I guess the other possibility is to generate Latex itself (instead XSL-FO) and then use something like for example
http://www.nixo-soft.de/tutorials/jlr/JLRTutorial.html http://www.nixo-soft.de/de/category/Downloads/page/libs/JavaLatexReport.php Another project I have found is http://forge.scilab.org/index.php/p/jlatexmath/ which seems to generate images, which can be used together with FOP. I guess the more "brutal" way is to install and call pdflatex (once the latex has been generated) from within Java: http://tex.stackexchange.com/a/41633 Thanks Michael Am 24.01.14 10:21, schrieb Michael Wechner: > Thanks very much for the pointer. Seems to work only with > > FOP 0.95beta or 0.95 > > but I think we are still using 0.93 and I guess we could upgrade ;-) > > IIUC probably more effort is to transform Latex to MathML in order to > use this library, but maybe I misunderstand something. Will have a > closer look at it. > > Thanks > > Michael > > Am 24.01.14 10:14, schrieb Luis Bernardo: >> This is not very recent, but take a look at >> http://jeuclid.sourceforge.net/trunk/jeuclid-fop/index.html. >> >> >> >> On Fri, Jan 24, 2014 at 8:54 AM, Michael Wechner >> <michael.wech...@wyona.com>wrote: >> >>> Hi >>> >>> I recently learned about >>> >>> http://www.mathjax.org/ >>> >>> which is a great library to render Latex snippets inside HTML. See for >>> example the abstract contained by >>> >>> http://projecteuclid.org/euclid.aos/1388545673 >>> >>> I would like to do the "same" thing with PDF, which means I have an XML >>> containing Latex snippets, e.g. >>> >>> <p> >>> We study sparse principal components analysis in high dimensions, where >>> $p$ (the number of variables) can be much larger than $n$ (the number of >>> observations), and analyze the problem of estimating the subspace >>> spanned by the principal eigenvectors of the population covariance >>> matrix. We introduce two complementary notions of $\ell_{q}$ subspace >>> sparsity: row sparsity and column sparsity. We prove nonasymptotic lower >>> and upper bounds on the minimax subspace estimation error for $0\leq >>> q\leq1$. The bounds are optimal for row sparse subspaces and nearly >>> optimal for column sparse subspaces, they apply to general classes of >>> covariance matrices, and they show that $\ell_{q}$ constrained estimates >>> can achieve optimal minimax rates without restrictive spiked covariance >>> conditions. Interestingly, the form of the rates matches known results >>> for sparse regression when the effective noise variance is defined >>> appropriately. Our proof employs a novel variational $\sin\Theta$ >>> theorem that may be useful in other regularized spectral estimation >>> problems. >>> </p> >>> >>> and then I would like to use XSL-FO and FOP to generate PDF. >>> >>> Is that possible somehow? Or any other ideas how I could generate such a >>> PDF? >>> >>> Thanks >>> >>> Michael >>> >>> --------------------------------------------------------------------- >>> To unsubscribe, e-mail: fop-users-unsubscr...@xmlgraphics.apache.org >>> For additional commands, e-mail: fop-users-h...@xmlgraphics.apache.org >>> >>> > --------------------------------------------------------------------- To unsubscribe, e-mail: fop-users-unsubscr...@xmlgraphics.apache.org For additional commands, e-mail: fop-users-h...@xmlgraphics.apache.org
