We have updated our course material. Have a look at the Courses section for two new distance learning courses on PCRaster Python; without tutor support the courses are free of charge.
This entry was posted in Uncategorized on December 16, 2013 by derek.
Users interested in a convenient development and analysis environment for PCRaster Python models could consider the Enthought Python distribution. On this page we show how you can use the PCRaster 4.0 release with Enthought Canopy.
This entry was posted in Uncategorized on December 16, 2013 by oliver.
We are happy to announce the final release of PCRaster-4.0.0! For more information, visit the PCRaster 4.0.0 download page.
This entry was posted in Uncategorized on December 4, 2013 by kor.
It is possible that your PCRaster models are not executing as fast as the could on your Linux system. That can happen because the default memory allocation and de-allocation rules that are in effect on Linux are optimized for system wide efficiency, instead of raw performance. For more information about this, see this document.
This entry was posted in Uncategorized on December 2, 2013 by kor.