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.

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.

We are happy to announce the final release of PCRaster-4.0.0! For more information, visit the PCRaster 4.0.0 download page.

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.

You can tune the memory allocation and de-allocation logic using environment variables (note the trailing underscore):

export MALLOC_MMAP_MAX_=0
export MALLOC_TRIM_THRESHOLD_=-1

You may want to check if these values help you to squeeze a bit of extra performance out of your models.