dir_tr fname(1:idx1(end))])
end
prev_fname=fname(1:idx1(end));
% load (sphere format) speech signal
x=read_sphere([WSJ_dir_name,'/data/', fname]);
x=x/(2^15); % conversion from short-int to float
% load RIR and noise for "THIS" utterance
eval(['RIR=wavread(RIR_sim',num2str(rcount),');']);
eval(['NOISE=wavread([noise_sim',num2str(ceil(rcount/4)),',''_',num2str(ncount),'.wav'']);']);
% Generate 8ch noisy reverberant data
y=gen_obs(x,RIR,NOISE,SNRdB);
% cut to length of original signal
y = y(1:size(x,2),:);
% rotine to cyclicly switch RIRs and noise, utterance by utterance
rcount=rcount+1;
if rcount>num_RIRvar;rcount=1;ncount=ncount+1;end
if ncount>10;ncount=1;end
% save the data
y=y/4; % common normalization to all the data to prevent clipping
% denominator was decided experimentally
for ch=1:8
eval(['wavwrite(y(:,',num2str(ch),'),16000,''',save_dir_tr fname,'_ch',num2str(ch),'.wav'');']);
end
display(['sentence ',num2str(fcount),' (out of 7861) finished! (Multi-condition training data)'])
fcount=fcount+1;
end
end
%%%%
function [y]=gen_obs(x,RIR,NOISE,SNRdB)
% function to generate noisy reverberant data
x=x';
% calculate direct+early reflection signal for calculating SNR
[val,delay]=max(RIR(:,1));
before_impulse=floor(16000*0.001);
after_impulse=floor(16000*0.05);
RIR_direct=RIR(delay-before_impulse:delay+after_impulse,1);
direct_signal=fconv(x,RIR_direct);
% obtain reverberant speech
for ch=1:8
rev_y(:,ch)=fconv(x,RIR(:,ch));
end
% normalize noise data according to the prefixed SNR value
NOISE=NOISE(1:size(rev_y,1),:);
NOISE_ref=NOISE(:,1);
iPn = diag(1./mean(NOISE_ref.^2,1));
Px = diag(mean(direct_signal.^2,1));
Msnr = sqrt(10^(-SNRdB/10)*iPn*Px);
scaled_NOISE = NOISE*Msnr;
y = rev_y + scaled_NOISE;
y = y(delay:end,:);
%%%%
function [y]=fconv(x, h)
%FCONV Fast Convolution
% [y] = FCONV(x, h) convolves x and h, and normalizes the output
% to +-1.
%
% x = input vector
% h = input vector
%
% See also CONV
%
% NOTES:
%
% 1) I have a short article explaining what a convolution is. It
% is available at http://stevem.us/fconv.html.
%
%
%Version 1.0
%Coded by: Stephen G. McGovern, 2003-2004.
%
%Copyright (c) 2003, Stephen McGovern
%All rights reserved.
%
%THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
%AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
%IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
%ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
%LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
%CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
%SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
%INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
%CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
%ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
%POSSIBILITY OF SUCH DAMAGE.
Ly=length(x)+length(h)-1; %
Ly2=pow2(nextpow2(Ly)); % Find smallest power of 2 that is > Ly
X=fft(x, Ly2); % Fast Fourier transform
H=fft(h, Ly2); % Fast Fourier transform
Y=X.*H; %
y=real(ifft(Y, Ly2)); % Inverse fast Fourier transform
y=y(1:1:Ly); % Take just the first N elements
|
github
|
h-delgado/binary-key-diarizer-master
|
pdist22.m
|
.m
|
binary-key-diarizer-master/matlab/external/pdist22.m
| 5,461 |
utf_8
|
173103b09eefbe457c081a3d41cddd3d
|
% This function belongs to Piotr Dollar's Toolbox
% http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html
% Please refer to the above web page for definitions and clarifications
%
% 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
% [X,IDX] = demoGenData(100,0,5,4,10,2,0);
% D = pdist2( X, X, 'sqeuclidean' );
% distMatrixShow( D, IDX );
%
% See also PDIST, DISTMATRIXSHOW
% Piotr's Image&Video Toolbox Version 2.0
% Copyright (C) 2007 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]
function D = pdist2( X, Y, metric )
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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function D = distCosine( X, Y )
if( ~isa(X,'double') || ~isa(Y,'double'))
error( 'Inputs must be of type double'); end;
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';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function D = distChiSq( X, Y )
%%% 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;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function D = distEucSq( X, Y )
%if( ~isa(X,'double') || ~isa(Y,'double'))
% error( 'Inputs must be of type double'); end;
m = size(X,1); n = size(Y,1);
%Yt = Y';
XX = sum(X.*X,2);
YY = sum(Y'.*Y',1);
D = XX(:,ones(1,n)) + YY(ones(1,m),:) - 2*X*Y';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% function D = distEucSq( X, Y )
%%%% code from Charles Elkan with variables renamed
% 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';
%%% 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 then 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
|
github
|
KGuo26/WADG_Matlab-master
|
bern_sem_basis.m
|
.m
|
WADG_Matlab-master/bern_sem_basis.m
| 1,476 |
utf_8
|
a00141b6425a6b137bfc093d5ed345aa
|
% wedge basis
% function V = bern_sem_basis(N,r,s,t)
% Vtri = bern_tri(N,r(:),s(:));
% % [r2D s2D] = Nodes2D(N); [r2D s2D] = xytors(r2D,s2D);
% % VWB = Vandermonde2D(N,r2D,s2D); Vtri = Vandermonde2D(N,r(:),s(:))/VWB;
% r1D = JacobiGL(0,0,N);
% VSEM = Vandermonde1D(N,r1D(:));
% V1D = Vandermonde1D(N,t(:))/VSEM; % sem in vertical direction
%
% Np = (N+1)*(N+2)/2;
%
% sk = 1;
% for i = 1:N+1
% for j = 1:Np
% V(:,sk) = Vtri(:,j).*V1D(:,i);
% sk = sk + 1;
% end
% end
function [V Vr Vs Vt V1 V2 V3] = bern_sem_basis(N,r,s,t)
% [Vtri Vrtri Vstri V1tri V2tri V3tri] = bern_tri(N,r(:),s(:));
[r2D s2D] = Nodes2D(N); [r2D s2D] = xytors(r2D,s2D);
VWB = Vandermonde2D(N,r2D,s2D);
Vtri = Vandermonde2D(N,r(:),s(:))/VWB;
[Vrtri Vstri] = GradVandermonde2D(N,r(:),s(:));
Vrtri = Vrtri/VWB; Vstri = Vstri/VWB;
V1tri = Vrtri*0; V2tri = Vrtri*0; V3tri = Vrtri*0; % dummy arrays
r1D = JacobiGL(0,0,N);
VSEM = Vandermonde1D(N,r1D(:));
V1D = Vandermonde1D(N,t(:))/VSEM; % sem in vertical direction
Vt1D = GradVandermonde1D(N,t(:))/VSEM; % sem in vertical direction
Nptri = (N+1)*(N+2)/2;
sk = 1;
for i = 1:N+1
for j = 1:Nptri
V(:,sk) = Vtri(:,j).*V1D(:,i);
Vr(:,sk) = Vrtri(:,j).*V1D(:,i);
Vs(:,sk) = Vstri(:,j).*V1D(:,i);
Vt(:,sk) = Vtri(:,j).*Vt1D(:,i);
V1(:,sk) = V1tri(:,j).*V1D(:,i);
V2(:,sk) = V2tri(:,j).*V1D(:,i);
V3(:,sk) = V3tri(:,j).*V1D(:,i);
sk = sk + 1;
end
end
|
github
|
KGuo26/WADG_Matlab-master
|
bern_basis_1D.m
|
.m
|
WADG_Matlab-master/bern_basis_1D.m
| 424 |
utf_8
|
d9d840acea7aa008762de1bb0bfe1d93
|
function [V Vr] = bern_basis_1D(N,r)
r = (1+r)/2; % convert to unit
for i = 0:N
V(:,i+1) = bern_1D(N,i,r);
Vr(:,i+1) = d_bern(N,i,r)*.5; % change of vars
end
function bi = bern_1D(N,i,r)
bi = nchoosek(N,i)*(r.^i).*(1-r).^(N-i);
function dbi = d_bern(N,i,r)
if (i==0)
dbi = -N*(1-r).^(N-1);
elseif (i==N)
dbi = N*(r.^(N-1));
else
dbi = nchoosek(N,i)*r.^(i - 1).*(1 - r).^(N - i - 1).*(i - N*r);
end
|
github
|
KGuo26/WADG_Matlab-master
|
bern_quad.m
|
.m
|
WADG_Matlab-master/bern_quad.m
| 689 |
utf_8
|
355d83ac78285e23030c96d0fadf8fcc
|
function [V Vr Vs] = bern_quad(N,r,s)
r = (1+r)/2; % convert to unit
s = (1+s)/2; % convert to unit
V = zeros(length(r),(N+1)^2);
Vr = zeros(length(r),(N+1)^2);
Vs = zeros(length(r),(N+1)^2);
sk = 1;
for j = 0:N
for i = 0:N
V(:,sk) = bern_1D(N,i,r).*bern_1D(N,j,s);
Vr(:,sk) = .5*d_bern_1D(N,i,r).*bern_1D(N,j,s);
Vs(:,sk) = .5*bern_1D(N,i,r).*d_bern_1D(N,j,s);
sk = sk + 1;
end
end
function bi = bern_1D(N,i,r)
bi = nchoosek(N,i)*(r.^i).*(1-r).^(N-i);
function dbi = d_bern_1D(N,i,r)
if (i==0)
dbi = -N*(1-r).^(N-1);
elseif (i==N)
dbi = N*(r.^(N-1));
else
dbi = nchoosek(N,i)*r.^(i - 1).*(1 - r).^(N - i - 1).*(i - N*r);
end
|
github
|
KGuo26/WADG_Matlab-master
|
bern_basis_tet.m
|
.m
|
WADG_Matlab-master/bern_basis_tet.m
| 1,786 |
utf_8
|
402f402665ab18ed94d19eb13672619c
|
% V = VDM. Vrst = deriv matrices. V1-4 barycentric derivs. ids =
% permutation to match equispaced node ordering on tet.
function [V, Vr, Vs, Vt, V1, V2, V3, V4, id] = bern_basis_tet(N,r,s,t)
%[L1 L2 L3 L4] = rsttobary(r,s,t);
L1 = -(1+r+s+t)/2;
L2 = (1+r)/2;
L3 = (1+s)/2;
L4 = (1+t)/2;
dL1r = -.5; dL2r = .5; dL3r = 0; dL4r = 0;
dL1s = -.5; dL2s = 0; dL3s = .5; dL4s = 0;
dL1t = -.5; dL2t = 0; dL3t = 0; dL4t = .5;
sk = 1;
for l = 0:N
for k = 0:N-l
for j = 0:N-k-l;
i = N-j-k-l;
C=factorial(N)/(factorial(i)*factorial(j)*factorial(k)*factorial(l));
% C = 1;
V(:,sk) = C*(L1.^i).*(L2.^j).*(L3.^k).*(L4.^l);
dL1 = C*i*(L1.^(i-1)).*(L2.^j).*(L3.^k).*(L4.^l);
dL2 = C*j*(L1.^(i)).*(L2.^(j-1)).*(L3.^k).*(L4.^l);
dL3 = C*k*(L1.^(i)).*(L2.^j).*(L3.^(k-1)).*(L4.^l);
dL4 = C*l*(L1.^(i)).*(L2.^j).*(L3.^k).*(L4.^(l-1));
if i==0
dL1 = zeros(size(dL1));
end
if j==0
dL2 = zeros(size(dL1));
end
if k ==0
dL3 = zeros(size(dL1));
end
if l ==0
dL4 = zeros(size(dL1));
end
Vr(:,sk) = dL1.*dL1r + dL2.*dL2r + dL3.*dL3r + dL4.*dL4r;
Vs(:,sk) = dL1.*dL1s + dL2.*dL2s + dL3.*dL3s + dL4.*dL4s;
Vt(:,sk) = dL1.*dL1t + dL2.*dL2t + dL3.*dL3t + dL4.*dL4t;
V1(:,sk) = dL1;
V2(:,sk) = dL2;
V3(:,sk) = dL3;
V4(:,sk) = dL4;
sk = sk + 1;
end
end
end
function bi = bern(N,i,r)
bi = nchoosek(N,i)*(r.^i).*(1-r).^(N-i);
function dbi = d_bern(N,i,r)
dbi = nchoosek(N,i)*r.^(i - 1).*(1 - r).^(N - i - 1).*(i - N*r);
|
github
|
KGuo26/WADG_Matlab-master
|
Wave3D.m
|
.m
|
WADG_Matlab-master/Wave3D.m
| 4,005 |
utf_8
|
61a2d10ebd5610a1443301f0f99b2ba0
|
clear
Globals3D;
N = 4;
FinalTime = 1;
cfun = @(x,y,z) ones(size(x));
%cfun = @(x,y,z) 1 + 0.5*sin(pi*x).*sin(pi*y).*sin(pi*z); % smooth velocity
%cfun = @(x,y,z) (1 + .5*sin(2*pi*x).*sin(2*pi*y) + (y > 0)); % piecewise smooth velocity
%pfun = @(x,y,z,t) cos(pi*x).*cos(pi*y).*cos(pi*z).*cos(sqrt(3)*pi*t);
pfun = @(x,y,z,t) sin(pi*x).*sin(pi*y).*sin(pi*z).*cos(pi*t);
ufun = @(x,y,z,t) -cos(pi*x).*sin(pi*y).*sin(pi*z).*sin(pi*t);
vfun = @(x,y,z,t) -sin(pi*x).*cos(pi*y).*sin(pi*z).*sin(pi*t);
wfun = @(x,y,z,t) -sin(pi*x).*sin(pi*y).*cos(pi*z).*sin(pi*t);
%ffun = @(x,y,z,t) sqrt(3)*pi*(1-1./cfun(x,y,z)).*cos(pi*x).*cos(pi*y).*cos(pi*z).*sin(sqrt(3)*pi*t);
ffun = @(x,y,z,t) pi*2*sin(pi*x).*sin(pi*y).*sin(pi*z).*sin(pi*t);
%generate 3D mesh
[Nv, VX, VY, VZ, K, EToV] = MeshReaderGmsh3D('cube2.msh');
StartUp3D;
Dr(abs(Dr)<1e-8) = 0;
Nq = 2*N+1;
[rq sq tq wq] = tet_cubature(Nq); % integrate u*v*c
Vq = Vandermonde3D(N,rq,sq,tq)/V;
xq = Vq*x; yq = Vq*y; zq=Vq*z;
Pq=V*V'*Vq'*diag(wq);
Cq = cfun(xq,yq,zq);
Lift3D(N, r, s, t);
% %% Adptive m on each element
% VMq = Vandermonde3D(M,rq,sq,tq);
% CqM = VMq*VMq'*diag(wq)*Cq;
%% initial condition
p = pfun(x,y,z,0);
u = ufun(x,y,z,0);
v = vfun(x,y,z,0);
w = wfun(x,y,z,0);
% p = pfun(x,y,z,0);
% u = zeros(Np,K);
% v = zeros(Np,K);
% w = zeros(Np,K);
%%
time = 0;
% Runge-Kutta residual storage
resu = zeros(Np,K); resv = zeros(Np,K); resw = zeros(Np,K); resp = zeros(Np,K);
CN = (N+1)*(N+2)*(N+3)/6; % trace inequality constant
CNh = max(CN*max(Fscale(:)));
dt = 2/CNh;
%dt = 0.00390625; %coincide with the time step in occa
% outer time step loop
tstep = 0;
while (time<FinalTime)
if(time+dt>FinalTime), dt = FinalTime-time; end
for INTRK = 1:5
timelocal = time + rk4c(INTRK)*dt;
f=ffun(x,y,z,timelocal);
[rhsp, rhsu, rhsv, rhsw] = acousticsRHS3D_WADG(p,u,v,w,f);
% initiate and increment Runge-Kutta residuals
resp = rk4a(INTRK)*resp + dt*rhsp;
resu = rk4a(INTRK)*resu + dt*rhsu;
resv = rk4a(INTRK)*resv + dt*rhsv;
resw = rk4a(INTRK)*resw + dt*rhsw;
% update fields
u = u+rk4b(INTRK)*resu;
v = v+rk4b(INTRK)*resv;
w = w+rk4b(INTRK)*resw;
p = p+rk4b(INTRK)*resp;
end
time = time+dt; tstep = tstep+1;
end
p_exact = pfun(x,y,z,FinalTime);
p_exact_quadrature = pfun(xq,yq,zq,FinalTime);
p_quadrature = Vq * p;
[d1,d2]=size(p_quadrature);
error_accumulation = 0;
for j1=1:d2
for j2=1:d1
err = p_quadrature(j2,j1)-p_exact_quadrature(j2,j1);
error_accumulation = error_accumulation + err*err*wq(j2)*J(1,j1);
end
end
error_l2 = sqrt(error_accumulation)
%error_fro = norm(p-p_exact,'fro');
function [rhsp, rhsu, rhsv, rhsw] = acousticsRHS3D_WADG(p,u,v,w,f)
Globals3D;
% Define field differences at faces
dp = zeros(Nfp*Nfaces,K); dp(:) = p(vmapP)-p(vmapM);
du = zeros(Nfp*Nfaces,K); du(:) = u(vmapP)-u(vmapM);
dv = zeros(Nfp*Nfaces,K); dv(:) = v(vmapP)-v(vmapM);
dw = zeros(Nfp*Nfaces,K); dw(:) = w(vmapP)-w(vmapM);
% evaluate upwind fluxes
ndotdU = nx.*du + ny.*dv + nz.*dw;
% % Impose reflective boundary conditions (p+ = -p-)
% ndotdU(mapB) = 0;
% dp(mapB) = -2*p(vmapB);
% Impose Dirichlet BCs
ndotdU(mapB) = 0;
p(vmapB) = 0;
dp(mapB) = 0;
tau = 1;
fluxp = tau*dp - ndotdU;
fluxu = (tau*ndotdU - dp).*nx;
fluxv = (tau*ndotdU - dp).*ny;
fluxw = (tau*ndotdU - dp).*nz;
pr = Dr*p; ps = Ds*p; pt = Dt*p;
dpdx = rx.*pr + sx.*ps + tx.*pt;
dpdy = ry.*pr + sy.*ps + ty.*pt;
dpdz = rz.*pr + sz.*ps + tz.*pt;
divU = Dr*(u.*rx + v.*ry + w.*rz) + Ds*(u.*sx + v.*sy + w.*sz)+ Dt*(u.*tx + v.*ty + w.*tz);
% compute right hand sides of the PDE's
rhsp = -divU + f + LIFT*(Fscale.*fluxp)/2.0;
rhsu = -dpdx + LIFT*(Fscale.*fluxu)/2.0;
rhsv = -dpdy + LIFT*(Fscale.*fluxv)/2.0;
rhsw = -dpdz + LIFT*(Fscale.*fluxw)/2.0;
rhsp = Pq*(Cq.*(Vq*rhsp));
return;
end
|
github
|
KGuo26/WADG_Matlab-master
|
bern_hex.m
|
.m
|
WADG_Matlab-master/bern_hex.m
| 930 |
utf_8
|
c1ba4aade744a74da97433f578142f21
|
function [V Vr Vs Vt] = bern_hex(N,r,s,t)
r = (1+r)/2; % convert to unit
s = (1+s)/2; % convert to unit
t = (1+t)/2; % convert to unit
Np = (N+1)^3;
V = zeros(length(r),Np);
Vr = zeros(length(r),Np);
Vs = zeros(length(r),Np);
Vt = zeros(length(r),Np);
sk = 1;
for k = 0:N
for j = 0:N
for i = 0:N
V(:,sk) = bern_1D(N,i,r).*bern_1D(N,j,s).*bern_1D(N,k,t);
Vr(:,sk) = .5*d_bern_1D(N,i,r).*bern_1D(N,j,s).*bern_1D(N,k,t);
Vs(:,sk) = .5*bern_1D(N,i,r).*d_bern_1D(N,j,s).*bern_1D(N,k,t);
Vt(:,sk) = .5*bern_1D(N,i,r).*bern_1D(N,j,s).*d_bern_1D(N,k,t);
sk = sk + 1;
end
end
end
function bi = bern_1D(N,i,r)
bi = nchoosek(N,i)*(r.^i).*(1-r).^(N-i);
function dbi = d_bern_1D(N,i,r)
if (i==0)
dbi = -N*(1-r).^(N-1);
elseif (i==N)
dbi = N*(r.^(N-1));
else
dbi = nchoosek(N,i)*r.^(i - 1).*(1 - r).^(N - i - 1).*(i - N*r);
end
|
github
|
KGuo26/WADG_Matlab-master
|
bern_tet.m
|
.m
|
WADG_Matlab-master/bern_tet.m
| 2,132 |
utf_8
|
4dc141800a89b218f1b7dff33be86eef
|
% V = VDM. Vrst = deriv matrices. V1-4 barycentric derivs. ids =
% permutation to match equispaced node ordering on tet.
% function [V Vr Vs Vt V1 V2 V3 V4 id] = bern_basis_tet(N,r,s,t)
%
% % use equivalence between W&B and equispaced nodes - get ordering
% [re se te] = EquiNodes3D(N);
% Ve = bern_tet(N,re,se,te);
% for i = 1:size(Ve,2)
% [val iid] = max(Ve(:,i));
% id(i) = iid;
% end
%
% [V Vr Vs Vt V1 V2 V3 V4] = bern_tet(N,r,s,t);
% V = V(:,id);
% Vr = Vr(:,id);
% Vs = Vs(:,id);
% Vt = Vt(:,id);
% V1 = V1(:,id);
% V2 = V2(:,id);
% V3 = V3(:,id);
% V4 = V4(:,id);
function [V Vr Vs Vt V1 V2 V3 V4] = bern_tet(N,r,s,t)
% [L1 L2 L3 L4] = rsttobary(r,s,t);
L1 = -(1+r+s+t)/2;
L2 = (1+r)/2;
L3 = (1+s)/2;
L4 = (1+t)/2;
dL1r = -.5; dL2r = .5; dL3r = 0; dL4r = 0;
dL1s = -.5; dL2s = 0; dL3s = .5; dL4s = 0;
dL1t = -.5; dL2t = 0; dL3t = 0; dL4t = .5;
sk = 1;
% for i = 0:N
% for j = 0:N-i
% for k = 0:N-i-j
% l = N-i-j-k;
for l = 0:N
for k = 0:N-l
for j = 0:N-l-k
i = N-j-k-l;
C=factorial(N)/(factorial(i)*factorial(j)*factorial(k)*factorial(l));
V(:,sk) = C*(L1.^i).*(L2.^j).*(L3.^k).*(L4.^l);
dL1 = C*i*(L1.^(i-1)).*(L2.^j).*(L3.^k).*(L4.^l);
dL2 = C*j*(L1.^(i)).*(L2.^(j-1)).*(L3.^k).*(L4.^l);
dL3 = C*k*(L1.^(i)).*(L2.^j).*(L3.^(k-1)).*(L4.^l);
dL4 = C*l*(L1.^(i)).*(L2.^j).*(L3.^k).*(L4.^(l-1));
if i==0
dL1 = zeros(size(dL1));
end
if j==0
dL2 = zeros(size(dL1));
end
if k ==0
dL3 = zeros(size(dL1));
end
if l ==0
dL4 = zeros(size(dL1));
end
Vr(:,sk) = dL1.*dL1r + dL2.*dL2r + dL3.*dL3r + dL4.*dL4r;
Vs(:,sk) = dL1.*dL1s + dL2.*dL2s + dL3.*dL3s + dL4.*dL4s;
Vt(:,sk) = dL1.*dL1t + dL2.*dL2t + dL3.*dL3t + dL4.*dL4t;
V1(:,sk) = dL1;
V2(:,sk) = dL2;
V3(:,sk) = dL3;
V4(:,sk) = dL4;
sk = sk + 1;
end
end
end
|
github
|
KGuo26/WADG_Matlab-master
|
Wave2D_mesh_fullquadrature.m
|
.m
|
WADG_Matlab-master/Wave2D_mesh_fullquadrature.m
| 4,216 |
utf_8
|
8353d0375b964df4092a4909937506a5
|
clear
Globals2D
kd=[4 8 16 32];
h=2./kd;
N = 4;
M = 1;
for i = 1:length(kd)
K1D = kd(i);
FinalTime = 1.0;
[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);
StartUp2D;
%% Set up wavespeed function
%cfun = @(x,y) ones(size(x));
cfun = @(x,y) 1+ sin(pi*x).*sin(pi*y); % smooth velocity
%cfun = @(x,y) (1 + .5*sin(2*pi*x).*sin(2*pi*y) + (y > 0)); % piecewise smooth velocity
%% generate quadrature points
Nq = 2*N+1;
[rq sq wq] = Cubature2D(Nq); % integrate u*v*c
Vq = Vandermonde2D(N,rq,sq)/V;
xq = Vq*x; yq = Vq*y;
%% construct the projection matrix for nodal basis Pq
Pq = V*V'*Vq'*diag(wq);
%% construct matrix Cq
Cq = cfun(xq,yq);
%% construct the projection matrix for cfun to degree M
VMq = Vandermonde2D(M,rq,sq);
CqM = VMq*VMq'*diag(wq)*Cq;
%% initial condition
x0 = 0; y0 = .1;
p = exp(-25*((x-x0).^2 + (y-y0).^2));
u = zeros(Np, K);
v=zeros(Np,K);
p_M = p;
u_M = u;
v_M = v;
time = 0;
%% Runge-Kutta residual storage
resu = zeros(Np,K); resv = zeros(Np,K); resp = zeros(Np,K);
resu_M = resu; resv_M = resv; resp_M = resp;
%% compute time step size
CN = (N+1)*(N+2)/2; % trace inequality constant
CNh = max(CN*max(Fscale(:)));
dt = 2/CNh;
%% outer time step loop
tstep = 0;
while (time<FinalTime)
if(time+dt>FinalTime), dt = FinalTime-time; end
for INTRK = 1:5
timelocal = time + rk4c(INTRK)*dt;
[rhsp, rhsu, rhsv] = acousticsRHS2D_fullWADG(p,u,v);
[rhsp_M, rhsu_M, rhsv_M] = acousticsRHS2D_adaptiveWADG(p_M,u_M,v_M);
% initiate and increment Runge-Kutta residuals
resp = rk4a(INTRK)*resp + dt*rhsp;
resu = rk4a(INTRK)*resu + dt*rhsu;
resv = rk4a(INTRK)*resv + dt*rhsv;
resp_M = rk4a(INTRK)*resp_M + dt*rhsp_M;
resu_M = rk4a(INTRK)*resu_M + dt*rhsu_M;
resv_M = rk4a(INTRK)*resv_M + dt*rhsv_M;
% update fields
u = u+rk4b(INTRK)*resu;
v = v+rk4b(INTRK)*resv;
p = p+rk4b(INTRK)*resp;
u_M = u_M + rk4b(INTRK)*resu_M;
v_M = v_M + rk4b(INTRK)*resv_M;
p_M = p_M + rk4b(INTRK)*resp_M;
end
% Increment time
time = time+dt; tstep = tstep+1;
end
p_quadrature = Vq * p;
p_M_quadrature = Vq * p_M;
[d1,d2]=size(p_quadrature);
error_accumulation = 0;
for j1=1:d2
for j2=1:d1
err = p_quadrature(j2,j1)-p_M_quadrature(j2,j1);
error_accumulation = error_accumulation + err*err*wq(j2)*J(1,j1);
end
end
error_l2(i) = sqrt(error_accumulation);
error_fro(i) = norm(p-p_M,'fro');
end
function [rhsp, rhsu, rhsv] = acousticsRHS2D_adaptiveWADG(p,u,v)
Globals2D;
% Define field differences at faces
dp = zeros(Nfp*Nfaces,K); dp(:) = p(vmapP)-p(vmapM);
du = zeros(Nfp*Nfaces,K); du(:) = u(vmapP)-u(vmapM);
dv = zeros(Nfp*Nfaces,K); dv(:) = v(vmapP)-v(vmapM);
% evaluate upwind fluxes
ndotdU = nx.*du + ny.*dv;
% Impose reflective boundary conditions (p+ = -p-)
ndotdU(mapB) = 0;
dp(mapB) = -2*p(vmapB);
tau = 1;
fluxp = tau*dp - ndotdU;
fluxu = (tau*ndotdU - dp).*nx;
fluxv = (tau*ndotdU - dp).*ny;
pr = Dr*p; ps = Ds*p;
dpdx = rx.*pr + sx.*ps;
dpdy = ry.*pr + sy.*ps;
divU = Dr*(u.*rx + v.*ry) + Ds*(u.*sx + v.*sy);
% compute right hand sides of the PDE's
rhsp = -divU + LIFT*(Fscale.*fluxp)/2.0;
rhsu = -dpdx + LIFT*(Fscale.*fluxu)/2.0;
rhsv = -dpdy + LIFT*(Fscale.*fluxv)/2.0;
rhsp = Pq*(CqM.*(Vq*rhsp));
return;
end
function [rhsp, rhsu, rhsv] = acousticsRHS2D_fullWADG(p,u,v)
Globals2D;
% Define field differences at faces
dp = zeros(Nfp*Nfaces,K); dp(:) = p(vmapP)-p(vmapM);
du = zeros(Nfp*Nfaces,K); du(:) = u(vmapP)-u(vmapM);
dv = zeros(Nfp*Nfaces,K); dv(:) = v(vmapP)-v(vmapM);
% evaluate upwind fluxes
ndotdU = nx.*du + ny.*dv;
% Impose reflective boundary conditions (p+ = -p-)
ndotdU(mapB) = 0;
dp(mapB) = -2*p(vmapB);
tau = 1;
fluxp = tau*dp - ndotdU;
fluxu = (tau*ndotdU - dp).*nx;
fluxv = (tau*ndotdU - dp).*ny;
pr = Dr*p; ps = Ds*p;
dpdx = rx.*pr + sx.*ps;
dpdy = ry.*pr + sy.*ps;
divU = Dr*(u.*rx + v.*ry) + Ds*(u.*sx + v.*sy);
% compute right hand sides of the PDE's
rhsp = -divU + LIFT*(Fscale.*fluxp)/2.0;
rhsu = -dpdx + LIFT*(Fscale.*fluxu)/2.0;
rhsv = -dpdy + LIFT*(Fscale.*fluxv)/2.0;
rhsp = Pq*(Cq.*(Vq*rhsp));
return;
end
|
github
|
KGuo26/WADG_Matlab-master
|
Wave2D_k_manufactured.m
|
.m
|
WADG_Matlab-master/Wave2D_k_manufactured.m
| 3,325 |
utf_8
|
0034b8526d028e8da590042ba197b35b
|
clear
Globals2D
k=[1 4 8 16]
N = 4;
M = 1
for i = 1:length(k)
K1D = 32;
FinalTime = 1.0;
[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);
StartUp2D;
%% Set up wavespeed function
%cfun = @(x,y) ones(size(x));
cfun = @(x,y) 1+ 0.5*sin(pi*x).*sin(pi*y); % smooth velocity
%cfun = @(x,y) (1 + .5*sin(2*pi*x).*sin(2*pi*y) + (y > 0)); % piecewise smooth velocity
%% Set periodic manufactured solution
pfun = @(x,y,t) sin(k(i)*pi*x).*sin(k(i)*pi*y).*cos(k(i)*pi*t);
ufun = @(x,y,t) -cos(k(i)*pi*x).*sin(k(i)*pi*y).*sin(k(i)*pi*t);
vfun = @(x,y,t) -sin(k(i)*pi*x).*cos(k(i)*pi*y).*sin(k(i)*pi*t);
ffun = @(x,y,t) k(i)*pi*(-1./(cfun(x,y))+2).*sin(k(i)*pi*x).*sin(k(i)*pi*y).*sin(k(i)*pi*t);
%% generate quadrature points
Nq = 2*N+1;
[rq sq wq] = Cubature2D(Nq); % integrate u*v*c
Vq = Vandermonde2D(N,rq,sq)/V;
xq = Vq*x; yq = Vq*y;
%% construct the projection matrix for nodal basis Pq
Pq = V*V'*Vq'*diag(wq);
%% construct matrix Cq
Cq = cfun(xq,yq);
%% construct the projection matrix for cfun to degree M
VMq = Vandermonde2D(M,rq,sq);
CqM = VMq*VMq'*diag(wq)*Cq;
%% initial condition
p = pfun(x,y,0);
u = ufun(x,y,0);
v = vfun(x,y,0);
time = 0;
%% Runge-Kutta residual storage
resu = zeros(Np,K); resv = zeros(Np,K); resp = zeros(Np,K);
%% compute time step size
CN = (N+1)*(N+2)/2; % trace inequality constant
CNh = max(CN*max(Fscale(:)));
dt = 2/CNh;
%% outer time step loop
tstep = 0;
while (time<FinalTime)
if(time+dt>FinalTime), dt = FinalTime-time; end
for INTRK = 1:5
timelocal = time + rk4c(INTRK)*dt;
f=ffun(x,y,timelocal);
[rhsp, rhsu, rhsv] = acousticsRHS2D_compare(p,u,v,f);
% initiate and increment Runge-Kutta residuals
resp = rk4a(INTRK)*resp + dt*rhsp;
resu = rk4a(INTRK)*resu + dt*rhsu;
resv = rk4a(INTRK)*resv + dt*rhsv;
% update fields
u = u+rk4b(INTRK)*resu;
v = v+rk4b(INTRK)*resv;
p = p+rk4b(INTRK)*resp;
end
% Increment time
time = time+dt; tstep = tstep+1;
end
p_exact = pfun(x,y,FinalTime);
p_exact_quadrature = pfun(xq,yq,FinalTime);
p_quadrature = Vq * p;
[d1,d2]=size(p_quadrature);
error_accumulation = 0;
for j1=1:d2
for j2=1:d1
err = p_quadrature(j2,j1)-p_exact_quadrature(j2,j1);
error_accumulation = error_accumulation + err*err*wq(j2)*J(1,j1);
end
end
error_l2(i) = sqrt(error_accumulation);
error_fro(i) = norm(p-p_exact,'fro');
end
function [rhsp, rhsu, rhsv] = acousticsRHS2D_compare(p,u,v,f)
Globals2D;
% Define field differences at faces
dp = zeros(Nfp*Nfaces,K); dp(:) = p(vmapP)-p(vmapM);
du = zeros(Nfp*Nfaces,K); du(:) = u(vmapP)-u(vmapM);
dv = zeros(Nfp*Nfaces,K); dv(:) = v(vmapP)-v(vmapM);
% evaluate upwind fluxes
ndotdU = nx.*du + ny.*dv;
% Impose reflective boundary conditions (p+ = -p-)
ndotdU(mapB) = 0;
dp(mapB) = -2*p(vmapB);
tau = 1;
fluxp = tau*dp - ndotdU;
fluxu = (tau*ndotdU - dp).*nx;
fluxv = (tau*ndotdU - dp).*ny;
pr = Dr*p; ps = Ds*p;
dpdx = rx.*pr + sx.*ps;
dpdy = ry.*pr + sy.*ps;
divU = Dr*(u.*rx + v.*ry) + Ds*(u.*sx + v.*sy);
% compute right hand sides of the PDE's
rhsp = -divU + f + LIFT*(Fscale.*fluxp)/2.0;
rhsu = -dpdx + LIFT*(Fscale.*fluxu)/2.0;
rhsv = -dpdy + LIFT*(Fscale.*fluxv)/2.0;
rhsp = Pq*(CqM.*(Vq*rhsp));
return;
end
|
github
|
KGuo26/WADG_Matlab-master
|
Wave2D_mesh_manufactured.m
|
.m
|
WADG_Matlab-master/Wave2D_mesh_manufactured.m
| 3,326 |
utf_8
|
322817f72f150c1acafea4eaefbd319b
|
clear
Globals2D
kd=[64]
h=2./kd
N = 4;
M = 1;
k = 16
for i = 1:length(kd)
K1D = kd(i);
FinalTime = 1.5;
[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);
StartUp2D;
%% Set up wavespeed function
%cfun = @(x,y) ones(size(x));
cfun = @(x,y) 1+ 0.5*sin(pi*x).*sin(pi*y); % smooth velocity
%cfun = @(x,y) (1 + .5*sin(2*pi*x).*sin(2*pi*y) + (y > 0)); % piecewise smooth velocity
%% Set periodic manufactured solution
pfun = @(x,y,t) sin(k*pi*x).*sin(k*pi*y).*cos(k*pi*t);
ufun = @(x,y,t) -cos(k*pi*x).*sin(k*pi*y).*sin(k*pi*t);
vfun = @(x,y,t) -sin(k*pi*x).*cos(k*pi*y).*sin(k*pi*t);
ffun = @(x,y,t) k*pi*(-1./(cfun(x,y))+2).*sin(k*pi*x).*sin(k*pi*y).*sin(k*pi*t);
%% generate quadrature points
Nq = 2*N+1;
[rq sq wq] = Cubature2D(Nq); % integrate u*v*c
Vq = Vandermonde2D(N,rq,sq)/V;
xq = Vq*x; yq = Vq*y;
%% construct the projection matrix for nodal basis Pq
Pq = V*V'*Vq'*diag(wq);
%% construct matrix Cq
Cq = cfun(xq,yq);
%% construct the projection matrix for cfun to degree M
VMq = Vandermonde2D(M,rq,sq);
CqM = VMq*VMq'*diag(wq)*Cq;
%% initial condition
p = pfun(x,y,0);
u = ufun(x,y,0);
v = vfun(x,y,0);
time = 0;
%% Runge-Kutta residual storage
resu = zeros(Np,K); resv = zeros(Np,K); resp = zeros(Np,K);
%% compute time step size
CN = (N+1)*(N+2)/2; % trace inequality constant
CNh = max(CN*max(Fscale(:)));
dt = 2/CNh;
%% outer time step loop
tstep = 0;
while (time<FinalTime)
if(time+dt>FinalTime), dt = FinalTime-time; end
for INTRK = 1:5
timelocal = time + rk4c(INTRK)*dt;
f=ffun(x,y,timelocal);
[rhsp, rhsu, rhsv] = acousticsRHS2D_compare(p,u,v,f);
% initiate and increment Runge-Kutta residuals
resp = rk4a(INTRK)*resp + dt*rhsp;
resu = rk4a(INTRK)*resu + dt*rhsu;
resv = rk4a(INTRK)*resv + dt*rhsv;
% update fields
u = u+rk4b(INTRK)*resu;
v = v+rk4b(INTRK)*resv;
p = p+rk4b(INTRK)*resp;
end
% Increment time
time = time+dt; tstep = tstep+1;
end
p_exact = pfun(x,y,FinalTime);
p_exact_quadrature = pfun(xq,yq,FinalTime);
p_quadrature = Vq * p;
[d1,d2]=size(p_quadrature);
error_accumulation = 0;
for j1=1:d2
for j2=1:d1
err = p_quadrature(j2,j1)-p_exact_quadrature(j2,j1);
error_accumulation = error_accumulation + err*err*wq(j2)*J(1,j1);
end
end
error_l2(i) = sqrt(error_accumulation);
error_fro(i) = norm(p-p_exact,'fro');
end
function [rhsp, rhsu, rhsv] = acousticsRHS2D_compare(p,u,v,f)
Globals2D;
% Define field differences at faces
dp = zeros(Nfp*Nfaces,K); dp(:) = p(vmapP)-p(vmapM);
du = zeros(Nfp*Nfaces,K); du(:) = u(vmapP)-u(vmapM);
dv = zeros(Nfp*Nfaces,K); dv(:) = v(vmapP)-v(vmapM);
% evaluate upwind fluxes
ndotdU = nx.*du + ny.*dv;
% Impose reflective boundary conditions (p+ = -p-)
ndotdU(mapB) = 0;
dp(mapB) = -2*p(vmapB);
tau = 1;
fluxp = tau*dp - ndotdU;
fluxu = (tau*ndotdU - dp).*nx;
fluxv = (tau*ndotdU - dp).*ny;
pr = Dr*p; ps = Ds*p;
dpdx = rx.*pr + sx.*ps;
dpdy = ry.*pr + sy.*ps;
divU = Dr*(u.*rx + v.*ry) + Ds*(u.*sx + v.*sy);
% compute right hand sides of the PDE's
rhsp = -divU + f + LIFT*(Fscale.*fluxp)/2.0;
rhsu = -dpdx + LIFT*(Fscale.*fluxu)/2.0;
rhsv = -dpdy + LIFT*(Fscale.*fluxv)/2.0;
rhsp = Pq*(CqM.*(Vq*rhsp));
return;
end
|
github
|
KGuo26/WADG_Matlab-master
|
bern_tri.m
|
.m
|
WADG_Matlab-master/bern_tri.m
| 1,444 |
utf_8
|
3a04a72548c550e48525a3a60987827c
|
% function [V Vr Vs V1 V2 V3 id] = bern_tri(N,r,s)
%
% % use equivalence between W&B and equispaced nodes - get ordering
% [re se] = EquiNodes2D(N); [re se] = xytors(re,se);
% Ve = bern_tri_b(N,re,se);
% for i = 1:size(Ve,2)
% [val iid] = max(Ve(:,i));
% id(i) = iid;
% % id(i) = i;
% end
%
% [V Vr Vs V1 V2 V3] = bern_tri_b(N,r,s);
% V = V(:,id);
% Vr = Vr(:,id); Vs = Vs(:,id);
% V1 = V1(:,id); V2 = V2(:,id); V3 = V3(:,id);
function [V Vr Vs VL1 VL2 VL3] = bern_tri(N,r,s)
% barycentric version
L1 = -(r+s)/2; L2 = (1+r)/2; L3 = (1+s)/2;
dL1r = -.5; dL2r = .5; dL3r = 0;
dL1s = -.5; dL2s = 0; dL3s = .5;
sk = 1;
% for i = 0:N
% for j = 0:N-i
% k = N-i-j;
for k = 0:N
for j = 0:N-k
i = N-j-k;
C=factorial(N)/(factorial(i)*factorial(j)*factorial(k));
V(:,sk) = C*(L1.^i).*(L2.^j).*(L3.^k);
dL1 = C*i*(L1.^(i-1)).*(L2.^j).*(L3.^k);
dL2 = C*j*(L1.^(i)).*(L2.^(j-1)).*(L3.^k);
dL3 = C*k*(L1.^(i)).*(L2.^j).*(L3.^(k-1));
if i==0
dL1 = zeros(size(dL1));
end
if j==0
dL2 = zeros(size(dL2));
end
if k == 0
dL3 = zeros(size(dL3));
end
Vr(:,sk) = dL1.*dL1r + dL2.*dL2r + dL3.*dL3r;
Vs(:,sk) = dL1.*dL1s + dL2.*dL2s + dL3.*dL3s;
VL1(:,sk) = dL1;
VL2(:,sk) = dL2;
VL3(:,sk) = dL3;
sk = sk + 1;
end
end
return
|
github
|
KGuo26/WADG_Matlab-master
|
Wave2D_modified.m
|
.m
|
WADG_Matlab-master/Wave2D_modified.m
| 2,725 |
utf_8
|
16165584c54009cc2f3f03cf162a8f73
|
Globals2D
N = 4;
K1D = 16;
c_flag = 0;
FinalTime = 0.5;
%cfun = @(x,y) ones(size(x));
cfun = @(x,y) 1 + sin(pi*x).*sin(pi*y); % smooth velocity
%cfun = @(x,y) (1 + .5*sin(2*pi*x).*sin(2*pi*y) + (y > 0)); % piecewise smooth velocity
[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);
StartUp2D;
[rp sp] = EquiNodes2D(50); [rp sp] = xytors(rp,sp);
Vp = Vandermonde2D(N,rp,sp)/V;
xp = Vp*x; yp = Vp*y;
Nq = 2*N+1;
[rq sq wq] = Cubature2D(Nq); % integrate u*v*c
Vq = Vandermonde2D(N,rq,sq)/V;
xq = Vq*x; yq = Vq*y;
%construct Pq
Pq=V*V'*Vq'*diag(wq);
%Pq is the projection matrix for Nodal Basis
%construct the matrix C
Cq=cfun(xq,yq);
%% initial condition
x0 = 0; y0 = .1;
p = exp(-25*((x-x0).^2 + (y-y0).^2));
u = zeros(Np, K);
v=zeros(Np,K);
%%
time = 0;
% Runge-Kutta residual storage
resu = zeros(Np,K); resv = zeros(Np,K); resp = zeros(Np,K);
% compute time step size
CN = (N+1)*(N+2)/2; % trace inequality constant
CNh = max(CN*max(Fscale(:)));
dt = 2/CNh;
% outer time step loop
tstep = 0;
while (time<FinalTime)
if(time+dt>FinalTime), dt = FinalTime-time; end
for INTRK = 1:5
timelocal = time + rk4c(INTRK)*dt;
[rhsp, rhsu, rhsv] = acousticsRHS2D_WADG(p,u,v);
% initiate and increment Runge-Kutta residuals
%apply invM*M_c^2
resp = rk4a(INTRK)*resp + dt*rhsp;
resu = rk4a(INTRK)*resu + dt*rhsu;
resv = rk4a(INTRK)*resv + dt*rhsv;
% update fields
u = u+rk4b(INTRK)*resu;
v = v+rk4b(INTRK)*resv;
p = p+rk4b(INTRK)*resp;
end;
if 1 && nargin==0 && mod(tstep,10)==0
clf
vv = Vp*p;
plot3(xp,yp,vv,'.');
axis equal
axis tight
colorbar
title(sprintf('time = %f',time))
drawnow
end
% Increment time
time = time+dt; tstep = tstep+1;
end
function [rhsp, rhsu, rhsv] = acousticsRHS2D_WADG(p,u,v)
Globals2D;
% Define field differences at faces
dp = zeros(Nfp*Nfaces,K); dp(:) = p(vmapP)-p(vmapM);
du = zeros(Nfp*Nfaces,K); du(:) = u(vmapP)-u(vmapM);
dv = zeros(Nfp*Nfaces,K); dv(:) = v(vmapP)-v(vmapM);
% evaluate upwind fluxes
ndotdU = nx.*du + ny.*dv;
% Impose reflective boundary conditions (p+ = -p-)
ndotdU(mapB) = 0;
dp(mapB) = -2*p(vmapB);
tau = 1;
fluxp = tau*dp - ndotdU;
fluxu = (tau*ndotdU - dp).*nx;
fluxv = (tau*ndotdU - dp).*ny;
pr = Dr*p; ps = Ds*p;
dpdx = rx.*pr + sx.*ps;
dpdy = ry.*pr + sy.*ps;
divU = Dr*(u.*rx + v.*ry) + Ds*(u.*sx + v.*sy);
% compute right hand sides of the PDE's
rhsp = -divU + LIFT*(Fscale.*fluxp)/2.0;
rhsu = -dpdx + LIFT*(Fscale.*fluxu)/2.0;
rhsv = -dpdy + LIFT*(Fscale.*fluxv)/2.0;
rhsp = Pq*(Cq.*(Vq*rhsp));
return;
end
|
github
|
KGuo26/WADG_Matlab-master
|
Wave2D_simple.m
|
.m
|
WADG_Matlab-master/Wave2D_simple.m
| 2,633 |
utf_8
|
977017e5edb69468778850473fbb3602
|
function Wave2D
Globals2D
N = 4;
K1D = 16;
c_flag = 0;
FinalTime = 3;
%cfun = @(x,y) ones(size(x));
cfun = @(x,y) .5*sin(pi*x).*sin(pi*y); % smooth velocity
%cfun = @(x,y) (1 + .5*sin(2*pi*x).*sin(2*pi*y) + (y > 0)); % piecewise smooth velocity
[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);
StartUp2D;
% plotting nodes
[rp sp] = EquiNodes2D(50); [rp sp] = xytors(rp,sp);
Vp = Vandermonde2D(N,rp,sp)/V;
xp = Vp*x; yp = Vp*y;
Nq = 2*N+1;
[rq sq wq] = Cubature2D(Nq); % integrate u*v*c
Vq = Vandermonde2D(N,rq,sq)/V;
xq = Vq*x; yq = Vq*y;
%% initial condition
x0 = 0; y0 = .1;
p = exp(-25*((x-x0).^2 + (y-y0).^2));
u = zeros(Np, K);
v= zeros(Np,K);
vv = Vp*p;
color_line3(xp,yp,vv,vv)
%%
time = 0;
% Runge-Kutta residual storage
resu = zeros(Np,K); resv = zeros(Np,K); resp = zeros(Np,K);
% compute time step size
CN = (N+1)*(N+2)/2; % trace inequality constant
CNh = max(CN*max(Fscale(:)));
dt = 2/CNh;
% outer time step loop
tstep = 0;
figure
while (time<FinalTime)
if(time+dt>FinalTime), dt = FinalTime-time; end
for INTRK = 1:5
timelocal = time + rk4c(INTRK)*dt;
[rhsp, rhsu, rhsv] = acousticsRHS2D(p,u,v,timelocal);
% initiate and increment Runge-Kutta residuals
resp = rk4a(INTRK)*resp + dt*rhsp;
resu = rk4a(INTRK)*resu + dt*rhsu;
resv = rk4a(INTRK)*resv + dt*rhsv;
% update fields
u = u+rk4b(INTRK)*resu;
v = v+rk4b(INTRK)*resv;
p = p+rk4b(INTRK)*resp;
end;
if 1 && nargin==0 && mod(tstep,10)==0
clf
vv = Vp*p;
color_line3(xp,yp,vv,vv,'.');
axis equal
axis tight
colorbar
title(sprintf('time = %f',time))
drawnow
end
% Increment time
time = time+dt; tstep = tstep+1;
end
function [rhsp, rhsu, rhsv] = acousticsRHS2D(p,u,v,time)
Globals2D;
% Define field differences at faces
dp = zeros(Nfp*Nfaces,K); dp(:) = p(vmapP)-p(vmapM);
du = zeros(Nfp*Nfaces,K); du(:) = u(vmapP)-u(vmapM);
dv = zeros(Nfp*Nfaces,K); dv(:) = v(vmapP)-v(vmapM);
% evaluate upwind fluxes
ndotdU = nx.*du + ny.*dv;
% Impose reflective boundary conditions (p+ = -p-)
ndotdU(mapB) = 0;
dp(mapB) = -2*p(vmapB);
tau = 1;
fluxp = tau*dp - ndotdU;
fluxu = (tau*ndotdU - dp).*nx;
fluxv = (tau*ndotdU - dp).*ny;
pr = Dr*p; ps = Ds*p;
dpdx = rx.*pr + sx.*ps;
dpdy = ry.*pr + sy.*ps;
divU = Dr*(u.*rx + v.*ry) + Ds*(u.*sx + v.*sy);
% compute right hand sides of the PDE's
rhsp = -divU + LIFT*(Fscale.*fluxp)/2.0;
rhsu = -dpdx + LIFT*(Fscale.*fluxu)/2.0;
rhsv = -dpdy + LIFT*(Fscale.*fluxv)/2.0;
return;
|
github
|
KGuo26/WADG_Matlab-master
|
bern_pyr.m
|
.m
|
WADG_Matlab-master/bern_pyr.m
| 1,162 |
utf_8
|
e05be17e730cbb437109d06c7b108ec5
|
function [V Vr Vs Vt Va Vb Vc] = bern_pyr(N,r,s,t)
a = 2*(r+1)./(1-t)-1;
b = 2*(s+1)./(1-t)-1;
c = t;
ids = abs(t-1)<1e-8;
a(ids) = -1;
b(ids) = -1;
dadr = 2./(1-t);
dbds = 2./(1-t);
dadt = (1+a)./(1-t);
dbdt = (1+b)./(1-t);
sk = 1;
for k = 0:N
for i = 0:N-k
for j = 0:N-k
V(:,sk) = bern(N-k,i,a).*bern(N-k,j,b).*bern(N,k,c);
va = .5*d_bern(N-k,i,a).*bern(N-k,j,b).*bern(N,k,c);
vb = .5*bern(N-k,i,a).*d_bern(N-k,j,b).*bern(N,k,c);
vc = .5*bern(N-k,i,a).*bern(N-k,j,b).*d_bern(N,k,c);
Vr(:,sk) = va.*dadr;
Vs(:,sk) = vb.*dbds;
Vt(:,sk) = va.*dadt + vb.*dbdt + vc;
% for testing
Va(:,sk) = va; Vb(:,sk) = vb; Vc(:,sk) = vc;
sk = sk + 1;
end
end
end
function [bi] = bern(N,i,r)
r = (1+r)/2;
bi = nchoosek(N,i)*(r.^i).*(1-r).^(N-i);
% bi = (r.^i).*(1-r).^(N-i);
function dbi = d_bern(N,i,r)
if (i==0)
dbi = -N*(1-r).^(N-1);
elseif (i==N)
dbi = N*(r.^(N-1));
else
dbi = nchoosek(N,i)*r.^(i - 1).*(1 - r).^(N - i - 1).*(i - N*r);
end
|
github
|
KGuo26/WADG_Matlab-master
|
bern_basis_tri.m
|
.m
|
WADG_Matlab-master/bern_basis_tri.m
| 2,146 |
utf_8
|
b5c5ae2fee4e7f3bac98387d91b0b6d1
|
function [V Vr Vs V1 V2 V3 id] = bern_basis_tri(N,r,s)
[V Vr Vs V1 V2 V3] = bern_tri(N,r,s);
return
%
% % use equivalence between W&B and equispaced nodes - get ordering
% [re se] = EquiNodes2D(N); [re se] = xytors(re,se);
% Ve = bern_tri(N,re,se);
% for i = 1:size(Ve,2)
% [val iid] = max(Ve(:,i));
% id(i) = iid;
% % id(i) = i;
% end
%
% [V Vr Vs V1 V2 V3] = bern_tri(N,r,s);
% V = V(:,id);
% Vr = Vr(:,id);
% Vs = Vs(:,id);
% V1 = V1(:,id);
% V2 = V2(:,id);
% V3 = V3(:,id);
function [V Vr Vs VL1 VL2 VL3] = bern_tri(N,r,s)
% barycentric version
L1 = -(r+s)/2; L2 = (1+r)/2; L3 = (1+s)/2;
dL1r = -.5; dL2r = .5; dL3r = 0;
dL1s = -.5; dL2s = 0; dL3s = .5;
sk = 1;
for k = 0:N
for j = 0:N-k
i = N-j-k;
C=factorial(N)/(factorial(i)*factorial(j)*factorial(k));
V(:,sk) = C*(L1.^i).*(L2.^j).*(L3.^k);
dL1 = C*i*(L1.^(i-1)).*(L2.^j).*(L3.^k);
dL2 = C*j*(L1.^(i)).*(L2.^(j-1)).*(L3.^k);
dL3 = C*k*(L1.^(i)).*(L2.^j).*(L3.^(k-1));
if i==0
dL1 = zeros(size(dL1));
end
if j==0
dL2 = zeros(size(dL2));
end
if k == 0
dL3 = zeros(size(dL3));
end
Vr(:,sk) = dL1.*dL1r + dL2.*dL2r + dL3.*dL3r;
Vs(:,sk) = dL1.*dL1s + dL2.*dL2s + dL3.*dL3s;
VL1(:,sk) = dL1;
VL2(:,sk) = dL2;
VL3(:,sk) = dL3;
sk = sk + 1;
end
end
return
% [a b] = rstoab(r,s);
% a = .5*(a+1); % convert to unit
% b = .5*(b+1); % convert to unit
% be careful about derivatives at s = 1
a = .5*(r+1)./(1-.5*(s+1));
b = .5*(s+1);
a(abs(1-s)<1e-8) = 0;
dadr = 1./(1 - s);
dads = (r+1)./((1-s).^2);
dbds = .5;
sk = 1;
for i = 0:N
for j = 0:N-i
k = N-i-j;
V(:,sk) = bern(N-k,i,a).*bern(N,k,b);
Vr(:,sk) = d_bern(N-k,i,a).*bern(N,k,b).*dadr;
Vs(:,sk) = d_bern(N-k,i,a).*bern(N,k,b).*dads + bern(N-k,i,a).*d_bern(N,k,b)*dbds;
sk = sk + 1;
end
end
function bi = bern(N,i,r)
bi = nchoosek(N,i)*(r.^i).*(1-r).^(N-i);
function dbi = d_bern(N,i,r)
dbi = nchoosek(N,i)*r.^(i - 1).*(1 - r).^(N - i - 1).*(i - N*r);
|
github
|
KGuo26/WADG_Matlab-master
|
Sample2D.m
|
.m
|
WADG_Matlab-master/Sample2D.m
| 717 |
utf_8
|
be53a9f67b172f80cb774efda6b67d4b
|
function [sampleweights,sampletri] = Sample2D(xout, yout)
% function [sampleweights,sampletri] = Sample2D(xout, yout)
% purpose: input = coordinates of output data point
% output = number of containing tri and interpolation weights
% [ only works for straight sided triangles ]
Globals2D;
% find containing tri
[sampletri,tribary] = ...
tsearchn([VX', VY'], EToV, [xout,yout]);
% Matlab barycentric coordinates -> biunit triangle coordinates
sout = 2*tribary(:,3)-1; rout = 2*tribary(:,2)-1;
% build generalized Vandermonde for the sample point
Vout = Vandermonde2D(N, rout, sout);
% build interpolation matrix for the sample point
sampleweights = Vout*invV;
return;
|
github
|
KGuo26/WADG_Matlab-master
|
tet_cubature.m
|
.m
|
WADG_Matlab-master/tet_cubature.m
| 103,616 |
utf_8
|
8f12d404868158cf03a4dc3cae2976bb
|
function [r s t w] = tet_cubature(N)
% CubatureData3D_GX.cpp
% database of precalculated cubature data
% 2012/05/02
%---------------------------------------------------------
%
% N [1 1 1] [2 2] [3 3] [4
% cubN 1 2 3 4 5 6 7 8
% Ncub 1 4 6 11 14 23 31 44
%---------------------------------------------------------
%
% N 4 [5 5] [6 6] [7 7]
% cubN 9 10 11 12 13 14 15
% Ncub 57 74 95 122 146 177 214
%---------------------------------------------------------
%----------------------
% N cubN Ncub
%----------------------
% 1 ( 2, 3) [ 4, 6]
% 2 ( 4, 5) [ 11, 14]
% 3 ( 6, 7) [ 23, 31]
% 4 ( 8, 9) [ 44, 57]
% 5 (10,11) [ 74, 95]
% 6 (12,13) [122,146]
% 7 (14,15) [177,214]
% 8 ( 15) [ 214]
%----------------------
% NodeSet 1 1
TN2012_1 = [ % 1*4
0.000000000000000E+00, 0.000000000000000E+00, 0.000000000000000E+00, 0.942809041582063E+00 ];
% NodeSet 2 4
TN2012_2 = [ % 4*4
-.267702858591007E+00, 0.593901700619949E-01, -.396942694114215E+00, 0.283333858791636E+00,
0.151093184153314E+00, 0.435408773247631E+00, 0.215171925430692E+00, 0.262834019890433E+00,
-.136769919523739E+00, -.328034111559041E+00, 0.295084651993713E+00, 0.300844647504794E+00,
0.806744930905196E+00, -.340097731428529E+00, -.343037895100255E+00, 0.957965153951996E-01 ];
% NodeSet 3 6
TN2012_3 = [ % 6*4
-.168503718027600E+00, 0.191091491627171E+00, -.389626731458516E+00, 0.124986334616479E+00,
0.278379942753442E-01, -.230493283883966E-01, 0.548135066324183E+00, 0.211580648438021E+00,
-.351221617734345E+00, 0.183514402633999E+00, 0.514781533034353E-01, 0.120742171826672E+00,
0.430853246354904E+00, -.247471582318045E+00, -.168331564100703E+00, 0.237591631621872E+00,
-.467676374796738E+00, -.423525082726438E+00, -.158697307788931E+00, 0.132581964894968E+00,
0.149283125384843E+00, 0.639784768516452E+00, -.108021925305539E+00, 0.115326290184051E+00 ];
% NodeSet 4 11
TN2012_4 = [ % 11*4
-.145961228097999E-01, 0.242656457919971E-02, 0.709356578010363E+00, 0.100379025241264E+00,
-.293724489630999E+00, 0.176439650676461E+00, 0.110886049494113E+00, 0.103929831025446E+00,
0.323087833715827E+00, -.193207041095656E+00, 0.169542639670465E+00, 0.146112883409388E+00,
-.881965417939658E-01, 0.231788427010598E-01, -.201181939132559E+00, 0.182349462340124E+00,
0.127020366331471E+00, 0.545141067721522E+00, 0.130950399075932E+00, 0.718068965649525E-01,
-.441430768809118E+00, -.384822563159018E+00, 0.922542967916253E-01, 0.748841921109801E-01,
0.393941858963343E+00, 0.206874467063953E+00, -.311156042619824E+00, 0.654969120044006E-01,
-.603446971461421E-01, -.457373907492708E+00, -.330221532932238E+00, 0.565055488469881E-01,
-.891405983460118E-01, 0.726809265959946E+00, -.350793173736374E+00, 0.522257744237005E-01,
0.654521303360376E+00, -.397035624387057E+00, -.297042483395114E+00, 0.521122321989204E-01,
-.730764225967786E+00, -.266942008864898E+00, -.386103757024185E+00, 0.370062834158988E-01 ];
% NodeSet 5 14
TN2012_5 = [ % 14*4
0.166418072157768E-15, 0.109436232414623E-15, 0.770436782529672E+00, 0.692899055434865E-01,
0.408992591748701E+00, -.236131982942675E+00, 0.333941052787584E+00, 0.401127730719707E-01,
0.633846727543000E-16, 0.472263965885350E+00, 0.333941052787583E+00, 0.401127730719707E-01,
-.408992591748701E+00, -.236131982942675E+00, 0.333941052787583E+00, 0.401127730719708E-01,
0.372390093103578E-16, 0.354800148930181E-16, -.298278869430759E+00, 0.106243195244073E+00,
-.243543677053202E+00, 0.140610007506098E+00, 0.994262898102530E-01, 0.106243195244073E+00,
0.771865760070526E-17, -.281220015012195E+00, 0.994262898102531E-01, 0.106243195244073E+00,
0.243543677053203E+00, 0.140610007506098E+00, 0.994262898102530E-01, 0.106243195244073E+00,
0.629058998756435E+00, -.363187382268184E+00, -.256812260843224E+00, 0.692899055434864E-01,
-.629058998756435E+00, -.363187382268184E+00, -.256812260843224E+00, 0.692899055434865E-01,
-.140776895379933E-15, 0.726374764536368E+00, -.256812260843224E+00, 0.692899055434864E-01,
0.408992591748701E+00, 0.236131982942675E+00, -.333941052787584E+00, 0.401127730719707E-01,
-.104796521146490E-15, -.472263965885350E+00, -.333941052787583E+00, 0.401127730719707E-01,
-.408992591748701E+00, 0.236131982942675E+00, -.333941052787584E+00, 0.401127730719707E-01 ];
% NodeSet 6 23
TN2012_6 = [ % 23*4
0.491994195152311E-02, -.139223850343092E-01, 0.106622826396772E+01, 0.668997954335606E-02,
0.341505806021954E-01, 0.254207383054340E+00, 0.631588696703923E+00, 0.297073565323308E-01,
-.456119832861723E+00, -.311038544744538E+00, 0.230026765510699E+00, 0.228454763572233E-01,
0.214449922976949E+00, -.123490387324972E+00, 0.563235163630283E+00, 0.460940056044558E-01,
-.154069305999594E+00, -.686521447230217E-01, 0.620626489628262E+00, 0.500002972857723E-01,
0.306452540322295E+00, 0.181849550192318E+00, 0.125468587644291E+00, 0.407663998761417E-01,
-.130588295951477E-01, -.248889116907340E-01, -.335770592628266E+00, 0.632962545391904E-01,
0.153524468983807E-01, -.326114310053292E+00, 0.114778898592700E+00, 0.564001074548194E-01,
-.156778448777534E+00, 0.252245430100092E+00, 0.214059780070552E+00, 0.590334934291369E-01,
-.397361694050676E+00, -.197715908337850E+00, -.796336180288233E-01, 0.606546975975660E-01,
0.976488461664955E-02, 0.650418429113368E+00, 0.586844628157869E-02, 0.410974214177224E-01,
-.522808798464745E+00, -.663505530412342E-01, 0.193427268845595E-02, 0.212751151391656E-01,
0.556560570221033E+00, -.318669005422011E+00, 0.137974025142484E-01, 0.439472843110177E-01,
0.842308197902346E-01, 0.213805982286452E-01, 0.319575612691255E-01, 0.106159736633763E+00,
0.217416609116024E+00, 0.459950700499800E+00, -.302723230375316E+00, 0.508504156781972E-01,
0.321215894522565E+00, -.426005242154505E+00, -.299485560260426E+00, 0.469223144374328E-01,
-.259364807854540E+00, 0.385198021113758E+00, -.282519709925312E+00, 0.594595362053551E-01,
0.563645476720258E+00, -.111160622782170E+00, -.306814049530041E+00, 0.444515894220345E-01,
-.641094964462573E+00, -.193431833850761E+00, -.366047360516159E+00, 0.240457075184084E-01,
-.334415109058215E+00, -.449175869972365E+00, -.310242329274613E+00, 0.374428103418277E-01,
-.189458838689202E-01, 0.901059945993096E+00, -.347395205043690E+00, 0.150146857671485E-01,
-.821465589619935E+00, -.510986035935518E+00, -.263564524010136E+00, 0.982721516374585E-02,
0.859741547561566E+00, -.520808514122574E+00, -.360343258536933E+00, 0.682714132625144E-02 ];
% NodeSet 7 31
TN2012_7 = [ % 31*4
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0.323006856476517E+00, -.116660814480029E+00, 0.596321726821282E+00, 0.113172838083258E-01,
0.179003094309949E-02, 0.276022304167135E-02, 0.919167034243546E+00, 0.218890177509953E-01,
-.174985856285022E+00, 0.149255307127258E+00, 0.516292138739824E+00, 0.211296379289743E-01,
0.351581822451223E-01, 0.322257000983196E+00, 0.549210359947022E+00, 0.219589530907310E-01,
-.114891280250506E+00, 0.359815024699799E-01, -.388691765396646E+00, 0.257668903243402E-01,
0.108024261958047E+00, -.212321108274526E+00, 0.513821465706857E+00, 0.271493965375484E-01,
0.996088601920952E-01, 0.337060034616579E+00, -.273250222640217E+00, 0.405324562077599E-01,
0.255258965717304E+00, 0.234593138765271E-01, -.128926642642749E+00, 0.497630316079319E-01,
0.301897209396454E+00, -.279744081272853E+00, -.336815502815560E+00, 0.394457458861626E-01,
-.363799525166287E-01, 0.678415814779263E+00, 0.496515459049994E-01, 0.195275383339862E-01,
-.197974843267435E+00, -.109926012070685E+00, 0.368810136731088E+00, 0.568528141204943E-01,
0.170554760858719E+00, 0.585448114971668E-01, 0.324539845033623E+00, 0.628959292907061E-01,
0.538271457535773E+00, -.332620243815606E+00, 0.653099846338948E-01, 0.296146949713317E-01,
-.582625595155966E+00, -.286639428866960E+00, 0.405007368919429E-01, 0.266935521529227E-01,
-.663882748425514E-01, 0.339664373274517E+00, 0.433913762115470E-01, 0.629225940565768E-01,
0.139486545048336E+00, -.307850231836867E+00, -.855418822812698E-02, 0.605178177170064E-01,
-.252544821156148E+00, -.968770785232076E-01, -.121223434312817E+00, 0.776291699936313E-01,
-.336417947935678E+00, -.422280930562356E+00, 0.224493699213366E-01, 0.196789751488124E-01,
0.508817409509134E+00, -.460796001465295E-01, -.538915514978018E-01, 0.231576809797211E-01,
-.399767677129958E+00, 0.169411793400156E+00, -.133176911076194E-01, 0.210705295411276E-01,
0.223357920899763E+00, 0.507026392231846E+00, -.399617306009708E-01, 0.209118054635657E-01,
-.288768983232798E+00, -.432041161224173E+00, -.330437075713885E+00, 0.348234147884134E-01,
0.243005680598846E+00, 0.526215548808249E+00, -.363369754803439E+00, 0.157467976153176E-01,
-.240245918466879E+00, 0.491923965978304E+00, -.318467007553824E+00, 0.318630473755232E-01,
-.573712249516894E+00, -.745318233435191E-01, -.334800074141430E+00, 0.264563326318226E-01,
0.553074058634518E+00, -.335502653046674E-02, -.350744403540142E+00, 0.198864503476964E-01,
0.763216087154036E+00, -.434539428837598E+00, -.312047989157562E+00, 0.194127668598410E-01,
0.110832352531628E-02, 0.883581509323175E+00, -.315533688400763E+00, 0.181045845852361E-01,
-.766658972551922E+00, -.454032742499625E+00, -.314870223157801E+00, 0.170243759166119E-01,
0.331437926843703E+00, -.541903403372277E+00, -.310441026876215E+00, 0.117984136552630E-01 ];
% NodeSet 8 44
TN2012_8 = [ % 44*4
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-.169910697181091E-13, -.249020633102366E-15, 0.427541621225973E+00, 0.136188175091050E-01,
0.156992255283360E-12, 0.102872783354632E-12, 0.145464511562067E+00, 0.887275759574957E-02,
0.535998565692471E-13, 0.781044393753285E-13, -.107279857079785E+00, 0.855159201414087E-02 ];
maptorst = true;
switch N
case 1
rstw = TN2012_1;
case 2
rstw = TN2012_2;
case 3
rstw = TN2012_3;
case 4
rstw = TN2012_4;
case 5
rstw = TN2012_5;
case 6
rstw = TN2012_6;
case 7
rstw = TN2012_7;
case 8
rstw = TN2012_8;
case 9
rstw = TN2012_9;
case 10
rstw = TN2012_10;
case 11
rstw = TN2012_11;
case 12
rstw = TN2012_12;
case 13
rstw = TN2012_13;
case 14
rstw = TN2012_14;
case 15
rstw = TN2012_15;
otherwise
% error('Quadrature degree too high.')
[r s t w] = tet_cubature_TP(ceil(N/2));
rstw = [r(:) s(:) t(:) w(:)];
maptorst = false;
end
r = rstw(:,1); s = rstw(:,2); t = rstw(:,3);
if maptorst
[r s t] = xyztorst(r,s,t);
end
w = rstw(:,4); w = (4/3)*w/sum(w);
% integrates order (2*N+1)
function [r s t w] = tet_cubature_TP(N)
[ra wa] = JacobiGQ(0,0,N);
[rb wb] = JacobiGQ(1,0,N);
[rc wc] = JacobiGQ(2,0,N);
[a b c] = meshgrid(ra, rb, rc);
[wa wb wc] = meshgrid(wa,wb,wc);
a = a(:); b = b(:); c = c(:);
wa = wa(:); wb = wb(:); wc = wc(:);
w = wa.*wb.*wc;
w = (4/3)*w/sum(w);
[r s t] = tet_abctorst(a,b,c);
|
github
|
KGuo26/WADG_Matlab-master
|
ElasticityAcoustic2D_unified.m
|
.m
|
WADG_Matlab-master/ElasticityAcoustic2D_unified.m
| 12,686 |
utf_8
|
a6984b83845491ed80d1ac4cd5a5ea33
|
% function ElasticAcoustic2D_unified
clear all, clear
% clear -global *
Globals2D
global tau1 tau2
global ue3 ue4 ue5 ua3
global fa1 fa2 fe1 fe2
K1D = 16;
N = 4;
c_flag = 0;
FinalTime = 2.0;
tau1 = 1;
tau2 = 1;
[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);
StartUp2D;
[rp sp] = EquiNodes2D(15); [rp sp] = xytors(rp,sp);
Vp = Vandermonde2D(N,rp,sp)/V;
xp = Vp*x; yp = Vp*y;
Nq = 2*N;
[rq sq wq] = Cubature2D(Nq); % integrate u*v*c
Vq = Vandermonde2D(N,rq,sq)/V;
Pq = V*V'*Vq'*diag(wq); % J's cancel out
Mref = inv(V*V');
xq = Vq*x; yq = Vq*y;
Jq = Vq*J;
% partition mesh: y > 0 = acoustic
global Ka Ke
% Ke = 1:K; Ka = [];
% Ka = 1:K; Ke = [];
Ka = find(mean(y) > 0); Ke = find(mean(y) < 0);
%% find dividing boundary
global mapAM vmapAM mapAP vmapAP
global mapEM vmapEM mapEP vmapEP
mapAM = []; mapAP = [];
vmapAM = []; vmapAP = [];
mapEM = []; mapEP = [];
vmapEM = []; vmapEP = [];
if ~isempty(Ke) && ~isempty(Ka)
mapAM = zeros(Nfp,K1D); mapAP = zeros(Nfp,K1D);
vmapAM = zeros(Nfp,K1D); vmapAP = zeros(Nfp,K1D);
mapEM = zeros(Nfp,K1D); mapEP = zeros(Nfp,K1D);
vmapEM = zeros(Nfp,K1D); vmapEP = zeros(Nfp,K1D);
yM = reshape(y(vmapM),Nfp*Nfaces,K);
ska = 1; ske = 1;
for e = 1:K
yf = reshape(yM(:,e),Nfp,Nfaces);
for f = 1:Nfaces
if norm(yf(:,f))<1e-8
ids = (1:Nfp) + (f-1)*Nfp + (e-1)*Nfp*Nfaces;
if ismember(e,Ka) % if in acoustic region
mapAM(:,ska) = mapM(ids);
mapAP(:,ska) = mapP(ids);
vmapAM(:,ska) = vmapM(ids);
vmapAP(:,ska) = vmapP(ids);
ska = ska + 1;
elseif ismember(e,Ke) % if in elastic region
mapEM(:,ske) = mapM(ids);
mapEP(:,ske) = mapP(ids);
vmapEM(:,ske) = vmapM(ids);
vmapEP(:,ske) = vmapP(ids);
ske = ske + 1;
else
keyboard
end
end
end
end
% keyboard
end
%%
global Nfld mu lambda Vq Pq c2
Nfld = 5; %(u1,u2,sxx,syy,sxy)
mu = ones(size(xq));
lambda = ones(size(xq));
c2 = ones(size(xq));
% k = 1;
% mu = 1 + .5*cos(k*pi*xq).*cos(k*pi*yq);
% c2 = 1 + .5*cos(k*pi*xq).*cos(k*pi*yq);
% mu = V\(Pq*mu);
% c2 = V\(Pq*c2);
% mu = repmat(mu(1,:),length(wq),1)/sqrt(2);
% c2 = repmat(c2(1,:),length(wq),1)/sqrt(2);
% vv = Vp*Pq*c2; color_line3(xp,yp,vv,vv,'.');return
% keyboard
%% implement exact Scholte wave
Scholte;
%keyboard
%% params setup
x0 = 0;
y0 = .1;
pp = exp(-100^2*((x-x0).^2 + (y-y0).^2));
f0 = 10;
t0 = 1/f0;
% point source
%global fsrc
%fsrc = @(t) (t < t0).*(1-2*(pi*f0*(t-t0))^2)*exp(-(pi*f0*(t-t0)^2)).* (Pq * exp(-100^2*((xq-x0).^2 + (yq-y0).^2)));
%fsrc = @(t) 0;
y0 = -.25;
%p = exp(-25^2*((x-x0).^2 + (y-y0).^2));
u = zeros(Np, K);
%% set initial value using exact solution of scholte wave
U{1}(:,Ka) = u1a(x(:,Ka),y(:,Ka),0);
U{1}(:,Ke) = u1e(x(:,Ke),y(:,Ke),0);
U{2}(:,Ka) = u2a(x(:,Ka),y(:,Ka),0);
U{2}(:,Ke) = u2e(x(:,Ke),y(:,Ke),0);
U{3}(:,Ka) = s1ax(x(:,Ka),y(:,Ka),0);
U{3}(:,Ke) = s1ex(x(:,Ke),y(:,Ke),0);
U{4}(:,Ka) = s2ay(x(:,Ka),y(:,Ka),0);
U{4}(:,Ke) = s2ey(x(:,Ke),y(:,Ke),0);
U{5}(:,Ka) = zeros(Np,length(Ka));
U{5}(:,Ke) = s12exy(x(:,Ke),y(:,Ke),0);
% U{1} = u;
% U{2} = u;
% U{3} = u;
% U{4} = u;
% U{5} = u;
%% test numerical stability
if Nfld*Np*K < 2500
u = zeros(Nfld*Np*K,1);
rhs = zeros(Nfld*Np*K,1);
A = zeros(Nfld*Np*K);
ids = 1:Np*K;
for i = 1:Nfld*Np*K
u(i) = 1;
for fld = 1:Nfld
U{fld} = reshape(u(ids + (fld-1)*Np*K),Np,K);
end
% ue1 = s1ex(x,y,0);
% ue2 = s2ey(x,y,0);
% ue3 = s12exy(x,y,0);
% ua1 = s1ax(x,y,0);
rE = ElasRHS2D(U,0);
rA = AcousRHS2D(U,0);
u(i) = 0;
for fld = 1:Nfld
rU = zeros(Np,K);
rU(:,Ke) = rE{fld}(:,Ke);
if fld <= 3
rU(:,Ka) = rA{fld}(:,Ka);
end
rhs(ids + (fld-1)*Np*K) = rU;
end
A(:,i) = rhs(:);
if (mod(i,100)==0)
disp(sprintf('on col %d out of %d\n',i,Np*K*Nfld))
end
end
lam = eig(A);
plot(lam,'.','markersize',24)
hold on
title(sprintf('Largest real part = %g\n',max(real(lam))))
axis equal
% drawnow
max(abs(lam))
keyboard
end
%%
time = 0;
% Runge-Kutta residual storage
for fld = 1:Nfld
res{fld} = zeros(Np,K);
%rhs{fld} = zeros(Np,K);
end
% compute time step size
CN = (N+1)*(N+2)/3; % guessing...
dt = 2/(max(2*mu(:)+lambda(:))*CN*max(Fscale(:)));
Nsteps = ceil(FinalTime/dt);
dt = FinalTime/Nsteps;
% outer time step loop
tstep = 0;
M = inv(V*V');
wqJ = diag(wq)*(Vq*J);
% figure
% colormap(gray)
% colormap(hot)
for tstep = 1:Nsteps
time = tstep*dt;
for INTRK = 1:5
timeloc = time + rk4c(INTRK)*dt;
ue3 = s1ex(x,y,timeloc);
ue4 = s2ey(x,y,timeloc);
ue5 = s12exy(x,y,timeloc);
ua3 = s1ax(x,y,timeloc);
%fa1 = f1a(x,y,timeloc);
%fa2 = f2a(x,y,timeloc);
%fe1 = f1e(x,y,timeloc);
%fe2 = f2e(x,y,timeloc);
rhsE = ElasRHS2D(U,timeloc);
rhsA = AcousRHS2D(U,timeloc);
% initiate and increment Runge-Kutta residuals
for fld = 1:Nfld
rhs = zeros(Np,K);
if fld <= 3
rhs(:,Ka) = rhsA{fld}(:,Ka);
end
rhs(:,Ke) = rhsE{fld}(:,Ke);
res{fld} = rk4a(INTRK)*res{fld} + dt*rhs;
U{fld} = U{fld} + rk4b(INTRK)*res{fld};
end
end
if 1 && (mod(tstep,10)==0 || tstep==Nsteps)
clf
pe = (U{3} + U{4})/2; % average trace(S)
% pe = U{3}; % average trace(S)
%pa = (U{3} + U{4})/2;
pa = U{3}; % average trace(S)
p(:,Ke) = pe(:,Ke);
p(:,Ka) = pa(:,Ka);
vv = Vp*p;
color_line3(xp,yp,vv,vv,'.');
axis tight
title(sprintf('time = %f',time));
colorbar;
drawnow
p_exact(:,Ka) = s1ax(xq(:,Ka),yq(:,Ka),time);
p_exact(:,Ke) = 0.5*s1ex(xq(:,Ke),yq(:,Ke),time)+0.5*s2ey(xq(:,Ke),yq(:,Ke),time);
p_quadrature = Vq * p;
[d1,d2]=size(p_quadrature);
error_accumulation = 0;
for j1=1:d2
for j2=1:d1
err = p_quadrature(j2,j1)-p_exact(j2,j1);
error_accumulation = error_accumulation + err*err*wq(j2)*J(1,j1);
end
end
error_l2 = sqrt(error_accumulation)
end
if mod(tstep,100)==0
disp(sprintf('On timestep %d out of %d\n',tstep,round(FinalTime/dt)))
end
end
% keyboard
set(gca,'fontsize',14)
title('')
axis tight
p_exact(:,Ka) = s1ax(xq(:,Ka),yq(:,Ka),FinalTime);
p_exact(:,Ke) = 0.5*s1ex(xq(:,Ke),yq(:,Ke),FinalTime)+0.5*s2ey(xq(:,Ke),yq(:,Ke),FinalTime);
p_quadrature = Vq * p;
[d1,d2]=size(p_quadrature);
error_accumulation = 0;
for j1=1:d2
for j2=1:d1
err = p_quadrature(j2,j1)-p_exact(j2,j1);
error_accumulation = error_accumulation + err*err*wq(j2)*J(1,j1);
end
end
error_l2 = sqrt(error_accumulation)
function [rhs] = ElasRHS2D(U,time)
% function [rhsu, rhsv, rhsp] = acousticsRHS2D(u,v,p)
% Purpose : Evaluate RHS flux in 2D acoustics TM form
Globals2D;
global Nfld mu lambda Vq Pq tau1 tau2 useWADG
global C11 C12 C13 C22 C23 C33
global ue3 ue4 ue5 ua3
global fa1 fa2 fe1 fe2
% Define field differences at faces
for fld = 1:Nfld
u = U{fld};
% compute jumps
dU{fld} = zeros(Nfp*Nfaces,K);
dU{fld}(:) = u(vmapP)-u(vmapM);
ur = Dr*u;
us = Ds*u;
Ux{fld} = rx.*ur + sx.*us;
Uy{fld} = ry.*ur + sy.*us;
end
divSx = Ux{3} + Uy{5}; % d(Sxx)dx + d(Sxy)dy
divSy = Ux{5} + Uy{4}; % d(Sxy)dx + d(Syy)dy
du1dx = Ux{1}; % du1dx
du2dy = Uy{2}; % du2dy
du12dxy = Ux{2} + Uy{1}; % du2dx + du1dy
% velocity fluxes An^T*sigma
nSx = nx.*dU{3} + ny.*dU{5};
nSy = nx.*dU{5} + ny.*dU{4};
% stress fluxes An*u
nUx = dU{1}.*nx;
nUy = dU{2}.*ny;
nUxy = dU{2}.*nx + dU{1}.*ny;
opt=1;
if opt==1 % traction BCs
% set traction equal to exact traction
nSx(mapB) = -2*(nx(mapB).*U{3}(vmapB) + ny(mapB).*U{5}(vmapB)) + 2*(nx(mapB).*ue3(vmapB) + ny(mapB).*ue5(vmapB));
nSy(mapB) = -2*(nx(mapB).*U{5}(vmapB) + ny(mapB).*U{4}(vmapB)) + 2*(nx(mapB).*ue5(vmapB) + ny(mapB).*ue4(vmapB));
%nSx(mapB) = -2*(nx(mapB).*ue1(vmapB) + ny(mapB).*ue3(vmapB));
%nSy(mapB) = -2*(nx(mapB).*ue3(vmapB) + ny(mapB).*ue2(vmapB));
%nSx(mapB) = -2*(nx(mapB).*U{3}(vmapB) + ny(mapB).*U{5}(vmapB));
%nSy(mapB) = -2*(nx(mapB).*U{5}(vmapB) + ny(mapB).*U{4}(vmapB));
elseif opt==2 % basic ABCs
nSx(mapB) = -(nx(mapB).*U{3}(vmapB) + ny(mapB).*U{5}(vmapB));
nSy(mapB) = -(nx(mapB).*U{5}(vmapB) + ny(mapB).*U{4}(vmapB));
dU{1}(mapB) = -U{1}(vmapB);
dU{2}(mapB) = -U{2}(vmapB);
elseif opt==3 % zero velocity
dU{1}(mapB) = -2*U{1}(vmapB);
dU{2}(mapB) = -2*U{2}(vmapB);
end
% coupling
global mapEM vmapEM mapEP vmapEP
nxf = nx(mapEM); nyf = ny(mapEM);
u = U{1}(vmapEP);
v = U{2}(vmapEP);
p = U{3}(vmapEP);
Snx = U{3}(vmapEM).*nxf + U{5}(vmapEM).*nyf;
Sny = U{5}(vmapEM).*nxf + U{4}(vmapEM).*nyf;
Un = u.*nxf + v.*nyf;
dU1 = (Un.*nxf-U{1}(vmapEM));
dU2 = (Un.*nyf-U{2}(vmapEM));
UnM = (u-U{1}(vmapEM)).*nxf + (v-U{2}(vmapEM)).*nyf;
dU1M = UnM.*nxf;
dU2M = UnM.*nyf;
nSx(mapEM) = (1*p.*nxf - Snx);
nSy(mapEM) = (1*p.*nyf - Sny);
nUx(mapEM) = dU1.*nxf;
nUy(mapEM) = dU2.*nyf;
nUxy(mapEM) = dU1.*nyf + dU2.*nxf;
% evaluate central fluxes
fc{1} = nSx;
fc{2} = nSy;
fc{3} = nUx;
fc{4} = nUy;
fc{5} = nUxy;
% penalization terms - reapply An
fp{1} = nx.*fc{3} + ny.*fc{5};
fp{2} = nx.*fc{5} + ny.*fc{4};
fp{1}(mapEM) = dU1M;
fp{2}(mapEM) = dU2M;
fp{3} = fc{1}.*nx;
fp{4} = fc{2}.*ny;
fp{5} = fc{2}.*nx + fc{1}.*ny;
flux = cell(5,1);
for fld = 1:2
flux{fld} = zeros(Nfp*Nfaces,K);
flux{fld}(:) = fc{fld}(:) + tau1.*fp{fld}(:);
end
for fld = 3:5
flux{fld} = zeros(Nfp*Nfaces,K);
flux{fld}(:) = fc{fld}(:) + tau2.*fp{fld}(:);
end
% compute right hand sides of the PDE's
rr{1} = divSx + LIFT*(Fscale.*flux{1})/2.0;
rr{2} = divSy + LIFT*(Fscale.*flux{2})/2.0;
rr{3} = du1dx + LIFT*(Fscale.*flux{3})/2.0;
rr{4} = du2dy + LIFT*(Fscale.*flux{4})/2.0;
rr{5} = du12dxy + LIFT*(Fscale.*flux{5})/2.0;
if 0
rhs{1} = rr{1};
rhs{2} = rr{2};
rhs{3} = (2*mu+lambda).*rr{3} + lambda.*rr{4};
rhs{4} = lambda.*rr{3} + (2*mu+lambda).*rr{4};
rhs{5} = (mu) .* rr{5};
else
global Pq Vq
rhs{1} = rr{1};
rhs{2} = rr{2};
rhs{3} = Pq*((2*mu+lambda).*(Vq*rr{3}) + lambda.*(Vq*rr{4}));
rhs{4} = Pq*(lambda.*(Vq*rr{3}) + (2*mu+lambda).*(Vq*rr{4}));
rhs{5} = Pq*(mu .* (Vq*rr{5}));
end
% global Ka
% for fld = 1:5
% rhs{fld}(:,Ka) = 0;
% end
end
function [rhs] = AcousRHS2D(U,time)
% function [rhsu, rhsv, rhsp] = acousticsRHS2D(u,v,p)
% Purpose : Evaluate RHS flux in 2D acoustics TM form
Globals2D;
global ua3
global fa1 fa2 fe1 fe2
u = U{1};
v = U{2};
p = U{3}; % should be same as U{4}
% Define field differences at faces
dp = zeros(Nfp*Nfaces,K); dp(:) = p(vmapP)-p(vmapM);
du = zeros(Nfp*Nfaces,K); du(:) = u(vmapP)-u(vmapM);
dv = zeros(Nfp*Nfaces,K); dv(:) = v(vmapP)-v(vmapM);
% evaluate upwind fluxes
ndotdU = nx.*du + ny.*dv;
% Impose reflective boundary conditions (p+ = -p-)
%ndotdU(mapB) = 0; % will not affect, naturally zero
%dp(mapB) = -2*p(vmapB);
dp(mapB) = -2*p(vmapB) + 2*ua3(vmapB);
% elastic-acoustic coupling
global mapAM vmapAM mapAP vmapAP
nxf = nx(mapAM); nyf = ny(mapAM);
v1 = U{1}(vmapAP); v2 = U{2}(vmapAP);
sxx = U{3}(vmapAP); syy = U{4}(vmapAP); sxy = U{5}(vmapAP);
Snx = sxx.*nxf + sxy.*nyf;
Sny = sxy.*nxf + syy.*nyf;
nSn = Snx.*nxf + Sny.*nyf;
dU1 = (v1-u(vmapAM));
dU2 = (v2-v(vmapAM));
ndotdU(mapAM) = dU1.*nxf + dU2.*nyf;
dp(mapAM) = nSn-1*p(vmapAM);
global tau1 tau2;
fluxp = tau2*dp + ndotdU;
fluxu = (tau1*ndotdU + dp).*nx;
fluxv = (tau1*ndotdU + dp).*ny;
pr = Dr*p; ps = Ds*p;
dpdx = rx.*pr + sx.*ps;
dpdy = ry.*pr + sy.*ps;
divU = Dr*(u.*rx + v.*ry) + Ds*(u.*sx + v.*sy);
% compute right hand sides of the PDE's
rhs{1} = dpdx + LIFT*(Fscale.*fluxu)/2.0;
rhs{2} = dpdy + LIFT*(Fscale.*fluxv)/2.0;
rhs{3} = divU + LIFT*(Fscale.*fluxp)/2.0;
if 0
global c2 Pq Vq
%global fsrc
%rhs{3} = rhs{3} + fsrc(time);
rhs{3} = Pq*(c2.*(Vq*rhs{3}));
end
end
|
github
|
KGuo26/WADG_Matlab-master
|
Wave2D_M_manufactured.m
|
.m
|
WADG_Matlab-master/Wave2D_M_manufactured.m
| 3,244 |
utf_8
|
7f7a6ac983321b0bffeaf09bbd71fa7f
|
clear
Globals2D
N = 8;
k=1
for M = 0:N
K1D = 32;
FinalTime = 1.0;
[Nv, VX, VY, K, EToV] = unif_tri_mesh(K1D);
StartUp2D;
%% Set up wavespeed function
%cfun = @(x,y) ones(size(x));
cfun = @(x,y) 1+ 0.5*sin(k*pi*x).*sin(k*pi*y); % smooth velocity
%cfun = @(x,y) (1 + .5*sin(2*pi*x).*sin(2*pi*y) + (y > 0)); % piecewise smooth velocity
%% Set periodic manufactured solution
pfun = @(x,y,t) sin(pi*x).*sin(pi*y).*cos(pi*t);
ufun = @(x,y,t) -cos(pi*x).*sin(pi*y).*sin(pi*t);
vfun = @(x,y,t) -sin(pi*x).*cos(pi*y).*sin(pi*t);
ffun = @(x,y,t) pi*(-1./(cfun(x,y))+2).*sin(pi*x).*sin(pi*y).*sin(pi*t);
%% generate quadrature points
Nq = 2*N+1;
[rq sq wq] = Cubature2D(Nq); % integrate u*v*c
Vq = Vandermonde2D(N,rq,sq)/V;
xq = Vq*x; yq = Vq*y;
%% construct the projection matrix for nodal basis Pq
Pq = V*V'*Vq'*diag(wq);
%% construct matrix Cq
Cq = cfun(xq,yq);
%% construct the projection matrix for cfun to degree M
VMq = Vandermonde2D(M,rq,sq);
CqM = VMq*VMq'*diag(wq)*Cq;
%% initial condition
p = pfun(x,y,0);
u = ufun(x,y,0);
v = vfun(x,y,0);
time = 0;
%% Runge-Kutta residual storage
resu = zeros(Np,K); resv = zeros(Np,K); resp = zeros(Np,K);
%% compute time step size
CN = (N+1)*(N+2)/2; % trace inequality constant
CNh = max(CN*max(Fscale(:)));
dt = 2/CNh;
%% outer time step loop
tstep = 0;
while (time<FinalTime)
if(time+dt>FinalTime), dt = FinalTime-time; end
for INTRK = 1:5
timelocal = time + rk4c(INTRK)*dt;
f=ffun(x,y,timelocal);
[rhsp, rhsu, rhsv] = acousticsRHS2D_compare(p,u,v,f);
% initiate and increment Runge-Kutta residuals
resp = rk4a(INTRK)*resp + dt*rhsp;
resu = rk4a(INTRK)*resu + dt*rhsu;
resv = rk4a(INTRK)*resv + dt*rhsv;
% update fields
u = u+rk4b(INTRK)*resu;
v = v+rk4b(INTRK)*resv;
p = p+rk4b(INTRK)*resp;
end
% Increment time
time = time+dt; tstep = tstep+1;
end
p_exact = pfun(x,y,FinalTime);
p_exact_quadrature = pfun(xq,yq,FinalTime);
p_quadrature = Vq * p;
[d1,d2]=size(p_quadrature);
error_accumulation = 0;
for j1=1:d2
for j2=1:d1
err = p_quadrature(j2,j1)-p_exact_quadrature(j2,j1);
error_accumulation = error_accumulation + err*err*wq(j2)*J(1,j1);
end
end
error_l2(M+1) = sqrt(error_accumulation);
error_fro(M+1) = norm(p-p_exact,'fro');
end
function [rhsp, rhsu, rhsv] = acousticsRHS2D_compare(p,u,v,f)
Globals2D;
% Define field differences at faces
dp = zeros(Nfp*Nfaces,K); dp(:) = p(vmapP)-p(vmapM);
du = zeros(Nfp*Nfaces,K); du(:) = u(vmapP)-u(vmapM);
dv = zeros(Nfp*Nfaces,K); dv(:) = v(vmapP)-v(vmapM);
% evaluate upwind fluxes
ndotdU = nx.*du + ny.*dv;
% Impose reflective boundary conditions (p+ = -p-)
ndotdU(mapB) = 0;
dp(mapB) = -2*p(vmapB);
tau = 1;
fluxp = tau*dp - ndotdU;
fluxu = (tau*ndotdU - dp).*nx;
fluxv = (tau*ndotdU - dp).*ny;
pr = Dr*p; ps = Ds*p;
dpdx = rx.*pr + sx.*ps;
dpdy = ry.*pr + sy.*ps;
divU = Dr*(u.*rx + v.*ry) + Ds*(u.*sx + v.*sy);
% compute right hand sides of the PDE's
rhsp = -divU + f + LIFT*(Fscale.*fluxp)/2.0;
rhsu = -dpdx + LIFT*(Fscale.*fluxu)/2.0;
rhsv = -dpdy + LIFT*(Fscale.*fluxv)/2.0;
rhsp = Pq*(CqM.*(Vq*rhsp));
return;
end
|
github
|
caghangir/MATLAB-Grid-Search-for-Neural-Networks-master
|
gridSearchNN.m
|
.m
|
MATLAB-Grid-Search-for-Neural-Networks-master/gridSearchNN.m
| 2,393 |
utf_8
|
40eb11e58ad1cf4dfde388d97884b636
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Grid Search for Matlab %
% %
% Copyright (C) 2017 Cagatay Demirel. All rights reserved. %
% [email protected] %
% %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% **** Example (How to use function) *******
%
% hidlaysize1 = [15 30 70];
% hidlaysize2 = [10 20 50];
% trainopt = {'traingd' 'traingda' 'traingdm' 'traingdx'};
% maxepoch = [10 20 40 90];
% transferfunc = {'logsig' 'tansig'};
% bestparameters = gridSearchNN(x_train',y_train',hidlaysize1,...
% hidlaysize2,trainopt,maxepoch,transferfunc);
function out = gridSearchNN(trainX,trainY,param1,param2,param3,param4,...
param5,varargin)
if(nargin > 4)
[p,q,r,s,t] = ndgrid(param1,param2,1:length(param3),param4,1:length(param5));
pairs = [p(:) q(:) r(:) s(:) t(:)];
% scoreboard = cell(size(pairs,1),4);
else
[p,q] = meshgrid(param1,param2);
pairs = [p(:) q(:)];
% scoreboard = cell(size(pairs,1),3);
end
valscores = zeros(size(pairs,1),1);
for i=1:size(pairs,1)
setdemorandstream(672880951)
net = patternnet([pairs(i,1) pairs(i,2)]);
net.trainFcn = param3{pairs(i,3)};
net.trainParam.epochs = pairs(i,4);
net.layers{2}.transferFcn = param5{pairs(i,5)};
net.divideParam.trainRatio = 0.9;
net.divideParam.valRatio = 0.1;
net.divideParam.testRatio = 0;
vals = crossval(@(XTRAIN, YTRAIN, XTEST, YTEST)NNtrain(XTRAIN, YTRAIN, XTEST, YTEST, net),...
trainX, trainY);
valscores(i) = mean(vals);
% net = train(net,trainX,trainY);
% y_pred = net(valX);
% [~,indicesReal] = max(valY, [], 1); %336x1 matrix
% [~, indicesPredicted] = max(y_pred,[],1);
% valscores(i) = mean(double(indicesPredicted == indicesReal));
end
[~,ind] = max(valscores);
out = {pairs(ind,1) pairs(ind,2) param3{pairs(ind,3)} ...
pairs(ind,4) param5{pairs(ind,5)}};
end
function testval = NNtrain(XTRAIN, YTRAIN, XTEST, YTEST, net)
net = train(net, XTRAIN', YTRAIN');
y_pred = net(XTEST');
[~,indicesReal] = max(YTEST',[],1);
[~,indicesPredicted] = max(y_pred,[],1);
testval = mean(double(indicesPredicted == indicesReal));
end
|
github
|
mrmushfiq/qalma-master
|
qalma.m
|
.m
|
qalma-master/qalma.m
| 5,230 |
utf_8
|
0e9226a0f71ab8eb22b7073be7ef050e
|
function varargout = qalma(varargin)
% QALMA MATLAB code for qalma.fig
% QALMA, by itself, creates a new QALMA or raises the existing
% singleton*.
%
% H = QALMA returns the handle to a new QALMA or the handle to
% the existing singleton*.
%
% QALMA('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in QALMA.M with the given input arguments.
%
% QALMA('Property','Value',...) creates a new QALMA or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before qalma_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to qalma_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 qalma
% Last Modified by GUIDE v2.5 08-Jun-2017 23:41:29
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @qalma_OpeningFcn, ...
'gui_OutputFcn', @qalma_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 qalma is made visible.
function qalma_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 qalma (see VARARGIN)
% Choose default command line output for qalma
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes qalma wait for user response (see UIRESUME)
% uiwait(handles.figure1);
%set(handles.date_txt, 'String', date);
% --- Outputs from this function are returned to the command line.
function varargout = qalma_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;
% --- Executes on button press in ss_button.
function ss_button_Callback(hObject, eventdata, handles)
% hObject handle to ss_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
close(qalma);
run('star_shot');
% --- Executes on button press in pf_button.
function pf_button_Callback(hObject, eventdata, handles)
% hObject handle to pf_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
close(qalma);
%run('./picket_panda8/picket_panda.m');
run('picket_panda');
% --- Executes on button press in wl_button.
function wl_button_Callback(hObject, eventdata, handles)
% hObject handle to wl_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
close(qalma);
run('wl');
% --- Executes on button press in lr_button.
function lr_button_Callback(hObject, eventdata, handles)
% hObject handle to lr_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
close(qalma);
run('ci');
% --- Executes on button press in lf_button.
function lf_button_Callback(hObject, eventdata, handles)
% hObject handle to lf_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
close(qalma);
run('dynalog');
% --- Executes on button press in rotate_button.
function rotate_button_Callback(hObject, eventdata, handles)
% hObject handle to rotate_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% --- Executes on button press in crop_button.
function crop_button_Callback(hObject, eventdata, handles)
% hObject handle to crop_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% --- Executes on button press in invert_button.
function invert_button_Callback(hObject, eventdata, handles)
% hObject handle to invert_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
|
github
|
mrmushfiq/qalma-master
|
star_shot.m
|
.m
|
qalma-master/star_shot/star_shot.m
| 26,383 |
utf_8
|
2b44cdb1fdf5db0023e49a8971a36020
|
% M. Mushfiqur Rahman
% Florida Atlantic University
% August, 2017
function varargout = star_shot(varargin)
% STAR_SHOT MATLAB code for star_shot.fig
% STAR_SHOT, by itself, creates a new STAR_SHOT or raises the existing
% singleton*.
%
% H = STAR_SHOT returns the handle to a new STAR_SHOT or the handle to
% the existing singleton*.
%
% STAR_SHOT('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in STAR_SHOT.M with the given input arguments.
%
% STAR_SHOT('Property','Value',...) creates a new STAR_SHOT or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before star_shot_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to star_shot_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 star_shot
% Last Modified by GUIDE v2.5 25-Aug-2017 22:35:28
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @star_shot_OpeningFcn, ...
'gui_OutputFcn', @star_shot_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 star_shot is made visible.
function star_shot_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 star_shot (see VARARGIN)
% Choose default command line output for star_shot
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes star_shot wait for user response (see UIRESUME)
% uiwait(handles.figure1);
set(handles.axes1, 'box','off','XTickLabel',[],'xtick',[],'YTickLabel',[],'ytick',[]);
%set(handles.num_arms_pop, 'String',{4:24});
set(handles.axes2, 'visible', 'off');
set(handles.axes3, 'visible', 'off');
set(handles.radius_name, 'visible', 'off');
set(handles.radius_txt, 'visible', 'off');
set(handles.comment_txt, 'visible', 'on');
set(handles.report_button, 'visible', 'on');
set(handles.mm_txt, 'visible', 'off');
set(handles.uitable1, 'visible', 'off');
set(handles.uitable1, 'Data', []);
set(handles.uitable1, 'ColumnName', {'Image','Gantry','Couch', 'Collimator', 'Radius(mm)','Comment'});
set(handles.wiener_check, 'Value', 1);
set(handles.w1_pop, 'String',{1:10}, 'Value', 8);
set(handles.w2_pop, 'String',{1:10}, 'Value', 8);
set(handles.avg_txt, 'String', '2');
set(handles.crop_check, 'Value', 1);
set(handles.resolution_txt, 'String', '0.39');
set(handles.mag_factor_txt, 'String', '1');
set(handles.analyze_button, 'Enable', 'off');
set(handles.report_button, 'Enable', 'off');
set(handles.add_button, 'Enable', 'off');
warning('off','all')
global analyzer;
analyzer = 0;
global c1;
global c2;
global c3;
c1 = get(handles.gantry_check, 'Value');
c2 = get(handles.couch_check, 'Value');
c3 = get(handles.collimator_check, 'Value');
% --- Outputs from this function are returned to the command line.
function varargout = star_shot_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;
% --- Executes on button press in browse_button.
function browse_button_Callback(hObject, eventdata, handles)
% hObject handle to browse_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global img;
global file_name;
[f p] = uigetfile(...
{'*.jpg; *.JPG; *.jpeg; *.JPEG; *.img; *.IMG; *.tif;*.png; *.TIF; *.tiff, *.TIFF','Supported Files (*.jpg,*.img,*.tiff,*.png)'; ...
'*.jpg','jpg Files (*.jpg)';...
'*.JPG','JPG Files (*.JPG)';...
'*.jpeg','jpeg Files (*.jpeg)';...
'*.JPEG','JPEG Files (*.JPEG)';...
'*.img','img Files (*.img)';...
'*.IMG','IMG Files (*.IMG)';...
'*.tif','tif Files (*.tif)';...
'*.TIF','TIF Files (*.TIF)';...
'*.tiff','tiff Files (*.tiff)';...
'*.TIFF','TIFF Files (*.TIFF)'},...
'MultiSelect', 'off');
try
img_d = imread([p f]);
img=im2double(img_d);
img=uint8(255*mat2gray(img));
imshow(img,'Parent',handles.axes1);
img = img_d;
file_name = f;
set(handles.analyze_button, 'Enable', 'on');
set(handles.report_button, 'Enable', 'on');
set(handles.add_button, 'Enable', 'on');
catch
h = msgbox('Please upload an image');
end
% --- Executes on button press in analyze_button.
function analyze_button_Callback(hObject, eventdata, handles)
% hObject handle to analyze_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
set(handles.analyze_button, 'Enable', 'off');
global img;
global file_name;
global analyzer;
analyzer = analyzer + 1;
%starF(handles,img);
[radii,pass] = starF(handles,img);
if pass == 1
set(handles.browse_button, 'Visible', 'off');
setpixelposition(handles.uitable1,[65, 10, 490, 127]);
set(handles.uitable1, 'visible', 'on');
global c1;
global c2;
global c3;
c1 = get(handles.gantry_check, 'Value');
c2 = get(handles.couch_check, 'Value');
c3 = get(handles.collimator_check, 'Value');
D = get(handles.uitable1, 'Data');
[r,c] = size(D);
D{r+1,1} = file_name;
if c1==1
D{r+1,2} = ' X ';
elseif c2 == 1
D{r+1,3} = ' X ';
elseif c3 == 1
D{r+1,4} = ' X ';
else
h = msgbox({'Check at least one of the three components'});
pause(2)
delete(h);
end
D{r+1,5} = get(handles.radius_txt, 'String');
% if pass == 1
% D{r+1,5} = get(handles.radius_txt, 'String');
% else
% D{r+1,5} = '-';
% end
D{r+1,6} = get(handles.comment_txt, 'String');
set(handles.uitable1, 'Data', D);
set(handles.row_pop, 'String', {1:r+1});
end
% --- Executes on selection change in num_arms_pop.
function num_arms_pop_Callback(hObject, eventdata, handles)
% hObject handle to num_arms_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns num_arms_pop contents as cell array
% contents{get(hObject,'Value')} returns selected item from num_arms_pop
% --- Executes during object creation, after setting all properties.
function num_arms_pop_CreateFcn(hObject, eventdata, handles)
% hObject handle to num_arms_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu 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
function radius_txt_Callback(hObject, eventdata, handles)
% hObject handle to radius_txt (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 radius_txt as text
% str2double(get(hObject,'String')) returns contents of radius_txt as a double
% --- Executes during object creation, after setting all properties.
function radius_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to radius_txt (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
function comment_txt_Callback(hObject, eventdata, handles)
% hObject handle to comment_txt (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 comment_txt as text
% str2double(get(hObject,'String')) returns contents of comment_txt as a double
% --- Executes during object creation, after setting all properties.
function comment_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to comment_txt (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 report_button.
function report_button_Callback(hObject, eventdata, handles)
% hObject handle to report_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
tc = get(handles.comment_txt, 'String');
tc = strcat('Comment:', tc);
result = get(handles.radius_txt, 'String');
result = strcat('Radius: ',result);
D = get(handles.uitable1, 'Data');
f2=figure
t = uitable(f2,'Data',D,'Position',[20 60 500 300]);
t.ColumnName = {'Image','Gantry','Couch', 'Collimator', 'Radius(mm)','Comment'}
saveas(f2,'Star_Report.pdf');
% --- Executes on button press in add_button.
function add_button_Callback(hObject, eventdata, handles)
% hObject handle to add_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global img;
global file_name;
%set(handles.axes2, 'visible', 'off');
set(handles.axes2, 'Units', 'pixels', 'Position', [300, 300, 10, 10]);
[f p] = uigetfile(...
{'*.jpg; *.JPG; *.jpeg; *.JPEG; *.img; *.IMG; *.tif;*.png; *.TIF; *.tiff, *.TIFF','Supported Files (*.jpg,*.img,*.tiff,*.png)'; ...
'*.jpg','jpg Files (*.jpg)';...
'*.JPG','JPG Files (*.JPG)';...
'*.jpeg','jpeg Files (*.jpeg)';...
'*.JPEG','JPEG Files (*.JPEG)';...
'*.img','img Files (*.img)';...
'*.IMG','IMG Files (*.IMG)';...
'*.tif','tif Files (*.tif)';...
'*.TIF','TIF Files (*.TIF)';...
'*.tiff','tiff Files (*.tiff)';...
'*.TIFF','TIFF Files (*.TIFF)'},...
'MultiSelect', 'off');
try
img_d = imread([p f]);
img=im2double(img_d);
img=uint8(255*mat2gray(img));
set(handles.axes1, 'Units', 'pixels', 'Position', [37, 99, 544, 397]);
imshow(img,'Parent',handles.axes1);
setpixelposition(handles.uitable1,[65, 10, 490, 70]);
img = img_d;
file_name = f;
set(handles.analyze_button, 'Enable', 'on');
catch
h = msgbox('Please upload an image');
end
%imshow(img,'Parent',handles.axes1);
% --- Executes on button press in gantry_check.
function gantry_check_Callback(hObject, eventdata, handles)
% hObject handle to gantry_check (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of gantry_check
set(handles.couch_check,'Value',0);
set(handles.collimator_check,'Value',0);
% --- Executes on button press in couch_check.
function couch_check_Callback(hObject, eventdata, handles)
% hObject handle to couch_check (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of couch_check
set(handles.gantry_check,'Value',0);
set(handles.collimator_check,'Value',0);
% --- Executes on button press in collimator_check.
function collimator_check_Callback(hObject, eventdata, handles)
% hObject handle to collimator_check (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of collimator_check
set(handles.couch_check,'Value',0);
set(handles.gantry_check,'Value',0);
% --- Executes on button press in delete_row_button.
function delete_row_button_Callback(hObject, eventdata, handles)
% hObject handle to delete_row_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% --- Executes on selection change in row_pop.
function row_pop_Callback(hObject, eventdata, handles)
% hObject handle to row_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns row_pop contents as cell array
% contents{get(hObject,'Value')} returns selected item from row_pop
% --- Executes during object creation, after setting all properties.
function row_pop_CreateFcn(hObject, eventdata, handles)
% hObject handle to row_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu 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 manual_check.
function manual_check_Callback(hObject, eventdata, handles)
% hObject handle to manual_check (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of manual_check
function rotate_txt_Callback(hObject, eventdata, handles)
% hObject handle to rotate_txt (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 rotate_txt as text
% str2double(get(hObject,'String')) returns contents of rotate_txt as a double
% global img;
% angle = str2num(get(handles.rotate_txt,'String'));
%
% if get(handles.manual_check, 'Value') == 1
% img = imrotate(img, angle);
% imshow(img,'Parent',handles.axes2);
% else
% h = msgbox({'Please Activate Manual mode'});
% pause(1)
% delete(h);
% end
% --- Executes during object creation, after setting all properties.
function rotate_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to rotate_txt (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 center_check.
function center_check_Callback(hObject, eventdata, handles)
% hObject handle to center_check (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of center_check
% --- Executes on button press in invert_check.
function invert_check_Callback(hObject, eventdata, handles)
% hObject handle to invert_check (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of invert_check
global img;
v1 = get(handles.manual_check, 'Value');
v2 = get(handles.invert_check, 'Value');
if v1 == 1 & v2 == 1
img = imcomplement(img);
imshow(img,'Parent',handles.axes1);
end
set(handles.invert_check, 'Value',0);
% --- Executes on button press in analyze_button.
function pushbutton8_Callback(hObject, eventdata, handles)
% hObject handle to analyze_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
function avg_txt_Callback(hObject, eventdata, handles)
% hObject handle to avg_txt (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 avg_txt as text
% str2double(get(hObject,'String')) returns contents of avg_txt as a double
% --- Executes during object creation, after setting all properties.
function avg_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to avg_txt (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 selection change in w1_pop.
function w1_pop_Callback(hObject, eventdata, handles)
% hObject handle to w1_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns w1_pop contents as cell array
% contents{get(hObject,'Value')} returns selected item from w1_pop
% --- Executes during object creation, after setting all properties.
function w1_pop_CreateFcn(hObject, eventdata, handles)
% hObject handle to w1_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu 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 selection change in w2_pop.
function w2_pop_Callback(hObject, eventdata, handles)
% hObject handle to w2_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns w2_pop contents as cell array
% contents{get(hObject,'Value')} returns selected item from w2_pop
% --- Executes during object creation, after setting all properties.
function w2_pop_CreateFcn(hObject, eventdata, handles)
% hObject handle to w2_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu 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 delete_button.
function delete_button_Callback(hObject, eventdata, handles)
% hObject handle to delete_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
D = get(handles.uitable1, 'Data');
[r,c] = size(D);
row = get(handles.row_pop, 'Value');
if r>0
D(row,:) = [];
end
set(handles.uitable1, 'Data', D);
function edit9_Callback(hObject, eventdata, handles)
% hObject handle to edit9 (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 edit9 as text
% str2double(get(hObject,'String')) returns contents of edit9 as a double
% --- Executes during object creation, after setting all properties.
function edit9_CreateFcn(hObject, eventdata, handles)
% hObject handle to edit9 (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 report_button.
function pushbutton12_Callback(hObject, eventdata, handles)
% hObject handle to report_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% --- Executes on button press in wiener_check.
function wiener_check_Callback(hObject, eventdata, handles)
% hObject handle to wiener_check (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of wiener_check
function edit10_Callback(hObject, eventdata, handles)
% hObject handle to comment_txt (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 comment_txt as text
% str2double(get(hObject,'String')) returns contents of comment_txt as a double
% --- Executes during object creation, after setting all properties.
function edit10_CreateFcn(hObject, eventdata, handles)
% hObject handle to comment_txt (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
function mag_factor_txt_Callback(hObject, eventdata, handles)
% hObject handle to mag_factor_txt (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 mag_factor_txt as text
% str2double(get(hObject,'String')) returns contents of mag_factor_txt as a double
% --- Executes during object creation, after setting all properties.
function mag_factor_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to mag_factor_txt (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
function resolution_txt_Callback(hObject, eventdata, handles)
% hObject handle to resolution_txt (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 resolution_txt as text
% str2double(get(hObject,'String')) returns contents of resolution_txt as a double
% --- Executes during object creation, after setting all properties.
function resolution_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to resolution_txt (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 crop_check.
function crop_check_Callback(hObject, eventdata, handles)
% hObject handle to crop_check (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of crop_check
% --- Executes on button press in home_button.
function home_button_Callback(hObject, eventdata, handles)
% hObject handle to home_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
close(star_shot);
run('qalma');
% --- Executes when user attempts to close figure1.
function figure1_CloseRequestFcn(hObject, eventdata, handles)
% hObject handle to figure1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: delete(hObject) closes the figure
delete(hObject);
if exist('starshot.png', 'file')==2
delete('starshot.png');
end
if exist('starshot_zoomed.png', 'file')==2
delete('starshot_zoomed.png');
end
clearvars;
|
github
|
mrmushfiq/qalma-master
|
picket_panda.m
|
.m
|
qalma-master/picket_fence/picket_panda.m
| 26,770 |
utf_8
|
d8e6b4b5b54d088f497dc1f0bef488f7
|
% M. Mushfiqur Rahman
% Florida Atlantic University
% August, 2017
function varargout = picket_panda(varargin)
% PICKET_PANDA MATLAB code for picket_panda.fig
% PICKET_PANDA, by itself, creates a new PICKET_PANDA or raises the existing
% singleton*.
%
% H = PICKET_PANDA returns the handle to a new PICKET_PANDA or the handle to
% the existing singleton*.
%
% PICKET_PANDA('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in PICKET_PANDA.M with the given input arguments.
%
% PICKET_PANDA('Property','Value',...) creates a new PICKET_PANDA or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before picket_panda_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to picket_panda_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 picket_panda
% Last Modified by GUIDE v2.5 23-Aug-2017 18:14:46
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @picket_panda_OpeningFcn, ...
'gui_OutputFcn', @picket_panda_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 picket_panda is made visible.
function picket_panda_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 picket_panda (see VARARGIN)
% Choose default command line output for picket_panda
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes picket_panda wait for user response (see UIRESUME)
% uiwait(handles.figure1);
global count;
count = 0; % go button click counter
global mag_count; %mag button click counter
mag_count = 0;
set(handles.axes2, 'visible', 'off', 'box','off','XTickLabel',[],'XTick',[],'YTickLabel',[],'YTick',[]);
set(handles.axes1, 'box','off','XTickLabel',[],'XTick',[],'YTickLabel',[],'YTick',[]);
set(handles.data_transfer_min, 'Data',[]);
set(handles.smoothing_param,'string','3');
set(handles.picket_num_pop, 'String',{1:10});
set(handles.level_txt, 'String', '26');
set(handles.width_left_txt, 'String', '37');
set(handles.width_right_txt, 'String', '37');
set(handles.w1_pop, 'String', {1:10});
set(handles.w2_pop, 'String', {1:10});
% set(handles.w1_pop, 'Value', 3);
% set(handles.w2_pop, 'Value', 2);
set(handles.w1_pop, 'Value', 5);
set(handles.w2_pop, 'Value', 5);
set(handles.rotate_pop, 'String', [0,90,180,270,360]);
set(handles.rotate_check, 'Value', 1);
set(handles.rotate_pop, 'Value', 1);
set(handles.profile_check, 'Value', 0);
set(handles.wiener_check, 'Value',1);
set(handles.mag_txt, 'String', '1.5');
set(handles.res_txt, 'String', '0.781');
set(handles.go_button, 'Enable', 'off');
set(handles.recalculate_button, 'Enable', 'off');
set(handles.plus_button, 'Enable', 'off');
set(handles.minus_button, 'Enable', 'off');
set(handles.column_mlc, 'Enable', 'off');
set(handles.select_line, 'Enable', 'off');
set(handles.report_button, 'Enable', 'off');
set(handles.rotate_pop, 'Enable', 'off');
set(handles.magnify_button, 'Enable', 'off');
set(handles.clear, 'Enable', 'off');
% --- Outputs from this function are returned to the command line.
function varargout = picket_panda_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;
% --- Executes on button press in pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
%......
%dicomF(handles);
global img;
[f p] = uigetfile({'*.dcm','DICOM Files'});
try
img_d = dicomread([p f]);
img=im2double(img_d);
img=uint8(255*mat2gray(img));
%img = imrotate(img, 90);
imshow(img,'Parent',handles.axes1);
h = msgbox({'Please select the number of pickets and select the pickets'});
pause(2)
delete(h);
set(handles.go_button, 'Enable', 'on');
set(handles.rotate_pop, 'Enable', 'on');
set(handles.magnify_button, 'Enable', 'on');
set(handles.clear, 'Enable', 'on');
catch
h = msgbox('Please upload an image');
end
% --- Executes when entered data in editable cell(s) in data_transfer_min.
function data_transfer_min_CellEditCallback(hObject, eventdata, handles)
% hObject handle to data_transfer_min (see GCBO)
% eventdata structure with the following fields (see MATLAB.UI.CONTROL.TABLE)
% Indices: row and column indices of the cell(s) edited
% PreviousData: previous data for the cell(s) edited
% EditData: string(s) entered by the user
% NewData: EditData or its converted form set on the Data property. Empty if Data was not changed
% Error: error string when failed to convert EditData to appropriate value for Data
% handles structure with handles and user data (see GUIDATA)
% --- Executes on selection change in select_line.
function select_line_Callback(hObject, eventdata, handles)
% hObject handle to select_line (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns select_line contents as cell array
% contents{get(hObject,'Value')} returns selected item from select_line
set(handles.plus_button, 'Enable', 'on');
set(handles.minus_button, 'Enable', 'on');
% --- Executes during object creation, after setting all properties.
function select_line_CreateFcn(hObject, eventdata, handles)
% hObject handle to select_line (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu 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 plus_button.
function plus_button_Callback(hObject, eventdata, handles)
% hObject handle to plus_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global img;
moveF(handles,img,-1); %sign = -1 , one pixel movement
%magnifier('aacircle', 20, 'current_positions.png');
set(handles.recalculate_button, 'Enable', 'on');
% --- Executes on button press in minus_button.
function minus_button_Callback(hObject, eventdata, handles)
% hObject handle to minus_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global img;
moveF(handles,img,+1);
set(handles.recalculate_button, 'Enable', 'on');
%sign = +1 , one pixel movement
%magnifier('aacircle', 20, 'current_positions.png');
% --- Executes on button press in clear.
function clear_Callback(hObject, eventdata, handles)
% hObject handle to clear (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global img;
imshow(img);
h = msgbox({'Please select the number of pickets (or keep as it is) and select the pickets'});
pause(2)
delete(h);
set(handles.go_button, 'Enable', 'on');
set(handles.data_transfer_min, 'Data',[]);
set(handles.column_mlc, 'Enable', 'off');
set(handles.select_line, 'Enable', 'off');
% --- Executes on selection change in column_mlc.
function column_mlc_Callback(hObject, eventdata, handles)
% hObject handle to column_mlc (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns column_mlc contents as cell array
% contents{get(hObject,'Value')} returns selected item from column_mlc
set(handles.select_line, 'Enable', 'on');
global img;
min_data = get(handles.data_transfer_min, 'Data');
[r,c]= size(min_data);
column = get(handles.column_mlc, 'Value');
min = min_data(:,column); %just getting the column required
my_line = get(handles.select_line,'Value');
xy = get(handles.xy_data,'Data');
x=xy(:,1);
level = str2num(get(handles.level_txt, 'String'));
%for i=my_line:length(min)-1
% min(i) = min(i) + 1;
%end
for i=1:length(min)-1
leafpos(i) = (min(i) + min(i+1))/2;
end
imshow(img);
min_s = min;
hold on
p = column;
col = num2str(p);
text([round(x(p))+level],[0],col, 'Color','black')
for j=1:length(leafpos)
% line([round(x(1))-30 round(x(1))-20],[leafpos(j) leafpos(j)],'Color','r')
name = num2str(j);
text([round(x(p))],[leafpos(j)],name, 'Color','yellow')
line([round(x(p))+level],[leafpos(j)],'Color','r','Marker','*', 'MarkerSize',3)
end
hold off
hold on
for j=1:length(min_s)
line([round(x(p))+level-4 round(x(p))+level+11],[min_s(j) min_s(j)],'Color','g')
end
hold off
% --- Executes during object creation, after setting all properties.
function column_mlc_CreateFcn(hObject, eventdata, handles)
% hObject handle to column_mlc (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu 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 magnify_button.
function magnify_button_Callback(hObject, eventdata, handles)
% hObject handle to magnify_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
%im= getimage(handles.axes1);
%magnifier('aacircle', 20, 'current_positions.png');
global mag_count;
mag_count = mag_count + 1;
while mag_count > 0
if mod(mag_count,2) == 0
set(handles.axes2, 'visible', 'off');
set(get(handles.axes2,'children'),'visible','off');
%delete(handles.axes2);
axes(handles.axes1);
%ginput(0);
break;
else
%[x,y] = ginput;
[x,y] = ginput(1);
set(handles.axes2, 'visible', 'on');
frame = getframe(handles.axes1);
im = frame2im(frame);
[r,c] = size(im);
%magnifier('aacircle', 20, im);
imshow(im,'Parent',handles.axes2);
axes(handles.axes2);
zoom(3);
axis([double(x+10) double(x + 110) double(y-40) double(y)]);
set(handles.axes2, 'Units', 'pixels', 'Position', [x-3, r-y, 200, 140],...
'box','off','XTickLabel',[],'xtick',[],'YTickLabel',[],'ytick',[]);
end
end
%mag_counter =0;
% --- Executes on button press in recalculate_button.
function recalculate_button_Callback(hObject, eventdata, handles)
% hObject handle to recalculate_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
set(handles.recalculate_button, 'Enable', 'off');
set(handles.plus_button, 'Enable', 'off');
set(handles.minus_button, 'Enable', 'off');
set(handles.select_line, 'Enable', 'on');
global img;
recalcF(handles,img);
% --- Executes on button press in report_button.
function report_button_Callback(hObject, eventdata, handles)
% hObject handle to report_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
f2 = figure
f2.Position = [100, 100, 740, 900];
[snap,map1] = imread('current_positions.png');
[gaps,map2] = imread('edge_gaps.png');
title('Report');
subplot(8,10,1:30), imshow(snap,[]); axis equal;
subplot(8,10,31:80),imshow(gaps,[]); axis equal;
saveas(f2,'Picket_Report.pdf');
function smoothing_param_Callback(hObject, eventdata, handles)
% hObject handle to smoothing_param (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 smoothing_param as text
% str2double(get(hObject,'String')) returns contents of smoothing_param as a double
% --- Executes during object creation, after setting all properties.
function smoothing_param_CreateFcn(hObject, eventdata, handles)
% hObject handle to smoothing_param (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 selection change in picket_num_pop.
function picket_num_pop_Callback(hObject, eventdata, handles)
% hObject handle to picket_num_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns picket_num_pop contents as cell array
% contents{get(hObject,'Value')} returns selected item from picket_num_pop
v=get(handles.picket_num_pop, 'Value');
set(handles.column_mlc, 'String', {1:v});
% --- Executes during object creation, after setting all properties.
function picket_num_pop_CreateFcn(hObject, eventdata, handles)
% hObject handle to picket_num_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu 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 go_button.
function go_button_Callback(hObject, eventdata, handles)
% hObject handle to go_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
set(handles.go_button, 'Enable', 'off');
set(handles.column_mlc, 'Enable', 'on');
set(handles.report_button, 'Enable', 'on');
global img;
dicomF2(handles,img);
min_data = get(handles.data_transfer_min, 'Data');
[r,c]= size(min_data);
pickets = get(handles.picket_num_pop,'Value');
set(handles.column_mlc, 'String',{1:pickets});
set(handles.select_line, 'String',{1:r});
global count;
count = count +1;
function width_right_txt_Callback(hObject, eventdata, handles)
% hObject handle to width_right_txt (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 width_right_txt as text
% str2double(get(hObject,'String')) returns contents of width_right_txt as a double
% --- Executes during object creation, after setting all properties.
function width_right_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to width_right_txt (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
function width_left_txt_Callback(hObject, eventdata, handles)
% hObject handle to width_left_txt (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 width_left_txt as text
% str2double(get(hObject,'String')) returns contents of width_left_txt as a double
% --- Executes during object creation, after setting all properties.
function width_left_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to width_left_txt (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 selection change in w2_pop.
function w2_pop_Callback(hObject, eventdata, handles)
% hObject handle to w2_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns w2_pop contents as cell array
% contents{get(hObject,'Value')} returns selected item from w2_pop
% --- Executes during object creation, after setting all properties.
function w2_pop_CreateFcn(hObject, eventdata, handles)
% hObject handle to w2_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu 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 selection change in w1_pop.
function w1_pop_Callback(hObject, eventdata, handles)
% hObject handle to w1_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns w1_pop contents as cell array
% contents{get(hObject,'Value')} returns selected item from w1_pop
% --- Executes during object creation, after setting all properties.
function w1_pop_CreateFcn(hObject, eventdata, handles)
% hObject handle to w1_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu 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 rotate_check.
function rotate_check_Callback(hObject, eventdata, handles)
% hObject handle to rotate_check (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of rotate_check
% --- Executes on selection change in rotate_pop.
function rotate_pop_Callback(hObject, eventdata, handles)
% hObject handle to rotate_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns rotate_pop contents as cell array
% contents{get(hObject,'Value')} returns selected item from rotate_pop
global img;
v = get(handles.rotate_check, 'Value');
if v ==1
g = get(handles.rotate_pop, 'Value');
if g == 2
img = imrotate(img, 90);
elseif g == 3
img = imrotate(img, 180);
elseif g == 4
img = imrotate(img, 270);
elseif g == 5
img = imrotate(img, 360);
end
end
imshow(img,'Parent',handles.axes1);
% --- Executes during object creation, after setting all properties.
function rotate_pop_CreateFcn(hObject, eventdata, handles)
% hObject handle to rotate_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu 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 profile_check.
function profile_check_Callback(hObject, eventdata, handles)
% hObject handle to profile_check (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of profile_check
global count;
if count > 0 & get(handles.profile_check,'Value')== 1
openfig('f_x.fig');
end
% --- Executes on button press in wiener_check.
function wiener_check_Callback(hObject, eventdata, handles)
% hObject handle to wiener_check (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of wiener_check
% --- Executes on button press in home_button.
function home_button_Callback(hObject, eventdata, handles)
% hObject handle to home_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
close(picket_panda);
%run('../qalma.m');
run('qalma')
function mag_txt_Callback(hObject, eventdata, handles)
% hObject handle to mag_txt (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 mag_txt as text
% str2double(get(hObject,'String')) returns contents of mag_txt as a double
% --- Executes during object creation, after setting all properties.
function mag_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to mag_txt (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
function res_txt_Callback(hObject, eventdata, handles)
% hObject handle to res_txt (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 res_txt as text
% str2double(get(hObject,'String')) returns contents of res_txt as a double
% --- Executes during object creation, after setting all properties.
function res_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to res_txt (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
function level_txt_Callback(hObject, eventdata, handles)
% hObject handle to level_txt (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 level_txt as text
% str2double(get(hObject,'String')) returns contents of level_txt as a double
% --- Executes during object creation, after setting all properties.
function level_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to level_txt (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 profile_v_check.
function profile_v_check_Callback(hObject, eventdata, handles)
% hObject handle to profile_v_check (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of profile_v_check
global count;
if count > 0 & get(handles.profile_v_check,'Value')== 1
if exist('f_y.fig', 'file')==2
openfig('f_y.fig');
end
end
% --- Executes when user attempts to close figure1.
function figure1_CloseRequestFcn(hObject, eventdata, handles)
% hObject handle to figure1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: delete(hObject) closes the figure
delete(hObject);
if exist('f_x.fig', 'file')==2
delete('f_x.fig');
end
if exist('f_y.fig', 'file')==2
delete('f_y.fig');
end
if exist('f_x_s.fig', 'file')==2
delete('f_x_s.fig');
end
if exist('f_x.fig', 'file')==2
delete('f_x.fig');
end
if exist('edge_gaps.png', 'file')==2
delete('edge_gaps.png');
end
if exist('current_positions.png', 'file')==2
delete('current_positions.png');
end
clearvars;
|
github
|
mrmushfiq/qalma-master
|
dynalog.m
|
.m
|
qalma-master/dynalog/dynalog.m
| 7,639 |
utf_8
|
5c135192ef465a4baa5b642159850bd1
|
% M. Mushfiqur Rahman
% Florida Atlantic University
% August, 2017
function varargout = dynalog(varargin)
% DYNALOG MATLAB code for dynalog.fig
% DYNALOG, by itself, creates a new DYNALOG or raises the existing
% singleton*.
%
% H = DYNALOG returns the handle to a new DYNALOG or the handle to
% the existing singleton*.
%
% DYNALOG('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in DYNALOG.M with the given input arguments.
%
% DYNALOG('Property','Value',...) creates a new DYNALOG or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before dynalog_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to dynalog_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 dynalog
% Last Modified by GUIDE v2.5 25-Aug-2017 22:37:45
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @dynalog_OpeningFcn, ...
'gui_OutputFcn', @dynalog_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 dynalog is made visible.
function dynalog_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 dynalog (see VARARGIN)
% Choose default command line output for dynalog
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes dynalog wait for user response (see UIRESUME)
% uiwait(handles.figure1);
set(handles.axes1,'visible','off');
set(handles.report_button,'visible','off');
set(handles.comment_txt,'visible','off');
set(handles.mag_panel, 'visible', 'off');
set(handles.axes2,'visible','off');
set(handles.axes3,'visible','off');
set(handles.axes4,'visible','off');
set(handles.mag_txt, 'String', '1');
% --- Outputs from this function are returned to the command line.
function varargout = dynalog_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;
% --- Executes on button press in a_button.
function a_button_Callback(hObject, eventdata, handles)
% hObject handle to a_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global f_a;
global p_a;
[f_a p_a] = uigetfile(...
{'*.dlg'},...
'MultiSelect', 'off');
set(handles.file_a_txt, 'String', f_a);
% --- Executes on button press in b_button.
function b_button_Callback(hObject, eventdata, handles)
% hObject handle to b_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global f_b;
global p_b;
[f_b p_b] = uigetfile(...
{'*.dlg'},...
'MultiSelect', 'off');
set(handles.file_b_txt, 'String', f_b);
% --- Executes on button press in dynalyze_button.
function dynalyze_button_Callback(hObject, eventdata, handles)
% hObject handle to dynalyze_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global f_a;
global p_a;
global f_b;
global p_b;
dynalogF(handles, f_a, f_b, p_a, p_b);
% --- Executes on button press in report_button.
function report_button_Callback(hObject, eventdata, handles)
% hObject handle to report_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
tc = get(handles.comment_txt, 'String');
tc = strcat('Comment: ', tc);
f2 = figure('Position', [100, 100, 724, 700]);
[picket,map1] = imread('picket_dyn.png');
title('Dynalog Report');
h1=subplot(4,4,1:12);
imshow(picket,[]);
axis equal;
h2=subplot(4,4,[14,15]);
text(0.3,1,tc); axis off;
saveas(f2,'Dynalog_Report.pdf');
function comment_txt_Callback(hObject, eventdata, handles)
% hObject handle to comment_txt (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 comment_txt as text
% str2double(get(hObject,'String')) returns contents of comment_txt as a double
% --- Executes during object creation, after setting all properties.
function comment_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to comment_txt (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 home_button.
function home_button_Callback(hObject, eventdata, handles)
% hObject handle to home_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
close(dynalog);
run('qalma');
function mag_txt_Callback(hObject, eventdata, handles)
% hObject handle to mag_txt (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 mag_txt as text
% str2double(get(hObject,'String')) returns contents of mag_txt as a double
% --- Executes during object creation, after setting all properties.
function mag_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to mag_txt (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 when user attempts to close figure1.
function figure1_CloseRequestFcn(hObject, eventdata, handles)
% hObject handle to figure1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: delete(hObject) closes the figure
delete(hObject);
if exist('picket_dyn.png', 'file')==2
delete('picket_dyn.png');
end
clearvars;
|
github
|
mrmushfiq/qalma-master
|
wl.m
|
.m
|
qalma-master/winston_lutz/wl.m
| 21,479 |
utf_8
|
5c9533af6cf6ffa9e93af23d9a8cf14a
|
% M. Mushfiqur Rahman
% Florida Atlantic University
% August, 2017
function varargout = wl(varargin)
% WL MATLAB code for wl.fig
% WL, by itself, creates a new WL or raises the existing
% singleton*.
%
% H = WL returns the handle to a new WL or the handle to
% the existing singleton*.
%
% WL('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in WL.M with the given input arguments.
%
% WL('Property','Value',...) creates a new WL or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before wl_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to wl_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 wl
% Last Modified by GUIDE v2.5 25-Aug-2017 22:33:26
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @wl_OpeningFcn, ...
'gui_OutputFcn', @wl_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 wl is made visible.
function wl_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 wl (see VARARGIN)
% Choose default command line output for wl
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes wl wait for user response (see UIRESUME)
% uiwait(handles.figure1);
global mag_count;
mag_count = 0;
set(handles.radiation_slider, 'Min',0,'Max',100,'Value', 14);
set(handles.ball_slider,'Min',0,'Max',100,'Value', 13);
set(handles.ball_txt, 'String', 13);
set(handles.radiation_txt, 'String', 14);
set(handles.axes1, 'Visible', 'off');
set(handles.axes2, 'Visible', 'off');
set(handles.axes3, 'Visible', 'off');
set(handles.radiation_slider, 'Visible', 'off');
set(handles.radiation_txt, 'Visible', 'off');
set(handles.ball_slider, 'Visible', 'off');
set(handles.ball_txt, 'Visible', 'off');
set(handles.text7, 'Visible', 'off');
set(handles.original_label, 'Visible', 'off');
set(handles.filter1_label, 'Visible', 'off');
set(handles.filter2_label, 'Visible', 'off');
set(handles.analyze_button, 'Visible', 'on');
set(handles.distance_label, 'Visible', 'off');
set(handles.distance_txt, 'Visible', 'off');
set(handles.report_button, 'Visible', 'on');
set(handles.uitable1, 'visible', 'off');
set(handles.uitable1, 'Data', []);
set(handles.uitable1, 'ColumnName', {'Image', 'Distance(mm)', 'Comment'});
set(handles.mag_txt, 'String', '1');
set(handles.res_txt, 'String', '0.34');
set(handles.zoom_pop, 'String', {1:10}, 'Value', 6);
set(handles.analyze_button, 'Enable', 'off');
set(handles.report_button, 'Enable', 'off');
set(handles.zoom_pop, 'Enable', 'off');
% --- Outputs from this function are returned to the command line.
function varargout = wl_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;
% --- Executes on slider movement.
function ball_slider_Callback(hObject, eventdata, handles)
% hObject handle to ball_slider (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,'Value') returns position of slider
% get(hObject,'Min') and get(hObject,'Max') to determine range of slider
global img;
global img_url;
v = get(handles.ball_slider, 'Value');
set(handles.ball_txt, 'String', num2str(v));
wlF_loading(handles,img_url);
% --- Executes during object creation, after setting all properties.
function ball_slider_CreateFcn(hObject, eventdata, handles)
% hObject handle to ball_slider (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: slider controls usually have a light gray background.
if isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor',[.9 .9 .9]);
end
function ball_txt_Callback(hObject, eventdata, handles)
% hObject handle to ball_txt (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 ball_txt as text
% str2double(get(hObject,'String')) returns contents of ball_txt as a double
global img;
global img_url;
s= get(handles.ball_txt, 'String');
set(handles.ball_slider, 'Value', str2num(s));
wlF_loading(handles,img_url);
% --- Executes during object creation, after setting all properties.
function ball_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to ball_txt (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 slider movement.
function radiation_slider_Callback(hObject, eventdata, handles)
% hObject handle to radiation_slider (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,'Value') returns position of slider
% get(hObject,'Min') and get(hObject,'Max') to determine range of slider
global img;
global img_url;
v = get(handles.radiation_slider, 'Value');
set(handles.radiation_txt, 'String', num2str(v));
wlF_loading(handles,img_url);
% --- Executes during object creation, after setting all properties.
function radiation_slider_CreateFcn(hObject, eventdata, handles)
% hObject handle to radiation_slider (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: slider controls usually have a light gray background.
if isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor',[.9 .9 .9]);
end
function radiation_txt_Callback(hObject, eventdata, handles)
% hObject handle to radiation_txt (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 radiation_txt as text
% str2double(get(hObject,'String')) returns contents of radiation_txt as a double
global img;
global img_url;
s = get(handles.radiation_txt, 'String');
set(handles.radiation_slider, 'Value', str2num(s));
wlF_loading(handles,img_url);
% --- Executes during object creation, after setting all properties.
function radiation_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to radiation_txt (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 load_button.
function load_button_Callback(hObject, eventdata, handles)
% hObject handle to load_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global img;
global file_name;
global img_url;
[f p] = uigetfile(...
{'*.dcm','Supported Files (*.dcm)'; ...
},...
'MultiSelect', 'off');
try
%img_d = imread([p f]);
img_d = dicomread([p f]);
img=im2double(img_d);
img=uint8(255*mat2gray(img));
file_name = f;
set(handles.axes1, 'Visible', 'on', 'box','off','XTickLabel',[],'XTick',[],'YTickLabel',[],'YTick',[]);
set(handles.axes2, 'Visible', 'on', 'box','off','XTickLabel',[],'XTick',[],'YTickLabel',[],'YTick',[]);
set(handles.axes3, 'Visible', 'on', 'box','off','XTickLabel',[],'XTick',[],'YTickLabel',[],'YTick',[]);
set(handles.radiation_slider, 'Visible', 'on');
set(handles.radiation_txt, 'Visible', 'on');
set(handles.ball_slider, 'Visible', 'on');
set(handles.ball_txt, 'Visible', 'on');
set(handles.original_label, 'Visible', 'on');
set(handles.filter1_label, 'Visible', 'on');
set(handles.filter2_label, 'Visible', 'on');
set(handles.analyze_button, 'Visible', 'on');
set(handles.distance_label, 'Visible', 'on');
set(handles.distance_txt, 'Visible', 'on');
imshow(img,'Parent',handles.axes1);
img_url = strcat(p,f);
wlF_loading(handles,img_url);
set(handles.analyze_button, 'Enable', 'on');
set(handles.zoom_pop, 'Enable', 'on');
catch
h = msgbox('Please upload an image');
end
% --- Executes on button press in analyze_button.
function analyze_button_Callback(hObject, eventdata, handles)
% hObject handle to analyze_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global img;
global file_name;
global img_url;
set(handles.report_button, 'Visible', 'on');
wlF(handles,img_url);
set(handles.uitable1, 'visible', 'on');
set(handles.load_button, 'Visible', 'off');
set(handles.text7, 'Visible', 'on');
set(handles.report_button, 'Enable', 'on');
D = get(handles.uitable1, 'Data');
[r,c] = size(D);
D{r+1,1} = file_name;
D{r+1,2} = get(handles.distance_txt, 'String');
D{r+1,3} = get(handles.comment_txt, 'String');
set(handles.uitable1, 'Data', D);
set(handles.row_pop, 'String', {1:r+1});
% --- Executes on button press in instructions_button.
function instructions_button_Callback(hObject, eventdata, handles)
% hObject handle to instructions_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% --- Executes on button press in report_button.
function report_button_Callback(hObject, eventdata, handles)
% hObject handle to report_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
frame = getframe(handles.axes1);
imf = frame2im(frame);
%imwrite(imf, 'wl_result.png');
tc = get(handles.comment_txt, 'String');
tc = strcat('Comment : ', tc);
dist = get(handles.distance_txt, 'String');
dist = strcat('Distance between the centers : ', dist);
texts = {dist,' ', tc};
D = get(handles.uitable1, 'Data');
f1=figure
t = uitable(f1,'Data',D,'Position',[20 60 400 300]);
t.ColumnName = {'Image','Distance(mm)','Comment'};
saveas(f1, 'wl_report.pdf')
function comment_txt_Callback(hObject, eventdata, handles)
% hObject handle to comment_txt (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 comment_txt as text
% str2double(get(hObject,'String')) returns contents of comment_txt as a double
% --- Executes during object creation, after setting all properties.
function comment_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to comment_txt (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 add_image.
function add_image_Callback(hObject, eventdata, handles)
% hObject handle to add_image (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global img;
global file_name;
global img_url;
[f p] = uigetfile(...
{'*.dcm','Supported Files (*.dcm)'; ...
},...
'MultiSelect', 'off');
% [f p] = uigetfile(...
% {'*.jpg; *.JPG; *.jpeg; *.JPEG; *.img; *.IMG; *.tif;*.png; .dcm;*.TIF; *.tiff, *.TIFF','Supported Files (*.dcm,*.jpg,*.img,*.tiff,*.png)'; ...
% '*.jpg','jpg Files (*.jpg)';...
% '*.JPG','JPG Files (*.JPG)';...
% '*.jpeg','jpeg Files (*.jpeg)';...
% '*.JPEG','JPEG Files (*.JPEG)';...
% '*.img','img Files (*.img)';...
% '*.IMG','IMG Files (*.IMG)';...
% '*.tif','tif Files (*.tif)';...
% '*.TIF','TIF Files (*.TIF)';...
% '*.tiff','tiff Files (*.tiff)';...
% '*.TIFF','TIFF Files (*.TIFF)'},...
% 'MultiSelect', 'off');
try
%img_d = imread([p f]);
img_d = dicomread([p f]);
img=im2double(img_d);
img=uint8(255*mat2gray(img));
file_name = f;
set(handles.axes1, 'Visible', 'on', 'box','off','XTickLabel',[],'XTick',[],'YTickLabel',[],'YTick',[]);
set(handles.axes2, 'Visible', 'on', 'box','off','XTickLabel',[],'XTick',[],'YTickLabel',[],'YTick',[]);
set(handles.axes3, 'Visible', 'on', 'box','off','XTickLabel',[],'XTick',[],'YTickLabel',[],'YTick',[]);
set(handles.radiation_slider, 'Visible', 'on');
set(handles.radiation_txt, 'Visible', 'on');
set(handles.ball_slider, 'Visible', 'on');
set(handles.ball_txt, 'Visible', 'on');
set(handles.original_label, 'Visible', 'on');
set(handles.filter1_label, 'Visible', 'on');
set(handles.filter2_label, 'Visible', 'on');
set(handles.analyze_button, 'Visible', 'on');
set(handles.distance_label, 'Visible', 'on');
set(handles.distance_txt, 'Visible', 'on');
imshow(img,'Parent',handles.axes1);
img_url = strcat(p,f);
wlF_loading(handles,img_url);
set(handles.analyze_button, 'Enable', 'on');
set(handles.zoom_pop, 'Enable', 'on');
catch
h = msgbox('Please upload an image');
end
function res_txt_Callback(hObject, eventdata, handles)
% hObject handle to res_txt (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 res_txt as text
% str2double(get(hObject,'String')) returns contents of res_txt as a double
% --- Executes during object creation, after setting all properties.
function res_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to res_txt (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
function mag_txt_Callback(hObject, eventdata, handles)
% hObject handle to mag_txt (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 mag_txt as text
% str2double(get(hObject,'String')) returns contents of mag_txt as a double
% --- Executes during object creation, after setting all properties.
function mag_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to mag_txt (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
function edit8_Callback(hObject, eventdata, handles)
% hObject handle to comment_txt (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 comment_txt as text
% str2double(get(hObject,'String')) returns contents of comment_txt as a double
% --- Executes during object creation, after setting all properties.
function edit8_CreateFcn(hObject, eventdata, handles)
% hObject handle to comment_txt (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 home_button.
function home_button_Callback(hObject, eventdata, handles)
% hObject handle to home_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
close(wl);
run('qalma');
% --- Executes on selection change in zoom_pop.
function zoom_pop_Callback(hObject, eventdata, handles)
% hObject handle to zoom_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns zoom_pop contents as cell array
% contents{get(hObject,'Value')} returns selected item from zoom_pop
global img;
global img_url;
wlF_loading(handles,img_url);
% --- Executes during object creation, after setting all properties.
function zoom_pop_CreateFcn(hObject, eventdata, handles)
% hObject handle to zoom_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu 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 pushbutton15.
function pushbutton15_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton15 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% --- Executes on button press in delete_button.
function delete_button_Callback(hObject, eventdata, handles)
% hObject handle to delete_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
D = get(handles.uitable1, 'Data');
[r,c] = size(D);
row = get(handles.row_pop, 'Value');
if r>0
D(row,:) = [];
end
set(handles.uitable1, 'Data', D);
% --- Executes on selection change in row_pop.
function row_pop_Callback(hObject, eventdata, handles)
% hObject handle to row_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns row_pop contents as cell array
% contents{get(hObject,'Value')} returns selected item from row_pop
% --- Executes during object creation, after setting all properties.
function row_pop_CreateFcn(hObject, eventdata, handles)
% hObject handle to row_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu 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 when user attempts to close figure1.
function figure1_CloseRequestFcn(hObject, eventdata, handles)
% hObject handle to figure1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: delete(hObject) closes the figure
delete(hObject);
clearvars;
|
github
|
mrmushfiq/qalma-master
|
ci.m
|
.m
|
qalma-master/ci/ci.m
| 25,849 |
utf_8
|
bda1d740da1c89b97e876fc2f9ffe66e
|
% M. Mushfiqur Rahman
% Florida Atlantic University
% August, 2017
function varargout = ci(varargin)
% CI MATLAB code for ci.fig
% CI, by itself, creates a new CI or raises the existing
% singleton*.
%
% H = CI returns the handle to a new CI or the handle to
% the existing singleton*.
%
% CI('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in CI.M with the given input arguments.
%
% CI('Property','Value',...) creates a new CI or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before ci_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to ci_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 ci
% Last Modified by GUIDE v2.5 31-Aug-2017 18:50:29
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @ci_OpeningFcn, ...
'gui_OutputFcn', @ci_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 ci is made visible.
function ci_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 ci (see VARARGIN)
% Choose default command line output for ci
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes ci wait for user response (see UIRESUME)
% uiwait(handles.figure1);
set(handles.bbs_txt, 'Visible', 'off');
setpixelposition(handles.radius1_pop,[3, 10, 70, 40]);
setpixelposition(handles.radius2_pop,[87, 10, 70, 40]);
setpixelposition(handles.w_1_pop,[79, 5, 70, 40]);
setpixelposition(handles.w_2_pop,[153, 5, 70, 40]);
set(handles.axes1,...
'Visible', 'on', 'box','off','XTickLabel',[],'XTick',[],'YTickLabel',[],'YTick',[]);
set(handles.wiener_check,'Value', 1);
set(handles.dark_check,'Value', 1);
set(handles.w_1_pop, 'string', {1:10}, 'Value', 5);
set(handles.w_2_pop, 'string', {1:10}, 'Value', 5);
set(handles.radius1_pop, 'string', {1:20}, 'Value', 1, 'Visible' , 'on');
set(handles.radius2_pop, 'string', {1:20}, 'Value', 3, 'Visible' , 'on');
set(handles.sensitivity_slider, 'SliderStep', [0.01, 0.1], 'Value', 0.94);
set(handles.threshold_slider, 'SliderStep', [0.01, 0.1], 'Value', 0.14);
set(handles.sensitivity_txt, 'String', '0.94');
set(handles.threshold_txt, 'String', '0.14');
set(handles.contrast_low_slider,'Min', 0, 'Max', 1, 'SliderStep', [0.01, 0.1], 'Value', 0);
set(handles.contrast_high_slider, 'Min', 0, 'Max', 1,'SliderStep', [0.01, 0.1], 'Value', 0.8);
set(handles.contrast_low_txt, 'String', '0');
set(handles.contrast_high_txt, 'String', '0.8');
set(handles.contrast_low_txt, 'Enable', 'off');
set(handles.contrast_high_txt, 'Enable', 'off');
set(handles.contrast_low_slider, 'Enable', 'off');
set(handles.contrast_high_slider, 'Enable', 'off');
set(handles.sensitivity_txt, 'Enable', 'off');
set(handles.threshold_txt, 'Enable', 'off');
set(handles.sensitivity_slider, 'Enable', 'off');
set(handles.threshold_slider, 'Enable', 'off');
set(handles.radius1_pop, 'Enable', 'off');
set(handles.radius2_pop, 'Enable', 'off');
set(handles.dark_check,'Enable', 'off');
set(handles.bright_check,'Enable', 'off');
set(handles.analyze_button, 'Enable', 'off');
set(handles.add_button, 'Enable', 'off');
set(handles.wiener_check, 'Enable','off');
set(handles.w_1_pop, 'Enable','off');
set(handles.w_2_pop, 'Enable','off');
set(handles.manual_check, 'Enable','off');
set(handles.reset_button, 'Enable', 'off');
set(handles.text5, 'Visible', 'off');
set(handles.dice_txt, 'Visible', 'off');
% --- Outputs from this function are returned to the command line.
function varargout = ci_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;
% --- Executes on button press in load_button.
function load_button_Callback(hObject, eventdata, handles)
% hObject handle to load_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global img;
global img_url;
[f p] = uigetfile({'*.dcm','DICOM Files'});
try
img_d = dicomread([p f]);
img=im2double(img_d);
img=uint8(255*mat2gray(img));
imshow(img,'Parent',handles.axes1);
img_url = strcat(p,f);
ci_loadingF(handles,img_url);
set(handles.contrast_low_txt, 'Enable', 'on');
set(handles.contrast_high_txt, 'Enable', 'on');
set(handles.contrast_low_slider, 'Enable', 'on');
set(handles.contrast_high_slider, 'Enable', 'on');
set(handles.sensitivity_txt, 'Enable', 'on');
set(handles.threshold_txt, 'Enable', 'on');
set(handles.sensitivity_slider, 'Enable', 'on');
set(handles.threshold_slider, 'Enable', 'on');
set(handles.radius1_pop, 'Enable', 'on');
set(handles.radius2_pop, 'Enable', 'on');
set(handles.dark_check,'Enable', 'on');
set(handles.bright_check,'Enable', 'on');
set(handles.analyze_button, 'Enable', 'on');
set(handles.wiener_check, 'Enable','on');
set(handles.w_1_pop, 'Enable','on');
set(handles.w_2_pop, 'Enable','on');
set(handles.manual_check, 'Enable','on');
set(handles.reset_button, 'Enable', 'on');
set(handles.text5, 'Visible', 'off');
set(handles.dice_txt, 'Visible', 'off');
catch
h = msgbox('Please upload an image');
end
% --- Executes on button press in analyze_button.
function analyze_button_Callback(hObject, eventdata, handles)
% hObject handle to analyze_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
set(handles.analyze_button, 'Enable', 'off');
global img_url;
ciF(handles,img_url);
set(handles.load_button, 'Visible', 'off');
set(handles.add_button, 'Enable', 'on');
set(handles.text5, 'Visible', 'on');
set(handles.dice_txt, 'Visible', 'on');
% --- Executes on button press in add_button.
function add_button_Callback(hObject, eventdata, handles)
% hObject handle to add_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global img;
global img_url;
[f p] = uigetfile({'*.dcm','DICOM Files'});
try
img_d = dicomread([p f]);
img=im2double(img_d);
img=uint8(255*mat2gray(img));
imshow(img,'Parent',handles.axes1);
img_url = strcat(p,f);
ci_loadingF(handles,img_url);
set(handles.analyze_button, 'Enable', 'on');
set(handles.text5, 'Visible', 'off');
set(handles.dice_txt, 'Visible', 'off');
catch
h = msgbox('Please upload an image');
end
% --- Executes on selection change in radius2_pop.
function radius2_pop_Callback(hObject, eventdata, handles)
% hObject handle to radius2_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns radius2_pop contents as cell array
% contents{get(hObject,'Value')} returns selected item from radius2_pop
global img_url;
ci_loadingF(handles,img_url);
% --- Executes during object creation, after setting all properties.
function radius2_pop_CreateFcn(hObject, eventdata, handles)
% hObject handle to radius2_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu 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 selection change in radius1_pop.
function radius1_pop_Callback(hObject, eventdata, handles)
% hObject handle to radius1_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns radius1_pop contents as cell array
% contents{get(hObject,'Value')} returns selected item from radius1_pop
global img_url;
ci_loadingF(handles,img_url);
% --- Executes during object creation, after setting all properties.
function radius1_pop_CreateFcn(hObject, eventdata, handles)
% hObject handle to radius1_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu 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
function comment_txt_Callback(hObject, eventdata, handles)
% hObject handle to comment_txt (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 comment_txt as text
% str2double(get(hObject,'String')) returns contents of comment_txt as a double
% --- Executes during object creation, after setting all properties.
function comment_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to comment_txt (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 slider movement.
function contrast_high_slider_Callback(hObject, eventdata, handles)
% hObject handle to contrast_high_slider (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,'Value') returns position of slider
% get(hObject,'Min') and get(hObject,'Max') to determine range of slider
a = get(handles.contrast_high_slider, 'Value');
set(handles.contrast_high_txt, 'String', num2str(a));
global img_url;
ci_loadingF(handles,img_url);
% --- Executes during object creation, after setting all properties.
function contrast_high_slider_CreateFcn(hObject, eventdata, handles)
% hObject handle to contrast_high_slider (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: slider controls usually have a light gray background.
if isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor',[.9 .9 .9]);
end
function contrast_high_txt_Callback(hObject, eventdata, handles)
% hObject handle to contrast_high_txt (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 contrast_high_txt as text
% str2double(get(hObject,'String')) returns contents of contrast_high_txt as a double
a = get(handles.contrast_high_txt, 'String');
set(handles.contrast_high_slider, 'Value', str2num(a));
global img_url;
ci_loadingF(handles,img_url);
% --- Executes during object creation, after setting all properties.
function contrast_high_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to contrast_high_txt (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 wiener_check.
function wiener_check_Callback(hObject, eventdata, handles)
% hObject handle to wiener_check (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of wiener_check
global img_url;
ci_loadingF(handles,img_url);
% --- Executes on selection change in w_1_pop.
function w_1_pop_Callback(hObject, eventdata, handles)
% hObject handle to w_1_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns w_1_pop contents as cell array
% contents{get(hObject,'Value')} returns selected item from w_1_pop
global img_url;
ci_loadingF(handles,img_url);
% --- Executes during object creation, after setting all properties.
function w_1_pop_CreateFcn(hObject, eventdata, handles)
% hObject handle to w_1_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu 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 selection change in w_2_pop.
function w_2_pop_Callback(hObject, eventdata, handles)
% hObject handle to w_2_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = cellstr(get(hObject,'String')) returns w_2_pop contents as cell array
% contents{get(hObject,'Value')} returns selected item from w_2_pop
global img_url;
ci_loadingF(handles,img_url);
% --- Executes during object creation, after setting all properties.
function w_2_pop_CreateFcn(hObject, eventdata, handles)
% hObject handle to w_2_pop (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu 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 slider movement.
function threshold_slider_Callback(hObject, eventdata, handles)
% hObject handle to threshold_slider (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,'Value') returns position of slider
% get(hObject,'Min') and get(hObject,'Max') to determine range of slider
a = get(handles.threshold_slider, 'Value');
set(handles.threshold_txt, 'String', num2str(a));
global img_url;
ci_loadingF(handles,img_url);
% --- Executes during object creation, after setting all properties.
function threshold_slider_CreateFcn(hObject, eventdata, handles)
% hObject handle to threshold_slider (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: slider controls usually have a light gray background.
if isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor',[.9 .9 .9]);
end
function threshold_txt_Callback(hObject, eventdata, handles)
% hObject handle to threshold_txt (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 threshold_txt as text
% str2double(get(hObject,'String')) returns contents of threshold_txt as a double
a = get(handles.threshold_txt, 'String');
set(handles.threshold_slider, 'Value', str2num(a));
global img_url;
ci_loadingF(handles,img_url);
% --- Executes during object creation, after setting all properties.
function threshold_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to threshold_txt (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 slider movement.
function sensitivity_slider_Callback(hObject, eventdata, handles)
% hObject handle to sensitivity_slider (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,'Value') returns position of slider
% get(hObject,'Min') and get(hObject,'Max') to determine range of slider
a = get(handles.sensitivity_slider, 'Value');
set(handles.sensitivity_txt, 'String', num2str(a));
global img_url;
ci_loadingF(handles,img_url);
% --- Executes during object creation, after setting all properties.
function sensitivity_slider_CreateFcn(hObject, eventdata, handles)
% hObject handle to sensitivity_slider (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: slider controls usually have a light gray background.
if isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor',[.9 .9 .9]);
end
function sensitivity_txt_Callback(hObject, eventdata, handles)
% hObject handle to sensitivity_txt (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 sensitivity_txt as text
% str2double(get(hObject,'String')) returns contents of sensitivity_txt as a double
a = get(handles.sensitivity_txt,'String');
set(handles.sensitivity_slider, 'Value', str2num(a));
global img_url;
ci_loadingF(handles,img_url);
% --- Executes during object creation, after setting all properties.
function sensitivity_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to sensitivity_txt (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 dark_check.
function dark_check_Callback(hObject, eventdata, handles)
% hObject handle to dark_check (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of dark_check
set(handles.bright_check, 'Value', 0);
global img_url;
ci_loadingF(handles,img_url);
% --- Executes on button press in bright_check.
function bright_check_Callback(hObject, eventdata, handles)
% hObject handle to bright_check (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of bright_check
set(handles.dark_check, 'Value', 0);
global img_url;
ci_loadingF(handles,img_url);
% --- Executes on slider movement.
function contrast_low_slider_Callback(hObject, eventdata, handles)
% hObject handle to contrast_low_slider (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,'Value') returns position of slider
% get(hObject,'Min') and get(hObject,'Max') to determine range of slider
a = get(handles.contrast_low_slider, 'Value');
set(handles.contrast_low_txt, 'String', num2str(a));
global img_url;
ci_loadingF(handles,img_url);
% --- Executes during object creation, after setting all properties.
function contrast_low_slider_CreateFcn(hObject, eventdata, handles)
% hObject handle to contrast_low_slider (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: slider controls usually have a light gray background.
if isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor',[.9 .9 .9]);
end
function contrast_low_txt_Callback(hObject, eventdata, handles)
% hObject handle to contrast_low_txt (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 contrast_low_txt as text
% str2double(get(hObject,'String')) returns contents of contrast_low_txt as a double
a = get(handles.contrast_low_txt, 'String');
set(handles.contrast_low_slider, 'Value', str2num(a));
global img_url;
ci_loadingF(handles,img_url);
% --- Executes during object creation, after setting all properties.
function contrast_low_txt_CreateFcn(hObject, eventdata, handles)
% hObject handle to contrast_low_txt (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 home_button.
function home_button_Callback(hObject, eventdata, handles)
% hObject handle to home_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
close(ci);
run('qalma');
% --- Executes on button press in manual_check.
function manual_check_Callback(hObject, eventdata, handles)
% hObject handle to manual_check (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of manual_check
global img_url;
if get(handles.manual_check, 'Value') == 1
h = msgbox('Please click on the BBs after clicking the analyze button');
try
ci_loadingF(handles,img_url);
catch
h1 = msgbox('Please load an image first');
end
else
try
ci_loadingF(handles,img_url);
catch
h1 = msgbox('Please load an image first');
end
end
% --- Executes when user attempts to close figure1.
function figure1_CloseRequestFcn(hObject, eventdata, handles)
% hObject handle to figure1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: delete(hObject) closes the figure
delete(hObject);
clearvars;
% --- Executes on button press in reset_button.
function reset_button_Callback(hObject, eventdata, handles)
% hObject handle to reset_button (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
set(handles.wiener_check,'Value', 1);
set(handles.dark_check,'Value', 1);
set(handles.w_1_pop, 'string', {1:10}, 'Value', 5);
set(handles.w_2_pop, 'string', {1:10}, 'Value', 5);
set(handles.radius1_pop, 'string', {1:20}, 'Value', 1, 'Visible' , 'on');
set(handles.radius2_pop, 'string', {1:20}, 'Value', 3, 'Visible' , 'on');
set(handles.sensitivity_slider, 'SliderStep', [0.01, 0.1], 'Value', 0.94);
set(handles.threshold_slider, 'SliderStep', [0.01, 0.1], 'Value', 0.14);
set(handles.sensitivity_txt, 'String', '0.94');
set(handles.threshold_txt, 'String', '0.14');
set(handles.contrast_low_slider,'Min', 0, 'Max', 1, 'SliderStep', [0.01, 0.1], 'Value', 0);
set(handles.contrast_high_slider, 'Min', 0, 'Max', 1,'SliderStep', [0.01, 0.1], 'Value', 0.8);
set(handles.contrast_low_txt, 'String', '0');
set(handles.contrast_high_txt, 'String', '0.8');
set(handles.text5, 'Visible', 'off');
set(handles.dice_txt, 'Visible', 'off');
global img_url;
ci_loadingF(handles,img_url);
|
github
|
andyzeng/arc-robot-vision-master
|
fill_depth_cross_bf.m
|
.m
|
arc-robot-vision-master/suction-based-grasping/external/bxf/fill_depth_cross_bf.m
| 1,990 |
utf_8
|
b7e5bbcb1bedcb7426978f7df1777af9
|
% In-paints the depth image using a cross-bilateral filter. The operation
% is implemented via several filterings at various scales. The number of
% scales is determined by the number of spacial and range sigmas provided.
% 3 spacial/range sigmas translated into filtering at 3 scales.
%
% Args:
% imgRgb - the RGB image, a uint8 HxWx3 matrix
% imgDepthAbs - the absolute depth map, a HxW double matrix whose values
% indicate depth in meters.
% spaceSigmas - (optional) sigmas for the spacial gaussian term.
% rangeSigmas - (optional) sigmas for the intensity gaussian term.
%
% Returns:
% imgDepthAbs - the inpainted depth image.
function imgDepthAbs = fill_depth_cross_bf(imgRgb, imgDepthAbs, ...
spaceSigmas, rangeSigmas)
error(nargchk(2,4,nargin));
assert(isa(imgRgb, 'uint8'), 'imgRgb must be uint8');
assert(isa(imgDepthAbs, 'double'), 'imgDepthAbs must be a double');
if nargin < 3
%spaceSigmas = [ 12];
spaceSigmas = [12 5 8];
end
if nargin < 4
% rangeSigmas = [0.2];
rangeSigmas = [0.2 0.08 0.02];
end
assert(numel(spaceSigmas) == numel(rangeSigmas));
assert(isa(rangeSigmas, 'double'));
assert(isa(spaceSigmas, 'double'));
% Create the 'noise' image and get the maximum observed depth.
imgIsNoise = imgDepthAbs == 0 | imgDepthAbs == 10;
maxDepthObs = max(imgDepthAbs(~imgIsNoise));
% If depth map is empty, exit function
if isempty(maxDepthObs)
return;
end
% Convert the depth image to uint8.
imgDepth = imgDepthAbs ./ maxDepthObs;
imgDepth(imgDepth > 1) = 1;
imgDepth = uint8(imgDepth * 255);
% Run the cross-bilateral filter.
if ispc
imgDepthAbs = mex_cbf_windows(imgDepth, rgb2gray(imgRgb), imgIsNoise, spaceSigmas(:), rangeSigmas(:));
else
imgDepthAbs = mex_cbf(imgDepth, rgb2gray(imgRgb), imgIsNoise, spaceSigmas(:), rangeSigmas(:));
end
% Convert back to absolute depth (meters).
imgDepthAbs = im2double(imgDepthAbs) .* maxDepthObs;
end
|
github
|
andyzeng/arc-robot-vision-master
|
sub2ind2d.m
|
.m
|
arc-robot-vision-master/parallel-jaw-grasping/baseline/sub2ind2d.m
| 135 |
utf_8
|
4970286e0c7d89b91364ca0d54668cff
|
% A faster version of sub2ind for 2D case
function linIndex = sub2ind2d(sz, rowSub, colSub)
linIndex = (colSub-1) * sz(1) + rowSub;
|
github
|
drbenvincent/darc-experiments-matlab-master
|
addSubFoldersToPath.m
|
.m
|
darc-experiments-matlab-master/darc-toolbox/addSubFoldersToPath.m
| 792 |
utf_8
|
1e9a817e1ab0ef5e56a1b36ec9f9397c
|
function addSubFoldersToPath()
pathOfThisFunction = mfilename('fullpath');
[currentpath, ~, ~]= fileparts(pathOfThisFunction);
allSubpaths = strsplit( genpath(currentpath) ,':');
blacklist={'.git','.ignore','.graffle','.'}; % '.' is any hidden folder
pathsToAdd={};
for n=1:numel(allSubpaths)
if shouldAddThisPath(allSubpaths{1,n},blacklist)
pathsToAdd{end+1} = allSubpaths{n};
end
end
disp('Temporarily adding toolbox subdirecties to the path: ')
fprintf('\t%s\n',pathsToAdd{:})
addpath( strjoin(pathsToAdd, ':') )
end
function addThisPath = shouldAddThisPath(path,blacklist)
addThisPath = true;
for ignoreStr = blacklist
if isStringMatch(path,ignoreStr{1})
addThisPath=false;
end
end
end
function matchFound = isStringMatch(str,pattern)
matchFound = ~strfind(str,pattern);
end
|
github
|
drbenvincent/darc-experiments-matlab-master
|
checkGitHubDependencies.m
|
.m
|
darc-experiments-matlab-master/darc-toolbox/checkGitHubDependencies.m
| 2,973 |
utf_8
|
30b69fd5ee94882628374c7dbfc419ea
|
function checkGitHubDependencies(dependencies)
% This function takes a cell array of url's to hithub repositories, loop through
% them and ensure they exist on the path, or clone them to your local machine.
%
% Example input:
%
% dependencies={...
% 'https://github.com/drbenvincent/mcmc-utils-matlab',...
% 'https://github.com/altmany/export_fig'};
assert(iscellstr(dependencies),'Input to function should be a cell array of url''s to github repositories')
% ensure dependencies is a row
if iscolumn(dependencies)
dependencies = dependencies';
end
assert(isrow(dependencies))
for url=dependencies
processDependency(url{:});
end
end
function processDependency(url)
displayDependencyToCommandWindow(url);
repoName = getRepoNameFromUrl(url);
if ~isRepoFolderOnPath(repoName)
targetPath = fullfile(defineInstallPath(),repoName);
targetPath = removeTrailingColon(targetPath);
cloneGitHubRepo(url, repoName, targetPath);
else
updateGitHubRepo(defineInstallPath(),repoName);
end
end
function displayDependencyToCommandWindow(url)
disp( makeHyperlink(url, makeWeblinkCode(url)) )
end
function repoName = getRepoNameFromUrl(url)
[~,repoName] = fileparts(url);
end
function installPath = defineInstallPath()
% installPath will be the Matlab userpath (eg /Users/Username/Documents/MATLAB)
if isempty(userpath)
userpath('reset')
end
installPath = userpath;
% Fix the trailing ":" which only sometimes appears (or ";" on PC)
installPath = removeTrailingColon(installPath);
end
function str = removeTrailingColon(str)
if str(end)==systemDelimiter()
str(end)='';
end
end
function onPath = isRepoFolderOnPath(repoName)
onPath = exist(repoName,'dir')==7;
end
function cloneGitHubRepo(repoAddress, repoName, installPath)
% ensure the folder exists
%targetPath = removeTrailingColon(fullfile(defineInstallPath(),repoName));
ensureFolderExists(installPath);
addpath(installPath);
% do the cloning
originalPath = cd;
try
cd(defineInstallPath())
command = sprintf('git clone %s.git', repoAddress);
[status, cmdout] = system(command);
catch ME
rethrow(ME)
end
cd(originalPath)
end
function updateGitHubRepo(installPath,repoName)
originalPath = cd;
try
cd(fullfile(installPath,repoName))
[status, cmdout] = system('git pull');
catch ME
rethrow(ME)
%warning('Unable to update GitHub repository')
end
cd(originalPath)
end
function weblinkCode = makeWeblinkCode(url)
assert(ischar(url))
weblinkCode = sprintf('web(''%s'')', url);
end
function hyperlink = makeHyperlink(text, codeToRun)
assert(ischar(text))
assert(ischar(codeToRun))
codeToRun = ['matlab: ' codeToRun];
hyperlink = sprintf('<a href="%s">%s</a>', codeToRun, text);
end
function delimiter = systemDelimiter()
if ismac
delimiter = ':';
elseif ispc
delimiter = ';';
end
end
% TODO: Work out how to make this work in Matlab
% function results = exectuteFunctionInPathProvided(func, targetPath)
% originalPath = cd;
% cd(targetPath)
% results = func();
% cd(originalPath)
% end
|
github
|
drbenvincent/darc-experiments-matlab-master
|
pdftops.m
|
.m
|
darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/pdftops.m
| 6,161 |
utf_8
|
5edac4bbbdae30223cb246a4ec7313d6
|
function varargout = pdftops(cmd)
%PDFTOPS Calls a local pdftops executable with the input command
%
% Example:
% [status result] = pdftops(cmd)
%
% Attempts to locate a pdftops executable, finally asking the user to
% specify the directory pdftops was installed into. The resulting path is
% stored for future reference.
%
% Once found, the executable is called with the input command string.
%
% This function requires that you have pdftops (from the Xpdf package)
% installed on your system. You can download this from:
% http://www.foolabs.com/xpdf
%
% IN:
% cmd - Command string to be passed into pdftops (e.g. '-help').
%
% OUT:
% status - 0 iff command ran without problem.
% result - Output from pdftops.
% Copyright: Oliver Woodford, 2009-2010
% Thanks to Jonas Dorn for the fix for the title of the uigetdir window on Mac OS.
% Thanks to Christoph Hertel for pointing out a bug in check_xpdf_path under linux.
% 23/01/2014 - Add full path to pdftops.txt in warning.
% 27/05/2015 - Fixed alert in case of missing pdftops; fixed code indentation
% 02/05/2016 - Added possible error explanation suggested by Michael Pacer (issue #137)
% 02/05/2016 - Search additional possible paths suggested by Jonas Stein (issue #147)
% 03/05/2016 - Display the specific error message if pdftops fails for some reason (issue #148)
% Call pdftops
[varargout{1:nargout}] = system([xpdf_command(xpdf_path()) cmd]);
end
function path_ = xpdf_path
% Return a valid path
% Start with the currently set path
path_ = user_string('pdftops');
% Check the path works
if check_xpdf_path(path_)
return
end
% Check whether the binary is on the path
if ispc
bin = 'pdftops.exe';
else
bin = 'pdftops';
end
if check_store_xpdf_path(bin)
path_ = bin;
return
end
% Search the obvious places
if ispc
paths = {'C:\Program Files\xpdf\pdftops.exe', 'C:\Program Files (x86)\xpdf\pdftops.exe'};
else
paths = {'/usr/bin/pdftops', '/usr/local/bin/pdftops'};
end
for a = 1:numel(paths)
path_ = paths{a};
if check_store_xpdf_path(path_)
return
end
end
% Ask the user to enter the path
errMsg1 = 'Pdftops not found. Please locate the program, or install xpdf-tools from ';
url1 = 'http://foolabs.com/xpdf';
fprintf(2, '%s\n', [errMsg1 '<a href="matlab:web(''-browser'',''' url1 ''');">' url1 '</a>']);
errMsg1 = [errMsg1 url1];
%if strncmp(computer,'MAC',3) % Is a Mac
% % Give separate warning as the MacOS uigetdir dialogue box doesn't have a title
% uiwait(warndlg(errMsg1))
%end
% Provide an alternative possible explanation as per issue #137
errMsg2 = 'If you have pdftops installed, perhaps Matlab is shaddowing it as described in ';
url2 = 'https://github.com/altmany/export_fig/issues/137';
fprintf(2, '%s\n', [errMsg2 '<a href="matlab:web(''-browser'',''' url2 ''');">issue #137</a>']);
errMsg2 = [errMsg2 url1];
state = 0;
while 1
if state
option1 = 'Install pdftops';
else
option1 = 'Issue #137';
end
answer = questdlg({errMsg1,'',errMsg2},'Pdftops error',option1,'Locate pdftops','Cancel','Cancel');
drawnow; % prevent a Matlab hang: http://undocumentedmatlab.com/blog/solving-a-matlab-hang-problem
switch answer
case 'Install pdftops'
web('-browser',url1);
case 'Issue #137'
web('-browser',url2);
state = 1;
case 'Locate pdftops'
base = uigetdir('/', errMsg1);
if isequal(base, 0)
% User hit cancel or closed window
break
end
base = [base filesep]; %#ok<AGROW>
bin_dir = {'', ['bin' filesep], ['lib' filesep]};
for a = 1:numel(bin_dir)
path_ = [base bin_dir{a} bin];
if exist(path_, 'file') == 2
break
end
end
if check_store_xpdf_path(path_)
return
end
otherwise % User hit Cancel or closed window
break
end
end
error('pdftops executable not found.');
end
function good = check_store_xpdf_path(path_)
% Check the path is valid
good = check_xpdf_path(path_);
if ~good
return
end
% Update the current default path to the path found
if ~user_string('pdftops', path_)
warning('Path to pdftops executable could not be saved. Enter it manually in %s.', fullfile(fileparts(which('user_string.m')), '.ignore', 'pdftops.txt'));
return
end
end
function good = check_xpdf_path(path_)
% Check the path is valid
[good, message] = system([xpdf_command(path_) '-h']); %#ok<ASGLU>
% system returns good = 1 even when the command runs
% Look for something distinct in the help text
good = ~isempty(strfind(message, 'PostScript'));
% Display the error message if the pdftops executable exists but fails for some reason
if ~good && exist(path_,'file') % file exists but generates an error
fprintf('Error running %s:\n', path_);
fprintf(2,'%s\n\n',message);
end
end
function cmd = xpdf_command(path_)
% Initialize any required system calls before calling ghostscript
% TODO: in Unix/Mac, find a way to determine whether to use "export" (bash) or "setenv" (csh/tcsh)
shell_cmd = '';
if isunix
% Avoids an error on Linux with outdated MATLAB lib files
% R20XXa/bin/glnxa64/libtiff.so.X
% R20XXa/sys/os/glnxa64/libstdc++.so.X
shell_cmd = 'export LD_LIBRARY_PATH=""; ';
end
if ismac
shell_cmd = 'export DYLD_LIBRARY_PATH=""; ';
end
% Construct the command string
cmd = sprintf('%s"%s" ', shell_cmd, path_);
end
|
github
|
drbenvincent/darc-experiments-matlab-master
|
crop_borders.m
|
.m
|
darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/crop_borders.m
| 5,133 |
utf_8
|
b744bf935914cfa6d9ff82140b48291e
|
function [A, vA, vB, bb_rel] = crop_borders(A, bcol, padding, crop_amounts)
%CROP_BORDERS Crop the borders of an image or stack of images
%
% [B, vA, vB, bb_rel] = crop_borders(A, bcol, [padding])
%
%IN:
% A - HxWxCxN stack of images.
% bcol - Cx1 background colour vector.
% padding - scalar indicating how much padding to have in relation to
% the cropped-image-size (0<=padding<=1). Default: 0
% crop_amounts - 4-element vector of crop amounts: [top,right,bottom,left]
% where NaN/Inf indicate auto-cropping, 0 means no cropping,
% and any other value mean cropping in pixel amounts.
%
%OUT:
% B - JxKxCxN cropped stack of images.
% vA - coordinates in A that contain the cropped image
% vB - coordinates in B where the cropped version of A is placed
% bb_rel - relative bounding box (used for eps-cropping)
%{
% 06/03/15: Improved image cropping thanks to Oscar Hartogensis
% 08/06/15: Fixed issue #76: case of transparent figure bgcolor
% 21/02/16: Enabled specifying non-automated crop amounts
% 04/04/16: Fix per Luiz Carvalho for old Matlab releases
% 23/10/16: Fixed issue #175: there used to be a 1px minimal padding in case of crop, now removed
%}
if nargin < 3
padding = 0;
end
if nargin < 4
crop_amounts = nan(1,4); % =auto-cropping
end
crop_amounts(end+1:4) = NaN; % fill missing values with NaN
[h, w, c, n] = size(A);
if isempty(bcol) % case of transparent bgcolor
bcol = A(ceil(end/2),1,:,1);
end
if isscalar(bcol)
bcol = bcol(ones(c, 1));
end
% Crop margin from left
if ~isfinite(crop_amounts(4))
bail = false;
for l = 1:w
for a = 1:c
if ~all(col(A(:,l,a,:)) == bcol(a))
bail = true;
break;
end
end
if bail
break;
end
end
else
l = 1 + abs(crop_amounts(4));
end
% Crop margin from right
if ~isfinite(crop_amounts(2))
bcol = A(ceil(end/2),w,:,1);
bail = false;
for r = w:-1:l
for a = 1:c
if ~all(col(A(:,r,a,:)) == bcol(a))
bail = true;
break;
end
end
if bail
break;
end
end
else
r = w - abs(crop_amounts(2));
end
% Crop margin from top
if ~isfinite(crop_amounts(1))
bcol = A(1,ceil(end/2),:,1);
bail = false;
for t = 1:h
for a = 1:c
if ~all(col(A(t,:,a,:)) == bcol(a))
bail = true;
break;
end
end
if bail
break;
end
end
else
t = 1 + abs(crop_amounts(1));
end
% Crop margin from bottom
bcol = A(h,ceil(end/2),:,1);
if ~isfinite(crop_amounts(3))
bail = false;
for b = h:-1:t
for a = 1:c
if ~all(col(A(b,:,a,:)) == bcol(a))
bail = true;
break;
end
end
if bail
break;
end
end
else
b = h - abs(crop_amounts(3));
end
if padding == 0 % no padding
% Issue #175: there used to be a 1px minimal padding in case of crop, now removed
%{
if ~isequal([t b l r], [1 h 1 w]) % Check if we're actually croppping
padding = 1; % Leave one boundary pixel to avoid bleeding on resize
bcol(:) = nan; % make the 1px padding transparent
end
%}
elseif abs(padding) < 1 % pad value is a relative fraction of image size
padding = sign(padding)*round(mean([b-t r-l])*abs(padding)); % ADJUST PADDING
else % pad value is in units of 1/72" points
padding = round(padding); % fix cases of non-integer pad value
end
if padding > 0 % extra padding
% Create an empty image, containing the background color, that has the
% cropped image size plus the padded border
B = repmat(bcol,[(b-t)+1+padding*2,(r-l)+1+padding*2,1,n]); % Fix per Luiz Carvalho
% vA - coordinates in A that contain the cropped image
vA = [t b l r];
% vB - coordinates in B where the cropped version of A will be placed
vB = [padding+1, (b-t)+1+padding, padding+1, (r-l)+1+padding];
% Place the original image in the empty image
B(vB(1):vB(2), vB(3):vB(4), :, :) = A(vA(1):vA(2), vA(3):vA(4), :, :);
A = B;
else % extra cropping
vA = [t-padding b+padding l-padding r+padding];
A = A(vA(1):vA(2), vA(3):vA(4), :, :);
vB = [NaN NaN NaN NaN];
end
% For EPS cropping, determine the relative BoundingBox - bb_rel
bb_rel = [l-1 h-b-1 r+1 h-t+1]./[w h w h];
end
function A = col(A)
A = A(:);
end
|
github
|
drbenvincent/darc-experiments-matlab-master
|
isolate_axes.m
|
.m
|
darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/isolate_axes.m
| 4,851 |
utf_8
|
611d9727e84ad6ba76dcb3543434d0ce
|
function fh = isolate_axes(ah, vis)
%ISOLATE_AXES Isolate the specified axes in a figure on their own
%
% Examples:
% fh = isolate_axes(ah)
% fh = isolate_axes(ah, vis)
%
% This function will create a new figure containing the axes/uipanels
% specified, and also their associated legends and colorbars. The objects
% specified must all be in the same figure, but they will generally only be
% a subset of the objects in the figure.
%
% IN:
% ah - An array of axes and uipanel handles, which must come from the
% same figure.
% vis - A boolean indicating whether the new figure should be visible.
% Default: false.
%
% OUT:
% fh - The handle of the created figure.
% Copyright (C) Oliver Woodford 2011-2013
% Thank you to Rosella Blatt for reporting a bug to do with axes in GUIs
% 16/03/12: Moved copyfig to its own function. Thanks to Bob Fratantonio
% for pointing out that the function is also used in export_fig.m
% 12/12/12: Add support for isolating uipanels. Thanks to michael for suggesting it
% 08/10/13: Bug fix to allchildren suggested by Will Grant (many thanks!)
% 05/12/13: Bug fix to axes having different units. Thanks to Remington Reid for reporting
% 21/04/15: Bug fix for exporting uipanels with legend/colorbar on HG1 (reported by Alvaro
% on FEX page as a comment on 24-Apr-2014); standardized indentation & help section
% 22/04/15: Bug fix: legends and colorbars were not exported when exporting axes handle in HG2
% Make sure we have an array of handles
if ~all(ishandle(ah))
error('ah must be an array of handles');
end
% Check that the handles are all for axes or uipanels, and are all in the same figure
fh = ancestor(ah(1), 'figure');
nAx = numel(ah);
for a = 1:nAx
if ~ismember(get(ah(a), 'Type'), {'axes', 'uipanel'})
error('All handles must be axes or uipanel handles.');
end
if ~isequal(ancestor(ah(a), 'figure'), fh)
error('Axes must all come from the same figure.');
end
end
% Tag the objects so we can find them in the copy
old_tag = get(ah, 'Tag');
if nAx == 1
old_tag = {old_tag};
end
set(ah, 'Tag', 'ObjectToCopy');
% Create a new figure exactly the same as the old one
fh = copyfig(fh); %copyobj(fh, 0);
if nargin < 2 || ~vis
set(fh, 'Visible', 'off');
end
% Reset the object tags
for a = 1:nAx
set(ah(a), 'Tag', old_tag{a});
end
% Find the objects to save
ah = findall(fh, 'Tag', 'ObjectToCopy');
if numel(ah) ~= nAx
close(fh);
error('Incorrect number of objects found.');
end
% Set the axes tags to what they should be
for a = 1:nAx
set(ah(a), 'Tag', old_tag{a});
end
% Keep any legends and colorbars which overlap the subplots
% Note: in HG1 these are axes objects; in HG2 they are separate objects, therefore we
% don't test for the type, only the tag (hopefully nobody but Matlab uses them!)
lh = findall(fh, 'Tag', 'legend', '-or', 'Tag', 'Colorbar');
nLeg = numel(lh);
if nLeg > 0
set([ah(:); lh(:)], 'Units', 'normalized');
try
ax_pos = get(ah, 'OuterPosition'); % axes and figures have the OuterPosition property
catch
ax_pos = get(ah, 'Position'); % uipanels only have Position, not OuterPosition
end
if nAx > 1
ax_pos = cell2mat(ax_pos(:));
end
ax_pos(:,3:4) = ax_pos(:,3:4) + ax_pos(:,1:2);
try
leg_pos = get(lh, 'OuterPosition');
catch
leg_pos = get(lh, 'Position'); % No OuterPosition in HG2, only in HG1
end
if nLeg > 1;
leg_pos = cell2mat(leg_pos);
end
leg_pos(:,3:4) = leg_pos(:,3:4) + leg_pos(:,1:2);
ax_pos = shiftdim(ax_pos, -1);
% Overlap test
M = bsxfun(@lt, leg_pos(:,1), ax_pos(:,:,3)) & ...
bsxfun(@lt, leg_pos(:,2), ax_pos(:,:,4)) & ...
bsxfun(@gt, leg_pos(:,3), ax_pos(:,:,1)) & ...
bsxfun(@gt, leg_pos(:,4), ax_pos(:,:,2));
ah = [ah; lh(any(M, 2))];
end
% Get all the objects in the figure
axs = findall(fh);
% Delete everything except for the input objects and associated items
delete(axs(~ismember(axs, [ah; allchildren(ah); allancestors(ah)])));
end
function ah = allchildren(ah)
ah = findall(ah);
if iscell(ah)
ah = cell2mat(ah);
end
ah = ah(:);
end
function ph = allancestors(ah)
ph = [];
for a = 1:numel(ah)
h = get(ah(a), 'parent');
while h ~= 0
ph = [ph; h];
h = get(h, 'parent');
end
end
end
|
github
|
drbenvincent/darc-experiments-matlab-master
|
im2gif.m
|
.m
|
darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/im2gif.m
| 6,234 |
utf_8
|
8ee74d7d94e524410788276aa41dd5f1
|
%IM2GIF Convert a multiframe image to an animated GIF file
%
% Examples:
% im2gif infile
% im2gif infile outfile
% im2gif(A, outfile)
% im2gif(..., '-nocrop')
% im2gif(..., '-nodither')
% im2gif(..., '-ncolors', n)
% im2gif(..., '-loops', n)
% im2gif(..., '-delay', n)
%
% This function converts a multiframe image to an animated GIF.
%
% To create an animation from a series of figures, export to a multiframe
% TIFF file using export_fig, then convert to a GIF, as follows:
%
% for a = 2 .^ (3:6)
% peaks(a);
% export_fig test.tif -nocrop -append
% end
% im2gif('test.tif', '-delay', 0.5);
%
%IN:
% infile - string containing the name of the input image.
% outfile - string containing the name of the output image (must have the
% .gif extension). Default: infile, with .gif extension.
% A - HxWxCxN array of input images, stacked along fourth dimension, to
% be converted to gif.
% -nocrop - option indicating that the borders of the output are not to
% be cropped.
% -nodither - option indicating that dithering is not to be used when
% converting the image.
% -ncolors - option pair, the value of which indicates the maximum number
% of colors the GIF can have. This can also be a quantization
% tolerance, between 0 and 1. Default/maximum: 256.
% -loops - option pair, the value of which gives the number of times the
% animation is to be looped. Default: 65535.
% -delay - option pair, the value of which gives the time, in seconds,
% between frames. Default: 1/15.
% Copyright (C) Oliver Woodford 2011
function im2gif(A, varargin)
% Parse the input arguments
[A, options] = parse_args(A, varargin{:});
if options.crop ~= 0
% Crop
A = crop_borders(A, A(ceil(end/2),1,:,1));
end
% Convert to indexed image
[h, w, c, n] = size(A);
A = reshape(permute(A, [1 2 4 3]), h, w*n, c);
map = unique(reshape(A, h*w*n, c), 'rows');
if size(map, 1) > 256
dither_str = {'dither', 'nodither'};
dither_str = dither_str{1+(options.dither==0)};
if options.ncolors <= 1
[B, map] = rgb2ind(A, options.ncolors, dither_str);
if size(map, 1) > 256
[B, map] = rgb2ind(A, 256, dither_str);
end
else
[B, map] = rgb2ind(A, min(round(options.ncolors), 256), dither_str);
end
else
if max(map(:)) > 1
map = double(map) / 255;
A = double(A) / 255;
end
B = rgb2ind(im2double(A), map);
end
B = reshape(B, h, w, 1, n);
% Bug fix to rgb2ind
map(B(1)+1,:) = im2double(A(1,1,:));
% Save as a gif
imwrite(B, map, options.outfile, 'LoopCount', round(options.loops(1)), 'DelayTime', options.delay);
end
%% Parse the input arguments
function [A, options] = parse_args(A, varargin)
% Set the defaults
options = struct('outfile', '', ...
'dither', true, ...
'crop', true, ...
'ncolors', 256, ...
'loops', 65535, ...
'delay', 1/15);
% Go through the arguments
a = 0;
n = numel(varargin);
while a < n
a = a + 1;
if ischar(varargin{a}) && ~isempty(varargin{a})
if varargin{a}(1) == '-'
opt = lower(varargin{a}(2:end));
switch opt
case 'nocrop'
options.crop = false;
case 'nodither'
options.dither = false;
otherwise
if ~isfield(options, opt)
error('Option %s not recognized', varargin{a});
end
a = a + 1;
if ischar(varargin{a}) && ~ischar(options.(opt))
options.(opt) = str2double(varargin{a});
else
options.(opt) = varargin{a};
end
end
else
options.outfile = varargin{a};
end
end
end
if isempty(options.outfile)
if ~ischar(A)
error('No output filename given.');
end
% Generate the output filename from the input filename
[path, outfile] = fileparts(A);
options.outfile = fullfile(path, [outfile '.gif']);
end
if ischar(A)
% Read in the image
A = imread_rgb(A);
end
end
%% Read image to uint8 rgb array
function [A, alpha] = imread_rgb(name)
% Get file info
info = imfinfo(name);
% Special case formats
switch lower(info(1).Format)
case 'gif'
[A, map] = imread(name, 'frames', 'all');
if ~isempty(map)
map = uint8(map * 256 - 0.5); % Convert to uint8 for storage
A = reshape(map(uint32(A)+1,:), [size(A) size(map, 2)]); % Assume indexed from 0
A = permute(A, [1 2 5 4 3]);
end
case {'tif', 'tiff'}
A = cell(numel(info), 1);
for a = 1:numel(A)
[A{a}, map] = imread(name, 'Index', a, 'Info', info);
if ~isempty(map)
map = uint8(map * 256 - 0.5); % Convert to uint8 for storage
A{a} = reshape(map(uint32(A{a})+1,:), [size(A) size(map, 2)]); % Assume indexed from 0
end
if size(A{a}, 3) == 4
% TIFF in CMYK colourspace - convert to RGB
if isfloat(A{a})
A{a} = A{a} * 255;
else
A{a} = single(A{a});
end
A{a} = 255 - A{a};
A{a}(:,:,4) = A{a}(:,:,4) / 255;
A{a} = uint8(A(:,:,1:3) .* A{a}(:,:,[4 4 4]));
end
end
A = cat(4, A{:});
otherwise
[A, map, alpha] = imread(name);
A = A(:,:,:,1); % Keep only first frame of multi-frame files
if ~isempty(map)
map = uint8(map * 256 - 0.5); % Convert to uint8 for storage
A = reshape(map(uint32(A)+1,:), [size(A) size(map, 2)]); % Assume indexed from 0
elseif size(A, 3) == 4
% Assume 4th channel is an alpha matte
alpha = A(:,:,4);
A = A(:,:,1:3);
end
end
end
|
github
|
drbenvincent/darc-experiments-matlab-master
|
read_write_entire_textfile.m
|
.m
|
darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/read_write_entire_textfile.m
| 961 |
utf_8
|
775aa1f538c76516c7fb406a4f129320
|
%READ_WRITE_ENTIRE_TEXTFILE Read or write a whole text file to/from memory
%
% Read or write an entire text file to/from memory, without leaving the
% file open if an error occurs.
%
% Reading:
% fstrm = read_write_entire_textfile(fname)
% Writing:
% read_write_entire_textfile(fname, fstrm)
%
%IN:
% fname - Pathname of text file to be read in.
% fstrm - String to be written to the file, including carriage returns.
%
%OUT:
% fstrm - String read from the file. If an fstrm input is given the
% output is the same as that input.
function fstrm = read_write_entire_textfile(fname, fstrm)
modes = {'rt', 'wt'};
writing = nargin > 1;
fh = fopen(fname, modes{1+writing});
if fh == -1
error('Unable to open file %s.', fname);
end
try
if writing
fwrite(fh, fstrm, 'char*1');
else
fstrm = fread(fh, '*char')';
end
catch ex
fclose(fh);
rethrow(ex);
end
fclose(fh);
end
|
github
|
drbenvincent/darc-experiments-matlab-master
|
pdf2eps.m
|
.m
|
darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/pdf2eps.m
| 1,522 |
utf_8
|
4c8f0603619234278ed413670d24bdb6
|
%PDF2EPS Convert a pdf file to eps format using pdftops
%
% Examples:
% pdf2eps source dest
%
% This function converts a pdf file to eps format.
%
% This function requires that you have pdftops, from the Xpdf suite of
% functions, installed on your system. This can be downloaded from:
% http://www.foolabs.com/xpdf
%
%IN:
% source - filename of the source pdf file to convert. The filename is
% assumed to already have the extension ".pdf".
% dest - filename of the destination eps file. The filename is assumed to
% already have the extension ".eps".
% Copyright (C) Oliver Woodford 2009-2010
% Thanks to Aldebaro Klautau for reporting a bug when saving to
% non-existant directories.
function pdf2eps(source, dest)
% Construct the options string for pdftops
options = ['-q -paper match -eps -level2 "' source '" "' dest '"'];
% Convert to eps using pdftops
[status, message] = pdftops(options);
% Check for error
if status
% Report error
if isempty(message)
error('Unable to generate eps. Check destination directory is writable.');
else
error(message);
end
end
% Fix the DSC error created by pdftops
fid = fopen(dest, 'r+');
if fid == -1
% Cannot open the file
return
end
fgetl(fid); % Get the first line
str = fgetl(fid); % Get the second line
if strcmp(str(1:min(13, end)), '% Produced by')
fseek(fid, -numel(str)-1, 'cof');
fwrite(fid, '%'); % Turn ' ' into '%'
end
fclose(fid);
end
|
github
|
drbenvincent/darc-experiments-matlab-master
|
print2array.m
|
.m
|
darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/print2array.m
| 10,376 |
utf_8
|
a2022c32ae3efa6007a326692227bd39
|
function [A, bcol] = print2array(fig, res, renderer, gs_options)
%PRINT2ARRAY Exports a figure to an image array
%
% Examples:
% A = print2array
% A = print2array(figure_handle)
% A = print2array(figure_handle, resolution)
% A = print2array(figure_handle, resolution, renderer)
% A = print2array(figure_handle, resolution, renderer, gs_options)
% [A bcol] = print2array(...)
%
% This function outputs a bitmap image of the given figure, at the desired
% resolution.
%
% If renderer is '-painters' then ghostcript needs to be installed. This
% can be downloaded from: http://www.ghostscript.com
%
% IN:
% figure_handle - The handle of the figure to be exported. Default: gcf.
% resolution - Resolution of the output, as a factor of screen
% resolution. Default: 1.
% renderer - string containing the renderer paramater to be passed to
% print. Default: '-opengl'.
% gs_options - optional ghostscript options (e.g.: '-dNoOutputFonts'). If
% multiple options are needed, enclose in call array: {'-a','-b'}
%
% OUT:
% A - MxNx3 uint8 image of the figure.
% bcol - 1x3 uint8 vector of the background color
% Copyright (C) Oliver Woodford 2008-2014, Yair Altman 2015-
%{
% 05/09/11: Set EraseModes to normal when using opengl or zbuffer
% renderers. Thanks to Pawel Kocieniewski for reporting the issue.
% 21/09/11: Bug fix: unit8 -> uint8! Thanks to Tobias Lamour for reporting it.
% 14/11/11: Bug fix: stop using hardcopy(), as it interfered with figure size
% and erasemode settings. Makes it a bit slower, but more reliable.
% Thanks to Phil Trinh and Meelis Lootus for reporting the issues.
% 09/12/11: Pass font path to ghostscript.
% 27/01/12: Bug fix affecting painters rendering tall figures. Thanks to
% Ken Campbell for reporting it.
% 03/04/12: Bug fix to median input. Thanks to Andy Matthews for reporting it.
% 26/10/12: Set PaperOrientation to portrait. Thanks to Michael Watts for
% reporting the issue.
% 26/02/15: If temp dir is not writable, use the current folder for temp
% EPS/TIF files (Javier Paredes)
% 27/02/15: Display suggested workarounds to internal print() error (issue #16)
% 28/02/15: Enable users to specify optional ghostscript options (issue #36)
% 10/03/15: Fixed minor warning reported by Paul Soderlind; fixed code indentation
% 28/05/15: Fixed issue #69: patches with LineWidth==0.75 appear wide (internal bug in Matlab's print() func)
% 07/07/15: Fixed issue #83: use numeric handles in HG1
% 11/12/16: Fixed cropping issue reported by Harry D.
%}
% Generate default input arguments, if needed
if nargin < 2
res = 1;
if nargin < 1
fig = gcf;
end
end
% Warn if output is large
old_mode = get(fig, 'Units');
set(fig, 'Units', 'pixels');
px = get(fig, 'Position');
set(fig, 'Units', old_mode);
npx = prod(px(3:4)*res)/1e6;
if npx > 30
% 30M pixels or larger!
warning('MATLAB:LargeImage', 'print2array generating a %.1fM pixel image. This could be slow and might also cause memory problems.', npx);
end
% Retrieve the background colour
bcol = get(fig, 'Color');
% Set the resolution parameter
res_str = ['-r' num2str(ceil(get(0, 'ScreenPixelsPerInch')*res))];
% Generate temporary file name
tmp_nam = [tempname '.tif'];
try
% Ensure that the temp dir is writable (Javier Paredes 26/2/15)
fid = fopen(tmp_nam,'w');
fwrite(fid,1);
fclose(fid);
delete(tmp_nam); % cleanup
isTempDirOk = true;
catch
% Temp dir is not writable, so use the current folder
[dummy,fname,fext] = fileparts(tmp_nam); %#ok<ASGLU>
fpath = pwd;
tmp_nam = fullfile(fpath,[fname fext]);
isTempDirOk = false;
end
% Enable users to specify optional ghostscript options (issue #36)
if nargin > 3 && ~isempty(gs_options)
if iscell(gs_options)
gs_options = sprintf(' %s',gs_options{:});
elseif ~ischar(gs_options)
error('gs_options input argument must be a string or cell-array of strings');
else
gs_options = [' ' gs_options];
end
else
gs_options = '';
end
if nargin > 2 && strcmp(renderer, '-painters')
% First try to print directly to tif file
try
% Print the file into a temporary TIF file and read it into array A
[A, err, ex] = read_tif_img(fig, res_str, renderer, tmp_nam);
if err, rethrow(ex); end
catch % error - try to print to EPS and then using Ghostscript to TIF
% Print to eps file
if isTempDirOk
tmp_eps = [tempname '.eps'];
else
tmp_eps = fullfile(fpath,[fname '.eps']);
end
print2eps(tmp_eps, fig, 0, renderer, '-loose');
try
% Initialize the command to export to tiff using ghostscript
cmd_str = ['-dEPSCrop -q -dNOPAUSE -dBATCH ' res_str ' -sDEVICE=tiff24nc'];
% Set the font path
fp = font_path();
if ~isempty(fp)
cmd_str = [cmd_str ' -sFONTPATH="' fp '"'];
end
% Add the filenames
cmd_str = [cmd_str ' -sOutputFile="' tmp_nam '" "' tmp_eps '"' gs_options];
% Execute the ghostscript command
ghostscript(cmd_str);
catch me
% Delete the intermediate file
delete(tmp_eps);
rethrow(me);
end
% Delete the intermediate file
delete(tmp_eps);
% Read in the generated bitmap
A = imread(tmp_nam);
% Delete the temporary bitmap file
delete(tmp_nam);
end
% Set border pixels to the correct colour
if isequal(bcol, 'none')
bcol = [];
elseif isequal(bcol, [1 1 1])
bcol = uint8([255 255 255]);
else
for l = 1:size(A, 2)
if ~all(reshape(A(:,l,:) == 255, [], 1))
break;
end
end
for r = size(A, 2):-1:l
if ~all(reshape(A(:,r,:) == 255, [], 1))
break;
end
end
for t = 1:size(A, 1)
if ~all(reshape(A(t,:,:) == 255, [], 1))
break;
end
end
for b = size(A, 1):-1:t
if ~all(reshape(A(b,:,:) == 255, [], 1))
break;
end
end
bcol = uint8(median(single([reshape(A(:,[l r],:), [], size(A, 3)); reshape(A([t b],:,:), [], size(A, 3))]), 1));
for c = 1:size(A, 3)
A(:,[1:l-1, r+1:end],c) = bcol(c);
A([1:t-1, b+1:end],:,c) = bcol(c);
end
end
else
if nargin < 3
renderer = '-opengl';
end
% Print the file into a temporary TIF file and read it into array A
[A, err, ex] = read_tif_img(fig, res_str, renderer, tmp_nam);
% Throw any error that occurred
if err
% Display suggested workarounds to internal print() error (issue #16)
fprintf(2, 'An error occured with Matlab''s builtin print function.\nTry setting the figure Renderer to ''painters'' or use opengl(''software'').\n\n');
rethrow(ex);
end
% Set the background color
if isequal(bcol, 'none')
bcol = [];
else
bcol = bcol * 255;
if isequal(bcol, round(bcol))
bcol = uint8(bcol);
else
bcol = squeeze(A(1,1,:));
end
end
end
% Check the output size is correct
if isequal(res, round(res))
px = round([px([4 3])*res 3]); % round() to avoid an indexing warning below
if ~isequal(size(A), px)
% Correct the output size
A = A(1:min(end,px(1)),1:min(end,px(2)),:);
end
end
end
% Function to create a TIF image of the figure and read it into an array
function [A, err, ex] = read_tif_img(fig, res_str, renderer, tmp_nam)
err = false;
ex = [];
% Temporarily set the paper size
old_pos_mode = get(fig, 'PaperPositionMode');
old_orientation = get(fig, 'PaperOrientation');
set(fig, 'PaperPositionMode','auto', 'PaperOrientation','portrait');
try
% Workaround for issue #69: patches with LineWidth==0.75 appear wide (internal bug in Matlab's print() function)
fp = []; % in case we get an error below
fp = findall(fig, 'Type','patch', 'LineWidth',0.75);
set(fp, 'LineWidth',0.5);
% Fix issue #83: use numeric handles in HG1
if ~using_hg2(fig), fig = double(fig); end
% Print to tiff file
print(fig, renderer, res_str, '-dtiff', tmp_nam);
% Read in the printed file
A = imread(tmp_nam);
% Delete the temporary file
delete(tmp_nam);
catch ex
err = true;
end
set(fp, 'LineWidth',0.75); % restore original figure appearance
% Reset the paper size
set(fig, 'PaperPositionMode',old_pos_mode, 'PaperOrientation',old_orientation);
end
% Function to return (and create, where necessary) the font path
function fp = font_path()
fp = user_string('gs_font_path');
if ~isempty(fp)
return
end
% Create the path
% Start with the default path
fp = getenv('GS_FONTPATH');
% Add on the typical directories for a given OS
if ispc
if ~isempty(fp)
fp = [fp ';'];
end
fp = [fp getenv('WINDIR') filesep 'Fonts'];
else
if ~isempty(fp)
fp = [fp ':'];
end
fp = [fp '/usr/share/fonts:/usr/local/share/fonts:/usr/share/fonts/X11:/usr/local/share/fonts/X11:/usr/share/fonts/truetype:/usr/local/share/fonts/truetype'];
end
user_string('gs_font_path', fp);
end
|
github
|
drbenvincent/darc-experiments-matlab-master
|
append_pdfs.m
|
.m
|
darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/append_pdfs.m
| 2,759 |
utf_8
|
9b52be41aff48bea6f27992396900640
|
%APPEND_PDFS Appends/concatenates multiple PDF files
%
% Example:
% append_pdfs(output, input1, input2, ...)
% append_pdfs(output, input_list{:})
% append_pdfs test.pdf temp1.pdf temp2.pdf
%
% This function appends multiple PDF files to an existing PDF file, or
% concatenates them into a PDF file if the output file doesn't yet exist.
%
% This function requires that you have ghostscript installed on your
% system. Ghostscript can be downloaded from: http://www.ghostscript.com
%
% IN:
% output - string of output file name (including the extension, .pdf).
% If it exists it is appended to; if not, it is created.
% input1 - string of an input file name (including the extension, .pdf).
% All input files are appended in order.
% input_list - cell array list of input file name strings. All input
% files are appended in order.
% Copyright: Oliver Woodford, 2011
% Thanks to Reinhard Knoll for pointing out that appending multiple pdfs in
% one go is much faster than appending them one at a time.
% Thanks to Michael Teo for reporting the issue of a too long command line.
% Issue resolved on 5/5/2011, by passing gs a command file.
% Thanks to Martin Wittmann for pointing out the quality issue when
% appending multiple bitmaps.
% Issue resolved (to best of my ability) 1/6/2011, using the prepress
% setting
% 26/02/15: If temp dir is not writable, use the output folder for temp
% files when appending (Javier Paredes); sanity check of inputs
function append_pdfs(varargin)
if nargin < 2, return; end % sanity check
% Are we appending or creating a new file
append = exist(varargin{1}, 'file') == 2;
output = [tempname '.pdf'];
try
% Ensure that the temp dir is writable (Javier Paredes 26/2/15)
fid = fopen(output,'w');
fwrite(fid,1);
fclose(fid);
delete(output);
isTempDirOk = true;
catch
% Temp dir is not writable, so use the output folder
[dummy,fname,fext] = fileparts(output); %#ok<ASGLU>
fpath = fileparts(varargin{1});
output = fullfile(fpath,[fname fext]);
isTempDirOk = false;
end
if ~append
output = varargin{1};
varargin = varargin(2:end);
end
% Create the command file
if isTempDirOk
cmdfile = [tempname '.txt'];
else
cmdfile = fullfile(fpath,[fname '.txt']);
end
fh = fopen(cmdfile, 'w');
fprintf(fh, '-q -dNOPAUSE -dBATCH -sDEVICE=pdfwrite -dPDFSETTINGS=/prepress -sOutputFile="%s" -f', output);
fprintf(fh, ' "%s"', varargin{:});
fclose(fh);
% Call ghostscript
ghostscript(['@"' cmdfile '"']);
% Delete the command file
delete(cmdfile);
% Rename the file if needed
if append
movefile(output, varargin{1});
end
end
|
github
|
drbenvincent/darc-experiments-matlab-master
|
using_hg2.m
|
.m
|
darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/using_hg2.m
| 1,100 |
utf_8
|
47ca10d86740c27b9f6b397373ae16cd
|
%USING_HG2 Determine if the HG2 graphics engine is used
%
% tf = using_hg2(fig)
%
%IN:
% fig - handle to the figure in question.
%
%OUT:
% tf - boolean indicating whether the HG2 graphics engine is being used
% (true) or not (false).
% 19/06/2015 - Suppress warning in R2015b; cache result for improved performance
% 06/06/2016 - Fixed issue #156 (bad return value in R2016b)
function tf = using_hg2(fig)
persistent tf_cached
if isempty(tf_cached)
try
if nargin < 1, fig = figure('visible','off'); end
oldWarn = warning('off','MATLAB:graphicsversion:GraphicsVersionRemoval');
try
% This generates a [supressed] warning in R2015b:
tf = ~graphicsversion(fig, 'handlegraphics');
catch
tf = ~verLessThan('matlab','8.4'); % =R2014b
end
warning(oldWarn);
catch
tf = false;
end
if nargin < 1, delete(fig); end
tf_cached = tf;
else
tf = tf_cached;
end
end
|
github
|
drbenvincent/darc-experiments-matlab-master
|
eps2pdf.m
|
.m
|
darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/eps2pdf.m
| 8,793 |
utf_8
|
474e976cf6454d5d7850baf14494fedf
|
function eps2pdf(source, dest, crop, append, gray, quality, gs_options)
%EPS2PDF Convert an eps file to pdf format using ghostscript
%
% Examples:
% eps2pdf source dest
% eps2pdf(source, dest, crop)
% eps2pdf(source, dest, crop, append)
% eps2pdf(source, dest, crop, append, gray)
% eps2pdf(source, dest, crop, append, gray, quality)
% eps2pdf(source, dest, crop, append, gray, quality, gs_options)
%
% This function converts an eps file to pdf format. The output can be
% optionally cropped and also converted to grayscale. If the output pdf
% file already exists then the eps file can optionally be appended as a new
% page on the end of the eps file. The level of bitmap compression can also
% optionally be set.
%
% This function requires that you have ghostscript installed on your
% system. Ghostscript can be downloaded from: http://www.ghostscript.com
%
% Inputs:
% source - filename of the source eps file to convert. The filename is
% assumed to already have the extension ".eps".
% dest - filename of the destination pdf file. The filename is assumed
% to already have the extension ".pdf".
% crop - boolean indicating whether to crop the borders off the pdf.
% Default: true.
% append - boolean indicating whether the eps should be appended to the
% end of the pdf as a new page (if the pdf exists already).
% Default: false.
% gray - boolean indicating whether the output pdf should be grayscale
% or not. Default: false.
% quality - scalar indicating the level of image bitmap quality to
% output. A larger value gives a higher quality. quality > 100
% gives lossless output. Default: ghostscript prepress default.
% gs_options - optional ghostscript options (e.g.: '-dNoOutputFonts'). If
% multiple options are needed, enclose in call array: {'-a','-b'}
% Copyright (C) Oliver Woodford 2009-2014, Yair Altman 2015-
% Suggestion of appending pdf files provided by Matt C at:
% http://www.mathworks.com/matlabcentral/fileexchange/23629
% Thank you to Fabio Viola for pointing out compression artifacts, leading
% to the quality setting.
% Thank you to Scott for pointing out the subsampling of very small images,
% which was fixed for lossless compression settings.
% 9/12/2011 Pass font path to ghostscript.
% 26/02/15: If temp dir is not writable, use the dest folder for temp
% destination files (Javier Paredes)
% 28/02/15: Enable users to specify optional ghostscript options (issue #36)
% 01/03/15: Upon GS error, retry without the -sFONTPATH= option (this might solve
% some /findfont errors according to James Rankin, FEX Comment 23/01/15)
% 23/06/15: Added extra debug info in case of ghostscript error; code indentation
% 04/10/15: Suggest a workaround for issue #41 (missing font path; thanks Mariia Fedotenkova)
% 22/02/16: Bug fix from latest release of this file (workaround for issue #41)
% 20/03/17: Added informational message in case of GS croak (issue #186)
% Intialise the options string for ghostscript
options = ['-q -dNOPAUSE -dBATCH -sDEVICE=pdfwrite -dPDFSETTINGS=/prepress -sOutputFile="' dest '"'];
% Set crop option
if nargin < 3 || crop
options = [options ' -dEPSCrop'];
end
% Set the font path
fp = font_path();
if ~isempty(fp)
options = [options ' -sFONTPATH="' fp '"'];
end
% Set the grayscale option
if nargin > 4 && gray
options = [options ' -sColorConversionStrategy=Gray -dProcessColorModel=/DeviceGray'];
end
% Set the bitmap quality
if nargin > 5 && ~isempty(quality)
options = [options ' -dAutoFilterColorImages=false -dAutoFilterGrayImages=false'];
if quality > 100
options = [options ' -dColorImageFilter=/FlateEncode -dGrayImageFilter=/FlateEncode -c ".setpdfwrite << /ColorImageDownsampleThreshold 10 /GrayImageDownsampleThreshold 10 >> setdistillerparams"'];
else
options = [options ' -dColorImageFilter=/DCTEncode -dGrayImageFilter=/DCTEncode'];
v = 1 + (quality < 80);
quality = 1 - quality / 100;
s = sprintf('<< /QFactor %.2f /Blend 1 /HSample [%d 1 1 %d] /VSample [%d 1 1 %d] >>', quality, v, v, v, v);
options = sprintf('%s -c ".setpdfwrite << /ColorImageDict %s /GrayImageDict %s >> setdistillerparams"', options, s, s);
end
end
% Enable users to specify optional ghostscript options (issue #36)
if nargin > 6 && ~isempty(gs_options)
if iscell(gs_options)
gs_options = sprintf(' %s',gs_options{:});
elseif ~ischar(gs_options)
error('gs_options input argument must be a string or cell-array of strings');
else
gs_options = [' ' gs_options];
end
options = [options gs_options];
end
% Check if the output file exists
if nargin > 3 && append && exist(dest, 'file') == 2
% File exists - append current figure to the end
tmp_nam = tempname;
try
% Ensure that the temp dir is writable (Javier Paredes 26/2/15)
fid = fopen(tmp_nam,'w');
fwrite(fid,1);
fclose(fid);
delete(tmp_nam);
catch
% Temp dir is not writable, so use the dest folder
[dummy,fname,fext] = fileparts(tmp_nam); %#ok<ASGLU>
fpath = fileparts(dest);
tmp_nam = fullfile(fpath,[fname fext]);
end
% Copy the file
copyfile(dest, tmp_nam);
% Add the output file names
options = [options ' -f "' tmp_nam '" "' source '"'];
try
% Convert to pdf using ghostscript
[status, message] = ghostscript(options);
catch me
% Delete the intermediate file
delete(tmp_nam);
rethrow(me);
end
% Delete the intermediate file
delete(tmp_nam);
else
% File doesn't exist or should be over-written
% Add the output file names
options = [options ' -f "' source '"'];
% Convert to pdf using ghostscript
[status, message] = ghostscript(options);
end
% Check for error
if status
% Retry without the -sFONTPATH= option (this might solve some GS
% /findfont errors according to James Rankin, FEX Comment 23/01/15)
orig_options = options;
if ~isempty(fp)
options = regexprep(options, ' -sFONTPATH=[^ ]+ ',' ');
status = ghostscript(options);
if ~status, return; end % hurray! (no error)
end
% Report error
if isempty(message)
error('Unable to generate pdf. Check destination directory is writable.');
elseif ~isempty(strfind(message,'/typecheck in /findfont'))
% Suggest a workaround for issue #41 (missing font path)
font_name = strtrim(regexprep(message,'.*Operand stack:\s*(.*)\s*Execution.*','$1'));
fprintf(2, 'Ghostscript error: could not find the following font(s): %s\n', font_name);
fpath = fileparts(mfilename('fullpath'));
gs_fonts_file = fullfile(fpath, '.ignore', 'gs_font_path.txt');
fprintf(2, ' try to add the font''s folder to your %s file\n\n', gs_fonts_file);
error('export_fig error');
else
fprintf(2, '\nGhostscript error: perhaps %s is open by another application\n', dest);
if ~isempty(gs_options)
fprintf(2, ' or maybe the%s option(s) are not accepted by your GS version\n', gs_options);
end
fprintf(2, ' or maybe you have another gs executable in your system''s path\n');
fprintf(2, 'Ghostscript options: %s\n\n', orig_options);
error(message);
end
end
end
% Function to return (and create, where necessary) the font path
function fp = font_path()
fp = user_string('gs_font_path');
if ~isempty(fp)
return
end
% Create the path
% Start with the default path
fp = getenv('GS_FONTPATH');
% Add on the typical directories for a given OS
if ispc
if ~isempty(fp)
fp = [fp ';'];
end
fp = [fp getenv('WINDIR') filesep 'Fonts'];
else
if ~isempty(fp)
fp = [fp ':'];
end
fp = [fp '/usr/share/fonts:/usr/local/share/fonts:/usr/share/fonts/X11:/usr/local/share/fonts/X11:/usr/share/fonts/truetype:/usr/local/share/fonts/truetype'];
end
user_string('gs_font_path', fp);
end
|
github
|
drbenvincent/darc-experiments-matlab-master
|
export_fig.m
|
.m
|
darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/export_fig.m
| 64,681 |
utf_8
|
6eb52ba116cd2632d3e98cfafa45bca1
|
function [imageData, alpha] = export_fig(varargin)
%EXPORT_FIG Exports figures in a publication-quality format
%
% Examples:
% imageData = export_fig
% [imageData, alpha] = export_fig
% export_fig filename
% export_fig filename -format1 -format2
% export_fig ... -nocrop
% export_fig ... -c[<val>,<val>,<val>,<val>]
% export_fig ... -transparent
% export_fig ... -native
% export_fig ... -m<val>
% export_fig ... -r<val>
% export_fig ... -a<val>
% export_fig ... -q<val>
% export_fig ... -p<val>
% export_fig ... -d<gs_option>
% export_fig ... -depsc
% export_fig ... -<renderer>
% export_fig ... -<colorspace>
% export_fig ... -append
% export_fig ... -bookmark
% export_fig ... -clipboard
% export_fig ... -update
% export_fig ... -nofontswap
% export_fig ... -font_space <char>
% export_fig ... -linecaps
% export_fig ... -noinvert
% export_fig(..., handle)
%
% This function saves a figure or single axes to one or more vector and/or
% bitmap file formats, and/or outputs a rasterized version to the workspace,
% with the following properties:
% - Figure/axes reproduced as it appears on screen
% - Cropped borders (optional)
% - Embedded fonts (vector formats)
% - Improved line and grid line styles
% - Anti-aliased graphics (bitmap formats)
% - Render images at native resolution (optional for bitmap formats)
% - Transparent background supported (pdf, eps, png, tiff)
% - Semi-transparent patch objects supported (png, tiff)
% - RGB, CMYK or grayscale output (CMYK only with pdf, eps, tiff)
% - Variable image compression, including lossless (pdf, eps, jpg)
% - Optional rounded line-caps (pdf, eps)
% - Optionally append to file (pdf, tiff)
% - Vector formats: pdf, eps
% - Bitmap formats: png, tiff, jpg, bmp, export to workspace
%
% This function is especially suited to exporting figures for use in
% publications and presentations, because of the high quality and
% portability of media produced.
%
% Note that the background color and figure dimensions are reproduced
% (the latter approximately, and ignoring cropping & magnification) in the
% output file. For transparent background (and semi-transparent patch
% objects), use the -transparent option or set the figure 'Color' property
% to 'none'. To make axes transparent set the axes 'Color' property to
% 'none'. PDF, EPS, TIF & PNG are the only formats that support a transparent
% background; only TIF & PNG formats support transparency of patch objects.
%
% The choice of renderer (opengl, zbuffer or painters) has a large impact
% on the quality of output. The default value (opengl for bitmaps, painters
% for vector formats) generally gives good results, but if you aren't
% satisfied then try another renderer. Notes: 1) For vector formats (EPS,
% PDF), only painters generates vector graphics. 2) For bitmaps, only
% opengl can render transparent patch objects correctly. 3) For bitmaps,
% only painters will correctly scale line dash and dot lengths when
% magnifying or anti-aliasing. 4) Fonts may be substitued with Courier when
% using painters.
%
% When exporting to vector format (PDF & EPS) and bitmap format using the
% painters renderer, this function requires that ghostscript is installed
% on your system. You can download this from:
% http://www.ghostscript.com
% When exporting to eps it additionally requires pdftops, from the Xpdf
% suite of functions. You can download this from:
% http://www.foolabs.com/xpdf
%
% Inputs:
% filename - string containing the name (optionally including full or
% relative path) of the file the figure is to be saved as. If
% a path is not specified, the figure is saved in the current
% directory. If no name and no output arguments are specified,
% the default name, 'export_fig_out', is used. If neither a
% file extension nor a format are specified, a ".png" is added
% and the figure saved in that format.
% -format1, -format2, etc. - strings containing the extensions of the
% file formats the figure is to be saved as.
% Valid options are: '-pdf', '-eps', '-png',
% '-tif', '-jpg' and '-bmp'. All combinations
% of formats are valid.
% -nocrop - option indicating that the borders of the output are not to
% be cropped.
% -c[<val>,<val>,<val>,<val>] - option indicating crop amounts. Must be
% a 4-element vector of numeric values: [top,right,bottom,left]
% where NaN/Inf indicate auto-cropping, 0 means no cropping,
% and any other value mean cropping in pixel amounts.
% -transparent - option indicating that the figure background is to be
% made transparent (png, pdf, tif and eps output only).
% -m<val> - option where val indicates the factor to magnify the
% on-screen figure pixel dimensions by when generating bitmap
% outputs (does not affect vector formats). Default: '-m1'.
% -r<val> - option val indicates the resolution (in pixels per inch) to
% export bitmap and vector outputs at, keeping the dimensions
% of the on-screen figure. Default: '-r864' (for vector output
% only). Note that the -m option overides the -r option for
% bitmap outputs only.
% -native - option indicating that the output resolution (when outputting
% a bitmap format) should be such that the vertical resolution
% of the first suitable image found in the figure is at the
% native resolution of that image. To specify a particular
% image to use, give it the tag 'export_fig_native'. Notes:
% This overrides any value set with the -m and -r options. It
% also assumes that the image is displayed front-to-parallel
% with the screen. The output resolution is approximate and
% should not be relied upon. Anti-aliasing can have adverse
% effects on image quality (disable with the -a1 option).
% -a1, -a2, -a3, -a4 - option indicating the amount of anti-aliasing to
% use for bitmap outputs. '-a1' means no anti-
% aliasing; '-a4' is the maximum amount (default).
% -<renderer> - option to force a particular renderer (painters, opengl or
% zbuffer). Default value: opengl for bitmap formats or
% figures with patches and/or transparent annotations;
% painters for vector formats without patches/transparencies.
% -<colorspace> - option indicating which colorspace color figures should
% be saved in: RGB (default), CMYK or gray. CMYK is only
% supported in pdf, eps and tiff output.
% -q<val> - option to vary bitmap image quality (in pdf, eps and jpg
% files only). Larger val, in the range 0-100, gives higher
% quality/lower compression. val > 100 gives lossless
% compression. Default: '-q95' for jpg, ghostscript prepress
% default for pdf & eps. Note: lossless compression can
% sometimes give a smaller file size than the default lossy
% compression, depending on the type of images.
% -p<val> - option to pad a border of width val to exported files, where
% val is either a relative size with respect to cropped image
% size (i.e. p=0.01 adds a 1% border). For EPS & PDF formats,
% val can also be integer in units of 1/72" points (abs(val)>1).
% val can be positive (padding) or negative (extra cropping).
% If used, the -nocrop flag will be ignored, i.e. the image will
% always be cropped and then padded. Default: 0 (i.e. no padding).
% -append - option indicating that if the file (pdfs only) already
% exists, the figure is to be appended as a new page, instead
% of being overwritten (default).
% -bookmark - option to indicate that a bookmark with the name of the
% figure is to be created in the output file (pdf only).
% -clipboard - option to save output as an image on the system clipboard.
% Note: background transparency is not preserved in clipboard
% -d<gs_option> - option to indicate a ghostscript setting. For example,
% -dMaxBitmap=0 or -dNoOutputFonts (Ghostscript 9.15+).
% -depsc - option to use EPS level-3 rather than the default level-2 print
% device. This solves some bugs with Matlab's default -depsc2 device
% such as discolored subplot lines on images (vector formats only).
% -update - option to download and install the latest version of export_fig
% -nofontswap - option to avoid font swapping. Font swapping is automatically
% done in vector formats (only): 11 standard Matlab fonts are
% replaced by the original figure fonts. This option prevents this.
% -font_space <char> - option to set a spacer character for font-names that
% contain spaces, used by EPS/PDF. Default: ''
% -linecaps - option to create rounded line-caps (vector formats only).
% -noinvert - option to avoid setting figure's InvertHardcopy property to
% 'off' during output (this solves some problems of empty outputs).
% handle - The handle of the figure, axes or uipanels (can be an array of
% handles, but the objects must be in the same figure) to be
% saved. Default: gcf.
%
% Outputs:
% imageData - MxNxC uint8 image array of the exported image.
% alpha - MxN single array of alphamatte values in the range [0,1],
% for the case when the background is transparent.
%
% Some helpful examples and tips can be found at:
% https://github.com/altmany/export_fig
%
% See also PRINT, SAVEAS, ScreenCapture (on the Matlab File Exchange)
%{
% Copyright (C) Oliver Woodford 2008-2014, Yair Altman 2015-
% The idea of using ghostscript is inspired by Peder Axensten's SAVEFIG
% (fex id: 10889) which is itself inspired by EPS2PDF (fex id: 5782).
% The idea for using pdftops came from the MATLAB newsgroup (id: 168171).
% The idea of editing the EPS file to change line styles comes from Jiro
% Doke's FIXPSLINESTYLE (fex id: 17928).
% The idea of changing dash length with line width came from comments on
% fex id: 5743, but the implementation is mine :)
% The idea of anti-aliasing bitmaps came from Anders Brun's MYAA (fex id:
% 20979).
% The idea of appending figures in pdfs came from Matt C in comments on the
% FEX (id: 23629)
% Thanks to Roland Martin for pointing out the colour MATLAB
% bug/feature with colorbar axes and transparent backgrounds.
% Thanks also to Andrew Matthews for describing a bug to do with the figure
% size changing in -nodisplay mode. I couldn't reproduce it, but included a
% fix anyway.
% Thanks to Tammy Threadgill for reporting a bug where an axes is not
% isolated from gui objects.
%}
%{
% 23/02/12: Ensure that axes limits don't change during printing
% 14/03/12: Fix bug in fixing the axes limits (thanks to Tobias Lamour for reporting it).
% 02/05/12: Incorporate patch of Petr Nechaev (many thanks), enabling bookmarking of figures in pdf files.
% 09/05/12: Incorporate patch of Arcelia Arrieta (many thanks), to keep tick marks fixed.
% 12/12/12: Add support for isolating uipanels. Thanks to michael for suggesting it.
% 25/09/13: Add support for changing resolution in vector formats. Thanks to Jan Jaap Meijer for suggesting it.
% 07/05/14: Add support for '~' at start of path. Thanks to Sally Warner for suggesting it.
% 24/02/15: Fix Matlab R2014b bug (issue #34): plot markers are not displayed when ZLimMode='manual'
% 25/02/15: Fix issue #4 (using HG2 on R2014a and earlier)
% 25/02/15: Fix issue #21 (bold TeX axes labels/titles in R2014b)
% 26/02/15: If temp dir is not writable, use the user-specified folder for temporary EPS/PDF files (Javier Paredes)
% 27/02/15: Modified repository URL from github.com/ojwoodford to /altmany
% Indented main function
% Added top-level try-catch block to display useful workarounds
% 28/02/15: Enable users to specify optional ghostscript options (issue #36)
% 06/03/15: Improved image padding & cropping thanks to Oscar Hartogensis
% 26/03/15: Fixed issue #49 (bug with transparent grayscale images); fixed out-of-memory issue
% 26/03/15: Fixed issue #42: non-normalized annotations on HG1
% 26/03/15: Fixed issue #46: Ghostscript crash if figure units <> pixels
% 27/03/15: Fixed issue #39: bad export of transparent annotations/patches
% 28/03/15: Fixed issue #50: error on some Matlab versions with the fix for issue #42
% 29/03/15: Fixed issue #33: bugs in Matlab's print() function with -cmyk
% 29/03/15: Improved processing of input args (accept space between param name & value, related to issue #51)
% 30/03/15: When exporting *.fig files, then saveas *.fig if figure is open, otherwise export the specified fig file
% 30/03/15: Fixed edge case bug introduced yesterday (commit #ae1755bd2e11dc4e99b95a7681f6e211b3fa9358)
% 09/04/15: Consolidated header comment sections; initialize output vars only if requested (nargout>0)
% 14/04/15: Workaround for issue #45: lines in image subplots are exported in invalid color
% 15/04/15: Fixed edge-case in parsing input parameters; fixed help section to show the -depsc option (issue #45)
% 21/04/15: Bug fix: Ghostscript croaks on % chars in output PDF file (reported by Sven on FEX page, 15-Jul-2014)
% 22/04/15: Bug fix: Pdftops croaks on relative paths (reported by Tintin Milou on FEX page, 19-Jan-2015)
% 04/05/15: Merged fix #63 (Kevin Mattheus Moerman): prevent tick-label changes during export
% 07/05/15: Partial fix for issue #65: PDF export used painters rather than opengl renderer (thanks Nguyenr)
% 08/05/15: Fixed issue #65: bad PDF append since commit #e9f3cdf 21/04/15 (thanks Robert Nguyen)
% 12/05/15: Fixed issue #67: exponent labels cropped in export, since fix #63 (04/05/15)
% 28/05/15: Fixed issue #69: set non-bold label font only if the string contains symbols (\beta etc.), followup to issue #21
% 29/05/15: Added informative error message in case user requested SVG output (issue #72)
% 09/06/15: Fixed issue #58: -transparent removed anti-aliasing when exporting to PNG
% 19/06/15: Added -update option to download and install the latest version of export_fig
% 07/07/15: Added -nofontswap option to avoid font-swapping in EPS/PDF
% 16/07/15: Fixed problem with anti-aliasing on old Matlab releases
% 11/09/15: Fixed issue #103: magnification must never become negative; also fixed reported error msg in parsing input params
% 26/09/15: Alert if trying to export transparent patches/areas to non-PNG outputs (issue #108)
% 04/10/15: Do not suggest workarounds for certain errors that have already been handled previously
% 01/11/15: Fixed issue #112: use same renderer in print2eps as export_fig (thanks to Jesús Pestana Puerta)
% 10/11/15: Custom GS installation webpage for MacOS. Thanks to Andy Hueni via FEX
% 19/11/15: Fixed clipboard export in R2015b (thanks to Dan K via FEX)
% 21/02/16: Added -c option for indicating specific crop amounts (idea by Cedric Noordam on FEX)
% 08/05/16: Added message about possible error reason when groot.Units~=pixels (issue #149)
% 17/05/16: Fixed case of image YData containing more than 2 elements (issue #151)
% 08/08/16: Enabled exporting transparency to TIF, in addition to PNG/PDF (issue #168)
% 11/12/16: Added alert in case of error creating output PDF/EPS file (issue #179)
% 13/12/16: Minor fix to the commit for issue #179 from 2 days ago
% 22/03/17: Fixed issue #187: only set manual ticks when no exponent is present
% 09/04/17: Added -linecaps option (idea by Baron Finer, issue #192)
% 15/09/17: Fixed issue #205: incorrect tick-labels when Ticks number don't match the TickLabels number
% 15/09/17: Fixed issue #210: initialize alpha map to ones instead of zeros when -transparent is not used
% 18/09/17: Added -font_space option to replace font-name spaces in EPS/PDF (workaround for issue #194)
% 18/09/17: Added -noinvert option to solve some export problems with some graphic cards (workaround for issue #197)
%}
if nargout
[imageData, alpha] = deal([]);
end
hadError = false;
displaySuggestedWorkarounds = true;
% Ensure the figure is rendered correctly _now_ so that properties like axes limits are up-to-date
drawnow;
pause(0.05); % this solves timing issues with Java Swing's EDT (http://undocumentedmatlab.com/blog/solving-a-matlab-hang-problem)
% Parse the input arguments
fig = get(0, 'CurrentFigure');
[fig, options] = parse_args(nargout, fig, varargin{:});
% Ensure that we have a figure handle
if isequal(fig,-1)
return; % silent bail-out
elseif isempty(fig)
error('No figure found');
end
% Isolate the subplot, if it is one
cls = all(ismember(get(fig, 'Type'), {'axes', 'uipanel'}));
if cls
% Given handles of one or more axes, so isolate them from the rest
fig = isolate_axes(fig);
else
% Check we have a figure
if ~isequal(get(fig, 'Type'), 'figure')
error('Handle must be that of a figure, axes or uipanel');
end
% Get the old InvertHardcopy mode
old_mode = get(fig, 'InvertHardcopy');
end
% Hack the font units where necessary (due to a font rendering bug in print?).
% This may not work perfectly in all cases.
% Also it can change the figure layout if reverted, so use a copy.
magnify = options.magnify * options.aa_factor;
if isbitmap(options) && magnify ~= 1
fontu = findall(fig, 'FontUnits', 'normalized');
if ~isempty(fontu)
% Some normalized font units found
if ~cls
fig = copyfig(fig);
set(fig, 'Visible', 'off');
fontu = findall(fig, 'FontUnits', 'normalized');
cls = true;
end
set(fontu, 'FontUnits', 'points');
end
end
try
% MATLAB "feature": axes limits and tick marks can change when printing
Hlims = findall(fig, 'Type', 'axes');
if ~cls
% Record the old axes limit and tick modes
Xlims = make_cell(get(Hlims, 'XLimMode'));
Ylims = make_cell(get(Hlims, 'YLimMode'));
Zlims = make_cell(get(Hlims, 'ZLimMode'));
Xtick = make_cell(get(Hlims, 'XTickMode'));
Ytick = make_cell(get(Hlims, 'YTickMode'));
Ztick = make_cell(get(Hlims, 'ZTickMode'));
Xlabel = make_cell(get(Hlims, 'XTickLabelMode'));
Ylabel = make_cell(get(Hlims, 'YTickLabelMode'));
Zlabel = make_cell(get(Hlims, 'ZTickLabelMode'));
end
% Set all axes limit and tick modes to manual, so the limits and ticks can't change
% Fix Matlab R2014b bug (issue #34): plot markers are not displayed when ZLimMode='manual'
set(Hlims, 'XLimMode', 'manual', 'YLimMode', 'manual');
set_tick_mode(Hlims, 'X');
set_tick_mode(Hlims, 'Y');
if ~using_hg2(fig)
set(Hlims,'ZLimMode', 'manual');
set_tick_mode(Hlims, 'Z');
end
catch
% ignore - fix issue #4 (using HG2 on R2014a and earlier)
end
% Fix issue #21 (bold TeX axes labels/titles in R2014b when exporting to EPS/PDF)
try
if using_hg2(fig) && isvector(options)
% Set the FontWeight of axes labels/titles to 'normal'
% Fix issue #69: set non-bold font only if the string contains symbols (\beta etc.)
texLabels = findall(fig, 'type','text', 'FontWeight','bold');
symbolIdx = ~cellfun('isempty',strfind({texLabels.String},'\'));
set(texLabels(symbolIdx), 'FontWeight','normal');
end
catch
% ignore
end
% Fix issue #42: non-normalized annotations on HG1 (internal Matlab bug)
annotationHandles = [];
try
if ~using_hg2(fig)
annotationHandles = findall(fig,'Type','hggroup','-and','-property','Units','-and','-not','Units','norm');
try % suggested by Jesús Pestana Puerta (jespestana) 30/9/2015
originalUnits = get(annotationHandles,'Units');
set(annotationHandles,'Units','norm');
catch
end
end
catch
% should never happen, but ignore in any case - issue #50
end
% Fix issue #46: Ghostscript crash if figure units <> pixels
oldFigUnits = get(fig,'Units');
set(fig,'Units','pixels');
% Set to print exactly what is there
if options.invert_hardcopy
set(fig, 'InvertHardcopy', 'off');
end
% Set the renderer
switch options.renderer
case 1
renderer = '-opengl';
case 2
renderer = '-zbuffer';
case 3
renderer = '-painters';
otherwise
renderer = '-opengl'; % Default for bitmaps
end
% Handle transparent patches
hasTransparency = ~isempty(findall(fig,'-property','FaceAlpha','-and','-not','FaceAlpha',1));
hasPatches = ~isempty(findall(fig,'type','patch'));
if hasTransparency
% Alert if trying to export transparent patches/areas to non-supported outputs (issue #108)
% http://www.mathworks.com/matlabcentral/answers/265265-can-export_fig-or-else-draw-vector-graphics-with-transparent-surfaces
% TODO - use transparency when exporting to PDF by not passing via print2eps
msg = 'export_fig currently supports transparent patches/areas only in PNG output. ';
if options.pdf
warning('export_fig:transparency', '%s\nTo export transparent patches/areas to PDF, use the print command:\n print(gcf, ''-dpdf'', ''%s.pdf'');', msg, options.name);
elseif ~options.png && ~options.tif % issue #168
warning('export_fig:transparency', '%s\nTo export the transparency correctly, try using the ScreenCapture utility on the Matlab File Exchange: http://bit.ly/1QFrBip', msg);
end
end
try
% Do the bitmap formats first
if isbitmap(options)
if abs(options.bb_padding) > 1
displaySuggestedWorkarounds = false;
error('For bitmap output (png,jpg,tif,bmp) the padding value (-p) must be between -1<p<1')
end
% Get the background colour
if options.transparent && (options.png || options.alpha)
% Get out an alpha channel
% MATLAB "feature": black colorbar axes can change to white and vice versa!
hCB = findall(fig, 'Type','axes', 'Tag','Colorbar');
if isempty(hCB)
yCol = [];
xCol = [];
else
yCol = get(hCB, 'YColor');
xCol = get(hCB, 'XColor');
if iscell(yCol)
yCol = cell2mat(yCol);
xCol = cell2mat(xCol);
end
yCol = sum(yCol, 2);
xCol = sum(xCol, 2);
end
% MATLAB "feature": apparently figure size can change when changing
% colour in -nodisplay mode
pos = get(fig, 'Position');
% Set the background colour to black, and set size in case it was
% changed internally
tcol = get(fig, 'Color');
set(fig, 'Color', 'k', 'Position', pos);
% Correct the colorbar axes colours
set(hCB(yCol==0), 'YColor', [0 0 0]);
set(hCB(xCol==0), 'XColor', [0 0 0]);
% The following code might cause out-of-memory errors
try
% Print large version to array
B = print2array(fig, magnify, renderer);
% Downscale the image
B = downsize(single(B), options.aa_factor);
catch
% This is more conservative in memory, but kills transparency (issue #58)
B = single(print2array(fig, magnify/options.aa_factor, renderer));
end
% Set background to white (and set size)
set(fig, 'Color', 'w', 'Position', pos);
% Correct the colorbar axes colours
set(hCB(yCol==3), 'YColor', [1 1 1]);
set(hCB(xCol==3), 'XColor', [1 1 1]);
% The following code might cause out-of-memory errors
try
% Print large version to array
A = print2array(fig, magnify, renderer);
% Downscale the image
A = downsize(single(A), options.aa_factor);
catch
% This is more conservative in memory, but kills transparency (issue #58)
A = single(print2array(fig, magnify/options.aa_factor, renderer));
end
% Set the background colour (and size) back to normal
set(fig, 'Color', tcol, 'Position', pos);
% Compute the alpha map
alpha = round(sum(B - A, 3)) / (255 * 3) + 1;
A = alpha;
A(A==0) = 1;
A = B ./ A(:,:,[1 1 1]);
clear B
% Convert to greyscale
if options.colourspace == 2
A = rgb2grey(A);
end
A = uint8(A);
% Crop the background
if options.crop
%[alpha, v] = crop_borders(alpha, 0, 1, options.crop_amounts);
%A = A(v(1):v(2),v(3):v(4),:);
[alpha, vA, vB] = crop_borders(alpha, 0, options.bb_padding, options.crop_amounts);
if ~any(isnan(vB)) % positive padding
B = repmat(uint8(zeros(1,1,size(A,3))),size(alpha));
B(vB(1):vB(2), vB(3):vB(4), :) = A(vA(1):vA(2), vA(3):vA(4), :); % ADDED BY OH
A = B;
else % negative padding
A = A(vA(1):vA(2), vA(3):vA(4), :);
end
end
if options.png
% Compute the resolution
res = options.magnify * get(0, 'ScreenPixelsPerInch') / 25.4e-3;
% Save the png
imwrite(A, [options.name '.png'], 'Alpha', double(alpha), 'ResolutionUnit', 'meter', 'XResolution', res, 'YResolution', res);
% Clear the png bit
options.png = false;
end
% Return only one channel for greyscale
if isbitmap(options)
A = check_greyscale(A);
end
if options.alpha
% Store the image
imageData = A;
% Clear the alpha bit
options.alpha = false;
end
% Get the non-alpha image
if isbitmap(options)
alph = alpha(:,:,ones(1, size(A, 3)));
A = uint8(single(A) .* alph + 255 * (1 - alph));
clear alph
end
if options.im
% Store the new image
imageData = A;
end
else
% Print large version to array
if options.transparent
% MATLAB "feature": apparently figure size can change when changing
% colour in -nodisplay mode
pos = get(fig, 'Position');
tcol = get(fig, 'Color');
set(fig, 'Color', 'w', 'Position', pos);
A = print2array(fig, magnify, renderer);
set(fig, 'Color', tcol, 'Position', pos);
tcol = 255;
else
[A, tcol] = print2array(fig, magnify, renderer);
end
% Crop the background
if options.crop
A = crop_borders(A, tcol, options.bb_padding, options.crop_amounts);
end
% Downscale the image
A = downsize(A, options.aa_factor);
if options.colourspace == 2
% Convert to greyscale
A = rgb2grey(A);
else
% Return only one channel for greyscale
A = check_greyscale(A);
end
% Outputs
if options.im
imageData = A;
end
if options.alpha
imageData = A;
alpha = ones(size(A, 1), size(A, 2), 'single');
end
end
% Save the images
if options.png
res = options.magnify * get(0, 'ScreenPixelsPerInch') / 25.4e-3;
imwrite(A, [options.name '.png'], 'ResolutionUnit', 'meter', 'XResolution', res, 'YResolution', res);
end
if options.bmp
imwrite(A, [options.name '.bmp']);
end
% Save jpeg with given quality
if options.jpg
quality = options.quality;
if isempty(quality)
quality = 95;
end
if quality > 100
imwrite(A, [options.name '.jpg'], 'Mode', 'lossless');
else
imwrite(A, [options.name '.jpg'], 'Quality', quality);
end
end
% Save tif images in cmyk if wanted (and possible)
if options.tif
if options.colourspace == 1 && size(A, 3) == 3
A = double(255 - A);
K = min(A, [], 3);
K_ = 255 ./ max(255 - K, 1);
C = (A(:,:,1) - K) .* K_;
M = (A(:,:,2) - K) .* K_;
Y = (A(:,:,3) - K) .* K_;
A = uint8(cat(3, C, M, Y, K));
clear C M Y K K_
end
append_mode = {'overwrite', 'append'};
imwrite(A, [options.name '.tif'], 'Resolution', options.magnify*get(0, 'ScreenPixelsPerInch'), 'WriteMode', append_mode{options.append+1});
end
end
% Now do the vector formats
if isvector(options)
% Set the default renderer to painters
if ~options.renderer
if hasTransparency || hasPatches
% This is *MUCH* slower, but more accurate for patches and transparent annotations (issue #39)
renderer = '-opengl';
else
renderer = '-painters';
end
end
options.rendererStr = renderer; % fix for issue #112
% Generate some filenames
tmp_nam = [tempname '.eps'];
try
% Ensure that the temp dir is writable (Javier Paredes 30/1/15)
fid = fopen(tmp_nam,'w');
fwrite(fid,1);
fclose(fid);
delete(tmp_nam);
isTempDirOk = true;
catch
% Temp dir is not writable, so use the user-specified folder
[dummy,fname,fext] = fileparts(tmp_nam); %#ok<ASGLU>
fpath = fileparts(options.name);
tmp_nam = fullfile(fpath,[fname fext]);
isTempDirOk = false;
end
if isTempDirOk
pdf_nam_tmp = [tempname '.pdf'];
else
pdf_nam_tmp = fullfile(fpath,[fname '.pdf']);
end
if options.pdf
pdf_nam = [options.name '.pdf'];
try copyfile(pdf_nam, pdf_nam_tmp, 'f'); catch, end % fix for issue #65
else
pdf_nam = pdf_nam_tmp;
end
% Generate the options for print
p2eArgs = {renderer, sprintf('-r%d', options.resolution)};
if options.colourspace == 1 % CMYK
% Issue #33: due to internal bugs in Matlab's print() function, we can't use its -cmyk option
%p2eArgs{end+1} = '-cmyk';
end
if ~options.crop
% Issue #56: due to internal bugs in Matlab's print() function, we can't use its internal cropping mechanism,
% therefore we always use '-loose' (in print2eps.m) and do our own cropping (in crop_borders)
%p2eArgs{end+1} = '-loose';
end
if any(strcmpi(varargin,'-depsc'))
% Issue #45: lines in image subplots are exported in invalid color.
% The workaround is to use the -depsc parameter instead of the default -depsc2
p2eArgs{end+1} = '-depsc';
end
try
% Generate an eps
print2eps(tmp_nam, fig, options, p2eArgs{:});
% Remove the background, if desired
if options.transparent && ~isequal(get(fig, 'Color'), 'none')
eps_remove_background(tmp_nam, 1 + using_hg2(fig));
end
% Fix colorspace to CMYK, if requested (workaround for issue #33)
if options.colourspace == 1 % CMYK
% Issue #33: due to internal bugs in Matlab's print() function, we can't use its -cmyk option
change_rgb_to_cmyk(tmp_nam);
end
% Add a bookmark to the PDF if desired
if options.bookmark
fig_nam = get(fig, 'Name');
if isempty(fig_nam)
warning('export_fig:EmptyBookmark', 'Bookmark requested for figure with no name. Bookmark will be empty.');
end
add_bookmark(tmp_nam, fig_nam);
end
% Generate a pdf
eps2pdf(tmp_nam, pdf_nam_tmp, 1, options.append, options.colourspace==2, options.quality, options.gs_options);
% Ghostscript croaks on % chars in the output PDF file, so use tempname and then rename the file
try
% Rename the file (except if it is already the same)
% Abbie K's comment on the commit for issue #179 (#commitcomment-20173476)
if ~isequal(pdf_nam_tmp, pdf_nam)
movefile(pdf_nam_tmp, pdf_nam, 'f');
end
catch
% Alert in case of error creating output PDF/EPS file (issue #179)
if exist(pdf_nam_tmp, 'file')
error(['Could not create ' pdf_nam ' - perhaps the folder does not exist, or you do not have write permissions']);
else
error('Could not generate the intermediary EPS file.');
end
end
catch ex
% Delete the eps
delete(tmp_nam);
rethrow(ex);
end
% Delete the eps
delete(tmp_nam);
if options.eps || options.linecaps
try
% Generate an eps from the pdf
% since pdftops can't handle relative paths (e.g., '..\'), use a temp file
eps_nam_tmp = strrep(pdf_nam_tmp,'.pdf','.eps');
pdf2eps(pdf_nam, eps_nam_tmp);
% Issue #192: enable rounded line-caps
if options.linecaps
fstrm = read_write_entire_textfile(eps_nam_tmp);
fstrm = regexprep(fstrm, '[02] J', '1 J');
read_write_entire_textfile(eps_nam_tmp, fstrm);
if options.pdf
eps2pdf(eps_nam_tmp, pdf_nam, 1, options.append, options.colourspace==2, options.quality, options.gs_options);
end
end
if options.eps
movefile(eps_nam_tmp, [options.name '.eps'], 'f');
else % if options.pdf
try delete(eps_nam_tmp); catch, end
end
catch ex
if ~options.pdf
% Delete the pdf
delete(pdf_nam);
end
try delete(eps_nam_tmp); catch, end
rethrow(ex);
end
if ~options.pdf
% Delete the pdf
delete(pdf_nam);
end
end
end
% Revert the figure or close it (if requested)
if cls || options.closeFig
% Close the created figure
close(fig);
else
% Reset the hardcopy mode
set(fig, 'InvertHardcopy', old_mode);
% Reset the axes limit and tick modes
for a = 1:numel(Hlims)
try
set(Hlims(a), 'XLimMode', Xlims{a}, 'YLimMode', Ylims{a}, 'ZLimMode', Zlims{a},...
'XTickMode', Xtick{a}, 'YTickMode', Ytick{a}, 'ZTickMode', Ztick{a},...
'XTickLabelMode', Xlabel{a}, 'YTickLabelMode', Ylabel{a}, 'ZTickLabelMode', Zlabel{a});
catch
% ignore - fix issue #4 (using HG2 on R2014a and earlier)
end
end
% Revert the tex-labels font weights
try set(texLabels, 'FontWeight','bold'); catch, end
% Revert annotation units
for handleIdx = 1 : numel(annotationHandles)
try
oldUnits = originalUnits{handleIdx};
catch
oldUnits = originalUnits;
end
try set(annotationHandles(handleIdx),'Units',oldUnits); catch, end
end
% Revert figure units
set(fig,'Units',oldFigUnits);
end
% Output to clipboard (if requested)
if options.clipboard
% Delete the output file if unchanged from the default name ('export_fig_out.png')
if strcmpi(options.name,'export_fig_out')
try
fileInfo = dir('export_fig_out.png');
if ~isempty(fileInfo)
timediff = now - fileInfo.datenum;
ONE_SEC = 1/24/60/60;
if timediff < ONE_SEC
delete('export_fig_out.png');
end
end
catch
% never mind...
end
end
% Save the image in the system clipboard
% credit: Jiro Doke's IMCLIPBOARD: http://www.mathworks.com/matlabcentral/fileexchange/28708-imclipboard
try
error(javachk('awt', 'export_fig -clipboard output'));
catch
warning('export_fig -clipboard output failed: requires Java to work');
return;
end
try
% Import necessary Java classes
import java.awt.Toolkit
import java.awt.image.BufferedImage
import java.awt.datatransfer.DataFlavor
% Get System Clipboard object (java.awt.Toolkit)
cb = Toolkit.getDefaultToolkit.getSystemClipboard();
% Add java class (ImageSelection) to the path
if ~exist('ImageSelection', 'class')
javaaddpath(fileparts(which(mfilename)), '-end');
end
% Get image size
ht = size(imageData, 1);
wd = size(imageData, 2);
% Convert to Blue-Green-Red format
try
imageData2 = imageData(:, :, [3 2 1]);
catch
% Probably gray-scaled image (2D, without the 3rd [RGB] dimension)
imageData2 = imageData(:, :, [1 1 1]);
end
% Convert to 3xWxH format
imageData2 = permute(imageData2, [3, 2, 1]);
% Append Alpha data (unused - transparency is not supported in clipboard copy)
alphaData2 = uint8(permute(255*alpha,[3,2,1])); %=255*ones(1,wd,ht,'uint8')
imageData2 = cat(1, imageData2, alphaData2);
% Create image buffer
imBuffer = BufferedImage(wd, ht, BufferedImage.TYPE_INT_RGB);
imBuffer.setRGB(0, 0, wd, ht, typecast(imageData2(:), 'int32'), 0, wd);
% Create ImageSelection object from the image buffer
imSelection = ImageSelection(imBuffer);
% Set clipboard content to the image
cb.setContents(imSelection, []);
catch
warning('export_fig -clipboard output failed: %s', lasterr); %#ok<LERR>
end
end
% Don't output the data to console unless requested
if ~nargout
clear imageData alpha
end
catch err
% Display possible workarounds before the error message
if displaySuggestedWorkarounds && ~strcmpi(err.message,'export_fig error')
if ~hadError, fprintf(2, 'export_fig error. '); end
fprintf(2, 'Please ensure:\n');
fprintf(2, ' that you are using the <a href="https://github.com/altmany/export_fig/archive/master.zip">latest version</a> of export_fig\n');
if ismac
fprintf(2, ' and that you have <a href="http://pages.uoregon.edu/koch">Ghostscript</a> installed\n');
else
fprintf(2, ' and that you have <a href="http://www.ghostscript.com">Ghostscript</a> installed\n');
end
try
if options.eps
fprintf(2, ' and that you have <a href="http://www.foolabs.com/xpdf">pdftops</a> installed\n');
end
catch
% ignore - probably an error in parse_args
end
fprintf(2, ' and that you do not have <a href="matlab:which export_fig -all">multiple versions</a> of export_fig installed by mistake\n');
fprintf(2, ' and that you did not made a mistake in the <a href="matlab:help export_fig">expected input arguments</a>\n');
try
% Alert per issue #149
if ~strncmpi(get(0,'Units'),'pixel',5)
fprintf(2, ' or try to set groot''s Units property back to its default value of ''pixels'' (<a href="matlab:web(''https://github.com/altmany/export_fig/issues/149'',''-browser'');">details</a>)\n');
end
catch
% ignore - maybe an old MAtlab release
end
fprintf(2, '\nIf the problem persists, then please <a href="https://github.com/altmany/export_fig/issues">report a new issue</a>.\n\n');
end
rethrow(err)
end
end
function options = default_options()
% Default options used by export_fig
options = struct(...
'name', 'export_fig_out', ...
'crop', true, ...
'crop_amounts', nan(1,4), ... % auto-crop all 4 image sides
'transparent', false, ...
'renderer', 0, ... % 0: default, 1: OpenGL, 2: ZBuffer, 3: Painters
'pdf', false, ...
'eps', false, ...
'png', false, ...
'tif', false, ...
'jpg', false, ...
'bmp', false, ...
'clipboard', false, ...
'colourspace', 0, ... % 0: RGB/gray, 1: CMYK, 2: gray
'append', false, ...
'im', false, ...
'alpha', false, ...
'aa_factor', 0, ...
'bb_padding', 0, ...
'magnify', [], ...
'resolution', [], ...
'bookmark', false, ...
'closeFig', false, ...
'quality', [], ...
'update', false, ...
'fontswap', true, ...
'font_space', '', ...
'linecaps', false, ...
'invert_hardcopy', true, ...
'gs_options', {{}});
end
function [fig, options] = parse_args(nout, fig, varargin)
% Parse the input arguments
% Set the defaults
native = false; % Set resolution to native of an image
options = default_options();
options.im = (nout == 1); % user requested imageData output
options.alpha = (nout == 2); % user requested alpha output
% Go through the other arguments
skipNext = false;
for a = 1:nargin-2
if skipNext
skipNext = false;
continue;
end
if all(ishandle(varargin{a}))
fig = varargin{a};
elseif ischar(varargin{a}) && ~isempty(varargin{a})
if varargin{a}(1) == '-'
switch lower(varargin{a}(2:end))
case 'nocrop'
options.crop = false;
options.crop_amounts = [0,0,0,0];
case {'trans', 'transparent'}
options.transparent = true;
case 'opengl'
options.renderer = 1;
case 'zbuffer'
options.renderer = 2;
case 'painters'
options.renderer = 3;
case 'pdf'
options.pdf = true;
case 'eps'
options.eps = true;
case 'png'
options.png = true;
case {'tif', 'tiff'}
options.tif = true;
case {'jpg', 'jpeg'}
options.jpg = true;
case 'bmp'
options.bmp = true;
case 'rgb'
options.colourspace = 0;
case 'cmyk'
options.colourspace = 1;
case {'gray', 'grey'}
options.colourspace = 2;
case {'a1', 'a2', 'a3', 'a4'}
options.aa_factor = str2double(varargin{a}(3));
case 'append'
options.append = true;
case 'bookmark'
options.bookmark = true;
case 'native'
native = true;
case 'clipboard'
options.clipboard = true;
options.im = true;
options.alpha = true;
case 'svg'
msg = ['SVG output is not supported by export_fig. Use one of the following alternatives:\n' ...
' 1. saveas(gcf,''filename.svg'')\n' ...
' 2. plot2svg utility: http://github.com/jschwizer99/plot2svg\n' ...
' 3. export_fig to EPS/PDF, then convert to SVG using generic (non-Matlab) tools\n'];
error(sprintf(msg)); %#ok<SPERR>
case 'update'
% Download the latest version of export_fig into the export_fig folder
try
zipFileName = 'https://github.com/altmany/export_fig/archive/master.zip';
folderName = fileparts(which(mfilename('fullpath')));
targetFileName = fullfile(folderName, datestr(now,'yyyy-mm-dd.zip'));
urlwrite(zipFileName,targetFileName);
catch
error('Could not download %s into %s\n',zipFileName,targetFileName);
end
% Unzip the downloaded zip file in the export_fig folder
try
unzip(targetFileName,folderName);
catch
error('Could not unzip %s\n',targetFileName);
end
case 'nofontswap'
options.fontswap = false;
case 'font_space'
options.font_space = varargin{a+1};
skipNext = true;
case 'linecaps'
options.linecaps = true;
case 'noinvert'
options.invert_hardcopy = false;
otherwise
try
wasError = false;
if strcmpi(varargin{a}(1:2),'-d')
varargin{a}(2) = 'd'; % ensure lowercase 'd'
options.gs_options{end+1} = varargin{a};
elseif strcmpi(varargin{a}(1:2),'-c')
if numel(varargin{a})==2
skipNext = true;
vals = str2num(varargin{a+1}); %#ok<ST2NM>
else
vals = str2num(varargin{a}(3:end)); %#ok<ST2NM>
end
if numel(vals)~=4
wasError = true;
error('option -c cannot be parsed: must be a 4-element numeric vector');
end
options.crop_amounts = vals;
options.crop = true;
else % scalar parameter value
val = str2double(regexp(varargin{a}, '(?<=-(m|M|r|R|q|Q|p|P))-?\d*.?\d+', 'match'));
if isempty(val) || isnan(val)
% Issue #51: improved processing of input args (accept space between param name & value)
val = str2double(varargin{a+1});
if isscalar(val) && ~isnan(val)
skipNext = true;
end
end
if ~isscalar(val) || isnan(val)
wasError = true;
error('option %s is not recognised or cannot be parsed', varargin{a});
end
switch lower(varargin{a}(2))
case 'm'
% Magnification may never be negative
if val <= 0
wasError = true;
error('Bad magnification value: %g (must be positive)', val);
end
options.magnify = val;
case 'r'
options.resolution = val;
case 'q'
options.quality = max(val, 0);
case 'p'
options.bb_padding = val;
end
end
catch err
% We might have reached here by raising an intentional error
if wasError % intentional raise
rethrow(err)
else % unintentional
error(['Unrecognized export_fig input option: ''' varargin{a} '''']);
end
end
end
else
[p, options.name, ext] = fileparts(varargin{a});
if ~isempty(p)
options.name = [p filesep options.name];
end
switch lower(ext)
case {'.tif', '.tiff'}
options.tif = true;
case {'.jpg', '.jpeg'}
options.jpg = true;
case '.png'
options.png = true;
case '.bmp'
options.bmp = true;
case '.eps'
options.eps = true;
case '.pdf'
options.pdf = true;
case '.fig'
% If no open figure, then load the specified .fig file and continue
if isempty(fig)
fig = openfig(varargin{a},'invisible');
varargin{a} = fig;
options.closeFig = true;
else
% save the current figure as the specified .fig file and exit
saveas(fig(1),varargin{a});
fig = -1;
return
end
case '.svg'
msg = ['SVG output is not supported by export_fig. Use one of the following alternatives:\n' ...
' 1. saveas(gcf,''filename.svg'')\n' ...
' 2. plot2svg utility: http://github.com/jschwizer99/plot2svg\n' ...
' 3. export_fig to EPS/PDF, then convert to SVG using generic (non-Matlab) tools\n'];
error(sprintf(msg)); %#ok<SPERR>
otherwise
options.name = varargin{a};
end
end
end
end
% Quick bail-out if no figure found
if isempty(fig), return; end
% Do border padding with repsect to a cropped image
if options.bb_padding
options.crop = true;
end
% Set default anti-aliasing now we know the renderer
if options.aa_factor == 0
try isAA = strcmp(get(ancestor(fig, 'figure'), 'GraphicsSmoothing'), 'on'); catch, isAA = false; end
options.aa_factor = 1 + 2 * (~(using_hg2(fig) && isAA) | (options.renderer == 3));
end
% Convert user dir '~' to full path
if numel(options.name) > 2 && options.name(1) == '~' && (options.name(2) == '/' || options.name(2) == '\')
options.name = fullfile(char(java.lang.System.getProperty('user.home')), options.name(2:end));
end
% Compute the magnification and resolution
if isempty(options.magnify)
if isempty(options.resolution)
options.magnify = 1;
options.resolution = 864;
else
options.magnify = options.resolution ./ get(0, 'ScreenPixelsPerInch');
end
elseif isempty(options.resolution)
options.resolution = 864;
end
% Set the default format
if ~isvector(options) && ~isbitmap(options)
options.png = true;
end
% Check whether transparent background is wanted (old way)
if isequal(get(ancestor(fig(1), 'figure'), 'Color'), 'none')
options.transparent = true;
end
% If requested, set the resolution to the native vertical resolution of the
% first suitable image found
if native && isbitmap(options)
% Find a suitable image
list = findall(fig, 'Type','image', 'Tag','export_fig_native');
if isempty(list)
list = findall(fig, 'Type','image', 'Visible','on');
end
for hIm = list(:)'
% Check height is >= 2
height = size(get(hIm, 'CData'), 1);
if height < 2
continue
end
% Account for the image filling only part of the axes, or vice versa
yl = get(hIm, 'YData');
if isscalar(yl)
yl = [yl(1)-0.5 yl(1)+height+0.5];
else
yl = [min(yl), max(yl)]; % fix issue #151 (case of yl containing more than 2 elements)
if ~diff(yl)
continue
end
yl = yl + [-0.5 0.5] * (diff(yl) / (height - 1));
end
hAx = get(hIm, 'Parent');
yl2 = get(hAx, 'YLim');
% Find the pixel height of the axes
oldUnits = get(hAx, 'Units');
set(hAx, 'Units', 'pixels');
pos = get(hAx, 'Position');
set(hAx, 'Units', oldUnits);
if ~pos(4)
continue
end
% Found a suitable image
% Account for stretch-to-fill being disabled
pbar = get(hAx, 'PlotBoxAspectRatio');
pos = min(pos(4), pbar(2)*pos(3)/pbar(1));
% Set the magnification to give native resolution
options.magnify = abs((height * diff(yl2)) / (pos * diff(yl))); % magnification must never be negative: issue #103
break
end
end
end
function A = downsize(A, factor)
% Downsample an image
if factor == 1
% Nothing to do
return
end
try
% Faster, but requires image processing toolbox
A = imresize(A, 1/factor, 'bilinear');
catch
% No image processing toolbox - resize manually
% Lowpass filter - use Gaussian as is separable, so faster
% Compute the 1d Gaussian filter
filt = (-factor-1:factor+1) / (factor * 0.6);
filt = exp(-filt .* filt);
% Normalize the filter
filt = single(filt / sum(filt));
% Filter the image
padding = floor(numel(filt) / 2);
for a = 1:size(A, 3)
A(:,:,a) = conv2(filt, filt', single(A([ones(1, padding) 1:end repmat(end, 1, padding)],[ones(1, padding) 1:end repmat(end, 1, padding)],a)), 'valid');
end
% Subsample
A = A(1+floor(mod(end-1, factor)/2):factor:end,1+floor(mod(end-1, factor)/2):factor:end,:);
end
end
function A = rgb2grey(A)
A = cast(reshape(reshape(single(A), [], 3) * single([0.299; 0.587; 0.114]), size(A, 1), size(A, 2)), class(A)); % #ok<ZEROLIKE>
end
function A = check_greyscale(A)
% Check if the image is greyscale
if size(A, 3) == 3 && ...
all(reshape(A(:,:,1) == A(:,:,2), [], 1)) && ...
all(reshape(A(:,:,2) == A(:,:,3), [], 1))
A = A(:,:,1); % Save only one channel for 8-bit output
end
end
function eps_remove_background(fname, count)
% Remove the background of an eps file
% Open the file
fh = fopen(fname, 'r+');
if fh == -1
error('Not able to open file %s.', fname);
end
% Read the file line by line
while count
% Get the next line
l = fgets(fh);
if isequal(l, -1)
break; % Quit, no rectangle found
end
% Check if the line contains the background rectangle
if isequal(regexp(l, ' *0 +0 +\d+ +\d+ +r[fe] *[\n\r]+', 'start'), 1)
% Set the line to whitespace and quit
l(1:regexp(l, '[\n\r]', 'start', 'once')-1) = ' ';
fseek(fh, -numel(l), 0);
fprintf(fh, l);
% Reduce the count
count = count - 1;
end
end
% Close the file
fclose(fh);
end
function b = isvector(options)
b = options.pdf || options.eps;
end
function b = isbitmap(options)
b = options.png || options.tif || options.jpg || options.bmp || options.im || options.alpha;
end
% Helper function
function A = make_cell(A)
if ~iscell(A)
A = {A};
end
end
function add_bookmark(fname, bookmark_text)
% Adds a bookmark to the temporary EPS file after %%EndPageSetup
% Read in the file
fh = fopen(fname, 'r');
if fh == -1
error('File %s not found.', fname);
end
try
fstrm = fread(fh, '*char')';
catch ex
fclose(fh);
rethrow(ex);
end
fclose(fh);
% Include standard pdfmark prolog to maximize compatibility
fstrm = strrep(fstrm, '%%BeginProlog', sprintf('%%%%BeginProlog\n/pdfmark where {pop} {userdict /pdfmark /cleartomark load put} ifelse'));
% Add page bookmark
fstrm = strrep(fstrm, '%%EndPageSetup', sprintf('%%%%EndPageSetup\n[ /Title (%s) /OUT pdfmark',bookmark_text));
% Write out the updated file
fh = fopen(fname, 'w');
if fh == -1
error('Unable to open %s for writing.', fname);
end
try
fwrite(fh, fstrm, 'char*1');
catch ex
fclose(fh);
rethrow(ex);
end
fclose(fh);
end
function set_tick_mode(Hlims, ax)
% Set the tick mode of linear axes to manual
% Leave log axes alone as these are tricky
M = get(Hlims, [ax 'Scale']);
if ~iscell(M)
M = {M};
end
%idx = cellfun(@(c) strcmp(c, 'linear'), M);
idx = find(strcmp(M,'linear'));
%set(Hlims(idx), [ax 'TickMode'], 'manual'); % issue #187
%set(Hlims(idx), [ax 'TickLabelMode'], 'manual'); % this hides exponent label in HG2!
for idx2 = 1 : numel(idx)
try
% Fix for issue #187 - only set manual ticks when no exponent is present
hAxes = Hlims(idx(idx2));
props = {[ax 'TickMode'],'manual', [ax 'TickLabelMode'],'manual'};
if isempty(strtrim(hAxes.([ax 'Ruler']).SecondaryLabel.String))
% Fix for issue #205 - only set manual ticks when the Ticks number match the TickLabels number
if numel(hAxes.([ax 'Tick'])) == numel(hAxes.([ax 'TickLabel']))
set(hAxes, props{:}); % no exponent and matching ticks, so update both ticks and tick labels to manual
end
end
catch % probably HG1
set(hAxes, props{:}); % revert back to old behavior
end
end
end
function change_rgb_to_cmyk(fname) % convert RGB => CMYK within an EPS file
% Do post-processing on the eps file
try
% Read the EPS file into memory
fstrm = read_write_entire_textfile(fname);
% Replace all gray-scale colors
fstrm = regexprep(fstrm, '\n([\d.]+) +GC\n', '\n0 0 0 ${num2str(1-str2num($1))} CC\n');
% Replace all RGB colors
fstrm = regexprep(fstrm, '\n[0.]+ +[0.]+ +[0.]+ +RC\n', '\n0 0 0 1 CC\n'); % pure black
fstrm = regexprep(fstrm, '\n([\d.]+) +([\d.]+) +([\d.]+) +RC\n', '\n${sprintf(''%.4g '',[1-[str2num($1),str2num($2),str2num($3)]/max([str2num($1),str2num($2),str2num($3)]),1-max([str2num($1),str2num($2),str2num($3)])])} CC\n');
% Overwrite the file with the modified contents
read_write_entire_textfile(fname, fstrm);
catch
% never mind - leave as is...
end
end
|
github
|
drbenvincent/darc-experiments-matlab-master
|
ghostscript.m
|
.m
|
darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/ghostscript.m
| 7,902 |
utf_8
|
ff62a40d651197dbea5d3c39998b3bad
|
function varargout = ghostscript(cmd)
%GHOSTSCRIPT Calls a local GhostScript executable with the input command
%
% Example:
% [status result] = ghostscript(cmd)
%
% Attempts to locate a ghostscript executable, finally asking the user to
% specify the directory ghostcript was installed into. The resulting path
% is stored for future reference.
%
% Once found, the executable is called with the input command string.
%
% This function requires that you have Ghostscript installed on your
% system. You can download this from: http://www.ghostscript.com
%
% IN:
% cmd - Command string to be passed into ghostscript.
%
% OUT:
% status - 0 iff command ran without problem.
% result - Output from ghostscript.
% Copyright: Oliver Woodford, 2009-2015, Yair Altman 2015-
%{
% Thanks to Jonas Dorn for the fix for the title of the uigetdir window on Mac OS.
% Thanks to Nathan Childress for the fix to default location on 64-bit Windows systems.
% 27/04/11 - Find 64-bit Ghostscript on Windows. Thanks to Paul Durack and
% Shaun Kline for pointing out the issue
% 04/05/11 - Thanks to David Chorlian for pointing out an alternative
% location for gs on linux.
% 12/12/12 - Add extra executable name on Windows. Thanks to Ratish
% Punnoose for highlighting the issue.
% 28/06/13 - Fix error using GS 9.07 in Linux. Many thanks to Jannick
% Steinbring for proposing the fix.
% 24/10/13 - Fix error using GS 9.07 in Linux. Many thanks to Johannes
% for the fix.
% 23/01/14 - Add full path to ghostscript.txt in warning. Thanks to Koen
% Vermeer for raising the issue.
% 27/02/15 - If Ghostscript croaks, display suggested workarounds
% 30/03/15 - Improved performance by caching status of GS path check, if ok
% 14/05/15 - Clarified warning message in case GS path could not be saved
% 29/05/15 - Avoid cryptic error in case the ghostscipt path cannot be saved (issue #74)
% 10/11/15 - Custom GS installation webpage for MacOS. Thanks to Andy Hueni via FEX
%}
try
% Call ghostscript
[varargout{1:nargout}] = system([gs_command(gs_path()) cmd]);
catch err
% Display possible workarounds for Ghostscript croaks
url1 = 'https://github.com/altmany/export_fig/issues/12#issuecomment-61467998'; % issue #12
url2 = 'https://github.com/altmany/export_fig/issues/20#issuecomment-63826270'; % issue #20
hg2_str = ''; if using_hg2, hg2_str = ' or Matlab R2014a'; end
fprintf(2, 'Ghostscript error. Rolling back to GS 9.10%s may possibly solve this:\n * <a href="%s">%s</a> ',hg2_str,url1,url1);
if using_hg2
fprintf(2, '(GS 9.10)\n * <a href="%s">%s</a> (R2014a)',url2,url2);
end
fprintf('\n\n');
if ismac || isunix
url3 = 'https://github.com/altmany/export_fig/issues/27'; % issue #27
fprintf(2, 'Alternatively, this may possibly be due to a font path issue:\n * <a href="%s">%s</a>\n\n',url3,url3);
% issue #20
fpath = which(mfilename);
if isempty(fpath), fpath = [mfilename('fullpath') '.m']; end
fprintf(2, 'Alternatively, if you are using csh, modify shell_cmd from "export..." to "setenv ..."\nat the bottom of <a href="matlab:opentoline(''%s'',174)">%s</a>\n\n',fpath,fpath);
end
rethrow(err);
end
end
function path_ = gs_path
% Return a valid path
% Start with the currently set path
path_ = user_string('ghostscript');
% Check the path works
if check_gs_path(path_)
return
end
% Check whether the binary is on the path
if ispc
bin = {'gswin32c.exe', 'gswin64c.exe', 'gs'};
else
bin = {'gs'};
end
for a = 1:numel(bin)
path_ = bin{a};
if check_store_gs_path(path_)
return
end
end
% Search the obvious places
if ispc
default_location = 'C:\Program Files\gs\';
dir_list = dir(default_location);
if isempty(dir_list)
default_location = 'C:\Program Files (x86)\gs\'; % Possible location on 64-bit systems
dir_list = dir(default_location);
end
executable = {'\bin\gswin32c.exe', '\bin\gswin64c.exe'};
ver_num = 0;
% If there are multiple versions, use the newest
for a = 1:numel(dir_list)
ver_num2 = sscanf(dir_list(a).name, 'gs%g');
if ~isempty(ver_num2) && ver_num2 > ver_num
for b = 1:numel(executable)
path2 = [default_location dir_list(a).name executable{b}];
if exist(path2, 'file') == 2
path_ = path2;
ver_num = ver_num2;
end
end
end
end
if check_store_gs_path(path_)
return
end
else
executable = {'/usr/bin/gs', '/usr/local/bin/gs'};
for a = 1:numel(executable)
path_ = executable{a};
if check_store_gs_path(path_)
return
end
end
end
% Ask the user to enter the path
while true
if strncmp(computer, 'MAC', 3) % Is a Mac
% Give separate warning as the uigetdir dialogue box doesn't have a
% title
uiwait(warndlg('Ghostscript not found. Please locate the program.'))
end
base = uigetdir('/', 'Ghostcript not found. Please locate the program.');
if isequal(base, 0)
% User hit cancel or closed window
break;
end
base = [base filesep]; %#ok<AGROW>
bin_dir = {'', ['bin' filesep], ['lib' filesep]};
for a = 1:numel(bin_dir)
for b = 1:numel(bin)
path_ = [base bin_dir{a} bin{b}];
if exist(path_, 'file') == 2
if check_store_gs_path(path_)
return
end
end
end
end
end
if ismac
error('Ghostscript not found. Have you installed it (http://pages.uoregon.edu/koch)?');
else
error('Ghostscript not found. Have you installed it from www.ghostscript.com?');
end
end
function good = check_store_gs_path(path_)
% Check the path is valid
good = check_gs_path(path_);
if ~good
return
end
% Update the current default path to the path found
if ~user_string('ghostscript', path_)
filename = fullfile(fileparts(which('user_string.m')), '.ignore', 'ghostscript.txt');
warning('Path to ghostscript installation could not be saved in %s (perhaps a permissions issue). You can manually create this file and set its contents to %s, to improve performance in future invocations (this warning is safe to ignore).', filename, path_);
return
end
end
function good = check_gs_path(path_)
persistent isOk
if isempty(path_)
isOk = false;
elseif ~isequal(isOk,true)
% Check whether the path is valid
[status, message] = system([gs_command(path_) '-h']); %#ok<ASGLU>
isOk = status == 0;
end
good = isOk;
end
function cmd = gs_command(path_)
% Initialize any required system calls before calling ghostscript
% TODO: in Unix/Mac, find a way to determine whether to use "export" (bash) or "setenv" (csh/tcsh)
shell_cmd = '';
if isunix
shell_cmd = 'export LD_LIBRARY_PATH=""; '; % Avoids an error on Linux with GS 9.07
end
if ismac
shell_cmd = 'export DYLD_LIBRARY_PATH=""; '; % Avoids an error on Mac with GS 9.07
end
% Construct the command string
cmd = sprintf('%s"%s" ', shell_cmd, path_);
end
|
github
|
drbenvincent/darc-experiments-matlab-master
|
fix_lines.m
|
.m
|
darc-experiments-matlab-master/darc-toolbox/dependencies/export-fig/fix_lines.m
| 6,441 |
utf_8
|
ffda929ebad8144b1e72d528fa5d9460
|
%FIX_LINES Improves the line style of eps files generated by print
%
% Examples:
% fix_lines fname
% fix_lines fname fname2
% fstrm_out = fixlines(fstrm_in)
%
% This function improves the style of lines in eps files generated by
% MATLAB's print function, making them more similar to those seen on
% screen. Grid lines are also changed from a dashed style to a dotted
% style, for greater differentiation from dashed lines.
%
% The function also places embedded fonts after the postscript header, in
% versions of MATLAB which place the fonts first (R2006b and earlier), in
% order to allow programs such as Ghostscript to find the bounding box
% information.
%
%IN:
% fname - Name or path of source eps file.
% fname2 - Name or path of destination eps file. Default: same as fname.
% fstrm_in - File contents of a MATLAB-generated eps file.
%
%OUT:
% fstrm_out - Contents of the eps file with line styles fixed.
% Copyright: (C) Oliver Woodford, 2008-2014
% The idea of editing the EPS file to change line styles comes from Jiro
% Doke's FIXPSLINESTYLE (fex id: 17928)
% The idea of changing dash length with line width came from comments on
% fex id: 5743, but the implementation is mine :)
% Thank you to Sylvain Favrot for bringing the embedded font/bounding box
% interaction in older versions of MATLAB to my attention.
% Thank you to D Ko for bringing an error with eps files with tiff previews
% to my attention.
% Thank you to Laurence K for suggesting the check to see if the file was
% opened.
% 01/03/15: Issue #20: warn users if using this function in HG2 (R2014b+)
% 27/03/15: Fixed out of memory issue with enormous EPS files (generated by print() with OpenGL renderer), related to issue #39
function fstrm = fix_lines(fstrm, fname2)
% Issue #20: warn users if using this function in HG2 (R2014b+)
if using_hg2
warning('export_fig:hg2','The fix_lines function should not be used in this Matlab version.');
end
if nargout == 0 || nargin > 1
if nargin < 2
% Overwrite the input file
fname2 = fstrm;
end
% Read in the file
fstrm = read_write_entire_textfile(fstrm);
end
% Move any embedded fonts after the postscript header
if strcmp(fstrm(1:15), '%!PS-AdobeFont-')
% Find the start and end of the header
ind = regexp(fstrm, '[\n\r]%!PS-Adobe-');
[ind2, ind2] = regexp(fstrm, '[\n\r]%%EndComments[\n\r]+');
% Put the header first
if ~isempty(ind) && ~isempty(ind2) && ind(1) < ind2(1)
fstrm = fstrm([ind(1)+1:ind2(1) 1:ind(1) ind2(1)+1:end]);
end
end
% Make sure all line width commands come before the line style definitions,
% so that dash lengths can be based on the correct widths
% Find all line style sections
ind = [regexp(fstrm, '[\n\r]SO[\n\r]'),... % This needs to be here even though it doesn't have dots/dashes!
regexp(fstrm, '[\n\r]DO[\n\r]'),...
regexp(fstrm, '[\n\r]DA[\n\r]'),...
regexp(fstrm, '[\n\r]DD[\n\r]')];
ind = sort(ind);
% Find line width commands
[ind2, ind3] = regexp(fstrm, '[\n\r]\d* w[\n\r]');
% Go through each line style section and swap with any line width commands
% near by
b = 1;
m = numel(ind);
n = numel(ind2);
for a = 1:m
% Go forwards width commands until we pass the current line style
while b <= n && ind2(b) < ind(a)
b = b + 1;
end
if b > n
% No more width commands
break;
end
% Check we haven't gone past another line style (including SO!)
if a < m && ind2(b) > ind(a+1)
continue;
end
% Are the commands close enough to be confident we can swap them?
if (ind2(b) - ind(a)) > 8
continue;
end
% Move the line style command below the line width command
fstrm(ind(a)+1:ind3(b)) = [fstrm(ind(a)+4:ind3(b)) fstrm(ind(a)+1:ind(a)+3)];
b = b + 1;
end
% Find any grid line definitions and change to GR format
% Find the DO sections again as they may have moved
ind = int32(regexp(fstrm, '[\n\r]DO[\n\r]'));
if ~isempty(ind)
% Find all occurrences of what are believed to be axes and grid lines
ind2 = int32(regexp(fstrm, '[\n\r] *\d* *\d* *mt *\d* *\d* *L[\n\r]'));
if ~isempty(ind2)
% Now see which DO sections come just before axes and grid lines
ind2 = repmat(ind2', [1 numel(ind)]) - repmat(ind, [numel(ind2) 1]);
ind2 = any(ind2 > 0 & ind2 < 12); % 12 chars seems about right
ind = ind(ind2);
% Change any regions we believe to be grid lines to GR
fstrm(ind+1) = 'G';
fstrm(ind+2) = 'R';
end
end
% Define the new styles, including the new GR format
% Dot and dash lengths have two parts: a constant amount plus a line width
% variable amount. The constant amount comes after dpi2point, and the
% variable amount comes after currentlinewidth. If you want to change
% dot/dash lengths for a one particular line style only, edit the numbers
% in the /DO (dotted lines), /DA (dashed lines), /DD (dot dash lines) and
% /GR (grid lines) lines for the style you want to change.
new_style = {'/dom { dpi2point 1 currentlinewidth 0.08 mul add mul mul } bdef',... % Dot length macro based on line width
'/dam { dpi2point 2 currentlinewidth 0.04 mul add mul mul } bdef',... % Dash length macro based on line width
'/SO { [] 0 setdash 0 setlinecap } bdef',... % Solid lines
'/DO { [1 dom 1.2 dom] 0 setdash 0 setlinecap } bdef',... % Dotted lines
'/DA { [4 dam 1.5 dam] 0 setdash 0 setlinecap } bdef',... % Dashed lines
'/DD { [1 dom 1.2 dom 4 dam 1.2 dom] 0 setdash 0 setlinecap } bdef',... % Dot dash lines
'/GR { [0 dpi2point mul 4 dpi2point mul] 0 setdash 1 setlinecap } bdef'}; % Grid lines - dot spacing remains constant
% Construct the output
% This is the original (memory-intensive) code:
%first_sec = strfind(fstrm, '% line types:'); % Isolate line style definition section
%[second_sec, remaining] = strtok(fstrm(first_sec+1:end), '/');
%[remaining, remaining] = strtok(remaining, '%');
%fstrm = [fstrm(1:first_sec) second_sec sprintf('%s\r', new_style{:}) remaining];
fstrm = regexprep(fstrm,'(% line types:.+?)/.+?%',['$1',sprintf('%s\r',new_style{:}),'%']);
% Write the output file
if nargout == 0 || nargin > 1
read_write_entire_textfile(fname2, fstrm);
end
end
|
github
|
drbenvincent/darc-experiments-matlab-master
|
PosteriorPrediction1D.m
|
.m
|
darc-experiments-matlab-master/darc-toolbox/dependencies/mcmc-utils-matlab/+mcmc/PosteriorPrediction1D.m
| 7,063 |
utf_8
|
3b31fa324f33b1e3af91b9ef1a04d68e
|
classdef PosteriorPrediction1D < handle
%% PosteriorPrediction1D
properties
variableNames
fh
xInterp
%samples
Y
nSamples
samples
ciType
nExamples
pointEstimateType
pointEstimate
shouldPlotData
xData, yData
ciWidth
h % a structure containing handles to figure and plot objects
end
properties(Access = protected)
end
methods
% Class constructor
function obj=PosteriorPrediction1D(fh, varargin)
p = inputParser;
p.FunctionName = mfilename;
p.addRequired('fh',@(x) isa(x,'function_handle'));
p.addParameter('xInterp',[],@isvector);
p.addParameter('samples',[],@ismatrix);
p.addParameter('variableNames',{},@iscellstr);
p.addParameter('ciType','examples',@(x)any(strcmp(x,{'examples','range','probMass'})));
p.addParameter('nExamples',100,@isscalar);
p.addParameter('ciWidth',0.95,@isscalar);
p.addParameter('pointEstimateType','mean',@isstr);
p.addParameter('shouldPlotData',true,@islogical);
p.addParameter('xData',[],@isvector);
p.addParameter('yData',[],@isvector);
p.addParameter('pointEstimate',[],@isvector);% if we have precomputed point estimate due to numerical problems (eg with log transformed data etc).
p.parse(fh, varargin{:});
% add p.Results fields into obj
fields = fieldnames(p.Results);
for n=1:numel(fields)
obj.(fields{n}) = p.Results.(fields{n});
end
obj.nSamples= size(obj.samples,1);
% predefine handles for point estimates
obj.h.hPointEst=[];
if isempty(p.Results.pointEstimate)
temp = mcmc.UnivariateDistribution(obj.samples,...
'shouldPlot',false,...
'pointEstimateType',p.Results.pointEstimateType);
obj.pointEstimate = temp.(p.Results.pointEstimateType);
end
% High-level plotting commands
if p.Results.shouldPlotData
switch obj.ciType
case{'examples'}
obj.plotExamples();
case{'range'}
obj.plotCI();
case{'probMass'}
obj.plotProbMass();
end
axis tight
obj.plotPointEstimate();
obj.plotData();
end
end
function evaluateFunction(obj,ExamplesToPlot)
% Evaluate the 1D function for the x-values specified and for
% each of the MCMC samples. This will result in a matrix with:
% rows = number of x-axis values
% cols = number of MCMC samples
%fprintf('Evaluating the function over %d MCMC samples...\n', numel(ExamplesToPlot));
if isempty(ExamplesToPlot)
ExamplesToPlot = [1:obj.nSamples];
end
try
% If the function handle can deal with vectorised inputs
% then this should compute much faster
%obj.Y = obj.fh(obj.xInterp, obj.samples(:,:))';
obj.Y = obj.fh(obj.xInterp, obj.samples(ExamplesToPlot,:))';
catch
% but if that fails, fall back on looping through mcmc
% samples
warning('*** SLOWNESS WARNING ***')
warning('Recommend writing your function in a way that can handle vectorised inputs')
Y = zeros(numel(obj.xInterp),numel(ExamplesToPlot));
for s=1:numel(ExamplesToPlot)
obj.Y(:,s) = obj.fh(obj.xInterp, obj.samples(ExamplesToPlot(s),:));
end
end
end
function obj = plotPointEstimate(obj)
% Plot a single curve with the single set of parameters. These
% might correspond to the mode of the MCMC parameters, for
% example.
% Calculate the y-values
YpointEstimate = obj.fh(obj.xInterp, obj.pointEstimate);
% plot the point estimate
hold on
hPointEst = plot(obj.xInterp, YpointEstimate,'k-', 'LineWidth', 3);
% concatenate handle onto a list, so that we can plot multiple
% point estimates and have handles to each
if numel(obj.h.hPointEst)==0
obj.h.hPointEst = hPointEst;
else
obj.h.hPointEst = [obj.h.hPointEst hPointEst];
end
end
function obj = plotExamples(obj)
% Plots a random set of example functions, each one corresponds
% to a particular MCMC sample.
% If we've asked for more examples, than MCMC samples, then
% just plot all.
if obj.nExamples > obj.nSamples
obj.nExamples = obj.nSamples;
end
% shuffle the deck and pick the top nExamples
shuffledExamples = randperm(obj.nSamples);
ExamplesToPlot = shuffledExamples([1:obj.nExamples]);
% Evaluate the function just for these examples
obj.evaluateFunction(ExamplesToPlot);
try
hExamples = plot(obj.xInterp, obj.Y,'-',...
'Color',[0.5 0.5 0.5 0.1]);
catch % backward compatability
hExamples = plot(obj.xInterp, obj.Y,'-',...
'Color',[0.5 0.5 0.5]);
end
% hExamples = plot(obj.xInterp, obj.Y(:,ExamplesToPlot),'k-');
obj.h.Axis = gca;
obj.h.hExamples = hExamples;
formatAxes(obj)
end
function obj = plotCI(obj)
obj.evaluateFunction([1:obj.nSamples]);
% Plots shaded 95%
vals = [(1-0.95)/2 1-((1-0.95)/2)].*100;
CI = prctile(obj.Y',vals);
% draw the shaded error bar zone
x = [obj.xInterp,fliplr(obj.xInterp)];
y = [CI(2,:),fliplr(CI(1,:))];
hCI =patch(x,y,[0.8 0.8 0.8]);
hCI.EdgeColor='none';
% save handle to CI
obj.h.hCI = hCI;
formatAxes(obj)
end
function obj = plotProbMass(obj)
% Plots the posterior predictive distribution in the form of a
% 2D probability mass function.
obj.evaluateFunction([1:obj.nSamples]);
yi = linspace( min(obj.Y(:)), max(obj.Y(:)), 100);
[PM]=calcProbabilityMass(obj,yi);
hProbMass=imagesc(obj.xInterp, yi, PM);
axis xy
% save handle to prob mass
obj.h.hExamples = hProbMass;
formatAxes(obj)
end
function obj = plotData(obj)
hold on
if isempty(obj.xData), return, end
if isempty(obj.yData), return, end
if ~obj.shouldPlotData, return, end
hData=plot(obj.xData,obj.yData,...
'o',...
'MarkerSize',8,...
'MarkerEdgeColor','k',...
'MarkerFaceColor','w');
% save handle to data points
obj.h.hData = hData;
formatAxes(obj)
end
end
end
%% Private functions
function [PM]=calcProbabilityMass(obj,yi)
% The matrix obj.Y has a number of rows equal to the number of x-axis
% values that we are evaluating the function over, and has a number of
% columns equal to the number of MCMC samples provided. What we do here is
% to loop over the x-values and convert the set of samples into a
% probability mass function by use of the hist function. We do this for the
% y values specified in the vector yi.
display('Calculating probability mass...')
obj.evaluateFunction([1:obj.nSamples]);
% preallocate
PM = zeros(size(obj.Y,1), numel(yi));
% loop over x-values, calculating posterior mass for the corresponding
% samples
for x=1:size(obj.Y,1)
PM(x,:) = hist( obj.Y(x,:) , yi );
% scale so the max of each column (x-value) is equal to 1
PM(x,:) =PM(x,:) / max(PM(x,:));
end
PM=PM'; % transpose
end
function formatAxes(obj)
if ~isempty(obj.xData)
xlim([min(obj.xData) max(obj.xData)])
end
% axes on top layer
ah=gca; ah.Layer='top';
axis square
box off
if ~isempty(obj.variableNames)
xlabel(obj.variableNames{1},'Interpreter','latex')
ylabel(obj.variableNames{2},'Interpreter','latex')
end
end
|
github
|
drbenvincent/darc-experiments-matlab-master
|
kde.m
|
.m
|
darc-experiments-matlab-master/darc-toolbox/dependencies/mcmc-utils-matlab/+mcmc/+kde/kde.m
| 5,689 |
utf_8
|
910728965b89850c5c417eef4698a074
|
function [bandwidth,density,xmesh,cdf]=kde(data,n,tstar_max,MIN,MAX)
% Tom Edit: added a tstar_max to limit the bandwidth if desired. It is a
% scaled value, default = inf;
%
% Reliable and extremely fast kernel density estimator for one-dimensional data;
% Gaussian kernel is assumed and the bandwidth is chosen automatically;
% Unlike many other implementations, this one is immune to problems
% caused by multimodal densities with widely separated modes (see example). The
% estimation does not deteriorate for multimodal densities, because we never assume
% a parametric model for the data.
% INPUTS:
% data - a vector of data from which the density estimate is constructed;
% n - the number of mesh points used in the uniform discretization of the
% interval [MIN, MAX]; n has to be a power of two; if n is not a power of two, then
% n is rounded up to the next power of two, i.e., n is set to n=2^ceil(log2(n));
% the default value of n is n=2^12;
% MIN, MAX - defines the interval [MIN,MAX] on which the density estimate is constructed;
% the default values of MIN and MAX are:
% MIN=min(data)-Range/10 and MAX=max(data)+Range/10, where Range=max(data)-min(data);
% OUTPUTS:
% bandwidth - the optimal bandwidth (Gaussian kernel assumed);
% density - column vector of length 'n' with the values of the density
% estimate at the grid points;
% xmesh - the grid over which the density estimate is computed;
% - If no output is requested, then the code automatically plots a graph of
% the density estimate.
% cdf - column vector of length 'n' with the values of the cdf
% Reference:
% Kernel density estimation via diffusion
% Z. I. Botev, J. F. Grotowski, and D. P. Kroese (2010)
% Annals of Statistics, Volume 38, Number 5, pages 2916-2957.
%
% Example:
% data=[randn(100,1);randn(100,1)*2+35 ;randn(100,1)+55];
% kde(data,2^14,min(data)-5,max(data)+5);
if ~exist('tstar_max','var')
tstar_max = inf;
end
data=data(:); %make data a column vector
if nargin<2 % if n is not supplied switch to the default
n=2^14;
end
n=2^ceil(log2(n)); % round up n to the next power of 2;
if nargin<5 %define the default interval [MIN,MAX]
minimum=min(data); maximum=max(data);
Range=maximum-minimum;
MIN=minimum-Range/2; MAX=maximum+Range/2;
end
% set up the grid over which the density estimate is computed;
R=MAX-MIN; dx=R/(n-1); xmesh=MIN+[0:dx:R]; N=length(unique(data));
%bin the data uniformly using the grid defined above;
initial_data=histc(data,xmesh)/N; initial_data=initial_data/sum(initial_data);
a=dct1d(initial_data); % discrete cosine transform of initial data
% now compute the optimal bandwidth^2 using the referenced method
I=[1:n-1]'.^2; a2=(a(2:end)/2).^2;
% use fzero to solve the equation t=zeta*gamma^[5](t)
t_star=root(@(t)fixed_point(t,N,I,a2),N);
t_star = min(t_star,tstar_max);
% smooth the discrete cosine transform of initial data using t_star
a_t=a.*exp(-[0:n-1]'.^2*pi^2*t_star/2);
% now apply the inverse discrete cosine transform
if (nargout>1)|(nargout==0)
density=idct1d(a_t)/R;
end
% take the rescaling of the data into account
bandwidth=sqrt(t_star)*R;
density(density<0)=eps; % remove negatives due to round-off error
if nargout==0
figure(1), plot(xmesh,density)
end
% for cdf estimation
if nargout>3
f=2*pi^2*sum(I.*a2.*exp(-I*pi^2*t_star));
t_cdf=(sqrt(pi)*f*N)^(-2/3);
% now get values of cdf on grid points using IDCT and cumsum function
a_cdf=a.*exp(-[0:n-1]'.^2*pi^2*t_cdf/2);
cdf=cumsum(idct1d(a_cdf))*(dx/R);
% take the rescaling into account if the bandwidth value is required
bandwidth_cdf=sqrt(t_cdf)*R;
end
end
%################################################################
function out=fixed_point(t,N,I,a2)
% this implements the function t-zeta*gamma^[l](t)
l=7;
f=2*pi^(2*l)*sum(I.^l.*a2.*exp(-I*pi^2*t));
for s=l-1:-1:2
K0=prod([1:2:2*s-1])/sqrt(2*pi); const=(1+(1/2)^(s+1/2))/3;
time=(2*const*K0/N/f)^(2/(3+2*s));
f=2*pi^(2*s)*sum(I.^s.*a2.*exp(-I*pi^2*time));
end
out=t-(2*N*sqrt(pi)*f)^(-2/5);
end
%##############################################################
function out = idct1d(data)
% computes the inverse discrete cosine transform
[nrows,ncols]=size(data);
% Compute weights
weights = nrows*exp(i*(0:nrows-1)*pi/(2*nrows)).';
% Compute x tilde using equation (5.93) in Jain
data = real(ifft(weights.*data));
% Re-order elements of each column according to equations (5.93) and
% (5.94) in Jain
out = zeros(nrows,1);
out(1:2:nrows) = data(1:nrows/2);
out(2:2:nrows) = data(nrows:-1:nrows/2+1);
% Reference:
% A. K. Jain, "Fundamentals of Digital Image
% Processing", pp. 150-153.
end
%##############################################################
function data=dct1d(data)
% computes the discrete cosine transform of the column vector data
[nrows,ncols]= size(data);
% Compute weights to multiply DFT coefficients
weight = [1;2*(exp(-i*(1:nrows-1)*pi/(2*nrows))).'];
% Re-order the elements of the columns of x
data = [ data(1:2:end,:); data(end:-2:2,:) ];
% Multiply FFT by weights:
data= real(weight.* fft(data));
end
function t=root(f,N)
% try to find smallest root whenever there is more than one
N=50*(N<=50)+1050*(N>=1050)+N*((N<1050)&(N>50));
tol=10^-12+0.01*(N-50)/1000;
flag=0;
while flag==0
try
t=fzero(f,[0,tol]);
flag=1;
catch
tol=min(tol*2,.1); % double search interval
end
if tol==.1 % if all else fails
t=fminbnd(@(x)abs(f(x)),0,.1); flag=1;
end
end
end
|
github
|
drbenvincent/darc-experiments-matlab-master
|
kde2d.m
|
.m
|
darc-experiments-matlab-master/darc-toolbox/dependencies/mcmc-utils-matlab/+mcmc/+kde2d/kde2d.m
| 7,506 |
utf_8
|
6d82435d2728e267990a5041d6b289b2
|
function [bandwidth,density,X,Y]=kde2d(data,n,MIN_XY,MAX_XY)
% fast and accurate state-of-the-art
% bivariate kernel density estimator
% with diagonal bandwidth matrix.
% The kernel is assumed to be Gaussian.
% The two bandwidth parameters are
% chosen optimally without ever
% using/assuming a parametric model for the data or any "rules of thumb".
% Unlike many other procedures, this one
% is immune to accuracy failures in the estimation of
% multimodal densities with widely separated modes (see examples).
% INPUTS: data - an N by 2 array with continuous data
% n - size of the n by n grid over which the density is computed
% n has to be a power of 2, otherwise n=2^ceil(log2(n));
% the default value is 2^8;
% MIN_XY,MAX_XY- limits of the bounding box over which the density is computed;
% the format is:
% MIN_XY=[lower_Xlim,lower_Ylim]
% MAX_XY=[upper_Xlim,upper_Ylim].
% The dafault limits are computed as:
% MAX=max(data,[],1); MIN=min(data,[],1); Range=MAX-MIN;
% MAX_XY=MAX+Range/4; MIN_XY=MIN-Range/4;
% OUTPUT: bandwidth - a row vector with the two optimal
% bandwidths for a bivaroate Gaussian kernel;
% the format is:
% bandwidth=[bandwidth_X, bandwidth_Y];
% density - an n by n matrix containing the density values over the n by n grid;
% density is not computed unless the function is asked for such an output;
% X,Y - the meshgrid over which the variable "density" has been computed;
% the intended usage is as follows:
% surf(X,Y,density)
% Example (simple Gaussian mixture)
% clear all
% % generate a Gaussian mixture with distant modes
% data=[randn(500,2);
% randn(500,1)+3.5, randn(500,1);];
% % call the routine
% [bandwidth,density,X,Y]=kde2d(data);
% % plot the data and the density estimate
% contour3(X,Y,density,50), hold on
% plot(data(:,1),data(:,2),'r.','MarkerSize',5)
%
% Example (Gaussian mixture with distant modes):
%
% clear all
% % generate a Gaussian mixture with distant modes
% data=[randn(100,1), randn(100,1)/4;
% randn(100,1)+18, randn(100,1);
% randn(100,1)+15, randn(100,1)/2-18;];
% % call the routine
% [bandwidth,density,X,Y]=kde2d(data);
% % plot the data and the density estimate
% surf(X,Y,density,'LineStyle','none'), view([0,60])
% colormap hot, hold on, alpha(.8)
% set(gca, 'color', 'blue');
% plot(data(:,1),data(:,2),'w.','MarkerSize',5)
%
% Example (Sinusoidal density):
%
% clear all
% X=rand(1000,1); Y=sin(X*10*pi)+randn(size(X))/3; data=[X,Y];
% % apply routine
% [bandwidth,density,X,Y]=kde2d(data);
% % plot the data and the density estimate
% surf(X,Y,density,'LineStyle','none'), view([0,70])
% colormap hot, hold on, alpha(.8)
% set(gca, 'color', 'blue');
% plot(data(:,1),data(:,2),'w.','MarkerSize',5)
%
% Notes: If you have a more accurate density estimator
% (as measured by which routine attains the smallest
% L_2 distance between the estimate and the true density) or you have
% problems running this code, please email me at [email protected]
% Reference: Z. I. Botev, J. F. Grotowski and D. P. Kroese
% "KERNEL DENSITY ESTIMATION VIA DIFFUSION" ,Submitted to the
% Annals of Statistics, 2009
global N A2 I
if nargin<2
n=2^8;
end
n=2^ceil(log2(n)); % round up n to the next power of 2;
N=size(data,1);
if nargin<3
MAX=max(data,[],1); MIN=min(data,[],1); Range=MAX-MIN;
MAX_XY=MAX+Range/4; MIN_XY=MIN-Range/4;
end
scaling=MAX_XY-MIN_XY;
if N<=size(data,2)
error('data has to be an N by 2 array where each row represents a two dimensional observation')
end
transformed_data=(data-repmat(MIN_XY,N,1))./repmat(scaling,N,1);
%bin the data uniformly using regular grid;
initial_data=ndhist(transformed_data,n);
% discrete cosine transform of initial data
a= dct2d(initial_data);
% now compute the optimal bandwidth^2
I=(0:n-1).^2; A2=a.^2;
t_star=fzero( @(t)(t-evolve(t)),[0,0.1]);
p_02=func([0,2],t_star);p_20=func([2,0],t_star); p_11=func([1,1],t_star);
t_y=(p_02^(3/4)/(4*pi*N*p_20^(3/4)*(p_11+sqrt(p_20*p_02))))^(1/3);
t_x=(p_20^(3/4)/(4*pi*N*p_02^(3/4)*(p_11+sqrt(p_20*p_02))))^(1/3);
% smooth the discrete cosine transform of initial data using t_star
a_t=exp(-(0:n-1)'.^2*pi^2*t_x/2)*exp(-(0:n-1).^2*pi^2*t_y/2).*a;
% now apply the inverse discrete cosine transform
if nargout>1
density=idct2d(a_t)*(numel(a_t)/prod(scaling));
[X,Y]=meshgrid(MIN_XY(1):scaling(1)/(n-1):MAX_XY(1),MIN_XY(2):scaling(2)/(n-1):MAX_XY(2));
end
bandwidth=sqrt([t_x,t_y]).*scaling;
end
%#######################################
function [out,time]=evolve(t)
global N
Sum_func = func([0,2],t) + func([2,0],t) + 2*func([1,1],t);
time=(2*pi*N*Sum_func)^(-1/3);
out=(t-time)/time;
end
%#######################################
function out=func(s,t)
global N
if sum(s)<=4
Sum_func=func([s(1)+1,s(2)],t)+func([s(1),s(2)+1],t); const=(1+1/2^(sum(s)+1))/3;
time=(-2*const*K(s(1))*K(s(2))/N/Sum_func)^(1/(2+sum(s)));
out=psi(s,time);
else
out=psi(s,t);
end
end
%#######################################
function out=psi(s,Time)
global I A2
% s is a vector
w=exp(-I*pi^2*Time).*[1,.5*ones(1,length(I)-1)];
wx=w.*(I.^s(1));
wy=w.*(I.^s(2));
out=(-1)^sum(s)*(wy*A2*wx')*pi^(2*sum(s));
end
%#######################################
function out=K(s)
out=(-1)^s*prod((1:2:2*s-1))/sqrt(2*pi);
end
%#######################################
function data=dct2d(data)
% computes the 2 dimensional discrete cosine transform of data
% data is an nd cube
[nrows,ncols]= size(data);
if nrows~=ncols
error('data is not a square array!')
end
% Compute weights to multiply DFT coefficients
w = [1;2*(exp(-i*(1:nrows-1)*pi/(2*nrows))).'];
weight=w(:,ones(1,ncols));
data=dct1d(dct1d(data)')';
function transform1d=dct1d(x)
% Re-order the elements of the columns of x
x = [ x(1:2:end,:); x(end:-2:2,:) ];
% Multiply FFT by weights:
transform1d = real(weight.* fft(x));
end
end
%#######################################
function data = idct2d(data)
% computes the 2 dimensional inverse discrete cosine transform
[nrows,ncols]=size(data);
% Compute wieghts
w = exp(i*(0:nrows-1)*pi/(2*nrows)).';
weights=w(:,ones(1,ncols));
data=idct1d(idct1d(data)');
function out=idct1d(x)
y = real(ifft(weights.*x));
out = zeros(nrows,ncols);
out(1:2:nrows,:) = y(1:nrows/2,:);
out(2:2:nrows,:) = y(nrows:-1:nrows/2+1,:);
end
end
%#######################################
function binned_data=ndhist(data,M)
% this function computes the histogram
% of an n-dimensional data set;
% 'data' is nrows by n columns
% M is the number of bins used in each dimension
% so that 'binned_data' is a hypercube with
% size length equal to M;
[nrows,ncols]=size(data);
bins=zeros(nrows,ncols);
for i=1:ncols
[dum,bins(:,i)] = histc(data(:,i),[0:1/M:1],1);
bins(:,i) = min(bins(:,i),M);
end
% Combine the vectors of 1D bin counts into a grid of nD bin
% counts.
binned_data = accumarray(bins(all(bins>0,2),:),1/nrows,M(ones(1,ncols)));
end
|
github
|
drbenvincent/darc-experiments-matlab-master
|
my_shaded_errorbar_zone_UL.m
|
.m
|
darc-experiments-matlab-master/darc-toolbox/utils-plotting/my_shaded_errorbar_zone_UL.m
| 815 |
utf_8
|
5455a1384ba84136387029c983c8a775
|
function [h]=my_shaded_errorbar_zone_UL(x,upper,lower,col)
% Plots a shaded region of error
%
% my_shaded_errorbar_zone_UL([-10:0.1:10],[-10:0.1:10]+1,[-10:0.1:10]-1,[0.7 0.7 0.7])
%
% eg, my_shaded_errorbar_zone([-10:0.1:10],x,abs(randn(size(x)))+2,[0 0 1])
%
%handle=patch([x max(x)-x+min(x)],[y+e flipud(y'-e')'],[0.8 0.8 0.8]);
%handle=patch([x max(x)-x+min(x)],[upper flipud(lower')'],[0.8 0.8 0.8]);
%
%
% written by: Benjamin T Vincent
h = holdDecorator( plotErrorBarZone(x, upper, lower, col) );
end
function h = plotErrorBarZone(x, upper, lower, col)
% draw the shaded error bar zone
x = [x, fliplr(x)];
y = [upper, fliplr(lower)];
h = patch(x, y, [0.8 0.8 0.8]);
% formatting
uistack(h,'bottom')
set(h,'EdgeColor','none')
set(h,'FaceColor',col)
set(h,'FaceAlpha',0.5)
end
|
github
|
drbenvincent/darc-experiments-matlab-master
|
plotUtilityFunction.m
|
.m
|
darc-experiments-matlab-master/darc-experiments/@Model/plotUtilityFunction.m
| 2,211 |
utf_8
|
e70f242efd3d46c6ec678cceec49d126
|
function plotUtilityFunction(obj, thetaStruct, varargin)
p = inputParser;
p.FunctionName = mfilename;
p.addRequired('thetaStruct',@isstruct_or_table);
% p.addParameter('xScale','linear',@(x)any(strcmp(x,{'linear','log'})));
p.addParameter('data',[],@istable);
p.addParameter('pointEstimateType','mean',@isstr);
% p.addParameter('maxDelay', [], @isscalar);
p.addParameter('utility_func_function_handle','', @(x) isa(x,'function_handle'))
p.parse(thetaStruct, varargin{:});
data = p.Results.data;
plotCurve(data, thetaStruct, p.Results.utility_func_function_handle, p)
%plotData(data);
formatAxes(data, p);
end
function plotCurve(data, thetaStruct, utility_func_function_handle, p)
% create set of rewards to calculate & plot ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
xVals = linspace(-100, 100, 100);
% evaluate and plot just the first N particles
N = 200;
fnames = fieldnames(thetaStruct);
for n=1:numel(fnames)
thetaStruct.(fnames{n}) = thetaStruct.(fnames{n})([1:N]);
end
% calculate discount fraction for the given theta samples ~~~~~~~~~~~~~~
prospect.reward = xVals;
% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
utilityOfReward = utility_func_function_handle(prospect, thetaStruct);
plot(xVals, utilityOfReward,...
'Color',[0 0.4 1 0.1])
hold on
%% plot posterior median as black line
for n=1:numel(fnames)
thetaStruct.(fnames{n}) = median(thetaStruct.(fnames{n}));
end
prospect.reward = xVals;
utilityOfReward = utility_func_function_handle(prospect, thetaStruct);
plot(xVals, utilityOfReward,...
'Color', 'k',...
'LineWidth', 2)
end
function formatAxes(data, p)
%opts = calc_opts(data, p);
xlabel('$R$', 'interpreter','Latex')
ylabel('$u(R)$', 'interpreter','Latex')
set(gca,'XAxisLocation','origin',...
'YAxisLocation','origin',...
'box', 'off')
% box off
% a = get(gca,'YLim');
% if opts.maxX>0
% xlim( [0 opts.maxX] )
% end
% ylim([0 min([a(2),10]) ])
drawnow
end
% function opts = calc_opts(data, p)
% if ~isempty(data)
% opts.maxlogB = max( abs(data.R_B) );
% opts.maxX = max( data.D_B ) *1.1;
% else
% opts.maxlogB = 1000;
% opts.maxX = 20;
% end
%
% if ~isempty(p.Results.maxDelay)
% opts.maxX = p.Results.maxDelay;
% end
% end
|
github
|
drbenvincent/darc-experiments-matlab-master
|
plotProbFunction.m
|
.m
|
darc-experiments-matlab-master/darc-experiments/@Model/plotProbFunction.m
| 4,833 |
utf_8
|
2fd2b28515f7226b9bda9d45b14e68a6
|
function plotProbFunction(obj, thetaStruct, varargin)
p = inputParser;
p.FunctionName = mfilename;
p.addRequired('thetaStruct',@isstruct_or_table);
p.addParameter('xScale','linear',@(x)any(strcmp(x,{'linear','log'})));
p.addParameter('data',[],@istable);
p.addParameter('pointEstimateType','mean',@isstr);
p.addParameter('discounting_function_handle','', @(x) isa(x,'function_handle'))
p.parse(thetaStruct, varargin{:});
data = p.Results.data;
plotCurve(data, thetaStruct, p.Results.discounting_function_handle, p)
plotData(data);
formatAxes(data);
end
function plotCurve(data, thetaStruct, discounting_function_handle, p)
switch p.Results.xScale
case{'linear'}
xProbVector = linspace(10^-4, 1, 1000);
xOddsVector = prob2oddsagainst(xProbVector);
% % create set of probs to calculate & plot ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
% if isempty(data)
% xOddsVector = linspace(0,100,1000);
% else
% xOddsVector = linspace(0,100,1000);
% % % zoom to data (only useful with the odds discounting type plot)
% % xOddsVector = xOddsVector( xOddsVector < max(prob2oddsagainst(data.P_B)));
% end
% % ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%% Plot calibration line for h = 1 = risk neutral
risk_neutral = 1 ./ (1+1.*xOddsVector); % hyperbolic discounting function
% converting x axis from odds to probability makes it like the
% traditional "probability weighting plot", as opposed to the
% "discounting of odds plot"
plot(oddsagainst2prob(xOddsVector), risk_neutral, '--',...
'Color', [0.7 0.7 0.7],...
'LineWidth', 4)
hold on
% evaluate and plot just the first N particles
N = 200;
fnames = fieldnames(thetaStruct);
for n=1:numel(fnames)
thetaStruct.(fnames{n}) = thetaStruct.(fnames{n})([1:N]);
end
% calculate discount fraction for the given theta samples ~~~~~~~~~~~~~~
prospect.prob = xProbVector;
% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
dF = discounting_function_handle(prospect, thetaStruct);
plot(oddsagainst2prob(xOddsVector), dF,...
'Color',[0 0.4 1 0.1])
hold on
%% plot posterior median as black line
for n=1:numel(fnames)
thetaStruct.(fnames{n}) = median(thetaStruct.(fnames{n}));
end
prospect.prob = xProbVector;
utilityOfReward = discounting_function_handle(prospect, thetaStruct);
plot(oddsagainst2prob(xOddsVector), utilityOfReward,...
'Color', 'k',...
'LineWidth', 2)
case{'log'}
error('not yet implemented plotting discount fractions with log x-axis')
end
end
function plotData(data)
if isempty(data)
return
end
[x, y, markerCol, markerSize] = convertDataIntoMarkers(data);
plotMarkers(oddsagainst2prob(x), y, markerCol, markerSize)
end
% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
function [x, y, markerCol, markerSize] = convertDataIntoMarkers(data)
% find unique experimental designs
uniqueDesigns = [abs(data.R_A), abs(data.R_B), data.P_A, data.P_B, data.D_A, data.D_B];
[C, ia, ic] = unique(uniqueDesigns, 'rows');
%loop over unique designs (ic)
for n=1:max(ic)
% binary set of which trials this design was used on
myset = ic==n;
% Size = number of times this design has been run
F(n) = sum(myset);
% Colour = proportion of times that participant chose immediate
% for that design
markerCol(n) = sum(data.R(myset)==0) ./ F(n);
markerSize(n) = F(n);
x(n) = prob2oddsagainst(data.P_B( ia(n) ));
y(n) = abs(data.R_A( ia(n) )) ./ abs(data.R_B( ia(n) ));
end
end
% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
function plotMarkers(x, y, markerCol, markerSize)
hold on
for i=1:max(numel(x))
h = plot(x(i), y(i),'o');
h.Color='k';
h.MarkerFaceColor=[1 1 1] .* (1-markerCol(i));
h.MarkerSize = markerSize(i)+4;
hold on
end
end
% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
function formatAxes(data)
opts = calc_opts(data);
xlabel('odds against Prospect B', 'interpreter','Latex')
xlabel('objective probability, $P$', 'interpreter','Latex')
ylabel('$\pi(P)$', 'interpreter','Latex')
box off
% a = get(gca,'YLim');
% if opts.maxX > 0
% xlim( [0 opts.maxX*1.1] )
% else
% x=get(gca,'XLim');
% xlim([0 a(2)])
% end
xlim([0 1])
ylim([0 1])
%% Add descriptive helper text
addTextToFigure('TL', 'risk seeking domain', 10)
addTextToFigure('BR', 'risk avoidant domain', 10)
drawnow
end
function opts = calc_opts(data)
if ~isempty(data)
opts.maxlogB = max( abs(data.R_B) );
opts.maxX = max( prob2oddsagainst(data.P_B) );
else
opts.maxlogB = 1000;
opts.maxX = 365;
end
end
% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
github
|
drbenvincent/darc-experiments-matlab-master
|
plotDiscountFunction.m
|
.m
|
darc-experiments-matlab-master/darc-experiments/@Model/plotDiscountFunction.m
| 4,074 |
UNKNOWN
|
cd3e7d94e8978e564eef501175a3a9c6
|
function plotDiscountFunction(obj, thetaStruct, varargin)
p = inputParser;
p.FunctionName = mfilename;
p.addRequired('thetaStruct',@isstruct_or_table);
p.addParameter('xScale','linear',@(x)any(strcmp(x,{'linear','log'})));
p.addParameter('data',[],@istable);
p.addParameter('pointEstimateType','mean',@isstr);
p.addParameter('maxDelay', [], @isscalar);
p.addParameter('discounting_function_handle','', @(x) isa(x,'function_handle'))
p.parse(thetaStruct, varargin{:});
data = p.Results.data;
plotCurve(data, thetaStruct, p.Results.discounting_function_handle, p)
plotData(data);
formatAxes(data, p);
end
function plotCurve(data, thetaStruct, discounting_function_handle, p)
switch p.Results.xScale
case{'linear'}
% create set of delays to calculate & plot ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
if ~isempty(p.Results.maxDelay)
% if isempty(data)
xVals = logspace(-3,3,1000);
xVals = xVals(xVals<p.Results.maxDelay);
else
max_delay_of_data = max([ data.D_A; data.D_B]);
xVals = logspace(-3,4,1000);
xVals = xVals(xVals<max_delay_of_data);
end
% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
% evaluate and plot just the first N particles
N = 200;
fnames = fieldnames(thetaStruct);
for n=1:numel(fnames)
thetaStruct.(fnames{n}) = thetaStruct.(fnames{n})([1:N]);
end
% calculate discount fraction for the given theta samples ~~~~~~~~~~~~~~
prospect.delay = xVals;
% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
dF = discounting_function_handle(prospect, thetaStruct);
plot(xVals, dF,...
'Color',[0 0.4 1 0.1])
hold on
%% plot posterior median as black line
for n=1:numel(fnames)
thetaStruct.(fnames{n}) = median(thetaStruct.(fnames{n}));
end
prospect.reward = xVals;
utilityOfReward = discounting_function_handle(prospect, thetaStruct);
plot(xVals, utilityOfReward,...
'Color', 'k',...
'LineWidth', 2)
case{'log'}
error('not yet implemented plotting discount fractions with log x-axis')
end
end
function plotData(data)
if isempty(data)
return
end
[x, y, markerCol, markerSize] = convertDataIntoMarkers(data);
plotMarkers(x, y, markerCol, markerSize)
end
function [x, y, markerCol, markerSize] = convertDataIntoMarkers(data)
% find unique experimental designs
uniqueDesigns = [abs(data.R_A), abs(data.R_B), data.D_A, data.D_B, data.D_A, data.D_B];
[C, ia, ic] = unique(uniqueDesigns, 'rows');
%loop over unique designs (ic)
for n=1:max(ic)
% binary set of which trials this design was used on
myset = ic==n;
% Size = number of times this design has been run
F(n) = sum(myset);
% Colour = proportion of times that participant chose immediate
% for that design
markerCol(n) = sum(data.R(myset)==0) ./ F(n);
markerSize(n) = F(n);
x(n) = data.D_B( ia(n) ); % delay to get �R_B
y(n) = abs(data.R_A( ia(n) )) ./ abs(data.R_B( ia(n) ));
end
end
function plotMarkers(x, y, markerCol, markerSize)
hold on
for i=1:max(numel(x))
h = plot(x(i), y(i),'o');
h.Color='k';
h.MarkerFaceColor=[1 1 1] .* (1-markerCol(i));
h.MarkerSize = markerSize(i)+4;
hold on
end
end
% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
function formatAxes(data, p)
opts = calc_opts(data, p);
xlabel('delay, $D^b$', 'interpreter','Latex')
ylabel('$d(D)$', 'interpreter','Latex')
box off
a = get(gca,'YLim');
if opts.maxX>0
xlim( [0 opts.maxX] )
else
x=get(gca,'XLim');
xlim([0 a(2)])
end
ylim([0 min(10, max([a(2),1])) ])
%% Add descriptive helper text
addTextToFigure('TR', 'choose immediate', 10, 'Color', [0.7 0.7 0.7])
addTextToFigure('BL', ' choose delayed', 10, 'Color', [0.7 0.7 0.7])
drawnow
end
function opts = calc_opts(data, p)
if ~isempty(data)
opts.maxlogB = max( abs(data.R_B) );
opts.maxX = max( data.D_B ) *1.1;
else
opts.maxlogB = 1000;
opts.maxX = 20;
end
if ~isempty(p.Results.maxDelay)
opts.maxX = p.Results.maxDelay;
end
end
% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
github
|
drbenvincent/darc-experiments-matlab-master
|
plotDiscountSurface.m
|
.m
|
darc-experiments-matlab-master/darc-experiments/@Model_hyperbolic1_time_and_prob/plotDiscountSurface.m
| 4,143 |
UNKNOWN
|
09dea0179f9b5215e875502ae0b37f08
|
function plotDiscountSurface(obj, thetaStruct, varargin)
% plots prob and time discount surface
p = inputParser;
p.FunctionName = mfilename;
p.addRequired('thetaStruct',@isstruct);
p.addParameter('xScale','linear',@(x)any(strcmp(x,{'linear','log'})));
p.addParameter('data',[],@isstruct_or_table)
p.addParameter('pointEstimateType','mean',@isstr);
p.addParameter('prob_discounting_function_handle','', @(x) isa(x,'function_handle'))
p.addParameter('time_discounting_function_handle','', @(x) isa(x,'function_handle'))
p.parse(thetaStruct, varargin{:});
data = p.Results.data;
plotSurface(data, thetaStruct, p.Results.prob_discounting_function_handle, p.Results.time_discounting_function_handle, p)
plotData(data)
formatAxes(data);
title({'P_A chance of �R_A in D_A days','P_B chance of �R_B in D_B days'})
end
function plotSurface(data, thetaStruct, probDiscountingFunctionFH, timeDiscountingFunctionFH, p)
% create set of delays to calculate & plot
N_DELAYS = 10;
N_ODDS = 12;
if isempty(data)
delays = linspace(0,365,N_DELAYS);
else
max_delay_of_data = max([ data.D_A; data.D_B]);
delays = linspace(0, max_delay_of_data, N_DELAYS);
end
if isempty(data)
odds = linspace(0, 20, N_ODDS);
else
odds = linspace(0, max(prob2oddsagainst(data.P_B)), N_ODDS);
end
% Evaluate only the posterior median
fnames = fieldnames(thetaStruct);
for n=1:numel(fnames)
thetaStruct.(fnames{n}) = median(thetaStruct.(fnames{n}));
end
warning('CREATE THIS NESTED PARAMETER STRUCTURE AUTOMATICALLY')
nestedParamStruct.prob.h = thetaStruct.h;
nestedParamStruct.delay.logk = thetaStruct.logk;
%opts = calc_opts(data);
% create grid of values
[odds_grid, delays_grid] = meshgrid(odds,delays); % create x,y (b,d) grid values
warning('this is duplication of model.calcPresentSubjectiveValue()')
prospect.reward = [];
prospect.delay = delays_grid(:);
prospect.prob = oddsagainst2prob(odds_grid(:));
V = ...
probDiscountingFunctionFH(prospect, nestedParamStruct.prob) .* ...
timeDiscountingFunctionFH(prospect, nestedParamStruct.delay);
V = reshape(V, size(odds_grid));
%% PLOT
hmesh = mesh(odds_grid, delays_grid, V);
% shading
hmesh.FaceColor ='w';
hmesh.FaceAlpha =0.7;
% edges
hmesh.MeshStyle ='both';
hmesh.EdgeColor ='k';
hmesh.EdgeAlpha =1;
% plot isolines
hold on
[c,h] = contour3(odds_grid, delays_grid, V, [0.2:0.2:0.8]);
h.LineColor = 'k';
h.LineWidth = 4;
end
function plotData(data)
if isempty(data)
return
end
[x,y,z,markerCol,markerSize] = convertDataIntoMarkers(data);
plotMarkers(x, y, z, markerCol, markerSize)
end
function [x,y,z,markerCol,markerSize] = convertDataIntoMarkers(data)
% find unique experimental designs
uniqueDelays = [abs(data.R_A), abs(data.R_B), data.D_A, data.D_B, data.D_A, data.D_B];
[C, ia, ic] = unique(uniqueDelays,'rows');
% loop over unique designs (ic)
for n=1:max(ic)
% binary set of which trials this design was used on
myset=ic==n;
% markerSize = number of times this design has been run
markerSize(n) = sum(myset);
% Colour = proportion of times participant chose immediate for that design
markerCol(n) = sum(data.R(myset)==0) ./ markerSize(n);
x(n) = prob2oddsagainst( data.P_B( ia(n) ) ); % odds against
y(n) = data.D_B( ia(n) ); % delay
z(n) = abs(data.R_A(ia(n))) ./ abs(data.R_B( ia(n)));
end
end
function plotMarkers(x, y, z, markerCol, markerSize)
hold on
for i=1:numel(x)
h = stem3(x(i), y(i), z(i));
h.Color='k';
h.MarkerFaceColor=[1 1 1] .* (1-markerCol(i));
h.MarkerSize = markerSize(i)+4;
hold on
end
end
function formatAxes(data)
%opts = calc_opts(data);
xlabel('Odds against, $\frac{1-P^B}{P^B}$', 'interpreter','latex')
ylabel('delay $D^B$', 'interpreter','latex')
zlabel('discount factor (and $\frac{R_A}{R_B}$)', 'interpreter','latex')
view([90+45, 20])
axis vis3d
axis tight
axis square
zlim([0 1])
camproj('perspective')
set(gca,'ZTick',[0:0.2:1])
end
function opts = calc_opts(data)
if ~isempty(data)
opts.maxlogB = max( abs(data.R_B) );
opts.maxD = max( data.D_B );
else
opts.maxlogB = 1000;
opts.maxD = 365;
end
% what does this even do?
opts.nIndifferenceLines = 10;
pow=1; while opts.maxlogB > 10^pow; pow=pow+1; end
opts.pow = pow;
end
|
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